53879
AGRICULTURAL
PRICE
DISTORTIONS,
INEQUALITY,
AND POVERTY
Editors
Kym Anderson � John Cockburn � Will Martin
Agricultural
Price Distortions,
Inequality, and
Poverty
Agricultural
Price Distortions,
Inequality, and
Poverty
Kym Anderson,
John Cockburn, and
Will Martin, Editors
Washington, D.C.
� 2010 The International Bank for Reconstruction and Development / The World Bank
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ISBN: 978-0-8213-8184-7
eISBN: 978-0-8213-8185-4
DOI: 10.1596/978-0-8213-8184-7
Library of Congress Cataloging-in-Publication Data
Agricultural price distortions, inequality, and poverty / Kym Anderson, John Cockburn and Will Martin, editors.
p. cm.
ISBN 978-0-8213-8184-7 -- ISBN 978-0-8213-8185-4 (electronic)
1. Agricultural prices--Government policy. 2. Farm income--Developing countries. 3. Agricultural wages--
Developing countries. 4. Poverty. I. Anderson, Kym. II. Cockburn, John. III. Martin, Will, 1953 -
HD1447.A37 2010
339.4'6--dc22
2009041807
Cover design: Tomoko Hirata/World Bank
Cover photo: � Curt Carnemark/World Bank Photo Library
OTHER TITLES IN THE SERIES
Distortions to Agricultural Incentives: A Global Perspective, 1955�2007
edited by Kym Anderson, 2009
Distortions to Agricultural Incentives in Africa
edited by Kym Anderson and William A. Masters, 2009
Distortions to Agricultural Incentives in Asia
edited by Kym Anderson and Will Martin, 2009
Distortions to Agricultural Incentives in Europe's Transition Economies
edited by Kym Anderson and Johan Swinnen, 2008
Distortions to Agricultural Incentives in Latin America
edited by Kym Anderson and Alberto Vald�s, 2008
CONTENTS
Foreword xvii
Acknowledgments xix
Contributors xxi
Abbreviations xxv
PART I INTRODUCTION 1
1 Introduction and Summary 3
Kym Anderson, John Cockburn, and Will Martin
PART II GLOBAL CGE APPROACHES 47
2 Global Welfare and Poverty Effects:
Linkage Model Results 49
Kym Anderson, Ernesto Valenzuela, and
Dominique van der Mensbrugghe
3 Global Poverty and Distributional Impacts:
The GIDD Model 87
Maurizio Bussolo, Rafael De Hoyos, and Denis Medvedev
4 Poverty Impacts in 15 Countries: The GTAP Model 119
Thomas W. Hertel and Roman Keeney
PART III NATIONAL CGE APPROACHES: ASIA 145
5 China 147
Fan Zhai and Thomas W. Hertel
6 Indonesia 179
Peter Warr
7 Pakistan 209
Caesar B. Cororaton and David Orden
vii
viii Contents
8 The Philippines 247
Caesar B. Cororaton, Erwin Corong, and John Cockburn
9 Thailand 283
Peter Warr
PART IV NATIONAL CGE APPROACHES: AFRICA 301
10 Mozambique 303
Channing Arndt and James Thurlow
11 South Africa 331
Nicolas H�rault and James Thurlow
PART V NATIONAL CGE APPROACHES: LATIN AMERICA 357
12 Argentina 359
Mart�n Cicowiez, Carolina D�az-Bonilla,
and Eugenio D�az-Bonilla
13 Brazil 391
Joaquim Bento de Souza Ferreira Filho and Mark Horridge
14 Nicaragua 423
Marco V. S�nchez and Rob Vos
Appendix: Border Price and Export Demand
Shocks in Developing Countries from
Rest-of-the-World Trade Liberalization:
The Linkage Model 457
Dominique van der Mensbrugghe,
Ernesto Valenzuela, and Kym Anderson
Index 489
Contents ix
Figures
3.1 Income Distributions among Agricultural and Nonagricultural
Populations of the World, 2000 90
3.2 Relationship between Income Levels and Share of Employment
in Agriculture, by Country, 2000 92
3.3 Share of the Population in Agriculture and of Agriculture
in Total Income, Developing Countries, 2000 93
3.4 Inequality Variation in Agricultural, Nonagricultural,
and All Households, Developing Countries, 2000 95
3.5 The GIDD Methodological Framework 97
3.6 The Effects on Real National Consumption of the Global
Removal of Agricultural and All Merchandise Trade Distortions 100
3.7 The Effects on National Real Factor Rewards of the Global
Removal of Only Agricultural Price and Trade Policies 102
3.8 National GICs: The Effects on the Per Capita Household Income
Distribution of a Hypothetical 1 Percent Increase in Agricultural
Versus Nonagricultural Incomes, Developing Countries 104
3.9 Regional and National GICs: The Effects on the Per Capita
Household Income Distribution of Full Global Agricultural
Policy Reform 108
3.10 Changes in Poverty as a Share of the Total Change among the
Greatest Winners and Losers in Full Global Agricultural
Policy Reform, Developing Countries 110
3.11 The Theil Index of Overall and Between-Group Income
Distributional Changes after Full Global Agricultural
Policy Reform 114
5.1 The Impacts of Prospective Liberalizations on Welfare of
Five Types of Households, China 167
6.1 Initial and Simulated Levels of Poverty Incidence, Indonesia 201
7.1 Poverty Incidence, Pakistan, 1986�87 to 2004�05 224
8.1 Trends in Poverty Indexes, the Philippines, 1985�2000 253
12.1 Export Taxes, Argentina, 2001�07 362
12.2 Tax Revenue, Argentina, 1991�2007 363
12.3 Agricultural Producer Price Indexes, Argentina, 1992�2006 364
12.4 Indexes of International Agricultural Prices, Relative Domestic
Agricultural Prices, and the Real Effective Exchange Rate,
Argentina, 1996�2008 365
13.1 The Share of the Poor, by State, Brazil, 2001 399
14.1 Tariff Rates on Agricultural and Total Imports from the United
States under DR-CAFTA, Nicaragua, 2003�20 428
14.2 The NRAs for Exportable, Importable, and All Covered Farm Prod-
ucts, Nicaragua, 1991�2004 429
14.3 The NRAs for Agricultural and Nonagricultural Tradable Sectors
and the RRAs for Farmers, Nicaragua, 1991�2004 430
14.4 The Fiscal Cost of Trade Liberalization, Nicaragua 446
x Contents
Tables
1.1 Global Poverty and Inequality, by Region, 1981�2005 4
1.2 The NRAs for Tradable Agricultural and Nonagricultural
Products and the RRA, Focus Regions, 1980�2004 7
1.3 The Linkage Model's Effects of Full Global Liberalization of
Agricultural and All Merchandise Trade on Economic
Welfare and Real GDP, by Country and Region 15
1.4 The Linkage Model's Effects of Full Global Merchandise
Trade Liberalization on Real Factor Prices, by Country and Region 17
1.5 The Linkage Model's Effects of Full Global Merchandise
Trade Liberalization on the Number of Extreme Poor, by Region 18
1.6 The GIDD Model's Effects of the Removal of Agricultural and
All Merchandise Trade Distortions on the Number of Extreme
Poor, by Region 20
1.7 The GIDD Model's Effects of the Removal of Agricultural
and All Merchandise Trade Distortions on Global Poverty and
Inequality, Farm and Nonfarm Households 21
1.8 The GIDD Model's Effects of the Removal of Agricultural and
All Merchandise Trade Distortions on Global Poverty and
Inequality, Farm and Nonfarm Households, by Region 22
1.9 The GTAP Model's Effects of Full Global Liberalization of
Agricultural and All Merchandise Trade on the Number of
Extreme Poor, by Country 24
1.10 Characteristics of the Models in the Global and National
Country Studies 26
1.11 The Impact of Reform on the Incidence of Extreme Poverty,
by Country 28
1.12 The Impact of Reform on the Incidence of Income
Inequality, by Country 32
1.13 The Direction of the Effects of Global Reform on Extreme
Poverty, by Country 36
2.1 Structure of Producer Price Distortions in Global Goods
Markets, 2004 53
2.2 The Impact on Real Income of the Full Liberalization of Global
Merchandise Trade, by Country and Region, 2004 58
2.3 Regional and Sectoral Sources of the Welfare Gains from the
Full Liberalization of Global Merchandise Trade, 2004 61
2.4 The Impact of Full Global Liberalization on the Shares of
Global Output Exported, by Product, 2004 62
2.5 The Impact of Full Global Trade Liberalization on Agricultural
and Food Output and Trade, by Country and Region, 2004 63
2.6 The Impact of Global Liberalization on the Share of Agricultural
and Food Production Exports, by Country and Region, 2004 65
Contents xi
2.7 The Impact of Global Liberalization on Self-sufficiency in
Agricultural and Other Products, by Region, 2004 66
2.8 Shares of Production Exported and of Consumption Imported
before and after Full Global Liberalization of all Merchandise
Trade, by Product, 2004 67
2.9 The Impact of Full Global Liberalization on Real International
Product Prices, 2004 68
2.10 The Impacts of Full Global Merchandise Trade Liberalization
on Real Pretax Factor Prices, by Country and Region, 2004 70
2.11 The Effects of the Full Global Liberalization of Agricultural
and Merchandise Trade on Sectoral Value Added, by Country
and Region, 2004 73
2.12 The Poverty Effects of Full Global Liberalization of Merchandise
Trade Reform, by Region, 2004 77
3.1 Characteristics of the Poor in Agricultural and Nonagricultural
Households, Developing Countries, 2000 91
3.2 The Household Effects of the Removal of Agricultural and All
Merchandise Trade Distortions on Global Poverty and Inequality 105
3.3 The GIDD Model: The Effects of the Removal of Agricultural
and All Merchandise Trade Distortions on the Incidence of
Poverty, by Region 107
3.4 The Effects of the Full Global Liberalization of Agricultural
and All Merchandise Trade on the Incidence of Inequality and
Poverty, Developing Countries 111
4.1 Estimates of Elasticities of the US$1-a-Day Poverty
Headcount with Respect to Total Income, 15 Countries 122
4.2 Stratum Contributions to the US$1-a-Day Poverty
Population, 15 Countries 124
4.3 Stratum- and Earnings-Specific Poverty Elasticities, Bangladesh 126
4.4 The Contributions of Earnings to the Total US$1-a-Day Poverty
Response, 15 Countries 127
4.5 Summary of the Poverty Impacts of Global Reforms, 15 Countries 131
4.6 Earnings Differences after Agricultural and Nonagricultural
Reforms, 15 Countries 136
4.7 Earnings-Specific Differences between Agricultural and
Nonagricultural Reforms in the Changes in Poverty, 15 Countries 137
4.8 Stratum-Specific Differences in the Changes in Poverty after
Agricultural and Nonagricultural Reforms, 15 Countries 138
4.9 Change in Poverty after Commodity-Specific Reforms,
15 Countries 140
4.10 Change in Poverty Caused by Foodgrain and Feedgrain
Reforms, 15 Countries 141
5.1 The Sectoral Structure of GDP, Trade, Import Tariffs, and
Export Subsidies, China, Around 2004 154
xii Contents
5.2 Modeled Liberalization Scenarios, China 157
5.3 Exogenous Demand and Price Shocks Caused by Liberalization
in the Rest of the World, China 158
5.4 Aggregate Simulation Results of Prospective Liberalizations,
China 161
5.5 The Effects of Prospective Liberalizations on Income Inequality
and Poverty, China 164
5.6 The Effects of Prospective Liberalizations on Sectoral Outputs
and Trade, China 171
5.7 Sensitivity Analysis of the Simulation Results, China 174
6.1 The Cost Shares of Major Factors of Production, Paddy and
Other Industries, Indonesia, 2000 184
6.2 Sources of the Factor Incomes of 10 Broad Household Groups,
Indonesia, 2000 187
6.3 Expenditure and Poverty Incidence by Household Category,
Indonesia, 2000 187
6.4 Industry Assistance Rates Used in Modeling, Indonesia, 2004 191
6.5 Exogenous Border Price Shocks That Would Result from
Rest-of-the-World Liberalization, Indonesia 193
6.6 Aggregate Simulation Results of the Prospective Liberalization
of All Commodities, Indonesia 195
6.7 Poverty Effects of the Prospective Liberalization of All
Commodities, Indonesia 198
6.8 Income Inequality Effects of the Prospective Liberalization of
All Commodities, Indonesia 202
6.9 Aggregate Simulation Results of the Prospective Liberalization
Only of Agricultural Commodities, Indonesia 203
6.10 Poverty Effects of the Prospective Liberalization of Only
Agricultural Commodities, Indonesia 204
6.11 Income Inequality Effects of the Prospective Liberalization of
Only Agricultural Commodities, Indonesia 206
7.1 Elasticity Parameters and Production Structure, Pakistan,
2001�02 215
7.2 Model Household Categories, Pakistan 218
7.3 Parameters and Exogenous Demand and Price Shocks Caused
by Rest-of-the-World Liberalization, Pakistan 220
7.4 Household Income Sources and Income Taxes, Pakistan, 2001�02 222
7.5 Poverty Estimates, Pakistan, 1998�99, 2001�02, and 2004�05 224
7.6 The Poverty Effects of Prospective Liberalizations, Pakistan 227
7.7 Aggregate Simulation Results of Prospective Agricultural and
Nonagricultural Liberalization, Pakistan 228
7.8 Household Welfare and Price Effects of the Liberalization in
All Goods Trade by the Rest of the World, Pakistan 232
Contents xiii
7.9 The Sectoral Effects of the Unilateral Liberalization of All
Goods Trade, Pakistan 236
7.10 Sensitivity Analysis of Household Welfare Effects, Two
Tax Replacement Schemes, Pakistan 242
7.11 Sensitivity Analysis of Poverty Effects, Two Tax Replacement
Schemes, Pakistan 243
8.1 The NRAs to Major Agricultural Commodities, the Philippines,
1960�2004 251
8.2 Poverty Incidence and Food Expenditure Shares,
the Philippines, 1997 and 2000 252
8.3 Production Structure, the Philippines, 2000 255
8.4 Trade Structure and Elasticity Parameters,
the Philippines, 2000 258
8.5 Structure of Household Expenditure, by Decile,
the Philippines, 2000 261
8.6 Exogenous Demand and Price Shocks Caused by
Rest-of-the-World Liberalization 263
8.7 Aggregate Simulation Results of Prospective Liberalizations
in Agriculture and Nonagriculture, the Philippines 268
8.8 Effects of Prospective Liberalizations on Real Household
Consumption, by Income Group, the Philippines 273
8.9 Income Inequality and the Poverty Effects of Prospective
Liberalizations, by Location, Gender, and Skills, the Philippines 277
9.1 Cost Shares of the Major Factors of Production, Paddy and
Other Industries, Thailand, 2000 286
9.2 Sources of the Factor Incomes of Broad Household Groups,
Thailand, 2000 286
9.3 Characteristics of the Major Household Categories,
Thailand, 2000 287
9.4 Industry Assistance Rates Used in Modeling, Thailand, 2004 289
9.5 Exogenous Border Price Shocks Caused by Rest-of-the-World
Liberalization, Thailand 290
9.6 Aggregate Simulation Results of the Prospective Liberalization
of All Commodities, Thailand 291
9.7 The Poverty and Inequality Effects of the Prospective
Liberalization of All Commodities, Thailand 293
9.8 Aggregate Simulation Results of the Prospective Liberalization
of Only Agricultural Commodities, Thailand 295
9.9 The Poverty and Inequality Effects of the Prospective
Liberalization of Only Agricultural Commodities, Thailand 296
10.1 The Economic Structure in 2002 and Price Distortions
in 2004, Mozambique 306
10.2 Factor Intensities of Production, Mozambique, 2002 309
xiv Contents
10.3 Household Income and Expenditure Shares, Mozambique, 2002 312
10.4 The Exogenous Demand and Price Shocks Caused by
Rest-of-the-World Liberalization, Mozambique 316
10.5 The Macroeconomic Simulation Results of Prospective
Liberalization Abroad and Nationally, Mozambique 318
10.6 The Effects of Prospective Liberalization Abroad and Nationally
on GDP, by Sector, Mozambique 319
10.7 The Effects of Prospective Liberalization Abroad and Nationally
on the Real Value of Exports, Mozambique 321
10.8 The Effects of Prospective Liberalization Abroad and Nationally
on the Real Value of Imports, Mozambique 323
10.9 The Effects of Prospective Liberalization Abroad and Nationally
on Employment, Welfare, and Poverty, Mozambique 325
11.1 Economic Structure and Price Distortions, South Africa, 2002 333
11.2 Direct Tax Rates on Households, South Africa, 2000 337
11.3 Household Income Shares, South Africa, 2000 338
11.4 Household Expenditure Shares, South Africa, 2000 339
11.5 Exogenous Demand and Border Price Shocks of
Rest-of-the-World Liberalization, South Africa 341
11.6 Macroeconomic Simulation Results of Prospective Liberalization
Abroad and Nationally, South Africa 342
11.7 The Effects of Prospective Liberalization Abroad and Nationally
on GDP, by Sector at Factor Cost, South Africa 343
11.8 Factor Intensity Structure, South Africa, 2002 345
11.9 The Effects of Prospective Liberalization Abroad and Nationally
on Factor Rewards, Employment, and Welfare, South Africa 346
11.10 The Effects of Prospective Liberalization Abroad and
Nationally on Sectoral Employment, Income Inequality,
and Poverty, South Africa 347
12.1 Components of the Social Accounting Matrix, Argentina, 2005 367
12.2 External Trade Structure, Argentina, 2005 369
12.3 Export Taxes and Import Tariffs, Argentina, 2005 370
12.4 Aggregate Simulation Results of Prospective Liberalization,
Argentina 375
12.5 Sectoral Simulation Results of Prospective Liberalization,
Argentina 377
12.6 Poverty Effects of Prospective Liberalization, Argentina 380
12.7 Income Inequality Effects of Prospective Liberalization,
Argentina 381
12.8 Fiscal and Real Investment Effects of Prospective Liberalization,
Argentina 384
13.1 Poverty Indicators, by Poverty Group, Brazil, 2001 398
13.2 Poverty and Income Inequality by State, Brazil, 2001 400
Contents xv
13.3 Share of Occupations in the Activity Labor Bill, by Wage
Group, Brazil, 2001 402
13.4 The Structure of External Trade, Brazil, 2001 405
13.5 Exogenous Demand and Border Price Shocks Caused by
Prospective Global Trade Liberalization, Brazil 407
13.6 The Macroeconomic Impacts of Prospective Trade
Liberalizations, Brazil 410
13.7 The Effects of Prospective Global Trade Liberalization on
Sectoral Outputs, Exports, and Imports, Brazil 411
13.8 The Effects of Prospective Global Trade Liberalization on
Output, by State, Brazil 413
13.9 The Effects of Prospective Global Trade Liberalization on Poverty
and Income Inequality, by Household Income Group, Brazil 414
13.10 Decomposition of the Effects of Liberalization on Real Incomes,
by Household Income Group, Brazil 416
13.11 The Effects of Prospective Global Trade Liberalization on Poverty,
by State, Brazil 417
14.1 Macroeconomic Indicators, Nicaragua, 1990�2005 425
14.2 Structure of Value Added by Sector at Factor Cost, Nicaragua,
1995, 2000, and 2004 426
14.3 The Poverty Headcount Ratio and the Gini Coefficient of
Income Inequality, Nicaragua, 1993�2005 431
14.4 Trade Structure and World Price Shocks Imposed in the Global
Trade Liberalization Simulations, Nicaragua, 2004 440
14.5 The Impact of Prospective Trade Liberalization on the
Macroeconomy, Nicaragua, 2004 441
14.6 The Impact of Prospective Trade Liberalization on Real
Sectoral GDP, Nicaragua 443
14.7 The Impact of Prospective Trade Liberalization on Employment
and Real Wages, Nicaragua 447
14.8 The Impact of Prospective Trade Liberalization on Employment,
by Sector, Nicaragua 448
14.9 Microsimulation Results for Poverty and Inequality, Nicaragua 451
A.1 Sectoral Concordance of the Linkage Model and the GTAP
Version 7 Database 459
A.2 Regional Concordance of the Linkage Model and the GTAP
Version 7 Database 460
A.3 Key Elasticities in the Linkage Model 464
A.4 Price Distortion Structure in GTAP Version 7p5 and the
Distortion Rates Drawn from the World Bank Project, 2004 468
A.5 Export Price, Import Price, and Export Demand Shocks to
National Models from Rest-of-the-World Agricultural and
Trade Policy Reform, Selected Developing Countries 471
FOREWORD
The vast majority of the world's poorest households depend to a considerable
extent on farming for their incomes, while food represents a large component of
the consumption of all poor households. The prices of farm products are thus
crucial determinants of the extent of poverty and inequality in the world. Yet, for
generations, these prices have been heavily distorted by government policies in
high-income countries and in developing countries. True, many countries began
to reform their agricultural price and trade policies in the 1980s, but Distortions to
Agricultural Incentives: A Global Perspective, 1955�2007 (edited by Kym Anderson),
a 2009 World Bank publication, shows that the extent of government policy inter-
vention is still considerable and still favors farmers in high-income countries at the
expense of farmers in developing countries.
What would be the poverty and inequality consequences of the removal of the
remaining distortions to agricultural incentives? This question is of great rele-
vance to governments in evaluating ways to engage in multilateral and regional
trade negotiations or to improve their own policies unilaterally. The answer is
often not clear in any one country and is certainly an empirical matter for groups
of countries because the positive and negative effects in different settings may be
offsetting. Some analysts have sought answers in past events using ex post econo-
metric analysis of historical data, but it is not easy to find natural experiments that
are suitable for analysis and from which it is possible to generalize. An alternative
approach--the one adopted in the present study--is to undertake ex ante analysis
using economy-wide models, including global models, so as to be able to simulate
the prospective effects of the multilateral removal of all price-distorting policies.
This volume represents a first attempt to exploit new methodologies, models,
and databases (developed partly as a consequence of recent World Bank�sponsored
research) to assess the relative impacts on national, regional, and global poverty
xvii
xviii Foreword
and inequality of agricultural and nonagricultural trade policies at home and
abroad. While clear, definitive answers are not easy to determine, the volume
extends our understanding of the contributions of various policies to inequality
and poverty in selected developing countries and regions. Its broad finding is that
the removal of all current price-distorting policies is likely to reduce global
poverty and inequality, but there may be a few countries in which such a policy
change on its own might worsen poverty nationally. In particular, it highlights the
fact that the results are sensitive to assumptions about the changes in taxation
needed to compensate the national treasury for losses in trade tax revenue, the
degree of flexibility in each country's labor markets, the complementary measures
taken to increase the opportunities for farm families and mitigate any adverse
poverty impacts, and so on. As always, agricultural and trade policy reforms have
better prospects of becoming politically and socially sustainable, the more govern-
ments also provide optimal domestic safety net policies.
Justin Yifu Lin
Senior Vice President and Chief Economist
The World Bank
ACKNOWLEDGMENTS
This book analyzes the effects of agricultural policies and farm and nonfarm trade
policies around the world on national and regional economic welfare, on income
inequality among and within countries, and, most importantly, on the level and
incidence of poverty in developing countries. The studies in this volume include
economy-wide analyses of the inequality and poverty effects of own-country poli-
cies compared with rest-of-the-world policies for 10 individual developing coun-
tries in three continents. In addition, the book includes three chapters that each
use a separate global economic model to examine the effects of policies on aggre-
gate poverty and the distribution of poverty across many identified developing
countries.
The book is motivated by two policy issues: first, the World Trade Organiza-
tion's struggle to conclude the Doha Round of multilateral trade negotiations, in
which agricultural policy reform is, once again, one of the most contentious top-
ics in the talks, and, second, the struggle of the developing countries to achieve
their Millennium Development Goals by 2015, notably the alleviation of hunger
and poverty, which depends crucially on policies that affect agricultural incen-
tives. The study's timing is facilitated by recent methodological developments
involving microsimulation modeling based on household survey data, in conjunc-
tion with economy-wide computable general equilibrium modeling. Recent
surveys are now available at the World Bank on more than 100 countries. More-
over, the World Bank has recently compiled a comprehensive new global database
that updates and expands substantially our understanding of the distortions to
agricultural incentives, particularly in developing countries. The authors of the
chapters in this book are therefore indebted to the many contributors to these
recent developments in methodology and data. In turn, we are grateful to the
xix
xx Acknowledgments
authors for taking part in our project, which is an extension of the project that
generated the new distortions database (details of which are available at
http://go.worldbank.org/5XY7A7LH40).
We extend our thanks to the senior advisory board of the overall project. The
board's members have provided advice and encouragement throughout the plan-
ning and implementation stages of the project. The board consists of Yujiro
Hayami, Bernard Hoekman, Anne Krueger, John Nash, Johan Swinnen, Stefan
Tangermann, Alberto Vald�s, Alan Winters, and, until his untimely death in
March 2008, Bruce Gardner.
The editors, the other authors, and the advisory board are all grateful for the
untiring administrative support provided throughout the entire project by
Michelle Chester in the trade unit of the Development Research Group of the
World Bank. They also appreciate the feedback and helpful comments of col-
leagues at the 2008 and 2009 annual Global Economic Analysis Conference, in
Helsinki and Santiago, respectively, where earlier versions of many of the chapters
herein were presented.
For financial assistance, grateful thanks go to the Development Research Group
of the World Bank and the trust funds of the governments of the Netherlands and
the United Kingdom. This combined support has made it possible for the study to
include a wide range of developing countries.
Kym Anderson, John Cockburn, and Will Martin
August 2009
CONTRIBUTORS
Kym Anderson is George Gollin Professor of Economics at the University of
Adelaide, Australia and a fellow of the Centre for Economic Policy Research,
London. During 2004�07, he was on an extended sabbatical as lead
economist (trade policy) in the Development Research Group of the World Bank
in Washington, DC.
Channing Arndt, following a five-year period based in Maputo, Mozambique, is a
professor at the Department of Economics at the University of Copenhagen. His
publications cover poverty measurement, trade policy, the macroeconomic impli-
cations of HIV/AIDS, agricultural productivity growth, and demand systems
estimation.
Maurizio Bussolo is a senior economist at the World Bank in Washington, DC. At
the time of his contribution to this volume, he was with the Development
Prospects Group; he is now in the economic policy unit of the Latin America and
the Caribbean Region of the World Bank.
Martin Cicowiez is a research economist and lecturer at the Center for Distribu-
tional, Labor, and Social Studies at the Universidad Nacional de La Plata, in
Buenos Aires, Argentina.
John Cockburn is a professor of economics at the Universit� Laval in Quebec and
co-director of the Poverty and Economic Policy Research Network. He conducts
research on child welfare, poverty, growth, and trade policy.
xxi
xxii Contributors
Erwin Corong is a PhD student at the Centre of Policy Studies at Monash Univer-
sity, Melbourne. At the time of his contribution to this volume, he was with the
Poverty and Economic Policy Research Network at the Universit� Laval in
Quebec. He was subsequently with the International Food Policy Research
Institute in Washington, DC.
Caesar B. Cororaton is a research fellow at the Global Issues Initiative at the
Institute for Society, Culture, and Environment of Virginia Polytechnic and State
University in Alexandria, Virginia. At the time of his contribution to this volume,
he was with the International Food Policy Research Institute in Washington, DC.
Carolina D�az-Bonilla is an economist in the Poverty Reduction and Economic
Management Network of the Latin America and the Caribbean Region at the
World Bank in Washington, DC. At the time of her contribution to this volume,
she was in the Development Prospects Group of the World Bank.
Eugenio D�az-Bonilla is executive director for Argentina and Haiti at the Inter-
American Development Bank, in Washington, DC. He was formerly a senior
fellow in the Macro and Trade Division of the International Food Policy Research
Institute in Washington, DC.
Joaquim Bento de Souza Ferreira Filho is a professor in the Department of
Economics in the Escola Superior de Agricultura "Luiz de Queiroz," Universidade
de S�o Paulo at Piracicaba, S�o Paulo, Brazil. He has served as president of the
Brazilian Society of Rural Economics, Management, and Sociology.
Nicolas H�rault is a research fellow with the Melbourne Institute of Applied
Economic and Social Research at the University of Melbourne, which he joined in
March 2007 following doctoral studies at the University of Bordeaux IV in France,
where he began his microsimulation analyses of South Africa.
Thomas W. Hertel is a distinguished professor, Department of Agricultural
Economics, and founding executive director, Center for Global Trade Analysis
at Purdue University, West Lafayette, Indiana, where he teaches and conducts
research on the economy-wide impacts of trade and environmental policies.
Mark Horridge is a professor of economics at the Centre of Policy Studies at
Monash University in Melbourne. He specializes in computable general equilib-
rium modeling, including the ongoing development of widely used economic
modeling software such as Gempack.
Contributors xxiii
Rafael De Hoyos, at the time of his contribution to this volume, was a senior
economist in the Development Prospects Group of the World Bank in
Washington, DC. He is now chief of advisers to the Mexican Under-Secretary of
Education, Mexico City.
Roman Keeney is an assistant professor in the Department of Agricultural Eco-
nomics, Purdue University, West Lafayette, Indiana, where he specializes in teaching
and research on the global economy-wide modeling of economic policies.
Will Martin is the research manager of the Rural Development Unit in the Devel-
opment Research Group at the World Bank in Washington, DC. He specializes in
trade and agricultural policy issues globally, especially in Asia, and has written
extensively on trade policies affecting developing countries.
Denis Medvedev is a young professional in the World Bank in Washington, DC. At
the time of his contribution to this volume, he was with the Development Prospects
Group at the World Bank. He specializes in the global economy-wide modeling of
economic policies, especially as these policies affect inequality and poverty.
David Orden is a senior research fellow at the International Food Policy Research
Institute in Washington, DC and director of the Global Issues Initiative at the
Institute for Society, Culture, and Environment at Virginia Polytechnic Institute
and State University in Alexandria, Virginia. His research focuses on the econom-
ics and political economy of agricultural policies.
Marco V. S�nchez is economic affairs officer at the Department of Economic and
Social Affairs of the United Nations, New York. He was previously associated with
the Sub-Regional Office in Mexico City of the United Nations Economic
Commission for Latin America and the Caribbean.
James Thurlow is a research fellow at the University of Copenhagen and at
the International Food Policy Research Institute in Washington, DC. His research
focuses on the role of agricultural transformation in the development process,
particularly strategies for growth and poverty reduction in Sub-Saharan Africa.
Ernesto Valenzuela is a lecturer and research fellow at the School of Economics and
the Centre for International Economic Studies at the University of Adelaide,
Australia. During 2005�07, he was a consultant at the Development Research
Group of the World Bank in Washington, DC.
xxiv Contributors
Dominique van der Mensbrugghe is lead economist in the Development Prospects
Group of the Development Economics Vice-Presidency of the World Bank in
Washington, DC, where he specializes in global economy-wide modeling. He is
the architect of the global Linkage model.
Rob Vos is the director of the Development Policy and Analysis Division at the
Department of Economic and Social Affairs of the United Nations, New York, and
affiliated professor of finance and development at the Institute of Social Studies in
The Hague.
Peter Warr is the John Crawford Professor of Agricultural Economics and found-
ing director of the Poverty Research Centre in the Arndt-Corden Division of Eco-
nomics, Research School of Pacific and Asian Studies, at the Australian National
University, Canberra.
Fan Zhai was, at the time of his contribution to this volume, an economist in the
Asian Development Bank in Manila on secondment to the Asian Development
Bank Institute in Tokyo as a research fellow. He is now with the China Investment
Bank in Beijing. His research has focused on modeling Asian economies.
ABBREVIATIONS
CGE computable general equilibrium (model)
DR-CAFTA Dominican Republic�Central America Free Trade Agreement
EU15 The 15 members of the European Union prior to 2004
GDP gross domestic product
GIC growth incidence curve
GIDD Global Income Distribution Dynamics (data set)
GTAP Global Trade Analysis Project
MS microsimulation
NRA nominal rate of assistance
OECD Organisation for Economic Co-operation and Development
PPP purchasing power parity
RRA relative rate of assistance
SAM social accounting matrix
WTO World Trade Organization
Note: All dollar amounts are U.S. dollars (US$) unless otherwise indicated.
xxv
Part I
Introduction
1
INTRODUCTION
AND SUMMARY
Kym Anderson, John Cockburn, and Will Martin
For decades, the earnings from farming in many developing countries have been
depressed because of a pro-urban, antiagricultural bias in own-country policies
and because governments in more well off countries are favoring their farmers by
imposing import barriers and providing subsidies. These policies have reduced
national and global economic welfare, inhibited economic growth, and added to
inequality and poverty because no less than three-quarters of the billion poorest
people in the world have been dependent directly or indirectly on farming for
their livelihoods (World Bank 2007). Over the past two or three decades, however,
numerous developing-country governments have reduced sectoral and trade
policy distortions, while governments in some high-income countries have also
begun reforming protectionist farm policies. Partly as a consequence of these and
associated domestic policy reforms and the consequent growth in incomes in
many developing countries, the number of people living on less than US$1 a day
fell by nearly half between 1981 and 2005, while the share of these people fell from
42 to 16 percent of the global population (table 1.1).
Notwithstanding the dramatic achievement in poverty reduction, there were
still almost 900 million extremely poor people in 2005, and the number may have
risen following the eruption of the global financial crisis in 2008. Moreover, most
of the improvement has been in Asia (especially China), while, in Sub-Saharan
Africa, the incidence of poverty was not much different in 2005 than in 1981, at
around 40 percent (amounting to 300 million people in 2005). Despite the success
of China, more than 100 million people were still living on less than US$1 a day
there in 2005, 90 percent of whom lived in rural areas. In India, the number of
3
4
Table 1.1. Global Poverty and Inequality, by Region, 1981�2005
(number and percent of people living on less than 2005 PPP US$1 a day)
Share of Index of
poor who income
are rural, inequality,
Region 1981 1987 1993 1999 2005 2002, % 2004a
Number of people, millions
World 1,528 1,228 1,237 1,146 879 74 --
Sub-Saharan Africa 157 202 247 299 299 69 --
East Asia and Pacific 948 598 600 425 180 85 0.37
China 730 412 444 302 106 90 0.36
South Asia 387 384 341 359 350 75 0.35
India 296 285 280 270 267 74 0.33
Latin America and the Caribbean 27 35 34 40 28 34 0.52
Rest of world 9 9 15 23 22 50 --
East and South Asia share in world, % 87 80 76 68 60 -- --
Share of population, %
World 42 30 27 23 16 -- --
Sub-Saharan Africa 40 42 44 46 39 -- --
East Asia and Pacific 69 39 36 24 10 -- --
China 74 38 38 24 8 -- --
South Asia 42 37 29 27 24 -- --
India 42 36 31 27 24 -- --
Latin America and the Caribbean 7 8 7 8 5 -- --
Sources: Chen and Ravallion (2008); for the rural share, Ravallion, Chen, and Sangraula (2007); for the Gini coefficient, PovcalNet (2008).
Note: The calculations are based on 2005 PPP (purchasing power parity) dollars. -- no data are available.
a. The index is the Gini coefficient calculated as the population-weighted cross-country average of national Gini coefficients in the region for the available year nearest
to 2004.
Introduction and Summary 5
extreme poor remains stubbornly close to 300 million (74 percent in rural areas)
despite large farm subsidies.
Less pressing than the problem of extreme poverty, though nonetheless still
important to the welfare of individuals, is the extent of income inequality.1 In the
past, only inequality at the local level affected the utility of individuals, but the
information and communication technology revolution has increased awareness
of income differences nationally and even internationally. At national levels, there
are concerns about rural-urban inequality, as well as inequality within other clas-
sifications. Within rural areas, for example, differences in incomes may be vast
among landless unskilled farm workers, subsistence farmers, the owners of larger
commercial farms, and nonfarm workers in rural towns.2
At the global level, Milanovic (2005) points to three possible means of assess-
ing the changes in income distribution in recent decades. One revolves around
intercountry inequality, the comparison of average incomes across countries
wherein each country has an equal weight in the world distribution regardless of
population size; measured in this way, income distribution appears to have
become more unequal. The second focuses on international inequality, the com-
parison of average incomes across countries wherein each country is weighted by
population; measured in this way, income inequality appears to have decreased,
although the decrease has occurred mostly because of rapid population growth in
China and India (see Bourguignon, Levin, and Rosenblatt 2004; Atkinson and
Brandolini 2004). The third method of assessment focuses on global inequality,
the comparison of individual incomes regardless of the country of citizenship.
This method thus takes into account within-country inequality, which is ignored
in the intercountry and international inequality approaches wherein individuals
are all assumed to earn the average income in their countries. The rapid growth in
the large emerging economies has tended to offset the increase in inequality
within countries, and, so, by this last methodology, global inequality appears to
have remained roughly constant since the late 1980s.3
Given the evidence currently available, our book focuses on one main question:
how much scope is there to reduce poverty and inequality in the world and in spe-
cific developing countries by unilaterally or globally eliminating the distortions in
the incentives affecting the producers and consumers of tradable goods? This
question is of great interest to the agricultural, trade, and development policy
communities in many developing countries and in nongovernmental organiza-
tions and international agencies. The answer is by no means obvious. While it is
true that recent studies indicate that agricultural policies are responsible for the
majority of the global welfare costs of the remaining distortions to goods markets,
removing these policies could affect national poverty levels either negatively or
positively. The answer in each country to our question depends on current food
6 Agricultural Price Distortions, Inequality, and Poverty
and agricultural policies at home and abroad, as well as on earning and spending
patterns and the taxes on the poor, among other factors. Account also needs to be
taken of three other facts. First, the dependence of the extreme poor on agricul-
ture for their livelihoods has been declining in numerous countries as alternative
opportunities have emerged outside agriculture, especially in off-farm, part-time
employment. Second, the lowering of trade barriers has improved the opportuni-
ties for farmers to specialize in cash crops for export, increasing their potential
benefits from any improvements in market access abroad for these crops or
related products. Third, practical realities too important to ignore in some coun-
tries are high levels of unemployment (as in South Africa) or policies that inhibit
intersectoral labor mobility (as in China).
Empirical studies undertaken as background for the World Trade Organiza-
tion's ongoing Doha Round of multilateral trade negotiations suggest that, in 2001,
when the Doha Round was launched, policy-driven distortions to
agricultural incentives contributed around two-thirds of the global welfare cost of
merchandise trade barriers and subsidies (for example, see Anderson, Martin, and
van der Mensbrugghe 2006). While these studies did not have access to compre-
hensive estimates of the distortions to farmer and food consumer incentives in
developing countries other than the applied tariffs on imports, a more recent
study that draws on a new database of distortions to agricultural incentives has
confirmed the earlier result. Valenzuela, van der Mensbrugghe, and Anderson
(2009) find that agricultural price and trade policies as of 2004 accounted for
60 percent of the global welfare cost of these and other merchandise trade policies.
This is a striking outcome given that the shares of agriculture and food in global
gross domestic product (GDP) and trade are less than 6 percent. The contribution
of farm and food policies to the welfare cost of global trade-distorting policies in
developing countries alone is estimated by these authors to be even greater, at
83 percent, of which a little more than one-third is generated by the policies of the
developing countries themselves. Nonetheless, the price distortion estimates used
in the modeling study (see Anderson and Valenzuela 2008) show that many devel-
oping countries protect their less-competitive farmers from import competition;
so, some of this subset of farmers might be hurt if all markets were opened.
Thus, despite much reform over the past quarter century in policies leading to
distortions in world trade, many of the relevant intervention measures, especially
the agricultural ones, are still in place. Table 1.2 summarizes the average extent of
these measures in developing and high-income countries. It shows that the rate of
assistance to farmers relative to producers of nonfarm tradables has fallen by one-
third in high-income countries since the late 1980s (from 51 to 32 percent), while,
in developing countries, this relative rate of assistance has risen from 41 percent
in the early 1980s to 1 percent in 2000�04. The latter trend in developing countries
is caused partly by the phasing out of agricultural export taxes and partly by the
Table 1.2. The NRAs for Tradable Agricultural and Nonagricultural
Products and the RRA, Focus Regions, 1980�2004
(percent)
Region, indicator 1980�84 1985�89 1990�94 1995�99 2000�04
Africa
NRA agricultural exportables 35 37 36 26 25
NRA agricultural import-
competing 13 58 5 10 2
NRA agricultural tradables 14 0 15 9 12
NRA nonagricultural tradables 2 9 3 2 7
RRA 13 8 17 10 18
South Asia
NRA agricultural exportables 28 21 16 12 6
NRA agricultural import-
competing 38 63 25 15 27
NRA agricultural tradables 2 47 0 2 13
NRA nonagricultural tradables 55 40 19 15 10
RRA 33 5 16 15 3
China and Southeast Asia
NRA agricultural exportables 50 41 21 2 0
NRA agricultural import-
competing 1 15 3 13 12
NRA agricultural tradables 35 28 12 5 7
NRA nonagricultural tradables 21 23 20 10 6
RRA 43 42 26 4 2
Latin America
NRA agricultural exportables 27 25 11 4 5
NRA agricultural import-
competing 14 5 19 13 21
NRA agricultural tradables 13 11 4 6 5
NRA nonagricultural tradables 19 17 7 7 5
RRA 27 24 3 1 1
All developing countries
NRA agricultural exportables 41 36 19 6 3
NRA agricultural import-
competing 17 38 23 22 23
NRA agricultural tradables 21 16 4 4 7
NRA nonagricultural tradables 35 27 17 10 6
RRA 41 34 18 5 1
High-income countries
NRA agricultural exportables 12 22 16 8 7
NRA agricultural import-
competing 58 71 62 54 51
NRA agricultural tradables 43 56 48 37 34
NRA nonagricultural tradables 3 3 3 2 1
RRA 38 51 45 34 32
Source: Anderson and Valenzuela (2008), based on estimates reported in the project country studies.
Note: The relative rate of assistance (RRA) is defined as 100 * [(100 NRAagt) (100 NRAnonagt) 1],
where NRAagt and NRAnonagt are the percentage nominal rates of assistance (NRAs) for the tradables
parts of the agricultural and nonagricultural sectors, respectively, and NRAagt is the weighted average of
the nominal rates of assistance for the exporting and import-competing subsectors of agriculture.
7
8 Agricultural Price Distortions, Inequality, and Poverty
rise in assistance via food import restrictions over the period. Thus, both in high-
income countries and in developing countries, there is now a large gap between
the nominal rates of assistance for import-competing and export agriculture, as
well as a continuing gap (albeit smaller than the corresponding gap in the 1980s)
between the relative rates of assistance in the two groups of countries. In light of
this evidence, our question above may be expressed more specifically for any
developing country of interest as two additional questions: How important are a
developing country's own agricultural and other trade policies compared with
those of the rest of the world in determining the welfare of the poor in the coun-
try? And what is the contribution of agricultural policies to these outcomes? Clear
answers to these questions are crucial in guiding countries in national policy
making and in negotiating bilateral and multilateral trade agreements.
Now is an appropriate time to address these multifaceted questions for at least
two policy reasons. One is that the World Trade Organization is struggling to con-
clude the Doha Round of multilateral trade negotiations, and agricultural policy
reform is once again one of the most contentious issues in these talks. The other is
that poorer countries are striving to achieve their United Nations�encouraged
Millennium Development Goals by 2015, and the prime goals are the reduction of
hunger and poverty. Farm-subsidizing rich countries are not alone in resisting
reform; some developing countries likewise do not wish to remove food import
barriers and farmer subsidies.
There are also several analytical reasons for focusing more thoroughly on this
issue now. First, methodologies to address the issue have been improving rapidly,
most notably through the combination of economy-wide computable general
equilibrium (CGE) modeling and microsimulation modeling based on household
survey data. Prominent examples include the studies in Hertel and Winters (2006)
and in Bourguignon, Bussolo, and Pereira da Silva (2008). Household income
information is increasingly important for poverty and inequality analysis because
farm households and rural areas in developing countries are rapidly diversifying
their sources of income beyond the income generated by agricultural land and
farm labor, including part-time off-farm work and remittances (Otsuka and
Yamano 2006; Otsuka, Estudillo, and Sawada 2009). Hence, the once close corre-
spondence between net farm income or agricultural GDP and farm household
welfare is fading even in low-income countries (Davis, Winters, and Carletto
2009).
Second, the compilation of national household surveys that are comparable for
the purpose of cross-country analysis has progressed rapidly. Recent surveys are
now available at the World Bank for more than 100 countries. The Global Income
Distribution Dynamics (GIDD) data set has already begun to be used in conjunc-
tion with the World Bank's Linkage model of the global economy to assess global
Introduction and Summary 9
income distribution issues (for example, see Bussolo, De Hoyos, and Medvedev
2008; see also the GIDD Database).
Third, the World Bank has recently compiled a comprehensive global database
that substantially updates and expands our information on the distortions to agri-
cultural incentives in developing countries.4 The estimates in this database have
since been expressed so as to make them usable in national and global economy-
wide models (Valenzuela and Anderson 2008). They differ from the usual estimates
relied on by trade modelers of developing-country policies in that they are based
on direct domestic-to-border price comparisons rather than on applied rates of
import tariffs and other key border measures (see Narayanan and Walmsley 2008
for the latest compilation of the Global Trade Analysis Project [GTAP] data set).
The present volume is a first attempt to exploit these new methodologies and
databases to assess the relative impacts on national, regional, and global poverty
and inequality of agricultural and nonagricultural trade policies at home and
abroad. Poverty is defined in purchasing power parity terms at a threshold of
US$1 a day per capita (the extreme poverty line) and also sometimes at a thresh-
old of US$2 a day (the moderate poverty line). If these indicators are not available,
then the national poverty line is used. The incidence of poverty (the share of the
population below the poverty line) and the headcount (the absolute number of
poor people) are also used. The Gini coefficient of income distribution is the key
measure of inequality adopted here. Where possible, the national indicators
for both poverty and inequality are calculated for farm and nonfarm households
separately, in addition to the national averages.
In undertaking this set of studies, we are acutely aware that agricultural and
trade or domestic price subsidies are by no means the first-best policy instru-
ments for achieving national poverty or income distribution objectives; this is
largely the prerogative of public finance policies such as the supply of public
goods or tax and transfer measures, including the provision of social safety nets
funded through general tax revenues. However, should studies reveal that national
trade-related policies are worsening poverty or inequality in particular countries,
they provide a reason, in addition to the usual national gains-from-trade reason,
for unilateral policy reform in these countries. Should the inequality- and
poverty-reducing effects of national trade-related policy reforms be contingent on
the rest of the world also reforming, this would provide another reason for a
country to participate actively in promoting multilateral trade negotiations at the
World Trade Organization. Furthermore, if global modeling studies reveal that
multilateral trade reform would reduce global inequality and poverty, this would
underline the importance of bringing the Doha Development Agenda of the
World Trade Organization expeditiously to a successful conclusion through com-
mitments to ambitious agricultural reform.
9
10 Agricultural Price Distortions, Inequality, and Poverty
A negative finding--for example, that trade liberalization or farm subsidy cuts
would increase poverty in a particular developing country--need not be a reason
to shun welfare-enhancing reform, but would be a reason to use the results to pro-
vide guidance to determine where tax or social programs might become more well
targeted so that all groups in society share in the economic benefits of reform (see
Ravallion 2008). The results of global reform also provide bargaining power
to developing countries that are seeking aid-for-trade side payments to reduce
any increase in poverty projected to be generated by trade reform that has been
multilaterally agreed upon.
The purpose of the rest of this chapter is to outline the analytical framework
and the common empirical methodology adopted in the global and national case
studies reported in subsequent chapters, to summarize and compare the modeling
results from the global and national models, and to draw some general policy
implications. The findings are based on three chapters (part II) that each use a
global model to examine the effects of farm and nonfarm price and trade policies
on global poverty and the distribution of poverty within and across many of the
countries identified, plus 10 individual developing-country studies (parts III�V)
spanning the three key regions: Asia (where nearly two-thirds of the world's poor
live), Sub-Saharan Africa, and Latin America.
Analytical Framework
To capture the poverty and inequality effects of price-distorting policies ade-
quately, one must give careful consideration to the impacts of the policies on
household incomes and expenditures. Many farm households in developing coun-
tries rely on the farm enterprise for virtually all of their incomes, and, in the world's
poorest countries, the share of national poverty concentrated in such households is
large. That the poorest households in the poorest countries are concentrated in
agriculture means these households are likely to benefit from farm producer price
increases generated by global trade policy reform, all else being equal. However,
this outcome is not certain for several reasons. First, if a country provides protec-
tion from import competition for the farm commodities produced by the poor,
the domestic prices of the commodities may decline after liberalization. Second,
poor farm households also spend the majority of their incomes on staple foods
(Cranfield et al. 2003); so, if food prices rise as a consequence of reform, then this
adverse effect on household expenditure may more than offset any beneficial effect
of higher earnings. Third, the rural nonfarm and urban poor, too, would be
adversely affected by a rise in the consumer prices of staple foods. However, it is
possible that a trade reform that induced a rise in food prices may also raise the
demand for unskilled labor (according to the relative factor intensity of production
Introduction and Summary 11
in the economy's expanding sectors), which--depending on the intersectoral
mobility of labor--might raise the incomes of poor households more than it raises
the price of the consumption bundle of these households. The outcome is therefore
not always going to be clear for any particular country and is certainly an empirical
matter for groups of countries because the positive and negative effects in different
settings will be more or less offsetting.
Some analysts have sought answers in past events by using ex post econometric
or micro household data analysis (as in the set of studies in Harrison 2007), but it
is not easy to find natural experiments of specific policy reforms that are appro-
priate for analysis and from which it is possible to generalize. An alternative
approach--the one adopted in the present study--is to undertake ex ante analysis
using economy-wide models. While such models have well-recognized limita-
tions, they are the only option available if one is seeking to simulate the prospec-
tive effects of the removal of all remaining price-distorting policies (see, for exam-
ple, Francois and Martin 2007). This is particularly the case if global reform is one
of the scenarios of interest and, even more so, if insights into the effects on overall
world poverty and inequality are being sought.
The approach adopted in our study is a variant on the pathbreaking approach
pioneered by Hertel and Winters (2006) in their study of the poverty consequences
of a prospective Doha Round agreement under the World Trade Organization.
Like Hertel and Winters (2006), our study uses global models to assess the impli-
cations of global reform for poverty, plus a series of national models to focus more
attention on specific aspects of importance to particular countries. However, the
present study contrasts with the earlier one compiled by Hertel and Winters in
three key respects. First, our study focuses on the impacts of agricultural policies
and distinguishes these from the impacts of other merchandise trade policies;
moreover, it relies on the new database on distortions to agricultural incentives in
developing countries that has only recently become available in a format that is
readily usable by CGE modelers (Valenzuela and Anderson 2008). These distor-
tion estimates (for 2004) are used to represent agricultural and food policies
in each of the 10 national CGE models employed in our country case studies in
parts III�V, as well as in the three global models described in part II. A second dis-
tinction is that our study examines inequality, in addition to poverty. The third
difference is that the present study is able to draw on the massive data collection
and modeling effort undertaken for the GIDD Database, which includes data on
more than a million surveyed households in a set of countries representing more
than 90 percent of the world's population.
The national CGE models are able, on their own, to provide estimates of the
effects of the unilateral reform of agricultural policies or of all merchandise trade-
distorting policies in each relevant country. To estimate the effects of the policies
12 Agricultural Price Distortions, Inequality, and Poverty
of other countries, however, a national modeler requires input from a global
model. We have decided to use the World Bank Linkage model for this purpose. It
is based on version 7 of the GTAP Database on global protection that is calibrated
to 2004 except that we have replaced the applied agricultural tariffs for developing
countries in the GTAP Database with the more comprehensive set of estimates of
distortion rates derived during the World Bank's research project, Distortions to
Agricultural Incentives, as collated by Valenzuela and Anderson (2008), which are
also calibrated to 2004.5 The latter distortion estimates suggest that, despite
reforms over the past 25 years, there was still a considerable range of price distor-
tions across commodities and countries in 2004, including a strong antitrade bias
in national agricultural policies in many developing countries, plus considerable
nonagricultural protection in some developing countries (see table 1.2 above).
There are various ways to transmit the results derived from a global CGE
model such as Linkage to a single-country CGE model. Like Hertel and Winters
(2006), we adopt the approach developed by Horridge and Zhai (2006). For
imports, Horridge and Zhai propose the use of border price changes from the
global model's simulation of rest-of-the-world liberalization (that is, without the
developing country that is the focus). For the exports of the developing country of
interest, the shift in the export demand curve following liberalization in the rest of
the world is given as percentage changes, as follows:
x (1 ).q, (1.1)
where x is the percentage vertical shift in the export demand curve; is the elas-
ticity of substitution between the exports of country i and the exports of other
countries; and q is the percentage change in the quantity of exports under the sce-
nario of liberalization in the rest of the world, excluding the country of focus.
All the CGE models used in the present study are applied in the comparative
static mode, and they assume constant returns to scale and perfectly competitive
product markets and homogeneous firms. In all cases other than the exceptional
case of South Africa and, to a much smaller extent, the cases of Argentina and
Nicaragua, unemployment is assumed to be unaffected by changes in the trade
policy regime. These assumptions are imposed simply because of insufficient
empirical evidence for the use of alternative assumptions across all the countries
modeled in our study. This application of a standard set of assumptions reduces
the risk that differences in the results across countries are driven by differing
assumptions about investment behavior, productivity growth, the degree of
monopolistic competition or firm heterogeneity, economies of scale, or the aggre-
gate employment response to changes in trade policy (see Helpman, Itskhoki, and
Redding 2009). Our workhorse specifications almost certainly lead to underesti-
mations of the welfare gains that would accrue from trade reform, however. In
Introduction and Summary 13
particular, without dynamics, the models will not generate a growth dividend from
the freeing up of markets or from the eventual productivity gains from trade. This div-
idend may be substantial.6 Moreover, because economic growth is the predominant
way poverty is reduced in developing countries (see the literature review in Ravallion
2006), the absence of dynamics implies that the results of this study will grossly
underestimate the potential poverty-reducing consequences of liberalization and
might, in some situations, indicate poverty increases when, in fact, they would be
decreases had the growth consequences been incorporated.
All the country case studies and two of the global modeling studies presented
in this volume make use of household survey data, in addition to a social account-
ing matrix. The matrix is the basis for the data in the CGE model, while the house-
hold survey data are used in microsimulation modeling.
Typically, the experiments are performed in two stages. The first stage involves
the imposition on the national CGE model of the policy shock (either unilateral
liberalization, or an exogenous shock to border prices and export demand pro-
vided by the Linkage model). This generates changes in domestic product and fac-
tor markets. The consequent changes in consumer and factor prices are then
transmitted to the microsimulation model to determine how they alter the earn-
ings of various household types (according to the shares of household income
generated by the various factors) and the cost of living of these households
(according to the shares of their expenditure on the various consumer products).
In turn, this provides information on changes in the distribution of real house-
hold incomes and, hence, in inequality, as well as in the number of people living
below any poverty line, such as the US$1-a-day extreme poverty line.
In all the country case studies, a common set of simulations has been run to
compare the inequality and poverty effects of the own-country versus rest-of-the-
world policies affecting the markets for agricultural goods (including lightly
processed food) relative to the markets for other merchandise. The precise nature
of the rest-of-the-world simulation, which employs the global Linkage model, is
made clear in the next chapter and in the appendix. The other two global studies
in part II use the same 2004 global protection data set, but rely on a different
global model that includes national household survey data for microsimulations.
In most cases, additional simulations have also been run, often to illustrate the
sensitivity of the results to key assumptions pertinent to the particular case study.
One assumption that the contributors to Hertel and Winters (2006) find impor-
tant and that is confirmed in our study as well revolves around the nature of the
change in raising tax revenue required to make up for the loss in tariff revenue
associated with trade policy reform.
Although the models used in this study are all standard, perfectly competitive,
constant-returns-to-scale, comparative static, economy-wide CGE models, they
14 Agricultural Price Distortions, Inequality, and Poverty
nonetheless differ somewhat so as to capture important realities, such as labor
market characteristics or data limitations, in the particular national settings. How-
ever, to ensure the comparability of the models within this volume, we have aimed
at conforming the models to a common set of factor market assumptions and clo-
sure rules, in addition to our use of 2004 as the base year for the data and under-
taking a common set of simulations with the same global distortions data set.
We know from trade theory that factor market assumptions are crucial deter-
minants of the income distributional effects of trade policies; so, all modelers have
assumed the following: (1) a fixed aggregate stock of factors (including no inter-
national mobility in labor or capital or in international technology transfers),
with the exception of labor in the studies on Argentina, Nicaragua, and South
Africa, in which some aggregate employment responsiveness to trade policy is
allowed because of the high level of unemployment in the baseline; (2) possibly
some sector-specific capital and labor, but most capital and labor types are
assumed to be intersectorally mobile and have a common, flexible rate of return
or wage, except in the case of Argentina, in which the labor market is modeled
with a switching regime between employment or wage adjustments; and (3) land
is assumed to be specific to the agricultural sector, but mobile across crop and
livestock activities within the sector.
The key agreed macroeconomic closure rules that the authors have aimed to
adopt in each case study are a fixed current account in foreign currency (so as to
avoid foreign debt considerations) and fixed real government spending and fiscal
balance (so as not to affect household utility other than through traceable changes
in factor and product prices and taxes). Fiscal balance is achieved by using a uni-
form (generally direct income) tax to replace the net losses in revenue caused by
the elimination of sectoral trade taxes and subsidies. Technologies are also
assumed to be unchanged by reform; so, no account is taken of any dynamic gains
arising from the opening of trade and the prospective impacts on poverty and
inequality generated by more rapid productivity growth.
Synopsis of Empirical Findings:
Global Model Results
This section summarizes the results of the three global models (denoted Linkage,
GIDD, and GTAP). The subsequent section then brings together the results from
the 10 more-detailed national case studies. Finally, we draw together the lessons
learned from both sets of analyses. It would be surprising if all the studies came to
the same conclusions, but the strength of this blend of somewhat different global
and national models is the fact that it is more likely to expose the various determi-
nants of the measured effects in different settings than would be the case if only a
single type of model were employed.
Introduction and Summary 15
Linkage model results
Chapter 2, by Anderson, Valenzuela, and van der Mensbrugghe, sets the scene for
the rest of the book in that it uses the World Bank's global Linkage model (see
van der Mensbrugghe 2005) to assess the market effects of the world's agricultural
and trade policies as of 2004. It serves two purposes. One is to provide the basis
for estimating, in each of the 10 country case studies in parts III�V, the effects of
rest-of-the-world policies on the import and export prices and the demand for
the various exports of any one developing country. The details of these results are
reported in the appendix. The other purpose of chapter 2 is to provide estimates
of various economic effects on individual countries and country groups so as to
be able to say something about international inequality (in the Milanovic [2005]
sense, that is, taking into account the economic size of countries) and poverty
(using a simple elasticities approach).
The Linkage model results reported in chapter 2 suggest that developing coun-
tries would gain nearly two times more than high-income countries in welfare
terms if 2004 agricultural and trade policies were removed globally (an average
welfare increase of 0.9 percent in developing countries compared with 0.5 percent
in high-income countries; see table 1.3, column 1). Thus, in this broad sense of a
world of only two large country groups, completing the global trade reform
Table 1.3. The Linkage Model's Effects of Full Global Liberalization
of Agricultural and All Merchandise Trade on Economic
Welfare and Real GDP, by Country and Region
(percent change relative to benchmark data)
Policies in Agricultural policies Policies in all sectors
all sectors
Economic Agric Nonag Agric Nonag
Region welfare (EV) GDP GDP GDP GDP
East and South Asia 0.9 0.3 0.7 0.5 2.9
China 0.2 2.8 0.2 5.7 3.0
India 0.2 6.1 1.4 8.3 0.3
Africa 0.2 0.1 0.8 0.9 0.0
Latin America 1.0 36.3 2.8 37.0 2.3
All developing
countries 0.9 5.4 1.0 5.6 1.9
Eastern Europe and
Central Asia 1.2 4.4 0.3 5.2 0.3
All high-income
countries 0.5 13.8 0.2 14.7 0.1
World total 0.6 1.0 0.4 1.2 0.5
Source: Linkage model simulations of Anderson, Valenzuela, and van der Mensbrugghe (chapter 2).
Note: EV equivalent variation in income. Agric agricultural. Nonag nonagricultural.
16 Agricultural Price Distortions, Inequality, and Poverty
process would reduce international inequality.7 The results vary widely across
developing countries, however, ranging from slight losses in the case of some
South Asian and Sub-Saharan African countries that would suffer exceptionally
large adverse changes in the terms of trade to an 8 percent increase in the case of
Ecuador (of which the main export item, bananas, is currently facing heavy dis-
crimination in the markets of the European Union, where former colonies and
least developed countries enjoy preferential duty-free access).8
Because three-quarters of the world's poorest people depend directly or indi-
rectly on agriculture for their main incomes and because farm sizes are far larger
in high-income countries than in developing countries, chapter 2 also looks at the
extent to which agricultural and trade policies in place as of 2004 reduced the
rewards of farming in developing countries and thereby added to international
inequality in farm incomes.9 It finds that net farm incomes in developing coun-
tries would rise by 5.6 percent, compared with 1.9 percent in nonagricultural
value added, if these policies were eliminated (see table 1.3, the final two
columns). This suggests that the inequality between farm and nonfarm house-
holds in developing countries would fall. In contrast, in high-income countries,
net farm incomes would fall by 15 percent on average, compared with a slight rise
in real nonfarm value added, that is, inequality between farm and nonfarm house-
holds in high-income countries would probably increase.10 However, inequality
between farm households in developing countries and those in high-income
countries would decline substantially. These inequality results would not be so
different if only agricultural policies were to be removed (see table 1.3, columns 2
and 3), underscoring the large magnitude of the distortions caused by agricultural
trade policies relative to the effects of nonagricultural trade policies.
Chapter 2 also reports that unskilled workers in developing countries--the
majority of whom work on farms--would benefit most from reform (followed
by skilled workers and then capital owners): the average change in the real
unskilled wage over all developing countries would be a rise of 3.5 percent. How-
ever, the most relevant consumer prices for the poor, including those many poor
farm households and other rural households that earn most of their income
from their labor and are net buyers of food, are the prices relating to food and
clothing. Hence, if we deflate by a food and clothing price index rather than the
aggregate consumer price index, we obtain a better indication of the welfare
change among the poor. As shown in the final column of table 1.4, the real
unskilled wage over all developing countries would show a rise of 5.9 percent if
we use the food and clothing deflator, that is, the inequality in real incomes
between unskilled wage earners and the much wealthier owners of (human or
physical) capital within developing countries would likely be reduced if there
were full trade reform.
Introduction and Summary 17
Table 1.4. The Linkage Model's Effects of Full Global Merchandise
Trade Liberalization on Real Factor Prices, by Country
and Region
(percent change relative to benchmark data)
Nominal change deflated Real change in unskilled
by the aggregate CPI wages, deflated
Skilled Capital user Land user By aggregate By food By food and
Region wages costa costa CPI CPI clothing CPI
East and South Asia 3.4 3.0 1.8 3.2 4.6 4.8
Africa 4.7 4.3 0.1 4.4 5.8 6.9
Latin America 1.4 1.9 21.1 4.5 2.4 4.1
All developing
countries 3.0 2.9 1.6 3.5 5.5 5.9
Eastern Europe and
Central Asia 3.2 2.6 4.5 1.7 4.2 4.5
High-income
countries 1.0 0.5 17.9 0.2 3.3 3.3
World total 1.3 1.2 3.1 0.9 3.6 3.8
Source: Anderson, Valenzuela, and van der Mensbrugghe (chapter 2).
Note: CPI consumer price index.
a. The user cost of capital and land represents the subsidy-inclusive rental cost.
The results on real factor rewards and net farm incomes suggest that poverty, as
well as international and intra-developing-country inequality, might be reduced
globally by agricultural and trade policy liberalization. The authors of chapter 2
take a further step to assess the impacts of reform on poverty explicitly even
though the Linkage model has only a single representative household per country.
They do so using the elasticities approach, which involves taking the estimated
impact on real household income and applying an estimated income to poverty
elasticity to estimate the impacts on the poverty headcount index for each coun-
try. They focus on the change in the average wage of unskilled workers, deflated by
the food and clothing consumer price index and assume that these workers are
exempt from the direct income tax imposed to replace the lost customs revenue
following trade reform (a realistic assumption for many developing countries).
Table 1.5 reports that, under the full merchandise trade reform scenario,
extreme poverty--the number of people surviving on less than US$1 a day--
would drop by 26 million people in developing countries relative to the baseline
level of slightly less than 1 billion, a reduction of 2.7 percent. The proportional
reduction is much higher in China and Sub-Saharan Africa, falling in each by
around 4 percent. It is even higher in Latin America (7 percent) and in South Asia
outside India (10 percent). In contrast, the number of extreme poor in India is
18
Table 1.5. The Linkage Model's Effects of Full Global Merchandise Trade Liberalization on the Number
of Extreme Poor, by Region
Change in Change in
number of number of
poor relative poor relative
Baseline to baseline to baseline
Real change headcount, % New levels, US$1 a day New levels, US$2 a day levels, millions levels, %
in average
unskilled US$1 US$2 Headcount, Total poor, Headcount, Total poor, US$1 US$2 US$1 US$2
Region wage,a % a day a day % millions % millions a day a day a day a day
East Asia 4.4 9 37 8 151 34 632 17 52 10.3 7.6
China 2.1 10 35 9 123 34 440 5 12 4.0 2.7
Other East Asia 8.1 9 50 6 29 42 192 12 40 30.1 17.1
South Asia 1.9 31 77 32 454 78 1,124 8 8 1.8 0.7
India 3.8 34 80 36 386 82 883 15 15 4.2 1.7
Other South Asia 4.0 29 94 26 68 92 241 8 7 9.9 2.7
Sub-Saharan Africa 5.3 41 72 39 287 70 508 11 14 3.8 2.7
Latin America 4.1 9 22 8 44 21 115 3 6 6.8 4.7
Middle East and
North Africa 14.3 1 20 1 3 13 40 2 19 36.4 32.7
Developing-country total 5.9 18 48 18 944 46 2,462 26 87 2.7 3.4
Excluding China 6.5 21 52 20 820 50 2,022 21 74 2.5 4.7
Eastern Europe and
Central Asia 4.5 1 10 1 4 9 43 0 4 6.8 8.0
Source: Anderson, Valenzuela, and van der Mensbrugghe (chapter 2).
a. Nominal unskilled wage deflated by the food and clothing consumer price index.
Introduction and Summary 19
estimated to rise, by 4 percent.11 This follows from the estimated decline in overall
income in India following trade liberalization that is noted in table 1.3. Under the
more moderate definition of poverty (people living on no more than US$2 per
day), the number of poor in developing countries would fall by nearly 90 million
compared to an aggregate baseline level of slightly less than 2.5 billion in 2004, or
by 3.4 percent (although the number of people in India living on less than US$2 a
day would still increase, but by only 1.7 percent).
The GIDD model results
Bussolo, De Hoyos, and Medvedev, in chapter 3, make direct use of the global
CGE Linkage model, but combine it with the newly developed GIDD tool
(Bussolo, De Hoyos, and Medvedev 2008). The GIDD is a framework for ex ante
analysis of the effects on income distribution and poverty of changes in macroeco-
nomic, trade, and sectoral policies or trends in global markets. It thus offers an
alternative to the elasticity approach adopted in chapter 2. It complements a global
CGE analysis by providing global microsimulations based on standardized house-
hold surveys. The tool pools information from most of the currently available
household surveys covering 1.2 million households in 73 developing countries.
Information on households in developed countries and Eastern European transi-
tion economies completes the data set. Overall, the GIDD sample countries cover
more than 90 percent of the world's population.12 In contrast to the modeling
approach used in chapter 2, the GIDD approach is able to distinguish between farm
and nonfarm households by examining the employment of heads of household.
However, because of differences across surveys in the coverage of sources of house-
hold income, the database does not provide precise information on the sources of
income for each household, and behind the data is the assumption that the propor-
tional change in household incomes is driven only by changes in wages.
The key inputs in the microsimulation model are the results on changes in
incomes from labor that have been obtained through the use of a variation on
the Linkage model that assumes full labor mobility.13 Two liberalization scenar-
ios are examined: first, the full liberalization of the markets for agricultural prod-
ucts and lightly processed food without the liberalization of nonfarm goods mar-
kets and, second, the full liberalization of the markets for all goods. Neither
scenario is accompanied by large effects on global poverty according to the
GIDD. The results summarized in table 1.6 show the incidence of extreme
poverty (US$1 a day) rising by 1.0 percent (0.5 percent each from full global
trade reform in the farm sector and the nonfarm sector). This increase in poverty
is largely caused by the increase in poverty in South Asia, where the number
of poor people rises by 3.9 percent after complete global trade reform, a result
20 Agricultural Price Distortions, Inequality, and Poverty
Table 1.6. The GIDD Model's Effects of the Removal of Agricultural
and All Merchandise Trade Distortions on the Number
of Extreme Poor, by Region
(millions and percent)
Change in number of poor after
global trade reform
Agricultural All merchandise
Share of reform only trade reform
global
Region poverty, % millions % millions %
Extreme poverty, US$1 a day
East Asia 24 6.4 2.8 6.3 2.8
South Asia 50 15.4 3.3 18.2 3.9
Sub-Saharan Africa 21 1.0 0.5 0.5 0.3
Latin America 4 2.8 6.9 3.5 8.7
Globala 100 5.0 0.5 8.9 1.0
Moderate and extreme poverty, US$2 a day
East Asia 33 12.8 1.6 13.2 1.7
South Asia 46 3.6 0.3 2.0 0.2
Sub-Saharan Africa 14 0.1 0.0 1.1 0.3
Latin America 4 4.8 4.6 5.7 5.4
Globala 100 22.1 0.9 19.8 0.8
Source: Bussolo, De Hoyos, and Medvedev (chapter 3).
a. Includes Middle East and North Africa, Eastern Europe and Central Asia, and high-income countries,
which, together, account for no more than 2 percent of the world's poor.
similar to the result reported in chapter 2. Moderate poverty (US$2 a day), on
the other hand, is projected to fall by a similar amount (0.9 percent because of
agricultural reform alone and 0.8 percent if nonfarm reform is included).
These small aggregate global changes are produced by a combination of offset-
ting trends among farm and nonfarm households (table 1.7). At the US$1-a-day
extreme poverty level, global liberalization would raise the share of agricultural
households among the world's total poor households by one percentage point
(from 76 to 77 percent). It would also increase the incidence of poverty among the
world's agricultural households (from 32 to 33 percent), while the incidence of
poverty among the world's nonfarm households would drop slightly, to 8 percent.
However, at the moderate poverty line of US$2 a day, both agricultural and all
merchandise trade liberalization would globally lower the incidence of poverty by
nearly 1 percent, and it would reduce poverty among farm and nonfarm house-
holds (compare table 1.7, last two columns).
There are several possible explanations for the differences between the signs of
the effects of reforms on extreme poverty in chapters 2 and 3. First is that the
Introduction and Summary 21
Table 1.7. The GIDD Model's Effects of the Removal of Agricultural
and All Merchandise Trade Distortions on Global Poverty
and Inequality, Farm and Nonfarm Households
US$1- US$1- US$2- US$2-
Real average a-day a-day a-day a-day
Gini monthly income, poverty poverty poverty poverty
Indicator coefficient 2000 US$ PPP incidence share incidence share
Initial levels
Agricultural 0.45 65 31.5a 76a 73.8a 70a
a
Nonagricultural 0.63 320 8.3 24a 26.7a 30a
All households 0.67 204 18.9a 100a 48.2a 100a
Agricultural liberalization, change from baseline, percentage points
Agricultural 0.7 1.1a 0.86 1.1 0.86 0.5
Nonagricultural 0.1 0.2a 0.29 1.1 0.90 0.5
All households 0.1 0.3a 0.23 0.0 0.88 0.0
All merchandise trade liberalization, change from baseline, percentage points
Agricultural 0.8 0.8a 1.09 1.0 0.66 0.6
Nonagricultural 0.2 0.4a 0.19 1.0 0.95 0.6
All households 0.0 0.4a 0.39 0.0 0.82 0.0
Source: Bussolo, De Hoyos, and Medvedev (chapter 3).
Note: PPP purchasing power parity.
a. Expressed in percentages.
GIDD poverty data refer to 2000, whereas the Linkage poverty numbers relate to
2004. A large share of the developing-country population was bunched around
the extreme poverty line in 2000 (see chapter 3, figure 3.1), but, by 2004, poverty
had shrunk quite a bit, at least in East Asia. Second, the GIDD results are based on
changes in labor incomes only rather than on changes in incomes from all factors
of production. In particular, by not including the effect on nonlabor (especially
land) incomes, the study presented in chapter 3 understates the poverty-reducing
impacts of trade reform on farm households, thereby contributing to its finding
that extreme poverty among farm households would increase. Third, the assump-
tion of full labor mobility implies that unskilled farm workers share in the gains
from increased agricultural prices, along with their nonfarm counterparts,
because less labor is required to migrate to nonfarm jobs.
The GIDD results suggest that there might also be a considerable change in
inequality following global trade reform. Indeed, table 1.7 shows that agricultural
incomes would increase by twice as much as nonfarm household incomes in the all-
goods reform scenario (0.8 compared with 0.4 percent) and by five times as much in
the reform in agriculture only scenario (1.1 compared with 0.2 percent). While this
reduction in the nonagricultural income premium on its own would reduce
inequality, income dispersion within the agricultural sector would also increase
22 Agricultural Price Distortions, Inequality, and Poverty
Table 1.8. The GIDD Model's Effects of the Removal of Agricultural
and All Merchandise Trade Distortions on Global Poverty
and Inequality, Farm and Nonfarm Households, by Region
(percentage point change)
Agricultural reform only All merchandise trade reform
US$1 US$2 US$1 US$2
Gini a day, a day, Gini a day, a day,
Region coefficient headcount headcount coefficient headcount headcount
East Asia 0.72 0.38 0.76 0.62 0.37 0.78
South Asia 0.82 1.16 0.27 0.81 1.37 0.15
India 1.01 1.49 0.33 1.04 1.71 0.26
Other South Asia 0.22 0.06 0.09 0.02 0.21 0.17
Africa 0.04 0.23 0.02 0.06 0.11 0.25
Latin America 0.51 0.61 1.06 0.65 0.77 1.26
World 0.10 0.23 0.88 0.00 0.39 0.82
Source: Bussolo, De Hoyos, and Medvedev (chapter 3).
a. Weighted averages across the selected countries in each region.
given the differences in the impacts of reform on income distribution across
developing-country regions, such that the final change in global inequality would
be close to zero (table 1.7, column 1).
Chapter 3 also provides results on poverty and inequality at the national and
regional levels; these are summarized in table 1.8. Improvements (that is, reduc-
tions) in these indicators are pervasive among the 19 countries of Latin America
and the Caribbean and the 5 East Asian countries. There are far fewer African
examples of improvements, regardless of whether the reform scenario is only in
agriculture or also includes nonfarm goods, but most of the changes in the indica-
tors on these countries are close to zero. It is mainly in India where extreme
poverty--but not moderate poverty--would worsen according to the GIDD
results; this was also the finding derived in the study in chapter 2 through the
application of poverty elasticities directly to the Linkage model results.
The impact of agricultural reform on poverty in India is important, but the
existing evidence is quite mixed. In an econometric analysis of historical data,
Topalova (2007) concludes that the reductions in agricultural protection associ-
ated with India's tariff reforms of the 1990s increased national poverty. In con-
trast, using detailed information on household incomes and expenditures, Cai,
de Janvry, and Sadoulet (2008) conclude that 70 percent of the farmers in India
(86 percent of those with less than 0.2 hectares, 73 percent of those with between
0.2 and 1 hectare, and 49 percent of those with more than 1 hectare) would have
lost from increases in the prices of staple foods during 2007�08. According to their
data, this is because even the smallest farmers receive only about half their
Introduction and Summary 23
incomes from farming. Unfortunately, we have been unable to locate an appropri-
ate model or commission a study for India to include in part III.
One needs to bear in mind that the GIDD microsimulation model assumes
that changes in total household incomes are proportional to the changes in the
wage rates for the agricultural or nonagricultural labor of the households. While
labor income is the most important source of income for households at or near
the poverty line, it is not the only one. Thus, accounting for changes in other fac-
tor returns may yield somewhat different results, especially in terms of inequality.
In the remaining studies under review, all sources of income are taken into
account so that we may provide more reliable national results, albeit for a smaller
sample of developing countries.
GTAP model results
Hertel and Keeney, in chapter 4, draw on the widely used global economy-wide
GTAP model. The model adopts the same price distortions as the other studies in
this volume and runs the same scenarios, but generates its own world price
changes from the GTAP model for scenarios of multilateral trade reform scenar-
ios. These price changes alter the border prices in the various countries in the
GTAP model, a subset of which has associated detailed household survey data.
This allows the authors to make statements about poverty impacts across a range
of diverse economies using an internally consistent framework that represents an
alternative to the framework used by Bussolo, De Hoyos, and Medvedev in chap-
ter 3. While the number of countries on which household survey data are available
to Hertel and Keeney is much smaller, the income data are richer, allowing the
authors to capture the distributive effects of all changes in factor incomes rather
than restricting their examination only to labor income shocks, as in chapter 3.
The multicountry study described in chapter 4 focuses on 15 developing
countries: four African (Malawi, Mozambique, Uganda, and Zambia), five Asian
(Bangladesh, Indonesia, the Philippines, Thailand, and Vietnam), and six Latin
American countries (Brazil, Chile, Colombia, Mexico, Peru, and Rep�blica
Bolivariana de Venezuela). Overall, it concludes that the removal of current
agricultural and trade policies globally would tend to reduce poverty, primarily
via the agricultural reforms (table 1.9). The unweighted average for all 15 devel-
oping countries is a decline in the headcount for extreme poverty (US$1 a day) of
1.7 percent. The average fall for the Asian subsample is two times higher, however,
and nearly two-thirds of the world's extremely poor people live in Asia
(although the sample does not include China and India). The results of Hertel
and Keeney on specific countries indicate that, in the sample, the greatest reduc-
tion in poverty would occur among the exporters of agricultural products,
namely, Chile, Thailand, and Vietnam (table 1.9, column 3). The majority of the
24 Agricultural Price Distortions, Inequality, and Poverty
Table 1.9. The GTAP Model's Effects of Full Global Liberalization of
Agricultural and All Merchandise Trade on the Number
of Extreme Poor, by Country
(percentage point change in US$1-a-day poverty)
Default tax replacement Alternative tax
replacementa
Agricultural Nonagricultural All merchandise All merchandise
Country reform only reform only trade reform trade reform
Asia
Bangladesh 0.3 0.5 0.3 5.3
Indonesia 1.1 0.5 0.6 5.2
Philippines 1.4 0.4 1.0 6.4
Thailand 11.2 0.9 10.3 28.1
Vietnam 0.5 5.3 5.7 23.6
Africa
Malawi 1.6 0.3 1.9 5.6
Mozambique 1.2 0.2 1.0 4.3
Uganda 0.0 0.1 0.1 6.0
Zambia 0.0 0.1 0.1 2.0
Latin America
Brazil 2.5 0.4 2.2 10.0
Chile 4.8 0.1 4.6 12.3
Colombia 0.7 0.6 0.1 4.1
Mexico 0.8 0.4 1.1 0.5
Peru 0.6 0.2 0.8 5.2
Venezuela, R.B. de 0.2 0.7 0.9 2.1
Unweighted average
Asia 2.9 0.6 3.5 13.7
Africa 0.7 0.1 0.7 4.5
Latin America 1.3 0.3 1.0 5.7
All 15 developing
countries 1.7 0.1 1.7 8.0
Source: Hertel and Keeney (chapter 4), table 4.5.
a. The poor are exempt under the alternative tax replacement.
15 countries would experience small increases in poverty after nonagricultural
reforms, although the unweighted average across the 15 countries suggests that
there would be a slight decrease, primarily because of a significant decline in
Vietnam (table 1.9, column 2).
The magnitude of the estimated reduction in extreme poverty in Asia and
Latin America is somewhat larger according to Hertel and Keeney (chapter 4)
than the average reductions estimated for the same countries by Bussolo, De
Hoyos, and Medvedev (chapter 3, table 3.4) using the GIDD model. Hertel and
Keeney also estimate a small reduction in poverty in Africa.14 These GTAP results
are thus closer to the results of the Linkage model described in chapter 2.
Introduction and Summary 25
Hertel and Keeney explore the relative poverty-friendliness of agricultural
trade reforms in detail by examining the differential impacts on real after-tax fac-
tor returns of agricultural versus nonagricultural trade policy reforms. They
extend their analysis to the distribution of households by looking at changes in
stratum-specific poverty. They find that the more favorable impacts of agricul-
tural reforms are driven by increased returns to the labor of farm households, as
well as higher returns to unskilled work off-farm. They also find that the liberal-
ization of foodgrain markets accounts for the largest contribution to poverty
reduction and that the removal of import tariffs in these commodity markets
dominates among the poverty-increasing impacts of the removal of subsidies by
high-income countries.
The final column of table 1.9 reports the percentage change in the national
poverty headcount if the poor are not subject to the rise in the income tax
required to replace trade tax revenue following trade reform. This assumption
represents a significant implicit income transfer from nonpoor to poor house-
holds and thus generates a marked difference in the predicted reduction in
poverty. Trade reforms are no longer marginally poverty reducing in most of the
15 cases, but, instead, are poverty reducing in all cases and by a considerable mag-
nitude. This lowers the poverty rate by roughly one-quarter in Thailand and
Vietnam, for example. Overall, the regional and total average extent of poverty
reduction is around four times larger under this scenario relative to the scenario
whereby the poor are also assumed to be subject to the income taxes levied to
replace lost trade tax revenue.
The reduction in the unweighted average poverty headcount in the three
regions shown in the final column of table 1.9 is remarkably similar to the reduc-
tion in the population-weighted averages found by Anderson, Valenzuela, and van
der Mensbrugghe (chapter 2) and reported in table 1.5, above, under a similar tax-
replacement assumption: the 17 percent for Asia, excluding China and India, and
the 6.4 percent for Latin America found by the latter authors are only slightly
above the 14 and 5.7 percent, respectively, of Hertel and Keeney, while the 3.7 per-
cent for Sub-Saharan Africa in chapter 2 is only slightly below the 4.5 percent
obtained by Hertel and Keeney.
Synopsis of the Empirical Findings:
National Model Results
We turn now to a comparison of the results of the 10 detailed individual coun-
try case studies with the results obtained from the global models described
above. The features of the national models are summarized in table 1.10.15 Like
the three global models, they focus on price-distorting policies as of 2004, even
though the database for the CGE models and the household survey data on
26
Table 1.10. Characteristics of the Models in the Global and National Country Studies
Protection Household Sectors, Products, Types of labor, Intersectoral
Study Chapter data, year SAM,a year survey, year number number number labor mobility
Global
Linkage 2 2004 2004 None 23 23 2 Yes
GIDD 3 2004 2004 circa 2000 23 23 4 Partial
GTAP 4 2004 2001 circa 2000 31 6 6 Partial
National
China 5 2004 2002 2000 48 48 8 Partial
Indonesia 6 2004 2000 1999 65 20 2 Yes
Pakistan 7 2004 2000�01 2001�02 34 28 3 Farm, no
Philippines 8 2004 2000 2000 41 34 2 Yes
Thailand 9 2004 2000 2000 65 65 2 Yes
Mozambique 10 2004 2003 2002 56 56 8 No
South Africa 11 2004 2002 2000 110 110 3 Unemployment
Argentina 12 2004 2005 2005 24 26 6 Unemployment
Brazil 13 2004 2001 2001 42 52 10 Yes
Nicaragua 14 2004 2000 2001 40 40 4 Unemployment
Sources: Global and country case studies in parts II�V.
a. Social accounting matrix of production and trade data.
Introduction and Summary 27
which these models are based typically date back a little earlier in the decade.
They all include more sectoral and product disaggregation than the global mod-
els and cover multiple types of households and labor. All the national studies
include microsimulations that draw on the CGE model results, as in the GIDD
and GTAP global models.
The results of the national studies on real GDP and household consumption
suggest that GDP would increase after full global trade reform, but only by 1 or
2 percent, in all 10 countries (except in Argentina if export taxes are removed dur-
ing full liberalization).16 Given falling consumer prices, real household consump-
tion would increase by considerably more in most cases. (Argentina would again
be the notable exception for the reasons discussed elsewhere below.) Generally,
these numbers are a little larger than the numbers generated by the global Linkage
model, but they are still usually much lower than would be the case had the
authors used dynamic models. Like the global models, the studies therefore
underestimate the poverty-reducing benefits of trade reform given the broad con-
sensus in the literature that trade liberalization increases growth, which is, in turn,
a major contributor to poverty reduction.
Comparative tables 1.11 and 1.12 summarize the national results on the inci-
dence of extreme poverty and income inequality, respectively, resulting from own-
country and rest-of-the-world full global liberalization of either agricultural trade
or the trade in all goods.17 Some authors ran only six of the nine simulations
shown in these tables, but the authors who ran all nine found that the simulations
sum almost exactly (to within one decimal place). We have therefore inferred the
three missing results in the other country studies by assuming that the results in
the agriculture only and nonagriculture only simulations sum to the results of the
reform in the trade for all goods. The inferred numbers are shown in italics in
tables 1.11 and 1.12. In each case, the total effects on poverty and inequality are
subdivided into rural and urban effects.
One should not necessarily expect the unweighted averages of the poverty
results for each region to be similar to those generated by Hertel and Keeney
(chapter 4) because only half of the 10 countries on which we have case studies are
included among the 15 countries sampled by Hertel and Keeney. Nonetheless, the
unweighted averages of the national poverty effects for each of the key developing
country regions computed by Hertel and Keeney are reported in parentheses in
the last 4 rows of table 1.11, panel c so that these may be easily compared with the
unweighted regional averages derived from our national case studies. In all but 3
of the 12 comparisons on global liberalization (agriculture, nonagriculture, and
all merchandise), the projected regional average reductions in poverty after global
liberalization are larger in our sample of national case studies than in the sample
of 15 countries examined by Hertel and Keeney. This may mean that the poverty
28
Table 1.11. The Impact of Reform on the Incidence of Extreme Poverty, by Country
(percentage point change in the national or US$1-a-day poverty line)
a. Rural poverty
Agricultural reform only Nonagricultural reform only All merchandise trade reform
Base
Country study % Unilateral R of W Global Unilateral R of W Global Unilateral R of W Global
China (US$2/day) 58 0.3 1.4 1.1 0.2 0.5 0.3 0.5 1.9 1.4
Indonesia 29 0.1 1.1 1.1 0.2 3.2 3.3 0.1 4.3 4.4
Pakistan 38 1.4 0.1 1.5 6.2 1.1 7.1 7.6 1.2 8.6
Philippines 49 0.0 0.6 0.3 0.6 0.3 0.2 0.6 0.9 0.1
Thailand 30 0.3 1.6 1.3 3.8 0.7 3.1 3.5 0.9 4.4
Mozambique 36 1.6 0.0 1.6 0.5 1.5 2.0 2.1 1.5 3.6
South Africa 17 0.3 0.3 0.7 0.8 0.0 0.8 1.1 0.4 1.4
Argentina -- -- -- -- -- -- -- -- -- --
Brazil -- -- -- -- -- -- -- -- -- --
Nicaragua 63 0.7 0.3 0.4 0.6 0.3 0.9 1.3 0.0 1.3
b. Urban poverty
Agricultural reform only Nonagricultural reform only All merchandise trade reform
Base
Country study % Unilateral R of W Global Unilateral R of W Global Unilateral R of W Global
China (US$2/day) 3 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.1 0.1
Indonesia 12 0.1 0.3 0.4 0.1 1.7 1.8 0.2 2.0 2.2
Pakistan 20 2.4 0.1 2.7 4.7 1.4 3.1 2.3 1.5 0.4
Philippines 19 0.8 0.9 0.2 1.2 0.7 0.3 2.0 1.6 0.1
Thailand 6 0.0 0.8 0.7 3.3 0.2 3.2 3.3 0.6 3.9
Mozambique 37 0.5 0.0 0.5 0.4 1.3 1.7 0.9 1.3 2.2
South Africa 4 0.1 0.2 0.3 0.4 0.0 0.4 0.5 0.2 0.7
Argentina 13 1.3 0.1 1.5 0.4 0.1 0.5 0.9 0.0 1.0
Brazil -- -- -- -- -- -- -- -- -- --
Nicaragua 27 0.3 0.5 0.2 1.0 1.4 0.4 0.7 0.9 0.2
(Table continues on the following page.)
29
30
Table 1.11. The Impact of Reform on the Incidence of Extreme Poverty, by Country (continued)
(percentage point change in the national or US$1-a-day poverty line)
c. Total poverty
Agricultural reform only Nonagricultural reform only All merchandise trade reform
Base
Country study % Unilateral R of W Global Unilateral R of W Global Unilateral R of W Global
China (US$2/day) 36 0.2 0.8 0.6 0.1 0.4 0.3 0.3 1.2 0.9
Indonesia 23 0.0 0.8 0.8 0.1 2.7 2.8 0.1 3.5 3.6
Pakistan 31 1.6 0.1 1.8 3.6 1.2 4.6 5.2 1.3 6.4
Philippines 34 0.4 0.6 0.1 0.7 0.3 0.2 1.1 0.9 0.1
Thailand 14 0.1 1.1 0.8 3.5 0.4 3.3 3.4 0.7 4.1
Mozambique 36 1.3 0.0 1.3 0.4 1.4 1.8 1.7 1.4 3.1
South Africa 10 0.2 0.3 0.5 0.6 0.1 0.6 0.8 0.3 1.1
Argentina -- -- -- -- -- -- -- -- -- --
Brazil 31 0.5 2.3 2.8 0.4 0.1 0.5 0.9 2.4 3.5
Nicaragua 41 0.1 0.2 0.3 0.9 0.3 0.6 1.0 0.1 0.9
Unweighted averages
Asia 28 0.2 0.7 ( 2.9) 0.8 1.2 0.8 ( 0.6) 2.2 1.5 1.6 ( 3.5) 3.0
Africa 32 0.8 0.2 ( 0.7) 0.9 0.5 0.7 (0.1) 1.2 1.3 0.9 ( 0.7) 2.1
Latin America 36 0.3 1.3 ( 1.3) 1.6 0.7 0.1 (0.3) 0.6 1.0 1.2 ( 1.0) 2.2
All 9 developing
countriesa 43 0.4 0.6 ( 1.7) 1.0 0.9 0.7 ( 0.1) 1.6 1.3 1.3 ( 2.6) 2.6
Sources: Studies in parts II�V. Hertel and Keeney (chapter 4), table 4.5, for the numbers in parentheses.
Note: R of W rest of the world. The numbers in italics for individual countries have been inferred on the assumption that linearity holds (see the text). The numbers in
parentheses are from the GTAP model results and are provided for comparison (see table 1.9). Numbers may not sum because of rounding or interaction effects.
-- no estimates are available.
a. Excludes Argentina.
Introduction and Summary 31
elasticities used in the latter study (and also in the Linkage model, which gener-
ated similar results) are too small given the greater possibilities for adaptation
reflected in most of the national models.18
The individual country results show that poverty would be reduced in all
countries by both global agricultural and, with the exception of the Philippines,
nonagricultural liberalization (table 1.11, panel c). If all merchandise trade were
liberalized, the extent of the reduction in poverty ranges from close to zero to
about 3.5 percentage points, except in Pakistan, where it is more than 6.0 percent-
age points.19 If we examine the unweighted averages, we find that a greater share
of the reduction in poverty is generated by nonfarm trade reform, with the impor-
tant exception of Brazil, where agricultural reform is the major contributor to the
significant pro-poor outcome. However, if the average is weighted according to
the number of people involved, agricultural reform would dominate, as it does in
the results of the global modeling. The extreme Brazil result occurs despite the
existence of tariff protection for the country's poor import-competing farmers; it
is a consequence of the increase in the demand for unskilled labor following liber-
alization, which evidently outweighs the poverty impact of the removal of farm
tariffs. The contribution of own-country reforms to the decline in poverty
appears to be as important as the rest-of-the-world reforms, on average, although
there is considerable cross-country divergence in the extent of this parity in the
farm reforms and in the nonfarm reforms.
The reduction in poverty is divided into rural and urban sources of reduction
in table 1.11, panels a and b. A glance at the final column in these panels reveals
that rural poverty declines much more than urban poverty in every case. This is
true after farm or nonfarm trade reform, as well as after own-country or rest-of-
the-world reform. Since the rural poor are much poorer, on average, than the
urban poor, this would lead one to expect trade reform to reduce inequality also
(see Bussolo, De Hoyos, and Medvedev, chapter 3, figure 3.1).
Indeed, the results shown at the bottom of table 1.12, panel c on this sample of
countries indicate that inequality would decline in all three developing-country
regions after full trade liberalization of all goods or of only agricultural products in
the case of both own-country reform and rest-of-the-world reform. The effect of
nonfarm trade reform alone is more mixed, providing another reason trade nego-
tiators should not neglect agricultural reform in their discussions. Rest-of-the-
world and global agricultural reform both lead to a reduction in inequality in every
country in the sample except Thailand (plus Argentina and the Philippines slightly
in the case of global reform). Meanwhile, unilateral agricultural reform reduces (or
leaves constant) inequality in a small majority of countries; the exceptions are
Argentina, China, the Philippines, and Thailand (although the effects are small).
Nonfarm global reform increases inequality slightly in only three countries.
32
Table 1.12. The Impact of Reform on the Incidence of Income Inequality, by Country
(percentage point change in the Gini coefficient)
a. Rural
Agricultural reform only Nonagricultural reform only All merchandise trade reform
Base
Country study % Unilateral R of W Global Unilateral R of W Global Unilateral R of W Global
China 0.32 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.2 0.2
Indonesia 0.29 0.0 0.0 0.0 0.1 0.0 0.1 0.1 0.0 0.1
Pakistan 0.26 0.1 0.0 0.1 0.3 0.0 0.3 0.2 0.0 0.2
Philippines 0.43 0.2 0.1 0.1 0.3 0.0 0.1 0.5 0.1 0.2
Thailand 0.33 0.0 0.5 0.5 0.4 0.0 0.4 0.4 0.5 0.9
Mozambique -- -- -- -- -- -- -- -- -- --
South Africa 0.63 0.1 0.1 0.2 0.3 0.0 0.3 0.4 0.1 0.5
Argentina -- -- -- -- -- -- -- -- -- --
Brazil -- -- -- -- -- -- -- -- -- --
Nicaragua -- -- -- -- -- -- -- -- -- --
b. Urban
Agricultural reform only Nonagricultural reform only All merchandise trade reform
Base
Country study % Unilateral R of W Global Unilateral R of W Global Unilateral R of W Global
China 0.26 0.0 0.1 0.1 0.0 0.1 0.1 0.0 0.0 0.0
Indonesia 0.36 0.0 0.1 0.1 0.3 0.3 0.6 0.3 0.2 0.5
Pakistan 0.40 0.1 0.0 0.1 1.9 0.0 1.9 2.0 0.0 2.0
Philippines 0.48 0.3 0.2 0.1 0.1 0.0 0.1 0.4 0.2 0.2
Thailand 0.15 0.1 0.6 0.7 0.5 0.0 0.5 0.6 0.6 1.2
Mozambique -- -- -- -- -- -- -- -- -- --
South Africa 0.62 0.1 0.1 0.2 0.5 0.0 0.5 0.6 0.1 0.7
Argentina 0.50 0.3 0.1 0.2 0.2 0.1 0.3 0.1 0.2 0.0
Brazil -- -- -- -- -- -- -- -- -- --
Nicaragua -- -- -- -- -- -- -- -- -- --
(Table continues on the following page.)
33
34
Table 1.12. The Impact of Reform on the Incidence of Income Inequality, by Country (continued)
(percentage point change in the Gini coefficient)
c. Total
Agricultural reform only Nonagricultural reform only All merchandise trade reform
Base
Country study % Unilateral R of W Global Unilateral R of W Global Unilateral R of W Global
China 0.44 0.1 0.4 0.3 0.0 0.1 0.1 0.1 0.5 0.4
Indonesia 0.34 0.0 0.1 0.1 0.2 0.2 0.4 0.2 0.1 0.3
Pakistan 0.34 0.1 0.0 0.2 3.2 0.1 3.1 3.3 0.1 3.3
Philippines 0.51 0.3 0.2 0.1 0.1 0.0 0.1 0.4 0.2 0.2
Thailand 0.34 0.1 0.7 0.8 0.4 0.0 0.4 0.5 0.7 1.2
Mozambique 0.48 1.2 0.1 1.3 0.3 0.2 0.1 1.5 0.1 1.4
South Africa 0.67 0.1 0.1 0.2 0.4 0.0 0.4 0.5 0.1 0.6
Argentina -- -- -- -- -- -- -- -- -- --
Brazil 0.58 0.2 1.4 1.6 0.1 0.1 0.0 0.1 1.5 1.7
Nicaragua 0.53 0.1 0.1 0.0 0.1 0.2 0.3 0.2 0.1 0.3
Unweighted averages
Asia 0.39 0.1 0.0 0.1 0.5 0.0 0.5 0.4 0.0 0.4
Africa 0.58 0.7 0.1 0.8 0.4 0.1 0.3 1.0 0.0 1.0
Latin America 0.56 0.2 0.7 0.8 0.0 0.2 0.1 0.2 0.8 1.0
All 9 developing
countriesa 0.59 0.2 0.2 0.4 0.3 0.0 0.3 0.5 0.2 0.7
Sources: Country case studies in parts III�V.
Note: R of W rest of the world. The numbers in italics for individual countries have been inferred on the assumption that linearity holds (see the text). Numbers may not sum
because of rounding or interaction effects. -- no data are available.
a. Excludes Argentina.
Introduction and Summary 35
In Indonesia and Thailand, the inequality-increasing impact of nonfarm reform
more than offsets the egalitarian effect of farm trade reform, whereas both types of
reform increase inequality in the Philippines and Thailand.
Inequality within the rural or urban household groups is not altered much by
trade reform relative to overall national inequality (compare table 1.12, panels a
and b with panel c). This underlines the point that trade reform would tend to
reduce urban-rural inequality predominantly rather than inequality separately
within either group.
Several of the national studies investigate the impact of reforms that might
complement trade reform, notably various approaches to address the elimination
of trade tax revenue. If the revenues can be recouped through taxes that do not
bear on the poor, then the impact of reform on poverty reduction is more favor-
able. The China study focuses on lowering the barriers to migration out of agri-
culture by improving the operation of land markets and diminishing the barriers
to mobility created by the hukou system, the household registration system in
China. Each of these two initiatives, along with international trade liberalization
(which increases access to foreign markets), would reduce poverty, and a combi-
nation of such measures would benefit all major household groups.
Argentina is a special case in several respects. First, Cicowiez, D�az-Bonilla, and
D�az-Bonilla, the authors of the country study (chapter 12), had access only to an
urban household survey; they were thus unable to make any determinations about
the effect of policy reform on rural poverty or urban-rural income inequality.
Second, Argentina imposed export taxes on farm products in 2002 and has
increased the taxes a number of times since then. Removing these taxes as part of a
shift toward free trade would clearly benefit farmers and rural areas, but would
also raise the price of food in urban areas, which, all else being equal, would tend
to increase urban poverty (see the results in table 1.11, panel b). Third, by their
assumptions, the authors allow reform to alter aggregate employment, unlike most
of the other studies, making their results less comparable. Together, these features
mean that the authors have found that global trade reform would reduce urban
poverty and inequality in Argentina, but only if export taxes are not included in
the reform. If export taxes are eliminated as well, the results in table 1.11, panel b
and table 1.12, panel b show that urban inequality would change little, but urban
poverty would rise. Although, on its own, nonfarm reform would reduce urban
poverty in Argentina, urban poverty would rise because of the strong negative
impact on the urban poor of the higher food prices resulting from the removal of
export taxes. In a global reform scenario in which export taxes are left unchanged,
the authors find that both poverty and inequality would fall in Argentina because
there would be less unemployment with respect to the scenario involving the
removal of export taxes.
36 Agricultural Price Distortions, Inequality, and Poverty
What Have We Learned?
As in previous studies, whether based on ex post econometrics (as in Harrison
2007) or on ex ante economy-wide simulation (as in Hertel and Winters 2006),
this study also finds that the results are mixed and not easy to summarize, partic-
ularly with regard to the poverty effects. There is nonetheless a high degree of sim-
ilarity in the most important sign: the direction of the estimated national effect on
extreme poverty that would result from freeing all merchandise trade globally.
The greatest amount of overlap across the studies occurs in relation to this effect;
the signs, summarized in table 1.13, agree in all but one-seventh of our available
cases. Moreover, apart from India, there is no case in which the majority of the
signs indicate that reform would increase poverty.
This beneficial impact of the full liberalization of global merchandise trade on
the world's poor would be more readily generated by agricultural reform than by
Table 1.13. The Direction of the Effects of Global Reform on Extreme
Poverty, by Country
(sign of the change in the share of the population living on less than US$1 a day
or the national poverty line)
Agricultural reform only All merchandise trade reform
GIDD GTAP National Linkage GIDD GTAP National
Country model model model model model model model
Brazil
Chile
China
Colombia
India
Indonesia
Mexico
Mozambique
Nicaragua
Pakistan
Peru
Philippines
South Africa
Thailand
Uganda
Venezuela, R.B. de
Vietnam
Sources: Country case studies in parts III�V.
Note: The table shows the only countries in our study on which results are available from at least two of
the models reported in the subsequent chapters. Blank cells indicate that there are no estimates.
Introduction and Summary 37
nonagricultural reform and, within agricultural reform, by the removal of the
substantial supports provided to farmers in developed countries rather than by
policy reform in developing countries. According to the economy-wide models
used in our study, such reform would raise the real earnings of unskilled laborers
in developing countries, most of whom are working in agriculture. The earnings
of these laborers would rise relative to the earnings of unskilled workers in devel-
oped countries and relative to the earnings of other income earners in developing
countries. In addition to reducing poverty, such reform would thus lower the
inequality within developing countries and between developing countries and
developed countries.
According to the estimates of the Linkage model, the number of extremely
poor people in developing countries--people living on less than US$1 a day--
would fall by 2.7 percent after the global opening of all goods markets; the num-
ber would decline by 4.0 percent in China and Sub-Saharan Africa, but rise by
4.0 percent in India (or by 1.7 percent if the more moderate US$2-a-day poverty
line is used). The GIDD model suggests that the decline in moderate poverty
would be less than the decline estimated in the Linkage model and that extreme
poverty would actually rise by 1.0 percent globally after full global trade reform
(almost all because of a rise in India). However, we should recall that the GIDD
model only takes labor income effects into account. The results for the 15 coun-
tries in the GTAP model are more in line with the Linkage results. They suggest
that, in Asia and Latin America, the poverty-reducing effect of global reform
would be twice as large as the estimates in the GIDD model and that, in Africa,
there would be a small decline (rather than a small rise) in poverty. The 10
national case studies all find that global trade liberalization is poverty reducing (if
the removal of export taxes is not part of the full liberalization in Argentina),
regardless of whether the reform involves only agricultural goods or all goods; the
benefit would arise roughly equally from reform at home and from reform
abroad. The case studies also find that rural poverty would be cut much more
than urban poverty in all cases, whether by reform at home or abroad and
whether or not the reform includes nonfarm goods.
Again according to the Linkage model, global trade liberalization would reduce
international inequality between developing countries and high-income countries
both in total and among only farm households. However, it cannot be guaranteed
that every developing country would be made more well off unless there is a
strong economic growth dividend associated with reform (which is not captured
in the comparative static modeling used in our study). The message emerging
from the GIDD analysis is less optimistic: the model finds that inequality would
change little after full global reform (inequality would fall in Latin America, but
rise in South Asia). This is mainly because of increased income dispersion within
38 Agricultural Price Distortions, Inequality, and Poverty
the agricultural sector, despite a reduction in the gap in farm-nonfarm household
incomes. The analysis based on the GTAP model, which reinforces the findings in
the Linkage model with respect to poverty, does not cover the inequality effects.
The full trade liberalization of all goods or only of agricultural products would
also cause inequality to decline after both own-country reform and rest-of-the-
world reform within each of the three developing country regions covered by our
sample. Inequality within the rural or urban household groups would not alter
much following full trade reform, suggesting that the predominant impact of
trade reform would be a reduction in urban-rural inequality.
The mechanism used by governments to adapt to the fall in tariff revenue is
shown to be crucial. If one assumes that, rather than distributing it proportion-
ately, governments do not require the poor to bear any of the tax burden in the
effort to replace lost trade tax revenue, the estimated degree of poverty reduction
is about four times greater in the 15 countries studied in the GTAP model.
The results of the three global analyses all indicate that the removal of the
remaining agricultural policies would have a much stronger impact on poverty
and inequality than would nonagricultural trade reforms. A weighted average
across the 10 country case studies would probably show similar results. This con-
trasts with the outcomes of reforms over the past three decades according to
Valenzuela, van der Mensbrugghe, and Anderson (2009): they estimate that global
reforms in nonfarm trade policy between the early 1980s and 2004 boosted the
value added in developing-country agriculture by more than twice as much as the
global reforms in agricultural policy lowered it, and the former might therefore be
expected to have had a dominant impact in any reduction in poverty and inequal-
ity over the period.
The 10 national case studies also shine some light on the relative importance of
domestic versus rest-of-the-world reform in these countries. The contribution of
own-country reforms to the fall in poverty appears to be equally as important as
the contribution of rest-of-the-world reform, on average, although there is con-
siderable cross-country divergence in the extent of the effects of both farm reform
and nonfarm reform.
Caveats
The impacts of agricultural and other trade reforms are complex. Such reforms
simultaneously affect product and factor markets, government budgets, and
external trade. The studies in this volume provide a broad range of ex ante model-
ing perspectives, including global and national models. They devote considerable
attention to capturing poverty effects through microsimulation and poverty elas-
ticity approaches and to using the same price distortion estimates, the same global
Introduction and Summary 39
model for measuring rest-of-the-world border shocks in the 10 national models,
and similar behavioral assumptions, tax replacement assumptions, and model
closures. Nonetheless, there is ample scope for further exploration of this issue
through additional comparisons, including by drilling down to examine the ori-
gins of each modeling result. Our space limitations mean that such exploratory
work needs to be left to future research efforts.
The reforms considered here cover only the liberalization of the trade in goods.
Freeing up the global trade in services would also likely produce gains in most
national economies, including among farmers. Freeing up capital would add to
the gains, as would freeing the international movement of low-skilled labor from
developing countries to higher-income countries (Prasad et al. 2007, World Bank
2005). How those reforms would interact with farm and other goods trade
reforms in terms of the impacts on global poverty and inequality awaits the devel-
opment of more sophisticated global simulation models.
Another key challenge that remains is to capture the growth effects of liberal-
ization and, in particular, the general equilibrium distributive (poverty and
inequality) consequences. This area of research has only recently been addressed
in the empirical literature by building on the advances in the theoretical literature
in the 1990s on endogenous growth (beginning with Grossman and Helpman
1991). Existing partial equilibrium analyses strongly suggest that the trade-
growth-poverty nexus is extremely important, possibly much more important
than the static reallocative impacts captured in the current set of studies. There is
every reason to believe that, once dynamics are included, they will reinforce the
basic finding of our study that agricultural and other merchandise trade policy
reforms are poverty and inequality reducing.
A further modeling change involves introducing a stochastic dimension so as
to capture changes in the probability of falling into poverty. This is important if
greater openness alters the risk of food price spikes given, for example, that an
upward spike might cause a food-deficit household to starve. Such general equi-
librium empirical modeling that contains sufficient sectoral and household detail
to be useful for poverty analysis, even without a dynamic component, is still in
infancy. However, this field may develop rapidly in response to the demand for
climate change studies; an early prototype is Ahmed, Diffenbaugh, and Hertel
(2009).
There is significant scope also for exploring empirically the possible effects of
complementary domestic reforms that might accompany agricultural price and
trade policy reforms. This is well illustrated in the China case study, which shows
that, if labor market reform were to accompany trade reform, the reduction in
poverty would be several times greater. Even in the extreme case of India, trade
reform would probably not increase poverty if more efficient transfer mechanisms
40 Agricultural Price Distortions, Inequality, and Poverty
were in place and high-payoff infrastructure investments were undertaken. The
politics behind implementing first-best domestic policies is not necessarily any
less complex than the politics associated with trade policies, however; this under-
scores the need for comprehensive political economy analysis that is not limited
only to border policy measures.20
Policy Implications
The empirical findings described above have a number of policy implications.
First, the generally attractive results in terms of the poverty- and inequality-
reducing effects of trade policy reforms, whether unilateral or multilateral, pro-
vide yet another reason why it is in the interest of countries to seek the further
liberalization of national and world markets.
Second, a recurring theme in the national case studies is that the gains in terms
of poverty and inequality reduction, in addition to the standard aggregate real
income gains associated with trade liberalization, are generally much greater after
global reform than after only own-country reform. In the Indonesia study, for
example, unilateral trade liberalization is expected to reduce poverty only slightly,
but liberalization by the rest of the world is expected to lower poverty substan-
tially. In the Philippines, domestic reform of the current levels of protection alone
may marginally increase poverty rates, whereas rest-of-the-world liberalization
would almost fully offset this increase (and more than offset it in the case of only
agricultural reform).
Third, the results of this set of studies show that the winners in trade reform
would overwhelmingly be found among the poorer countries and the poorest
individuals within countries. However, it is also clear that, even among the
extreme poor, some will lose out. Hence, there is merit in compensatory policies,
ideally ones that focus not on private goods, but on public goods that reduce
underinvestment in pro-growth factors such as rural human capital. At the
national level, India appears to be an important example of a potential loser from
global trade reform in terms of welfare, poverty, and inequality. The government
of India might therefore consider replacing its current extensive agricultural
input subsidies and import tariffs by targeted assistance aimed only at the poor-
est farmers and rural areas (which may also help the urban poor, save govern-
ment spending on fair price shops to offset the effects of tariffs on food, and
reduce the adverse environmental effects of subsidies for irrigation and farm
chemicals).21
Fourth, the most substantial benefits would be generated by agricultural
reform. This underscores the economic and social importance of securing
reforms in the agricultural sector, in addition to manufacturing, notwithstanding
Introduction and Summary 41
the political sensitivities involved. Other domestic policy instruments would more
directly address the Millennium Development Goals of governments in poverty
and hunger reduction and, hence, be more efficient than trade policies in this
effort. However, the former generally represent a greater net drain on the treasury,
which may be a challenge in low-income countries that still rely heavily on trade
tax revenue. One solution to this dilemma involves expanding aid-for-trade fund-
ing as part of official development assistance programs.
Finally, most of the national case studies find that unilateral policy reform
alone may represent a way to reduce poverty and inequality. This suggests that
developing countries should not hold back on national reforms while they are
negotiating in the World Trade Organization Doha Round or other international
forums. It also suggests that, from a poverty-reducing perspective, developing
countries have little to gain and, potentially, much to lose by negotiating exemp-
tions or delays in national reforms within the framework of World Trade Organi-
zation multilateral agreements.
Notes
1. For a review of the theoretical literature and the empirical evidence on individual and societal
preferences for redistribution, see Alesina and Giuliano (2009). Prasad et al. (2007) make the point
that, as the number of the extreme poor decline, concerns about poverty will diminish and be gradu-
ally replaced by concerns about income inequality.
2. Political economists are also interested in the ways policies affect the incomes and asset values of
various vested interests, but this is not a focus of our analysis.
3. A study by Sala-i-Martin (2006) finds that economies have converged in the sense that dispari-
ties in GDP per capita across countries have shrunk in recent decades. Analyses based on household
survey data rather than GDP per capita include the studies by Milanovic (2002, 2005, 2006). A recent
review of the evidence on global poverty and inequality is available in Ferreira and Ravallion (2008).
4. The distortions database is documented fully in Anderson and Valenzuela (2008). It is based on
the methodology summarized in Anderson et al. (2008) and detailed in appendix A of Anderson
(2009).
5. We were fortunate to have early access to the P5 preliminary version of the GTAP Database
ahead of the final release. Details on the GTAP Database are available in Narayanan and Walmsley
(2008).
6. See Wacziarg and Welch (2008) and Krueger (2010), as well as the collection of seminal papers
in Winters (2007). Rutherford and Tarr (2002) bring together these ideas using a numerical open-
economy growth model. This model allows for product variety, imperfect competition, economies of
scale, and international capital flows. It is also dynamic so that the model may be used to trace out an
adjustment path to trade reform. It is stochastic in that it draws randomly from uniform probability
distributions for eight key parameters of the model. The authors simulate a halving of the only policy
intervention (a 20 percent tariff on imports) and thereby fully replace the government's lost tariff rev-
enue by a lump-sum tax. This modest trade reform produces a welfare increase (in terms of a Hicksian
equivalent variation) of 11 percent of the present value of consumption in the central model. System-
atic sensitivity analysis with 34,000 simulations shows that there is virtually no chance of a welfare gain
of less than 3 percent and a 7 percent chance of a welfare gain larger than 18 percent of consumption.
See also the empirical study of four developing countries in Cockburn et al. (2008).
42 Agricultural Price Distortions, Inequality, and Poverty
7. This would continue a process that was initiated in the 1980s when many countries began to
reform trade and exchange rate regimes. Using the same Linkage model and database as the present
study, Valenzuela, van der Mensbrugghe, and Anderson (2009) find that the global reforms between
1980�84 and 2004 also boosted economic welfare proportionately more in developing countries than
in high-income economies (by 1.0 percent compared with 0.7 percent, respectively).
8. Even so, if one were to treat each of the 60 countries or the groups of countries in the global
study described in chapter 2 in terms of a single household (that is, ignoring intracountry inequality),
then intercountry income inequality (not taking account of the differing economic size of countries)
would be reduced at least slightly, as measured by the Gini coefficient, from 0.8513 to 0.8506.
9. According to data in the FAOSTAT Database, less than 15 million relatively wealthy farmers in
developed countries, with an average of almost 80 hectares per worker, are currently being helped, at
the expense of not only consumers and taxpayers in these rich countries, but also the majority of the
1.3 billion relatively impoverished farmers and their large families in developing countries, who, on
average, must earn a living from only 2.5 hectares per worker.
10. In some high-income economies, however, farm households now have higher incomes than
nonfarm households (Gardner and Sumner 2007, OECD 2009).
11. The rise in India is caused partly by the removal of the large subsidies and import tariffs that
assist Indian farmers and partly by the greater imports of farm products that raise the border prices of
these imports.
12. Information on the GIDD data set, methodology, and applications are available at http://
go.worldbank.org/YADEAFEJ30.
13. While changes in incomes from labor are the most important income change among house-
holds at or near the poverty line, accounting for the changes in other sources of income may yield
somewhat different results, particularly as they relate to inequality. The results for the Linkage
model used in chapter 3 are not identical to those in chapter 2 because, to make their results com-
patible with the GIDD, the authors of chapter 3 had to assume that labor is less than fully mobile
across sectors.
14. An African comparison is not possible because there was only one African country common to
the two sets of sample countries.
15. The 10 national studies cover Argentina (Cicowiez, D�az-Bonilla, and D�az-Bonilla; chap-
ter 12), Brazil (Ferreira Filho and Horridge; chapter 13), China (Zhai and Hertel; chapter 5),
Indonesia (Warr; chapter 6), Mozambique (Arndt and Thurlow; chapter 10), Nicaragua (Sanchez
and Vos; chapter 14), Pakistan (Cororaton and Orden; chapter 7), the Philippines (Cororaton,
Corong, and Cockburn; chapter 8), South Africa (H�rault and Thurlow; chapter 11), and Thailand
(Warr; chapter 9).
16. The results on Argentina are included only in the urban parts of tables 11 and 12 because the
relevant household survey does not include rural areas. However, it should be kept in mind that
Argentina is the most urbanized developing country in the sample; only 8 percent of the population
was living in rural areas in 2007. Hence, even if the poverty effects in rural areas had the opposite sign,
they might not offset substantially the results in the urban sector.
17. The data are based on national or US$1-a-day poverty lines, except in the case of China; the
results on China are available only at the US$2-a-day poverty line.
18. Hertel and Keeney (chapter 4) use stratum-specific poverty elasticities to map the impacts on
poverty of the changes in average incomes from all sources.
19. The Pakistan results have been generated on the assumption that the loss in trade taxes would
be offset by a rise in direct income taxes. Only nonpoor urban households pay direct taxes in Pakistan;
so, the removal of tariffs would raise the direct taxes paid by the urban nonpoor such that the benefits
of trade reform would go mainly to the poor.
20. A beginning has been made in such political econometric analysis in a set of studies reported in
Anderson (2010) that use the World Bank agricultural distortions database compiled by Anderson and
Valenzuela (2008).
Introduction and Summary 43
21. Caution is needed, as always, in proposing such interventions in countries in which it is diffi-
cult to implement even the most well intentioned policy. As Pritchett (2009) vividly points out, India
especially suffers from this problem of underdevelopment.
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Part II
GLOBAL CGE
APPROACHES
2
Global Welfare and
Poverty Effects:
Linkage Model
Results
Kym Anderson, Ernesto Valenzuela,
and Dominique van der Mensbrugghe*
Despite much reform over the past quarter century in the policy distortions to
agricultural incentives, many relevant intervention measures remain active
(Anderson 2009). How these policies affect economic welfare, inequality, and
poverty levels is an issue of great interest to the agricultural, trade, and develop-
ment policy communities in international agencies and in many developing coun-
tries. More specifically, for any developing country of interest, how important are
its own policies compared with those of the rest of the world in affecting the
welfare of the poor in that country? And, given that three-quarters of the world's
poor depend on agriculture directly or indirectly for their livelihoods and that,
according to an earlier finding, farm policies in 2001 were responsible for more
than three-fifths of the global welfare cost of trade distortions, what contributions
do agricultural policies contribute to these outcomes (World Bank 2007; Anderson,
Martin, and van der Mensbrugghe 2006a, 2006b)?
Now is an appropriate time to address this set of questions for at least three
reasons. One is that the World Trade Organization is struggling to conclude the
Doha Round of multilateral trade negotiations; agricultural policy reform is once
again one of the most contentious issues in these talks. Another reason is that
*The authors are grateful for the distortion estimates provided by authors of the focus country case
studies; for the assistance with spreadsheets by Johanna Croser, Marianne Kurzweil, and Signe Nelgen;
and for the helpful comments of workshop participants.
49
50 Agricultural Price Distortions, Inequality, and Poverty
poorer countries and their development partners are striving to achieve the
United Nations�encouraged Millennium Development Goals by 2015, of which
the prime goals are the reduction of hunger and poverty. The third reason is that a
new set of estimates of distortions to agricultural incentives in many countries has
been brought together recently by the World Bank (Anderson and Valenzuela
2008), and these estimates have since been provided as alternative measures of
price distortions for use in computable general equilibrium models (Valenzuela
and Anderson 2008). The estimates differ from the usual ones used by trade mod-
elers in that they are based on direct domestic-to-border price comparisons rather
than on merely applied import tariff rates (as with the Global Trade Analysis
Project [GTAP] data set; see Narayanan and Walmsley 2008).
This chapter serves two purposes. The first is to offer an analysis of the eco-
nomic effects of agricultural price and merchandise trade policies around the
world as of 2004 on global markets, net farm incomes, and national and regional
economic welfare and poverty, that is, it assesses the effort still necessary to
remove the disarray in world agriculture--to use the title of the seminal study by
Johnson (1991)--and to provide at least a crude indication of the poverty that
might be reduced by such a reform. In doing so, the chapter also serves a second
purpose: to explain the origin of the exogenous shocks used in the global model-
ing described in the following chapter and in the national modeling studies
described in parts III�V that show the market effects on particular developing
countries of rest-of-the-world agricultural and trade policies.
To quantify the impacts of current policies, we first amend the distortions in
version 7 of the GTAP global protection database (Narayanan and Walmsley
2008) by replacing the applied tariffs there with distortion rates that reproduce
the distortions estimated by contributors to the World Bank's research project,
Distortions to Agricultural Incentives, as collated by Valenzuela and Anderson
(2008).1 These distortion estimates suggest that, despite the reforms of the past
25 years, there was still a considerable range of rates across commodities and
countries in 2004, including a strong antitrade bias in national agricultural and
trade policies in many developing countries. Furthermore, nonagricultural pro-
tectionism is still rife in some developing countries, and agricultural price
supports in some high-income countries remain high.
The present analysis addresses the following questions: To what extent were
policies as of 2004 still reducing the rewards of farming in developing countries
and thereby adding to inequality in farm household incomes across countries? Are
policies depressing value added more in primary agriculture than in the rest of the
economies of developing countries? And are they depressing the earnings of
unskilled workers more than the earnings of the owners of other factors of pro-
duction, thereby potentially contributing to inequality and poverty within these
Global Welfare and Poverty Effects: Linkage Model Results 51
developing countries (given that farm incomes are well below nonfarm incomes
in most developing countries and that agriculture in these countries is intensive in
the use of unskilled labor)?
To provide answers to these and related questions, we use our amended GTAP
distortion database in a global computable general equilibrium model (the Linkage
model; see van der Mensbrugghe 2005). We use the model to assess how agricul-
tural markets, factor prices, and the value added in agriculture versus in nonfarm
sectors would differ if all price and trade policies that distort the markets for farm
and nonfarm goods (as of 2004) were removed. It is important to include nonagri-
cultural trade policies in the reform experiment because, as shown in the new study
reported in Anderson (2009), these policies may have been more harmful than
agricultural policies in depressing farmer incentives in many developing countries.
We present the results for the key countries and regions of the world and for the
world as a whole. While no one anticipates a complete freeing of global markets in
the near future, this prospective analysis serves as a benchmark to appreciate the
stakes in terms of further reforms through rounds of multilateral World Trade
Organization negotiations. At the same time, by showing how different the trade
patterns of various countries would be without distortions, we also provide indica-
tions of agricultural comparative advantages in different parts of the world that are
more accurate than the perspective offered through an examination of actual trade
and self-sufficiency indicators derived in the current distortion-ridden situation.
The chapter begins with an examination of the extent of price distortions in
2004 provided by various policy instruments as calibrated in Valenzuela and
Anderson (2008). The emphasis is mainly on import tariffs in the case of nonfarm
products, but, in the case of agriculture, it is on the full range of production, con-
sumption, and trade taxes and subsidies and their equivalent in the case of quan-
titative restrictions on markets. This is followed by a description of the Linkage
model of the global economy that we use to analyze the consequences of the
removal of the distortions. The key results of the two simulations are then pre-
sented: the full global liberalization of markets for all merchandise and--to
understand the relative contribution of farm policies to these outcomes--the full
global reform only of agricultural policies.2 The chapter concludes by highlighting
the main messages that emerge from the results. They are as follows: in a shift to
free markets, income inequality across countries would be reduced at least
slightly; all but one-sixth of the gains to developing countries would be generated
by agricultural policy reform; unskilled workers in developing countries--the
majority of whom work on farms--would benefit most from reform; net farm
incomes in developing countries would rise by 6 percent compared with 2 percent
in nonagricultural value added; and the number of people surviving on less than
US$1 a day would drop by 3 percent globally.
52 Agricultural Price Distortions, Inequality, and Poverty
Key Distortions in Global Markets
Border measures have traditionally been the main means by which governments
distort prices in their domestic markets for tradable products, given that the rela-
tive prices of the various goods are affected by trade taxes or subsidies. Product-
specific domestic output subsidies and farm input subsidies have played a more
limited role in part because of their much greater overt cost to the treasury.3
To quantify the impacts of current policies, we use the Altertax procedure
(Malcolm 1998) to amend the distortions in the prerelease of version 7 of the
GTAP global protection database. The amendments relate mainly to developing
countries, but, following Anderson and Valenzuela (2007b), we also alter cotton
distortions in the United States to reflect the policies there more accurately. To
simplify the discussion below, we treat European transition economies (among
which we include Turkey) as one of the world's developing-country regions,
alongside Africa, Asia, and Latin America.4
Version 7 of the GTAP database includes estimates of bilateral tariffs and
export subsidies and of domestic supports as of 2004 for more than 100 countries
and country groups spanning the world. As with version 6 of the GTAP data set
(which relates to 2001; see Dimaranan 2006), the protection data come from
MAcMaps, a joint project of the Centre d'Etudes Prospectives et d'Informations
Internationale (Paris) and the International Trade Centre (Geneva). MAcMaps is
a detailed database on bilateral import protection at the Harmonized System
6-digit tariff line classification level that integrates trade preferences, specific and
compound tariffs, and a partial evaluation of nontariff barriers such as tariff rate
quotas.5 The new 2004 version 7 of the GTAP database has lower tariffs than the
previous, 2001 version 6 database because of major reforms such as the comple-
tion of the implementation of the Uruguay Round agreements and unilateral
reforms, including those resulting from the World Trade Organization accession
negotiations of China and other recent acceding countries.
As noted above, the agricultural price distortion rates in the GTAP version 7
database have been replaced here by an alternative data set for numerous develop-
ing countries based on the estimates of the nominal rates of assistance for 2004
contained in Valenzuela and Anderson (2008). The sectoral averages of these
amended values are shown in table 2.1. In the case of the amendments to the
import tariffs on individual farm products for any particular developing country,
the bilateral tariff structure in the GTAP version 7 database is preserved by simply
lowering or raising the bilateral tariffs by the same proportion we use to amend
the country's average import tariff on each product for 2004.
According to this amended data set, the weighted average applied tariff for agri-
culture and lightly processed food in 2004 was 21.8 percent for developing coun-
tries and 22.3 percent for high-income countries, while, for nonfarm goods, it was
Global Welfare and Poverty Effects: Linkage Model Results 53
Table 2.1. Structure of Producer Price Distortions in Global
Goods Markets, 2004
(percent)
Agriculture and
Primary lightly processed Other
agriculture food goods
Domestic Export
Region, country support subsidy Tariff Tariff
Africa 0.8 0.1 20.4 11.2
Egypt, Arab Rep. 0.0 0.0 5.0 13.5
Madagascar 0.0 4.4 3.4 2.7
Mozambique 0.2 0.0 14.5 10.9
Nigeria 0.1 0.0 76.1 17.2
Senegal 0.0 1.1 6.2 8.9
South Africa 0.0 0.0 10.2 6.5
Tanzania 0.3 0.0 11.8 13.7
Uganda 0.0 2.6 9.2 5.5
Zambia 0.8 0.0 7.0 9.0
Zimbabwe 3.2 0.0 8.9 15.4
Rest of Africa 1.2 0.3 19.0 13.4
East and South Asia 2.4 0.6 29.6 8.1
China 0.0 0.2 6.5 7.1
Indonesia 0.0 1.6 7.3 4.9
Korea, Rep. 0.0 0.0 319.4 5.9
Malaysia 0.0 0.2 5.0 5.9
Philippines 4.7 0.0 7.1 3.4
Taiwan, China 0.4 0.0 84.2 3.9
Thailand 0.2 0.0 26.2 12.9
Vietnam 3.6 0.5 21.5 18.5
Bangladesh 1.0 0.0 9.9 22.5
India 10.1 2.5 2.9 20.8
Pakistan 0.0 0.2 19.4 18.5
Sri Lanka 0.6 0.3 23.8 5.8
Rest of East and South Asia 0.7 0.0 4.3 2.7
Latin America 0.2 1.4 7.2 6.7
Argentina 0.0 14.8 0.0 5.8
Brazil 0.0 0.0 4.8 8.9
Chile 0.0 0.0 2.4 1.8
Colombia 0.0 0.0 21.6 9.8
Ecuador 0.0 0.0 13.4 10.4
Mexico 1.2 0.0 6.2 3.4
Nicaragua 0.0 2.8 9.6 3.9
Rest of Latin America 1.7 0.3 9.9 9.9
(Table continues on the following page.)
54 Agricultural Price Distortions, Inequality, and Poverty
Table 2.1. Structure of Producer Price Distortions in Global
Goods Markets, 2004 (continued )
(percent)
Agriculture and
Primary lightly processed Other
agriculture food goods
Domestic Export
Region, country support subsidy Tariff Tariff
Eastern Europe and Central Asia 0.8 0.3 15.9 4.8
Baltic States 3.4 0.0 8.2 0.9
Bulgaria 0.6 0.0 14.8 11.5
Czech Republic 0.6 0.0 3.0 0.5
Hungary 3.1 0.0 6.2 0.5
Poland 0.4 0.0 6.2 0.8
Romania 1.3 0.0 18.0 9.8
Russian Federation 1.7 0.9 18.9 7.4
Slovak Republic 0.0 0.0 5.2 0.4
Slovenia 0.0 0.0 7.8 0.4
Kazakhstan 0.9 0.0 3.4 2.7
Turkey 0.8 0.0 33.3 3.1
Rest of Eastern Europe
and Central Asia 1.1 0.9 9.7 5.7
High-income countries 2.6 7.2 22.3 1.2
Australia 0.0 0.0 0.5 3.3
Canada 1.6 3.6 18.9 1.4
EU15 1.2 12.8 6.9 0.7
Japan 2.0 0.0 151.7 1.7
New Zealand 0.0 0.2 0.7 3.3
Rest of Western Europe 2.6 13.4 53.9 2.2
United States 5.2 0.6 6.1 1.3
Developing countries 1.4 0.0 21.8 7.5
Africa 0.8 0.1 20.4 11.2
East Asia 0.3 0.0 41.6 6.7
South Asia 7.2 1.7 6.9 20.2
Latin America 0.2 1.4 7.2 6.7
Middle East 12.4 0.0 7.5 5.7
Eastern Europe and Central Asia 0.8 0.3 15.9 4.8
World total 1.9 3.5 22.1 3.3
Sources: Valenzuela and Anderson (2008) based on calculations compiled by Anderson and Valenzuela
(2008).
Note: Data are weighted by the value of production at undistorted prices. EU15 the 15 members of
the European Union prior to 2004.
Global Welfare and Poverty Effects: Linkage Model Results 55
7.5 percent for developing countries and only 1.2 percent for high-income coun-
tries. Export subsidies for farm products in a few high-income regions and export
taxes in a few developing countries were still in place in 2004, but they are generally
small in impact compared with tariffs, as are production subsidies and taxes.6
The averages alone are not necessarily good indicators of the overall distortions
to farmer incentives. Also of importance is the composition of each country's
trade. Two examples serve to illustrate the point. First, if the tariffs of high-income
countries are at a near-prohibitive level for temperate farm products, but are zero
on tropical products such as coffee beans, the import-weighted average agricul-
tural tariff of these countries may be quite low even if the agricultural value added
has been enhanced substantially in these rich countries. Second, the nonagricul-
tural primary sector and the farm sector may receive a similar level of import pro-
tection (but less than the manufacturing sector), although the former is much
more export-focused than agriculture. In this case, trade reform may cause the
nonagricultural primary sector to expand at the expense not only of manufactur-
ing, but also of farming.
Although we have used production rather than trade weights to obtain sectoral
average rates of distortion in table 2.1 and although the ratio of agricultural tariffs
to the tariffs on other goods in 2004 shown in the table is well above unity in many
of the regions, it is not possible to say from those distortion rates alone whether
developing-country policies have an antiagricultural bias. Likewise, it is not possi-
ble to know how the benefits of the removal of agricultural tariffs in the protective
countries would be distributed among the various agricultural exporting coun-
tries. To address such issues, a global general equilibrium model is needed to esti-
mate the net effects of the distortions in all sectors in all countries in terms of the
agricultural markets and net farm incomes of the various nations. We now turn to
such a model.
The Linkage Model of the Global Economy
The model used for this analysis is the World Bank's global computable general
equilibrium model known as Linkage (van der Mensbrugghe 2005). For most of
this decade, this model has formed the basis for the standard World Bank long-term
projections of the world economy, for much of world trade policy analysis, and,
more recently, for migration policy analysis (see World Bank 2001, 2003, 2004,
2005, 2006). It is a relatively straightforward computable general equilibrium
model, but has some characteristics that distinguish it from other comparative
static models such as the GTAP model (described in Hertel 1997). Factor stocks are
fixed, which means, in the case of labor, that the extent of unemployment (if any) in
the baseline remains unchanged. Producers minimize costs, subject to constant
returns to scale in production; consumers maximize utility; and all markets,
56 Agricultural Price Distortions, Inequality, and Poverty
including for labor, are cleared with flexible prices. There are three types of produc-
tion structures. The crop sectors reflect the substitution possibilities between
extensive and intensive farming; the livestock sectors reflect the substitution possi-
bilities between pasture and intensive feeding; and all other sectors reflect standard
capital-labor substitution. There are two types of labor, skilled and unskilled, and
the total employment of each is assumed to be fixed (so there is no change in unem-
ployment levels), although both are assumed to be intersectorally mobile. There is a
single representative household per modeled region, and this household allocates
income to consumption using the extended linear expenditure system. Trade is
modeled using a nested Armington structure whereby aggregate import demand is
the outcome of the allocation of domestic absorption between domestic goods and
aggregate imports, and then aggregate import demand is allocated across source
countries to determine the bilateral trade flows.7
Government fiscal balances are fixed in U.S. dollar terms, and the fiscal objective
is met by changing the level of lump sum taxes on households. This implies that the
losses in tariff revenues are replaced by higher direct taxes on households. The cur-
rent account balance is also fixed. Given that other external financial flows are fixed,
this implies that ex ante changes to the trade balance are reflected in ex post changes
to the real exchange rate. For example, if import tariffs are reduced, the propensity
to import increases, and additional imports are financed by boosting export rev-
enues. This last step is typically achieved through depreciation of the real exchange
rate. Finally, investment is driven by savings. With fixed public and foreign savings,
investment occurs through changes in the savings behavior of households and
changes in the unit cost of investment. The model only solves for relative prices; the
num�raire, or price anchor, is the export price index of manufactured exports from
high-income countries. This price is fixed at unity in the base year.
A virtue of beginning with the latest GTAP database is that the database
includes bilateral tariffs that capture not only reciprocal, but also nonreciprocal
preferential trade agreements; the latter provide low-income exporters duty-free
access to protected markets in high-income countries. This allows us to take into
account the fact that future reform may cause a decline in the international terms
of trade in those developing countries that are enjoying preferential access to the
agricultural markets and other markets in high-income countries (in addition to
those that are net food importers because their comparative advantage lies in
other sectors, such as labor-intensive manufacturing).
The version of the Linkage model used in our study is based on an aggregation
involving 23 sectors and 49 individual countries, plus 11 country groups spanning
the world (see the appendix). There is an emphasis on agriculture and food, which
account for 16 of the 23 sectors. Note that, consistent with the World Trade Organi-
zation, we include the Republic of Korea and Taiwan, China in the developing-
country category.8
Global Welfare and Poverty Effects: Linkage Model Results 57
The results below are comparative static results; so, they do not include the
(often much larger) dynamic gains that result from an acceleration in investment
arising from the reduction in tariffs on industrial goods that lowers the cost of
investment. Also missing are therefore any costs of adjustment to reform. More-
over, because this version of the Linkage model assumes perfect competition and
constant returns to scale, it captures none of the benefits of freeing markets that
might be generated by accelerated productivity growth, scale economies, and the
creation of new markets (extensification versus intensification). There is also a
dampening effect on the estimates of welfare gains because of product and
regional aggregation, which hides many of the differences across products in rates
of distortions. The results should thus be treated as much lower-bound estimates
of the net economic welfare benefits of policy reform.9
The Prospective Effects of the Global Removal
of Price-Distorting Policies
In this section, to explore the possible outcome of the removal of the policies in
force as of 2004, we examine the results of two modeling simulations. The main
one involves the full global liberalization of agricultural policies and of trade poli-
cies in nonagricultural goods. We also discuss an additional simulation, which
involves the global liberalization of agricultural policies only, to give a sense of the
relative contribution of farm policies to various outcomes.
Global and national economic welfare
Beginning with the baseline projection of the world economy in 2004, we remove
globally all agricultural subsidies and taxes, plus import tariffs on other merchan-
dise (as summarized in table 2.1).10 Our Linkage model suggests that this would
lead to a global gain of US$168 billion per year (table 2.2). As a share of national
income, developing countries would gain nearly twice as much as high-income
countries by completing the reform process (an average increase of 0.9 compared
with 0.5 percent, respectively). Thus, in this broad sense of a world of only two
large country groups, completing the global reform process would reduce interna-
tional inequality, to use the Milanovic (2005) term, taking in to account the eco-
nomic size of each country.11 The results vary widely across developing countries,
however, ranging from slight losses in the case of some South Asian and Sub-
Saharan African countries that would suffer exceptionally large adverse terms of
trade changes to 8 percent increases in the case of Ecuador (of which the main
export item, bananas, is currently facing heavy discrimination in the markets of
the European Union, where former colonies and least developed countries enjoy
preferential duty-free access).
58 Agricultural Price Distortions, Inequality, and Poverty
Table 2.2. The Impact on Real Income of the Full Liberalization of Global
Merchandise Trade, by Country and Region, 2004
(2004 U.S. dollars and percent)
Change in annual Change in
real income, real income,
US$, billions % of benchmark
Change Change
deriving deriving
from from
Total change Total terms of
income in terms income trade
Country and region gain of trade gain effects
North Africa and Sub-Saharan Africa 0.9 6.0 0.2 1.1
Egypt, Arab Rep. 0.2 0.6 0.3 0.9
Madagascar 0.0 0.0 0.9 1.2
Mozambique 0.1 0.1 2.4 2.0
Nigeria 0.3 0.6 0.7 1.3
Senegal 0.0 0.1 2.3 4.0
South Africa 0.2 0.7 0.1 0.5
Tanzania 0.0 0.0 0.5 0.4
Uganda 0.0 0.0 0.6 0.1
Zambia 0.0 0.0 0.1 0.3
Zimbabwe 0.1 0.0 3.4 0.5
Rest of Africa 0.5 3.8 0.2 1.5
East and South Asia 29.7 4.9 0.9 0.1
China 3.3 0.5 0.2 0.0
Indonesia 0.5 0.0 0.2 0.0
Korea, Rep. 14.0 0.2 2.8 0.0
Malaysia 4.2 1.0 4.7 1.1
Philippines 0.0 0.5 0.1 0.7
Taiwan, China 1.0 0.0 0.4 0.0
Thailand 3.3 0.1 1.4 0.1
Vietnam 1.9 0.9 5.3 2.5
Bangladesh 0.2 0.8 0.4 1.7
India 0.8 2.9 0.2 0.6
Pakistan 0.1 0.6 0.2 0.8
Sri Lanka 0.8 0.5 5.1 3.1
Rest of East and South Asia 1.9 0.8 1.4 0.5
Latin America 15.8 2.5 1.0 0.2
Argentina 3.2 0.7 2.6 0.6
Brazil 6.8 5.6 1.6 1.3
Chile 0.3 0.2 0.4 0.3
Colombia 2.2 0.7 3.1 1.0
Ecuador 2.0 1.1 8.2 4.4
Mexico 0.7 3.4 0.1 0.6
Nicaragua 0.0 0.0 1.3 0.4
Rest of Latin America 2.0 1.0 0.5 0.3
Global Welfare and Poverty Effects: Linkage Model Results 59
Table 2.2. The Impact on Real Income of the Full Liberalization of Global
Merchandise Trade, by Country and Region, 2004 (continued)
(2004 U.S. dollars and percent)
Change in annual Change in
real income, real income,
US$, billions % of benchmark
Change Change
deriving deriving
from from
Total change Total terms of
income in terms income trade
Country and region gain of trade gain effects
Eastern Europe and Central Asia 14.2 3.6 1.2 0.3
Baltic States 0.5 0.1 1.8 0.3
Bulgaria 0.2 0.2 1.4 1.4
Czech Republic 1.0 0.1 1.4 0.2
Hungary 0.4 0.1 0.6 0.1
Poland 2.0 0.1 1.2 0.1
Romania 0.1 0.7 0.3 1.9
Russian Federation 5.4 3.1 1.2 0.7
Slovak Republic 0.7 0.1 2.3 0.4
Slovenia 0.3 0.1 1.5 0.3
Kazakhstan 0.4 0.2 1.1 0.6
Turkey 1.3 0.5 0.6 0.2
Rest of Eastern Europe and Central Asia 2.2 0.5 2.1 0.4
High-income countries 102.8 11.3 0.5 0.1
Australia 2.4 1.9 0.5 0.4
Canada 0.6 1.2 0.1 0.2
EU15 56.8 3.8 0.7 0.0
Japan 23.1 10.4 0.7 0.3
New Zealand 2.2 1.8 3.2 2.6
Rest of Western Europe 13.1 0.1 2.7 0.0
United States 2.8 0.9 0.0 0.0
Hong Kong, China; Singapore 1.7 1.4 1.4 1.1
Developing countries 64.9 12.2 0.9 0.2
North Africa 0.9 2.8 0.5 1.5
Sub-Saharan Africa 0.0 3.2 0.0 0.9
East Asia 30.1 1.0 1.1 0.0
South Asia 0.4 3.9 0.1 0.6
Latin America 15.8 2.5 1.0 0.2
Middle East 4.2 0.2 0.8 0.0
Eastern Europe and Central Asia 14.2 3.6 1.2 0.3
World total 167.7 1.0 0.6 0.0
Source: World Bank Linkage model simulations by the authors.
Note: The table shows results relative to the 2004 benchmark data. EU15 the 15 members of the
European Union prior to 2004.
60 Agricultural Price Distortions, Inequality, and Poverty
If one were to represent each of the 60 countries and regions in table 2.2 by a
single household (that is, ignoring intraregional inequality), the income inequal-
ity across countries as measured by the Gini coefficient would be reduced at least
slightly, from 0.8513 to 0.8506.12
The second and fourth columns in table 2.2 show the amount of the welfare
gain deriving from changes in the international terms of trade for each country.
For developing countries as a group, this terms of trade effect is slightly negative;
the converse is true for high-income countries.
The regional and sectoral distribution of welfare effects
One way to decompose the real income gains from the full removal of price distor-
tions globally so that we may understand more clearly the sources of the gains for
each region is to assess the impacts in various economic sectors of liberalization in
developing countries versus liberalization in high-income countries. These results
are provided in table 2.3. They suggest that the global liberalization of agriculture
and food markets would contribute 70 percent of the total global gains from mer-
chandise reform. This is slightly greater than the 63 percent found for 2015 by
Anderson, Martin, and van der Mensbrugghe (2006b) using the earlier version 6 of
the GTAP database anchored on 2001 estimates of distortions. This result is remark-
able given the low shares of agriculture and food in global GDP and global merchan-
dise trade (3 and 6 percent, respectively). For developing countries, the importance
of agricultural policies is even slightly greater, at 72 percent (see row 7 in table 2.3).
Slightly more than two-thirds of the global gains that may arise because of the
removal of agricultural policies are accounted for by the farm policies of high-
income countries (column 3 in table 2.3; US$82 billion of the US$117 billion per
year). These policies also account for nearly one-quarter of the overall gains to
developing countries from all global agricultural and trade policy reforms (col-
umn 1 in table 2.3; US$15 billion of the US$65 billion per year).
The quantities produced and traded
The results of full global liberalization suggest that there would be little change in
the aggregate shares of developing countries in the global output and exports of
nonfarm products other than textiles and apparel. The shares of these countries in
agricultural and processed food markets change noticeably, however. The export
share rises from 54 to 64 percent, and the output share rises from 46 to 50 percent.
More significantly, the rises occur in nearly all agricultural and food industries.
The share of the global production of farm products that is exported thus rises
dramatically in many industries and, for the sector as a whole, increases from 8 to
13 percent, excluding intra�European Union trade (table 2.4). This thickening of
international food markets would reduce substantially the fluctuations in food
prices and in the quantities of food traded in these markets.
Table 2.3. Regional and Sectoral Sources of the Welfare Gains from the Full Liberalization of Global
Merchandise Trade, 2004
(2004 U.S. dollars and percent)
Gains by region,a US$, billions Share of regional gain, %
Sector Developing High-income World Developing High-income World
Developing countries liberalize
Agriculture and light processing 31.8 3.9 35.6 48.6 3.8 21.2
Manufacturing and services 5.6 36.7 42.3 8.6 35.9 25.2
Total 37.4 40.6 77.9 57.2 39.6 46.5
High-income countries liberalize
Agriculture and light processing 15.1 66.4 81.6 23.2 64.9 48.6
Manufacturing and services 12.8 4.6 8.2 19.6 4.5 4.9
Total 28.0 61.8 89.8 42.8 60.4 53.5
All countries liberalize
Agriculture and light processing 46.9 70.3 117.2 71.8 68.7 69.9
Manufacturing and services 18.4 32.1 50.5 28.2 31.3 30.1
Total 65.3 102.3 167.7 100.0 100.0 100.0
Source: World Bank Linkage model simulations by the authors.
Note: The table shows results relative to the 2004 benchmark data.
a. Small interaction effects are distributed proportionately, and the numbers are rounded to sum to 100 percent.
61
Table 2.4. The Impact of Full Global Liberalization on the Shares of Global Output Exported, by Product, 2004
(percent)
62
Share in global output Developing countries in Developing countries in
exported global output global exports
Full global Full global Full global
Benchmark liberalization Benchmark liberalization Benchmark liberalization
Paddy rice 1 2 81 82 56 42
Wheat 16 22 67 71 25 39
Other grains 11 15 55 57 35 56
Oilseeds 21 28 69 74 54 68
Plant-based fibers 25 25 74 83 50 79
Vegetables and fruits 9 15 72 77 69 80
Other crops 14 17 49 49 75 62
Cattle, sheep, and so on 2 2 43 48 56 59
Other livestock 4 4 65 67 43 46
Wool 13 14 82 81 16 18
Beef and sheep meat 7 21 27 41 31 68
Other meat products 7 12 32 34 42 45
Vegetable oils and fats 20 30 52 58 80 84
Dairy products 5 11 29 33 28 41
Processed rice 5 7 76 79 85 87
Refined sugar 8 42 52 85 78 90
Other food, beverages, tobacco 9 12 35 36 50 59
Other primary products 31 33 64 63 76 76
Textiles and wearing apparel 28 35 53 57 74 77
Other manufacturing 24 26 32 31 43 43
Services 3 3 20 20 31 30
Agriculture and food 8 13 46 50 54 64
Agriculture 8 11 62 65 55 64
Processed foods 8 14 37 40 52 63
Source: World Bank Linkage model simulations by the authors.
Note: The developing-country shares of global output and exports exclude intra�European Union trade.
Global Welfare and Poverty Effects: Linkage Model Results 63
The impact of full trade reform on agricultural and food output and trade for
each country and region is shown in table 2.5. It is clear that global farm trade is
enhanced by more than one-third (39 percent), whereas the global value of output is
virtually unchanged (dropping only 2.6 percent). This suggests that, in aggregate, the
pro-agricultural policies of high-income countries are not quite fully offset by the
Table 2.5. The Impact of Full Global Trade Liberalization on
Agricultural and Food Output and Trade,
by Country and Region, 2004
(2004 U.S. dollars and percent)
Change relative
US$, billions to baseline, %
Country, region Output Exports Imports Output Exports Imports
North Africa and
Sub-Saharan Africa 13.8 20.5 10.0 7.2 99.1 46.0
Egypt, Arab Rep. 0.4 0.5 0.1 2.2 39.2 4.2
Madagascar 0.0 0.0 0.0 0.4 2.7 4.3
Mozambique 0.9 1.0 0.1 52.3 597.1 33.3
Nigeria 0.5 0.4 0.7 2.9 92.8 43.1
Senegal 0.0 0.0 0.0 1.9 35.0 0.3
South Africa 0.7 0.9 0.8 2.4 26.7 42.9
Tanzania 0.0 0.2 0.1 0.7 28.5 31.2
Uganda 0.0 0.0 0.0 0.6 1.3 1.5
Zambia 0.1 0.1 0.0 5.2 22.3 35.9
Zimbabwe 0.4 0.3 0.1 25.7 38.0 39.2
Rest of Africa 12.0 17.0 8.3 10.5 133.1 64.3
East and South Asia 25.0 39.5 24.7 2.7 83.4 36.7
China 6.2 7.7 6.7 1.7 76.5 27.5
Indonesia 1.1 1.6 1.0 1.8 21.6 21.5
Korea, Rep. 1.0 1.0 6.2 1.7 194.1 75.0
Malaysia 1.6 1.3 0.7 8.9 17.0 17.8
Philippines 1.1 1.9 0.8 3.5 120.5 35.0
Taiwan, China 1.9 0.3 1.5 9.1 62.8 35.5
Thailand 9.5 8.3 1.9 17.4 133.0 78.1
Vietnam 0.5 1.1 0.6 3.3 54.0 55.6
Bangladesh 0.6 0.4 0.8 2.4 261.2 38.3
India 1.1 9.0 1.4 0.5 131.2 24.2
Pakistan 0.6 0.5 1.0 1.3 45.0 43.0
Sri Lanka 0.1 0.1 0.6 1.2 18.2 69.3
Rest of East and
South Asia 8.0 6.4 1.4 41.5 266.1 29.5
(Table continues on the following page.)
Table 2.5. The Impact of Full Global Trade Liberalization on
Agricultural and Food Output and Trade,
by Country and Region, 2004 (continued)
(2004 U.S. dollars and percent)
Change relative
US$, billions to baseline, %
Country, region Output Exports Imports Output Exports Imports
Latin America 87.2 71.5 7.2 26.8 106.4 29.8
Argentina 12.2 15.1 0.3 37.8 95.6 81.8
Brazil 45.8 25.7 2.1 45.3 100.7 94.8
Chile 0.5 0.4 0.2 4.7 11.3 15.8
Colombia 3.1 4.9 1.1 14.6 161.4 81.7
Ecuador 4.2 4.6 0.3 46.1 198.7 71.8
Mexico 0.3 0.3 0.4 0.4 5.8 4.3
Nicaragua 0.0 0.1 0.0 2.9 21.6 19.4
Rest of Latin America 21.6 20.4 2.8 25.7 175.9 30.4
Eastern Europe and
Central Asia 10.4 17.4 20.3 2.6 79.7 77.6
Baltic States 1.2 0.1 0.4 16.9 15.5 30.9
Bulgaria 4.2 2.6 0.6 6.6 366.5 118.1
Czech Republic 2.2 0.1 0.7 12.0 10.9 40.5
Hungary 0.9 0.4 0.8 6.0 17.1 66.6
Poland 1.7 2.5 2.5 3.9 80.7 88.8
Romania 0.2 1.3 1.1 1.0 190.5 78.3
Russian Federation 12.9 3.2 8.8 13.1 179.4 98.9
Slovak Republic 0.9 0.1 0.4 11.3 12.0 64.1
Slovenia 0.6 0.1 0.2 17.1 54.1 26.2
Kazakhstan 1.5 1.4 0.0 11.8 142.9 11.6
Turkey 2.0 2.3 2.9 3.1 61.5 92.1
Rest of Eastern Europe
and Central Asia 3.0 4.1 2.0 7.7 71.3 53.4
High-income countries 233.2 9.2 89.8 13.1 4.0 38.3
Australia 12.0 7.0 0.2 19.8 41.2 11.1
Canada 1.6 3.6 2.7 2.4 24.1 32.8
EU15 190.9 38.8 50.9 21.2 29.2 31.9
Japan 39.1 0.4 16.8 22.9 87.7 69.1
New Zealand 10.6 6.4 0.2 46.6 74.3 27.1
Rest of Western Europe 11.6 11.7 9.8 19.4 312.0 132.7
United States 12.8 0.6 9.3 2.6 1.1 32.4
Hong Kong, China;
Singapore 0.1 0.0 0.1 2.1 6.3 1.6
Developing countries 137.6 163.6 64.6 7.1 100.0 40.4
North Africa 11.4 13.3 6.1 17.3 377.2 62.5
Sub-Saharan Africa 2.5 7.2 3.8 1.9 41.9 32.3
East Asia 25.1 29.5 20.8 4.0 77.4 37.4
South Asia 0.1 10.0 3.9 0.0 108.3 33.2
Latin America 87.2 71.5 7.2 26.8 106.4 29.8
Middle East 22.0 14.8 2.5 21.5 222.7 12.1
Eastern Europe and
Central Asia 10.4 17.4 20.3 2.6 79.7 77.6
World total 95.7 154.4 154.4 2.6 39.1 39.1
Source: World Bank Linkage model simulations by the authors.
Note: The table shows results relative to the 2004 benchmark data. EU15 the 15 members of the
European Union prior to 2004.
64
Global Welfare and Poverty Effects: Linkage Model Results 65
Table 2.6. The Impact of Global Liberalization on the Share of
Agricultural and Food Production Exports,
by Country and Region, 2004
(percent)
2004 benchmark Full global
Country, region data liberalization
Developing countries 9.5 16.9
North Africa 6.3 20.6
Sub-Saharan Africa 13.8 19.3
East Asia 8.4 15.1
South Asia 3.7 7.5
Latin America 18.1 28.2
Middle East 7.4 17.2
Eastern Europe and Central Asia 6.8 11.1
High-income countries 13.0 14.1
World total 11.4 15.4
Source: World Bank Linkage model simulations by the authors.
antiagricultural policies of developing countries, whereas the antitrade bias in the
policies of both groups of countries reinforce each other. The increase in the exports
of these goods from developing countries would be a huge US$163 billion per year.
Latin America accounts for nearly half this increase, but the exports of all developing
regions expand. This means that the share of these countries in production for export
would be much higher. It would increase in the case of almost all developing coun-
tries, rising in aggregate for the group from 10 to 17 percent (table 2.6).
Also of interest is the course of agricultural imports. Developing countries as a
group would see them growing less than farm exports (table 2.5). This means that
the food and agricultural self-sufficiency of these countries would rise, although,
in aggregate, only slightly. For high-income countries, it would fall five percentage
points (slightly less if Eastern Europe is included); for East Asia and Africa, it
would rise two or three points; for South Asia, it would be unchanged; and, for
Latin America, it would jump from 112 to 126 percent (table 2.7).
Such reform also raises substantially the share of agricultural and food produc-
tion that is exported globally, thereby thickening international markets. This would
restrict international food price fluctuations and thereby reduce the concerns
about vulnerability to import dependence. The extent of this global public good
aspect of agricultural and trade reform may be sensed for various products from the
results reported in table 2.8. The case of highly protected sugar and milk, as well as
grains and oilseeds, is especially noteworthy. Also noteworthy is the extent to which
the developing-country shares of output exported rise for certain products. The
share of grain production that is exported by these countries would double, and
Table 2.7. The Impact of Global Liberalization on Self-sufficiency in Agricultural and Other Products, by Region, 2004
66 (percent)
North Africa
and Eastern
High-income Developing Sub-Saharan Europe and
countries countries Africa Latin America East Asia South Asia Central Asia
Product BK GL BK GL BK GL BK GL BK GL BK GL BK GL
Paddy rice 101 105 100 99 97 96 93 72 100 101 101 101 95 92
Wheat 141 140 88 89 67 46 80 98 68 65 100 98 102 117
Other grains 108 102 94 98 94 91 98 119 88 81 103 105 103 113
Oilseeds 104 92 97 103 104 130 140 167 66 51 100 101 106 115
Plant-based fibers 161 112 88 97 177 265 94 107 54 58 93 95 104 118
Vegetables and fruits 90 78 105 109 108 103 153 221 102 104 99 98 99 92
Other crops 90 91 113 110 138 138 143 133 110 104 104 104 90 88
Cattle, sheep, and so on 100 100 100 100 101 99 102 102 98 97 100 100 102 102
Other livestock 101 101 100 100 101 100 101 100 99 99 100 100 99 98
Wool 161 180 92 91 103 104 103 102 78 75 96 93 96 99
Beef and sheep meat 101 85 97 134 96 102 108 183 83 77 126 652 95 85
Other meat products 100 99 100 103 92 85 121 143 101 103 96 95 96 93
Vegetable oils and fats 95 85 103 114 69 191 141 143 115 116 78 66 93 96
Dairy products 103 100 94 101 76 79 97 102 78 78 99 99 102 104
Processed rice 99 95 100 101 69 63 94 85 104 108 104 104 92 87
Refined sugar 98 41 102 133 95 100 131 227 98 196 96 91 98 70
Other food, beverages, tobacco 99 97 103 105 101 100 108 112 105 113 106 94 100 98
Other primary products 76 76 122 122 180 189 148 155 84 82 75 69 115 116
Textiles and wearing apparel 81 76 123 128 98 91 104 91 144 155 144 153 101 95
Other manufacturing 101 102 98 96 77 74 96 91 106 105 90 89 95 95
Services 101 101 101 101 101 102 100 100 101 100 100 101 101 101
Agriculture and food 100 95 101 105 100 103 112 126 100 102 100 100 99 98
Agriculture 99 96 100 102 104 103 115 126 96 95 100 100 100 101
Processed foods 100 95 101 108 94 103 110 126 104 111 100 101 99 96
Source: World Bank Linkage model simulations by the authors.
Note: Self-sufficiency domestic production as a percentage of domestic consumption measured in value terms at free-on-board prices. BK benchmark. GL global liberalization.
Table 2.8. Shares of Production Exported and of Consumption Imported before and after Full Global Liberalization
of all Merchandise Trade, by Product, 2004
(percent)
Share of production exported Share of consumption imported
High-income countries Developing countries High-income countries Developing countries
2004 Global 2004 Global 2004 Global 2004 Global
Product benchmark liberalization benchmark liberalization benchmark liberalization benchmark liberalization
Paddy rice 3 7 1 1 2 3 1 2
Wheat 37 47 6 12 11 25 17 21
Other grains 15 16 7 15 9 14 11 15
Oilseeds 31 34 16 25 26 36 16 22
Plant-based fibers 50 31 17 24 18 22 26 25
Vegetables and fruits 10 13 9 15 18 30 4 7
Other crops 7 13 21 22 16 20 11 14
Cattle, sheep, and so on 1 2 2 2 2 2 2 2
Other livestock 6 7 3 3 6 6 3 3
Wool 60 62 2 3 35 31 10 12
Beef and sheep meat 6 11 7 35 5 24 10 13
Other meat products 6 10 9 16 6 12 8 14
Vegetable oils and fats 8 11 31 43 12 24 26 34
Dairy products 5 10 4 14 2 10 10 14
Processed rice 3 4 5 8 4 9 5 7
Refined sugar 4 30 12 44 5 66 10 25
Other food, beverages, tobacco 7 8 12 20 8 10 9 16
Other primary products 20 21 37 39 38 39 22 24
Textiles and wearing apparel 15 19 39 48 30 37 23 31
Other manufacturing 20 21 32 36 19 20 32 38
Services 3 3 5 4 2 2 5 5
Agriculture and food 7 9 9 17 8 13 8 12
Agriculture 9 11 7 11 10 15 7 9
Processed foods 6 9 12 23 7 13 10 16
67
Source: World Bank Linkage model simulations by the authors.
Note: The data on the high-income countries exclude intra�European Union trade.
68 Agricultural Price Distortions, Inequality, and Poverty
the corresponding share of meat would more than double, while the share of sugar
would rise nearly fourfold. Global exports of cotton (plant-based fibers) would
become more dominated by developing countries, while the share of cotton
production exported by high-income countries would fall from 50 to 31 percent.
The effects on product and factor prices
The average real international prices of agricultural and lightly processed food prod-
ucts would be only 1.3 percent higher in the absence of all merchandise trade distor-
tions, or 2.0 percent if only agricultural policies were liberalized (see table 2.9). The
Table 2.9. The Impact of Full Global Liberalization on Real
International Product Prices, 2004
(percent relative to 2004 baseline)
Policies in all
Product Agricultural policies goods sectors
Paddy rice 6.9 6.6
Wheat 1.8 1.4
Other grains 2.6 2.7
Oilseeds 2.2 2.4
Sugarcane and beets 1.1 2.0
Plant-based fibers 4.7 2.9
Vegetables and fruits 2.4 1.8
Other crops 1.7 1.0
Cattle, sheep, and so on 0.2 1.1
Other livestock 1.2 2.1
Raw milk 0.7 0.2
Wool 3.5 3.3
Beef and sheep meat 5.6 4.6
Other meat products 1.3 0.6
Vegetable oils and fats 1.4 1.9
Dairy products 4.6 3.8
Processed rice 2.8 2.9
Refined sugar 2.5 1.3
Other food, beverages, tobacco 1.7 1.3
Textiles and wearing apparel 0.3 1.2
Other manufacturing 0.2 0.2
Merchandise trade 0.3 0.2
Agriculture and food 0.8 0.3
Agriculture 1.5 0.9
Agriculture and light processing 2.0 1.3
Source: World Bank Linkage model simulations by the authors.
Note: The model num�raire is the export price index of the manufactured exports of high-income countries.
Global Welfare and Poverty Effects: Linkage Model Results 69
net effects of distortions (as of 2004) are especially dampening on the international
prices of beef, milk, rice, and cotton. However, they prop up the international prices
of some other products because export taxes are still in place in some developing
countries, most notably Argentina.
The size of the redistribution of welfare among groups within each country
following trade reform may be much larger than the aggregate change partly
because of the impacts on real pretax rewards to labor, capital, and land. These
effects are reported in table 2.10, in which the data on factor rewards are deflated
by the overall consumer price index and also, in the case of unskilled wages, by the
food and the food plus clothing consumer price indexes (since these items are so
prominent in the spending of unskilled workers). Consistent with trade theory,
these results suggest that unskilled workers in developing countries--the majority
of whom work on farms--would benefit most from reform, followed by skilled
workers, then capital owners. The returns to immobile agricultural land would
also rise in developing countries, but by less than the rise in more mobile factors.
Land returns fall substantially in highly protected Japan and Western Europe,
change little in the United States, rise considerably in Australia and Canada, and
rise dramatically in dairy-intensive New Zealand.
The effects on sectoral value added
Also of crucial interest in terms of the impact of these policies on inequality and
poverty is the effect the policies have on value added in agriculture, that is, on net
farm incomes. The results of this issue in full global reform are reported in the first
four columns of table 2.11. They show that, for developing countries as a group, the
value added in agriculture rises by 5.6 percent following full global reform of all
merchandise trade. This compares with only 1.9 percent for nonagriculture. Net
farm income expands the most in Latin America, where the average rise is 37 per-
cent, but exceeds 100 percent in Argentina and Ecuador and 40�50 percent in
Brazil and Colombia. In East Asia, it also expands considerably, more than the
expansion in nonagricultural value added, including in China. However, among
the countries listed in Africa, net farm incomes would increase substantially only in
Mozambique, Zambia, and Zimbabwe, and, in the continent as a whole, including
North Africa, they would fall only slightly (by less than 1 percent). Partly this is
because the nonagricultural primary sector--in which numerous African coun-
tries have a strong comparative advantage--would expand (raising Africa's self-
sufficiency in that sector from 180 to 189 percent; see table 2.7), and this would, in
turn, boost the production and employment in nontradable goods and services. It
is estimated that net farm incomes would also fall in South Asia (by 7 percent),
although, there, textiles and clothing would expand (raising the self-sufficiency in
70 Table 2.10. The Impacts of Full Global Merchandise Trade Liberalization on Real Pretax Factor Prices, by Country
and Region, 2004
(percent)
Nominal change deflated by the Real change in unskilled wages,
aggregate CPI deflated
Skilled Capitala Landa By aggregate By food By food and
Country, region wages user cost user cost CPI CPI clothing CPI
North Africa and Sub-Saharan Africa 4.7 4.3 0.1 4.4 5.8 6.9
Egypt, Arab Rep. 3.2 1.7 3.4 2.3 2.2 2.9
Madagascar 2.0 0.2 0.3 0.8 0.8 0.7
Mozambique 0.2 4.3 5.8 10.7 12.0 13.0
Nigeria 10.1 10.5 1.3 3.2 7.7 8.6
Senegal 2.4 3.2 0.7 3.1 2.6 3.6
South Africa 1.8 2.4 0.1 1.6 1.7 3.6
Tanzania 2.1 2.4 1.6 2.8 2.6 3.0
Uganda 2.2 0.7 0.8 0.2 0.2 0.5
Zambia 2.6 3.2 0.7 3.0 3.1 3.5
Zimbabwe 6.7 11.8 23.1 13.6 15.9 16.8
Rest of Africa 6.2 5.5 1.1 6.4 7.2 8.5
East and South Asia 3.4 3.0 1.8 3.2 4.6 4.8
China 1.9 2.0 3.6 2.6 1.6 2.1
Indonesia 0.7 0.7 0.1 1.4 1.5 1.8
Korea, Rep. 7.1 6.5 14.5 5.8 26.6 22.7
Malaysia 10.7 10.2 3.6 11.1 32.3 29.2
Philippines 1.0 1.4 7.2 8.5 9.2 9.4
Taiwan, China 2.4 2.8 11.8 3.3 10.9 10.3
Thailand 2.6 3.5 7.5 5.6 8.5 7.8
Vietnam 17.7 16.0 9.1 19.6 23.9 26.7
Bangladesh 2.1 2.8 2.8 3.3 4.5 5.3
India 2.1 0.2 8.5 1.9 4.4 3.8
Pakistan 3.9 3.5 1.5 3.8 3.3 3.5
Sri Lanka 0.1 14.1 0.9 3.0 4.8 4.8
Rest of East and South Asia 2.0 2.9 6.5 5.1 8.3 9.1
Latin America 1.4 1.9 21.1 4.5 2.4 4.1
Argentina 3.1 4.1 43.6 8.8 4.9 7.2
Brazil 1.3 2.7 26.5 1.4 0.2 1.1
Chile 1.3 0.5 3.0 1.3 1.1 1.9
Colombia 0.8 0.3 30.2 6.0 4.4 5.6
Ecuador 2.1 1.2 61.7 15.1 12.1 13.9
Mexico 0.5 0.5 2.3 0.8 2.3 0.9
Nicaragua 1.9 2.5 2.1 3.8 3.9 4.7
Rest of Latin America 0.8 1.5 18.0 5.4 4.9 6.1
Eastern Europe and Central Asia 3.2 2.6 4.5 1.7 4.2 4.5
Baltic States 3.9 2.0 9.8 1.3 5.6 5.0
Bulgaria 0.3 1.6 5.9 2.5 1.8 2.3
Czech Republic 2.4 1.5 26.1 0.7 3.9 3.3
Hungary 2.2 1.1 19.9 1.0 1.6 1.4
Poland 3.9 2.6 24.6 0.5 5.2 4.6
Romania 4.5 3.5 3.4 3.4 4.5 5.7
Slovak Republic 2.9 2.2 15.9 1.0 4.9 4.0
Slovenia 2.3 1.8 17.2 1.3 4.9 4.2
Russian Federation 3.9 3.8 1.9 2.5 4.3 5.1
Kazakhstan 1.0 1.4 14.0 3.0 2.4 3.3
Turkey 2.1 1.4 3.4 0.4 1.8 1.8
71
Rest of Eastern Europe and Central Asia 3.7 4.2 12.7 6.3 9.7 10.3
(Table continues on the following page.)
72 Table 2.10. The Impacts of Full Global Merchandise Trade Liberalization on Real Pretax Factor Prices, by Country
and Region, 2004 (continued)
(percent)
Nominal change deflated by the Real change in unskilled wages,
aggregate CPI deflated
Skilled Capitala Landa By aggregate By food By food and
Country, region wages user cost user cost CPI CPI clothing CPI
High-income countries 1.0 0.5 17.9 0.2 3.3 3.3
Australia 0.4 0.8 9.4 1.3 0.0 1.6
Canada 0.5 0.4 6.3 0.4 1.7 2.7
EU15 1.7 0.6 39.5 0.1 4.2 3.6
Japan 1.7 1.2 29.3 0.9 6.5 6.0
New Zealand 1.2 1.5 34.8 5.9 6.2 7.3
Rest of Western Europe 3.1 3.1 50.6 0.8 19.3 14.0
United States 0.2 0.1 2.9 0.1 2.0 0.0
Hong Kong, China; Singapore 0.3 0.8 0.4 2.2 1.8 2.4
Developing countries 3.0 2.9 1.6 3.5 5.5 5.9
North Africa 7.7 5.3 0.5 7.0 9.3 10.4
Sub-Saharan Africa 3.2 3.8 0.2 3.2 4.4 5.3
East Asia and Pacific 3.4 3.3 1.9 4.0 6.9 6.9
South Asia 2.3 1.2 6.2 0.6 2.5 1.9
Latin America 1.4 1.9 21.1 4.5 2.4 4.1
Middle East 2.9 4.7 43.8 8.3 17.0 16.5
Eastern Europe and Central Asia 3.2 2.6 4.5 1.7 4.2 4.5
World total 1.3 1.2 3.1 0.9 3.6 3.8
Source: World Bank Linkage model simulations by the authors.
Note: The table shows results relative to the 2004 benchmark data. CPI consumer price index. EU15 the 15 members of the European Union prior to 2004.
a. The user cost of capital and land represents the subsidy-inclusive rental cost.
Table 2.11. The Effects of the Full Global Liberalization of Agricultural and Merchandise Trade on Sectoral Value
Added, by Country and Region, 2004
(2004 U.S. dollars and percent)
US$, billions Percent
Agricultural Policies in all Agricultural Policies in all
policies sectors policies sectors
Country, region Ag Nonag Ag Nonag Ag Nonag Ag Nonag
North and Sub Saharan Africa 0.1 5.1 0.9 0.2 0.1 0.8 0.9 0.0
Egypt, Arab Rep. 0.1 0.2 0.0 0.7 1.3 0.4 0.1 1.1
Madagascar 0.0 0.0 0.0 0.1 3.2 0.1 3.4 3.1
Mozambique 0.3 0.0 0.3 0.0 23.6 0.6 22.7 0.1
Nigeria 0.6 0.2 1.2 0.8 4.8 0.5 9.3 1.7
Senegal 0.0 0.0 0.0 0.0 1.5 0.8 1.1 0.8
South Africa 0.2 0.7 0.1 0.1 2.7 0.4 0.7 0.1
Tanzania 0.0 0.0 0.0 0.1 0.6 0.3 0.3 1.3
Uganda 0.1 0.0 0.1 0.1 1.6 0.4 2.9 1.6
Zambia 0.0 0.0 0.0 0.0 0.7 0.5 0.6 0.6
Zimbabwe 0.1 0.0 0.2 0.2 24.2 0.8 38.9 4.9
Rest of Africa 0.5 3.9 0.0 1.4 0.7 1.4 0.1 0.5
East and South Asia 1.4 24.4 2.0 100.7 0.3 0.7 0.5 2.9
China 4.6 2.5 9.4 37.5 2.8 0.2 5.7 3.0
Indonesia 0.3 1.1 0.2 2.7 1.1 0.5 0.8 1.2
Korea, Rep. 4.0 7.2 3.2 31.3 18.7 1.2 15.1 5.4
Malaysia 0.2 0.9 0.1 4.0 6.3 0.8 2.0 3.8
Philippines 1.7 0.3 1.9 1.0 13.8 0.5 15.6 1.7
Taiwan, China 0.5 0.8 0.5 10.1 11.3 0.3 9.9 3.7
73
(Table continues on the following pages.)
74 Table 2.11. The Effects of the Full Global Liberalization of Agricultural and Merchandise Trade on Sectoral Value
Added, by Country and Region, 2004 (continued)
(2004 U.S. dollars and percent)
US$, billions Percent
Agricultural Policies in all Agricultural Policies in all
policies sectors policies sectors
Country, region Ag Nonag Ag Nonag Ag Nonag Ag Nonag
Thailand 2.9 2.7 3.0 7.3 14.0 1.0 14.3 2.8
Vietnam 1.4 0.0 1.2 4.5 22.8 0.0 18.8 15.6
Bangladesh 0.2 0.4 0.3 2.1 2.6 0.9 3.8 4.4
India 7.8 6.3 10.6 1.3 6.1 1.4 8.3 0.3
Pakistan 0.2 0.1 0.1 0.2 1.0 0.1 0.5 0.2
Sri Lanka 0.0 0.0 0.3 1.3 0.0 0.1 7.1 9.6
Rest of East and South Asia 0.6 2.3 0.7 4.3 9.6 1.4 11.2 2.7
Latin America 40.0 42.2 40.7 34.6 36.3 2.8 37.0 2.3
Argentina 12.4 8.1 10.9 15.1 116.8 7.4 103.5 13.8
Brazil 12.2 22.7 13.0 21.3 40.1 4.4 42.6 4.2
Chile 0.2 0.3 0.2 0.7 5.0 0.3 5.5 0.9
Colombia 5.0 2.1 5.0 1.2 53.5 2.7 53.5 1.5
Ecuador 2.6 2.9 2.9 1.7 113.1 11.4 126.0 6.7
Mexico 0.2 0.6 0.1 3.4 1.0 0.2 0.3 1.0
Nicaragua 0.0 0.0 0.0 0.1 3.0 1.4 2.4 2.3
Rest of Latin America 7.9 5.5 8.6 2.1 26.3 1.5 28.7 0.6
Eastern Europe and Central Asia 5.2 4.4 6.2 4.4 4.4 0.3 5.2 0.3
Baltic States 0.1 0.1 0.1 0.2 7.5 0.3 8.9 0.5
Bulgaria 0.3 0.1 0.4 0.1 5.1 0.4 5.6 0.3
Czech Republic 0.7 0.4 0.7 0.3 19.2 0.4 20.9 0.3
Hungary 0.7 0.3 0.7 0.1 16.8 0.4 17.9 0.1
Poland 2.4 2.1 2.5 1.7 21.8 1.1 22.6 0.9
Romania 0.3 0.2 0.5 0.3 3.7 0.4 5.8 0.5
Russian Federation 2.2 0.7 2.3 1.3 6.3 0.2 6.6 0.3
Slovak Republic 0.1 0.1 0.1 0.1 11.8 0.2 13.5 0.4
Slovenia 0.0 0.1 0.0 0.1 9.2 0.4 11.1 0.4
Kazakhstan 0.5 0.4 0.5 0.5 23.1 1.1 23.0 1.2
Turkey 1.0 0.9 1.5 0.9 3.2 0.4 4.7 0.4
Rest of Eastern Europe and Central Asia 1.5 0.5 1.5 2.1 11.1 0.4 11.1 1.8
High-income countries 55.1 61.9 58.5 28.6 13.8 0.2 14.7 0.1
Australia 2.2 8.4 2.7 11.7 10.9 1.5 13.7 2.1
Canada 0.4 2.5 0.7 4.6 3.4 0.3 5.3 0.5
EU15 42.9 16.7 47.4 45.9 23.0 0.2 25.4 0.4
Japan 7.6 4.5 7.6 93.2 16.7 0.1 16.8 2.3
New Zealand 2.7 4.1 2.7 4.4 57.7 5.0 57.2 5.4
Rest of Western Europe 3.6 6.5 3.6 8.4 25.8 1.0 25.8 1.3
United States 6.4 18.6 6.0 25.2 5.7 0.2 5.3 0.2
Hong Kong, China; Singapore 0.0 0.6 0.0 3.4 3.7 0.4 2.2 2.1
Developing countries 42.7 79.5 44.4 145.6 5.4 1.0 5.6 1.9
North Africa 0.1 3.9 0.3 1.8 0.4 1.8 1.1 0.8
Sub-Saharan Africa 0.2 1.2 0.6 2.0 0.3 0.3 0.8 0.5
East Asia 6.8 17.7 12.6 102.8 2.6 0.6 4.7 3.5
South Asia 8.2 6.7 10.7 2.1 5.1 1.1 6.7 0.3
Latin America 40.0 42.2 40.7 34.6 36.3 2.8 37.0 2.3
Eastern Europe and Central Asia 5.2 4.4 6.2 4.4 4.4 0.3 5.2 0.3
World total 12.4 141.4 14.2 174.2 1.0 0.4 1.2 0.5
75
Source: World Bank Linkage model simulations by the authors.
Note: The table shows results relative to the 2004 benchmark data. Ag agriculture. Nonag nonagriculture. EU15 the 15 members of the European Union prior to 2004.
76 Agricultural Price Distortions, Inequality, and Poverty
the sector from 144 to 153 percent). For India, the skilled-unskilled wage differen-
tial rises, as does skill-intensive goods and services production.
The effects on poverty measured using the elasticities approach
The results for real factor rewards and net farm income suggest that inequality
and poverty might be reduced globally through agricultural and trade policy lib-
eralization. It is possible for us to take a step or two further in assessing the
impacts of reform on poverty through a global model, even with only a single rep-
resentative household per country. This involves using the elasticities approach,
which is employed here in two ways. The first way focuses on the impact on real
household income by applying an estimated income-to-poverty elasticity and
then assessing the impacts on the poverty headcount index for each country. This
simple approach assumes that there is distributional neutrality: the poor receive
the same proportional increase in real income as the average household in the
economy, and all are subject to the same higher rate of direct income taxation to
replace the customs revenue forgone because of trade liberalization.
A slightly more complex, but more reasonable approach involves linking key
model variables to the possible change in the average per capita consumption of
the poor, that is, to capture from the model's results some of the distributional
aspects of the changes in real income, rather than simply the average gain. We
have accomplished this by calculating the change in the average (pretax) wage of
unskilled workers, deflated by the food and clothing consumer price index. (Food
and clothing prices are presumably the most relevant consumer items among the
poor, including people in the many poor farm households and other rural house-
holds who earn most of their income from wages and are net buyers of food.)
These workers are assumed to be exempt from the direct income tax imposed to
replace the lost customs revenue following trade reform, a realistic assumption for
many developing countries.13
Table 2.12 summarizes the key poverty results that emerge from the global
reform scenario using these two approaches. As is clear from the comparison, the
more naive first approach yields little change in poverty numbers. We therefore
concentrate our attention here on the results generated using the more realistic
second approach.
Under the full merchandise trade reform scenario, extreme poverty--the num-
ber of people surviving on less than US$1 a day--would drop in developing coun-
tries by 26 million relative to the baseline level of slightly under 1 billion, a reduc-
tion of 2.7 percent. The proportional reduction is much higher in China than in
other developing countries, however: 3.7 compared with 2.6 percent. This would
continue the trend of the recent past whereby China has been the region in which
Table 2.12. The Poverty Effects of Full Global Liberalization of Merchandise Trade Reform, by Region, 2004
(number and percent)
a. The benchmark
Benchmark
Poverty
US$1 a day US$2 a day elasticities
Number of poor, Number of poor, US$1 US$2
Region Headcount, % millions Headcount, % millions a day a day
East Asia 9 169 37 684 n.a. n.a.
China 10 128 35 452 1.9 1.3
Other East Asia 9 41 50 232 3.7 2.1
South Asia 31 446 77 1,116 n.a. n.a.
India 34 371 80 868 1.1 0.5
Other South Asia 29 76 94 248 2.5 0.7
Eastern Europe and Central Asia 1 4 10 46 1.7 1.7
Middle East and North Africa 1 4 20 59 2.5 2.3
Sub-Saharan Africa 41 298 72 522 0.7 0.5
Latin America 9 47 22 121 1.7 1.1
Developing-country total 18 969 48 2,548 n.a. n.a.
Excluding China 21 841 52 2,096 n.a. n.a.
(Table continues on the following page.)
77
78
Table 2.12. The Poverty Effects of Full Global Liberalization of Merchandise Trade Reform, by Region, 2004
(continued)
(number and percent)
b. All are assumed to obtain the average income gain
Change in
US$1 a day US$2 a day number of poor
Average income Number of Number of US$1 US$2
change, real, Headcount, poor, Headcount, poor, a day, a day,
Region % % millions % millions millions millions
East Asia 1.1 9 166 36 675 2.9 8.9
China 0.2 10 128 35 451 0.6 1.4
Other East Asia 1.9 8 38 49 224 2.3 7.5
South Asia 0.1 31 446 77 1,116 0.1 0.3
India 0.2 34 371 80 868 0.6 0.7
Other South Asia 0.3 29 75 94 248 0.5 0.4
Eastern Europe and Central Asia 1.2 1 4 10 45 0.1 1.0
Middle East and North Africa 0.7 1 4 19 58 0.1 1.0
Sub-Saharan Africa 0.0 41 298 72 522 0.0 0.0
Latin America 1.0 9 46 22 119 0.8 1.3
Developing-country total 0.9 18 966 47 2,536 3.7 12.0
Excluding China 1.1 21 838 51 2,085 3.1 10.6
c. The gain in the real earnings of unskilled workers
Change in
US$1 a day US$2 a day number of poor
Average income Number of Number of US$1 US$2
change, real,a Headcount, poor, Headcount, poor, a day, a day,
Region % % millions % millions millions millions
East Asia 4.4 8 151 34 632 17 52
China 2.1 9 123 34 440 5 12
Other East Asia 8.1 6 29 42 192 12 40
South Asia 1.9 32 454 78 1,124 8 8
India 3.8 36 386 82 883 15 15
Other South Asia 4.0 26 68 92 241 8 7
Eastern Europe and Central Asia 4.5 1 4 9 43 0 4
Middle East and North Africa 14.3 1 3 13 40 2 19
Sub-Saharan Africa 5.3 39 287 70 508 11 14
Latin America 4.1 8 44 21 115 3 6
Developing-country total 5.9 18 944 46 2,462 26 87
Excluding China 6.5 20 820 50 2,022 21 74
Source: World Bank Linkage model simulations by the authors.
Note: n.a. not applicable.
a. Nominal unskilled wage deflated by the food and clothing consumer price index.
79
80 Agricultural Price Distortions, Inequality, and Poverty
poverty reduction has been most numerous (see Chen and Ravallion 2007, 2008).
Nonetheless, in this scenario, the number of the extreme poor in Sub-Saharan
Africa would fall by 3.7 percent. In India (though not in the rest of South Asia),
by contrast, we estimate that the number of the extreme poor would climb by
4.0 percent.
Recall that this set of poverty calculations is based on the change in the real
wage of unskilled workers, deflated by the food and clothing consumer price
index. The average change in real unskilled wages across all developing countries
is 5.9 percent, which is six times greater than the average net income increase in
developing countries, after accounting for a rise in direct taxes to offset the loss in
tariff revenues and assuming that the change in unskilled wages is fully passed
through to households. This suggests that such a reform would deliver a marked
average reduction in income inequality within developing countries.
Under the broader definition of poverty (a subsistence threshold of no
more than US$2 a day), the number of poor in developing countries would fall
by 87 million under the full reform scenario relative to the aggregate baseline
level of nearly 2.5 billion poor. This represents a somewhat larger proportion-
ate reduction in the number of poor in developing countries, by 3.4 percent, or
3.7 percent if China is excluded. The proportionate decline in Sub-Saharan
Africa is 2.7 percent, while, in India, there is an increase by 1.7 percent.
Caveats
As with all modeling, our results depend on our assumptions in structuring the
model. Several assumptions place a downward bias on our estimates of the welfare
gains arising from trade. They include the assumptions that returns to scale are
constant (rather than increasing), that there are no productivity effects of reform
(for example, of the sort stressed by Melitz 2003), and that there is no possibility
for the creation of new markets following reform. There is also always the issue of
product and regional aggregation: the less disaggregated the specification of the
world economy, the smaller the estimated benefits of reform. This is because there
is no accounting for welfare gains from adjustments within aggregated sectors or
regions.
As for the effects on poverty, the crude methodology used at the end of the pre-
vious subsection is meant simply as a beginning in our examination of the
poverty consequences of global trade reform. The results of global reform that we
have identified based on the Linkage model and presented above are used in the
next chapter, in association with microsimulation survey data on individual coun-
tries, to assess the effects on the distribution of income across and within 101
countries. Bussolo, De Hoyos, and Medvedev, the authors of the next chapter, are
Global Welfare and Poverty Effects: Linkage Model Results 81
thus able to identify much more precisely the inequality and poverty effects
worldwide of the policies as of 2004. In parts III�V, in individual developing-
country case studies, authors examine--also with the help of national microsim-
ulation survey data--the effects of own-country policies, but also the effects of
policies in the rest of the world. The border price and export demand shocks asso-
ciated with rest-of-the-world liberalization are almost the same as those outlined
above. The only difference is that, for each of the countries in the case studies, a
separate global simulation has been run that excludes the reforms undertaken by
the countries under examination. The border shocks in all the case studies are
reported in the appendix.
Conclusions
The findings presented above are aimed at indicating the global economic effects
of the agricultural and trade policies in place as of 2004. They may be summarized
as follows:
� As a share of national income, developing countries would gain nearly twice as
much as high-income countries by removing the policies (an average increase
of 0.9 percent compared with 0.5 percent, respectively), thereby reducing
income inequality across countries.
� Even intercountry inequality (not taking into account differences in the eco-
nomic size of nations) as measured by the Gini coefficient would improve
slightly, although some developing countries (notably in South Asia) would
lose, and a few (for example, Ecuador) would gain many times more than the
average.
� Of the prospective welfare gains from global liberalization, 60 percent would
be generated by agricultural and food policy reform, which is a striking result
given that the shares of agriculture and food in global GDP and global
merchandise trade are less than 9 percent.
� The contribution of agricultural policy reform to the prospective welfare gain
in developing countries is even greater, at 83 percent.
� Through the full liberalization of the trade in goods, the share of exports in the
global production of farm products would rise from 8 to 13 percent (excluding
intra�European Union trade), thereby thickening international food markets
and reducing the price fluctuations and the instability in the quantities traded
on these markets.
� Unskilled workers in developing countries--the majority of whom work on
farms--would benefit most from reform, followed by skilled workers and then
capital owners. The average change in the real unskilled wage across all
82 Agricultural Price Distortions, Inequality, and Poverty
developing countries would rise 3.8 percent or nearly five times more than the
average increase in net incomes in developing countries.
� Net farm incomes in developing countries would rise by 5.6 percent, compared
with 1.9 percent for nonagricultural value added, suggesting that inequality
between farm and nonfarm households in developing countries would fall.
� In contrast, in high-income countries, net farm incomes would fall by 15 per-
cent on average (compared with a slight rise in real nonfarm value added). So,
inequality between farm households in these countries and farm households in
developing countries would decline substantially.
� The number of people in extreme poverty--the number surviving on less than
US$1 a day--in developing countries would drop by 26 million relative to the
baseline level of slightly under 1 billion, a reduction of 3 percent (4 percent if
the more moderate US$2-a-day poverty level is used).
To obtain a more precise sense of the inequality and poverty effects within
countries and to explore the extent to which own-country policies rather than
rest-of world policies are causing the harm, we need country case studies using
national economy-wide models that are enhanced through the inclusion of
detailed earning and expenditure information on numerous types of urban and
rural households. This is the purpose of parts III�V.
Notes
1. This distortions database is documented fully in Anderson and Valenzuela (2008). It is based on
the methodology summarized in Anderson et al. (2008a, 2008b).
2. Some of the questions raised here have been addressed by Anderson, Martin, and van der
Mensbrugghe (2006a, 2006b), who use the same Linkage model we use in the present analysis, and by
Anderson and Valenzuela (2007a), who use the GTAP-AGR model. However, in each case, these
authors have relied on the GTAP version 6 protection database for 2001, which includes only the
applied import tariffs in developing countries.
3. In principle, service trade and foreign investment distortions may also affect incentives in the
agricultural and industrial sectors, but they are ignored here because much controversy still surrounds
the measurement of these distortions and the way they should be modeled. This is reflected in the
widely differing results emerging from attempts to include service distortions in trade reform model-
ing. Compare, for example, Brown, Deardorff, and Stern (2003); Francois, van Meijl, and van Tongeren
(2005); and Hertel and Keeney (2006).
4. We have no new distortion estimates for countries in the Middle East; so, in what follows, we give
little attention to this small and relatively affluent part of the global agricultural economy.
5. More information on the MAcMaps database is available in Bou�t et al. (2008) and at http://www.
cepii.fr/anglaisgraph/bdd/macmap.htm. For details on the incorporation of the database into the
GTAP version 7 data set, see Narayanan and Walmsley (2008).
6. Using the GTAP version 6 protection database for 2001, Anderson, Martin, and Valenzuela
(2006) find that agricultural production and export subsidies together contributed only 7 percent of
the global welfare cost of agricultural protection.
7. The size of the Armington elasticities is important; see Valenzuela, Anderson, and Hertel (2008)
and Zhang and Osborne (2009). The Linkage model assumes larger values relative to some other
Global Welfare and Poverty Effects: Linkage Model Results 83
models because it is seeking to estimate the long-run consequences of liberalization. An example of the
differences this may generate in the results is detailed in Anderson and Martin (2006, table 12A.2).
8. The more affluent economies of Hong Kong, China and of Singapore are in our high-income
category, but, since they have trade policies that approximate free trade policies and almost no farm
production anyway, their influence on the results is not noticeable.
9. The model also does not include any divergences between the private and social marginal costs
and the benefits that might arise from externalities, market failures, and other behind-the-border
policies not represented in our amended GTAP protection database. These omissions may affect the
welfare estimates in either direction.
10. The only other policy change is the removal of the export taxes on nonfarm products in
Argentina. We do this because these taxes were introduced at the same time (at the end of 2001) and
for the same reason (so the government might gain the support of the urban poor) as the country's
export taxes on farm products.
11. This would continue a process that began in the 1980s when many countries began to reform
their trade and exchange rate regimes. Using the same Linkage model and database as the present
study, Valenzuela, van der Mensbrugghe, and Anderson (2009) find that the global reforms between
1980�84 and 2004 also boosted economic welfare proportionately more in developing countries than
in high-income economies (by 1.0 percent compared with 0.7 percent, respectively).
12. The Gini coefficient is a measure of intercountry inequality in the Milanovic (2005) sense,
whereby each country is treated as a single observation without regard to the economic size of the
country. The measure is calculated using the 60 regions and Deaton's Gini coefficient calculation:
N + 1 2
G = - (� n P X )
N - 1 N(N - 1)u i = 1 i i
where N is the number of regions, u is the sample average GDP, Pi is the GDP sample rank (setting the
highest at 1 and the lowest at N), and Xi is the GDP of country i.
13. Even if the fiscal closure affects a domestic sales or value added tax instead of direct taxes on
households, food is exempt from taxation in many countries, or the tax is difficult to collect in practice
because of the informal nature of many food markets.
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3
Global Poverty and
Distributional Impacts:
The GIDD Model
Maurizio Bussolo, Rafael De Hoyos,
and Denis Medvedev*
Trade liberalization is almost always welfare increasing nationally and globally,
but it also brings about large redistributions of income. Simulation models
calibrated on real world data show that the aggregate gains for a country that elimi-
nates tariffs are, at best, within only a few percentage points of the initial GDP.
Similarly, the gains from multilateral trade policy reforms for the whole world tend
to be small. In contrast, the losses suffered by specific, initially protected sectors or
factors may be much larger. As Rodrik (1998) puts it, the static efficiency conse-
quences of trade reform pale in comparison with the redistributive effects.
These effects often create complicated policy challenges at domestic and inter-
national levels because, in most cases, losers tend to be a smaller and more vocal
group than winners.1 Perhaps the most recent and glaring example of this trade-
related distributional tension is the impasse in the Doha Round of the World
Trade Organization. Disputes over the reduction of agricultural market distortions
have stalled the entire multilateral trade negotiation process. The controversy is cen-
tered on the demands of developing and agricultural exporting countries to phase
out export subsidies and domestic farm supports that are mainly applied in devel-
oped countries, in addition to the reduction of import barriers.
This example illustrates that a distributional tension among countries may
have important implications for international relations and global welfare. An
*The authors are grateful to Rebecca Lessem for research assistance and to Will Martin, Hans Timmer,
and Dominique van der Mensbrugghe for discussions on the issues.
87
88 Agricultural Price Distortions, Inequality, and Poverty
additional question is this: would resolving trade disputes improve the distribu-
tion of income not only between countries, but also within national economies?
The answer depends, in part, on own-country distortions to agricultural and
other producer incentives in individual developing countries. Frequently, the
related policies privilege urban dwellers by protecting urban industries and main-
taining low prices for food items, which works to the disadvantage of (often)
poorer local farmers, although this is much less so now than it was in the 1960s
and 1970s (Krueger, Schiff, and Vald�s 1988; Anderson 2009). Given that poverty
is greatest among farmers, the poverty reduction potential of agricultural trade
liberalization is promising (Chen and Ravallion 2007).
Using an ex ante simulation analysis, this chapter answers the following ques-
tions: How much would global inequality and poverty be reduced if all distortions
to trade in agricultural and other goods were removed? How much of this change
would be generated by agricultural policy reform only? What share of the change
in inequality would arise from changes across countries versus changes within
countries (one should bear in mind that a lowering of the inequality between
agricultural and nonagricultural groups might be offset by, for example, increased
inequality within the agricultural sector)? What would happen to global poverty
and to the incidence of poverty in specific countries and regions? Does it matter
whether we use the US$1-a-day or US$2-a-day international poverty line (because,
for example, more nonagricultural households than farm households may be clus-
tered between these two poverty lines)?
The empirical results of the study represented in this chapter have been pro-
duced using the World Bank's Linkage global general equilibrium model and the
newly developed Global Income Distribution Dynamics tool (GIDD) (van der
Mensbrugghe 2005; Bussolo, De Hoyos, and Medvedev 2008; see also the GIDD
Database). The GIDD is a framework for ex ante analysis of the income distribu-
tional and poverty effects of changes in macroeconomic, trade, and sectoral poli-
cies and trends in global markets. It complements a global computable general
equilibrium (CGE) analysis by providing global microsimulations based on stan-
dardized household surveys. The tool pools most of the currently available household
surveys covering 1.2 million households in 73 developing countries. Information on
households in developed countries completes the data set. Overall, the GIDD sample
countries cover more than 90 percent of the world's population.2
The chapter is organized as follows. The next section presents the GIDD data
set and the main features of global income distribution as a way of establishing
the initial conditions or baseline. This descriptive analysis sets the stage for the
subsequent sections, which illustrate the modeling methodology, lay out the
reform scenarios, and summarize the main results. The two core simulations
involve the case of the liberalization of all merchandise trade and agricultural
Global Poverty and Distributional Impacts: The GIDD Model 89
domestic distortions and--to examine the contribution of farm and food
policies--the case of the liberalization of only agricultural trade and domestic
price-distorting measures. Final remarks are provided in the concluding section.
What Is at Stake? The Initial Conditions
Almost 45 percent of the world's people, most of them in developing countries,
live in households in which agricultural activities represent the main occupation
of the heads of household. A large share of this agriculture-dependent group,
close to 32 percent, is poor. Agricultural households thus contribute dispropor-
tionably to global poverty: three of every four poor people belong to this group.
Improving the economic opportunities available in agriculture may therefore
significantly affect global poverty and inequality. The specific opportunity consid-
ered in this study is the removal of agricultural subsidies and taxes and all
merchandise trade distortions. The direct effects of this global liberalization
would include changes in the international prices of food and other agricultural
products and changes in the returns to the factors used intensively in agriculture.
These changes would determine the winners and losers of reform through the
impacts on household earnings and spending.
Before examining these effects in detail, this section describes the stakes
involved in the reforms by considering the socioeconomic characteristics of
the world's population, especially the people engaged in agriculture. This ini-
tial descriptive analysis is based on the GIDD data set that has been recently
developed at the World Bank. The data set consists of 73 detailed household
surveys on low- and middle-income countries, complemented by more highly
aggregated information on income distribution among 25 high-income coun-
tries and 22 other developing countries.3 Together, the 120 countries in the
data set cover more than 90 percent of the global population. The country cov-
erage varies by region: while the countries in the GIDD data set represent more
than 97 percent of the population in East Asia, South Asia, Latin America, and
Eastern Europe and Central Asia, the coverage in Sub-Saharan Africa and in
the Middle East and North Africa is limited to 76 and 58 percent of the popu-
lation, respectively. Among the detailed surveys, the majority (54) rely on per
capita consumption as the main indicator of welfare, while the remaining
surveys--all but one are on countries in Latin America--rely on per capita
income alone as a measure of household welfare. The income and consump-
tion data are monthly; the data are standardized to the year 2000 and are
expressed in 1993 purchasing power parity (PPP) prices for consistency with
the US$1-a-day and US$2-a-day poverty lines, which are calculated at 1993
PPP exchange rates.4
90 Agricultural Price Distortions, Inequality, and Poverty
Figure 3.1. Income Distributions among Agricultural and
Nonagricultural Populations of the World, 2000
0.4
0.3
density
0.2
0.1
0
1 1 3 5 7 9
monthly household per capita income (1993 PPP, log)
global agricultural population global income distribution, 2000
global nonagricultural population poverty line
Source: Author compilation based on the GIDD Database.
Note: See the text for an explanation of the figure.
Three facts about the agricultural sector help identify the welfare effects of a
global-scale removal of trade distortions: the proportion of the world's people
whose real incomes depend on the agricultural sector, the initial position of the
agricultural population in the global income distribution, and the dispersion of
incomes among the agricultural population.
Based on the GIDD data set, figure 3.1 shows a kernel density of the global
income distribution of household per capita income and consumption and kernel
densities of the income and consumption of the population inside and outside the
agricultural sector.5 The area below the kernel density for the agricultural sector is
equal to 0.45, indicating that 45 percent of the world population relies on agricul-
ture for their livelihoods. The distribution of the agricultural population is
located to the left of the nonagricultural distribution, implying that households in
the agricultural sector earn, on average, lower incomes than their counterparts in
other sectors. In U.S. PPP dollars, the average agricultural household's per capita
monthly income is US$65, only 20 percent of the US$320 per capita income
earned by the average household in the nonagricultural group. The differences in
the shapes of the two distributions corroborate the hypothesis of Kuznets more
than 50 years ago that incomes in the traditional sector are less well dispersed than
incomes in the modern sectors. A more egalitarian traditional sector is indicated
by the taller and thinner distribution for the agricultural population in figure 3.1.
Global Poverty and Distributional Impacts: The GIDD Model 91
Income inequality is estimated based on the global income distribution data
depicted in figure 3.1. The Gini coefficient for the world is equal to 0.67, which
denotes a high level of inequality. In fact, this global Gini is about 0.28 points
higher (worse) than the Gini of the United Sates and even higher than the level
observed in extremely unequal countries such as Mexico. As Bourguignon, Levin,
and Rosenblatt (2004: 15) note, "if the world [were] a country, it would be one of
the most unequal countries in the world." How much of this inequality may be
explained by the disparity in average incomes between the agricultural group and
the rest? Inequality decomposition analysis shows that one-quarter of the global
income disparities may be explained by the difference in average incomes between
the two groups of households; the remaining three-quarters are generated by
within-group income variation.
Based on the preestablished poverty line of US$1 a day in PPP dollars, the GIDD
global income data also provide information about the differences in poverty inci-
dence among the two population subgroups. Despite the fact that incomes are more
evenly distributed among the agricultural population (the Gini coefficient is
0.18 points lower in agriculture than in nonagriculture), lower average incomes in
this sector result in a much higher poverty incidence: 32 percent of agricultural
households are poor compared with 8 percent of nonagricultural households.
In terms of the personal characteristics of the poor inside and outside the agri-
cultural sector, no noticeable differences are observed in the average age of the
heads of household or in the household size. However, poor people in agriculture
tend to have lower educational attainment: only 32 percent have completed pri-
mary education compared with 46 percent among nonfarm households. In agri-
culture, poor woman-headed households are a small minority, less than 9 percent,
which is significantly below the 14 percent observed among nonagricultural
households (table 3.1).
Up to this point, our welfare information on agricultural and nonagricultural
populations has been derived by agglomerating all households within these two
groups irrespective of nationality. In fact, the kernel densities in figure 3.1 exploit
Table 3.1. Characteristics of the Poor in Agricultural and
Nonagricultural Households, Developing Countries, 2000
Head of
household Age of Household Woman-
completed household members, headed
Population primary head, average households,
Household type share, % school, % average years number %
Agricultural 44.8 32.3 44.8 7.11 8.7
Nonagricultural 55.2 46.0 44.4 7.06 14.0
Source: Author compilation based on the GIDD Database.
92 Agricultural Price Distortions, Inequality, and Poverty
Figure 3.2. Relationship between Income Levels and Share of
Employment in Agriculture, by Country, 2000
80
employment in agriculture (%)
China
60
India
40
20 Brazil European Union
United States
Russian Federation
0
0 500 1,000 1,500
monthly household per capita income (1993 ppp)
Source: Author compilation based on the GIDD Database.
Note: Area of symbol proportional to the country's population.
full income heterogeneity across households, including variations across and
within countries. Countries display large differences in terms of population size,
level of development, and the importance of the agricultural sector in the econ-
omy. These three country-specific characteristics are important determinants in
any prospective change in global poverty and global inequality. As we may see in
figure 3.2, global poverty would be strongly reduced if China and India were to
achieve higher income levels. Given the initial large share of these countries in the
global population and their position in the global income distribution, the eco-
nomic expansion of these two giants is a key factor shaping the evolution of the
world economy.6 Figure 3.2 also depicts a negative relationship between income
levels and the share of workers in agriculture, and, although it is imperfectly
inferred from a cross-section of countries at a particular point in time, this rela-
tionship suggests that structural shifts would likely affect income distribution
within countries. Figure 3.3 shows this heterogeneity by displaying, for each
county in our sample, the shares of agriculture in total population and in national
income. Given the large variation in the proportion of the population for which
the incomes depend on the agricultural sector, the income effects following a
Figure 3.3. Share of the Population in Agriculture and of Agriculture
in Total Income, Developing Countries, 2000
Burundi
Burkina Faso
Lao PDR
Nepal
Tanzania
Ethiopia
Kenya
Madagascar
Cambodia
Vietnam
Bangladesh
Uganda
Cameroon
Yemen, Rep.
Kyrgyz Republic
Guyana
Albania
Gambia, The
Nigeria
C�te d'Ivoire
Pakistan
Moldova
India
Ghana
Haiti
Thailand
Georgia
Morocco
Philippines
Guatemala
Uzbekistan
Kazakhstan
Honduras
Armenia
country
Azerbaijan
Sri Lanka
Nicaragua
Indonesia
Turkey
Paraguay
Bolivia
Tajikistan
Peru
Mali
Macedonia, FYR
Guinea
Dominican Republic
Jamaica
Costa Rica
Colombia
South Africa
El Salvador
Ukraine
Senegal
Panama
Benin
Romania
Lithuania
Brazil
Venezuela, R.B. de
Chile
Mauritania
Poland
Ecuador
Estonia
Belarus
Hungary
Mexico agriculture incomes (% of total)
Jordan agriculture populations (% of total)
Russian Federation
Bulgaria
0 10 20 30 40 50 60 70 80 90 100
percent
Source: Author compilation based on the GIDD Database.
Note: The figure is based on the data set for developing countries and transition economies.
94 Agricultural Price Distortions, Inequality, and Poverty
removal of agricultural distortions would differ greatly between countries and be
especially important in countries in which more than half the population is in
agriculture. The majority of these agriculture-based countries are located in the
poorest region of the world, namely, Sub-Saharan Africa. Of the 25 countries in
the agriculture-based group, 12 are in Sub-Saharan Africa, 4 are in South Asia,
3 are in East Asia, 3 are in Eastern Europe and Central Asia, 2 are in Latin America
(Guyana and Haiti), and only 1 (the Republic of Yemen) is in the Middle East and
North Africa.
The pattern observed at the global level, namely, that agriculture-dependent
households, on average, earn less than other households, is replicated in all devel-
oping countries in the GIDD Database (figure 3.3); the share of the total popula-
tion employed in agricultural activities is always larger than the share of this
population in total income. The average income of nonagricultural households is
2.25 times greater than the average agricultural household income. This difference
is unconditional in the sense it does not take into account the fact that, in agricul-
ture, low-earning unskilled workers tend to be more abundant than skilled work-
ers or that other factors may explain the observed income gap. However, we have
undertaken a simple multivariate regression analysis on the GIDD data set, and it
shows that, even controlling for education, age, gender, household size, geo-
graphic region, and country fixed-effects, agriculture-related incomes are still
23 percent lower than incomes derived from other sectors.
An important element hidden in figure 3.1 and only partially shown in figure 3.3
is the degree of cross-country variation in income inequality. Figure 3.4 shows that
the differences in the Gini coefficient across countries are enormous. Former cen-
trally planned economies such as Hungary, Romania, and Ukraine show a coeffi-
cient below 0.3, whereas, in South Africa and much of Latin America, the coefficient
reaches values well above 0.5. Once again, the tendency toward higher inequality
within the agricultural group observed at the global level is corroborated by analysis
of country-specific inequality. For more than three-quarters of the countries
included in our data (56 of 73), Gini indicators of inequality within the agricultural
group are higher than those within the nonagricultural group (figure 3.4).
A global trade reform would be expected to reallocate resources intersectorally
within national states and between them. Given the global variations in the impor-
tance of the agricultural sector, the ratio of nonagricultural to agricultural incomes,
and the within-sector income inequality, the resource reallocation following the
trade reform would have significant distributional effects across and within coun-
tries. Is economic theory able to provide guidance on the expected global welfare
effects after the removal of agricultural distortions and other trade distortions?
As shown by Winters (2002) and McCulloch, Winters, and Cirera (2001), trade
liberalization and household welfare are linked via prices, factor markets, and
Figure 3.4. Inequality Variation in Agricultural, Nonagricultural,
and All Households, Developing Countries, 2000
Denmark
Finland
Sweden
Netherlands
Korea, Rep.
Austria
Luxembourg
Germany
Albania
France
Tajikistan
Switzerland
Canada
Australia
European Union
Spain
Greece
Italy
New Zealand
Georgia
Portugal
Ghana
Israel
Nigeria
United States
Singapore
China
Hong Kong, China
Gambia, The
El Salvador
Mexico
Nicaragua
Colombia
Ecuador
Bolivia
Brazil
Guatemala
Hungary
Ukraine
Czech Republic
Slovenia
Pakistan
Belarus
Russian Federation
Romania
Kyrgyz Republic
Lithuania
Armenia
country
Kazakhstan
Ethiopia
Bulgaria
Poland
Mongolia
Bangladesh
Estonia
India
Indonesia
Yemen, Rep.
Ireland
Uzbekistan
Moldova
Tanzania
Vietnam
Lao PDR
Nepal
Benin
Azerbaijan
Turkey
Macedonia, FYR
Sri Lanka
United Kingdom
Morocco
Mali
Senegal
Guinea
Thailand
Mauritania
Jamaica
Guyana
Cambodia
Jordan
Burundi
Venezuela, R.B. de
Uganda
Cameroon
Kenya
C�te d'Ivoire
Costa Rica
Madagascar
Philippines
Burkina Faso
Dominican Republic Gini in agriculture
Chile Gini in nonagriculture
Peru
Honduras overall Gini
Panama
Paraguay
South Africa
Haiti
0.15 0.25 0.35 0.45 0.55 0.65 0.75
Gini coefficient
Source: Author compilation based on the GIDD Database.
96 Agricultural Price Distortions, Inequality, and Poverty
consumer preferences. The international prices of many agricultural products
would increase after the removal of trade barriers (see chapter 2, by Anderson,
Valenzuela, and van der Mensbrugghe; see also Anderson and Martin 2005). If we
assume some degree of pass-through, the increase in international prices would
be followed by a rise in domestic agricultural prices, which would encourage a
redistribution of resources from nonagricultural activities to the agricultural sec-
tor of the economy. Based on figure 3.1, this redistribution might help reduce
global poverty and inequality. However, household consumption patterns would
also change as a result of the shift in prices, making the link between trade liberal-
ization and global household welfare more complex. As a consequence of the agri-
cultural price changes, a redistribution of real income would take place between
net sellers and net buyers of agricultural products; the welfare of the former would
improve at the expense of the welfare of the latter.7 Finally, factor prices would
also change after trade liberalization, thus changing the real incomes of house-
holds that are not directly involved in agricultural production.
The transition from trade theory to real world analysis presents serious chal-
lenges. A sound empirical strategy must estimate the effects of the reform on prices,
monetary incomes (via profits in the case of farm households and returns to factors
of production in the case of nonfarm households), consumption, and transfers.8
The framework used in this chapter and described in more detail below accounts for
the impact of trade liberalization through at least some of these channels.
Methodology
The empirical analysis in this chapter relies on the GIDD Database, a newly devel-
oped tool for analyzing the dynamics of global income distribution.9 Compiled by
the Development Prospects Group of the World Bank, the GIDD combines a con-
sistent set of price and volume changes from a global CGE model with microdata at
the household level to create a hypothetical or counterfactual income distribution
capturing the welfare effects of policies under evaluation.10 The GIDD therefore has
the ability to map CGE-consistent macroeconomic outcomes to disaggregated
household survey data.
The GIDD framework is based on microsimulation methodologies developed
in the recent literature, including Bourguignon and Pereira da Silva (2003),
Ferreira and Leite (2003, 2004), Chen and Ravallion (2003), and Bussolo, Lay, and
van der Mensbrugghe (2006). The starting point is the global income distribution
in 2000, which has been assembled using data from household surveys (see
above).11 The hypothetical distribution is then obtained by applying three main
exogenous changes to the initial distribution: changes in relative wages across
skills and sectors for each country, changes in household purchasing power
Global Poverty and Distributional Impacts: The GIDD Model 97
Figure 3.5. The GIDD Methodological Framework
removal of global agricultural distortions
CGE--linkage
real wages (by
growth effects skill and food prices
sector)
hypothetical income
distribution
Source: Construction by the authors.
caused by shifts in food prices and other prices, and changes in the average level of
welfare (real income) for each country.
The methodological framework used here is depicted in figure 3.5. The starting
point is the price and quantity effects after the removal of trade distortions; one
computes these by using the global CGE Linkage model (top part of figure 3.5).
The CGE model will compute the values of the three variables linking the macro-
and microlevels of the model (middle part of figure 3.5): overall economic
growth, real wage premiums among agricultural and nonagricultural and skilled
and unskilled groups, and the consumption (or real income) effects brought
about by the change in the relative prices of food. These CGE results are passed
on to the household survey data, creating a new, hypothetical household income
distribution (bottom part of figure 3.5). This is accomplished by differentiating
four types of households: those in which the household head is an: (1) unskilled
agricultural worker, (2) skilled agricultural worker, (3) unskilled nonagricultural
worker, or (4) skilled nonagricultural worker. The initial income premium earned
by groups (2)�(4) relative to group (1) is changed in accordance with changes in
the wage premiums in the CGE model, which uses the GIDD information on a
number of workers in each of the segments (1)�(4). For example, if, initially, a
household headed by an unskilled worker in nonagriculture earns 50 percent
more than a household headed by an unskilled worker in agriculture and if the
CGE results show that this premium would decline by a tenth, the microsimula-
tion part of the GIDD changes the incomes of all households headed by unskilled
98 Agricultural Price Distortions, Inequality, and Poverty
workers in nonagriculture such that the new wage premium is 45 percent. In addi-
tion to these wage shocks, the GIDD also accounts for changes in relative prices
and changes in per capita incomes, thereby indirectly picking up the impact on
the returns to factors other than labor.
In the real world, the changes depicted in figure 3.5 take place simultaneously,
but, in the GIDD simplified framework, they are accommodated in a sequential
fashion. In the first step, changes in labor remuneration by skill level and sectoral
location are applied to each household in the sample, depending on educational
attainment and sector of employment. In the second step, consistent with an over-
all growth rate of real income per capita, real household incomes are affected by
the change in the prices of food items versus the prices of nonfood; households
with a higher share of household income allocated to food consumption bear a
larger proportional impact after a change in the prices of food.
Comparisons between the initial and the counterfactual income distributions
capture the welfare and inequality effects of the removal of global trade distor-
tions. By taking into account labor market effects (returns to skills in the agricul-
tural sector and in the rest of the economy) and consumption effects, the GIDD
framework closely maps the theoretical links outlined in the previous section.12
However, the framework reshapes national income distributions under a set of
strong assumptions. In particular, income inequality within population sub-
groups formed by skills and by sector of employment is assumed to remain con-
stant after the trade reform. Moreover, data limitations affect the estimates of the
initial inequality and of the evolution of inequality. Although consumption
expenditure is a more reliable welfare measure than income and although its dis-
tribution is normally more equal than the distribution of income, consumption
data are not available in all country surveys. To obtain a global picture, we have
been obliged to include countries for which only income data are available, along
with countries for which consumption information is available. Finally, measure-
ment errors implicit in the PPP exchange rates that have been used to convert
local currency units also affect the comparability across countries. The resulting
hypothetical income distribution should thus not be considered a forecast of the
future distribution; rather, it should be interpreted as the result of an exercise that
captures the distributional effect of trade liberalization, all else being equal.
What Happens to Poverty and Income Distribution
If Trade Is Liberalized Globally?
In this section, we link the macro-outcomes of global agricultural reform and
other trade policy reforms to the changes in the distribution of income among
and within countries. Our analysis is carried out in three stages. First, we briefly
Global Poverty and Distributional Impacts: The GIDD Model 99
examine the macroeconomic results from the Linkage model simulations of
global trade reform (which are similar, but not identical to those presented in
chapter 2, by Anderson, Valenzuela, and van der Mensbrugghe) and focus on the
variables that are passed on to the household survey data.13 Second, we consider
the income distributional results from a global perspective by quantifying the
likely changes in global poverty and inequality and identifying groups of coun-
tries and individuals that are likely to benefit the most (least) from global trade
reform. Third, we assess the potential trends in the distribution of income within
countries by identifying countries in which inequality and poverty pressures may
heighten and thus erode the support for additional reforms.
Macroeconomic general equilibrium results
We have carried out the Linkage simulation analysis using version 7 of the Global
Trade Analysis Project Database, which has been amended by Valenzuela and
Anderson (2008) to take account of new estimates of the distortions to agricultural
incentives in developing countries compiled by these same authors. The Linkage
model disaggregates global trade into bilateral flows between more than 100 coun-
tries and regions in 57 commodity groups. The base year for the simulations is 2004,
and the baseline data have taken into account changes in the global trade and tariff
structure generated by the implementation of the Uruguay Round commitments,
European Union enlargement, China's accession to the World Trade Organization,
and most major preferential trade agreements in place at the time. The model is
solved in a comparative static mode, which means that simulations are imple-
mented as one-time shocks and do not take into account potential growth effects
through changes in capital accumulation rates or variations in productivity.
Our two simulations envision the full removal globally of trade taxes and sub-
sidies on all agricultural goods and lightly processed food without and then with
the trade reform of nonagricultural goods. With these two scenarios, we are able
to see the relative contribution to changes in the global economy after the removal
of agricultural distortions alone.
The removal of distortions to the trade in agricultural products causes global
consumption to rise by 0.29 percent, or two-thirds of the improvement expected
under a trade liberalization scenario involving all goods. Developing countries
gain more than the average; their consumption rises by 0.47 percent compared
with 0.24 percent for high-income countries. No less than 50 of 60 Linkage coun-
tries and regions--representing nearly 95 percent of the world's people--would
experience positive changes in consumption following the removal of agricultural
distortions compared with 47 countries and regions that would enjoy consump-
tion gains from the liberalization of all goods trade (figure 3.6).
100 Agricultural Price Distortions, Inequality, and Poverty
Figure 3.6. The Effects on Real National Consumption of the Global
Removal of Agricultural and All Merchandise Trade
Distortions
Ecuador
Lithuania
New Zealand
Bulgaria
Nigeria
Rest of North Africa
Colombia
Rest of Western Europe
Argentina
Mozambique
Slovak Republic
Estonia
Czech Republic
Brazil
Morocco
Slovenia
Poland
Russian Federation
Rest of East Asia
Vietnam
Nicaragua
Hungary
Malaysia
Thailand
Zimbabwe
Latvia
Rest of Latin America and Caribbean
Korea, Rep.
Indonesia
country
Rest of Eastern Europe and Central Asia
Kazakhstan
Philippines
Middle East
EU15
Bangladesh
Romania
Turkey
Australia
Japan
Zambia
Canada
Rest of Sub-Saharan Africa
Tanzania
Taiwan, China
Pakistan
India
Hong Kong, China; Singapore
United States
Egypt, Arab Rep.
South Africa
Other South Asia
Sri Lanka
Mexico
China
Rest of West and Central Africa
Chile global liberalization of trade in
Madagascar agriculture and food products
Uganda global liberalization of all trade
Senegal
Kyrgyz Republic
4 2 0 2 4 6 8 10 12
percent change in real consumption
Source: Author compilation using Linkage model results similar to those in chapter 2, by Anderson,
Valenzuela, and van der Mensbrugghe, except that factor mobility is more limited.
Note: The darker bars show the percent increase in consumption (at prereform prices) generated by the
removal of price and trade distortions in agriculture and food products (excluding beverages and tobacco).
The lighter bars show the additional gains in consumption generated by the removal of all remaining
barriers to merchandise trade. The combined length of the two bars shows the consumption gains from the
global trade reform of all merchandise. EU15 the 15 members of the European Union prior to 2004.
Global Poverty and Distributional Impacts: The GIDD Model 101
There are three main channels that transmit the trade reform shocks to house-
hold consumption in the Linkage model and help explain the heterogeneity of the
results in figure 3.6. The first channel is changes in the terms of trade, that is, the
ratio of export prices to import prices, without taking into account domestic price
distortions caused by own-country policies. Net exporters of agriculture and food,
such as Brazil, Ecuador, and New Zealand, reap significant welfare gains if the export
prices of their farm commodities rise by an average of 8, 19, and 11 percent, respec-
tively.14 On the other hand, net importers of food, such as China, Mexico, and
Senegal, experience real consumption losses due to higher import prices.
The second channel is tightly linked to the first and involves the impact of
own-country policies. Thus, countries with high prereform tariffs or export taxes,
such as Lithuania, Nigeria, and the group in North Africa, tend to experience
larger consumption gains relative to countries in which the initial distortions are
low. If initial agricultural import barriers are sufficiently high, consumers may
face lower postreform prices for food even if import prices are rising. This is the
case in North Africa, which experiences an increase in real consumption although
it is a net food importer.
The third channel is the impact of trade reform on government budgets. Since
the model does not include an explicit transversality condition, we maintain a
fixed budget deficit closure, which means that any losses in public revenue (such
as a reduction in tariff income) must be offset by a compensatory increase in the
direct tax rate on households.15 Therefore, welfare gains are more limited in coun-
tries, such as Tanzania and Zimbabwe, that rely heavily on international trade
taxes as an important provider of public revenue.16
In addition to changes in the levels of per capita consumption across countries,
the Linkage results hint at important distributional consequences of trade reform
within countries, which come about through changes in returns to labor in differ-
ent sectors and at varying skill levels. Figure 3.7 shows the contributions of the
payments for different factors to the total change in real GDP at factor cost (in
percentage points) after the removal of agricultural distortions alone. With the
exception of China, all the countries experiencing an increase in the payments for
unskilled labor in agriculture also register consumption gains after agricultural
policy reform. However, the converse does not hold: real consumption rises in 32
of the 41 countries that show a decline in unskilled agricultural wages. Since
unskilled workers in agriculture tend to be in the poorest part of the population,
these results suggest that pressures toward increased inequality may intensify.17
Furthermore, losses and gains in agricultural wages exhibit strong regional pat-
terns: the real wages of unskilled farmers rise in Latin America, the Middle East,
and East Asia, but decline in other developing regions and decline much more in
high-income countries.
102 Agricultural Price Distortions, Inequality, and Poverty
Figure 3.7. The Effects on National Real Factor Rewards of the
Global Removal of Only Agricultural Price and
Trade Policies
Argentina
Ecuador
Mozambique
New Zealand
Colombia
Bulgaria
Philippines
Vietnam
Zimbabwe
Kazakhstan
Middle East
Brazil
Rest of Latin America and Caribbean
Thailand
China
Rest of North Africa
Rest of East Asia
Australia
Rest of Eastern Europe and Central Asia
Hong Kong, China; Singapore
Canada unskilled agricultural labor
Indonesia unskilled nonagricultural labor
Chile
Senegal skilled agricultural labor
Egypt, Arab Rep. skilled nonagricultural labor
United States capital
Malaysia
Mexico land
Estonia
country
South Africa
Pakistan
Other South Asia
Kyrgyz Republic
Slovenia
Nicaragua
Taiwan, China
Tanzania
West and Central Africa
Japan
Rest of Western Europe
Russian Federation
Latvia
Sri Lanka
Zambia
Bangladesh
Romania
EU15
Slovak Republic
Rest of Sub-Saharan Africa
Korea, Rep.
Turkey
Hungary
Lithuania
Czech Republic
India
Uganda
Poland
Nigeria
Madagascar
Morocco
4 3 2 1 0 1 2 3 4 5
percent change in real value added
Source: Author compilation using Linkage model results similar to those in chapter 2, by Anderson,
Valenzuela, and van der Mensbrugghe, except that factor mobility is more limited.
Note: Each bar shows a contribution of the changes in the value added by a specific factor to the total
change in value added, deflated by the price of GDP at factor cost. Countries are sorted (in descending
order) by the increase in the payments to unskilled farm labor.
Global Poverty and Distributional Impacts: The GIDD Model 103
The initial level of protection in agriculture, combined with the terms of trade
shock, represents the main determinant of the trends in farm factor prices. Con-
sider the example of India, where unskilled farm wages decline by 6.1 percent fol-
lowing trade reform.18 Indian farmers must contend with the falling international
prices of imported farm products (a decline of 1.7 percent), as well as a loss in tar-
iff protection (2.0 percent), export subsidies (3.3 percent), and output subsidies
(6.9 percent) if all farm policies are scrapped. The first two channels decrease the
competitiveness of the farmers on the domestic market and lead to higher import
penetration; the third channel erodes their competitiveness on international mar-
kets; and the fourth channel increases production costs and makes Indian farmers
less competitive overall. Together, these effects result in a strong negative shock to
farm labor earnings.
In Mexico, the income losses among unskilled farmers are lower than they are
in India. This is partially attributable to the close trading relationship between
Mexico and the United States. Mexico purchases 75 percent of its agricultural
imports from the United States, the export prices of which rise by 5.7 percent
because of the elimination of export and production subsidies. Thus, the removal
of agricultural price supports in the United States exerts upward pressure on the
import prices of farm products in Mexico, which hurts consumers, but increases
the competitiveness of Mexican farmers on the domestic market. The trend is
counteracted by the removal of Mexico's tariff protection on agriculture (1.2 per-
cent) and farm output subsidies (0.8 percent), which leads to a decrease in the
competitiveness of farmers in Mexico and market share losses among them in the
domestic market and in export markets.
In contrast, Brazil is an example of a country in which a number of positive
developments combine to produce a nearly 34 percent gain in the wages of
unskilled agricultural workers.19 The import prices of farm products in Brazil rise
by 1.8 percent, bolstering the domestic competitiveness of Brazilian farmers,
while export prices increase by more than 10 percent. Because the farmers do not
receive export or production subsidies, they are well positioned to take advantage
of these opportunities and gain market share both domestically and abroad.
Although some of the gains among agricultural producers are offset by the loss in
domestic protection (import tariffs of 2.4 percent), Brazilian agriculture is still
able to increase production volumes by 18 percent after farm trade reform.
Microsimulation results:
A first look at global poverty and inequality effects
In this subsection, we use the GIDD model and data to simulate the likely changes
in global poverty and inequality arising from the elimination of trade distortions.
104 Agricultural Price Distortions, Inequality, and Poverty
Given the richness of the data and the numerous factors affecting global poverty
and inequality within the GIDD, this subsection starts with two simulations that
illustrate, in a simple way, the expected effects of global trade policy reform.
Focusing only on the developing countries in our data set, both simulations raise
the average income in the developing world by 1 percent. In the first simulation,
national income increases because of an increase in the incomes of agricultural
households only, while, in the second simulation, the increase originates entirely
in an expansion in nonagricultural incomes. The results of these examples are
shown in two growth incidence curves (GICs) in figure 3.8.20 The broken GIC line
captures the effects of assigning income gains only to agricultural households,
while the solid GIC line raises incomes only among those households in which the
heads work in nonagricultural activities.
As expected, the increase in agricultural incomes is more pro-poor than the
same income change taking place in other sectors in which households are rela-
tively richer. This pro-poor bias in the growth in agricultural incomes is reflected in
the downward slope of the broken GIC line, indicating that the poorest households
Figure 3.8. National GICs: The Effects on the Per Capita Household
Income Distribution of a Hypothetical 1 Percent Increase
in Agricultural Versus Nonagricultural Incomes,
Developing Countries
10
percent rise in household income
0.8
0.6
0.4
0.2
0
0 20 40 60 80 100
percentiles
1% increase in nonagricultural incomes
1% increase in agricultural incomes
extreme poverty line
Source: Author compilation based on the GIDD Database.
Note: The figure is based on the data set for developing countries only.
Global Poverty and Distributional Impacts: The GIDD Model 105
reap the largest income gains. A different way of interpreting the results shown in
figure 3.8 is to observe that, if the agricultural sectors in all developing countries
receive income gains above the gains in the nonagricultural sectors as an outcome
of the elimination of distortions, global poverty and inequality would fall. As
shown in the discussion below, the reality tends to be much more complex than
these simple simulations, but the central message of figure 3.8 nonetheless captures
the essence of the GIDD simulations.
Impacts on global poverty and inequality
Using the GIDD to translate the shocks from the Linkage model into poverty and
inequality outcomes, we find that the effects of the full removal of agricultural price
and trade distortions on extreme poverty globally are close to zero (table 3.2, col-
umn 3). This limited impact is explained by several factors. First, the estimated
impacts arise from a comparative static model rather than a dynamic model, and, so,
they do not capture the growth effects of the reform. Thus, the changes in per capita
consumption are small and arise only because of the income boost from more-
efficient resource allocation. According to the GIDD, the world's average monthly
Table 3.2. The Household Effects of the Removal of Agricultural
and All Merchandise Trade Distortions on Global
Poverty and Inequality
(percentage point change and U.S. dollars)
US$1-a-day US$2-a-day
Real average poverty, % poverty, %
Gini monthly income,
Household type coefficient 2000 US$ PPP Incidence Share Incidence Share
Baseline
Agricultural 0.45 65 31.5 76 73.8 70
Nonagricultural 0.63 320 8.3 24 26.7 30
All households 0.67 204 18.9 100 48.2 100
Agricultural liberalization, percentage point change
Agricultural 0.7 1.1a 0.86 1.13 0.86 0.47
Nonagricultural 0.1 0.2a 0.29 1.13 0.90 0.47
All households 0.1 0.3a 0.23 0.00 0.88 0.00
All merchandise trade liberalization, percentage point change
Agricultural 0.8 0.8a 1.09 1.02 0.66 0.57
Nonagricultural 0.2 0.4a 0.19 1.02 0.95 0.57
All households 0.0 0.4a 0.39 0.00 0.82 0.00
Source: Author compilation based on the GIDD Database and using Linkage model results similar to
those in chapter 2, by Anderson, Valenzuela, and van der Mensbrugghe, except that factor mobility is
more limited.
a. Changes in average income are expressed in percentages.
106 Agricultural Price Distortions, Inequality, and Poverty
household income increases 0.3 percent after the removal of only agricultural dis-
tortions, passing from an initial level of US$204 to a final value of US$210 at 1993
PPP. If nonagricultural trade is also liberalized, global income rises slightly more, but
the total number of households living on less than US$1 a day remains the same.
Second, there is an income redistribution from farm households to nonfarm
households, so that the incidence of extreme poverty among farm households
rises by around 1 percent in both scenarios, while the incidence among nonfarm
households falls by 0.2 or 0.3 percent. As a result, the share of extremely poor
households that are farm households rises from 76 to 77 percent (table 3.2,
column 4).
Third, the definition of poverty is important. The extreme level, at less than US$1
a day, suggests that only 8 percent of nonfarm households are poor and that they
account for only one in four poor households, whereas, at the moderate poverty def-
inition, at less than US$2 a day, the corresponding share is 27 percent, accounting for
nearly one in three poor households. If we use the moderate definition of poverty, we
find that both agricultural trade liberalization and all merchandise trade liberaliza-
tion globally lower the total poverty incidence by nearly 1 percent by reducing it
among farm and nonfarm households (compare table 3.2, columns 3 and 5).
The policy reforms also have only a slight impact in reducing income inequal-
ity at the global level. Incomes rise in both the agricultural and nonagricultural
sectors, and agricultural incomes increase by twice as much in the all-goods
reform scenario and by five times as much in the reform of agriculture only
(1.1 percent compared with only 0.2 percent; see table 3.2, column 2). Yet, while
the reduction in the nonagricultural income premium, on its own, reduces
inequality, income dispersion within the agricultural sector also increases such
that the final change in global income distribution is close to zero, judged accord-
ing to the changes in the Gini coefficient shown in table 3.2, column 1.
Because of these distributional changes taking place within the agricultural
sector, the incidence of extreme poverty (under US$1 a day at PPP) rises in the
agricultural sector. It increases by 0.9 percentage points as a consequence of the
elimination of agricultural price and trade distortions and by 1.1 points if non-
farm trade policies are also reformed. Meanwhile, poverty among nonagricul-
tural households experiences a reduction equal to 0.3 or 0.2 percentage points,
respectively. The combination of poverty changes occurring inside and outside
the agricultural sector increases extreme poverty by 0.2 percentage points with
respect to agricultural reform alone and by 0.4 points if all goods markets are
freed, the latter pushing 9 million additional individuals below the extreme
poverty line of US$1 a day. We must bear in mind, though, that the poverty effect
of these reforms depends on where the poverty line is set. While the global
number of people in poverty measured by the US$1-a-day poverty line shows
Global Poverty and Distributional Impacts: The GIDD Model 107
Table 3.3. The GIDD Model: The Effects of the Removal of
Agricultural and All Merchandise Trade Distortions
on the Incidence of Poverty, by Region
(number and percent)
Change in number of poor,
global trade reform
Share of Agriculture only All merchandise
global
Region poverty, % Millions % Millions %
Extremely poor, under US$1 a day
East Asia 24 6.4 2.8 6.3 2.8
South Asia 50 15.4 3.3 18.2 3.9
Sub-Saharan Africa 21 1.0 0.5 0.5 0.3
Latin America 4 2.8 6.9 3.5 8.7
Globala 100 5.0 0.5 8.9 1.0
Moderately and extremely poor, under US$2 a day
East Asia 33 12.8 1.6 13.2 1.7
South Asia 46 3.6 0.3 2.0 0.2
Sub-Saharan Africa 14 0.1 0.0 1.1 0.3
Latin America 4 4.8 4.6 5.7 5.4
Globala 100 22.1 0.9 19.8 0.8
Source: Author compilation based on the GIDD Database and using Linkage model results similar to
those in chapter 2, by Anderson, Valenzuela, and van der Mensbrugghe, except that factor mobility is
more limited.
a. Includes Middle East and North Africa, Eastern Europe and Central Asia, and high-income countries.
Together, these account for no more than 2 percent of the world's poor.
an increase of 1.0 percent as a consequence of the reform in all goods, the num-
ber measured using the US$2-a-day criterion actually falls by about 20 million or
0.8 percent (table 3.3, columns 4 and 5).
These results treat the world as a single entity, making no distinction among
regions or countries. Indeed, the lack of major changes at the global level may
be the outcome of offsetting impacts across regions. Farmers in Latin America are
big winners from agricultural price and trade reform, with an impressive increase
of 16 percent in household incomes. In contrast, the incomes of farmers in South
Asia would shrink more than 3 percent if agricultural distortions were dismantled
globally. To show the incidence of these changes among the population across
regions, figure 3.9 plots the GICs for Latin America, South Asia, and the rest of the
world (see chart a). The GIC for Latin America indicates that agriculture-based
growth in the region is quite pro-poor. In contrast, agriculture-based growth in
South Asia is highly regressive: the poorest households are the losers in such
reform. East Asia and, to a lesser extent, Sub-Saharan Africa would benefit from
108 Agricultural Price Distortions, Inequality, and Poverty
Figure 3.9. Regional and National GICs: The Effects on the
Per Capita Household Income Distribution of Full
Global Agricultural Policy Reform
a. Regional
12 % of poor in % of poor in South Asia
Latin America
10
% change in per capita income
8
6
4
2
0
2
4
0 20 40 60 80 100
percentiles
Latin America other developing countries South Asia
b. National: Brazil and India
10
% of poor in Brazil % of poor in India
% change in per capita income
5
0
5
0 20 40 60 80 100
percentiles
India Brazil
Source: Author compilation based on the GIDD Database.
Note: The figure is based on the data set for developing countries only.
Global Poverty and Distributional Impacts: The GIDD Model 109
global agricultural reform, and the effects of such reform would be progressive,
albeit small, in the rest of the world.
The differences in reform outcomes across regions help explain the lack of sig-
nificant change in global poverty. With nearly a half billion of its people in
extreme poverty and another 625 million moderately poor, South Asia alone
accounts for about half of global poverty, while Latin America accounts for less
than 5 percent (table 3.3, column 1). Hence, although removing agricultural dis-
tortions reduces extreme poverty in most regions of the world, the increase in the
headcount ratio in South Asia slightly more than offsets these gains.
The results based on the US$2-a-day poverty line show a more positive picture.
Poverty gauged by this standard is reduced in all key regions except (marginally)
Sub-Saharan Africa (table 3.3, final column). The results at the moderate poverty
line are particularly interesting in the case of South Asia, where agricultural reform
becomes pro-poor, instead of antipoor as it was according to the US$1-a-day
extreme poverty line. This result is explained by the large number of nonagricultural
households between the two poverty lines in South Asia; these would experience an
increase in purchasing power if global agricultural markets were liberalized. The
reduction in moderate poverty is somewhat less if the trade in nonfarm goods is also
liberalized, but is still 0.8 percent, or 20 million people globally.
Poverty and inequality effects within regions and countries
Global agricultural liberalization has distributional and poverty effects that vary
not only across regions, but also between neighboring countries and within coun-
tries. This subsection summarizes the poverty effects and distributional changes
in each of the countries included in our sample. Table 3.3 shows that the 5 million
individuals who would be pushed into poverty as a consequence of agricultural
reform may be broken down into an increase of 15 million people living in
poverty in South Asia and a decrease of 10 million people living in poverty in all
other regions. Figure 3.10 shows the specific countries that contribute, respec-
tively, the most to the reduction and the most to the increase in global poverty. On
the one hand, among the new poor, 92 percent--about 15 million--are in India,
while 2.2 percent are in Mexico, and 1.8 percent are in South Africa; both Mexico
and South Africa currently protect their farmers and would face higher prices for
imported farm products. On the other hand, the gross reduction in global poverty
is distributed more evenly among many winners among countries; the great
majority are located in East Asia and Latin America. In fact, no country in East
Asia and only Chile and Mexico in Latin America experience an increase in the
number of extreme poor as a result of global agricultural reform, and even the lat-
ter two would experience poverty reduction if the reform were broadened to
include nonfarm goods as well (table 3.4).
110 Agricultural Price Distortions, Inequality, and Poverty
Figure 3.10. Changes in Poverty as a Share of the Total
Change among the Greatest Winners and Losers
in Full Global Agricultural Policy Reform,
Developing Countries
a. Share of the global increase in poverty
India 92.0
Mexico 2.2
South Africa 1.8
Kenya 0.7
country
Pakistan 0.7
Morocco 0.7
Bangladesh 0.5
Turkey 0.3
Sri Lanka 0.2
rest of the world 1.0
0 20 40 60 80 100
percent
b. Share of the global reduction in poverty
Philippines 38.6
Brazil 12.1
Nigeria 11.5
Indonesia 8.3
Vietnam 6.1
country
Peru 5.5
Thailand 2.1
Yemen, Rep. 2.1
Ecuador 1.9
Colombia 1.6
rest of the world 10.1
0 10 20 30 40
percent
Source: Author compilation based on the GIDD Database.
Note: The figure is based on the data set for developing countries only.
Global Poverty and Distributional Impacts: The GIDD Model 111
Table 3.4. The Effects of the Full Global Liberalization of Agricultural
and All Merchandise Trade on the Incidence of Inequality
and Poverty, Developing Countries
(percentage point change)
Agricultural reform only All merchandise trade reform
US$1 US$2 US$1 US$2
Gini a day, a day, Gini a day, a day,
Region, country coefficient headcount headcount coefficient headcount headcount
East Asia 0.72 0.38 0.76 0.62 0.37 0.78
Cambodia 0.75 0.40 0.17 0.69 0.90 0.05
China 0.37 0.00 0.00 0.35 0.00 0.00
Indonesia 0.05 0.49 0.83 0.04 0.57 1.26
Philippines 6.88 6.31 9.91 6.65 6.15 9.80
Thailand 1.25 0.40 2.29 1.10 0.38 2.29
Vietnam 1.85 0.89 3.57 0.08 0.66 2.97
South Asia 0.82 1.16 0.27 0.81 1.37 0.15
Bangladesh 0.46 0.06 0.19 0.09 0.07 0.05
India 1.01 1.49 0.33 1.04 1.71 0.26
Nepal 0.05 0.12 0.04 0.05 0.11 0.18
Pakistan 0.04 0.09 0.03 0.10 0.49 0.44
Sri Lanka 0.03 0.14 0.01 0.17 0.20 0.69
Sub-Saharan Africa 0.04 0.23 0.02 0.06 0.11 0.25
Benin 0.00 0.05 0.09 0.02 0.79 0.74
Burkina Faso 0.02 0.00 0.05 0.02 0.78 0.43
Burundi 0.27 0.35 0.17 0.26 0.12 0.37
Cameroon 0.01 0.24 0.06 0.05 0.72 0.70
C�te d'Ivoire 0.02 0.14 0.61 0.06 0.43 0.89
Ethiopia 0.13 0.27 0.20 0.07 0.24 0.22
Gambia, The 0.12 0.05 0.00 0.11 0.61 0.40
Ghana 0.08 0.10 0.09 0.08 0.62 0.62
Guinea 0.04 0.10 0.04 0.03 0.62 0.42
Kenya 0.30 0.41 0.54 0.38 0.52 0.94
Madagascar 0.95 0.24 0.06 0.98 0.56 0.36
Mali 0.01 0.01 0.03 0.03 0.93 0.45
Mauritania 0.03 0.09 0.18 0.07 0.60 0.60
Nigeria 0.10 1.04 0.30 0.27 0.75 0.21
Senegal 0.28 0.12 0.24 0.01 0.75 0.31
South Africa 0.32 0.68 0.68 0.21 0.39 0.50
Tanzania 0.03 0.01 0.03 0.02 0.54 0.26
Uganda 0.30 0.05 0.01 0.45 0.12 0.03
Latin America 0.51 0.61 1.06 0.65 0.77 1.26
Bolivia 0.55 0.64 0.65 0.71 0.74 0.61
Brazil 0.58 0.82 1.16 0.70 0.92 1.31
Chile 0.03 0.02 0.02 0.09 0.02 0.10
Colombia 0.71 0.45 1.30 0.85 0.49 1.40
Costa Rica 0.56 0.13 0.50 0.72 0.22 0.62
(Table continues on the following page.)
112 Agricultural Price Distortions, Inequality, and Poverty
Table 3.4. The Effects of the Full Global Liberalization of Agricultural
and All Merchandise Trade on the Incidence of Inequality
and Poverty, Developing Countries (continued)
(percentage point change)
Agricultural reform only All merchandise trade reform
US$1 US$2 US$1 US$2
Gini a day, a day, Gini a day, a day,
Region, country coefficient headcount headcount coefficient headcount headcount
Dominican
Republic 0.76 0.24 0.75 0.95 0.24 0.89
Ecuador 0.90 1.75 2.78 1.29 1.98 3.09
El Salvador 1.09 1.25 0.79 1.29 1.34 0.88
Guatemala 0.87 1.26 1.54 1.11 1.59 1.44
Guyana 1.49 0.27 0.39 1.66 0.46 0.54
Honduras 1.06 0.57 1.58 1.30 0.72 1.63
Haiti 0.80 0.61 0.55 1.04 0.65 0.53
Jamaica 0.75 0.10 1.44 0.91 0.05 1.49
Mexico 0.20 0.36 0.35 0.05 0.01 0.07
Nicaragua 2.42 3.03 1.42 2.38 3.07 1.42
Panama 0.40 0.44 0.74 0.53 0.51 0.85
Paraguay 0.77 1.30 1.41 0.99 1.38 1.92
Peru 1.21 2.92 6.41 1.51 3.10 6.77
Venezuela, R.B. de 0.29 0.44 0.71 0.42 0.55 0.82
Europe and
Central Asia 0.07 0.02 0.26 0.22 0.00 0.12
Armenia 0.42 0.34 0.14 0.45 0.23 0.13
Azerbaijan 0.23 0.10 1.36 0.12 0.14 1.21
Georgia 0.48 0.49 0.57 0.37 0.45 0.61
Kazakhstan 0.97 0.39 2.07 1.00 0.43 2.22
Kyrgyz Republic 0.50 0.08 0.57 0.27 0.19 1.09
Moldova 0.20 0.22 0.75 0.06 0.09 0.24
Russian
Federation 0.16 0.00 0.32 0.06 0.00 0.31
Tajikistan 0.12 0.77 0.75 0.32 0.67 0.74
Ukraine 0.11 0.03 0.33 0.02 0.00 0.04
Uzbekistan 0.07 0.00 0.06 0.29 0.00 0.03
World 0.10 0.23 0.88 0.00 0.39 0.82
Source: Author compilation based on the GIDD Database and using Linkage model results similar to those in
chapter 2, by Anderson, Valenzuela, and van der Mensbrugghe, except that factor mobility is more limited.
The contributions to the global entries into and exits out of poverty depicted
in figure 3.10 are, to a certain extent, the outcomes of differences in population
size. For instance, a populous country such as India may have a substantial contri-
bution to global poverty without necessarily implying a large increase in the head-
count ratio of the country. Another way of ranking countries in terms of poverty
Global Poverty and Distributional Impacts: The GIDD Model 113
outcomes is to consider the postreform change in the headcount ratio following
all merchandise trade liberalization globally. Undertaking this exercise, we show
that, among countries in which poverty falls, Ecuador and Peru would enjoy the
largest declines in Latin America, while, in Asia, the Philippines, Thailand, and
Vietnam (and Kazakhstan) would be in this category. India is still the country
showing the largest increase in the incidence of extreme poverty, but the incidence
of moderate poverty (US$2 a day) falls by 0.3 percentage points (table 3.4). Note
that these changes in India occur although average household income remains
virtually constant; the changes are therefore entirely a result of a deterioration in
income distribution. This is reflected in the increase by 1.0 percentage points in
the Gini coefficient of India. Three-quarters of this increase in inequality is attrib-
utable to a rise in the agricultural-nonagricultural income gap in India. In
contrast, the poverty reduction in Brazil is the outcome of a combination of a
1 percent increase in average incomes and a reduction in the Gini coefficient of
inequality of around 0.6�0.7 percentage points (table 3.4).
The changes in overall growth and distribution that would take place in Brazil
and India after global agricultural reform are summarized through the GICs for
these two countries plotted in figure 3.9, chart b. Given the importance of Brazil
and India in their respective regions, it is not surprising that the shapes of the
GICs for these countries are similar to the GICs of their respective regions plotted
in figure 3.9, chart a.
Chart b in Figure 3.9 shows that the only beneficiaries of global agricultural
liberalization in India are people in households in the top 22 percent of the distri-
bution; given that 83 percent of the Indian population is living below the US$2-a-
day poverty line, part of the top 22 percent is accounted for by households living
in moderate poverty.
Agricultural reforms may have important real income distributional effects
in terms of the gap between agricultural and nonagricultural households (see
elsewhere above). Our results show that, for many countries in our sample, the
removal of distortions would have considerable distributional effects. In one-
third of the countries listed in table 3.4, the Gini coefficient shows a decline of
more than one-half of a percentage point. This pattern is also observed in the
changes in the country-specific Theil index plotted in figure 3.11. There are
distinguishable regional differences in the distributional effects of the reform;
countries in East Asia and Latin America experience a significant reduction in
income inequality, while the inequality in countries outside these regions
remains constant or rises marginally. The advantage of using the Theil index as
the inequality measure is that we are able to decompose the changes in the
index into an effect attributable to shifts in the agricultural-to-nonagricultural
wage gap (the between effect) and the effects arising from income changes
114 Agricultural Price Distortions, Inequality, and Poverty
Figure 3.11. The Theil Index of Overall and Between-Group
Income Distributional Changes after Full Global
Agricultural Policy Reform
Yemen, Rep.
Haiti
Vietnam change in the overall
Honduras Theil index
Guatemala
Peru change in the
Philippines between-group
Paraguay (agri/non-agri)
Thailand component of
Ecuador the Theil index
Jordan
Cambodia
Brazil
Colombia
Bolivia
Guyana
Jamaica
Dominican Republic
El Salvador
Costa Rica
Kazakhstan
Panama
Azerbaijan
Venezuela, R.B. de
China
Kyrgyz Republic
Georgia
Russian Federation
Moldova
Ukraine
Albania
Tajikistan
Bulgaria
country
Indonesia
Chile
Senegal
Tanzania
Uzbekistan
Mongolia
Mauritania
C�te d'Ivoire
Benin
Guinea
Pakistan
Mali
Romania
Ghana
Nicaragua
Gambia, The
Cameroon
Mexico
Burkina Faso
Nepal
Sri Lanka
Estonia
Armenia
Nigeria
Uganda
Hungary
Turkey
Burundi
South Africa
Poland
Bangladesh
India
Kenya
Ethiopia
Lithuania
0.035 0.025 0.015 0.005 0 0.005 0.015 0.025
Theil index
Source: Author compilation based on the GIDD Database.
Note: The Theil index is an inequality measure of the family of general entropy that has the property of
yielding perfectly additive inequality decompositions by population subgroups. The figure is based on
the data set for developing countries only.
Global Poverty and Distributional Impacts: The GIDD Model 115
within these two groups. Figure 3.11 shows the total changes in the Theil index
(depicted by an x) and the changes attributable to movements in the nonagri-
cultural income premium (the small horizontal bar). Since the between effect
is close to the total distributional effect for the majority of countries, we may
conclude that the total change in income distribution in these economies is
mainly the outcome of changes in the mean incomes in the agricultural and
nonagricultural sectors.
Conclusions
Trade distortions in agriculture and food represent the last major bastion of
merchandise protection and continue to be the main point of contention in
multilateral and preferential trade negotiations. Using a newly developed data
set and methodological approach for evaluating the poverty and inequality
effects of policy reforms, the GIDD, this chapter evaluates the potential effects
on global income distribution and poverty exerted by the removal of agricul-
tural distortions and other trade distortions.
There are three main messages emerging from our analysis. First, the liberaliza-
tion of agricultural and food markets is unlikely to have large effects on global
poverty. Our results show that the incidence of extreme poverty (US$1 a day, PPP)
might rise by 0.5 percent each from farm and nonfarm full global trade reform,
while moderate poverty (US$2 a day, PPP) is likely to fall by 0.9 percent from agri-
cultural reform alone. The second message is that these small aggregate changes
are produced by a combination of offsetting trends at the regional and national
levels. Farmers in Latin America, the region that accounts for less than 5 percent
of global poverty, experience significant income gains, while 15 million more
people in South Asia, where half of the world's poor reside, would fall below the
extreme poverty line after the liberalization of world agricultural markets, and
another 2.8 million would fall below this line if the barriers to the trade in
nonfarm goods were also removed. Some countries or regions would experience
considerable distributional changes following global trade reform. Inequality is
projected to fall in regions, such as Latin America, that are characterized by high
initial inequality, but is projected to rise in South Asia, which is characterized by
low initial inequality. As a result, the projected changes for developing countries as
a group are small overall.
There are several important caveats to our analysis. First, it should be empha-
sized that, although poverty reduction is a most worthy goal, it should not be the
only or even the first metric with which to measure trade policy. Trade reform
should not be expected to benefit all constituents and may do so only if it is
accompanied by other, complementary domestic policies. Second, our analysis is
116 Agricultural Price Distortions, Inequality, and Poverty
confined to an examination of the effects of static efficiency gains and does not
consider the potential growth effects of trade liberalization. Although our results
show that the static gains from agricultural reform and other trade reform may
not contribute to a reduction in extreme poverty and may do little to combat
moderate poverty, they do not imply that this pattern of trade liberalization is not
an effective tool for poverty reduction. Finally, our microsimulation model con-
siders only changes in labor income; while this is the most important income
source for households at or near the poverty line, accounting for changes in other
factor returns may yield somewhat different inequality results.
Notes
1. According to Anderson and Martin (2005), self-interested vocal groups lobbying aggressively in
favor of excluding agricultural liberalization from multilateral negotiations encompass not only farm-
ers in the highly protected countries and net food-importing developing countries, but also food
exporters receiving preferential access to these markets, including holders of tariff rate quotas, mem-
bers of regional trading agreements, and parties to nonreciprocal preference agreements, such as all
least developed countries.
2. The GIDD data set, methodology, and applications are available at http://go.worldbank.org/
YADEAFEJ30.
3. This more highly aggregated information usually consists of 20 data points for each country,
each data point representing the average per capita income (or consumption) of 5 percent of the coun-
try's population. In the absence of full survey data, the use of these vingtile data provides a close
approximation to most economy-wide measures of inequality.
4. The adjustment procedure for expressing welfare indicators in 1993 international dollars (PPP)
is as follows. First, for countries for which the survey year is not 2000, the welfare indicator (household
per capita income or consumption) is scaled to the year 2000 using the cumulative growth in real
income per capita between the survey year and 2000. Then, the welfare indicator is converted to 1993
national prices by multiplying the welfare indicator by the ratio of the consumer price index in 1993 to
the consumer price index in the survey year. Finally, the welfare indicator is converted to 1993 interna-
tional prices by multiplying the outcome of the above calculations by the 1993 PPP exchange rate.
5. The distributions for the agricultural and nonagricultural populations are not, strictly speaking,
density functions because the area below the curve does not add to 1. The densities of the agricultural
and nonagricultural populations were rescaled so that the area under the curve represents the share of
the world's population in these two groups.
6. For a specific analysis of the importance of China and India to global growth and income distri-
bution, see Bussolo et al. (2007).
7. A household is defined as a net producer (consumer) of agricultural products if the monetary
income it derives from merchandising these products is greater (smaller) than the amount spent on
them.
8. For empirical applications of the effect of trade on household welfare that take into account
these effects, see, for example, the case studies on Mexico by Nicita (2004) and De Hoyos (2007).
9. A detailed methodological description of the GIDD may be found in Bussolo, De Hoyos, and
Medvedev (2008).
10. The GIDD uses the Linkage model as a global CGE framework. See van der Mensbrugghe
(2005) for a detailed description of the Linkage model.
11. Throughout this chapter, global distribution means the GIDD sample of countries, which
include 92 percent of the world's population.
Global Poverty and Distributional Impacts: The GIDD Model 117
12. The GIDD does not take into account the welfare impacts via any changes in remittances or
transfers between households that result from trade reform.
13. The Linkage results used here are not identical to those in chapter 2 because labor mobility in
our chapter has been restricted to match the available microdata more closely. In the version of the
Linkage model used in chapter 2, the assumption of full factor mobility leads to an equalization of fac-
tor prices across sectors. However, the household survey data show large and persistent differences
between the labor earnings in agriculture and the labor earnings in nonagriculture after we have con-
trolled for other relevant characteristics. Imposing an equalization of wages in the GIDD would lead to
large and implausible changes in the distribution of income; to maintain consistency between macro-
and micromodels, we have therefore limited labor mobility in the macromodel.
14. The price increases are calculated using the Paasche price index, that is, using postreform
exports as weights to aggregate the prices of individual commodities. Unless explicitly noted, all price
indexes in this section are calculated using the Paasche formula. Price indexes differ by country
because of differences in the composition of exports (that is, aggregation weights).
15. This closure choice thus gives rise to a consistent measurement of household utility because the
utility function does not include the consumption of public goods.
16. In this situation, whether households gain or lose from trade reform, in addition to the impacts
of the first two channels, depends on their ability to substitute out of more expensive goods into now-
cheaper alternatives.
17. Note that trends in consumption per capita are unlikely to be representative of the welfare of
agricultural households given that, because of limited incomes and high poverty incidence, the weight
of these households in total consumption is low.
18. The 6.1 percent figure refers to the decline in nominal wages. The change in real wages depends
on the choice of deflator: while the consumer price index increases by 2 percent relative to the base
year, the GDP deflator falls by 1 percent.
19. This is a nominal increase, not a real increase. Consumer prices in Brazil rise by 4 percent after
trade reform.
20. A GIC shows the changes in welfare along the entire income distribution. It therefore captures,
in a single graph, the growth and distributional components of overall welfare changes. For a detailed
description of the properties of GICs, see Ravallion and Chen (2003).
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World Bank, Washington, DC. http://go.worldbank.org/7NP2KK1OH0.
Winters, L. A. 2002. "Trade Liberalization and Poverty: What Are the Links?" World Economy 25 (9):
1339�67.
4
Poverty Impacts in
15 Countries:
The GTAP Model
Thomas W. Hertel and Roman Keeney
Despite slow progress toward a multilateral trade reform agreement, the Doha
Development Agenda negotiations of the World Trade Organization continue to
generate interest because of their poverty reduction potential. A distinguishing
feature of the Doha agenda has been the lack of commitment to trade policy
reform by developing countries, particularly the poorest countries, which would
not be required to commit to reduce tariffs following a Doha agreement
(Anderson and Martin 2006).
Recent research suggests that developing-country tariff cuts, particularly in
agriculture, are among the most poverty-friendly elements of a broader multilat-
eral trade policy agenda (Hertel et al. 2009). Such analyses hinge critically on the
measured protection for agriculture in developing countries. Unlike the countries
of the Organisation for Economic Co-operation and Development (OECD), in
which the measurement of agricultural protection has received considerable
attention over the past two decades through the regular publication of producer
support estimates, there was considerable uncertainty about the current support
and recent trends in agricultural protection in developing countries, particularly
the smaller low-income economies, until the new World Bank agricultural distor-
tions database was compiled (Anderson and Valenzuela 2008).
In this chapter, we incorporate the new information on price distortions in an
assessment of the impact of agricultural and other trade reforms on poverty. The
new data on developing-country protection in agriculture afford us the opportunity
to consider the relative importance of agricultural versus nonagricultural protection
at home and abroad more accurately than has been previously possible.1
119
120 Agricultural Price Distortions, Inequality, and Poverty
Tracing the impacts of developments in multilateral trade policy from interna-
tional markets to the household level is a long and complex process (Winters,
McCulloch, and McKay 2004). A natural method for accomplishing this is to
divide the task into parts. This approach is taken in Hertel and Winters (2006),
who use a global model to generate the changes in world prices following various
multilateral trade reform scenarios and then national models rich in household-
level detail to assess the ensuing national poverty impacts. This method allows
the authors to benefit from the comparative advantage of separate modeling tools.
It has become widely accepted as the standard method for reaching definitive
answers to ex ante questions on the impacts of trade reforms on poverty in
individual countries.
An inherent limitation of the country-specific studies underlying the final
estimates on the changes in poverty derived through this approach is the short-
age of general conclusions on the broad pattern of impacts on poverty. Are rich-
country reforms pro-poor on average? How do they compare with poor-country
reforms?
For this purpose, it is important to assemble a framework that is based on
household surveys. The framework should allow us to make comparisons across
countries and develop conclusions about poverty impacts in a wide range of
diverse economies that are representative of the developing world. This approach
has been adopted by Ivanic (2006) and Hertel et al. (2007) in their analyses of the
Doha Development Agenda. These authors find that the set of measures envi-
sioned under the Doha Development Agenda are less poverty-friendly than alter-
native policy measures not being considered.2 Hertel et al. (2009) explore this
question in detail and determine that an overemphasis on export and domestic
production subsidies relative to the attention paid to market access in the rich
countries, together with the absence of commitments to deep agricultural tariff
cuts in developing countries, is the primary culprit in diminishing the prospects
for poverty reduction through the Doha Development Agenda. The international
cross-section approach--the framework approach mentioned above--has proven
especially fruitful in providing insights that are complementary to those provided
by more-detailed country-specific studies, despite the former's relatively undiffer-
entiated treatment of developing-country household behavior.
In addition to making use of the new developing-country protection estimates,
we also examine the poverty impacts of trade reform through a different lens,
namely, the lens of commodity-specific support. Given that many political
economy and distributional causes of agricultural protection are inherently
commodity specific, viewing the trade-poverty link through the commodity lens
in a comparative fashion represents an area of relative inattention and one that a
Poverty Impacts in 15 Countries: The GTAP Model 121
global model with a diverse developing-country sample is well suited to address.
For our present purposes, we focus on 15 developing countries: four in Africa
(Malawi, Mozambique, Uganda, and Zambia), five in Asia (Bangladesh, Indonesia,
the Philippines, Thailand, and Vietnam), and six in Latin America (Brazil, Chile,
Colombia, Mexico, Peru, and Rep�blica Bolivariana de Venezuela).
Our Analytical Approach to Poverty Modeling
Our poverty analysis begins with the specification of a utility function and an
associated consumer demand system for determining household consumption
and the maximum utility attainable at a given level of prices and incomes. The
utility of the household at the poverty line is defined as the poverty level of utility.
Households with utility at or below this level are deemed to be in poverty. In this
study, we follow Hertel et al. (2004, 2007) in using the AIDADS demand system of
Rimmer and Powell (1992) to represent consumer preferences. AIDADS (an
implicitly directly additive demand system) is particularly effective in poverty
analysis because it lends itself to international cross-section estimation and
devotes two-thirds of its parameters to consumption behavior in the neighbor-
hood of the poverty line (Cranfield et al. 2003).
The estimation of this demand system is undertaken using the 80-country per
capita consumption data set offered in version 6.1 of the Global Trade Analysis
Project (GTAP) data set; the resulting parameters are reported in Hertel et al. (2009).
The demand system estimates are then calibrated to reproduce base year per capita
demands in each country following the approach of Golub and Hertel (2008).
A key finding in the work of Hertel et al. (2004) is the importance of stratifying
households by primary source of income. Farm households in developing coun-
tries often rely on the farm enterprise for virtually all income, and the share of
national poverty concentrated in agriculture-specialized households is large in
the poorest countries in our sample: between one-quarter and one-half of the
US$1-a-day poverty line headcount in Chile, Colombia, Indonesia, Malawi,
Mozambique, and Zambia.
Not only are farm households in the poorest countries more likely to be spe-
cialized in farm earnings, these specialized farm households also tend to be poorer
than the rest of the population (Hertel et al. 2004). The implication is that the
poorest households in the poorest countries are more concentrated in agriculture
and therefore more likely to benefit from producer price increases engendered by
multilateral trade reforms. We follow Hertel et al. (2004) in identifying five house-
hold groups that rely almost exclusively (greater than 95 percent) on one source of
income: agricultural self-employment, nonagricultural self-employment, rural
122 Agricultural Price Distortions, Inequality, and Poverty
wage labor, urban wage labor, or transfer payments. The remaining households
are grouped into rural or urban diversified strata, leading to seven total strata.3
Hertel et al. (2004) have simulated the impact of trade reform on the full dis-
tribution of households within each of the seven strata using a global computable
general equilibrium (CGE) model and a household microsimulation framework.
Given our emphasis on poverty in this chapter, we follow Hertel et al. (2009) by
focusing on the households in the neighborhood of the poverty line and making
use of a highly disaggregated poverty elasticity approach. Specifically, we compute
the stratum-specific elasticity of the poverty headcount with respect to a change in
average income in the stratum. We denote this elasticity by ers and report the com-
puted values for the 15 countries in our sample in table 4.1. The values range from
a low of 0.0006 in the self-employed agriculture stratum in Zambia, where nearly
Table 4.1. Estimates of Elasticities of the US$1-a-Day Poverty
Headcount with Respect to Total Income,
15 Countries
Strata
Urban Rural Urban Rural National
Country Ag Nonag labor labor Transfer diverse diverse elasticity
Bangladesh 1.64 2.02 1.58 0.63 0.56 1.74 1.09 1.24
Brazil 0.75 1.28 1.94 2.19 0.34 3.63 2.69 1.35
Chile 1.90 2.24 2.06 1.55 2.45 2.29 2.60 2.18
Colombia 0.79 0.60 1.73 1.72 0.93 1.14 1.00 0.82
Indonesia 2.35 2.14 2.38 2.89 1.17 2.58 2.87 2.47
Malawi 0.49 0.30 2.26 1.97 0.43 1.04 0.76 0.58
Mexico 1.73 1.90 3.33 2.08 2.28 1.63 1.80 2.02
Mozambique 0.28 0.94 0.97 0.76 0.48 1.58 0.99 0.64
Peru 1.50 1.32 2.37 1.73 0.44 1.09 1.05 1.07
Philippines 2.25 1.96 2.98 2.44 1.69 2.42 1.98 2.15
Thailand 2.30 2.42 2.98 2.45 2.78 2.42 2.59 2.57
Uganda 0.28 0.40 1.71 0.34 0.01 0.36 0.21 0.24
Venezuela,
R.B. de 0.69 1.16 2.57 2.17 0.01 1.72 1.53 1.20
Vietnam 0.48 1.12 2.81 8.98 0.84 0.86 1.01 0.98
Zambia 0.00 0.64 2.28 0.91 0.45 1.29 0.37 0.61
Source: Author compilation.
Note: The values in the strata columns are the elasticities of the poverty headcount with respect to
changes in earnings. National elasticity in the final column is the aggregate elasticity for each country
weighted by the poverty share (see table 4.2). The authors have estimated the elasticities using country-
specific household survey data. For the first five strata, more than 95 percent of household income is
derived from only one income source. Ag agriculture. Nonag nonagriculture.
Poverty Impacts in 15 Countries: The GTAP Model 123
all of the population is living well below the poverty line, to a high of 3.63 in the
urban diversified stratum of Brazil, where the population density at the poverty
line is quite high.
The proportional change in the real incomes of households at the poverty line
in stratum s of region r may be written as the sum of the real after-tax factor earn-
ings of these households weighted by the income share, as follows:
Np N Np
y rs = a a p (W rj - C r ),
rsj (4.1)
j
where a p is the share of income obtained from factor j by households at the
rsj
N
poverty line in stratum s of region r; W rj is the proportional change in after-tax
earnings of factor j in region r; and C N r is the proportional change in the cost of
p
living at the poverty line in region r, that is, the expenditure required to remain at
the poverty level of utility. This last is obtained by solving the AIDADS demand
system for the expenditure required to remain at the poverty level of utility given
the new prices (that is, the post-trade reform prices).
We may now express the proportional change in the poverty headcount in
stratum s of region r as follows:
N N N
Hrs = ers # y p = ers # a # a p (W rj - C rp)
N rs rsj (4.2)
j
p
The earnings shares at the poverty line, a rsj, will play a critical role in our analysis.
Regardless of the household type (with the exception of transfer-dependent
households), the income from unskilled labor tends to dominate the stratum-
specific earnings shares (Hertel et al. 2009). For example, in the case of agriculture-
dependent households, most earnings show up in unskilled agricultural labor. In
the case of rural diversified households, earnings usually show a mix of sources,
including agricultural self-employed unskilled labor, unskilled wage labor, and
unskilled nonfarm (self-employed) labor. The fact that these households are so
poor means that they have little income from land or capital.
Having established the determinants of the stratum poverty headcount, we
may now progress to the national poverty headcount, Hr, which may be expressed
as a function of the stratum headcounts and stratum populations (POPrs):
Hr = c a POPrs * Hrs d n POPr , (4.3)
s
where POPr = � POPrs. So, the proportional change in the national poverty
s
N
headcount is Hr = � b rs * Hrs .
s
124 Agricultural Price Distortions, Inequality, and Poverty
Here,
b rs = C (POPrs * Hrs)>POPr D >Hr = (POPrs * Hrs)> a (POPrs * Hrs) (4.4)
s
is the share of stratum s poverty in nationwide poverty within region r. These
shares are reported in table 4.2 for our 15 focus countries. Agriculture-specialized
households and rural diversified households tend to dominate in the poverty
headcount although exceptions are evident in Colombia, Peru, and Rep�blica
Bolivariana de Venezuela, where self-employed nonagricultural households
represent a large share of the poor.
Combining equations (4.2) and (4.4), we obtain a useful expression for evalu-
ating the change in the national poverty headcount in response to a small change
in factor and commodity prices:
N N p
Hr = a b rs # ers # a a p (W rj - C r )
rsj (4.5)
s j
Table 4.2. Stratum Contributions to the US$1-a-Day Poverty
Population, 15 Countries
(percentage shares)
Strata
Urban Rural Urban Rural
Country Ag Nonag labor labor Transfer diverse diverse Total
Bangladesh 0.15 0.13 0.04 0.22 0.03 0.07 0.37 1.00
Brazil 0.14 0.09 0.24 0.15 0.32 0.04 0.03 1.00
Chile 0.26 0.01 0.09 0.09 0.28 0.15 0.12 1.00
Colombia 0.28 0.43 0.03 0.04 0.12 0.05 0.04 1.00
Indonesia 0.42 0.12 0.02 0.07 0.04 0.06 0.28 1.00
Malawi 0.54 0.11 0.00 0.03 0.07 0.01 0.25 1.00
Mexico 0.05 0.06 0.05 0.12 0.28 0.14 0.29 1.00
Mozambique 0.41 0.13 0.01 0.05 0.14 0.06 0.19 1.00
Peru 0.07 0.35 0.01 0.02 0.22 0.11 0.23 1.00
Philippines 0.12 0.06 0.03 0.05 0.03 0.23 0.49 1.00
Thailand 0.06 0.02 0.00 0.06 0.11 0.07 0.68 1.00
Uganda 0.10 0.04 0.00 0.03 0.02 0.07 0.75 1.00
Venezuela,
R.B. de 0.08 0.24 0.17 0.10 0.28 0.08 0.05 1.00
Vietnam 0.04 0.11 0.00 0.00 0.05 0.10 0.70 1.00
Zambia 0.34 0.23 0.10 0.07 0.07 0.09 0.11 1.00
Source: Author compilation.
Note: The values are the shares of the impoverished population that are specialized in a particular
stratum of earnings. The shares are derived from country-specific household surveys. The total column
reflects the assumption that the entire population in poverty is allocated among the seven strata.
Ag agriculture. Nonag nonagriculture.
Poverty Impacts in 15 Countries: The GTAP Model 125
Because the expression in parentheses in equation (4.5) denotes the proportional
change in the real after-tax income associated with each of the factors of produc-
N rj
tion (W R), we may rewrite this as follows:
N N rj
Hr = a a b rs # ers # a p # W R
rsj (4.6)
s j
From equation (4.6), it is clear that, to obtain the change in national poverty, each
real after-tax factor return must be premultiplied by a region-, stratum-, and
factor-specific poverty elasticity.
For example, table 4.3 reports the product b rs # ers # ap for Bangladesh. The
rsj
rows in the table correspond to strata, and the columns to earnings types. Because
� j arsj = 1, the sums in the rows of the table simply give the elasticity of national
poverty with respect to a 1 percent rise in stratum income, that is, b rs # ers. The
sums are obviously heavily influenced by the national poverty shares reported in
the Bangladesh row of table 4.2. The rural diversified stratum comprises nearly
37 percent of the poor; so it is not surprising that this row total is the largest in
table 4.3. This stratum is followed in importance by the self-employed agricultural
and nonagricultural strata, which show relatively high stratum-specific poverty
elasticities (recall the first row of table 4.1), and then by the rural labor stratum
and the urban diversified stratum.
The column sums in table 4.3 allow us to identify the factors that are most
important to poverty reduction in Bangladesh. In this case, it is clear that
unskilled labor is the primary endowment of the poor in Bangladesh. The key
issues are the strata of these workers and the effect of trade reform on their rela-
tive wages. From the bottom row in table 4.3, we learn that unskilled wage labor is
the most important stratum from the perspective of national poverty reduction,
followed closely by self-employed nonagricultural labor and unskilled agricultural
labor. Transfer payments, skilled wage labor, capital, and land play a much smaller
role in poverty reduction at the margin, according to the final row in the table.
The grand total in table 4.3 gives the national US$1-a-day poverty elasticity
(bottom right corner, 1.24) with respect to a uniform 1 percent rise in real after-
tax income from all sources.
The first row of table 4.4 takes the final row of table 4.3 for Bangladesh and
divides all the entries by 1.24 so that we may see the share of each earnings source
in national poverty reduction in the context of an across-the-board rise in real
after-tax incomes. Thus, unskilled wage labor (the fifth column entry in row 1)
contributes 33 percent of the total and so on. The remaining rows in table 4.4 pro-
vide the same calculation for the other 14 developing countries in our sample.
If we treat each country as an observation, we find that unskilled wage labor
shows the highest average share (table 4.4, bottom row), followed by agricultural
unskilled labor and then nonagricultural unskilled labor and transfers. Transfer
126
Table 4.3. Stratum- and Earnings-Specific Poverty Elasticities, Bangladesh
Ag Ag Unskilled Skilled Nonag Nonag
unskilled skilled Ag wage wage unskilled skilled Nonag
Strata Land labor labor capital labor labor labor labor capital Transfers Total
Ag 0.01 0.23 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.24
Nonag 0.00 0.00 0.00 0.00 0.00 0.00 0.25 0.00 0.01 0.00 0.26
Urban labor 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.06
Rural labor 0.00 0.00 0.00 0.00 0.12 0.01 0.00 0.00 0.00 0.00 0.14
Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.02
Urban diverse 0.00 0.02 0.00 0.00 0.05 0.01 0.02 0.00 0.00 0.01 0.12
Rural diverse 0.00 0.07 0.00 0.00 0.17 0.02 0.08 0.00 0.01 0.04 0.40
Total 0.01 0.33 0.00 0.01 0.41 0.03 0.36 0.00 0.02 0.07 1.24
Source: Author compilation.
Note: The table refers to households living on less than US$1 a day. The elasticities are calculated by multiplying earnings shares by stratum-specific elasticities for
Bangladesh. The total column shows the change in stratum poverty resulting from a 1 percent increase in income for each household type. The total row shows the
national change in poverty resulting from a 1 percent increase in the factor income of each column type. Ag agriculture. Nonag nonagriculture.
Table 4.4. The Contributions of Earnings to the Total US$1-a-Day Poverty Response, 15 Countries
(percent share of total)
Ag Ag Unskilled Skilled Nonag Nonag
unskilled skilled Ag wage wage unskilled skilled Nonag
Country Land labor labor capital labor labor labor labor capital Transfers Total
Bangladesh 0.01 0.27 0.00 0.01 0.33 0.02 0.29 0.00 0.02 0.06 1.00 (1.24)
Brazil 0.00 0.07 0.03 0.01 0.60 0.04 0.10 0.01 0.00 0.15 1.00 (1.35)
Chile 0.07 0.15 0.00 0.10 0.26 0.00 0.02 0.00 0.00 0.40 1.00 (2.18)
Colombia 0.00 0.29 0.00 0.00 0.18 0.00 0.34 0.00 0.01 0.16 1.00 (0.82)
Indonesia 0.03 0.50 0.00 0.02 0.19 0.01 0.17 0.00 0.04 0.03 1.00 (2.47)
Malawi 0.03 0.52 0.00 0.07 0.12 0.02 0.07 0.00 0.05 0.14 1.00 (0.58)
Mexico 0.00 0.09 0.00 0.00 0.38 0.00 0.08 0.00 0.00 0.43 1.00 (2.02)
Mozambique 0.00 0.35 0.00 0.02 0.11 0.00 0.14 0.00 0.15 0.20 1.00 (0.64)
Peru 0.01 0.16 0.00 0.01 0.09 0.00 0.45 0.07 0.06 0.14 1.00 (1.07)
Philippines 0.23 0.00 0.01 0.12 0.31 0.01 0.14 0.00 0.07 0.10 1.00 (2.15)
Thailand 0.03 0.20 0.02 0.02 0.24 0.05 0.04 0.01 0.01 0.38 1.00 (2.57)
Uganda 0.13 0.13 0.00 0.25 0.08 0.08 0.08 0.00 0.13 0.08 1.00 (0.24)
Venezuela,
R.B. de 0.00 0.06 0.00 0.00 0.58 0.03 0.28 0.01 0.00 0.05 1.00 (1.20)
Vietnam 0.01 0.12 0.00 0.00 0.00 0.00 0.18 0.00 0.47 0.20 1.00 (0.98)
Zambia 0.00 0.02 0.00 0.02 0.43 0.16 0.20 0.00 0.13 0.08 1.00 (0.61)
Average 0.04 0.19 0.00 0.04 0.26 0.03 0.17 0.01 0.08 0.17 n.a.
Source: Author compilation.
Note: The values are earnings-specific elasticities, divided by national poverty elasticities (in parentheses in the total column). The average row is the simple average
across countries of the contribution of a specific earnings source to total poverty responsiveness. The total column reflects the fact that contributions to total
127
responsiveness add to 1. Ag agriculture. Nonag nonagriculture. n.a. not applicable.
128 Agricultural Price Distortions, Inequality, and Poverty
payments are important in some of the richer developing countries (Brazil, Chile,
Colombia, Mexico, Thailand), as well as in lower-income countries with a large
share of migrant labor (for example, Mozambique). The entries in table 4.4 give a
clear idea of the factor price increases that are most likely to lower the national
poverty headcount in each country.
The Global CGE Model
Because the household-level poverty impacts hinge critically on factor rewards
that depend not only on the type of endowment, but also on where it is employed,
we need a global modeling framework with sufficient detail to separate out these
differential returns. We have adopted the GTAP model (Hertel 1997) and the ver-
sion 6.1 GTAP Database calibrated to 2001 (Dimaranan 2006). We have modified
both to be consistent with our needs for differentiated factor returns, as well as the
changes in price distortions identified in Anderson and Valenzuela (2008) and
prepared for CGE modeling by Valenzuela and Anderson (2008). We must also
make some updates to account for changes in market conditions and policy
reforms since 2001, the year of the version 6.1 database. The model is imple-
mented in Gempack software (Harrison and Pearson 1996).
We now turn to a discussion of the modeling assumptions and data changes
incorporated into our CGE modeling framework.
Database adjustments
Our starting point for the global CGE analysis of the impacts of trade policy is the
GTAP version 6.1 database (Dimaranan 2006). For our present purposes, we have
aggregated the database to 31 productive sectors and six groups of household con-
sumption items, following Ivanic (2006). We have updated this database by solving
an experiment that accounts for key policy reforms in border protection that were
undertaken between 2001 and 2005, most notably the accession of China to the
World Trade Organization, the enlargement of the European Union, and the com-
pletion of the Uruguay Round commitments by some members of the World Trade
Organization. We have also altered the database, now with a base year of 2005, to
reflect the recent estimates of agricultural price distortions in developing countries
by Anderson and Valenzuela (2008). We have likewise altered our database to reflect
the most recent OECD commodity support data on agricultural output subsidies as
of 2004. The aggregate changes in commodity support as measured by the producer
support estimate are relatively small over this time frame (OECD 2007). However, in
some instances, the support for particular commodities changed dramatically.
Because of our desired emphasis on viewing poverty impacts through a commodity
Poverty Impacts in 15 Countries: The GTAP Model 129
lens, our use of the most recent available information on initial protection is
warranted. In total, this has meant that we have adjusted the output subsidy levels
among 25 OECD members in the initial database.4
Modifications to the GTAP model
Our modifications to the standard GTAP model focus on features that enhance the
analysis of agricultural reforms and the simulation of impacts on poverty. We retain
the simplistic, yet empirically robust assumptions of constant returns to scale and
perfect competition typically featured in agricultural trade studies.5 The remaining
modifications are aimed at permitting us to shed new light on the distributional
consequences of trade reforms. They focus particularly on unraveling the puzzle
involved in the question, why is such reform not more poverty-friendly?
On the demand side of the model, we ensure consistency with the poverty
analysis by modifying the GTAP model to incorporate the AIDADS demand
system as discussed in Hertel et al. (2007). Thus, the aggregate preferences are
consistent with the preferences used to evaluate the impact of price changes on
households at the poverty line although expenditure patterns differ across income
levels because of the nonhomotheticity of demands.
The other modifications relate to the factor markets and follow from the model
changes made by Keeney and Hertel (2005). Frictions in agricultural factor
markets have featured prominently in the development economics literature,
particularly as an explanation for low agricultural supply response (de Janvry,
Fafchamps, and Sadoulet 1991). Modeling the complex processes leading to
limited farm-nonfarm and rural-urban mobility for the full range of countries in
our model is beyond the scope of this chapter and is better suited to the individual
country case study approach (see elsewhere above).
To maintain a general framework that reflects the imperfect factor mobility
between rural and urban employment, we specify a constant elasticity of transfor-
mation function that transforms farm-employed factors into nonfarm employ-
ment and vice-versa.6 This allows factor rewards to diverge between the farm and
nonfarm sectors and supplies us with the factor market segmentation we require
for our distributional analysis. We use the factor supply parameters that have been
adopted by Keeney and Hertel (2005) and that are drawn from the OECD (2001).
We assume that aggregate endowment levels are fixed in our static analysis.
This reflects a belief that the aggregate supply of factors is unaffected by trade
policy. This is not the full employment assumption sometimes ridiculed by
advocates of structural models of development. Rather, it holds that aggregate
employment is primarily determined by factors, such as labor market norms and
regulation, that are largely independent of trade policy in the long run.
130 Agricultural Price Distortions, Inequality, and Poverty
Recalling equation (4.6), we must tie our results to the factor earnings shares
for all household types in each region. We map the factor returns from the general
equilibrium model to the various earnings types according to the following
method. Agricultural labor and capital receive the corresponding farm factor
returns from the general equilibrium model, as do nonagricultural labor and cap-
ital. Wage labor in the household surveys is not distinguished by place of employ-
ment; so, we use the economy-wide average change. Transfer payments represent
an important source of earnings for many households and have no obvious corol-
lary in the CGE model. We choose to index these to the growth rate in net national
incomes, which allows us to maintain consistency with the representative house-
hold approach of the global model (see Hertel et al. 2007).
Finally, a few words about our macroclosure are in order. In this chapter, we fix
the ratio of key macroeconomic aggregates relative to net national income. These
include government spending, total tax revenues (net of subsidies), net national
savings, and the trade balance. In this way, we also ensure that public transfer pay-
ments (not explicitly modeled in our study) are implicitly fixed relative to net
national income. This provides a convenient method for indexing the transfer
payments accruing to households. Because tariff liberalization typically results in
a reduction in tax revenues, a replacement tax is needed. In this chapter, we
assume that income taxes on all earnings rise by an equal proportion so to ensure
that tax revenues remain fixed relative to net national income. Of course, in those
rich countries in which tariffs are low and agricultural subsidies are high, this tax
rate may fall in the wake of trade liberalization. While we do not believe that the
income tax will be the replacement tax of choice in many economies, particularly
the poorest economies, it is a convenient tool, and we do not possess sufficient
detail on the tax structure in many of these economies to improve greatly on this
simple assumption. As we show below, the omission of the tax replacement effect
has a dramatic impact on our poverty results. This impact is thereby highlighted
as a key issue for consideration in the country-specific case studies in this volume
and elsewhere.
Model Results and Discussion
In this chapter, we report the results of only one core simulation: the removal
globally of all agricultural production and export subsidies and all agricultural
and nonagricultural merchandise trade taxes.7 Table 4.5 reports our estimates of
the percent change in the national poverty headcount for each of our focus coun-
tries based on this global liberalization simulation. We use the decomposition
method of Harrison, Horridge, and Pearson (2000) to identify the impacts
of agricultural and nonagricultural policy reform separately from the total in
Table 4.5. Summary of the Poverty Impacts of Global Reforms, 15 Countries
(percent change in the headcount)
Core results of this study Different Different tax replacement
data,
Country Ag reform Nonag reform Total Level change, 1,000s GTAP 6 Poor are exempt VAT
Bangladesh 0.25 0.51 0.26 116 0.28 5.30 0.01
Brazil 2.53 0.38 2.15 50 1.41 10.00 1.42
Chile 4.76 0.12 4.64 14 4.99 12.25 4.79
Colombia 0.72 0.63 0.09 3 0.10 4.05 0.03
Indonesia 1.05 0.49 0.56 84 1.45 5.23 0.53
Malawi 1.64 0.26 1.91 81 1.84 5.62 1.31
Mexico 0.78 0.35 1.13 105 1.35 0.48 1.15
Mozambique 1.15 0.15 1.00 61 0.69 4.34 0.29
Peru 0.64 0.16 0.80 35 0.79 5.24 0.67
Philippines 1.37 0.42 0.95 108 0.75 6.39 1.92
Thailand 11.19 0.93 10.26 121 8.87 28.05 5.83
Uganda 0.01 0.09 0.09 15 0.06 5.96 0.09
Venezuela, R.B. de 0.15 0.71 0.86 28 0.86 2.12 1.14
Vietnam 0.48 5.26 5.74 88 5.85 23.58 6.96
Zambia 0.02 0.13 0.11 7 0.09 2.00 1.25
Average 1.66 0.05 1.71 n.a. 1.59 8.04 1.30
Absolute average 1.78 0.71 2.04 n.a. 1.96 8.04 1.83
Sign consistency 0.93 0.07 0.84 n.a. 0.81 1.00 0.71
Source: Model simulations by the authors.
Note: The table shows the results of simulations by the authors using Gempack software (Harrison and Pearson 1996) and the GTAP Database (Dimaranan 2006). The
impacts of agricultural (Ag) and nonagricultural (Nonag) reform add to the total percent change in poverty. We use the subtotal routine in Gempack that has been
developed by Harrison, Horridge, and Pearson (2000) to isolate these portions of the total impact. Level change is the calculated number of persons moving out of
poverty given the initial headcount and the predicted percent change. The different data simulation uses the GTAP version 6 (base year 2001) protection data with the
modifications of Hertel et al. (2007, 2009). The different tax replacement, poor are exempt scenario assumes that the poor are not subject to the higher income tax
needed to replace lost tariff revenue. The different tax replacement, value added tax (VAT) scenario replaces lost tariff revenue through a consumption tax that may be
131
viewed as a value added tax equivalent under which imports are taxed, while exports are exempted. Average is the simple average of the percent changes, while
absolute average is the simple average of the absolute value of individual percent changes. Sign consistency measures the consistency between the direction of effects
and the level of impact and is calculated as the ratio of the average to the absolute average, AV AAV (Ivanic 2006). n.a. not applicable.
132 Agricultural Price Distortions, Inequality, and Poverty
percentage terms. We also provide level changes (in thousands) in the national
poverty headcount (table 4.5, column 4).
Impacts of the new data on price distortions
Table 4.5 offers a comparison of the total poverty reduction based on previous
work (Hertel et al. 2009) that did not feature the distortion estimates compiled by
Anderson and Valenzuela (2008) or the updated OECD estimates of protection in
OECD member countries. Comparing table 4.5, columns 3 and 5, we find mostly
small differences in the percent changes in the poverty headcount of our study
(column 3) compared with the changes calculated in the GTAP Database (column 5).
However, some differences are worth noting. For Colombia, the previous results
indicated that global liberalization would lead to a slight increase in poverty (a
0.1 percent rise in the national headcount), whereas our results anticipate that
global trade reform will lead to a slight reduction in poverty. Colombia is one of
the countries in which the information on agricultural protection changed signif-
icantly because of the incorporation of the estimates of Anderson and Valenzuela
(2008). More significantly, our predicted poverty reduction in Indonesia is some-
what lower and, in Brazil, somewhat higher than the poverty reduction estimates
reported previously.
The crucial role of tax replacement
The last two columns of table 4.5 present differing estimates of the predicted
poverty changes that follow if we alter our assumption about tax adjustments. One
variant is to assume that the poor are not subjected to the income tax replacement
mechanism. Another is to use an alternative tax replacement instrument, namely, a
value added tax equivalent. This allows us to compare our results with the results of
studies that rely on different assumptions, and it highlights the importance of the
tax replacement assumption on the predicted changes in poverty.
Table 4.5, column 6 reports the percent changes in the national poverty head-
counts if the poor are not subject to the replacement income tax. This is the
assumption of Anderson, Martin, and van der Mensbrugghe (2006) in their analy-
sis of the poverty impacts of the Doha Development Agenda. As one may see, this
accounts for a marked difference in the predicted poverty reduction. Trade
reforms that, without this assumption, were marginally poverty reducing in most
cases are now universally poverty reducing by a considerable magnitude. The
assumption reduces the poverty rate by roughly one-quarter in Thailand and
Vietnam, for example. In this scenario, the poor are being given access to com-
modities or are able to sell them at undistorted prices without having to bear any
Poverty Impacts in 15 Countries: The GTAP Model 133
of the direct tax burden involved in replacing the lost tariff revenue. Indeed, this
reform represents a significant implicit income transfer from nonpoor house-
holds to poor households. We do not argue that such a fiscal transfer is not to be
desired, of course, because the transfer would have tremendous poverty reduction
benefits. However, we do not believe this is the measure that would most likely be
used, given that most developing countries seek to make up for lost tariff revenue
by resorting to a value added tax.
The final column in table 4.5 reports the poverty results if we replace the lost
tariff revenue by adjusting the value added tax. However, care must be taken in
adjusting this tax because some sectors are exempt from the tax (public con-
sumption, for example, and, often, basic foods). In this alternative scenario, we
adjust the tax through an equiproportional adjustment in the power of the con-
sumption tax (that is, one, plus the consumption tax rate) on taxable items in
each focus country. This might be viewed as equivalent, in effect, to a value added
tax replacement experiment if the value added tax applies to all imports and
exempts all exports. However, if the existing consumption tax structure is already
distorted (as in the case in our model, for example, because some sectors are
exempt), this replacement consumption tax exacerbates the distortions. In
particular, because it does not apply to public consumption, the tax distorts the
allocation of resources between public consumption and private consumption.
Therefore, in our framework, this value added tax scenario is expected to pro-
duce less beneficial outcomes in terms of poverty reduction relative to the core
scenario. Indeed, this is confirmed by a comparison of table 4.5, columns 3 and 7:
if lost tariff revenue is replaced by a consumption tax instead of an income tax,
the poverty gains are more modest, and the mix of countries that reduce poverty
is altered slightly.
Summary of the poverty impacts
In the final three rows of table 4.5, columns 1�3, we report on the summary mea-
sures introduced by Ivanic (2006). Specifically, we compute, first, the average
value across countries (treating each country as an observation with equal
weight); then, we report the average absolute value, which indicates the impor-
tance of a given change regardless of sign; and, finally, we report the ratio of these
two: AV AAV SC (average/absolute average sign consistency). Note that this
ratio--which may be viewed as the tendency for trade reforms to reduce (or
increase) poverty--is constructed such that 1 SC 1. If SC 1, then a
given trade reform (or set of reforms) is poverty reducing for all countries in the
sample because the average change and the absolute value of the changes are of
the same magnitude, but opposite in sign.
134 Agricultural Price Distortions, Inequality, and Poverty
From the average across countries, we see that our current results, based on
updated protection data, predict larger poverty-reducing impacts in developing
countries relative to the previous analysis (AV 1.71 versus AV 1.59) and a
slightly greater prevalence of poverty-reducing impacts across the countries in
our sample (AAV 0.84 versus AAV 0.81). In summary, the results predict
that full global trade liberalization would reduce poverty in 10 of our 15 focus
countries. Even if we ignore the growth-enhancing effects of trade reform (which
are not included in our comparative static framework), this would mean that no
less than 816,000 people would be lifted out of poverty.
The decomposition of total impacts between agricultural and nonagricultural
reforms (table 4.5, first two columns) shows that the impact of the global agricul-
tural reforms on the poor is more than twice the impact of the nonagricultural
reforms (AAVag 1.78 versus AAVnonag 0.71). Furthermore, the agricultural
reforms are nearly always poverty reducing (SC 0.93), whereas the nonagricul-
tural reforms tend to be only marginally poverty reducing among this sample of
countries (SC 0.07). Of the five countries experiencing increases in poverty in
the wake of combined agricultural and nonagricultural trade reforms, Bangladesh
and Mexico are the most important in terms of absolute numbers (more than
100,000 in each case). These countries currently enjoy preferential market access in
their most important markets (the European Union for Bangladesh and the United
States for Mexico). Global trade liberalization would lead to substantial preference
erosion for both countries in these preferred markets.
Why are agricultural reforms more poverty-friendly than
nonfarm trade reform?
We begin our quest to understand the differential impact of trade reforms across
products by focusing on the difference between the poverty impacts of agricul-
tural and nonagricultural reforms; we then turn to an individual commodity
decomposition of agricultural reforms.
A natural method of investigating the difference between farm and nonfarm
reforms is via the decomposition proposed in equation (4.6). Because the elastici-
ties in this expression are the same (initially) in both experiments, the entire dif-
ference in the change in the poverty headcount is accounted for by real after-tax
wage changes. Specifically, we have the following decomposition of the difference
in poverty headcount by region:
N p N
Hr,diff = a a b rs # ers # a rsj(W R NR
rj,agr - W rj,nagr), (4.7)
s j
N rj,agr
where W R is the real after-tax change in earnings for endowment j in region r
N rj,nagr is the nonagricultural counterpart.
owing to agricultural reforms, and W R
Poverty Impacts in 15 Countries: The GTAP Model 135
Our first task is to explain why the earnings are differentially affected; then, we
must consider the interaction of these changes with region, stratum, and factor
elasticities to determine the differential impact on national poverty.
N rj,agr - W R ) for all sources of earnings and
Table 4.6 reports the values of (W R N rj,nagr
all focus regions in our analysis. Relative to nonagricultural trade reforms, agricul-
tural trade reforms raise the returns to farming in all our focus developing countries.
Relative to nonagricultural reforms, they also raise the returns to unskilled wage labor
in most countries as the unskilled labor-intensive agricultural sector expands and
boosts wages among unskilled labor. (Indonesia, Mexico, and Vietnam are the excep-
tions because the nonagricultural reforms exert a stronger influence on unskilled
wages there.) Agricultural reforms are less favorable for skilled labor,but the reward of
this factor is relatively unimportant for the poor (see table 4.4).
Continuing across the columns in table 4.6, we see that agricultural reforms
often, but not always lead to relatively lower real after-tax earnings relative to
nonagricultural reforms for self-employed nonagricultural endowments; this is
evident in the many negative entries in columns 7�9. Relative to nonagricultural
reforms, agricultural reforms that increase food prices tend to hurt transfer-
dependent households because transfers (by assumption) are indexed to net
national income and not to the cost of living at the poverty line (column 10). This
effect is attributable to the relatively large share of food expenditures in the
consumption baskets of the poorest households.
If we take into account the aggregate poverty elasticities with respect to factor
earnings reported in table 4.4, it is not immediately clear from the pattern of earn-
ings differences shown in table 4.6 why agricultural trade reforms dominate nona-
gricultural reforms as a poverty reduction tool. However, this result depends on
the responsiveness of the national poverty headcounts to each of the sources of
earnings. We gain more insight into this issue by examining table 4.7, which
reports the poverty-weighted elasticity counterparts to the earnings differences
N rj,nfarm).
N rj,farm - W R
reported in table 4.6, that is, � b rs # ers # a p (W R
rsj
s
If we sum across a row in table 4.7 (that is, if we sum across all endowments in a
given country), we obtain the percent difference in the poverty headcount stem-
ming from agricultural and nonagricultural reforms (subject to any rounding error
arising from the differencing of percentages; this is the reason these entries differ
from the simple difference between the first two columns of table 4.5). The final col-
umn in table 4.7 indicates that, in all countries except Mexico and Vietnam, agricul-
tural reforms are more poverty friendly relative to nonagricultural reforms.
What is special about Mexico and Vietnam? We note that these are two of the
three countries in which real after-tax unskilled wages rise more under nonagri-
cultural trade reforms (the other country is Indonesia; see table 4.6). Unlike
Indonesia, where the share of the poor in agriculture-specialized households is
Table 4.6. Earnings Differences after Agricultural and Nonagricultural Reforms, 15 Countries
136
(percent change)
Ag Ag Unskilled Skilled Nonag Nonag
unskilled skilled Ag wage wage unskilled skilled Nonag
Country Land labor labor capital labor labor labor labor capital Transfers
Bangladesh 0.33 0.38 0.95 0.86 0.56 1.68 0.62 1.69 1.52 1.17
Brazil 40.01 16.35 15.10 14.81 1.34 0.75 0.72 0.91 1.58 0.13
Chile 15.35 7.57 6.93 6.74 0.29 0.86 1.03 0.88 1.33 0.85
Colombia 8.22 4.40 3.97 3.49 1.10 0.30 0.36 0.30 0.81 0.35
Indonesia 3.87 1.76 1.59 1.49 0.50 0.83 1.53 0.86 1.09 0.59
Malawi 4.76 3.20 2.68 2.82 1.75 0.77 0.75 0.77 0.68 1.21
Mexico 0.18 0.10 0.06 0.06 0.19 0.10 0.21 0.10 0.20 0.26
Mozambique 8.58 4.93 3.52 4.16 1.42 1.41 0.18 1.43 0.50 0.60
Peru 12.95 6.94 5.69 5.92 1.07 1.25 1.44 1.49 0.92 0.11
Philippines 3.07 1.68 1.53 1.21 0.35 0.07 0.56 0.03 0.67 0.36
Thailand 34.07 17.46 14.29 12.92 4.19 1.59 2.01 1.99 4.38 2.11
Uganda 0.62 0.50 0.47 0.44 0.40 0.34 0.25 0.34 0.20 0.37
Venezuela, R.B. de 0.87 0.68 0.70 0.44 0.49 0.55 0.46 0.55 0.01 0.11
Vietnam 15.75 3.87 3.89 5.83 9.07 8.93 12.81 8.93 4.87 6.40
Zambia 4.00 1.87 1.60 1.47 0.56 0.03 0.17 0.02 0.49 0.15
Average 10.15 4.76 4.19 4.17 0.25 0.80 1.19 0.86 0.96 0.49
Absolute average 10.17 4.78 4.20 4.18 1.55 1.30 1.54 1.35 1.28 0.98
Sign consistency 1.00 1.00 1.00 1.00 0.16 0.62 0.77 0.64 0.75 0.50
Source: Model simulations by the authors.
Note: The table shows the differences in impacts on real after-tax earnings according to endowment following agricultural and nonagricultural reforms. See the note to
table 4.5 for simulation information.
Table 4.7. Earnings-Specific Differences between Agricultural and Nonagricultural Reforms in the
Changes in Poverty, 15 Countries
(percent change in the headcount)
Ag Ag Unskilled Skilled Nonag Nonag
unskilled skilled Ag wage wage unskilled skilled Nonag
Country Land labor labor capital labor labor labor labor capital Transfers Total
Bangladesh 0.00 0.12 0.00 0.01 0.23 0.05 0.22 0.00 0.04 0.08 0.76
Brazil 0.10 1.43 0.60 0.15 1.09 0.04 0.10 0.00 0.00 0.03 3.21
Chile 2.30 2.45 0.00 1.48 0.16 0.00 0.04 0.00 0.00 0.75 5.60
Colombia 0.00 1.07 0.00 0.00 0.17 0.00 0.10 0.00 0.01 0.04 1.38
Indonesia 0.30 2.16 0.02 0.08 0.23 0.02 0.66 0.01 0.10 0.05 1.49
Malawi 0.10 0.95 0.00 0.11 0.12 0.00 0.03 0.00 0.02 0.10 1.43
Mexico 0.00 0.02 0.00 0.00 0.15 0.00 0.03 0.00 0.00 0.23 0.43
Mozambique 0.04 1.12 0.00 0.04 0.11 0.00 0.02 0.00 0.05 0.08 1.31
Peru 0.12 1.17 0.00 0.07 0.11 0.01 0.70 0.11 0.06 0.02 0.59
Philippines 1.54 0.01 0.04 0.32 0.23 0.00 0.17 0.00 0.09 0.08 1.79
Thailand 2.95 9.08 0.89 0.54 2.55 0.21 0.21 0.03 0.13 2.06 13.38
Uganda 0.02 0.02 0.00 0.03 0.01 0.01 0.00 0.00 0.01 0.01 0.10
Venezuela, R.B. de 0.00 0.05 0.00 0.00 0.34 0.02 0.16 0.00 0.00 0.01 0.56
Vietnam 0.17 0.45 0.00 0.03 0.01 0.00 2.27 0.01 2.24 1.31 5.20
Zambia 0.01 0.01 0.00 0.01 0.14 0.00 0.02 0.00 0.04 0.01 0.15
Average 0.51 1.34 0.10 0.19 0.32 0.01 0.24 0.01 0.18 0.28 1.74
Absolute average 0.51 1.34 0.10 0.19 0.38 0.02 0.32 0.01 0.19 0.32 2.49
Sign consistency 1.00 1.00 1.00 1.00 0.86 0.53 0.77 0.93 0.95 0.87 0.70
Source: Model simulations by the authors.
137
Note: The results correspond with equation (4.7) and represent the differences in the impacts of agricultural and nonagricultural reforms on poverty according to
endowment. See the note to table 4.5 for simulation information.
138 Agricultural Price Distortions, Inequality, and Poverty
about 40 percent (see table 4.2), Mexico and Vietnam show relatively low shares of
agricultural households in total poverty. Both these countries have relatively high
shares of rural diversified households in poverty that rely heavily on unskilled
wage labor, which is more favorably affected by nonagricultural trade reforms.
While higher returns to unskilled family labor in agriculture represent the
dominant driver of agricultural poverty reduction relative to nonagricultural
poverty reduction in most countries, there are important exceptions. In
Bangladesh and Zambia, agricultural reforms have a more favorable impact on
poverty than do nonagricultural reforms largely because of the wage labor chan-
nel. In the Philippines, farm households benefit relatively more from higher
returns to agricultural land.
We also examine the relative impact of agricultural and nonagricultural trade
reforms on poverty by stratum. This is shown in table 4.8, which reports the
Table 4.8. Stratum-Specific Differences in the Changes in Poverty after
Agricultural and Nonagricultural Reforms, 15 Countries
(contribution to percent change in the headcount)
Urban Rural Urban Rural
Country Ag Nonag labor labor Transfer diverse diverse Total
Bangladesh 0.10 0.17 0.03 0.09 0.02 0.08 0.27 0.76
Brazil 1.67 0.08 0.55 0.40 0.01 0.46 0.23 3.21
Chile 4.47 0.02 0.05 0.04 0.58 0.97 0.67 5.60
Colombia 0.98 0.09 0.06 0.07 0.04 0.08 0.06 1.38
Indonesia 1.76 0.39 0.02 0.10 0.03 0.03 0.23 1.49
Malawi 0.86 0.02 0.00 0.09 0.03 0.02 0.40 1.43
Mexico 0.01 0.02 0.03 0.05 0.17 0.05 0.10 0.43
Mozambique 0.58 0.04 0.02 0.05 0.04 0.21 0.45 1.31
Peru 0.68 0.64 0.01 0.03 0.01 0.18 0.33 0.59
Philippines 0.62 0.06 0.03 0.04 0.02 0.41 0.77 1.79
Thailand 2.62 0.09 0.05 0.57 0.63 0.94 9.94 13.38
Uganda 0.01 0.00 0.00 0.00 0.00 0.01 0.06 0.10
Venezuela, R.B. de 0.04 0.13 0.21 0.11 0.00 0.04 0.03 0.56
Vietnam 0.08 1.16 0.00 0.00 0.27 0.11 3.74 5.20
Zambia 0.00 0.01 0.08 0.03 0.00 0.04 0.01 0.15
Average 0.96 0.14 0.07 0.09 0.11 0.22 0.64 1.74
Absolute average 0.97 0.20 0.08 0.11 0.12 0.24 1.15 2.49
Sign consistency 1.00 0.72 0.91 0.82 0.86 0.91 0.56 0.70
Source: Model simulations by the authors.
Note: The results correspond with equation (4.7) and represent the differences in the impacts of agricul-
tural and nonagricultural reforms on poverty according to household type. See the note to table 4.5 for
simulation information.
Poverty Impacts in 15 Countries: The GTAP Model 139
N rj,nfarm) for all 15 countries and all
N rj,farm - W R
elements of � j b rs # ers # a p (W R
rsj
seven strata. The columns now refer to strata, and each element represents the
combined impact of all changes in earnings (adjusted for changes in taxes and the
cost of living) on national poverty through the changes in poverty in the individ-
ual household strata. Once again, the final column records the differences
between agricultural and nonagricultural reforms in terms of the percent change
in the national poverty headcount. Here, we see that agricultural trade reforms
reduce poverty among agriculture-specialized households in nearly all countries
(note that SC, the ratio of the average and absolute average headcounts, is 1.00
under this stratum).
The contribution of the agricultural stratum to poverty reduction is negligible
only in Mexico and Vietnam, and these are the only two countries in which
poverty among diversified rural households would fall more after nonagricultural
trade reforms than after agricultural reforms (leading to positive entries in the
rural diversified column in table 4.8). In both Mexico and Vietnam, the contribu-
tions of all nonagricultural household strata to reductions in poverty are more
significant after multilateral nonagricultural reforms than after agricultural
reforms.
Understanding the impacts on poverty of particular farm
commodity policies
Because of the importance of agricultural reform, we now turn to the task of
decomposing poverty impacts according to agricultural commodity. In
table 4.9, we decompose the reduction in poverty after agricultural reform
(see table 4.5) into component parts; in this case, our breakdown is according
to the global commodity market in which the reform has occurred. Thus, the
first set of columns reports the percent change in national poverty headcounts
caused by global reforms in the foodgrains sector. As a result, we see that, with
the exception of Vietnam (and Mexico and Rep�blica Bolivariana de
Venezuela, in which there is no effect), the liberalization in foodgrain markets
is generally poverty reducing. The disaggregation of the foodgrains reforms
according to instrument reveals that tariff cuts in foodgrains--as well as feed-
grains, which are also shown in table 4.10--are universally poverty reducing
(the sign consistency index is 1). If poverty increases in a country because of
reforms in food or feedgrains, this effect is caused by adverse price impacts that
arise from the elimination of export subsidies and domestic support (see the
export subsidies and domestic subsidies columns in table 4.10). Apart from
foodgrains, the reforms in other crops are the most poverty friendly. These
140 Agricultural Price Distortions, Inequality, and Poverty
Table 4.9. Change in Poverty after Commodity-Specific
Reforms, 15 Countries
(percent change in the headcount)
Other
Country Foodgrains Feedgrains Sugar Cotton crops Dairy Meat
Bangladesh 0.07 0.16 0.02 0.18 0.11 0.04 0.02
Brazil 0.29 0.20 0.20 0.01 0.27 0.01 1.56
Chile 0.18 0.04 0.03 0.03 3.11 0.40 1.03
Colombia 0.11 0.06 0.08 0.01 0.55 0.09 0.04
Indonesia 0.09 0.00 0.29 0.00 0.36 0.03 0.86
Malawi 0.14 0.02 0.05 0.04 1.51 0.02 0.00
Mexico 0.00 0.48 0.03 0.06 0.23 0.03 0.02
Mozambique 0.10 0.17 0.53 0.08 0.36 0.01 0.09
Peru 0.07 0.01 0.01 0.02 0.01 0.02 0.60
Philippines 0.20 0.23 0.01 0.07 0.79 0.11 0.19
Thailand 5.63 0.91 0.88 0.06 2.89 0.01 0.93
Uganda 0.01 0.02 0.00 0.02 0.03 0.01 0.00
Venezuela, R.B. de 0.00 0.02 0.00 0.00 0.04 0.03 0.04
Vietnam 0.03 0.40 0.07 0.00 0.18 0.13 0.01
Zambia 0.01 0.04 0.01 0.01 0.04 0.01 0.03
Average 0.46 0.11 0.10 0.01 0.64 0.01 0.33
Absolute average 0.46 0.18 0.15 0.04 0.70 0.06 0.36
Sign consistency 0.99 0.57 0.65 0.31 0.92 0.23 0.90
Source: Model simulations by the authors.
Note: The results correspond with equation (4.7) and represent the impacts of reforms on poverty
according to commodity sector. See the note to table 4.5 for simulation information.
crops include many of the tropical products of which developing countries
are net exporters. We report only foodgrains and feedgrains here, but an all-
commodity ranking on poverty friendliness, as we have used the term, would
appear as follows: foodgrains, other food and beverages, other crops, meats,
sugar, feedgrains, cotton, and dairy. In terms of the absolute size of the impacts
on poverty, the liberalization of other crops is the most significant, followed by
liberalization in foodgrains and then meats.
Summary and Conclusions
This chapter offers a complementary perspective to the detailed country case
studies of trade reform and poverty provided by others in this volume. By looking
at a wide range of developing countries in Africa, Asia, and Latin America, we
Poverty Impacts in 15 Countries: The GTAP Model 141
Table 4.10. Change in Poverty Caused by Foodgrain and
Feedgrain Reforms, 15 Countries
(percent change in the headcount)
Foodgrains Feedgrains
Export Domestic Export Domestic
Country Tariffs subsidies subsidies Total Tariffs subsidies subsidies Total
Bangladesh 0.02 0.02 0.03 0.07 0.17 0.00 0.01 0.16
Brazil 0.31 0.01 0.01 0.29 0.07 0.01 0.13 0.20
Chile 0.22 0.01 0.05 0.18 0.00 0.02 0.02 0.04
Colombia 0.14 0.00 0.02 0.11 0.12 0.03 0.14 0.06
Indonesia 0.18 0.01 0.08 0.09 0.05 0.01 0.04 0.00
Malawi 0.18 0.00 0.03 0.14 0.02 0.00 0.04 0.02
Mexico 0.16 0.00 0.15 0.00 0.44 0.01 0.92 0.48
Mozambique 0.13 0.01 0.02 0.10 0.17 0.00 0.01 0.17
Peru 0.11 0.01 0.04 0.07 0.05 0.02 0.04 0.01
Philippines 0.34 0.02 0.12 0.20 0.31 0.01 0.08 0.23
Thailand 5.45 0.03 0.15 5.63 1.09 0.00 0.18 0.91
Uganda 0.02 0.00 0.01 0.01 0.02 0.00 0.00 0.02
Venezuela, R.B. de 0.02 0.00 0.02 0.00 0.03 0.00 0.05 0.02
Vietnam 0.02 0.01 0.04 0.03 0.44 0.02 0.01 0.40
Zambia 0.01 0.00 0.00 0.01 0.05 0.00 0.01 0.04
Average 0.49 0.00 0.03 0.46 0.20 0.01 0.09 0.11
Absolute average 0.49 0.01 0.05 0.46 0.20 0.01 0.11 0.18
Sign consistency 1.00 0.17 0.54 0.99 1.00 0.60 0.82 0.57
Source: Model simulations by the authors.
Note: The results correspond with equation (4.7) and represent the impacts of reforms on poverty
according to type of reform in two commodity sectors. The totals are the sums across instruments and
reflect the total contribution of sector-specific reforms to the percent change in poverty headcounts
nationwide. See the note to table 4.5 for simulation information.
provide more general conclusions about the poverty impacts of commodity trade
reform. We find that, overall, trade reform tends to reduce poverty, and it does so
because of the agricultural trade reform components in the total package of
reforms. Indeed, nonagricultural trade reforms tend to increase poverty in most
of our focus developing countries.
We explore the relative poverty friendliness of agricultural trade reforms in
more detail by examining the differential impacts (relative to nonagricultural
reforms) on real after-tax factor returns and on poverty by stratum. Overall, the
more favorable impacts of agricultural reforms are driven by the increased factor
rewards for farm households, as well as the higher returns to unskilled wage labor,
which we have evaluated relative to the real cost of living at the poverty line.
142 Agricultural Price Distortions, Inequality, and Poverty
Finally, we have examined the poverty impacts of trade reform according to agri-
cultural commodity groups across our sample of countries. Not surprisingly,
foodgrain reforms are the most poverty-friendly group.
Notes
1. Note that, throughout this chapter, as with the rest of the book, nonagriculture includes highly
processed foods and beverages, while farm goods that require light processing before they may be
traded easily, such as rice, sugar, dairy products, and meat, are included in agriculture.
2. Specifically, the set of measures envisioned under the Doha Development Agenda focus on the
implementation of the July 2004 framework agreement of the Doha Development Agenda.
3. A clear limitation of this approach stems from the rigidity of the households' classification by
earnings specialization. Obviously, households may be induced to change their specialization or diver-
sify in response to changing relative factor returns. We believe that the relatively broad definition of the
seven strata circumvents this problem for the majority of households in the face of modest earnings
changes. However, this important qualification is considered in more detail in the subsequent results
section.
4. We make adjustments to the output subsidy ad valorem rate in instances in which the differ-
ence between the reported output subsidy rate in 2001 and the corresponding rate in 2004 is at least
1 percent. Most of the changes occur in the case of the United States, where the ad valorem output sub-
sidy for rice falls by 77 points and the subsidy for oilseeds falls by 24 points. The world prices for
oilseeds and rice were low in 2001 relative to 2004, and these commodities factor heavily in our
updates of protection in the OECD countries.
5. Francois, van Meijl, and van Tongeren (2005) introduce monopolistic competition in the manu-
facturing sector into their analysis of World Trade Organization reforms. The resulting variety and
scale effects generally boost the gains to rich countries and dampen the gains to poor countries gener-
ated by rich-country reforms. However, this makes their model less stable, and, given our focus on
agricultural reforms, this feature seems less critical.
6. Land in the GTAP Database is specific to agriculture. Therefore, we model imperfect mobility
across agricultural uses to represent the cost of converting land from one use to another.
7. More specifically, our global liberalization includes the removal of all border measures (export
subsidies and trade taxes) in all regions of the model and the removal of all input and output subsidies
in agriculture in the OECD and in those developing countries on which Anderson and Valenzuela
(2008) provide new information (see the text for explanations).
References
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London: Palgrave Macmillan; Washington, DC: World Bank.
Anderson, K., W. Martin, and D. van der Mensbrugghe. 2006. "Global Impacts of the Doha Scenarios
on Poverty." In Poverty and the WTO: Impacts of the Doha Development Agenda, ed. T. W. Hertel and
L. A. Winters, 497�528. London: Palgrave Macmillan; Washington, DC: World Bank.
Anderson, K., and E. Valenzuela. 2008. "Estimates of Global Distortions to Agricultural Incentives,
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Francois, J. F., H. van Meijl, and F. van Tongeren. 2005. "Trade Liberalization in the Doha Development
Round." Economic Policy 20 (42): 349�91.
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to Exogenous Shocks." Computational Economics 15 (3): 227�49.
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using Gempack." Computational Economics 9 (2): 83�127.
Hertel, T. W., ed. 1997. Global Trade Analysis: Modeling and Applications. New York: Cambridge
University Press.
Hertel, T. W., M. Ivanic, P. V. Preckel, and J. A. L. Cranfield. 2004. "The Earnings Effects of Multilateral
Trade Liberalization: Implications for Poverty." World Bank Economic Review 18 (2): 205�36.
Hertel, T. W., R. Keeney, M. Ivanic, and L. A. Winters. 2007. "Distributional Effects of WTO Agricul-
tural Reforms in Rich and Poor Countries." Economic Policy 22 (50): 289�337.
------. 2009. "Why Isn't the Doha Development Agenda More Poverty Friendly?" Review of Devel-
opment Economics. Wiley InterScience. http://www3.interscience.wiley.com/journal/122211891/
abstract (published online February 23).
Hertel, T. W., and L. A. Winters, eds. 2006. Poverty and the WTO: Impacts of the Doha Development
Agenda. London: Palgrave Macmillan; Washington, DC: World Bank.
Ivanic, M. 2006. "The Effects of a Prospective Multilateral Trade Reform on Poverty in Developing
Countries." In Poverty and the WTO: Impacts of the Doha Development Agenda, ed. T. W. Hertel and
L. A. Winters, 405�26. London: Palgrave Macmillan; Washington, DC: World Bank.
Keeney, R., and T. W. Hertel. 2005. "GTAP-AGR: A Framework for Assessing the Implications of Mul-
tilateral Changes in Agricultural Policies." GTAP Technical Paper 24 (August), Center for Global
Trade Analysis, Department of Agricultural Economics, Purdue University, West Lafayette, IN.
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1613�22.
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Developing Countries, 2004 and 1980�84." Research Memorandum 13 (December), Center for
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Part III
NATIONAL CGE
APPROACHES:
ASIA
5
China
Fan Zhai and Thomas W. Hertel
As the most populous nation in the world, China plays a critical role in the deter-
mination of the global poverty headcount. Indeed, a considerable portion of the
reduction in the headcount may be attributable to the remarkable reduction in
the incidence of poverty in China over the past two decades. Chen and Ravallion
(2004) find that, in 1981, 65 percent of the population in China was in extreme
poverty (US$1 a day), whereas by 2001, this share had fallen to around 12 percent.
They show that much of this poverty reduction was driven by reforms in the agri-
cultural sector. These advances notwithstanding, rural poverty continues to dom-
inate the national poverty headcount in China, and the headcount is highest
among households that are specialized in farming. Furthermore, there is evidence
that, despite rapid economic growth, the rural-urban wage gap is widening
(Sicular et al. 2007). Within the rural sector, rapid nonagricultural income growth
and slow agricultural income growth since the 1990s is contributing to increased
rural inequality (Benjamin et al. 2007). The agricultural sector therefore contin-
ues to play an important role in the determination of national poverty and
inequality in China. In this chapter, we focus on the impact on poverty and
inequality in China of agricultural, trade, and rural policy reforms both at home
and abroad.
The impact of trade reforms on poverty and inequality in China has been a
topic of intense research over the past decade, culminating in a number of studies
focusing on the impact on poverty of China's accession to the World Trade Organ-
ization (WTO) (for example, see Bhattasali, Li, and Martin 2004). Chen and
Ravallion (2004) examine these impacts at a highly disaggregate level using earn-
ings and price estimates from another study; they estimate that WTO accession
will benefit urban households, particularly poor urban households. However, they
predict that, as a result of the WTO accession, falling rural wages and increases in
147
148 Agricultural Price Distortions, Inequality, and Poverty
the consumer prices faced by rural households are likely to hurt the rural poor. In
a companion study, Hertel, Zhai, and Wang (2004) aggregate households to a
greater degree, but incorporate them directly into their computable general equi-
librium (CGE) model of China. They, too, conclude that WTO accession will be
relatively more favorable for urban households, but they argue that whether or
not rural households will lose from these reforms depends critically on the degree
of off-farm labor mobility. At low (or zero) mobility, as assumed by Chen and
Ravallion, the poorest rural households lose from reform, but, as the off-farm
labor supply elasticity rises, the potential for farm households to gain increases.
In closely related work, Hertel and Zhai (2006) contrast the impacts of com-
modity market reforms, such as those initiated through China's WTO accession,
with factor market reforms aimed at facilitating an improved flow of labor out of
agriculture and between the rural and urban markets. They find that the latter may
result in significant gains for rural households. Specifically, these authors explore
the implications of (1) reforming agricultural land markets to permit arm's-length
land rental in all rural areas, thereby facilitating the permanent movement of labor
out of farming; (2) enhancing off-farm labor mobility; and (3) abolishing the
hukou system, the household registration system in China, thereby reducing the
transaction costs imposed on rural-urban migrants. If combined, these reforms
reduce the estimated 2007 urban-rural income ratio from 2.58 (in the absence of
WTO accession) to 2.09. If WTO accession is added to this mix of policy reforms,
the 2007 urban-rural income ratio is still reduced, though not quite as much, to
2.12. Given the importance of labor market distortions to poverty and inequality
in China, we pay special attention in our study to their presence as well.
In this chapter, we update the model used in earlier studies (to reflect the most
recent social accounting matrix for China) and capitalize on the most recent esti-
mates of agricultural price distortions, which have changed significantly since
China's accession to the WTO. We also bring to bear new farm price distortion
estimates for other developing countries to assess the impact of global trade
reform on poverty and inequality in China.1 Unlike our earlier work (see below),
which focused solely on China's own reforms associated with WTO accession, we
explore here the impacts of reforms in the rest of the world, as well as in China.
Additionally, we decompose these impacts in two ways: first, by region (China
versus the rest of the world) and, second, by sector (agriculture versus nonagricul-
ture). We also examine the interplay between these commodity market reforms
and the factor market reforms aimed at improving the allocation of labor within
the Chinese economy.
This chapter is organized as follows. The next section describes the speci-
fication of the CGE model used in this study. We then assess the effects on the
macroeconomy, agricultural production, and poverty in China that are generated
China 149
by the elimination of agricultural price distortions in the rest of the world, as well
as by the reduction of China's own distortions in commodity and factor markets.
The final section offers conclusions.
The Model and the Data
The Chinese model we use is an updated version of the household-disaggregated
CGE model that we have relied on previously to study the economic and poverty
effects in China of WTO accession and Doha Round trade liberalization (see
Hertel, Zhai, and Wang 2004; Hertel and Zhai 2006; Zhai and Hertel 2006). The
model has intellectual roots in the group of single-country applied general equi-
librium models used over the past two decades to analyze the impact of trade
policy reform. The updated version in this chapter has a more recent benchmark
data set based on the 2002 input-output table (NBS 2006) and a detailed sectoral
disaggregation for agriculture and food. In this section, we describe the main
features of the model.
Household behavior
Following our previous work, we disaggregate rural and urban households into
40 rural and 60 urban representative households according to primary source
of income and relative income level. In light of the fact that our focus here is on
agricultural incentives, we seek to highlight those households that depend exclu-
sively on farming for their incomes. Accordingly, we stratify the rural households
by agriculture-specialized households (more than 95 percent of household
income is obtained through farming) and diversified (all other sources of
income). We are also interested in the impacts of restrictions on rural-urban labor
mobility; so, we separately identify urban households and group them into three
strata: transfer-specialized, labor-specialized, and diversified households. Within
each stratum, we order households from poorest to richest based on per capita
income and then group them into 20 vingtiles, each containing 5 percent of the
stratum population.
Household income is derived from labor income, the profits from family-owned
agricultural and nonagricultural enterprises, property income, and transfers. House-
holds consume goods and services according to a preference structure determined by
the extended linear expenditure system. Through the specification of a subsistence
quantity of each good or service, this expenditure function generates nonhomothetic
demands, whereby the greater the relative importance of subsistence consumption to
a household (for example, it would be high for rice, but low for automobiles), the
more income inelastic the demand of the household for that good.
150 Agricultural Price Distortions, Inequality, and Poverty
The other important dimension of household behavior is the supply of labor
for off-farm activities. In China, the off-farm labor supply decision is complicated
by institutional factors that have been built into the system to keep the agricul-
tural population in place; among these, the rural land tenure system is one of the
most widely discussed (Zhao 1999a). The absence of well-defined land tenure has
served to raise the opportunity cost of leaving the farm (Yang 1997). Households
that cease to farm the land may lose the rights to the land; so, they have a strong
incentive to continue some level of agricultural activity even if profitability is
quite low (Zhao 1999b). Because of only modest growth in rural nonfarm activi-
ties, this seriously limits the ability of households to obtain off-farm work (Zhao
1999a).2 Although an active land rental market has emerged in some regions in
recent years, the overall level of land rental transactions is still low; only around
10�15 percent of rural households rent land out or are land renters (Deininger
and Jin 2005, 2007; Wang, Herzfeld, and Glauben 2007). Empirical studies have
found that the transaction costs associated with land rental are significant, and the
absence of an efficient land rental market remains a substantial barrier to the
facilitation of off-farm labor participation by rural labor (Deininger and Jin 2005;
Wang, Herzfeld, and Glauben 2007).
In this chapter, we model rural households as if they maximize the total return
to their labor supply, which is offered on both the on-farm and the off-farm labor
markets. However, the ability of households to shift labor between these two labor
markets is constrained by a number of factors, including education, experience,
and simple geography, which may isolate farm households from the nonfarm
labor market. We proxy the combined impact of these factors through a single,
finite, constant elasticity of transformation. The labor allocation between farm
and off-farm jobs is determined by the ratio of the shadow value of labor in agri-
culture relative to the off-farm wage rate and this elasticity of transformation.3
The constant elasticity of transformation parameter governs the off-farm labor
supply elasticity, for which we adopt the estimate of 2.67 of Sicular and Zhao
(2004) as the overall farm�off-farm transformation elasticity for the total rural
labor force. The empirical study by Zhang, Huang, and Rozelle (2002) suggests
that this elasticity increases by 0.58 for an additional year of schooling. This is
translated into the farm�off-farm transformation elasticity of 0.68 for unskilled
labor and 4.01 for semiskilled labor.4
Owing to the absence of an effective land market, the shadow value of labor in
agriculture in this function takes into account the potential impact that reducing
agricultural employment would have on the household's claim to farmland. This
incremental factor is calculated as the marginal value product of land, multiplied
by the probability that the household will lose its land as a result of off-farm migra-
tion. To make this amenable to use in a model of a representative farm household
China 151
with continuously variable labor and land use, we translate the probability into a
simple elasticity of land income with respect to on-farm labor. The higher this
elasticity, the greater the probability that the farm household will lose its land if the
farmer shifts to an off-farm job. The benchmark elasticity in our model is 0.5, that
is, a 10 percent reduction in on-farm work results in a 5 percent loss of land
income. However, for purposes of sensitivity analysis, we also report results from
two extreme simulations. In the first, the elasticity of land income with respect to
off-farm work is zero. This is the case of a perfectly functioning land rental market
with no chance of land loss. In the second sensitivity analysis, the elasticity is set
equal to 1, such that the farmer leaving the farm to work in the city is virtually guar-
anteed to lose his land. By comparing these two extremes, we gain an appreciation
of the importance of land market reform in addressing inequality.
Rural-urban migration
Migration is a key part of the rural economy in China. According to rural household
survey data collected in 2003 and compiled by Liu, Park, and Zhao (2006), 19.4 per-
cent of all rural workers participated in migratory work in 2003, and more than
40 percent of all households had at least one member who was a labor migrant in
that year. More than half the migrants had left their provinces, and most of these had
migrated to the coastal provinces, where manufacturing activity and exports have
been booming. The 2000 census estimated that the total number of migrants in
China was 131 million, of which nearly two-thirds were non-hukou migrants.
(Households lacking the hukou urban registration face limited access to many urban
amenities, including housing and education.) Rural-urban migration was the largest
form of migration and accounted for more than 50 million people in the 2000 cen-
sus (Cai, Park, and Zhao 2007). This massive migration is a rational response to the
enormous rural-urban wage gap that exists in China, which Sicular et al. (2007)
recently placed at 2.27 (the ratio of urban to rural per capita disposable income in
2002) after adjusting for housing subsidies and spatial price differences. Sicular et al.
find no evidence of this gap declining. Indeed, the ratio of urban to rural incomes
appears to have risen slightly between 1997 and 2002. This is hardly the outcome that
a standard general equilibrium model with perfect labor mobility would predict.
Clearly, there are important barriers to labor movement in China that need to be
considered if one hopes to assess accurately the impact of commodity market
reforms on rural and urban employment, wages, and household incomes.
While the rural-urban per capita income gap is an indication of a potential
labor market distortion, we are more interested in discovering the hourly wage
differential among workers of comparable skill and ability. If there were no barri-
ers to the movement of labor between rural and urban areas, we would expect real
152 Agricultural Price Distortions, Inequality, and Poverty
wages to be equalized for an individual worker with given characteristics. In an
exploration of the issue of rural-urban inequality in greater detail in nine provinces
using the China Health and Nutrition Survey, Shi, Sicular, and Zhao (2002) con-
clude that the apparent labor market distortion is about 42 percent of the rural-
urban labor income differential and 48 percent of the hourly earnings differential.5
Applied to their estimated average wage differential, this amounts to an ad valorem
apparent transactions tax rate on rural wages of 81 percent.6
We model these transaction costs as real costs that are assumed by the tempo-
rary rural migrants who move to urban areas without possessing hukou urban
registrations. Of course, these migrants are heterogeneous, and the extent of the
burden varies widely. Individuals who are single and live close to the urban area
in which they are working are likely to experience minor inconvenience as a result
of the temporary migration. We expect them to be the first to migrate (all else
being equal) in response to higher urban wages. Other migrants have large fami-
lies and must travel a great distance. Their urban living conditions are often poor,
and, among these migrants, it is not uncommon to be robbed on their return
home by train after their jobs. For such individuals, the decision to migrate tem-
porarily is likely to be difficult, and they may or may not choose to repeat the
experience. With this heterogeneous population in mind, we postulate a transac-
tion cost function in which the cost increases in proportion to the increase in the
rural population engaged in temporary work. This transaction cost function has a
simple constant elasticity functional form, which begins at the origin, reflecting
those migrants for whom there is essentially no cost because of their proximity to
urban areas, and reaches the observed wage gap (adjusted for transportation and
living costs) at the current level of temporary migration (about 70 million work-
ers). We assume that additional increases in temporary migration have only a
modest impact on these transaction costs.7 Finally, only a portion of these
observed transaction costs may be attributed to the government's formal policy of
migration restriction, the hukou system. Indeed, Shi (2002) finds that only
28 percent of the rural-urban wage difference may be explained directly by the
coefficient on the hukou registration variable. We take this into account elsewhere
below in our investigation of prospective labor market reforms by the Chinese
government.
Production and trade
Production in each sector of the economy is modeled using nested constant elas-
ticity of substitution functions, and constant returns to scale are assumed. In the
top level of the nest, value added and a composite of intermediate inputs produce
outputs. Then, another constant elasticity of substitution function disaggregates
China 153
the value added into a capital-labor composite and agricultural land. The capital-
labor composite is split into the capital�skilled labor composite and aggregated
less-skilled labor. The capital�skilled labor composite consists of capital and
skilled labor, while aggregated less-skilled labor is composed of semiskilled labor
and unskilled labor. A low substitution elasticity of 0.3 between capital and skilled
labor is assumed to introduce the capital-skill complementarity. The elasticity of
substitution between semiskilled labor and unskilled labor is set at 1.5 based on
estimates for the United States by Katz and Murphy (1992) and Heckman,
Lochner, and Taber (1998).
Each sector employs a labor composite comprising both rural and urban labor
that substitute imperfectly. This is an indirect means of building a geographic fla-
vor into the model because some sectors will be located largely in urban areas,
while others will be predominantly in rural areas. By limiting the substitutability
of rural and urban labor in each sector, we are able to proxy the economic effect of
geographically distributed activity. Ideally, we would model the geographic distri-
bution of industrial activity, but the data do not exist to support this split.
All commodity and factor markets are assumed to clear through prices. In the
case of rural labor markets, there is a segmentation between agricultural and
nonagricultural labor: these two markets are linked imperfectly through the con-
stant elasticity of transformation parameter (see elsewhere above). Once the
transaction costs associated with temporary migration are accounted for, rural
wages are equated with urban wages. Capital is assumed to be fully mobile across
sectors. Import demand is modeled using the Armington assumption, that is,
domestic products are assumed to be differentiated from foreign products. On the
export side, it is assumed that firms treat the domestic and export markets equally.
Thus, the law of one price holds, that is, the export price is identical to the price of
domestic supply. The small country assumption is assumed for imports; so, world
import prices are exogenous in terms of foreign currency. Exports are demanded
according to constant elasticity demand curves. The terms of trade for China are
therefore endogenous in the simulations. The value of export demand and the
Armington elasticities are based on the elasticities used in the global CGE Linkage
model (van der Mensbrugghe 2005).
The benchmark data
A social accounting matrix is estimated for China for the year 2002 to serve as the
benchmark data set for model calibration. The matrix contains 48 sectors of
production and 100 representative households based on 2000 household survey
data for three provinces (Guangdong, Liaoning, and Sichuan) and the 2002 input-
output table (NBS 2006). Because the 2002 input-output table has only one crop
154 Agricultural Price Distortions, Inequality, and Poverty
sector and one livestock sector, we disaggregate these two sectors into eight crop
sectors and four livestock sectors according to the corresponding Global Trade
Analysis Project sector classification (Hertel 1997). The information on the struc-
ture of production, demand, inputs, and trade in the Global Trade Analysis Project
Database, version 7 are used for the sectoral disaggregation, and we employ the
cross-entropy method to balance the social accounting matrix (see Robinson,
Cattaneo, and El-Said 2001).
The base year tariffs and export subsidies and taxes are reported in table 5.1.
Protection rates for lightly processed food and agricultural products have been
obtained from Huang et al. (2009) and from the Global Trade Analysis Project
Database, version 7. For other primary goods and manufacturing products, tariffs
are estimated based on the collected revenue from import tariffs and base year
imports by commodity. As shown in table 5.1, China's tariff structure provides
more protection for food and agricultural products than for nonfood manufacturing
Table 5.1. The Sectoral Structure of GDP, Trade, Import Tariffs,
and Export Subsidies, China, Around 2004
(percent)
Tariff Export GDP Export Import
Product rate subsidy ratea share share share
Agriculture 6.5 0.8 13.4 1.6 2.5
Paddy rice 0.0 1.0 0.8 0.0 0.0
Wheat 4.0 0.0 0.3 0.0 0.1
Other grains 3.4 13.0 0.3 0.1 0.0
Vegetables and fruits 14.8 0.0 5.5 0.7 0.1
Oilseeds 15.9 0.0 0.4 0.1 0.7
Sugarcane and beets 15.3 0.0 0.0 0.0 0.0
Plant-based fibers 5.3 0.0 0.2 0.0 0.3
Other crops 9.4 0.0 0.1 0.3 0.0
Cattle, sheep, and so on 3.9 0.0 0.3 0.0 0.0
Other livestock 0.0 0.0 2.7 0.1 0.2
Raw milk 0.0 0.0 0.1 0.0 0.0
Wool 7.0 0.0 0.1 0.0 0.1
Forestry 2.8 0.0 1.3 0.0 0.9
Fishing 5.2 0.0 1.3 0.1 0.0
Mining 0.7 0.0 4.9 1.5 6.2
Coal mining 3.1 0.0 1.9 0.5 0.1
Crude oil and natural gas 0.9 0.0 1.9 0.4 4.1
Ore mining 0.0 0.0 0.5 0.1 1.4
Other mining 0.5 0.0 0.6 0.5 0.7
(Table continues on the following page.)
China 155
Table 5.1. The Sectoral Structure of GDP, Trade, Import Tariffs,
and Export Subsidies, China, Around 2004 (continued)
(percent)
Tariff Export GDP Export Import
Product rate subsidy ratea share share share
Food manufacturing 5.0 0.0 3.8 3.0 2.0
Meat products 10.5 0.0 0.2 0.5 0.3
Vegetable oils 12.5 0.0 0.3 0.1 0.5
Grain, milled 0.0 1.0 0.2 0.0 0.0
Sugar, refined 17.3 0.0 0.1 0.0 0.1
Forage 11.5 0.0 0.2 0.0 0.0
Prepared fish products 0.9 0.0 0.2 1.0 0.7
Other processed food 9.4 0.0 0.8 1.1 0.3
Beverages 12.7 0.0 0.6 0.2 0.1
Tobacco products 8.9 0.0 1.1 0.1 0.1
Nonfood manufacturing 2.9 0.0 28.6 74.9 80.5
Textiles 0.2 0.0 2.0 9.1 4.5
Apparel and leather 0.2 0.0 1.5 9.3 1.6
Sawmills and furniture 1.9 0.0 0.9 2.2 0.7
Paper, printing, and so on 3.0 0.0 2.0 3.3 2.0
Petroleum refining 3.6 0.0 0.9 0.9 1.5
Chemicals 3.3 0.0 4.9 7.3 13.0
Building materials 2.7 0.0 1.6 1.4 0.7
Metals 1.7 0.0 3.1 1.5 5.9
Metal products 2.1 0.0 1.2 3.6 2.0
Machinery 3.3 0.0 3.1 4.4 11.6
Transport equipment 16.4 0.0 2.1 2.2 3.7
Electrical machinery 2.9 0.0 1.5 6.8 6.2
Electronics 1.4 0.0 2.3 16.6 20.7
Instruments 2.1 0.0 0.4 5.0 6.0
Other manufacturing goods 0.7 0.0 1.2 1.4 0.4
Utilities, construction, services 0.0 0.0 49.3 19.0 8.8
Sources: Huang et al. (2009), drawing on version 7 of the Global Trade Analysis Database and the social
accounting matrix for China for 2002.
a. Negative numbers indicate the existence of an export tax.
goods. Moreover, the import tariff rates show considerable cross-sectoral variation
within agriculture: vegetables and fruits, oilseeds, and sugarcane and beets have
high tariff rates of around 15 percent, while imports of plant-based fibers appear to
be effectively subsidized.
Table 5.1, columns 3�5 present China's sectoral shares of GDP, exports, and
imports. Despite the diminishing importance of the agricultural sector in the
Chinese economy over the last two decades, agriculture still accounts for 13.4 percent
156 Agricultural Price Distortions, Inequality, and Poverty
of GDP. Vegetables, fruits, and livestock are key sources of agricultural value added.
Manufacturing value added represents 32 percent of the economy-wide total. Chem-
icals, metals, and machinery lead the way, followed by sectors related to electronics,
textiles, and apparel. Nonfood manufacturing is export intensive, accounting for
75 percent of Chinese exports. Electronics, textiles, apparel, chemicals, and machin-
ery are the major exporting sectors. These sectors also represent a relatively large
share of imports, reflecting the significant presence of the processing trade in China.
Meanwhile, China's agricultural and food manufacturing sectors have limited trade
exposure. Agriculture accounts for only 1.6 percent of exports and 2.5 percent of
imports. Vegetables and fruits are major agricultural exports, while agricultural
imports are concentrated in oilseeds, cotton, and forestry products.
Simulation Design
To explore the implications for the Chinese economy of agricultural distortions at
home and abroad, we consider six policy reform scenarios that eliminate various
distortions in global trade and in China's domestic commodity and factor mar-
kets. These scenarios are summarized in table 5.2. The first two scenarios examine
the effects of trade liberalization in the rest of the world. ROW-Ag considers the
impact of agricultural liberalization in the form of the elimination of import
tariffs and export subsidies, as well as subsidies for domestic production, in the
agricultural and lightly processed food sectors in the rest of the world. The second
scenario involving the rest of the world policies across all merchandise commodi-
ties (ROW) looks at more broadly based trade liberalization. It combines the
removal of policy distortions in the agricultural and lightly processed food sectors
in scenario 1 and the elimination of tariffs on nonagricultural goods in the rest of
the world.
We incorporate the impacts of trade reforms in the rest of the world in the
Chinese CGE model through exogenous changes in import prices and export
demands. The sizes of these exogenous trade shocks are obtained from the global
CGE Linkage model, omitting China's reforms in the process. Table 5.3 lists the
external shocks imposed in the ROW and ROW-Ag scenarios.8 It shows that
enormous percent increases in China's agricultural and food export volumes
(export demand) are generated by the elimination of high rates of protection
elsewhere in Asia. Rice, other grains, vegetables and fruits, and refined sugar all
show large proportionate increases. Of course, the associated changes in output
volume are often quite modest because China is not a large exporter of most of
these products (see table 5.1). China's export volume declines in most livestock
subsectors, reflecting the relatively smaller ROW barriers faced by China's
exporters in these subsectors.
China 157
Table 5.2. Modeled Liberalization Scenarios, China
Scenario Description
Agricultural liberalization in the rest of the world
ROW-Ag � Elimination of production taxes and subsidies in the agricultural
and lightly processed food sectors
� Elimination of export taxes and subsidies in the agricultural and
lightly processed food sectors
� Elimination of import tariffs in the agricultural and lightly
processed food sectors
All merchandise trade liberalization in the rest of the world
ROW � Elimination of production taxes and subsidies in the agricultural
and lightly processed food sectors
� Elimination of export taxes and subsidies in the agricultural and
lightly processed food sectors
� Elimination of import tariffs in all sectors
Agricultural liberalization in China
DOM-Ag � Elimination of export taxes and subsidies in the agricultural and
lightly processed food sectors
� Elimination of import tariffs in the agricultural and lightly
processed food sectors
All merchandise trade liberalization in China
DOM � Elimination of export taxes and subsidies in the agricultural and
lightly processed food sectors
� Elimination of import tariffs in all sectors
Relaxation of the hukou system
LABOR � Cut the indirect transaction costs from 81 to 34 percent of the
nonfarm rural wage
Introduction of land reform
LAND � Farm households do not include the returns to land in decisions
to migrate temporarily
Source: Specifications of the authors.
In the case of broadbased trade reform, the average export price in China
increases by 2.4 percent, while the average import price increases by only 0.6 per-
cent (both relative to the price of the manufacturing exports of members of the
Organisation for Economic Co-operation and Development), indicating a gain in
the country's terms of trade if other countries liberalize and China does not.
However, given the relatively greater importance to China of manufacturing
exports, if liberalization is confined to the agricultural sector, the improvement
in the terms of trade diminishes; the average increase in the export price is only
0.6 percent, and the average increase in the import price is only 0.4 percent.
158 Agricultural Price Distortions, Inequality, and Poverty
Table 5.3. Exogenous Demand and Price Shocks Caused by
Liberalization in the Rest of the World, China
(percent change)
Elimination of all trade Elimination of agricultural
distortions in ROW distortions in ROW
Export Export Import Export Export Import
Sector, products demand prices prices demand prices prices
Agriculture
Paddy rice 94.9 4.2 n.a. 123.6 0.0 1.8
Wheat 15.5 3.5 2.8 45.8 1.4 3.6
Other grains 105.1 3.9 6.5 157.7 1.6 6.5
Vegetables and fruits 185.5 4.2 1.9 232.9 1.8 1.6
Oilseeds 10.3 4.0 2.8 42.9 1.7 2.3
Sugarcane and beets n.a. n.a. n.a. n.a. n.a. n.a.
Plant-based fibers 30.0 3.3 10.0 51.4 1.3 11.5
Other crops 12.7 4.5 1.3 8.4 2.0 1.5
Cattle, sheep, and so on 18.6 4.4 6.5 3.1 1.9 6.6
Other livestock 20.8 3.8 0.7 0.2 1.6 1.6
Raw milk 48.3 4.1 1.8 31.7 1.7 0.7
Wool 13.1 3.8 4.9 10.1 1.6 4.9
Other primary products 7.8 2.7 0.5 2.0 0.6 1.1
Lightly processed food
Meat products 29.2 3.5 4.9 56.3 1.3 5.6
Vegetable oils 6.4 1.8 0.2 5.7 0.3 0.9
Grain, milled 148.8 3.0 4.2 192.1 0.9 3.4
Sugar, refined 410.2 3.0 1.4 560.4 0.8 2.0
Highly processed food 67.3 2.9 0.8 14.1 0.8 0.2
Nonfood manufacturing
Textiles; apparel and leather 13.7 2.6 0.2 2.1 0.8 0.4
Other manufacturing sectors 3.3 2.2 0.7 1.6 0.5 0.3
Services 10.5 2.5 0.1 0.9 0.5 0.2
Total 2.2 2.4 0.6 0.3 0.6 0.4
Source: Linkage model simulations by van der Mensbrugghe, Valenzuela, and Anderson (see the
appendix).
Note: n.a. not applicable.
The increase in sectoral export prices range from 1.8 to 4.5 percent in the case
of broadbased trade liberalization and from 0.3 to 2.0 percent in the case of
agricultural liberalization only; food and agricultural prices rise relative to non-
food prices in both cases.
China 159
The changes in China's import prices show much greater sectoral variation.
The import prices of most food and agricultural products rise more than the
import prices of nonfood products, reflecting the elimination of agricultural sub-
sidies in the countries of the Organisation for Economic Co-operation and Devel-
opment. However, the world price of China's oilseed imports declines by 2.8 and
2.3 percent, respectively, in these two scenarios, largely because of the elimination
of the high export taxes on soybean exports from Argentina; in the wake of the
elimination of these export taxes, the soybean exports become a dominant source
of oilseed imports into China.
The next two scenarios focus on the impacts of China's own trade liberaliza-
tion. Scenario 3 (DOM-Ag) eliminates the import tariffs and export taxes and
subsidies on China's agricultural goods and lightly processed foods. In scenario
4, DOM, the tariff elimination is extended to nonagricultural sectors. These two
scenarios are intended to show the effects of distortions in China's factor mar-
kets. Scenario 5 (LABOR) examines the impact of a relaxation of the hukou sys-
tem such that the ad valorem tax equivalent of the indirect transaction costs are
reduced from 81 to 34 percent (if evaluated at current levels of migration). This
is the portion of the observed differential in wages that has been directly attrib-
uted to possession of a hukou certificate (see elsewhere above). In scenario 6
(LAND), we consider the impact of the relaxation of one of the important bar-
riers to off-farm labor mobility, namely, the absence of well-defined property
rights for agricultural land. The existence of this barrier leads to the retention of
additional labor in the farm sector. The reason for this is that farm households
presently tend to include the returns to communal land in their decision to
work on- or off-farm because leaving the farm means potentially forgoing the
rights to the farmland. Scenario 6 introduces a land reform whereby farm
households migrating to cities may keep full land returns by renting their land
out; they thereby only need to consider the ratio of the marginal value of the
products of their labor in agriculture to their nonfarm rural wages in deciding
where to work.
In all six scenarios, government real spending and real savings (deflated by
the GDP deflator) are fixed at the levels of the base year. Thus, the policy
reforms are assumed to be revenue neutral and associated with a unified,
endogenous factor income tax designed to replace lost government tariff rev-
enue. The goal of this tax replacement closure is to avoid unrealistic macroeco-
nomic effects of tariff removal, while exerting a relatively neutral impact on
inequality. Foreign savings are also fixed in foreign currency terms, and the real
exchange rate adjusts endogenously to maintain the current account balance.
Total investment is endogenously adjusted according to changes in the savings
levels of households and enterprises.
160 Agricultural Price Distortions, Inequality, and Poverty
The Impacts of the Reforms on China's Economy
In this section, we examine the impacts of the six scenarios on the macroeconomy,
on poverty and inequality, and on households and sectors.
Macroeconomic effects
The macroeconomic outcomes of these simulations are reported in table 5.4. We
begin by focusing on the two scenarios of broadbased commodity trade liberaliza-
tion (ROW and DOM, reported in the first two columns). The elimination of
trade distortions in all commodity sectors gives a substantial boost to trade in
China; exports and imports rise by more than 5 percent in the unilateral liberal-
ization scenario and by 2�4 percent in the scenario of trade liberalization in the
rest of the world.
Aggregate welfare effects, which we proxy using the sum of the equivalent vari-
ation of individual households and a representative firm, are generally quite small,
as one would expect in a model with fixed endowments, perfect competition, and
constant returns to scale.9 The composite equivalent variation is projected to
increase by 0.5 percent of GDP in the case of trade liberalization in the rest of the
world because of improved terms of trade. In contrast, there is a small deteriora-
tion in welfare under unilateral liberalization because of a deterioration in China's
terms of trade. This reflects China's relatively limited import protection following
the WTO accession, as well as the country's growing influence in world export
markets, in which trade expansion tends to depress export prices.
At fixed labor endowments and capital stocks and on the assumption of fixed
unemployment and no productivity changes, real GDP changes little under both
trade liberalization scenarios. The small decrease under ROW is driven by the ensu-
ing labor reallocation from nonagriculture to agriculture. The stronger demand for
China's agricultural exports following the elimination of trade barriers in the rest of
the world diverts the labor force from high-productivity manufacturing sectors to
lower-productivity agricultural subsectors.10 As a consequence, real GDP declines
slightly. This contrasts with the case of China's unilateral trade liberalization,
whereby the elimination of the relatively higher import protection in the agricul-
tural sector encourages the movement of the labor force from the rural agricultural
sector to urban nonagricultural activities, leading to an increase in GDP.
As the bottom section of table 5.4 indicates, temporary migration from the
rural to urban sectors slows down as a result of the trade liberalization in the rest
of the world; this boosts the economic prospects in agriculture. Under ROW, there
are about 5.9 million fewer rural-urban migrants in the new equilibrium relative
to the base year. The larger rural labor force is mainly generated by the retention
China 161
Table 5.4. Aggregate Simulation Results of Prospective
Liberalizations, China
Indicator ROW DOM ROW-Ag DOM-Ag LABOR LAND
Macroeconomy, % change
Welfare, equivalent variation 0.5 0.1 0.04 0.01 1.0 0.1
Real GDP 0.1 0.2 0.2 0.1 0.8 0.3
Exports 1.9 5.8 0.3 0.7 1.6 0.6
Imports 4.3 5.5 0.1 0.7 1.4 0.6
Terms of trade 1.8 0.8 0.3 0.1 0.3 0.1
Consumer price index 2.9 0.9 1.0 0.3 1.4 0.7
Factor prices, %
Return to agricultural land 16.3 3.5 13.5 3.1 7.3 2.5
Return to capital 2.2 0.8 0.0 0.0 1.5 0.6
Unskilled wages
Urban 3.7 1.1 1.2 0.3 17.7 3.1
Rural nonagricultural 3.9 1.3 1.3 0.4 6.9 3.9
Agricultural 4.4 1.8 1.3 0.4 23.7 8.8
Semiskilled wages
Urban 3.9 1.2 1.3 0.3 5.4 3.1
Rural nonagricultural 4.9 1.1 2.2 0.4 25.5 4.5
Agricultural 2.7 1.1 0.0 0.0 20.1 11.7
Skilled wages
Urban 1.9 0.9 0.0 0.0 0.9 0.3
Rural nonagricultural 1.9 1.0 0.1 0.0 0.9 0.2
Labor force, millions
Farm labor 6.4 1.6 5.7 1.5 27.9 13.2
Unskilled 0.7 0.2 0.6 0.2 15.6 1.8
Semiskilled 5.7 1.4 5.1 1.3 12.3 11.3
Rural-urban temporary migration 5.9 1.5 5.3 1.4 35.7 12.1
Unskilled 0.6 0.1 0.5 0.1 18.2 1.5
Semiskilled 5.3 1.3 4.8 1.3 17.6 10.6
Skilled 0.0 0.0 0.0 0.0 0.0 0.0
Labor force, %
Farm labor 1.7 0.4 1.6 0.4 7.6 3.6
Unskilled 0.4 0.1 0.4 0.1 9.8 1.2
Semiskilled 2.7 0.7 2.4 0.6 5.9 5.4
Rural-urban temporary migration 6.0 1.5 5.4 1.4 36.5 12.3
Unskilled 1.5 0.4 1.4 0.4 46.7 3.9
Semiskilled 10.4 2.6 9.3 2.5 34.3 20.6
Skilled 0.0 0.0 0.0 0.0 0.0 0.0
Source: CGE model simulations of the authors.
162 Agricultural Price Distortions, Inequality, and Poverty
of additional on-farm labor (increased by 6.4 million) in the ROW scenario. In
contrast, China's unilateral trade liberalization accelerates off-farm migration;
about 1.5 million to 1.6 million workers would leave agriculture and migrate to
urban areas (relative to the baseline).
Table 5.4 also reports changes in factor prices. It is clear from these that trade
liberalization in the rest of the world favors unskilled and semiskilled labor over
skilled labor and rural labor over urban labor. This is caused by the relative
increase in the demand for agricultural exports and the rise in the prices of com-
peting agricultural imports. Agricultural profitability in China is also boosted by
the trade reforms in the rest of the world; this is reflected in the rise in the returns
to agricultural land under ROW. The returns to capital and skilled wages increase
less than the consumer price index, which rises by 2.9 percent under ROW. This
pattern of changes in factor prices contrasts sharply with the pattern emerging
after China's unilateral liberalization, whereby the returns to capital and skilled
wages increase the most relative to the consumer price index, while the returns to
agricultural land decrease the most.
In the macroeconomic results reported in table 5.4, columns 3 and 4, namely,
the results stemming from the liberalization of the agricultural and lightly
processed food sectors only, we see that agricultural liberalization has only modest
impacts on aggregate exports and imports, reflecting the minor role of the agri-
cultural and food sectors in China's total trade (see table 5.1). Consequently,
China's welfare gains from agricultural liberalization are trivial, ranging from 0.01
to 0.04 percent of GDP. In contrast to unilateral trade liberalization in all sectors,
China's unilateral agricultural liberalization leads to an aggregate gain in equiva-
lent variation of 0.01 percent of GDP, mainly because of much smaller losses in
the terms of trade. The changes in factor prices induced by agricultural liberaliza-
tion show patterns that are similar to the patterns resulting from broadbased
commodity trade liberalization, that is, agricultural liberalization in the rest of the
world would favor unskilled and semiskilled labor, as well as agricultural land, in
China, while China's unilateral agricultural reforms would favor the capital and
skilled labor that are intensively employed in the relatively lightly protected man-
ufacturing sectors. The changes in off-farm employment and rural-urban migra-
tion under the two agricultural liberalization scenarios are comparable to the cor-
responding changes in the broadbased trade liberalization scenarios, indicating
the dominant role played by distortions in the agricultural subsectors in deter-
mining the mobility of the rural labor force in China.
Relative to the reduction in China's trade distortions, the labor market reforms
investigated in scenarios LABOR and LAND generally have larger impacts on wel-
fare, GDP, and other macroeconomic aggregates. This reflects the large and per-
sistent rural-urban distortions in China's labor markets. It is evident from the last
China 163
two columns of table 5.4 that both the factor market reforms serve to increase
migration out of the relatively low-productivity agricultural sector into the
higher-productivity nonagricultural sectors and from the rural to the urban econ-
omy. In the case of land reform, 13.2 million additional workers leave agriculture
after they have been assured that they will retain ownership of their land even if
they migrate (see the farm labor row in table 5.4, final column). These individuals
initially migrate to the off-farm rural labor market, which releases an additional
12.1 million temporary rural migrants to the urban sector to restore equality in
rural and urban wages, net of transaction costs. The release of workers from agri-
culture tends to depress wages in the rural nonfarm economy, where wages fall by
3.9 percent in the case of land reform. This wage drop plays a role in dampening
the out-migration from agriculture.
While the LAND reform scenario focuses on the barriers to the off-farm
mobility of labor, the LABOR scenario focuses on rural-urban migration. If the
transaction costs associated with temporary migration are reduced because of the
elimination of the hukou system, rural-urban migration expands by 35.7 million
workers. Because the transaction costs associated with temporary rural-urban
migration operate, in effect, as a tax on rural labor, the first impact of their reduc-
tion is to increase the supply of rural labor to the urban economy, thereby boost-
ing rural wages and depressing urban wages. This represents a redistribution
of the rents associated with the hukou system from urban to rural households.
In addition, by raising rural wages, this hukou reform scenario also draws
27.9 million additional workers out of agriculture.
Poverty and inequality impacts
Because poverty and income distribution are central to our study, we provide sev-
eral related measures of inequality and poverty in table 5.5. The first column in
the table reports the initial level of each indicator in our database, while subse-
quent columns report changes or percentage changes in the indicators. The initial
urban-rural income ratio, at 3.5, is higher than the ratios in some of the studies
cited elsewhere above that are based on household surveys. This is largely because
of our inability to adjust for spatial price variations which, if taken fully into
account, would reduce the ratio considerably. The initial Gini coefficient in our
model, 0.442, is heavily influenced by rural-urban income disparity. This estimate
is also consistent with the recent work of Benjamin et al. (2007), who identify
limitations with many of the existing estimates of inequality and place the Gini in
the 0.4�0.5 range.
Using the US$2-a-day poverty line and the 1993 purchasing power parity
exchange rate, the World Bank estimates that 58.1 percent of the rural population
164
Table 5.5. The Effects of Prospective Liberalizations on Income Inequality and Poverty, China
Indicator Base ROW DOM ROW-Ag DOM-Ag LABOR LAND
Inequality
Urban/rural income ratio 3.538 0.052 0.009 0.042 0.010 0.303 0.167
Gini coefficient 0.442 0.005 0.001 0.004 0.001 0.021 0.008
Urban 0.259 0.000 0.000 0.001 0.000 0.006 0.003
Rural 0.315 0.002 0.000 0.002 0.000 0.008 0.003
Poverty headcount, US$2 a day Ratio, % Changes, percentage points
Total 36.4 1.2 0.3 0.8 0.2 4.1 1.0
Urban 2.5 0.1 0.0 0.0 0.0 0.7 0.3
Transfer specialized 0.5 0.0 0.0 0.0 0.0 0.0 0.0
Labor specialized 4.0 0.1 0.0 0.0 0.0 0.9 0.4
Diversified 1.6 0.0 0.0 0.0 0.0 0.6 0.3
Rural 58.1 1.9 0.5 1.4 0.3 7.1 1.9
Agriculture specialized 63.6 1.8 0.4 1.4 0.3 6.8 3.5
Diversified 57.5 1.9 0.5 1.4 0.3 7.1 1.7
Poverty headcount, US$2 a day Millions Changes, millions
Total 467.3 14.9 3.6 10.8 2.3 52.1 13.4
Urban 12.6 0.3 0.1 0.0 0.0 3.3 1.6
Transfer specialized 0.1 0.0 0.0 0.0 0.0 0.0 0.0
Labor specialized 8.1 0.2 0.1 0.0 0.0 1.7 0.9
Diversified 4.4 0.1 0.0 0.0 0.0 1.6 0.8
Rural 454.7 14.6 3.5 10.8 2.4 55.5 15.0
Agriculture specialized 52.2 1.5 0.4 1.1 0.3 5.6 2.9
Diversified 402.5 13.1 3.2 9.7 2.1 49.9 12.1
Source: CGE model simulations of the authors.
China 165
and 2.5 percent of the urban population were living in poverty in China in 2004.11
We start with these target rates of poverty and compute the poverty line in our
data set that reproduces this same poverty headcount. This yields a poverty line of
Y 3,520 per person for urban areas and Y 2,591 per person for rural areas. By
assuming a uniform distribution of the population within each of the income
vingtiles in our source data from the National Bureau of Statistics of China (NBS
2006), we are able to estimate the poverty headcounts in each stratum. This infor-
mation is also reported in table 5.5. As one may see there, the national poverty sit-
uation in China is largely driven by rural poverty; 455 million poor reside in rural
areas. The poverty headcount rate is highest in the agriculture-dependent house-
hold stratum, in which nearly two-thirds (63.7 percent) of the population is poor.
The two reform scenarios that do not reduce the rural-urban income disparity
are DOM and DOM-Ag (China's unilateral liberalization) because rural house-
holds generally lose if agricultural factor returns decline. Although the magnitude
of the change in the rural-urban income ratio is small in the cases of trade liberal-
ization, it is substantial in the factor market reform scenarios. In the case where
the hukou registration system is abolished (LABOR), for example, the ratio
declines from 3.54 to 3.23. The decline in the land reform scenario (LAND) is
likewise large, at 0.17 points.
Table 5.5 also reports the absolute changes in Gini coefficients. Because income
inequality in China is dominated by urban-rural inequality, the narrower urban-
rural income gaps in the scenarios of trade liberalization in the rest of the world and
of reforms in factor markets are reflected in an improvement in overall inequality, as
measured in the national Gini coefficient. There are no discernible changes in
inequality within urban and rural areas in the unilateral liberalization scenarios.
However, in the two factor market reform scenarios, the Gini coefficients show a
slight increase in inequality within urban areas and a slight decline within rural areas.
This is because low-income urban households dependent on the earnings from
unskilled labor are hurt most by the increase in the rural-urban migration of
unskilled workers after labor market reforms, whereas low-income, diversified rural
households gain more from the resulting increase in rural unskilled wages than do
households at high-income levels.
In the scenario of broadbased trade liberalization in the rest of the world (ROW),
the monetary poverty line increases by 2.9 percent after the change in the consumer
price index (table 5.4). Nonetheless, higher factor earnings mean that the poverty
headcount ratio declines among all household groups living in significant poverty.
Urban poverty decreases by 0.3 million people (the bottom panel reports changes in
poverty in millions), while rural households enjoy a 1.9 percentage point reduction
in the poverty headcount (the middle panel reports percentage point changes in
poverty). Because of the large population base in rural China, this translates into a
rural poverty reduction of 14.6 million people. In the case of agricultural liberalization
166 Agricultural Price Distortions, Inequality, and Poverty
in the rest of the world, the poverty reduction is smaller, but still significant; the rural
poverty headcount declines by 10.8 million.
Given the adverse impacts on agricultural subsectors after China's unilateral liber-
alization, rural poverty increases slightly, by 3.5 million people, in the DOM scenario
and by 2.4 million in the DOM-Ag scenario. Because of the predominance of rural
poverty in China, these increases in rural poverty translate into comparable changes
in total poverty. However, labor market reforms would significantly reduce rural
poverty, but slightly increase urban poverty. The rural poverty headcount ratio
declines from 58.1 percent in the base year to 51.0 percent in the LABOR scenario
and 56.2 percent in the LAND scenario, while the urban headcount ratio rises slightly,
from 2.5 to 2.8�3.2 percent. Overall, the share of the national population that is
impoverished falls quite sharply in the case of hukou reforms, from 36.4 percent of
the total population to 32.3 percent in the LABOR scenario (hukou reforms) and to
35.4 percent in the LAND scenario. If combined, these two scenarios, together, gener-
ate a poverty reduction of 65.5 million. It is thus clear that, if poverty reduction and
greater income equality are the objectives of the next round of reforms in China, then
factor market reforms should be part of the package.
Household impacts
It is important to dig below the aggregate indicators of poverty and inequality and
consider the disaggregated household incidence curves reported in figure 5.1, charts
a-f. These show the percentage changes in welfare (the equivalent variation as a per-
centage of initial income) by stratum across the income vingtile spectrum. The
largest increases in welfare after both trade and agricultural liberalization in ROW
(on the order of 2 percent) accrue to agriculture-specialized households (figure 5.1,
charts a and b). These households benefit from the fact that the returns to agricul-
tural land increase relative to other factor prices. Real incomes rise less among rural
diversified households because of the dominance of nonfarm wage earnings in the
income portfolios of these households. Among urban households, the largest wel-
fare increases in chart a of figure 5.1 are associated with labor-specialized house-
holds, followed by urban diversified households. This is consistent with the larger
increases in wage rates relative to the returns to capital. Because the transfers are
constant in real terms and because transfers make up most of the incomes of these
households, the real incomes of the transfer group are little affected by the agricul-
tural reform. However, because the share of food consumption in the total expendi-
ture of households at low income levels tend to be larger, the relative increase in
food prices leads to a higher household-specific consumer price index for these low-
income households relative to the national average consumer price index, causing
the modest welfare losses among the lowest income transfer groups.
China 167
Figure 5.1. The Impacts of Prospective Liberalizations on
Welfare of Five Types of Households, China
a. All goods liberalization in the rest of the world (ROW)
3.5
equivalent variation as a % of income
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0.5
1.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
vingtile
urban transfer specialized rural diversified urban diversified
urban labor specialized rural agriculture specialized
b. Agricultural liberalization in the rest of the world (ROW-ag)
3.0
equivalent variation as a % of income
2.5
2.0
1.5
1.0
0.5
0.0
0.5
1.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
vingtile
urban transfer specialized rural diversified urban diversified
urban labor specialized rural agriculture specialized
(Figure continues on the following page.)
168 Agricultural Price Distortions, Inequality, and Poverty
Figure 5.1. (continued)
c. Unilateral liberalization of all goods trade (TRA)
0.6
equivalent variation as a % of income
0.4
0.2
0.0
0.2
0.4
0.6
0.8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
vingtile
urban transfer specialized rural diversified urban diversified
urban labor specialized rural agriculture specialized
d. Unilateral liberalization of agricultural trade (TRA-ag)
0.3
equivalent variation as a % of income
0.2
0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
vingtile
urban transfer specialized rural diversified urban diversified
urban labor specialized rural agriculture specialized
China 169
Figure 5.1. (continued)
e. Hukou reform (LABOR)
15.0
equivalent variation as a % of income
10.0
5.0
0.0
5.0
10.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
vingtile
urban transfer specialized rural diversified urban diversified
urban labor specialized rural agriculture specialized
f. Land reform (LAND)
8.0
equivalent variation as a % of income
6.0
4.0
2.0
0.0
2.0
4.0
6.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
vingtile
urban transfer specialized rural diversified urban diversified
urban labor specialized rural agriculture specialized
Source: Author simulations.
170 Agricultural Price Distortions, Inequality, and Poverty
China's unilateral trade liberalization hurts all households except urban transfer-
specialized households (figure 5.1, chart c), although the magnitude of the welfare
losses is small. Rural agriculture-specialized households experience the largest
welfare losses, followed by rural diversified households, which suffer from the
depressed returns to agricultural activity. The welfare losses of urban households
are small, amounting to only around 0.1�0.2 percent of household income among
diversified households and labor-specialized households. The increased income
tax rate required to replace the loss in tariff revenue is the major factor contribut-
ing to the welfare losses among urban households. In the case of China's unilateral
agricultural liberalization, rural households are still the major losers, but all urban
households gain slightly because of the smaller tax replacement effects associated
with the less serious loss in tariff revenue (figure 5.1, chart d).
Recall from the preceding discussion that the largest poverty and inequality
impacts stem from the hukou reform. Chart e, figure 5.1 shows why this is true.
The population stratum showing the highest poverty headcount, the agriculture-
specialized households, is also the one reaping the largest proportionate gains in this
labor market reform scenario. The stratum benefits from the significant increase in
agricultural wages. Diversified rural households also benefit from the rise in rural
wages, although their welfare gains are somewhat less. These households supply less
of the temporary migrant labor to urban areas. These migrants bear the direct bur-
den of the heightened transaction costs associated with the hukou system; so, if this
burden is eliminated, the migrants benefit the most. While the benefits from hukou
reform are spread relatively evenly across income levels within each of the rural
strata, the higher-income households within the diversified strata, which have more
capital earnings in their incomes, tend to experience smaller proportionate gains; this
contributes to the decline in the Gini coefficient within the rural sector.
Most urban households suffer from the influx of unskilled and semiskilled
rural migrants, whose presence drags down the wage rates in urban areas. Almost
all urban households experience welfare losses, with the minor exception of the
richest transfer-specialized households. Overall, the urban index of income
inequality worsens slightly. However, this is overwhelmed by the reduction in
between-sector, rural-urban inequality; if coupled with the decline in rural
inequality, this leads to a decline in the national Gini coefficient of inequality of
0.021 (from 0.442 to 0.421). This is a substantial movement in a coefficient that is
generally quite robust in the face of policy reforms.
Similar to the case of the hukou reform, the largest gains from land reform accrue
to agriculture-specialized rural households (figure 5.1, chart f). These households are
currently constrained to remain active on the farm if they wish to retain the rights to
their land. By permitting some of these households to rent the land and migrate to
the city if they wish, land market reform raises the shadow value of the labor remain-
ing in agriculture substantially across all income levels. Diversified rural households
China 171
also gain; some of the highest gains come at the lowest income levels, where house-
holds are more heavily reliant on incomes from agriculture. Urban household welfare
falls across the board in this experiment, and it falls most among the poorest house-
holds. This is because of the large boost in rural-urban migration among unskilled
and semiskilled labor, as well as the increase in food prices following the reduction in
the agricultural labor force. As a consequence, the urban Gini coefficient rises. How-
ever, from the point of view of overall inequality in China, the main consequence of
this experiment is to redistribute income from urban to rural households, which
lowers the Gini coefficient by 0.008.
Sectoral impacts
In table 5.6, we report the effects of the policy reforms in the six aggregated sce-
narios we have investigated on output, exports, and imports. The first row of the
table shows that highly processed food products are the major winners from the
Table 5.6. The Effects of Prospective Liberalizations on
Sectoral Outputs and Trade, China
(percent)
Subsector ROW DOM ROW-Ag DOM-Ag LABOR LAND
Output
Agriculture 1.7 0.3 1.6 0.4 2.9 1.3
Other primary goods 0.8 0.1 0.2 0.1 0.8 0.5
Lightly processed food 1.7 2.0 1.7 2.2 2.6 1.1
Highly processed food 5.4 0.03 1.6 0.44 2.3 1.1
Nonfood manufacturing 0.5 0.4 0.9 0.3 2.0 0.9
Services 0.4 0.2 0.2 0.1 1.3 0.5
Exports
Agriculture 71.5 5.7 100.2 3.0 39.2 23.6
Other primary goods 6.0 5.6 1.7 0.6 3.7 1.3
Lightly processed food 31.2 11.0 58.3 7.7 25.0 14.4
Highly processed food 64.8 7.0 14.8 2.8 17.4 9.6
Nonfood manufacturing 1.6 6.4 2.1 0.7 3.0 1.4
Services 10.2 2.7 1.2 0.3 2.1 1.1
Imports
Agriculture 11.6 21.0 1.2 22.1 18.6 9.8
Other primary goods 5.5 1.2 2.4 0.1 6.9 3.0
Lightly processed food 8.8 46.4 0.1 48.6 10.3 5.8
Highly processed food 8.8 16.3 3.5 0.9 8.0 4.3
Nonfood manufacturing 3.9 5.7 0.2 0.0 0.5 0.1
Services 4.8 1.2 0.3 0.1 0.4 0.1
Source: CGE model simulations of the authors.
172 Agricultural Price Distortions, Inequality, and Poverty
elimination of market distortions in the rest of the world, with an average output
expansion of 5.4 percent. Production in the agriculture and lightly processed food
sectors expands by 1.7 percent. Substantial increases in exports are the key drivers
of the expansion in China's food and agricultural sectors; this flows from the
significant increase in international demand. Exports of agricultural products,
lightly processed food, and highly processed food increase by 72, 31, and 65 per-
cent, respectively, in the ROW scenario. Despite the absence of any decline in pro-
tection for agriculture in the ROW scenario, China's agricultural and food imports
increase by around 10 percent following agricultural liberalization in the rest of
the world because of the decline in the world prices of some of China's major agri-
cultural imports such as oilseeds and vegetable oils. In addition, there is a real
appreciation in China's currency that tends to boost the demand for imports
across the board.
If ROW liberalization is confined to the agricultural and lightly processed food
sectors, they become the only two aggregate sectors with expanding exports and
output. All the other aggregate sectors experience declining output and exports.
The impact of agricultural liberalization in the rest of the world on China's
imports is modest in comparison with the impact of broadbased trade liberaliza-
tion because the decline in total exports and a depreciation in the real exchange
rate both serve to dampen the expansion of imports in the ROW-Ag scenario.
The sectoral impacts of China's own reforms suggest that the current distor-
tions arising from China's tariff protection and labor market barriers generally
support the size of agriculture relative to the size of other industries. Under all
four scenarios involving China's own reforms, agriculture experiences output
losses, while the nonfood manufacturing sectors and services expand. The impact
of reducing China's distortions in commodity and factor markets on highly
processed food is mixed: this sector benefits from the elimination of import
tariffs, but loses from the reforms in factor markets.
In the two scenarios involving liberalization in the rest of the world, the disag-
gregated changes in sectoral output (not shown) generally follow the changes in
export demand reported in table 5.3: the sectors with larger increases (decreases)
in export demand and higher export dependence, such as prepared fish products,
sugar, textiles, and apparel and leather, experience relatively large increases
(decreases) in output. However, imports also play a role in determining changes in
sectoral output in oilseeds, in which output shrinks by 7.4 percent in the ROW
scenario as a result of the 2.8 percent decline in import prices, which spurs the
growth of oilseed imports.
Under China's unilateral trade liberalization (DOM), instruments, electronics,
textiles, apparel and leather are major manufacturing sectors with rapid output
expansion. As the most highly export-oriented sectors, they benefit from the real
China 173
depreciation in the Chinese currency in the wake of the country's unilateral trade
liberalization. At the other end of the spectrum, the most heavily protected sec-
tors, with sizable trade exposure, experience declining output, including oilseeds,
sugar, transportation equipment, other grains, and vegetable oil. In the case of
China's unilateral agricultural liberalization, the agricultural subsectors with high
levels of protection experience significant contractions in output.
In the scenarios of hukou reform (reduced transaction costs) and land reform,
agricultural output falls sharply because the farm labor force is diverted to off-
farm rural activities, as well as urban-based manufacturing. Within manufactur-
ing, the consumption goods subsectors experience declining output, but most
capital goods sectors expand because the changes in final demand favor invest-
ment over consumption in these two scenarios.
The sensitivity of the results to the land rental market assumption
China's rural land markets have been undergoing reform, and a nascent market
for land is emerging in many areas (see elsewhere above). In principle, this
should facilitate off-farm migration because migrants may no longer risk losing
control of their land if they leave their farms. However, to date, these reforms
have been restricted to certain regions, and it is not clear how efficiently this
market is functioning even in those special cases. Therefore, in our base case
results, we have assumed that the transfer of rural labor from farm activities to
off-farm activities would diminish the earnings from land rents by 50 percent, on
average (that is, there is a 50 percent probability that migrants will lose control of
their land). Since this parameter choice is somewhat arbitrary, we contrast the
base case results with the results arising from the two extreme assumptions about
the functioning of the rural land rental market: one in which there is a zero loss
in land returns following the onset of off-farm employment and the other in
which there is no land rental market (representing a 100 percent loss in land
returns if farmers switch to off-farm jobs). We then repeat the two trade liberal-
ization scenarios, ROW and DOM. The key simulation results are presented in
table 5.7. Since the macro aggregate results are essentially unchanged from our
base line results, only revised results on factor prices and labor migration are
reported.
The first pair of columns in table 5.7 report the results if the land market is
entirely absent so that migration produces the loss of all land farmed by the
migrants. The second pair of columns reports the results under the opposite
assumption, namely, the existence of a fully functioning land market. Consider,
first, the case of trade reforms in the rest of the world (ROW). In this case, the
returns to land and the wage rates in agriculture rise. Furthermore, the rise in
174 Agricultural Price Distortions, Inequality, and Poverty
Table 5.7. Sensitivity Analysis of the Simulation Results, China
No land rental market Fully functioning land market
Indicator ROW DOM ROW DOM
Factor prices, %
Return to agricultural land 16.8 3.6 15.8 3.4
Return to capital 2.1 0.7 2.2 0.8
Unskilled wages
Urban 4.0 1.1 3.4 1.0
Rural nonagricultural 4.3 1.3 3.5 1.3
Agricultural 3.5 1.3 5.3 1.8
Semiskilled wages
Urban 4.4 1.3 3.5 1.1
Rural nonagricultural 5.6 1.1 4.1 1.1
Agricultural 1.5 0.8 4.2 1.5
Skilled wages
Urban 1.8 0.9 1.9 0.9
Rural nonagricultural 1.7 1.0 1.9 1.0
Labor force, millions
Farm labor 8.0 1.9 4.7 1.3
Unskilled 0.8 0.2 0.5 0.1
Semiskilled 7.2 1.7 4.2 1.1
Rural-urban temporary
migration 7.4 1.7 4.4 1.2
Unskilled 0.7 0.2 0.4 0.1
Semiskilled 6.7 1.6 4.0 1.1
Skilled 0.0 0.0 0.0 0.0
Source: CGE model simulations of the authors.
the returns to land is greater than the rise in wages. Therefore, households that
had hitherto been considering leaving agriculture because of depressed factor
returns have an even stronger incentive to continue to devote their labor to
agriculture than do those households that, at the margin, had been indifferent
to the wage differential between the farm and nonfarm sectors (the fully func-
tioning land market). Thus, the movement of labor into agriculture in the
ROW scenario is greater if the land market is not functioning rather than
functioning, provided there is no change in the underlying structure of the
land market.
The same situation applies, but in reverse, in the case of unilateral trade
reforms if labor is leaving agriculture. Here, by including the returns to land in
the off-farm migration decision (because these fall more than the fall in wages),
the incentive to work off-farm is accentuated in the absence of a land market. As
China 175
we see above, the shift from the lack of a land market to a fully functioning land
market (the LAND experiment) generates a much larger flow of workers from
agriculture to the rest of the economy (more than 13 million), as well as a signifi-
cant reduction in poverty. So, we are not concluding that a poorly functioning
land market is good for poverty reduction. Because the impact of land reforms
dominates the impacts of trade reform on labor markets, the former rules the day
if both are undertaken together. However, this sensitivity analysis does show that
our predictions about the impact of trade reforms on intersectoral labor mobility
depend importantly on the extent to which farmers are able to lease their land if
they are migrating to the city for work.
Conclusions and Policy Implications
Absolute poverty in China is now largely a rural problem, and, within the rural
sector, the intensity of poverty is greatest on the farm. Thus, policy reforms that
either boost the returns to farming or enhance the off-farm opportunities for
those individuals presently working in agriculture offer the best prospects for
reducing poverty and inequality in China. Of the reforms considered, trade
reforms in the rest of the world, land reform, and hukou reform all reduce poverty
in China, while unilateral trade reforms result in a small increase in poverty.
Domestic agricultural distortions are important factors in determining the distri-
butional and poverty effects of trade reform packages, although the impacts of the
reforms on aggregate trade and welfare are small. Furthermore, the ROW trade
reforms, as well as the land and hukou reforms tend to favor rural households
over urban households, while the opposite is true of the unilateral tariff reforms.
So, it would seem desirable to bundle these reforms together in such a way that all
these broad household groups stand to benefit from the reform package. For
example, by combining the ROW and domestic trade reforms, a policy package
would reduce poverty and inequality, while benefitting all the household groups
in our study.
The land reform and the hukou reform scenarios benefit rural areas much
more significantly than urban ones. In the case of land reform, the changes may
hurt the lower-income urban households that currently benefit from the artificial
restriction of rural-urban labor mobility. This outcome may be avoided, however,
if the reforms are phased in over time; in the context of continued rapid economic
growth in the urban and coastal regions in China, the urban losses are likely to be
more than offset by ongoing income growth. Indeed, this is what appears to be
happening in many regions of China in which the restrictions on labor mobility
are being eroded, and land markets are emerging. This study suggests that such
labor and land market reforms are particularly impressive in their potential for
176 Agricultural Price Distortions, Inequality, and Poverty
reducing inequality and rural poverty in China, as well as their scope for allowing
China to realize more effectively the potential of the vast rural labor force.
Notes
1. Estimates of agricultural protection and assistance for China based on Huang et al. (2009) are
incorporated in the World Bank's Distortions to Agricultural Incentives Project Database (Anderson
and Valenzuela 2008). The former cover five decades, but the representative values for developing-
country agriculture as of 2004 that are used in the global CGE modeling for this study are summarized
in Valenzuela and Anderson (2008).
2. However, as noted by Parish, Zhe, and Li (1995), the rural labor market is looking more and
more like an open market.
3. See Hertel and Zhai (2006) for details on the behavior of off-farm labor supply in the model.
4. Given the small number of skilled farm workers in China, we ignore this group in our analysis.
See Zhai and Hertel (2006) for details of the derivation of the off-farm labor supply elasticity.
5. There are likely other, unobserved factors inducing this rural-urban wage differential, in which
case, the estimation of the labor market distortion via the subtraction of known factors is biased in the
direction of overstating the hukou-related distortion. Therefore, it is useful also to estimate the direct
impact of household registration status on the observed wage difference among households. Shi
(2002) takes this approach to the problem. Using the same China Health and Nutrition Survey data
set, he finds that only 28 percent of the rural-urban wage difference may be explained directly via
the coefficient on the hukou registration variable. This is quite a bit less than the 48 percent left
unexplained by the subtraction approach of Shi, Sicular, and Zhao (2002).
6. See Hertel and Zhai (2006) for a detailed description of the way this ad valorem distortion is
obtained.
7. We assume that a doubling in the number of temporary migrants would only increase the
marginal cost of migration by 10 percent.
8. Zhai and Hertel (2009), appendix table A.1 provides the mapping between 48 sectors in the
Chinese CGE model and 23 sectors in the Linkage model.
9. The equivalent variation of the representative firm is calculated based on the firm's utility
derived from the part of investment financed by the firm's retained earnings.
10. In reality, this is likely to show up in slower rates of out-migration from agriculture.
11. The World Bank's poverty estimates are available at http://go.worldbank.org/NT2A1XUWP0.
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6
Indonesia
Peter Warr*
Since the independence of Indonesia, the country's trade policies have taxed agri-
culture relative to manufacturing. However, since around 2000, the net impact of
the trade policies has been roughly neutral between these broad sectors. The
reversal occurred immediately following the Asian financial crisis of the late
1990s. It took the form of increases in the protection of the import-competing
commodities sugar and rice, declines in the taxation of agricultural exports, espe-
cially rubber and copra, and declines in manufacturing protection. The shift
toward a more democratic form of government has weakened the influence of
Indonesia's technocrats, who have generally favored liberal trade policies. The
greater protection of some key agricultural commodities has been a consequence.
The protection of agriculture has primarily involved import restrictions in the
import-competing sugar and rice sectors. Other agricultural sectors receive virtu-
ally no direct protection. Subsidies for fertilizer and other inputs have been an
indirect source of assistance to agriculture, but the rates of these subsidies have
declined.
The political explanations for the protection of the sugar and rice industries
are quite distinct to each case. The protection of the sugar industry is a conse-
quence of the political power of the highly concentrated sugar refining industry,
including the state-owned component of this industry, which is closely linked
with large-scale sugar plantations.1 In contrast, Indonesia's paddy industry--the
farm-level production of rice--is dominated by small-scale farm-level producers.
The rice milling sector is much more concentrated and well organized, however,
*The author has benefited greatly from the research assistance of Arief Anshory Yusuf in conducting
the model simulations, from helpful discussions with Ernesto Valenzuela, and from the comments of
John Cockburn.
179
180 Agricultural Price Distortions, Inequality, and Poverty
and this is relevant because imports compete with milled rice rather than
with the raw unmilled product (paddy) produced by farmers. The political power
of the rice millers has been an important source of support for the protection of
the rice industry. The enhanced political power of the Indonesian parliament
since the upheavals induced by the Asian crisis, together with the economic
nationalism that dominates the membership of the parliament, has strengthened
this support for the protection of the rice industry. Since 2000, imports of rice
have officially been banned. In part, this policy has reflected the dubious claim,
advanced by supporters of rice industry protection, that restricting rice imports
reduces poverty. A general equilibrium analysis presented in Warr (2005)
indicates that the policy boosts poverty in rural and urban areas because the
poverty-increasing effects of raising the consumer price of rice far exceed
the poverty-reducing effects of raising the producer price.
The purpose of our study is to analyze the effects of agricultural and other
trade policies both in Indonesia and at a global level. We focus particularly on the
effects on the incidence of poverty in Indonesia. We examine the effects of liberal-
ization in the markets for all tradable goods and the effects of liberalization in
agricultural markets alone. For this kind of analysis, a general equilibrium
approach is essential.
For illustrative purposes, consider the impact of reducing the protection of the
rice industry, a highly controversial issue in Indonesia. An adequate analysis of the
distributional impact of this policy needs to take account of the policy effects on
the expenditures of households, disaggregated by household group, but also the
policy effects on household incomes. This requires an examination of the impact
on wages, which operate through effects on the labor market, as well as the impact
on the returns to the agricultural land and capital owned by poor people. In
undertaking this analysis, however, we should not consider the rice industry in
isolation. A reduction in rice prices will induce some contraction in rice (paddy)
production. The paddy industry is a large employer of unskilled labor in absolute
terms. Depending on the labor intensity of this industry relative to the labor
intensity of other industries, a contraction of output might induce an increase or
a decrease in real unskilled wages. Any change in unskilled wages would affect the
profitability in other industries and generate impacts on outputs and prices in
those industries as well. These effects would have repercussions on household
incomes. The repercussions on incomes would have to be balanced against the
effects on consumers of a reduction in the price of rice. Moreover, the consump-
tion of rice may not be considered in isolation either. A reduction in the price of
rice will have implications for the demand for other staple foods, such as corn
and wheat flour, another significant import. Finally, the reduced protection may
lower government revenue if the instrument of protection is a tariff, or it
Indonesia 181
may lower private rents if the instrument is an import quota. The way this revenue
is spent by the government or the private quota holders will also influence the net
distributional outcome.
The debate over Indonesia's rice protection illustrates the necessity of a general
equilibrium approach. The economic issues involved are complex and interrelated.
A framework is required that accounts for these interactions and simultaneously
satisfies all relevant market clearing conditions and macroeconomic constraints.
To address the issues of poverty and inequality, such a framework must include a
disaggregated household sector. Moreover, as the above discussion illustrates, the
full impact of a reduction in the protection of the rice industry depends on the val-
ues of key economic parameters. In the case of the rice example, these include the
supply response of domestic producers and the elasticity of supply of rice imports
in Indonesia. However, the true values of these parameters are uncertain. A frame-
work is therefore needed in which the values of key parameters may be varied,
where appropriate, to determine the sensitivity of the results to the assumed values
of the parameters.
The next section describes the Wayang general equilibrium model of the Indone-
sian economy, the principal analytic tool we use in this study. The subsequent sec-
tion describes the simulations performed with this model, in combination with the
World Bank Linkage model of the world economy (van der Mensbrugghe 2005).
The simulations involve unilateral agricultural and trade policy reform in Indonesia
and reform in the rest of the world. Our purpose is to assess the relative importance
of own-country versus rest-of-the-world policies in terms of the effects on Indone-
sian households. The results are presented with a focus on the implications for the
incidence of poverty in Indonesia. The final section concludes.
The Wayang General Equilibrium Model
of the Indonesian Economy
This section briefly describes the major elements of the Wayang model. The
household sector of the model is crucial to the analysis of the incidence of
poverty. The most important features of the model are summarized in the
overview below. Following the overview, we describe the theoretical structure of
the model and the related database. We then discuss important features of the
parameter assumptions.
Overview
The Wayang model identifies 10 household types, which represent 10 socioeco-
nomic groups as defined in the social accounting matrix (SAM) published by
182 Agricultural Price Distortions, Inequality, and Poverty
Statistics Indonesia (BPS 2005). For the purposes of our study, each of these
10 SAM household categories is divided into centile groups--100 subcategories,
with an equal population segment in each--arranged by per capita consumption
expenditure. Working with a general equilibrium model containing a highly dis-
aggregated household sector allows us to conduct controlled experiments that
focus on the consequences for household incomes, expenditures, poverty, and
inequality that arise from selected economic shocks, taken one at a time.
The Wayang model also has a disaggregated industry and commodity struc-
ture. According to the assumptions adopted about microeconomic behavior, all
firms seek competitive profit maximization, and all consumers seek competitive
utility maximization. In the simulations reported in this chapter, the markets for
final outputs, intermediate goods, and factors of production are all assumed to
clear at prices that are determined endogenously within the model.2 The nominal
exchange rate between the rupiah and the U.S. dollar may be thought of as exoge-
nously fixed. The role in the model of the exogenous nominal exchange rate is to
determine, along with international prices, the nominal domestic price level.
Given that prices adjust flexibly to clear markets, a 1 percent increase in the
rupiah-dollar exchange rate will result in a 1 percent increase in all nominal
domestic prices, leaving all real variables unchanged.
The Wayang model belongs to the class of general equilibrium models that are
linear in proportional changes, meaning that relative effects are not altered by the
size of a shock. These models are sometimes referred to as Johansen models, after
the seminal work of Johansen (1964), who used this approach. The Wayang model
shares many structural features with the highly influential ORANI general equi-
librium model of the Australian economy (Dixon et al. 1982) and the general
equilibrium model of the global economy established through the Global Trade
Analysis Project (Hertel 1997). These two models also belong to the Johansen cat-
egory. The specific structure of the Wayang model draws on an earlier version of
the model (Warr et al. 1998) and on a revised version of the ORANI model, called
ORANI-G (Horridge 2004). The features of the Wayang model have been adapted
to reflect important aspects of the Indonesian economy and to facilitate the
analysis of poverty and inequality in Indonesia.3 The principal components of
the model are summarized below.
Industries
The national model contains 65 producer goods and services offered by 65 corre-
sponding industries, of which 18 are agricultural industries, 6 are resource sectors
(wood, other forestry, fishing, minerals mining, crude oil, and other mining), and
41 are other industries. Each industry produces a single output; so, the set of
Indonesia 183
commodities coincides with the set of industries. The various industries in the model
are classified as either export oriented or import competing. The level of exports of an
export-oriented industry is treated as endogenous, while any exports of an import-
competing industry are treated as exogenous.4 The criterion used to classify these
industries is the ratio of an industry's imports to the industry's exports. If this ratio
exceeds 1.5, then the industry is said to be producing an importable. If the import-
export ratio is less than 0.5, then the industry is deemed export oriented. For ratios
between 0.5 and 1.5, additional relevant information is used to classify the industry.
Commodities
The Wayang model contains two types of commodities: producer goods and con-
sumer goods. Producer goods are either domestically produced or imported. All 65
producer goods are, in principle, capable of being imported, although some show
zero import levels in the database, in which services and utilities account for most of
the examples. The 20 consumer goods identified in the model are each transformed
from the producer goods. The proportions of domestically produced and imported
producer goods of each kind used in this transformation are sensitive to the
(Armington) elasticities of substitution and the relative price changes of the goods.
Factors of production
The mobility of factors of production is a critical feature of any general equilib-
rium system. Mobility here means mobility across economic activities (industries)
rather than geographical mobility, although the two are clearly connected. The
greater the factor mobility built into the model, the greater the economy's simu-
lated capacity to respond to changes in the economic environment. It is essential
that assumptions about the mobility of factors of production be consistent with
the length of the run that the model is intended to represent.
Two types of labor are identified: unskilled and skilled. They are distinguished by
the educational characteristics of the workforce. Skilled labor is defined as those
workers with at least a lower secondary education. Both types of labor are assumed
to be fully mobile across all sectors. These assumptions imply that skilled wages
must be equal in all sectors and move together. The same applies to unskilled wages,
although the two need not be the same and need not move together.
An alternative treatment, popular in general equilibrium modeling studies, is to
assume labor mobility within the agricultural and nonagricultural sectors, but not
between them. This approach is rejected here because it denies a central reality of
the Indonesian economy (and of many other developing economies): the mobility
of labor between rural and urban areas even in the short run. The approach would
184 Agricultural Price Distortions, Inequality, and Poverty
rule out all resource mobility between the agricultural and nonagricultural indus-
tries, greatly limiting the scope for economic adjustment to changes in patterns
of incentives such as the changes that would be produced by trade liberalization.
In Indonesia, unskilled and semiskilled workers move readily, often seasonally,
between the agricultural and nonagricultural sectors of the economy. Indeed, this
mobility is more important than direct mobility among the various agricultural
regions of the country, although the latter sort of mobility also occurs.
The mobility of capital is a somewhat different case. It is assumed that there are
two kinds of mobile capital: one kind that is mobile among agricultural sectors and
another kind that is mobile among nonagricultural industries. Mobile agricultural
capital cannot be used outside agriculture, and mobile nonagricultural capital can-
not be used in agriculture. Mobile agricultural capital includes machinery such as
tractors of various types, which may be used in a range of agricultural activities. It is
best to consider land (as we use the term) as an immobile form of agricultural capi-
tal, which, in the short run, includes much true land. In nonagriculture, plant and
buildings are classified as mobile because they may be used for many purposes. A
factory building is a good example. Machinery is considered immobile because
most such machinery is more industry specific than are tractors in agriculture.
Table 6.1 summarizes some features of the cost structure of the paddy industry
and compares this cost structure with the situation in the rest of the agricultural sec-
tor and the rest of the economy. The paddy industry is intensive in its use of
unskilled labor, which accounts for 18.5 percent of the total costs and 31 percent of
the total variable costs excluding capital and land. Both shares are well above the
Table 6.1. The Cost Shares of Major Factors of Production,
Paddy and Other Industries, Indonesia, 2000
(percent of total costs)
Other Non- All
Cost components Paddy agriculture agriculture Industries
Unskilled labor 18.5 9.0 6.3 8.1
Skilled labor 3.1 6.6 7.1 7.0
Mobile agricultural capital 20.6 21.3 0.0 1.7
Mobile nonagricultural capital 0.0 0.0 25.3 23.2
Land 18.1 20.2 0.0 1.6
Nonland fixed capital 0.0 0.0 25.3 21.2
Intermediate inputs 39.7 42.9 36.0 37.2
Total 100.0 100.0 100.0 100.0
Source: Wayang model database, based on data of Statistics Indonesia (BPS 2001) and unpublished
agricultural cost survey data accessed at the Center for Agro-Social and Economic Research, Ministry
of Agriculture, Bogor, Indonesia, in March 2008.
Indonesia 185
shares in other agricultural industries and the rest of the economy, on average. This
point is important in our discussion elsewhere below.
It is assumed that, in every sector, the production technology is characterized
by constant elasticity of substitution and diminishing returns to scale to variable
factors alone. However, we introduce a sector-specific fixed factor in each sector to
assure that there are constant returns to scale in production to all factors. We refer
to the set of specific factors in the agricultural sectors as land and to the set of spe-
cific factors in the nonagricultural sectors as fixed capital. The assumption of con-
stant returns means that all factor demand functions are homogeneous of degree
1 in output. In each sector, there is a zero profit condition that equates the price of
output to the minimum unit cost of production. This condition may be thought
of as determining the price of the fixed factor in that sector.
Households
The model contains 10 major household categories--seven rural and three
urban--that are differentiated by socioeconomic group. The sources of income of
each household type depend on the ownership by the households of factors of
production. These differ among the household categories and are estimated from
the 2000 SAM compiled by Statistics Indonesia (BPS 2005). The SAM is based pri-
marily on the National Socioeconomic Survey--the Susenas Survey--conducted
by Statistics Indonesia. Drawing on the 1999 Susenas data (BPS 2000), we subdi-
vide each of the 10 household categories into 100 subcategories, each of which has
the same population size, and arrange them according to per capita real consump-
tion expenditure, thus establishing a total of 1,000 subcategories.5 The consumer
demand equations for the various household types are based on the linear expen-
diture system. Within each of the 10 major categories, the 100 subcategories differ
according to the budget shares in consumption.
Because the focus of our study centers on income distribution, the sources of
income of the various households are of particular interest. The source of the
factor ownership matrix used in the model is the SAM for Indonesia in 2000.
The households are described as follows:
� Rural 1, agricultural employees: agricultural workers who do not own land
� Rural 2, small farmers: agricultural workers with less than 0.5 hectare of land
� Rural 3, medium farmers: agricultural workers with 0.5 to 1 hectare of land
� Rural 4, large farmers: agricultural workers with more than 1 hectare of land
� Rural 5, low-income nonagricultural rural households: small retail store own-
ers, small entrepreneurs, small personal service providers, and clerical and
manual workers in rural areas
186 Agricultural Price Distortions, Inequality, and Poverty
� Rural 6, nonlabor rural households: nonlabor-force and unclassified house-
holds in rural areas
� Rural 7, high-income nonagricultural rural households: managers, technicians,
professionals, military officers, teachers, large entrepreneurs, large retail store
owners, large personal service providers, and skilled clerical workers in rural
areas
� Urban 1, low-income urban households: small retail store owners, small entre-
preneurs, small personal service providers, and clerical and manual workers in
urban areas
� Urban 2, nonlabor urban households: nonlabor-force and unclassified house-
holds in urban areas
� Urban 3, high-income urban households: managers, technicians, professionals,
military officers, teachers, large entrepreneurs, large retail store owners, large
personal service providers, and skilled clerical workers in urban areas
In the SAM, the income and expenditure items of each household are classified
as follows: wages and salaries, rent from capital, incoming transfers, outgoing
transfers, income taxes, final consumption, and savings.
The wages and salaries and rent from capital categories are each subdivided
into various subcategories. These categories do not correspond exactly to the cat-
egories in the model. In agriculture, returns to land and capital are not separated
in the SAM, but returns to owner-provided labor are separated. We have used a
previous study on the cost structure of paddy production to allocate returns
among the land and capital categories, and the various farming households
receive the same proportionate breakdown from this total (see Warr 2005).
The factor ownership characteristics of the 10 major household categories are
summarized in table 6.2. These household categories vary considerably in the
composition of factor incomes, but, for the purposes of our study, the limitations
in the available data have obliged us to impose the assumption that the composi-
tion of factor incomes is uniform across the 100 subcategories in each of the
10 major categories. These 100 subcategories thus obtain incomes from factors of
production in the same proportions. Of course, because the incomes of these
100 subcategories vary greatly, we should think of them as owning different quan-
tities of a uniform bundle of factors. The composition of the factor bundles varies
across the 10 major household categories, but is uniform within each category.
The composition of expenditures on final commodities does vary among the
100 subcategories, however, and also across the 10 major household categories.
The characteristics of the 10 household categories described above are summa-
rized in table 6.3. The table shows the importance of each category in the overall
incidence of poverty in Indonesia using the government's official poverty line. As
Indonesia 187
Table 6.2. Sources of the Factor Incomes of 10 Broad
Household Groups, Indonesia, 2000
Mobile Mobile Fixed Total
Skilled Unskilled agricultural nonagricultural capital factor
Category Land labor labor capital capital and land income
Rural 1 4.1 1.4 53.6 2.1 9.3 29.5 100.0
Rural 2 1.6 6.1 26.7 1.4 16.3 47.9 100.0
Rural 3 9.8 2.7 14.1 4.8 16.1 52.6 100.0
Rural 4 9.7 4.0 7.8 4.9 17.4 56.3 100.0
Rural 5 7.6 7.0 43.3 3.6 8.7 30.0 100.0
Rural 6 2.8 29.2 15.2 1.7 12.7 38.4 100.0
Rural 7 12.6 20.7 4.5 5.9 12.4 44.0 100.0
Urban 1 4.1 12.8 24.4 2.4 13.8 42.5 100.0
Urban 2 3.2 22.0 42.3 1.7 7.4 23.4 100.0
Urban 3 4.1 23.8 1.3 2.5 17.0 51.4 100.0
Source: Wayang model database (see Warr et al. 1998), based on data of Statistics Indonesia (BPS 2000).
Table 6.3. Expenditure and Poverty Incidence by Household
Category, Indonesia, 2000
Mean per Share of Share of
Share of total capita monthly poor in category in
Category population, % expenditure, Rp category, % all poor, %
Rural 1 8.0 6,358 39.8 13.9
Rural 2 14.8 3,608 34.9 22.4
Rural 3 7.1 7,584 32.3 9.9
Rural 4 9.0 6,618 27.8 10.9
Rural 5 16.0 3,891 23.8 16.5
Rural 6 4.9 12,795 28.0 5.9
Rural 7 5.0 16,060 10.5 2.3
Urban 1 20.4 4,210 15.2 13.4
Urban 2 6.1 17,813 11.2 2.9
Urban 3 8.7 14,353 5.0 1.9
Indonesia 100.0 12,084 23.1 100.0
Memorandum items
Headcount poverty incidence, national,% 23.10
Headcount poverty incidence, rural, % 29.09
Headcount poverty incidence, urban, % 11.97
Gini coefficient, national 0.335
Gini coefficient, rural 0.291
Gini coefficient, urban 0.356
Source: Wayang model database (see Warr et al. 1998), based on data of Statistics Indonesia (BPS 2000).
188 Agricultural Price Distortions, Inequality, and Poverty
in many developing countries, the incidence of poverty in Indonesia is highest
among the rural socioeconomic categories. Rural households account for 82 percent
of all poor people in Indonesia, but only 65 percent of the total population.
Theoretical structure of the model
The analytical structure of the model includes the following major components:
� The household consumption demands of each of the 10 broad household
types for 20 categories of consumer goods are derived from the linear expendi-
ture system.
� The household supplies of skilled and unskilled labor are assumed to be
exogenous.
� The factor demand system is based on an assumption of constant elasticity of
substitution in production technology that relates the demand for each pri-
mary factor to industry outputs and to the prices of each of the primary fac-
tors. This reflects the assumption that factors of production may be substituted
for one another in ways that depend on factor prices and on the elasticities of
substitution between the factors.
� The distinction between skilled and unskilled labor, which are nested within
the sectoral production functions, is addressed in each nonagricultural sec-
tor by allowing skilled and unskilled labor to enter a constant elasticity of
substitution production function to produce effective labor. Effective labor,
variable capital, and fixed capital then enter the production functions for
domestic output.
� Leontief assumptions are used to represent the demand for intermediate
goods. The demand for each intermediate good in each sector is assumed to be
in fixed proportion to the gross output of the sector.
� The demand for imported and domestically produced versions of each good
incorporates Armington elasticities of substitution between the two versions.
� The set of equations that determines the incomes of the 10 household types
from the (exogenous) household ownership of factors of production reflects
data derived from the official 2000 SAM (BPS 2005), the (endogenous) rates of
return to these factors, and any net transfers from elsewhere into the system.
� The import tariff and excise tax rates across commodities; the rates of business
taxes, value added taxes, and corporate income taxes across industries; and the
rates of personal income taxes across household types reflect the structure of
the Indonesian tax system and rely on data of the Ministry of Finance.6
� A set of macroeconomic identities ensure that standard macroeconomic
accounting conventions are observed.
Indonesia 189
The Wayang model database
This subsection provides a description of Indosam 2000, a disaggregated SAM for
Indonesia with a base year of 2000. This SAM is intended to serve as the database
for Wayang, but it has other, potential uses as well. At the time of our study, 2000
is the most recent year on which it is possible to assemble the information
required for the construction of a SAM for Indonesia.
Four data sources, all compiled by Statistics Indonesia, have been used to con-
struct Indosam 2000: the input-output tables for 2000 (BPS 2001), the 2000 SAM
(BPS 2005), the 1999 Susenas National Socioeconomic Survey (BPS 2000), and
supplementary data sources used in the construction of specific tables. The prin-
cipal data source is the 2000 SAM, produced by Statistics Indonesia (BPS 2005). It
contains 22 production sectors, which is insufficient for the purposes of our study.
Moreover, the SAM does not include adequate detail on tax payments and house-
hold sources of income that is also required for the study. The 2000 input-output
tables specify 66 production sectors.
For the purposes of our study, modifications to the data contained in the
input-output tables are needed for three reasons. First, the table specifies only the
total transactions for intermediate goods and services for each pair of producing
and purchasing industries at producer prices. Unlike the 1990 tables (BPS 1990),
these transactions are not divided into goods and services provided through
domestic and imported sources. Second, the tables include a sector--number 66,
labeled as an unspecified sector--as a balancing item. Sector 66 does not describe
a true sector of the economy, and, in any case, the data on this sector indicate neg-
ative final demand, an economic impossibility. Third, the tables obtained from
Statistics Indonesia are not fully balanced. The major imbalance is that, for most
industries defined in the table, the industry-specific elements in row 210 (total
input) are not equivalent to the corresponding elements in row 600 (total output),
and the elements in row 200 (total imports), plus row 600 (total output) are not
equivalent to the elements in row 700 (total supply).
We have overcome these problems as follows. First, we have used the shares of
imported intermediate goods and domestically produced intermediate goods
for each cell of the table, as implied by the 1990 input-output tables (BPS 1990),
to divide the transactions in intermediate goods into domestic and imported
components, a distinction that is required by the Armington theoretical struc-
ture of the demand for intermediate goods. Second, we have aggregated sector
66 with the much larger sector 65 (other services), which has eliminated the
problem of negative final demand. Third, we have balanced the revised table
using the RAS adjustment method to ensure that all required accounting identi-
ties are observed.
190 Agricultural Price Distortions, Inequality, and Poverty
The model's elasticities
All export demand elasticities are set equal to 20. The elasticities of supply of
imports in Indonesia are assumed to be infinite (import prices are set exoge-
nously) except in the case of rice, for which the assumed elasticity is 10. All pro-
duction functions are assumed to be constant elasticity of substitution functions
in primary factors with elasticities of substitution of 0.5, except for the paddy pro-
duction industry, for which this elasticity is set at 0.25, reflecting the empirical
observation of low elasticities of supply response in this industry. The Armington
elasticities of substitution in demand between imports and domestically pro-
duced goods are set equal to 2, except for the case of rice, for which the assumed
value is 6. The higher value for rice reflects the assumption that imported and
domestically produced rice are close substitutes, unlike the situation in most other
commodities.
Simulations
We first describe the policy shocks that are simulated and then summarize the
model closure characteristics.
The shocks
We simulate the effects of policy reform using the Wayang model of the Indone-
sian economy, combined with the Linkage model of the world economy (van der
Mensbrugghe 2005). The simulations involve unilateral agricultural and trade
policy reform in Indonesia and reform in the rest of the world. We do this to assess
the relative importance of the impacts of own-country versus rest-of-the-world
policies on Indonesian households. We also compare reform in agriculture alone
with reform in all goods markets to gauge the relative contribution of agricultural
policies to the measured impacts on Indonesian households.
We use the word reform here to mean the complete elimination of all tariffs,
the tariff equivalents of any nontariff barriers, export taxes and export subsidies,
and domestic agricultural policies in so far as they alter the producer or consumer
prices of farm products in various countries. We consider three sets of policy
reforms below: unilateral reform in Indonesia (simulation A); global reform,
excluding Indonesia (simulation B); and the combination, that is, global reform,
including Indonesia (simulation C).
In all three sets of simulations, the Wayang model treats as exogenous:
(1) all rates of industry assistance in Indonesia (tariffs, the tariff equivalents of
nontariff barriers, and export subsidies), (2) all import prices in Indonesia,
and (3) all shifters in the inverse export demand functions in Indonesia
Indonesia 191
Table 6.4. Industry Assistance Rates Used in Modeling,
Indonesia, 2004
(percent)
Commodity Tariff Export subsidy Output subsidy
Paddy rice 15.0 0.0 0.0
Wheat 0.8 0.0 0.0
Other grains 15.0 0.0 0.0
Vegetables and fruits 4.8 0.0 0.0
Oilseeds 4.7 9.0 0.0
Sugarcane 0.0 0.0 0.0
Plant-based fibers 3.7 0.0 0.0
Other crops 4.5 8.0 0.0
Cattle, sheep, and so on 3.1 0.0 0.0
Other livestock 3.1 0.0 0.0
Raw milk 0.0 0.0 0.0
Wool 3.8 0.0 0.0
Other primary products 2.9 0.6 0.3
Beef and sheep meat 4.8 0.0 0.0
Other meat products 4.5 0.0 0.0
Vegetable oils and fats 2.9 0.0 0.0
Dairy products 3.9 0.0 0.0
Processed rice 15.0 0.0 0.0
Refined sugar 18.3 0.0 0.0
Other food, beverages, tobacco 15.3 0.0 0.0
Textiles and wearing apparel 8.0 1.4 0.0
Other manufacturing 5.1 1.2 0.1
Services 0.0 0.0 0.2
Source: Valenzuela and Anderson (2008), based on the estimates compiled by Anderson and Valenzuela
(2008).
(equivalent to shifts in the prices at which Indonesia may export a given vol-
ume of exports).
Simulation A depicts reform in Indonesia alone. This simulation relies only on
the Wayang model and does not involve the Linkage model. The assumed rates of
industry assistance in Indonesia are set out in table 6.4, and the database of the
Wayang model has been amended to match these rates. Exogenous variables (1)
are set to zero (changed exogenously by 100 percent) in this scenario, in which
all Indonesian tariffs, the tariff equivalents of quantitative restrictions, and all
export subsidies are eliminated. Exogenous variables (2) and (3), import prices
and export demand shifters, do not change. Export prices are determined endoge-
nously in the Wayang model by export demand equations for Indonesia that relate
the export price of each commodity to the quantity of the commodity exported.7
192 Agricultural Price Distortions, Inequality, and Poverty
The export quantities are endogenously determined within the Wayang model,
and export prices are determined simultaneously by movements along the export
demand equations (recalling that the export demand shifters are zero).
Simulation B depicts reforms in the rest of the world (all countries except
Indonesia). In this case, exogenous variables (1) do not change, but exogenous
variables (2) and (3) do. The simulation uses the changes in import prices in
Indonesia and the shifts in the export demand equations for Indonesia that are
generated by simulations in the Linkage model. These Linkage model simulations,
conducted by van der Mensbrugghe, Valenzuela, and Anderson (see the appen-
dix), estimate the changes to these import prices and export demand shifters that
result from liberalization in all countries except Indonesia; these results are then
applied as shocks to the Wayang model. The changes to border prices derived
from the Linkage model are shown in table 6.5.
Simulation C combines simulations A and B to depict global reform, including
Indonesia. In this case, exogenous variables (1), (2), and (3) all change. The non-
linear approximation techniques used to solve the Wayang model mean that the
results of simulation C are not exactly the arithmetic sum of the results of simula-
tions A and B, but they are similar to the arithmetic sum.
Each of the above simulations is conducted twice: once to show the case in
which the reductions in the protection in Indonesia and in the rest of the world
apply to all traded commodities (labeled simulations A1, B1, and C1) and once to
show the case in which the reductions in protection apply only to agricultural and
lightly processed food commodities (labeled simulations A2, B2, and C2).
Model closure
Because we have chosen the real consumption expenditure of each household as
the basis for welfare measurement and the calculation of the incidence of poverty,
we must make the macroeconomic closure compatible with this measure and with
the single-period horizon of the model. We do this by ensuring that the full eco-
nomic effects of the shocks to be introduced are channeled into current-period
household consumption and do not leak in other directions so that we are unable
to capture real world intertemporal welfare implications in our welfare measure.
The choice of macroeconomic closure may thus be seen partly as a mechanism to
minimize inconsistencies between the use of a single-period model to analyze
welfare results and the multiperiod reality that the model imperfectly represents.
To prevent intertemporal and other welfare leakages from occurring, we con-
duct the simulations for the case of balanced trade (the exogenous balance on the
current account). This ensures that the potential benefits of the liberalization do
not flow to foreigners through a current account surplus or that increases in
Indonesia 193
Table 6.5. Exogenous Border Price Shocks That Would Result
from Rest-of-the-World Liberalization, Indonesia
(percent deviation from base)
Export price shocksa Import price shocksb
Reform Only Reform Only
of all agricultural of all agricultural
Commodity goods reform goods reform
Paddy rice 0.0 0.0 4.3 2.7
Wheat 0.0 0.0 6.8 7.1
Other grains 3.1 1.4 2.7 2.8
Oilseeds 3.0 1.3 1.8 1.3
Sugarcane and beets 0.0 0.0 0.0 0.0
Plant-based fibers 3.1 1.4 7.6 8.7
Vegetables and fruits 3.1 1.4 2.6 1.9
Other crops 3.0 1.3 1.5 1.8
Cattle, sheep, and so on 3.0 1.3 5.6 5.5
Other livestock 2.7 1.1 1.9 0.6
Raw milk 0.0 0.0 0.0 0.0
Wool 0.0 0.0 9.8 10.0
Beef and sheep meat 3.0 1.4 5.6 5.7
Other meat products 2.5 0.8 3.3 3.6
Vegetable oils and fats 2.5 0.9 0.4 1.1
Dairy products 2.7 1.1 8.6 8.8
Processed rice 3.0 1.3 3.7 2.8
Refined sugar 2.6 1.0 2.9 2.5
Other food, beverages,
tobacco 2.4 0.9 0.8 0.7
Other primary products 2.1 0.6 1.3 0.8
Textiles and wearing
apparel 2.1 1.0 0.3 0.4
Other manufacturing 1.9 0.6 0.4 0.4
Services 2.1 0.6 0.2 0.2
Agriculture and food 2.5 1.0 3.0 3.2
Agriculture 3.0 1.3 4.1 4.5
Processed foods 2.5 0.9 1.7 1.8
Other manufacturing 2.0 0.6 0.4 0.4
Nontradables 2.1 0.6 0.2 0.2
Total 2.1 0.7 0.5 0.6
Merchandise trade 2.1 0.7 0.7 0.8
Source: Linkage model simulations by van der Mensbrugghe, Valenzuela, and Anderson (see the appendix).
a. Simulated as shocks to the inverse export demand equations for Indonesia.
b. Simulated as shocks to the exogenous import prices for Indonesia.
194 Agricultural Price Distortions, Inequality, and Poverty
domestic consumption are not achieved at the expense of borrowing from abroad
in the case of a current account deficit. For the same reason, we exogenously fix
real government spending and real investment demand for each good. We hold
the government budget deficit fixed in nominal terms. We achieve this by endoge-
nous across-the-board adjustments to the sales tax rate so as to restore the base
level of the budgetary deficit. As the combined outcome of these features of the
closure, the full effects of the changes in policy are channeled into household con-
sumption and not into effects that are, in fact, relevant to economic welfare, but
are ignored in the single-period focus of the model.
The results of the liberalization of the markets for all goods
While the emphasis in our study is on the effects on poverty and income inequality,
an understanding of these effects requires that we first look at the macroeconomic
impacts.
Macroeconomic effects
The macroeconomic effects of trade reform in the markets for all commodities are
summarized in table 6.6. Real GDP rises in Indonesia under all three reform sce-
narios. The increases are small in the case of unilateral liberalization (Sim A1) and
moderate if other countries liberalize as well (Sim B1 and Sim C1). Under unilat-
eral liberalization, the domestic price levels in Indonesia decline (measured as the
GDP deflator and the consumer price index). Aggregate real household consump-
tion rises marginally under unilateral reform (Sim A1) and rises more substan-
tially if the rest of the world reforms (B1) and under global reform, including
Indonesia (C1).
As our description of model closure indicates, real investment, real inventories,
and real government spending (each deflated by the relevant price deflator), as
well as the real trade balance (measured in foreign exchange terms), are all held
constant in these simulations. The nominal values of each of these categories thus
change as the price levels of the components change. The nominal values of GDP
and consumption change in line with the fact that the GDP deflator and the con-
sumer price index both decline as tariffs are removed in Indonesia. The same is
true of investment and inventories.
However, nominal government spending increases. The reason is evident from
the changes in factor prices. The real value of skilled labor increases by a propor-
tion (5.7 percent) greater than the decline in the consumer price index (2.2 per-
cent). Nominal skilled wages therefore rise (3.5 percent). Because government
expenditure is heavily concentrated in the employment of skilled (educated)
labor, nominal government spending must rise to maintain the real value of
Indonesia 195
Table 6.6. Aggregate Simulation Results of the Prospective
Liberalization of All Commodities, Indonesia
Sim A1, Sim B1, Sim C1,
unilateral rest-of-the-world global
Indicator liberalization liberalization liberalization
Macroeconomic aggregates, % change from base
Real GDP, expenditure side,
GDP deflator 0.54 1.12 1.37
Real household consumption,
CPI deflator 0.54 5.21 5.78
Import volume index, duty-
paid weights 11.95 12.54 27.09
Export volume index 9.84 4.59 15.56
GDP price index,
expenditure side 1.75 9.06 7.59
Consumer price index 2.19 8.79 6.50
Nominal change, Rp, billions
GDP 17,923 150,926 133,071
Consumption 14,854 129,693 113,574
Investment 4,561 12,890 9,135
Inventory 109 1,580 1,829
Government expenditure 1,600 9,922 12,191
Exports, net of imports 0 0 0
Real return to factors, % change from base, using the CPI deflator
Unskilled labor 0.2 7.1 8.5
Skilled labor 5.7 1.8 8.1
Agricultural capital 5.6 11.9 9.0
Nonagricultural capital 3.1 4.6 1.5
Land 2.9 21.0 19.1
Real household expenditure, % change from base, using the CPI deflator
Rural 1 0.8 3.9 3.0
Rural 2 1.1 5.9 4.7
Rural 3 0.1 6.8 6.8
Rural 4 0.7 4.4 5.1
Rural 5 1.0 5.5 6.7
Rural 6 0.3 5.6 6.0
Rural 7 1.1 5.2 6.3
Urban 1 0.1 4.6 4.5
Urban 2 1.6 5.1 7.0
Urban 3 1.9 5.5 7.4
Source: CGE model simulations for Indonesia by the author.
Note: CPI consumer price index.
196 Agricultural Price Distortions, Inequality, and Poverty
government spending. Consumption is the only component of expenditure on
GDP the real value of which is not fixed exogenously. The increase in nominal
government spending therefore limits the amount by which real household con-
sumption may increase in response to the decline in protection.
The real value of skilled wages rises because, according to the rates of industry
assistance in Indonesia used in these simulations (see table 6.4), the country's pro-
tective structure acts against the interests of industries that are intensive in the use
of skilled labor. The protective structure is virtually neutral with regard to
unskilled labor. The existence of this protection reduces the real value of skilled
wages, and the removal of the protection accomplishes the reverse.
The bottom panel of table 6.6 summarizes the changes in the real consumption
expenditures of each of the 10 household groups. Under unilateral liberalization
(Sim A1), the poorer rural household groups (Rural 1, 2, and 3) and the poorest
urban household group (Urban 1) lose, and all other household groups gain. The
changes in real factor prices and in the sources of household incomes (see table
6.2) provide the main explanation for these outcomes. Real unskilled wages
remain virtually unchanged, and the real return to agricultural land and capital
decline in this simulation, thus harming poorer rural households. At the same
time, the real returns to skilled labor and nonagricultural capital increase, favor-
ing the richer households, especially households in urban areas. In addition, the
reduced protection directly lowers the consumer prices of the imported goods
subject to the protection.
These results necessarily reflect the structure of protection in Indonesia that
is assumed in our study, as represented in the Distortions to Agricultural
Incentives Project Database (Anderson and Valenzuela 2008). According to our
model results, protection disfavors skilled labor-intensive industries overall,
while favoring agricultural industries that are intensive in the use of land and
agricultural capital. The removal of the protection harms agricultural land and
capital and benefits skilled labor and nonagricultural capital. These factors are
owned most intensively by the richest urban households, and these households
are, in consequence, the largest beneficiaries of unilateral liberalization in
Indonesia.
Liberalization in the rest of the world (Sim B1) produces international price
changes that raise real GDP in Indonesia by about twice the increase that would
result from unilateral liberalization by Indonesia, but the effects within Indonesia
are qualitatively different. Liberalization in the rest of the world raises the real
value of both skilled and unskilled wages in Indonesia, especially the former, and
also raises the returns to agricultural capital and (especially) agricultural land.
This occurs because liberalization in the rest of the world raises agricultural prices
relative to nonagricultural prices internationally, favoring the agricultural sectors
in Indonesia. These results are important in our discussion of poverty (see below).
Indonesia 197
The real returns to nonagricultural capital decline, but all other factor returns
increase. A key point is that rest-of-the-world liberalization benefits unskilled
labor proportionately more than skilled labor in Indonesia. The increase in real
consumption in Indonesia in the scenario of rest-of-the-world liberalization is
10 times the corresponding increase in the scenario of unilateral liberalization.
Real household expenditures increase in all 10 socioeconomic categories in the
rest-of-the-world liberalization scenario.
Global reform (Sim C1) is a combination of the previous two simulations and
is dominated by rest-of-the-world liberalization. The results are qualitatively
similar to the effects of simulation B1.
Effects on inequality and the incidence of poverty
The simulated effects on the incidence of poverty according to socioeconomic
group broadly mimic the effects on average household consumption among these
groups, which is discussed above. The level of the incidence of poverty depends on
the poverty line used in the calculations, and this may also be true of the simu-
lated changes in the incidence of poverty that result from particular economic
shocks. The shifts in real expenditures generated by particular economic shocks
are not uniform across income groups because of differences in expenditure pat-
terns. Different poverty lines act on different sections of the cumulative distribu-
tion of real expenditures and may thus produce different patterns of change in the
incidence of poverty within the same simulation.
We use three poverty lines to investigate the effects on the incidence of poverty:
the government's national poverty line, the international US$1-a-day (extreme)
poverty line at purchasing power parity, and the international US$2-a-day (mod-
erate) poverty line at purchasing power parity.
In the case of each of these poverty lines, we apply the following calibration
method. First, we use the ex ante distribution of household expenditures revealed
in the model database to calculate the particular level of poverty incidence associ-
ated with the poverty line.
Second, we use the published level of poverty incidence based on the particular
poverty line to calculate the value of the poverty line in the domestic currency in
Indonesia. The published levels of poverty incidence are contained in the Statisti-
cal Yearbook of Indonesia in the case of the national poverty line (BPS 2007) and
the World Bank's World Development Indicators in the case of the US$1-a-day
and US$2-a-day poverty lines (PovcalNet 2008). These calculations yield the base
levels of the poverty lines used in subsequent calculations.
Third, we simulate the ex post levels of real expenditure for each household in
the model to reflect the effects of the shocks. These calculations of real expendi-
ture are performed using the individual household consumer price index as the
deflator to reflect the particular household's consumption bundle.
198 Agricultural Price Distortions, Inequality, and Poverty
Fourth, we compare these ex post real expenditures with the poverty lines
described above to obtain ex post levels of poverty incidence.
Fifth, the changes in poverty incidence reported in table 6.7 (see also table 6.10
elsewhere below) are the ex post levels of poverty incidence, minus the ex ante lev-
els corresponding to each of the three poverty lines described above. A positive
number thus indicates an increase in the simulated level of poverty incidence as a
result of the relevant shocks.
In addition to the effects on the incidence of poverty, we also report simulated
effects on inequality in the distribution of household real expenditures using the
Gini coefficient as the measure. The Gini coefficient has values between 0 and 1;
the higher values reflect greater inequality. These coefficients are estimated by
constructing Lorenz curves from the distributions of ex ante and ex post real
expenditures and then calculating the Gini coefficients corresponding to these
distributions. (These results are presented in tables 6.8 and 6.11; see elsewhere
below.)
Panel a in table 6.7 indicates that unilateral liberalization in Indonesia applied
to all commodities (simulation A1) raises the incidence of poverty in the poorest
Table 6.7. Poverty Effects of the Prospective Liberalization of
All Commodities, Indonesia
a. Measured at the national poverty line
Changes in poverty incidence (%),
Ex ante level ex post ex ante
of poverty
incidence, Sim A1, Sim B1, rest- Sim C1,
Household % of group unilateral of-the-world global
category population liberalization liberalization liberalization
Rural 1 39.81 0.81 4.82 3.52
Rural 2 34.89 0.76 5.71 4.95
Rural 3 32.29 0.09 5.53 5.49
Rural 4 27.82 0.44 2.64 4.17
Rural 5 23.78 0.82 3.91 4.60
Rural 6 28.01 0.13 3.45 3.85
Rural 7 10.50 0.77 2.07 2.35
Urban 1 15.22 0.02 2.31 2.27
Urban 2 11.16 0.52 2.35 3.09
Urban 3 5.00 0.39 1.03 1.36
Urban households 11.98 0.16 1.99 2.18
Rural households 29.09 0.05 4.26 4.36
All households 23.10 0.12 3.49 3.60
Indonesia 199
b. Measured at the US$1-a-day poverty line
Changes in poverty incidence (%),
Ex ante level
ex post ex ante
of poverty
incidence, Sim A1, Sim B1, Sim C1,
Household % of group unilateral rest-of-the-world global
category population liberalization liberalization liberalization
Rural 1 2.40 0.11 0.47 0.39
Rural 2 13.09 0.43 3.46 2.72
Rural 3 8.74 0.06 2.07 2.06
Rural 4 18.55 0.67 3.13 3.48
Rural 5 8.67 0.54 2.20 2.73
Rural 6 1.80 0.03 0.47 0.51
Rural 7 0.00 0.00 0.00 0.00
Urban 1 7.08 0.00 1.43 1.40
Urban 2 2.66 0.18 0.51 0.68
Urban 3 0.00 0.00 0.00 0.00
Urban households 4.56 0.03 0.91 0.93
Rural households 9.09 0.11 2.09 2.09
All households 7.50 0.08 1.68 1.68
c. Measured at the US$2-a-day poverty line
Changes in poverty incidence (%),
Ex ante level ex post ex ante
of poverty
incidence, Sim A1, Sim B1, Sim C1,
Household % of group unilateral rest-of-the-world global
category population liberalization liberalization liberalization
Rural 1 48.79 2.03 3.29 2.87
Rural 2 74.36 0.62 5.05 3.28
Rural 3 66.73 0.28 6.32 6.30
Rural 4 78.64 0.26 2.79 3.49
Rural 5 66.93 0.63 4.56 5.84
Rural 6 30.59 0.72 4.66 4.81
Rural 7 2.38 0.19 0.53 0.59
Urban 1 53.11 0.05 4.04 3.95
Urban 2 31.43 0.96 2.96 4.48
Urban 3 6.79 0.62 1.39 1.78
Urban households 37.87 0.29 3.19 3.50
Rural households 60.27 0.16 4.16 4.13
All households 52.40 0.00 3.82 3.91
Source: CGE model simulations for Indonesia by the author.
200 Agricultural Price Distortions, Inequality, and Poverty
three rural household categories, but lowers it among the richer rural categories
and in all but the poorest urban categories. The aggregate urban and rural inci-
dence of poverty both decline, but the decline is larger among urban households.
National poverty incidence necessarily declines. All these effects are quite small.
Table 6.7, panels b and c show that these effects are not particularly sensitive to the
poverty line used except that, at the US$2-a-day poverty line, the incidence of
rural poverty rises.
The effects of liberalization in the rest of the world (simulation B1) are quite
different. The incidence of poverty in Indonesia declines significantly in this sce-
nario. The decline occurs in all 10 socioeconomic groups, but is largest in rural
areas. At the national level, poverty incidence declines by 3.5 percent (national
poverty line). This pattern of results is not sensitive to the poverty line used. At the
US$1-a-day and national poverty lines, the incidence of poverty declines signifi-
cantly in both rural and urban areas. The explanation for these outcomes is evi-
dent from the changes in real factor returns described above. Rest-of-the-world
liberalization raises the international prices of agricultural commodities relative
to nonagricultural prices, and this produces an increase in the real value of
unskilled wages and of the returns to agricultural capital and land in Indonesia.
These effects benefit poor households, especially those in rural areas. The inci-
dence of poverty in both urban and, especially, rural areas declines.
Finally, we may compare the combined effects of unilateral reform in Indonesia
and liberalization elsewhere, summarized in the tables as the effects of global
reform (simulation C1). As in the case of simulation B1, the real returns to
unskilled and skilled labor rise significantly, along with the returns to agricultural
land and capital. But the real return to nonagricultural capital declines marginally.
Both rural poverty and urban poverty decline significantly, but, especially, rural
poverty. The central result is that the effects of the rest-of-the-world reform dom-
inate the effects of unilateral liberalization. The outcomes in this scenario are
essentially the same as those of the rest-of-the-world scenario (B1) tempered by
the counteracting domestic liberalization (A1) effects.
The method we use to estimate the changes in the incidence of poverty is illus-
trated in figure 6.1. The figure shows the ex ante (initial) distribution of expendi-
tures for the socioeconomic household group Rural 3 (medium-size farmers with
0.5 to 1 hectare of land), along with the simulated ex post (new) distribution of
expenditures that results from simulation C1. The two curves thus indicate
the cumulative distribution of expenditures per person at constant (year 2000)
prices before and after the shock, that is, the change in nominal expenditures for
each household is deflated by a household-specific index of consumer prices,
reflecting that household's base-period (initial) expenditure pattern. For any level
of expenditure (horizontal axis), each curve shows (on the vertical axis) the share
Indonesia 201
Figure 6.1. Initial and Simulated Levels of Poverty Incidence,
Indonesia
(as illustrated by socioeconomic household category Rural 3, simulation C1)
100
90
80
percent of population
70
60
50
40
30 32.3
28.8
20
10
0
0 5 10 15 20 25 30 35
expenditure per capita per month (Rp, thousands, at constant 1,000 prices)
intial simulation C1 poverty line
Source: CGE model simulations for Indonesia by the author.
Note: The cumulative distributions of expenditures corresponding to simulation C1 are calculated in real
terms using household-specific consumer price indexes at year 2000 prices as the deflator for each
household. It is therefore possible to compare the initial (year 2000) distribution of expenditures and the
distribution marked simulation C1 with the poverty line for the year 2000. The initial and simulated
(postliberalization) levels of poverty incidence for this household category, using the national poverty
line, resulting from simulation C1 are 32.3 percent and 26.8 percent, respectively. See table 6.7, panel a.
of the population with expenditures less than or equal to that level of expenditure.
For any poverty line, the incidence of poverty may thus be read as the vertical
value of the intersection between the poverty line (horizontal axis) and the cumula-
tive distribution.
Using the national poverty line for 2000, we find that the initial level of poverty
incidence among the Rural 3 group was 32.3 percent, the intersection of the poverty
line with the cumulative initial distribution in figure 6.1. Simulation C1 (global
reform in all commodities) shifts the entire distribution to the right, though not
uniformly, producing the simulated cumulative distribution of expenditures at con-
stant prices, which is labeled simulation C1 in the figure. It is important to note that
this is a shift in real expenditures measured at 2000 prices. The new level of poverty
incidence may therefore be read using the same poverty line that we use elsewhere
above. Poverty incidence declines to 26.8 percent, a decrease of 5.5 percent. It is
apparent from figure 6.1 that the incidence of poverty declined no matter which
202 Agricultural Price Distortions, Inequality, and Poverty
Table 6.8. Income Inequality Effects of the Prospective Liberalization
of All Commodities, Indonesia
Changes in Gini coefficient, ex post ex ante
Ex ante Sim A1, Sim B1, Sim C1,
Household level of Gini unilateral rest-of-the-world global
category coefficient liberalization liberalization liberalization
Urban households 0.3559 0.0025 0.0019 0.0045
Rural households 0.2912 0.0011 0.0002 0.0013
All households 0.3351 0.0023 0.0006 0.0030
Source: CGE model simulations for Indonesia by the author.
poverty line might be chosen. In the case of our simulation here, the conclusion that
the incidence of poverty declined among socioeconomic group Rural 3 is therefore
not dependent on the particular poverty line that we have selected, although the
magnitude of the decline is affected by the choice of poverty line.
The effects on inequality in Indonesia are summarized in table 6.8. Both uni-
lateral liberalization and global liberalization in all commodities raise inequality.
This effect is largest in the case of unilateral liberalization. The effect operates
through the increased returns to skilled labor and nonagricultural capital, which
are factors owned primarily by more affluent urban households.
Results of the liberalization only of agricultural markets
If liberalization is confined to agricultural and lightly processed food products only
(simulations A2, B2, and C2), unilateral liberalization in Indonesia (simulation A2)
lowers the returns to unskilled labor, agricultural capital, and land and raises the
returns to skilled labor and nonagricultural capital (table 6.9). Rice and sugar dom-
inate agricultural protection in Indonesia. These industries are intensive in unskilled
labor and land relative to the rest of the economy. Reducing protection in Indonesia
reduces the real incomes of the owners of unskilled labor and agricultural land
(table 6.10, panels a and c). Real incomes fall in most rural household categories and
rise in urban categories, but reduced agricultural protection also lowers food prices.
The net effects on real expenditures are small. Most rural household categories are
small net losers, and all urban categories are net gainers.
It has been argued elsewhere that the reduced protection of the rice industry
alone reduces the incidence of poverty among both urban and rural households
in Indonesia (Warr 2005). Rice differs from the rest of agriculture in two impor-
tant respects. First, it is more intensive in the use of unskilled labor than the rest of
agriculture. Second, rice is a staple food for most Indonesians and forms a high
share of the total expenditures among the poorest groups. Thus, reductions in the
Indonesia 203
Table 6.9. Aggregate Simulation Results of the Prospective
Liberalization Only of Agricultural Commodities,
Indonesia
Sim A2, Sim B2, Sim C2,
unilateral rest-of-the-world global
Indicator liberalization liberalization liberalization
Macroeconomic aggregates, % change from base
Real GDP, expenditure
side, GDP deflator 0.01 0.25 0.26
Real household consumption,
CPI deflator 0.00 1.07 1.08
Import volume index,
duty-paid weights 0.51 0.57 1.17
Export volume index 0.42 0.43 0.05
GDP price index,
expenditure side 0.17 1.46 1.25
Consumer price index 0.28 1.55 1.20
Nominal change, Rp, billions
GDP 2,411 25,137 22,206
Consumption 2,556 23,709 20,658
Investment 1 2,319 2,322
Inventory 81 728 840
Government expenditure 228 162 65
Exports, net of imports 0 0 0
Real return to factors, % change from base, using the CPI deflator
Unskilled labor 0.5 3.2 2.7
Skilled labor 0.7 2.5 1.8
Agricultural capital 2.0 15.7 13.4
Nonagricultural capital 0.4 1.6 1.2
Land 1.1 29.7 28.8
Real household expenditure, % change from base, using the CPI deflator
Rural 1 0.0 1.1 1.2
Rural 2 0.4 2.1 1.7
Rural 3 0.3 2.3 2.0
Rural 4 0.1 1.4 1.3
Rural 5 0.1 0.6 0.8
Rural 6 0.2 1.8 1.6
Rural 7 0.0 1.1 1.1
Urban 1 0.0 1.1 1.1
Urban 2 0.2 0.8 1.0
Urban 3 0.2 0.2 0.4
Source: CGE model simulations for Indonesia by the author.
Note: CPI consumer price index.
204 Agricultural Price Distortions, Inequality, and Poverty
Table 6.10. Poverty Effects of the Prospective Liberalization of
Only Agricultural Commodities, Indonesia
a. Measured at the national poverty line
Changes in poverty incidence (%),
Ex ante level ex post ex ante
of poverty
incidence, Sim A2, Sim B2, Sim C2,
Household % of group unilateral rest-of-the-world global
category population liberalization liberalization liberalization
Rural 1 39.81 0.00 1.93 2.00
Rural 2 34.89 0.25 1.73 1.37
Rural 3 32.29 0.17 1.40 1.25
Rural 4 27.82 0.06 0.78 0.74
Rural 5 23.78 0.15 0.39 0.58
Rural 6 28.01 0.11 0.76 0.70
Rural 7 10.50 0.02 0.73 0.72
Urban 1 15.22 0.11 0.47 0.52
Urban 2 11.16 0.05 0.16 0.25
Urban 3 5.00 0.05 0.00 0.06
Urban households 11.97 0.08 0.29 0.35
Rural households 29.09 0.05 1.11 1.06
All households 23.10 0.02 0.85 0.83
b. Measured at the US$1-a-day poverty line
Changes in poverty incidence (%),
Ex ante level ex post ex ante
of poverty
incidence, Sim A2, Sim B2, Sim C2,
Household % of group unilateral rest-of-the-world global
category population liberalization liberalization liberalization
Rural 1 2.40 0.00 0.14 0.15
Rural 2 13.09 0.13 0.81 0.67
Rural 3 8.74 0.11 0.84 0.77
Rural 4 18.55 0.09 0.96 0.92
Rural 5 8.67 0.10 0.23 0.35
Rural 6 1.80 0.01 0.15 0.14
Rural 7 0.00 0.00 0.00 0.00
Urban 1 7.08 0.04 0.30 0.35
Urban 2 2.66 0.03 0.05 0.09
Urban 3 0.00 0.00 0.00 0.00
Urban households 4.56 0.03 0.18 0.22
Rural households 9.09 0.03 0.50 0.48
All households 7.50 0.01 0.38 0.39
Indonesia 205
c. Measured at the US$2-a-day poverty line
Changes in poverty incidence (%),
Ex ante level ex post ex ante
of poverty
incidence, Sim A2, Sim B2, Sim C2,
Household % of group unilateral rest-of-the-world global
category population liberalization liberalization liberalization
Rural 1 48.79 0.01 0.89 0.92
Rural 2 74.36 0.20 1.27 1.03
Rural 3 66.73 0.41 2.42 2.21
Rural 4 78.64 0.05 0.56 0.51
Rural 5 66.93 0.09 0.39 0.51
Rural 6 30.59 0.46 1.70 1.58
Rural 7 2.38 0.00 0.17 0.17
Urban 1 53.11 0.04 1.04 1.12
Urban 2 31.43 0.38 0.60 0.69
Urban 3 6.79 0.08 0.01 0.10
Urban households 37.87 0.11 0.71 0.79
Rural households 60.27 0.11 0.98 0.92
All households 52.40 0.03 0.89 0.88
Source: CGE model simulations for Indonesia by the author.
protection of rice reduce real unskilled wages, but they also reduce the consumer
price of rice, the staple food of the poor. The latter effect dominates.
However, if the reduction in protection applies to all agricultural products,
including sugar, the effect on the incidence of poverty becomes more ambiguous.
Reductions in protection still reduce unskilled wages, but the poverty-reducing
effect of lowering consumer prices is less strong in protected agriculture in general
than in rice alone. Unilateral agricultural liberalization causes the incidence of
poverty to rise in rural areas and to fall in urban areas. The net effect on the national
incidence of poverty is a small decline, measured by the national poverty line, but a
small increase, measured by the US$1-a-day and US$2-a-day poverty lines.
Agricultural liberalization in the rest of the world (simulation B2) raises the
prices of agricultural commodities relative to the prices of nonagricultural com-
modities on international markets. The effect is to raise the returns to unskilled
labor in Indonesia and to lower the returns to skilled labor. The incidence of
poverty declines in both rural and urban areas, and this qualitative result holds
regardless of the poverty line. If the world liberalizes, including Indonesia (simu-
lation C2), the quantitative effects of this liberalization also dominate the effects
of domestic liberalization. The results of global agricultural liberalization are thus
qualitatively similar to the results of rest-of-the-world liberalization.
206 Agricultural Price Distortions, Inequality, and Poverty
Table 6.11. Income Inequality Effects of the Prospective Liberalization
of Only Agricultural Commodities, Indonesia
Changes in Gini coefficient, ex post ex ante
Ex ante Sim A2, Sim B2, Sim C2,
Household level of Gini unilateral rest-of-the-world global
category coefficient liberalization liberalization liberalization
Urban households 0.3559 0.0001 0.0006 0.0006
Rural households 0.2912 0.0000 0.0001 0.0000
All households 0.3351 0.0002 0.0007 0.0006
Source: CGE model simulations for Indonesia by the author.
Inequality in Indonesia rises slightly under unilateral agricultural liberalization
because unskilled wages decline relative to skilled wages. Within rural areas alone,
there is no change in inequality because, although real wages decline, this effect is
offset by the reduced returns to agricultural capital and land. Rest-of-the-world
reform and global agricultural reform reduce inequality in Indonesia because
the returns to unskilled labor, agricultural capital, and land (owned by the poor,
especially the rural poor) rise relative to other factor returns. These effects are
summarized in table 6.11.
Comparing the results of the liberalization in all commodities (simulations A1
to C1) with the liberalization in agricultural products only (simulations A2 to
C2), six key points emerge, as follows:
� Trade liberalization reduces the incidence of poverty in Indonesia, but the
effects are large only if they apply at a global level and to all commodities.
� The effects of rest-of-the-world liberalization dominate the effects of unilateral
liberalization, and rest-of-the-world liberalization is more strongly poverty
reducing than liberalization in Indonesia alone.
� Unilateral across-the-board liberalization in Indonesia reduces food prices and
raises the real returns to skilled labor, while lowering the returns to agriculture-
specific factors of production,which produces small net reductions in the incidence
of poverty overall, though the effects on individual rural household categories are
mixed.
� Liberalization in all commodities is more strongly poverty reducing in Indone-
sia than is liberalization in agricultural products alone, whether the liberaliza-
tion occurs unilaterally in Indonesia, or in the rest of the world, or both.
� Unilateral liberalization confined to agricultural products generates benefits
mainly among urban households; these benefits operate through reduced food
prices, plus increased returns to skilled labor and nonagricultural capital.
Indonesia 207
� Liberalization in all commodities raises inequality in Indonesia, whether the
liberalization is unilateral or global, but especially the former, whereas liberal-
ization only in agricultural products raises inequality slightly if the liberaliza-
tion is unilateral and reduces inequality if the liberalization is global.
Conclusions
The comparative static nature of our analysis in this chapter limits our capacity to
capture the full economic gains available through liberalization. Dynamic effects
are not captured, and these may be significant sources of additional welfare gains
and poverty reduction from trade policy liberalization. Nonetheless, within this
comparative static limitation, the analysis indicates that the global reform of trade
policies in all commodities would be a significant potential source of poverty
reduction in Indonesia. The poor in Indonesia--rural and urban--have a strong
interest in global trade policy reform, whether Indonesia is part of the liberaliza-
tion or not. If Indonesia liberalizes unilaterally, poverty incidence also declines,
but the comparative static benefits are much smaller.
If liberalization is confined to agricultural products, the pattern of the effects is
similar, but the declines in the incidence of poverty in Indonesia are much more
modest. Global reform in agricultural products generates significant reductions in
both rural and urban poverty. Indonesia's rural and urban poor have a strong
interest in the global reform of agricultural trade policy, but--according to the
comparative static analysis of our study--the rural poor do not necessarily have
an interest in unilateral agricultural liberalization because some rural socioeco-
nomic groups would lose out from this sort of liberalization.
Notes
1. For a fuller discussion of agricultural assistance in Indonesia, see Fane and Warr (2008, 2009).
The estimates by these authors of agricultural assistance in Indonesia are incorporated in the World
Bank database on global agricultural distortions (see Anderson and Valenzuela 2008). These estimates
cover over four decades. The representative values for the computable general equilibrium modeling as
of 2004 that are used here are available in Valenzuela and Anderson (2008).
2. Variations on this assumption are possible. For example, the possibility of unemployment
may be introduced by varying the closure to make either real or nominal wages exogenous, thereby
allowing the level of employment to be endogenously determined by demand.
3. For an application of an earlier version of this model to the relationship between economic
growth and the incidence of poverty in Indonesia, see Fane and Warr (2003).
4. Given that the exported and domestically sold goods are considered identical, this assumption is
required to separate the domestic price of the import-competing good from the price of the exported
good. Otherwise, the Armington structure we have described above would be redundant. An alterna-
tive treatment is to distinguish between the exported commodity and the commodity sold domesti-
cally; between them would be a finite elasticity of transformation in production.
208 Agricultural Price Distortions, Inequality, and Poverty
5. The population size of each of the 10 major categories is not the same, but, within each of these
10 categories, the population size of each of the 100 subcategories is the same.
6. Unpublished tax rate data of the Ministry of Finance were accessed in Jakarta in March 2008.
7. The assumption that each export demand elasticity is 20 means that export prices are close to
exogenous.
References
Anderson, K., and E. Valenzuela. 2008. "Estimates of Global Distortions to Agricultural Incentives,
1955�2007." Data spreadsheet, October, World Bank, Washington, DC. http://go.worldbank.org/
YAO39F35E0.
BPS (Badan Pusat Statistik, Statistics Indonesia). 1990. Input-Output Tables of Indonesia. Jakarta: BPS.
------. 2000. The National Socioeconomic Survey (Susenas), 1999. Jakarta: BPS.
------. 2001. Input-Output Tables of Indonesia, 2000. Jakarta: BPS.
------. 2005. Social Accounting Matrix for Indonesia, 2000. Jakarta: BPS.
------. 2007. Statistik Indonesia: Statistical Yearbook of Indonesia 2007. Jakarta: BPS.
Dixon, P. B., B. R. Parmenter, J. Sutton, and D. P. Vincent. 1982. ORANI: A Multisectoral Model of the
Australian Economy. Amsterdam: North-Holland.
Fane, G., and P. Warr. 2003. "How Economic Growth Reduces Poverty: A General Equilibrium Analy-
sis for Indonesia." In Perspectives on Growth and Poverty, ed. R. van der Hoeven and A. Shorrocks,
217�34. New York: United Nations University Press.
------. 2008. "Agricultural Protection in Indonesia." Bulletin of Indonesian Economic Studies 44 (1):
133�50.
------. 2009. "Indonesia." In Distortions to Agricultural Incentives in Asia, ed. K. Anderson and
W. Martin, 165�96. Washington, DC: World Bank.
Hertel, T. W., ed. 1997. Global Trade Analysis: Modeling and Applications. New York: Cambridge
University Press.
Horridge, M. 2004. "ORANI-G: A Generic Single-Country Computable General Equilibrium Model."
Centre of Policy Studies, Monash University, Melbourne.
Johansen, L. 1964. A Multi-sectoral Study of Economic Growth. Amsterdam: North-Holland.
PovcalNet. World Bank, Washington, DC. http://go.worldbank.org/NT2A1XUWP0 (accessed May
2008).
Valenzuela, E., and K. Anderson. 2008. "Alternative Agricultural Price Distortions for CGE Analysis of
Developing Countries, 2004 and 1980�84." Research Memorandum 13 (December), Center for
Global Trade Analysis, Department of Agricultural Economics, Purdue University, West Lafayette,
IN. https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=2925.
van der Mensbrugghe, D. 2005. "Linkage Technical Reference Document: Version 6.0." December,
World Bank, Washington, DC. http://go.worldbank.org/7NP2KK1OH0.
Warr, P. 2005. "Food Policy and Poverty in Indonesia: A General Equilibrium Analysis." Australian
Journal of Agricultural and Resource Economics 49 (4): 429�51.
Warr, P., M. Aziz, H. da Costa, and P. J. Thapa. 1998. "Wayang: An Empirically Based Applied
General Equilibrium Model of the Indonesian Economy." Unpublished report, Australian
National University, Canberra.
7
Pakistan
Caesar B. Cororaton and David Orden
This chapter analyzes the macroeconomic, sectoral, and poverty implications of
the removal of agricultural and nonagricultural price distortions in the domestic
markets of Pakistan and in the rest of the world. Our analysis uses the simulated
results of rest-of-the-world trade liberalization in the World Bank's global Linkage
model (hereafter referred to as the global model; see van der Mensbrugghe 2005)
and derives results for rest-of-the-world and own-country liberalization from the
Pakistan computable general equilibrium (CGE) model of Cororaton and Orden
(2008). The global model incorporates new estimates of assistance to farm indus-
tries for various developed and developing countries, including Pakistan, from the
World Bank's research project, Distortions to Agricultural Incentives.1 Using these
new estimates, the global model simulates two separate scenarios: one involving
full trade liberalization and one involving trade liberalization in agriculture alone.
Both scenarios exclude Pakistan. The global model simulations generate changes
in the import prices for Pakistan at the border, together with changes in world
export prices and shifts in the export demand for Pakistan products. We utilize
these results, along with the new estimates of industry assistance in Pakistan gen-
erated by Dorosh and Salam (2009), to analyze various liberalization scenarios
and measure their impacts on national welfare, income inequality, and poverty
using the Pakistan CGE model.
Trade reform entails a fiscal revenue loss to the government because trade taxes
are an important source of revenue. We conduct experiments using two alterna-
tive tax replacement schemes to retain a fixed fiscal balance: a direct tax on house-
hold incomes and an indirect tax on consumption. We are thus able to show how
the results differ according to the choice of the tax replacement method.
The simulation analysis is conducted in stages. In the first stage, we run two
separate experiments. One experiment involves using the changes in the border
209
210 Agricultural Price Distortions, Inequality, and Poverty
prices and the computed shifts in the world export demand for Pakistan products
from the global model (see the appendix by van der Mensbrugghe, Valenzuela, and
Anderson) as an exogenous shock to the Pakistan model without altering the exist-
ing structure of price-distorting policies in Pakistan. The other experiment involves
simulating unilateral trade liberalization in Pakistan without incorporating the
changes from the global model. In the second stage, we combine these two separate
experiments to examine the total effects. We conduct separate experiments in each
stage for trade liberalization in all tradable goods sectors and in agriculture only
(including lightly processed food). The simulations generate vectors of household
income and consumer prices, which we use in conjunction with data from the
2001�02 Pakistan Household Integration Economic Survey (Federal Bureau of
Statistics 2003) to calculate the impact on national income inequality and poverty.
The chapter is organized as follows. The next section discusses the structure of
agricultural and trade distortions in Pakistan based on the new estimates of indus-
try assistance. The Pakistan CGE model is then outlined. This section covers the
model database (which reveals the structure of sectoral production, trade, and con-
sumption), the sources of household income, and the tax structure based on the
2001�02 social accounting matrix (SAM). This is followed by a description of trends
in rural and urban poverty in Pakistan. The policy experiments and the results
generated by the various modeling scenarios are subsequently discussed in detail.
The last section presents a summary of the findings and policy insights. The choice
of tax replacement schemes plays an important role in the results we discuss.
Agricultural Policies and Industry Assistance
in Pakistan
The period from the 1960s to the mid-1980s involved heavy government interven-
tion in Pakistan (Dorosh and Salam 2009). The government's hand on agricul-
tural markets, trade policies, and the market for foreign exchange depressed the
real prices of tradable agricultural commodities. The fixed exchange rate policy
during these years, together with high domestic inflation, eroded significantly the
competitiveness of the export sectors. However, during these years, the green rev-
olution took place in agriculture. The revolution involved a package of inputs
such as seeds, fertilizer, and irrigation that boosted agricultural production
through higher farm productivity. Then, from the mid-1980s to the early 1990s,
the government started to liberalize the agricultural sector, though it maintained
control over the domestic wheat market and imposed high tariffs on vegetable oils
and milk products.
Prior to the 1990s, Pakistan had been pursuing an import-substituting industri-
alization strategy that involved high tariff rates and quantitative import restrictions
Pakistan 211
to promote the manufacturing sector. Then, major reforms were implemented in
1991 and 1997 that involved a series of tariff rate cuts and the phasing out of the
quantitative import restrictions. The maximum tariff rates were reduced from 65
to 45 percent, and the number of tariff categories was cut from 13 to 5. This led to a
significant drop in government revenue from trade taxes because tariffs had been
the major contributor to government funds.
The key policy changes affecting agricultural prices are summarized in the rest
of this section, while the policy changes affecting the manufacturing sector are
described elsewhere below.
Wheat is the staple food in Pakistan. The wheat market is still heavily con-
trolled by the government through various instruments, including government
procurement (to stabilize supply), support pricing (to assist farmers), and ceiling
prices (to ensure affordability among consumers). However, Pakistan's trade and
pricing policies on wheat effectively tax wheat producers, while providing sub-
stantial fiscal subsidies to wheat millers through the government sale of wheat at
below market prices (Dorosh 2005).
Government involvement in the market for cotton, which is the largest cash
crop in Pakistan, has changed substantially over time. In 1974, the government
prevented the private sector from engaging in the international cotton trade, but
this changed in 1989, when the private sector was allowed to buy cotton directly
from ginners, export, and sell cotton domestically. Also, exports of cotton were
subjected to an export tax. After the abolition of the export duty on cotton in
1994, domestic prices were more in line with international prices (Cororaton and
Orden 2008). Since the mid-1990s, exports and imports of cotton have been prac-
tically duty free, although seed cotton continues to enjoy indirect protection
because of import tariffs on vegetable oils that increase the price of cotton seed
oil. Otherwise, government intervention has recently been limited to an annual
review of the support prices for seed cotton and some public sector procurement
to maintain these prices.
Rice is the third largest crop after wheat and cotton. There were heavy controls
on rice in the early 1970s when the government instituted a monopoly procure-
ment scheme to limit domestic consumption and expand exports. The two varieties
of rice (basmati and the ordinary coarse rice called IRRI rice) are exported. The
intervention system still exists but, since 2003�04, government procurement has
been minimal. There were no export taxes on rice in the mid-2000s, but imports
were subject to a 10 percent customs duty. The average domestic price of rice is
below the export price (often about 20 percent) because of quality differences.
The domestic marketing and processing of sugarcane were highly regulated
until the mid-1980s. The zoning of sugar mills required farmers to sell sugarcane to
mills inside their zone until 1987. There has been no government procurement of
212 Agricultural Price Distortions, Inequality, and Poverty
sugarcane, but the government annually announces a support price that greatly
assists sugarcane and refined sugar production, and it adjusts import tariffs and the
related taxes to stabilize domestic prices. There are export bans on sugarcane and
refined sugar, but they do little to reduce the high level of assistance to the industry.
There was a minor tax on vegetable oils in the 1970s and 1980s, but, since the
1990s, vegetable oil imports have been taxed heavily. For example, in 2005�06, the
tariff was 32 percent on imported soybean oil and 40 percent on palm oil. Like-
wise, the domestic prices of sunflower oil are considerably higher than the border
price. Nonetheless, two-thirds of the edible oil requirements of Pakistan are
supplied by imports.
Maize is mainly used as feed in the livestock and poultry sectors. Maize pro-
duction has expanded rapidly in recent years because of the strong demand for
poultry products. The government has not intervened in the production and mar-
keting of maize. However, there are tariffs on imported maize; these range from 10
to 25 percent. Maize was a nontradable crop between 1990 and 2005; thus, import
tariffs had only minor effects on domestic prices.
The import tariffs on milk are high. In the 1970s and 1980s, the average pro-
tection was estimated at 74 percent, but the extent of protection has diminished,
and, in the first half of the present decade, protection averaged about 35 percent
(Dorosh and Salem 2009).
The Pakistan CGE Model
This section summarizes the structure of the Pakistan CGE model; more details
may be found in Cororaton and Orden (2008). It also discusses the method we use
to introduce changes in the model to interface with the results generated by the
global model. The model database representing the Pakistan economy is also
summarized, along with the key parameters of the model.
Structure of the national model
The Pakistan CGE model of Cororaton and Orden (2008) is calibrated to the
2001�02 SAM constructed by Dorosh, Niazi, and Nazli (2004).2 The model has
34 production sectors in primary agriculture, lightly processed food, other manu-
facturing, and services. There are five categories of productive factors: three types
of labor (skilled labor, unskilled labor, and farm labor), as well as capital and land.
There are also 19 household categories, a government sector, a firm sector, and the
rest of the world.
In the model, output (X) is a composite of value added (VA) and intermediate
inputs. Output is sold to the domestic market (D) and may also be sold to the
export market (E). Goods E and goods D are perfect substitutes. The sources of
Pakistan 213
supply in the domestic market are domestic output and imports (M); substitution
between D and M are dependent on the change in the relative prices of D and M and
on the substitution parameter in a constant elasticity of substitution function.
The primary factors of production in agriculture are unskilled labor (a com-
posite of the labor of farmers and hired unskilled labor), land, and capital, while,
in nonagriculture, they are skilled labor, unskilled labor, and capital. The on-farm
labor of farmers is used only in primary agriculture. Other unskilled labor
(including by farmers) is mobile across sectors and is employed in agricultural
and nonagricultural sectors, while skilled labor is only mobile among nonagricul-
tural sectors. Capital is fixed in each sector; so, the rates of return may differ across
sectors.3 The use of land may shift among agricultural industries.
The sources of household income are factors of production, transfers, foreign
remittances, and dividends. Household savings are a fixed proportion of dispos-
able income. According to the SAM, nonpoor urban households pay direct
income taxes to the government, while other households do not. Household
demand is specified as a linear expenditure system.
The government obtains revenue from direct taxes on household and firm
incomes, tariffs, other receipts, and indirect (consumption) taxes on domestic and
imported goods. It spends on the consumption of goods and services, transfers,
and other payments. We assume a fixed government fiscal balance in nominal
terms. Tariff policy reforms result in changes in government income and expendi-
ture, but the government balance is fixed through a tax replacement. We use a
direct income tax replacement, but also compare the results under an adjustment
via an indirect sales tax replacement on domestic consumption.4 In either case,
the tax replacement is endogenously determined so as to maintain a fixed level of
government balance.
Foreign savings are also fixed. The num�raire is a weighted index of the price of
value added in which the weights are the shares of the sectoral value added in the
base calibration. The nominal exchange rate is flexible. Furthermore, we introduce
a weighted price of investment and derive total investment in real prices. We hold
total investment in real prices fixed by introducing an adjustment factor in the
household savings function. Equilibrium in the model is achieved if the supply and
demand of goods and services are equal and if investment is equal to savings.
Linking the global model and the Pakistan model
There are various methods for transmitting the results derived from a global CGE
model to a single-country CGE model. Horridge and Zhai (2006) propose, for
imports, the use of border price changes from the global model simulation of rest-
of-the-world liberalization (that is, without Pakistan). For Pakistan's exports, their
proposed scheme is as follows.
214 Agricultural Price Distortions, Inequality, and Poverty
The export demand in the Pakistan model is
PWE0 h
E = E0 c d , (7.1)
PWE
where E is exports; PWE0 is international prices; PWE is the free on board (bor-
der) prices of Pakistan's exports; h is the export supply elasticity, the value of
which is equal to ESUBM (the Armington parameter in the global model); and E0
is the scale parameter in the demand function. Because exports and domestic
goods are perfect substitutes, the export price in local currency is equal to the local
price, which does not include indirect taxes.
The change in the export demand shifter, E0, is derived as:
E0 = 100 # (a - 1), (7.2)
where a = (1 + 0.01p)([1 + 0.01q][1/ESUBM]), in which p is the change in the
border export price, and q is the change in the export volume from the global
model of rest-of-the-world liberalization, excluding Pakistan (Horridge and Zhai
2006). The purpose of the introduction of the export demand shift calculated in
(7.2) is to allow the Pakistan model--not the simpler representation of Pakistan
in the global model--to determine export supply behavior and the equilibrium
prices and quantities of Pakistan's exports, taking into account the world demand
shift in the global model.
Economic structure in the SAM and key parameters
in the Pakistan model
Table 7.1 shows the sectoral structure of production and trade in the model based on
the 2001�02 SAM. Of the 34 sectors, 12 are primary agricultural sectors (sectors 1 to
12), while sectors 14 to 18, which represent lightly processed food, are part of the
broadly defined agricultural sector in this analysis. The nonagricultural sectors
include the mining industry (sector 13), other food (sector 19), manufacturing
industries (sectors 20 to 27), energy (sector 28), construction (sector 29), and five
service sectors (sectors 30 to 34). Among these broad sectoral groupings, agriculture
produces 27 percent of the sectoral value added and 28 percent of the gross value of
sectoral output. In the SAM, it accounts for 13 percent of total employment.5
There are 19 household groups in the model. The agriculture-based groups are
categorized according to household location (Punjab, Sindh, and other Pakistan)
and the size of land holdings (large, medium, and small farms; landless small-
farm renters, and agricultural workers without land). In addition, there are four
nonfarm national aggregates: rural nonfarm poor, rural nonfarm nonpoor, urban
poor, and urban nonpoor. Table 7.2 shows the 19 households in the SAM and
the corresponding characteristics of the 19 household groups in the Household
Integration Economic Survey.
Table 7.1. Elasticity Parameters and Production Structure, Pakistan, 2001�02
Production
Value
added Value Employ- Land- Indirect Trade
Share (%)
ratio added Output Capital- ment output tax Elasticities Exports (%) Imports (%)
va/x, share, share, labor share, Skilled Unskilled ratio, rate,
Sectors % % % ratioa % labor labor Farmers % sig_va b
% eta c
sig_m d
share intensity e
share intensityf
Agriculture 26.8 27.7 12.6 8.5 6.6
Primary agriculture 23.2 20.1 10.7 3.9 3.1
1. Wheat, irrigated 50.8 1.8 1.8 0.3 1.58 18.86 81.14 27.82 0.75 0.10 5.85 2.93 0.64 3.56 0.30 2.53
2. Wheat, nonirrigated 50.9 0.1 0.1 0.3 0.07 18.85 81.15 27.25 0.75 0.00 5.85 2.93
3. Paddy, IRRI 60.2 0.2 0.2 0.5 0.10 18.84 81.16 45.35 0.75 0.30 4.45 2.23
4. Paddy, basmati 60.2 0.5 0.4 0.5 0.12 18.86 81.14 51.27 0.75 0.00 4.45 2.23
5. Raw cotton 61.2 1.4 1.1 0.3 1.11 18.87 81.13 35.97 0.75 0.04 3.94 1.97
6. Sugarcane 60.0 1.0 0.8 0.7 0.32 18.87 81.13 46.68 0.75 0.07 5.91 2.96
7. Other major crops 71.0 2.8 2.0 0.3 2.42 18.87 81.13 38.88 0.75 0.05 3.94 1.97 0.52 2.65 0.60 4.53
8. Fruits and vegetables 64.2 3.6 2.8 0.6 1.75 18.87 81.13 44.37 0.75 0.34 3.94 1.97 1.05 3.78 1.31 6.94
9. Livestock, cattle
and dairy 53.2 10.3 9.7 9.0 2.56 100.00 0.75 0.00 3.94 1.97 0.05 0.06 0.70 1.08
10. Poultry 51.6 0.7 0.7 9.0 0.18 100.00 0.75 0.00 3.94 1.97 0.01 0.11
11. Forestry 82.1 0.3 0.2 0.0 0.12 18.88 81.12 65.68 0.75 0.00 4.31 2.15 0.48 31.36 0.23 25.16
12. Fishing industry 57.1 0.6 0.5 2.3 0.41 100.00 0.75 0.00 4.31 2.15 1.14 23.79 0.00 0.08
Lightly processed food 3.6 7.6 1.8 4.6 3.4
14. Vegetable oil 7.9 0.2 1.4 6.7 0.07 60.28 39.72 1.50 0.02 3.94 1.97 0.00 0.02 2.33 19.99
15. Wheat, milled 21.8 1.2 2.8 4.4 0.56 64.94 35.06 1.50 0.02 4.45 2.22 0.51 1.82 0.82 4.31
16. Rice, milled IRRI 30.7 0.2 0.4 3.7 0.12 56.75 43.25 1.50 0.00 4.45 2.22 1.72 46.60
17. Rice, milled basmati 29.0 0.5 0.8 3.7 0.25 56.77 43.23 1.50 0.00 4.45 2.22 2.34 28.58
18. Sugar 32.2 1.4 2.2 3.3 0.82 69.96 30.04 1.50 6.75 5.91 2.96 0.03 0.11 0.28 1.89
215
(Table continues on the following pages.)
216
Table 7.1. Elasticity Parameters and Production Structure, Pakistan, 2001�02 (continued)
Production
Value
added Value Employ- Land- Indirect Trade
Share (%)
ratio added Output Capital- ment output tax Elasticities Exports (%) Imports (%)
va/x, share, share, labor share, Skilled Unskilled ratio, rate,
Sectors % % % ratioa % labor labor Farmers % sig_va b
% eta c
sig_m d
share intensity e
share intensityf
Nonagriculture 73.2 72.3 87.4 91.5 93.4
Mining and
manufacturing 13.2 24.2 7.9 74.1 88.2
13. Mining 74.6 0.6 0.4 2.3 0.47 85.00 15.00 1.50 14.50 4.31 2.16 0.78 18.61 9.29 80.53
19. Other food 36.9 1.7 2.3 4.7 0.75 61.57 38.43 1.50 44.69 3.94 1.97 12.07 51.47 1.06 12.45
20. Cotton lint
and yarn 21.6 1.4 3.3 3.3 0.82 85.52 14.48 1.50 12.05 4.11 2.06 8.97 27.10 0.71 4.27
21. Textiles 22.2 3.6 8.0 2.7 2.43 78.91 21.09 1.50 0.00 4.11 2.06 31.91 39.66 1.61 4.81
22. Leather 8.3 0.1 0.5 2.9 0.06 60.36 39.64 1.50 0.00 4.11 2.06 2.27 42.79 0.11 5.21
23. Wood products 36.3 0.7 0.9 1.8 0.57 67.98 32.02 1.50 0.18 4.09 2.04 0.03 0.34 0.56 8.61
24. Chemicals 28.2 0.5 0.9 3.8 0.25 55.16 44.84 1.50 2.58 4.09 2.05 1.38 15.90 11.16 69.89
25. Cement and bricks 55.0 1.4 1.3 7.4 0.42 68.98 31.02 1.50 24.15 4.09 2.05 0.03 0.21
26. Petroleum
refining 19.4 0.6 1.5 2.9 0.36 71.95 28.05 1.50 28.96 4.09 2.05 9.71 50.11
27. Other
manufacturing 25.4 2.6 5.0 2.6 1.75 67.99 32.01 1.50 4.39 4.09 2.05 16.61 33.17 54.00 71.03
Other industry 6.6 6.6 7.2 0.0 0.0
28. Energy 60.8 3.4 2.8 4.0 1.69 80.00 20.00 1.50 5.02 2.08 1.04
29. Construction 41.6 3.2 3.8 0.4 5.48 50.00 50.00 1.50 0.16 2.08 1.04
Services 53.5 41.5 72.4 17.5 5.2
30. Commerce 84.0 15.3 9.1 0.4 26.52 20.00 80.00 1.50 0.00 2.08 1.04 0.07 0.07 0.21 0.35
31. Transport 53.9 11.8 10.9 1.5 11.73 20.00 80.00 1.50 0.27 2.08 1.04 17.38 15.88
32. Housing 81.8 4.9 3.0 1.50 0.03 2.08 1.04
33. Private services 53.5 12.9 12.0 1.5 12.79 20.00 80.00 1.50 0.00 2.08 1.04 0.01 0.01 5.03 5.98
34. Public services 66.2 8.6 6.5 21.35 100.00 0.00 2.08 1.04
Total 49.8 100.0 100 100.0 100.0 10.0 100.0 14.45
Source: Dorosh, Niazi, and Nazli (2004).
Note: va-value added; x-output.
a. total labor total capital
b. sig_va substitution parameter in CES production
c. eta export demand elasticity
d. sig_m substitution parameter in CES composite good
e. export output
f. imports composite good
217
218 Agricultural Price Distortions, Inequality, and Poverty
Table 7.2. Model Household Categories, Pakistan
2001�02 social 2001�02 household
accounting matrix integrated economic survey
Large-farm households Landowners with more than
(Punjab, Sindh, other Pakistan) 50 acres
Medium-farm households Landowners with more than 12.5,
(Punjab, Sindh, other Pakistan) but less than 50 acres
Small-farm households Landowners with less than
(Punjab, Sindh, other Pakistan) 12.5 acres
Small-farm renters, landless No landholdings, but rent land for
(Punjab, Sindh, other Pakistan) farm activities
Rural agricultural workers, landless No landholdings, agricultural
(Punjab, Sindh, other Pakistan) workers
Rural nonfarm nonpoor Rural nonpoor, nonfarmers,
nonagricultural workers
Rural nonfarm poor Rural poor, nonfarmers,
nonagricultural workers
Urban nonpoor Urban nonpoor
Urban poor Urban poor
Sources: Dorosh, Niazi, and Nazli (2004); Federal Bureau of Statistics (2003).
Note: The SAM covers three major provinces: Punjab, Sindh, and other Pakistan, which consists of
Azad Kashmir, Balochistan, North-West Frontier Province, Northern Areas (Gilgit-Baltistan), Federally
Administered, and Federally Administered Tribal Areas.
The structure of consumption varies among household groups. A composite
sector--livestock, cattle, and dairy--shows the highest share in the consumption
basket, varying from 14 percent among large- and medium-farm households in
other Pakistan to 25 percent among agricultural worker households in Punjab. The
other major items in the consumption basket are private services (about 14 per-
cent), transport (about 13 percent), milled wheat (from 4 percent among the urban
nonpoor to 12 percent among agricultural workers in other Pakistan), textiles
(from 5 percent among large- and medium-farm households in other Pakistan to
7 percent among agricultural worker households in Punjab and the urban poor),
other manufacturing (from 1 percent among agricultural worker households
in Sindh to 10 percent among large- and medium-farm households in other
Pakistan), sugar (from 3 percent among urban nonpoor households to 10 percent
among agricultural worker households in other Pakistan), and fruits and vegeta-
bles (from 4 percent among large- and medium-farm households in Punjab to
7 percent among agricultural worker households in other Pakistan). Commodities
with high foreign trade content are impacted significantly by changes in trade
policies and world prices. This has varying effects across household groups because
of differences in the household consumption bundles.
Pakistan 219
The sectoral indirect tax structure is presented in table 7.1. The highest tax rate,
45 percent, applies to other food, the share of which in the consumption of house-
holds is only about 1 percent. Indirect taxes are also relatively high on cement,
bricks, and petroleum refining, which generally account for less than 1 percent of
direct household consumption, but affect housing and transportation costs. The
tax rate on cotton lint and yarn is 12 percent, while it is zero on textiles. However,
because cotton lint and yarn are major inputs into textile production, an increase in
the tax on the former raises the cost of production of textiles. This affects con-
sumers because the share of textiles in the consumption basket is about 5 percent.
Sugar shows the highest tariff rate, 59 percent (table 7.3). Another commodity
showing high tariffs, averaging 55 percent, is livestock, cattle, and diary, which
account for a large share in the consumption basket of households. Other agricul-
tural commodities showing high tariffs and substantial consumption shares are
milled wheat and vegetable oil. A few primary agricultural and light food process-
ing sectors show low or even negative import tariffs. In contrast, tariffs are uni-
formly relatively high across the manufacturing sectors.
Overall, the foreign trade sector in Pakistan is not large relative to the domestic
sector (table 7.1). Of the total domestic output, only 10 percent goes to the export
market. Only 15 percent of the total goods and services available in the domestic
market are imported. However, there are large differences across sectors. Within
agriculture, the sectors with the highest share of production exported are milled
IRRI rice, 47 percent; forestry, 31 percent; and fishing, 24 percent. The shares
exported are small in the rest of the agricultural sectors. Within the nonagricultural
sectors, other food, at 52 percent, has the highest share of production exported;
leather is at 43 percent; textiles, 40 percent; and cotton lint and yarn, 27 percent. The
textile sector dominates in exports. In the SAM, textiles account for 32 percent of
total exports, cotton lint and yarn for 9 percent, and other food for 12 percent.
Because of the large volume of crude oil imports, the mining sector has the high-
est share of domestic consumption that is imported, at 81 percent. The share of
other manufacturing is 71 percent; of chemicals, 70 percent; and of petroleum,
50 percent. Other manufacturing accounts for 54 percent of overall imports; chem-
icals, 11 percent; and mining and petroleum refining, each about 9 percent. Except
for forestry (25 percent) and vegetable oil (20 percent), the shares of domestic con-
sumption that are imported in the agricultural sectors are well under 10 percent.
Table 7.1 includes the values of key elasticity parameters in the model: the import
substitution elasticity (sig_m) in the constant elasticity of substitution composite
good function and the production substitution elasticity (sig_va) in the constant
elasticity of substitution value added production function.6 The values of the export
demand elasticity (eta) are the Armington parameters of the global model.
The sources of household income in the model are labor income, capital
income, income from land, and other income (table 7.4). Other income is
Table 7.3. Parameters and Exogenous Demand and Price Shocks Caused by Rest-of-the-World Liberalization, Pakistan
220
Full trade Agricultural
liberalization, excluding trade liberalization,
Pakistan CGE model Linkage model Trade distortions Pakistan excluding Pakistan
World
import World
price Export import Export
Sector Sector Sector Tariff, Export change, demand price, % demand
no. description description % tax, % % shifterb change shifterb
Agriculture
Primary agriculture
1. Wheat, irrigated Wheat 4.9 0.0 2.41 1.0 3.12 1.00
2. Wheat, nonirrigated Wheat 4.9 0.0 2.41 1.0 3.12 1.00
3. Paddy, IRRI Paddy rice 0.0 4.0 0.00 1.2 0.00 1.23
4. Paddy, basmati Paddy rice 0.0 4.0 0.00 1.2 0.00 1.23
5. Cotton Plant-based fibers 4.9 0.0 4.44 1.1 6.68 1.14
6. Sugarcane Sugarcane and beets 4.3 0.0 0.00 1.0 0.00 1.00
7. Other major crops Other crops 15.3 0.0 1.91 1.0 0.00 1.01
8. Fruits and vegetables Vegetables and fruits 16.5 0.0 2.93 1.0 2.62 0.97
9. Livestock, cattle, and Cattle, sheep, and so on 55.4 0.0 4.41 1.0 5.17 1.00
dairya
10. Poultry Other livestock 10.8 0.0 8.00 1.0 6.77 0.99
11. Forestry Other primary products 14.3 18.1 0.14 1.1 0.79 1.01
Lightly processed food
12. Fishing industry Other primary products 14.3 18.1 0.14 1.1 0.79 1.01
14. Vegetable oil Vegetable oils and fats 23.7 0.0 1.78 0.9 0.40 0.93
15. Wheat, milled Other food, beverages 24.9 0.0 0.00 1.0 1.68 0.97
and tobacco
16. Rice, milled IRRI Processed rice 0.0 4.0 8.21 1.1 10.18 1.08
17. Rice, milled basmati Processed rice 0.0 4.0 8.21 1.1 10.18 1.08
18. Sugar Refined sugar 59.0 0.0 1.62 1.0 3.44 1.00
Nonagriculture
Mining and manufacturing
13. Mining Other primary products 14.3 18.1 0.14 1.1 0.79 1.01
19. Other food Other food, beverages 24.9 0.0 0.00 1.0 1.68 0.97
and tobacco
20. Cotton lint and yarn Textile and wearing 19.9 1.1 0.68 1.0 0.48 1.00
apparel
21. Textiles Textile and wearing 19.9 1.1 0.68 1.0 0.48 1.00
apparel
22. Leather Textile and wearing 19.9 1.1 0.68 1.0 0.48 1.00
apparel
23. Wood products Other manufacturing 16.6 5.4 0.38 1.0 0.38 1.00
24. Chemicals Other manufacturing 16.6 5.4 0.38 1.0 0.38 1.00
25. Cement and bricks Other manufacturing 16.6 5.4 0.38 1.0 0.38 1.00
26. Petroleum refining Other manufacturing 16.6 5.4 0.38 1.0 0.38 1.00
27. Other manufacturing Other manufacturing 16.6 5.4 0.38 1.0 0.38 1.00
Other industry
28. Energy Services 0.0 0.0 0.22 1.0 0.19 1.00
29. Construction Services 0.0 0.0 0.22 1.0 0.19 1.00
Services
30. Commerce Services 0.0 0.0 0.22 1.0 0.19 1.00
31. Transport Services 0.0 0.0 0.22 1.0 0.19 1.00
32. Housing Services 0.0 0.0 0.22 1.0 0.19 1.00
33. Private services Services 0.0 0.0 0.22 1.0 0.19 1.00
34. Public services Services 0.0 0.0 0.22 1.0 0.19 1.00
Source: Linkage model simulations by van der Mensbrugghe, Valenzuela, and Anderson (see the appendix).
221
a. This is the trade weighted average of cattle, sheep, other livestock, and dairy in the Linkage model.
b. In equation 2, this is a (1 0.01 * p)(1 0.01 * q)^(1/ESUBM); where p is export price change, q export volume change; and ESBUM Arimington elasticity,
all from the Linkage model.
Table 7.4. Household Income Sources and Income Taxes, Pakistan, 2001�02
222 2001�02 income Income sources, %
Per capita, 2001�02
population Labor
Total PRs, Direct
Households mil Rs 1,000s 1,000s % distribution Farm Unskilled Skilled Capital Land Other tax, %
Large-farm households
�Sindh 19,079 112.9 169 0.12 13.2 0.3 -- 31.4 49.8 5.3 --
�Punjab 64,116 173.7 369 0.25 8.6 0.5 -- 43.3 42.2 5.4 --
�Other Pakistana 10,755 152.9 70 0.05 9.8 0.2 -- 52.4 32.3 5.4 --
Medium-farm households
�Sindh 44,625 30.4 1,466 1.00 14.5 3.1 -- 39.6 37.4 5.3 --
�Punjab 145,995 48.4 3,014 2.07 10.8 4.3 -- 52.3 27.2 5.4 --
�Other Pakistan 35,572 34.2 1,040 0.71 14.9 2.9 -- 38.2 38.4 5.5 --
Small-farm households
�Sindh 57,648 14.9 3,873 2.65 6.8 9.0 -- 57.9 20.4 5.8 --
�Punjab 318,888 18.1 17,605 12.06 7.9 14.2 -- 51.8 20.3 5.8 --
�Other Pakistan 124,985 11.9 10,493 7.19 6.0 11.6 -- 63.6 12.5 6.2 --
Small-farm renters, landless
�Sindh 43,672 7.7 5,682 3.89 11.6 18.7 -- 48.5 15.6 5.7 --
�Punjab 45,963 10.7 4,307 2.95 9.0 20.5 -- 48.7 16.1 5.8 --
�Other Pakistan 14,970 8.2 1,818 1.25 10.1 14.5 -- 55.0 14.7 5.7 --
Rural agricultural workers, landless
�Sindh 20,782 6.4 3,241 2.22 -- 51.0 -- 42.8 --- 6.2 --
�Punjab 68,172 12.0 5,693 3.90 -- 49.3 -- 45.2 -- 5.5 --
�Other Pakistan 9,513 14.6 653 0.45 -- 18.7 -- 76.0 -- 5.4 --
Rural nonfarm households
�nonpoor 400,771 19.8 20,233 13.86 -- 43.0 -- 49.9 -- 7.2 --
�poor 134,398 5.5 24,525 16.80 -- 29.7 -- 63.4 -- 6.9 --
Urban households
�nonpoor 1,744,119 58.5 29,829 20.44 -- 11.9 33.3 10.6 -- 44.2 8.4
�poor 181,413 15.3 11,880 8.14 -- 76.2 -- 18.0 -- 5.8 --
Sources: Dorosh, Niazi, and Nazli (2004).
a. Other Pakistan--Azad Kashmir, Balochistan, North-West Frontier Province, Northern Areas (Gilgit-Baltistan), Federally Administered, and Federally Administered
Tribal Areas.
Pakistan 223
composed of foreign remittances, which is assumed in the SAM to be distributed
proportionately among all households, and dividend income, which is earned
only by urban nonpoor households. The sources of income vary across house-
hold groups. Farm households are dependent on income from land, farm labor,
and capital. Other rural households depend on income from unskilled labor and
capital. About three-fourths of the income of urban poor households comes
from unskilled labor. Urban nonpoor households derive 44 percent of their
incomes from other income (composed largely of dividend income) and 33 per-
cent from skilled labor income. According to the SAM, it is only the urban
nonpoor household group that pays income taxes, amounting to 8.4 percent of
their incomes.
Poverty Indicators
The overall poverty rate based on the official national poverty line declined in
Pakistan from about 30 percent in the late 1980s to 26 percent in 1990�91. During
these years, both urban and rural poverty decreased. However, in 1993�94, the
incidence of rural poverty and the incidence of urban poverty started to move in dif-
ferent directions: urban poverty continued to decline, while rural poverty began to
rise, thereby widening the poverty gap between urban and rural areas (figure 7.1).
The gap reached a peak in 2001�02, which was largely caused by the crippling
drought that severely affected agricultural output that year, together with rela-
tively low international agricultural commodity prices. Almost 70 percent of the
people live in rural areas, and, because the majority of these people (40 percent of
all households nationally) depend on agriculture for income, the incidence of
rural poverty increased to 39 percent that year, while urban poverty was stable at
23 percent.
There is some disagreement about the accuracy of recent estimates of poverty.
For 2004�05, the estimates of the Planning Commission of Pakistan show the
overall incidence of poverty declining from the peak of 34 percent in 2001�02 to
24 percent in 2004�05 (Dorosh and Salam 2009). The World Bank (2007) esti-
mates a smaller decline, to 29 percent. Despite the disparity between these
estimates (arising primarily because of the inflation factor used in computing
the relevant poverty lines), the estimates show the incidence of poverty declining
in urban and rural areas in the most recent years, but highlight the large gap
between the rural and urban poverty rates. The depth of poverty indicated by the
Foster, Greer, and Thorbecke (1984) poverty gap and the squared poverty gap also
suggest that the problem of poverty is more severe in rural areas and that this was
especially true during the 2001�02 drought (table 7.5).
224 Agricultural Price Distortions, Inequality, and Poverty
Figure 7.1. Poverty Incidence, Pakistan, 1986�87 to 2004�05
45
40
35
poverty headcount, %
30
25
20
15
10
5
0
1986�87 1987�88 1990�91 1992�93 1993�94 1996�97 1998�99 2001�02 2004�05
Pakistan urban rural
Sources: Ministry of Finance (2003); World Bank (2007) for the 2004�05 estimates.
Note: Poverty is defined as the share of the population living below the national poverty line. Official
data for 1993�94 indicate that the level of overall poverty was above the incidence of poverty in urban
and rural areas; see Ministry of Finance (2003).
Table 7.5. Poverty Estimates, Pakistan, 1998�99, 2001�02,
and 2004�05
Poverty index 1998�99 2001�02 2004�05
Poverty incidence
Pakistan 30.0 31.2 29.2
Urban 21.0 19.9 19.1
Rural 33.8 38.2 34.0
Poverty gap
Pakistan 6.3 6.5 6.1
Urban 4.3 3.9 3.9
Rural 7.1 8.0 7.2
Squared poverty gap, poverty severity
Pakistan 2.0 2.0 2.0
Urban 1.3 1.2 1.2
Rural 2.2 2.5 2.3
Sources: Ministry of Finance (2003); World Bank (2007) for the 2004�05 estimates.
Note: The table reflects the poverty concepts of Foster, Greer, and Thorbecke (1984).
Pakistan 225
Simulations
The first part of this section describes our six policy experiments, while the second
part discusses the results. The experiments use direct tax replacement to hold the
government fiscal balance fixed. The idea is to replace distorting trade taxes with
less-distorting income taxes. The fiscal burden falls on urban nonpoor house-
holds because, according to the SAM, other household groups do not pay income
taxes (table 7.4). We have also conducted an alternative indirect tax replacement
experiment to check the sensitivity of the results to the specification, given
that financing a trade reform is a nontrivial issue from the government's point
of view (Ahmed, Abbas, and Ahmed 2009). In our analysis, we separate the effects
on the economy of reducing distortions in the rest of the world and in domestic
markets in Pakistan, and we evaluate the effects of both on income inequality
and poverty.
Design of the policy experiments
Table 7.3 shows the sectoral correspondence between the Pakistan model and the
global model. It also shows the sectoral tariff rates and export taxes, which, where
possible, are based on the set of estimates on nominal rates of assistance for
Pakistan by Dorosh and Salam (2009). We use these trade distortions in all our
policy experiments. The table presents the changes in border import prices found
through the global model under full trade liberalization and agricultural liberal-
ization by the rest of the world, and it lists sectoral export demand shifters calcu-
lated on the basis of equation (7.2). We have used these data as inputs in the six
policy experiments we have conducted, as follows:
� S1A, full world trade liberalization in all tradable goods sectors by all countries,
excluding Pakistan: This experiment uses the results of the global model under
full trade liberalization in table 7.3. It retains all existing trade distortions in
Pakistan.
� S1B, agricultural price and trade liberalization by all countries, excluding
Pakistan: This scenario uses the results of the global model. As with S1A, all
existing distortions in Pakistan are retained.
� S2A, full goods trade liberalization carried out unilaterally in Pakistan: All
Pakistan trade distortions are set to zero. There are no changes in the sectoral
border export and import prices or in the export demand shifters because
there is no rest-of-the-world trade liberalization.
� S2B, agricultural trade liberalization carried out unilaterally in Pakistan: All
Pakistan distortions in primary agriculture and in lightly processed food are set
to zero. Similar to S2A, there are no changes in the sectoral border export and
226 Agricultural Price Distortions, Inequality, and Poverty
import prices and in the export demand shifters because there is no rest-of-
the-world trade liberalization.
� S3A, full world trade liberalization of all tradable goods, including in Pakistan:
This combines S1A and S2A.
� S3B, agricultural world trade liberalization, including in Pakistan: This
combines S1B and S2B.
In analyzing the results under each of the scenarios, we indicate, first, the
effects on poverty for the whole of Pakistan, for rural and urban areas, and for
major household groups. The results on poverty include changes in the incidence
of poverty and in the depth of poverty measured by the poverty gap and the
squared poverty gap. These poverty effects are traced and analyzed through the
various determining channels: macro, sectoral, commodity, and factor prices, as
well as household incomes. In estimating the poverty effects, we apply the results
on household incomes and consumer prices for each of the 19 household groups
in the CGE model simulations to the household classifications in the Household
Integration Economic Survey. Each of the CGE simulations generates a new vec-
tor of household income and consumer price for each of the groups, which we use
to compute new sets of poverty indexes to compare with the baseline indexes.
Simulation results
In this subsection, we present the modeling results of our six policy experiments.
We continue the discussion with additional results that show the sensitivity of the
core results to changes in the treatment of tax adjustments in the model.
S1A: Trade liberalization by the rest of the world without Pakistan
Full trade liberalization abroad, while retaining all existing trade distortions in
Pakistan, causes the overall poverty incidence index to decline by 1.3 percent from
its base value, from 31.2 to 30.8 (table 7.6). Households at the bottom of the
income ladder benefit the most, as indicated by the greater reduction in the poverty
gap (1.6 percent) and the squared poverty gap (1.9 percent). Among rural house-
holds, the poorest households--those in the rural nonfarmer group--benefit the
most. Thus, rural-urban income inequality is lowered in this scenario also.
What are the forces that drive these reductions in poverty and income inequal-
ity? The S1A simulation leads to a real exchange rate appreciation of 1.24 percent
(table 7.7).7 The terms of trade (the ratio of domestic export to import prices)
improve by 1.38 percent in agriculture and by 1.56 percent in nonagriculture. This
is because of the lower world import prices of some of the agricultural products,
as well as most of the nonfarm goods (table 7.3).
Table 7.6. The Poverty Effects of Prospective Liberalizations, Pakistan
2001�02 % change from 2001�02 index
Index in population
Indicator 2001�02 distribution, % S1A S1B S2A S2B S3A S3B
All Pakistan �P0 31.23 1.3 0.1 5.2 1.6 6.4 1.8
�P1 6.46 1.6 0.1 10.0 2.3 11.5 2.5
�P2 1.97 1.9 0.1 12.1 2.8 13.8 3.0
Gini coefficient 0.34 0.08 0.02 3.3 0.1 3.3 0.2
All urban �P0 19.86 29.7 1.5 0.1 2.3 2.4 0.4 2.7
�P1 3.91 1.5 0.1 13.3 3.6 14.8 3.7
�P2 1.16 1.7 0.1 16.9 4.2 18.3 4.3
Gini coefficient 0.40 0.03 0.01 2.0 0.1 2.0 0.1
All rural �P0 38.16 70.3 1.2 0.1 7.6 1.4 8.6 1.5
�P1 8.02 1.6 0.1 9.0 2.0 10.5 2.1
�P2 2.47 2.0 0.1 10.8 2.4 12.5 2.6
Gini coefficient 0.26 0.01 0.02 0.2 0.1 0.2 0.1
Large- and medium-farm households �P0 22.82 4.0 0.0 0.0 7.9 3.4 7.9 3.4
�P1 4.06 0.7 0.4 9.9 4.9 10.0 5.3
�P2 1.13 0.7 0.5 10.5 5.2 10.6 5.8
Small-farm households and agricultural workers �P0 37.40 30.6 1.4 0.1 8.3 0.9 8.7 0.9
�P1 7.47 1.3 0.0 9.3 1.3 10.3 1.3
�P2 2.20 1.6 0.0 11.2 1.7 12.4 1.6
Rural nonfarm households �P0 39.89 35.7 1.1 0.2 7.2 1.9 8.5 2.1
�P1 8.71 1.9 0.2 8.9 2.6 10.6 2.8
�P2 2.76 2.2 0.2 10.6 3.1 12.6 3.4
Source: National CGE model simulations by the authors.
227
Note: P0 poverty headcount. P1 poverty gap. P2 poverty severity.
228
Table 7.7. Aggregate Simulation Results of Prospective Agricultural and Nonagricultural Liberalization,
Pakistan
(% change from the base)
S1A S1B S2A S2B S3A S3B
Variables Ag Nonag Ag Nonag Ag Nonag Ag Nonag Ag Nonag Ag Nonag
Real GDP 0.15 0.04 0.81 0.26 0.95 0.29
Real and relative prices
Real exchange rate 1.24 0.13 6.09 1.87 4.79 1.73
Domestic terms of tradea 1.38 1.56 0.98 0.13 12.61 9.37 17.30 0.84 14.19 11.06 18.52 0.71
Prices
Export price in local currency 0.26 0.82 0.33 0.13 1.92 0.09 0.63 1.70 2.16 0.66 0.30 1.59
Import price in local currency 1.70 2.35 0.73 0.24 12.26 8.34 14.49 2.57 13.74 10.41 15.13 2.33
Domestic price 0.29 0.55 0.07 0.08 2.47 1.23 1.53 1.11 2.74 1.69 1.56 1.06
Output price 0.29 0.58 0.06 0.08 2.45 1.07 1.50 1.18 2.73 1.57 1.52 1.12
Value added price 0.16 0.12 0.03 0.06 3.14 1.32 2.88 1.01 2.98 1.24 2.79 0.97
Consumer price index 0.51 0.11 2.81 0.27 3.24 0.34
Volume
Imports 2.41 0.96 0.89 0.12 22.15 4.45 29.64 0.48 24.75 5.46 30.42 0.36
Exports 0.73 0.16 1.79 0.11 4.78 13.24 6.20 3.07 5.28 13.07 8.00 3.17
Domestic demand 0.09 0.07 0.01 0.02 0.87 1.12 1.05 0.03 0.76 1.19 1.06 0.01
Composite good 0.18 0.12 0.02 0.01 0.09 0.08 0.20 0.07 0.30 0.05 0.22 0.06
Output 0.11 0.08 0.05 0.01 0.69 0.39 0.82 0.39 0.57 0.50 0.77 0.38
Value added 0.07 0.03 0.03 0.01 0.51 0.19 0.39 0.13 0.43 0.16 0.35 0.12
Factor prices
Farm wages 0.95 0.27 0.58 2.16 1.67 2.44
Wages of unskilled labor 0.14 0.05 1.49 0.47 1.38 0.45
Wages of skilled labor 0.04 0.00 2.06 0.73 2.04 0.74
Return to land 1.00 0.32 1.90 2.52 3.06 2.85
Return to capital 1.00 0.14 0.27 0.09 5.02 1.00 3.80 1.41 3.92 0.93 3.41 1.34
Factor prices less inflation
Farm wages 0.44 0.16 2.23 1.88 1.56 2.10
Wages of unskilled labor 0.37 0.05 4.30 0.74 4.61 0.79
Wages of skilled labor 0.47 0.11 4.87 1.00 5.28 1.09
Return to land 0.49 0.21 0.91 2.25 0.18 2.51
Return to capital 1.51 0.37 0.37 0.02 2.21 3.81 3.53 1.68 0.69 4.17 3.07 1.68
Source: National CGE model simulations by the authors.
Note: Ag agriculture. Nonag nonagriculture.
a
Change in the ratio of domestic export and import prices.
229
230 Agricultural Price Distortions, Inequality, and Poverty
The import prices of agricultural goods drop by 1.7 percent (table 7.7),
despite the increases in the import prices of livestock, wheat, vegetable oil, and
sugar (table 7.3). This is caused by a number of factors, including the appre-
ciation of the currency; the decline in the border import prices of fruits and
vegetables and other major crops (table 7.3), all of which have relatively large
import components (table 7.1); and the slight reduction in the border import
prices of forestry products, which show high import intensity. The domestic
prices of farm products decline by 0.3 percent, which is lower than the drop
in import prices. This results in more imports of agricultural goods (a rise of
2.4 percent) and a marginal increase in the domestic demand for agriculture
(a rise of 0.1 percent). Because the demand for imported and domestic agricul-
tural products increases, the domestic consumption of farm products increases
(by 0.2 percent).
Table 7.3 shows that the border import prices of nonagricultural goods decline.
This, together with the appreciation of the exchange rate, reduces the import
prices of nonagricultural goods by 2.4 percent (table 7.7). The domestic prices of
nonagricultural products also decline, by 0.6 percent, which is lower than the
decline in the import prices. Thus, the imports of nonfarm products increase, by
1.0 percent. At the sectoral level, there is a relatively large rise in imports of cotton
lint and yarn, textiles, and leather because of the relatively greater decline in the
border prices of these products. More imports of nonfarm goods reduce margin-
ally the domestic demand, but, despite this, overall domestic nonagricultural
consumption increases by 0.1 percent.
The export prices of farm products decline by 0.3 percent. Because the border
prices of these products rise, the decline is caused by appreciation in the exchange
rate. There is a slightly greater decrease in the domestic prices of agricultural
products. Thus, exports of agriculture improve by 0.73 percent, and the overall
output of agriculture increases by 0.11 percent.
The effects on value added, value added prices, and factor prices in agriculture
are explained by the changes in sectoral export prices, factor intensities, and
import and export intensities. The overall output price of agriculture declines by
0.29, while the value added price increases by 0.16 percent. The difference in the
sign is generated by a relatively higher increase in the value added price of milled
rice (2 percent) and vegetable oil (1.7 percent).8 The increase by 1.18 percent in
the border export price of milled rice has larger effects on the value added price of
milled rice because rice has a high export-intensity ratio (table 7.1). Although the
border import price of milled rice increases more (10.18 percent), this has no
effects because there are zero imports. The increase by 1.78 percent in the import
border price of vegetable oil increases the value added price of vegetable oil
because vegetable oil has a high import-intensity ratio.
Pakistan 231
Farm wages and the returns to land decline by about 1.0 percent. This is caused
by the decline in the output and value added prices in primary agriculture, which
employs farmers and uses land. The average rate of return to capital in agriculture
improves by 1 percent. This is caused by the increase in the value added price of
milled rice and vegetable oil. These sectors are relatively capital intensive, showing
capital-labor ratios of 3.7 for rice and 6.7 for vegetable oil (table 7.1). Because
wage rates increase less than value added prices, returns to capital rise. The returns
to capital in these sectors increase by more than 2 percent for milled rice and
1.9 percent for vegetable oil. The change in the returns to capital in livestock and
poultry is also positive, but smaller. The change in the returns to capital in the
other primary agricultural commodities is negative.
The decline in the value added prices in primary agriculture and in nonagri-
culture lowers the wages of unskilled labor by 0.14 percent. However, because of
the increase in the value added prices of milled rice and vegetable oil, the wages of
skilled workers decrease by only 0.04 percent. The average returns to the capital
used in nonagriculture decline by 0.14 percent.
We have also included the results on factor prices net of the inflation effects.
The overall consumer price index in this experiment decreases by 0.5 percent. Net
of the inflation effects, there is a negative result for farm wages and the returns to
land, but the other factors show positive net price effects.
All these effects lead to changes in household incomes (table 7.8). The changes
in the nominal incomes of households are negative across groups except rural
nonfarmers and rural agricultural workers, the latter because of their heavy
reliance on agricultural capital incomes (mostly informal capital), as shown in
table 7.4, and the increase in the average returns to capital in agriculture (1 per-
cent; see table 7.7). However, the consumer prices for each of the groups decline
more rapidly than the drop in nominal incomes because of the greater reduction
in import prices. Thus, all household groups realize improvements in real
incomes. The largest increases in real incomes occur among rural nonfarmers
(0.63 and 0.53 for the nonpoor and poor, respectively; see table 7.8) and agricul-
tural workers in other Pakistan (0.58 percent). This explains the significant reduc-
tion in the depth of poverty in rural areas, particularly among rural nonfarmers.
In the scenario, full trade liberalization by the rest of the world thus reduces
both poverty and income inequality. It reduces import prices, especially on com-
modities that have relatively large shares in the consumption basket of consumers.
This translates into declining consumer prices. It also enhances agricultural pro-
duction relative to nonagricultural production because of improvements in the
world price of farm commodities. The poorest nonfarm households in rural areas
benefit the most from the improvement in the real wages of unskilled labor and in
the returns to capital and from the reduction in consumer prices.
232 Agricultural Price Distortions, Inequality, and Poverty
Table 7.8. Household Welfare and Price Effects of the Liberalization
in All Goods Trade by the Rest of the World, Pakistan
S1A S1B
2001�02 Change in Change in EV/ Change in Change in EV/ Change in
population nominal consumer income, nominal consumer income, nominal
Households distribution, % income, % price, % % income, % price, % % income, %
Large-farm households
�Sindh 0.14 0.49 0.51 0.02 0.24 0.08 0.16 0.53
�Punjab 0.17 0.35 0.58 0.23 0.20 0.09 0.11 0.48
�Other Pakistan 0.01 0.24 0.67 0.43 0.17 0.13 0.04 0.39
Medium-farm households
�Sindh 0.99 0.46 0.51 0.05 0.20 0.08 0.11 0.61
�Punjab 1.89 0.30 0.58 0.28 0.15 0.09 0.06 0.50
�Other Pakistan 0.75 0.47 0.67 0.20 0.20 0.13 0.07 0.62
Small-farm households
�Sindh 2.79 0.19 0.44 0.25 0.12 0.06 0.06 0.33
�Punjab 12.87 0.22 0.45 0.23 0.13 0.09 0.04 0.20
�Other Pakistan 5.67 0.10 0.46 0.36 0.09 0.11 0.01 0.19
Small-farm renters, landless
�Sindh 0.16 0.22 0.38 0.16 0.12 0.07 0.06 0.03
�Punjab 1.50 0.20 0.47 0.27 0.12 0.10 0.02 0.00
�Other Pakistan 0.58 0.18 0.52 0.35 0.11 0.12 0.01 0.13
Rural agricultural workers, landless
�Sindh 4.12 0.01 0.38 0.37 0.06 0.09 0.03 0.87
�Punjab 2.02 0.00 0.40 0.39 0.06 0.09 0.03 0.82
�Other Pakistan 0.86 0.10 0.48 0.58 0.04 0.13 0.09 0.07
Rural nonfarm households
�nonpoor 17.60 0.11 0.52 0.63 0.06 0.11 0.04 0.94
�poor 18.14 0.06 0.46 0.53 0.05 0.10 0.06 0.35
Urban households
�nonpoor 22.50 0.22 0.53 0.31 0.11 0.12 0.01 7.21
�poor 7.23 0.09 0.46 0.37 0.08 0.11 0.03 1.49
Source: National CGE model simulations by the authors.
Note: EV equivalent variation.
S1B: Agricultural liberalization by the rest of the world
This second experiment incorporates the results of the global model for agricul-
tural liberalization by the rest of the world, while retaining all existing trade distor-
tions in Pakistan. Compared with scenario S1A, the border import prices of
some commodities increase more in this scenario. For example, there is a greater
increase in the border import prices of wheat, livestock, cotton, milled rice, and
sugar (table 7.3). Furthermore, the border import prices of nonagricultural prod-
ucts increase in this scenario, while they decline in scenario S1A (table 7.7). Also, for
commodities that show declining border import prices, the drop is relatively greater
Pakistan 233
S2A S2B S3A S3B
Change in EV/ Change in Change in EV/ Change in Change in EV/ Change in Change in EV/
consumer income, nominal consumer income, nominal consumer income, nominal consumer income,
price, % % income, % price, % % income, % price, % % income, % price, % %
2.60 2.08 1.63 0.28 1.34 1.09 3.03 1.96 1.84 0.32 1.51
2.82 2.35 1.34 0.16 1.17 0.86 3.31 2.47 1.51 0.22 1.29
2.94 2.57 1.11 0.03 1.08 0.64 3.52 2.90 1.25 0.12 1.12
2.60 2.00 1.21 0.28 0.93 1.11 3.03 1.94 1.39 0.32 1.06
2.82 2.33 0.87 0.16 0.71 0.81 3.31 2.52 0.99 0.22 0.77
2.94 2.33 1.25 0.03 1.22 1.14 3.52 2.40 1.43 0.12 1.30
2.40 2.08 0.60 0.40 0.19 0.51 2.78 2.28 0.68 0.43 0.25
2.47 2.28 0.59 0.53 0.06 0.41 2.84 2.44 0.68 0.57 0.10
2.37 2.19 0.37 0.53 0.16 0.26 2.77 2.52 0.42 0.60 0.18
2.16 2.14 0.53 0.56 0.03 0.24 2.48 2.25 0.62 0.59 0.03
2.41 2.42 0.48 0.52 0.05 0.19 2.82 2.64 0.57 0.59 0.03
2.41 2.29 0.50 0.44 0.05 0.29 2.87 2.59 0.58 0.53 0.04
2.08 2.96 0.26 0.68 0.95 0.91 2.41 3.33 0.24 0.73 0.98
2.19 3.02 0.25 0.71 0.97 0.87 2.52 3.40 0.23 0.76 1.00
2.20 2.28 0.10 0.60 0.71 0.24 2.62 2.87 0.11 0.70 0.82
2.68 3.63 0.10 0.30 0.41 1.12 3.12 4.25 0.09 0.37 0.47
2.23 2.58 0.16 0.57 0.73 0.47 2.63 3.11 0.16 0.64 0.80
3.13 4.06 0.25 0.11 0.13 7.27 3.57 3.69 0.32 0.19 0.12
2.44 3.94 0.38 0.51 0.90 1.44 2.83 4.28 0.34 0.58 0.93
in this scenario compared with scenario S1A. Thus, the increase in the terms of
trade for both agriculture and nonagriculture is lower in this experiment compared
with scenario S1A. Also, the increase in the terms of trade is significantly lower in
nonagriculture than in agriculture in this scenario relative to scenario S1A.
Table 7.6 shows that, while Pakistan's overall poverty incidence index declines
marginally, poverty is not reduced across the board. Poverty in urban areas
declines, but not all rural households experience a drop in poverty. Rural non-
farmers show the highest poverty reduction, but, among farmers and agricultural
workers, there is a slight increase in poverty.
234 Agricultural Price Distortions, Inequality, and Poverty
What are the factors that drive these poverty results? The import prices of agri-
culture decline by 0.7 percent (table 7.7). This is caused by the real exchange rate
appreciation of 0.13 percent and the reduction in the border prices of milled
wheat, as well as fruits and vegetables, which are import intensive. There are a
number of primary agricultural commodities that show relatively greater increases
in import prices, but these commodities are not imported. The domestic prices of
agricultural goods decrease, but by less than the decline in the import prices of
these goods. Thus, the imports of agricultural goods increase by 0.9 percent.
In nonagriculture, the smaller decline in domestic prices relative to import
prices leads to a marginal increase in imports by 0.12 percent. This raises slightly
the domestic consumption of nonagricultural products.
The increase in the export prices in agriculture by 0.33 percent and the decline in
the domestic prices in agriculture by 0.07 percent result in a rise in exports by
1.8 percent. This increases the overall output of agriculture slightly, despite the
decline in the domestic demand because of more imports. However, the increase
in the exports of nonagricultural goods is not quite sufficient to offset the decline in
domestic demand; so, the overall output of nonagriculture declines by 0.01 percent.
The difference in the results between the prices of value added and the output
in agriculture is caused by variations in the results across industries within the
agricultural sector. The greater increase in the border price of milled rice leads to
a higher value added price, which offsets the decline in value added prices in the
rest of agriculture. The decline in farm wages by 0.27 percent and the decline in
the returns to land by 0.32 percent are caused by the decrease in the value added
prices in primary agriculture. There is an increase in the returns to capital in agri-
culture by 0.27 percent mainly because of the improvement in the value added
price of milled rice, a sector which has a high capital-labor ratio. The decline in
wages in unskilled labor is smaller than the decline in farm wages because of the
increase in the value added price of milled rice, which neutralizes much of the
effect of the falling value added prices in the rest of agriculture and in some non-
agricultural sectors. Because milled rice requires more skilled labor than unskilled
labor (table 7.1), the increase in the value added price of milled rice also offsets
the negative effects arising in the rest of the economy; thus, the wages of skilled
labor do not change.
Net of the inflation effects, the changes in factor prices involve declining farm
wages and declining returns to land, but otherwise rising factor prices. The nomi-
nal income effects are negative among all household groups (table 7.8), but they
are smaller than the effects generated in scenario S1A. Consumer prices decline,
but the decline is not sufficient to offset the drop in the nominal incomes of farm-
ers. However, rural nonfarmers and urban households enjoy marginal improve-
ments in real incomes.
Pakistan 235
In sum, agricultural liberalization by the rest of the world would generate a
marginal change in the terms of trade that favors agriculture compared with the
results of scenario S1A. Furthermore, although overall import prices decline, the
drop is much smaller in the present case than in scenario S1A. This translates into
a smaller decline in consumer prices across household groups that is not sufficient
to offset the drop in nominal incomes in some groups. These groups--farmers
and agricultural workers--experience a slight increase in poverty. Moreover, given
the small share of agriculture in the overall trade of Pakistan (less than 10 percent;
see table 7.1), a liberalization only in agriculture has much less impact on the
Pakistan economy than a liberalization in all goods trade. Thus, the poverty
impact is significantly less in the present case than in scenario S1A.
S2A: The unilateral liberalization of all goods trade by Pakistan
Our third experiment sets all sectoral import tariffs and export taxes in Pakistan
to zero and assumes that there are no changes in policies abroad. Table 7.6 shows
that this generates a significant drop in poverty, by 5.2 percent overall. There is
also a significant reduction in the depth of poverty: the poverty gap drops by
10 percent, and the squared poverty gap drops by 12 percent. However, the inci-
dence of poverty in urban areas increases by 2.3 percent. The detailed results show
that the urban nonpoor suffer a decline in incomes because of the additional tax
burden. This is the outcome of the tax replacement method, whereby we replace
trade-distorting taxes in Pakistan with a less-distorting income tax that falls
disproportionately on urban nonpoor households.9 The rest of the household
groups enjoy higher incomes and therefore lower poverty. Overall, income
inequality is also reduced.
Most of the effects arise from the elimination of tariffs, although there are also
effects because of the dismantling of the export taxes in a number of sectors
(table 7.3). The elimination of tariffs leads to a substantial reduction in import
prices. The greatest reduction is in sugar and in livestock, cattle, and dairy because
these sectors are exposed to the highest tariff rates. The import prices of vegetable
oil, milled wheat, other food, cotton lint and yarn, and textiles also decline notably
(table 7.9). Overall, the import prices in agriculture decline by 12 percent, while,
in nonagriculture, the import prices decline by 8.3 percent (table 7.7).
Domestic prices also decline. However, the decline in domestic prices in most
sectors is less than the decline in import prices. Thus, imports in these sectors
surge. The imports of sugar increase by 215 percent; livestock, cattle, and dairy, by
99 percent; milled wheat, by 40 percent; other food, by 38 percent; cotton lint and
yarn, by 38 percent; textiles, by 33 percent; and leather, by 28 percent (table 7.9).
Other sectors show notable increases as well. Overall, imports in agriculture rise
by 22 percent compared with only 4 percent in nonagriculture.
236
Table 7.9. The Sectoral Effects of the Unilateral Liberalization of All Goods Trade, Pakistan
Domestic Composite Value
Output demand good Exports Imports added Return
to
Sectors x px d pd q pq e pe m pm va pva capital
Agriculture
Primary agriculture
1. Wheat, irrigated 2.2 2.2 0.6 2.2 0.4 1.9 58.0 2.2 30.9 11.2 2.2 0.9 2.0
2. Wheat, nonirrigated 0.6 2.5 0.6 2.5 0.6 2.5 0.0 0.0 0.0 0.0 0.6 1.3 2.1
3. Paddy, IRRI 0.7 2.5 0.7 2.5 0.7 2.5 0.0 0.0 0.0 0.0 0.7 1.5 0.5
4. Paddy, basmati 0.9 2.6 0.9 2.6 0.9 2.6 0.0 0.0 0.0 0.0 0.9 1.7 0.5
5. Raw cotton 4.3 2.6 4.3 2.6 4.3 2.6 0.0 0.0 0.0 0.0 4.3 0.8 5.0
6. Sugarcane 1.3 2.7 1.3 2.7 1.3 2.7 0.0 0.0 0.0 0.0 1.3 1.8 3.5
7. Other major crops 0.2 1.6 1.0 1.6 0.4 1.9 32.6 1.6 13.9 8.3 0.2 1.3 1.5
8. Fruits and vegetables 0.8 1.5 2.0 1.5 0.4 2.3 32.2 1.5 15.2 9.3 0.8 1.7 2.7
9. Livestock, cattle, and dairy 0.5 3.3 0.6 3.3 0.1 3.6 41.8 3.3 99.0 32.0 0.5 5.0 5.7
10. Poultry 0.6 3.6 0.7 3.6 0.7 3.6 43.9 3.6 0.0 0.0 0.6 5.7 6.5
11. Forestry 21.2 1.9 3.3 1.9 0.3 3.2 41.7 1.9 9.9 7.5 21.2 1.6 0.0
12. Fishing industry 4.9 9.2 0.6 9.2 0.6 9.2 18.5 9.2 3.4 7.5 4.9 13.7 19.3
Lightly processed food
14. Vegetable oil 6.8 3.8 6.8 3.8 0.1 7.2 44.8 3.8 17.7 14.5 6.8 34.9 37.9
15. Wheat, milled 0.6 1.2 1.1 1.2 0.2 1.7 34.8 1.2 39.6 15.4 0.6 0.1 0.3
16. Rice, milled IRRI 1.5 0.7 0.1 0.7 0.1 0.7 3.3 0.7 0.0 0.0 1.5 5.5 6.5
17. Rice, milled basmati 1.3 0.3 0.3 0.3 0.3 0.3 5.2 0.3 0.0 0.0 1.3 5.0 5.9
18. Sugar 1.4 1.5 1.4 1.5 0.0 2.0 51.5 1.5 215.2 33.5 1.4 1.2 2.1
Nonagriculture
Mining and manufacturing
13. Mining 4.0 8.6 1.1 8.6 1.3 7.5 21.0 8.6 1.9 7.3 4.0 10.4 15.1
19. Other food 2.6 2.9 6.0 2.9 1.1 0.2 11.3 2.9 38.1 15.4 2.6 10.3 12.2
20. Cotton lint and yarn 5.0 1.7 2.5 1.7 3.7 1.1 12.0 1.7 37.5 11.8 5.0 13.8 17.6
21. Textiles 4.1 1.6 0.9 1.6 0.8 0.8 12.3 1.6 32.8 11.8 4.1 9.6 12.6
22. Leather 8.9 0.5 0.2 0.5 1.1 1.2 22.7 0.5 27.8 11.8 8.9 20.0 27.0
23. Wood products 1.8 1.7 1.8 1.7 0.5 2.3 7.2 1.7 15.9 9.3 1.8 0.3 1.5
24. Chemicals 2.5 4.6 6.7 4.6 0.6 8.0 21.2 4.6 3.6 9.3 2.5 4.6 6.2
25. Cement and bricks 1.4 4.7 1.4 4.7 1.4 4.7 21.7 4.7 0.0 0.0 1.4 4.8 5.7
26. Petroleum refining 3.8 4.8 3.8 4.8 1.1 7.1 0.0 0.0 6.3 9.3 3.8 5.5 7.9
27. Other manufacturing 0.8 3.5 7.9 3.5 1.0 7.7 15.5 3.5 4.7 9.3 0.8 0.5 0.0
Other industry
28. Energy 0.3 0.5 0.3 0.5 0.3 0.5 0.0 0.0 0.0 0.0 0.3 2.7 2.8
29. Construction 1.7 2.9 1.7 2.9 1.7 2.9 0.0 0.0 0.0 0.0 1.7 1.3 0.1
Services
30. Commerce 0.4 0.5 0.4 0.5 0.4 0.5 11.1 0.5 5.5 5.7 0.4 1.5 1.2
31. Transport 1.7 1.3 0.6 1.3 0.6 1.3 15.2 1.3 0.0 0.0 1.7 3.4 4.6
32. Housing 0.0 11.7 0.0 11.7 0.0 11.7 0.0 0.0 0.0 0.0 0.0 13.6 13.6
33. Private services 0.4 0.7 0.4 0.7 0.8 0.3 13.9 0.7 6.7 5.7 0.4 1.2 0.9
34. Public services 0.7 0.5 0.7 0.5 0.7 0.5 0.0 0.0 0.0 0.0 0.7 2.1 0.0
Source: National CGE model simulations by the authors.
Note: x output. px output price. d domestic demand. pd domestic price. q composite good. pq composite price. e exports. pe export price.
m imports. pm import price. va value added. pva value added price.
237
238 Agricultural Price Distortions, Inequality, and Poverty
Because world prices are fixed, the decline in prices in Pakistan because of
the trade reform increases the country's competitiveness.10 There is a real depreci-
ation of 6.1 percent in the exchange rate. The results, shown in table 7.9, indicate
that, except for forestry and fishing, the exports of agriculture--primary agricul-
ture and lightly processed food--improve. Overall, agricultural exports expand by
4.8 percent. However, this increase does not offset the displacement effects of the
surge in imports by 22 percent. Thus, the overall output of agriculture declines
by 0.7 percent. The biggest reductions are in forestry (21 percent), vegetable oil
(7 percent), and fishing (5 percent). However, there is an improvement in raw cot-
ton production because of the increase in the output of cotton lint and yarn, as
well as textiles (see elsewhere below).
In nonagriculture, almost all sectors realize positive growth in exports. Overall,
exports in nonagriculture increase by 13 percent. The increase in manufacturing
exports is also substantial, especially in major export items such as cotton lint and
yarn, textiles, other food, and other manufacturing. There is also a notable increase
in the exports of services such as commerce, transport, and private services.
For other food, the increase in imports displaces domestic demand by 6 per-
cent. However, this is offset by the increase in exports; thus, the output of other
food improves by 2.6 percent, while the output price improves by 2.9 percent. The
impact on textiles may be analyzed in relation to the effects on the cotton lint and
yarn sector and the raw cotton sector. The increase in textile imports displaces
domestic demand by 0.9 percent. However, this is offset by the increase in textile
exports; thus, textile output improves by 4.1 percent, while the output price
improves by 1.6 percent. Because the cotton lint and yarn sector supplies materials
to the textile sector, the improvement in the output of textiles because of the greater
exports leads to an improvement in the domestic demand for cotton lint and yarn
by 2.5 percent. The increase in both the exports and the domestic demand for cotton
lint and yarn leads to 5.0 percent more output and a rise in the output price by
1.7 percent, which, in turn, leads to 4.3 percent more output of raw cotton.
The negative change in the value added prices in agriculture leads to lower
prices for factors that are used heavily in agriculture. The wages of farmers
decrease by 0.6 percent; the returns to land fall by 1.9 percent; and the average
returns to agricultural capital fall by 5 percent.
The average output price in nonagriculture decreases by 1.1 percent, but the
value added price improves by 1.3 percent. The increase arises from the notable
improvement in the value added prices of leather (20 percent), cotton lint and
yarn (14 percent), other food (10 percent), textiles (10 percent), and transport
(3 percent) (see table 7.9). Thus, the prices of the factors used in nonagriculture
improve. The wages of unskilled workers increase by 1.5 percent and of skilled
labor by 2.1 percent, while the average returns to nonagricultural capital rise by
Pakistan 239
1 percent. Furthermore, there is a significant decline in the consumer price index.
Thus, net of the inflation effects, factor prices improve except for the average
returns to the capital used in agriculture.
The nominal incomes of farmers drop (table 7.8). This occurs largely because
of the declines in the wages of farmers, in the returns to land, and in the average
returns to capital in agriculture. Because of the higher wages of workers, the nom-
inal incomes of nonfarmers improve except for the nominal incomes of urban
nonpoor households. The incomes of the urban nonpoor decline because of the
income tax replacement imposed on this group. However, the decline in con-
sumer prices is significant for all groups. This offsets the decline in nominal
incomes except in the case of the urban nonpoor.
In sum, all households, except urban nonpoor households, realize an increase
in real incomes, which leads to a significant decline in poverty. The urban poor
experience the largest increase in incomes and the largest drop in the depth of
poverty. Income inequality is reduced.
S2B: Unilateral agricultural liberalization by Pakistan
Our fourth experiment sets only agricultural price distortions in Pakistan at zero,
while retaining all nonagricultural trade taxes and assuming no changes in
the global model.11 Overall, the poverty effects are significantly lower in this
experiment compared with the S2A scenario. Furthermore, there are differences in
the effects across households. Urban households experience a decline in poverty,
and, although overall poverty in rural areas declines, large- and medium-farm
households face increasing poverty.
The results at the macro, sectoral, factor, and commodity price levels explain
these poverty effects. At the sectoral level, the import prices of agriculture drop by
14 percent (table 7.7). The largest declines are in sugar (36 percent); livestock,
cattle, and dairy (34 percent); milled wheat (18 percent); and vegetable oil
(18 percent).12 There is also a reduction in domestic prices, but this is significantly
smaller than the drop in import prices. Thus, the imports of agricultural goods
surge by 30 percent.
This agricultural liberalization results in a real exchange rate depreciation.
Because tariffs and subsidies in nonagriculture are retained, the average import
prices in nonagriculture increase by only 2.6 percent, while the domestic prices in
nonagriculture increase by 1.11 percent. Thus, the imports of nonagricultural
products decline by 0.5 percent. On the other hand, the exports of nonagricul-
tural products improve by 3.1 percent. At the sectoral level, the increase is caused
by the strong export effect on leather, wood products, cotton lint and yarn, and
commerce. Because world prices are fixed and the domestic and output prices of
nonagriculture increase, the increase in nonagricultural exports is caused by the
240 Agricultural Price Distortions, Inequality, and Poverty
depreciation in the exchange rate. The increase in nonagricultural exports,
together with the marginal increase in the domestic demand for nonagricultural
products, leads to an improvement in nonagricultural output by 0.4 percent.
The prices of the factors used in agriculture decline. The wages of farmers
decrease by 2.2 percent; the returns to land, by 2.5 percent; and the average returns
to capital, by 3.8 percent. However, the prices of factors used heavily in nonagri-
culture improve. We observe a similar pattern in factor prices after we net out the
marginal decline of 0.27 percent in the consumer price index.
The nominal incomes of farmers decline, while the nominal incomes of non-
farmers improve. The marginal decline in the consumer price index does not offset
the decrease in the nominal incomes of farmers, especially large- and medium-farm
households. Thus, the real income of these farm households is lower. However, non-
farmers enjoy higher real incomes, except for the urban nonpoor, among whom real
incomes fall slightly as a result of the tax burden they bear. Nonetheless, the addi-
tional tax burden is not sufficiently large to push the urban nonpoor below the
poverty line (unlike the case of scenario S2A); so, poverty declines in urban areas.
Although the overall poverty in rural areas declines, large- and medium-farm
households face increasing poverty because of declining real incomes.
S3A: Full trade liberalization by Pakistan and the rest of the world
Our fifth experiment combines the trade liberalization in the rest of the world
with that in Pakistan in all sectors. The effects of the unilateral trade liberalization
in Pakistan are larger than the effects of the rest-of-the-world trade liberalization.
The combined impact on both exports and imports is strongly positive. There is
also a large decline in the consumer price index. Factor prices in agriculture
decline, but they improve in nonagriculture. However, net of the inflation effects,
the only decline in factor returns occurs in the average returns to the capital used
in agriculture. The nominal incomes of farmers decline, while the nominal
incomes among nonfarmers improve. The large reduction in the consumer price
index contributes to an increase in the real incomes of all households except
urban nonpoor households.
This scenario generates the largest reduction in poverty. Another point worth
highlighting is that, while the incidence of poverty among the urban nonpoor
increases, the increase is much smaller in this experiment than in scenario S2A.
S3B: Agricultural liberalization by Pakistan and the rest of the world
Our sixth experiment combines the agricultural liberalization of the rest of the
world with that in Pakistan. It turns out that the effects of the reform in Pakistan
dominate those of the agricultural liberalization in the rest of the world. There is
also an upward response in imports and exports, but in agriculture only. The
Pakistan 241
surge in agricultural imports displaces local production. This results in lower
prices for the factors used in agriculture. Factor prices in nonagriculture increase
because the sector remains protected. Therefore, farmers have lower incomes,
while nonfarmers benefit.
Sensitivity analysis: Indirect versus direct tax replacement
The results discussed above are derived using a replacement income tax. Because
the SAM we have used to calibrate the model has an income tax on urban non-
poor households only (table 7.4), the direct tax replacement puts all the burden of
financing the trade reform on this group. As an alternative, we consider, in this
subsection, the assessment of indirect taxes to offset the losses in government tar-
iff revenue that are generated by reform. We focus on the poverty effects of these
two tax replacement schemes in scenarios S3A (full trade liberalization of all
goods in the rest of the world and in Pakistan) and S3B (agricultural liberalization
in the rest of the world and in Pakistan).
The effects on real income across households are presented in table 7.10. In
scenario S3A, in which all sectors are liberalized, changing the tax replacement
from a direct tax to an indirect tax completely alters the results. Under the direct
tax replacement, all households enjoy higher real incomes except urban nonpoor
households. This tax replacement scheme therefore redistributes income from the
urban nonpoor to the rest of the household groups. These latter household
groups benefit from the reduction in consumer prices and from the redistribution
of income from the urban nonpoor households. However, if an indirect tax
replacement is used, consumer prices increase because of the taxes, and the bur-
den is shared among all household groups depending upon household consump-
tion structures. There is a reduction in household incomes among most of the
groups except the three relatively wealthy groups (the large-farm households in
other Pakistan, the rural nonpoor, and the urban nonpoor). Under this tax
replacement scheme, there is a significant increase in domestic prices because of
the higher indirect taxes.
If the trade liberalization is focused on agriculture only under S3B, the income
results are not sensitive to the tax replacement scheme used. This is because the
net government budget implications of the elimination of the distortions are not
as large in agriculture as in nonagriculture. Thus, the impact on domestic prices
through higher indirect taxes is not as significant in the case of agricultural liber-
alization as in the case of the trade liberalization of all goods. In both tax replace-
ment schemes, farmers, particularly large- and medium-farm households, are
negatively affected, while nonfarmers are favorably affected. However, in the
direct tax replacement scheme, the urban nonpoor are still negatively affected,
while they are favorably affected in the indirect tax replacement scheme.
242 Agricultural Price Distortions, Inequality, and Poverty
Table 7.10. Sensitivity Analysis of Household Welfare Effects,
Two Tax Replacement Schemes, Pakistan
S3A S3B
2001�02
population Direct Indirect Direct Indirect
Households distribution, % tax tax tax tax
Large-farm households
�Sindh 0.1 1.96 0.49 1.51 1.65
�Punjab 0.2 2.47 0.08 1.29 1.42
�Other Pakistan 0.0 2.90 0.18 1.12 1.26
Medium-farm households
�Sindh 1.0 1.94 0.75 1.06 1.20
�Punjab 1.9 2.52 0.31 0.77 0.92
�Other Pakistan 0.8 2.40 0.40 1.30 1.45
Small-farm households
�Sindh 2.8 2.28 0.70 0.25 0.40
�Punjab 12.9 2.44 0.55 0.10 0.25
�Other Pakistan 5.7 2.52 0.56 0.18 0.03
Small-farm renters, landless
�Sindh 0.2 2.25 0.92 0.03 0.18
�Punjab 1.5 2.64 0.53 0.03 0.13
�Other Pakistan 0.6 2.59 0.51 0.04 0.20
Rural agricultural workers, landless
�Sindh 4.1 3.33 0.48 0.98 0.80
�Punjab 2.0 3.40 0.36 1.00 0.82
�Other Pakistan 0.9 2.87 0.47 0.82 0.66
Rural nonfarm households
�nonpoor 17.6 4.25 0.55 0.47 0.29
�poor 18.1 3.11 0.41 0.80 0.63
Urban households
�nonpoor 22.5 3.69 0.63 0.12 0.10
�poor 7.2 4.28 0.14 0.93 0.71
Source: National CGE model simulations by the authors.
Table 7.11 presents the poverty results based on this sensitivity analysis. Trade
liberalization in all goods globally under the indirect tax replacement scheme is
poverty increasing in scenario S3A. This is because of the declining real incomes
among most groups. This effect arises largely because of the higher consumer
prices resulting from the indirect tax replacement scheme. The higher consumer
prices wipe out the gains accruing because of the higher border export prices, the
lower border import prices, and the lower tariffs.
Agricultural liberalization alone entails a smaller fiscal burden. Therefore, both
the direct income tax replacement scheme and the indirect tax replacement scheme
Pakistan 243
Table 7.11. Sensitivity Analysis of Poverty Effects, Two Tax
Replacement Schemes, Pakistan
S3A S3B
2001�02 Direct Indirect Direct Indirect
Poverty index index tax tax tax tax
All Pakistan
�P0 31.2 6.4 0.5 1.8 1.2
�P1 6.5 11.5 1.5 2.5 1.8
�P2 2.0 13.8 1.8 3.0 2.2
All urban
�P0 19.9 0.4 0.0 2.7 2.3
�P1 3.9 14.8 0.6 3.7 2.9
�P2 1.2 18.3 0.7 4.3 3.3
All rural
�P0 38.2 8.6 0.6 1.5 0.9
�P1 8.0 10.5 1.7 2.1 1.5
�P2 2.5 12.5 2.1 2.6 1.9
Large- and medium-farm households
�P0 22.8 7.9 2.2 3.4 3.4
�P1 4.1 10.0 2.4 5.3 6.0
�P2 1.1 10.6 2.8 5.8 6.6
Small-farm households and agricultural workers
�P0 37.4 8.7 1.5 0.9 0.3
�P1 7.5 10.3 2.2 1.3 0.6
�P2 2.2 12.4 2.6 1.6 0.9
Rural nonfarm households
�P0 39.9 8.5 0.0 2.1 1.5
�P1 8.7 10.6 1.5 2.8 2.2
�P2 2.8 12.6 1.8 3.4 2.7
Source: National CGE model simulations by the authors.
Note: PO = poverty headcount. P1 = poverty gap. P2 = poverty severity.
generate favorable effects on poverty. In the case of the indirect income tax replace-
ment scheme, although it increases consumer prices, it does not wipe out the gains
achieved through the higher border export prices, the lower border import prices,
and the lower trade taxes on agricultural commodities. Because of the negative
effects of the agricultural liberalization on domestic agriculture in Pakistan, farm-
ers will be hurt, especially large- and medium-farm households. However, this is a
small segment in the total population and exhibits the lowest incidence of poverty
(23 percent in 2001�02 compared with 37 percent among small-farm households
and agricultural workers and 40 percent among rural nonfarmers).
244 Agricultural Price Distortions, Inequality, and Poverty
Summary and Policy Implications
In this chapter, we have linked the results of two economic models--the Linkage
model of the World Bank and the Pakistan CGE model that we have developed--
to analyze and compare the poverty effects of trade liberalization abroad with the
corresponding effects of unilateral reform by Pakistan. We have conducted six
policy experiments: two rest-of-the-world trade liberalization experiments (full
liberalization that covers all goods sectors and agriculture only), two unilateral
trade liberalization cases (all goods and agriculture only), and two combined sce-
narios. The results are evaluated under a direct tax replacement scheme among
household incomes; the direct tax is paid only by the urban nonpoor. We also
examine an alternative tax replacement scheme, an indirect tax on commodities.
A number of policy insights may be drawn from our simulation results. The
impacts on the Pakistan economy and on the extent of poverty in the country
from own-country liberalization are significantly larger than the impacts from
rest-of-the-world trade liberalization. The effects of agricultural liberalization
(both in the rest-of-the-world market and in Pakistan) are considerably smaller
than the effects of the liberalization of all goods trade. This is because of the
smaller share of agricultural trade in overall exports and imports in Pakistan, in
which trade is dominated by nonagricultural products.
The income from trade taxes is a major source of revenue for the government.
The trade tax revenue is considerably lower from agricultural commodities than
from nonagricultural products. Thus, the elimination of the trade taxes on all
tradable commodities creates a large dent in government income and in the fiscal
balance. It therefore entails a significant government demand for tax revenue from
other sources. The poverty and income effects of full trade liberalization greatly
depend upon how the tax replacement is implemented. If an additional tax is
imposed on household incomes to generate funds to finance the reduction in
trade taxes in all sectors, there is a notable decline of consumer prices and a large
income redistribution from urban nonpoor households to the rest of the house-
hold groups. There is therefore a considerable decline in the incidence of poverty,
in the depth of poverty, and in income inequality. This is because the burden of
the additional tax falls entirely on the urban nonpoor, while the rest of the groups
benefit from higher real factor prices and larger reductions in consumer prices.
However, if the tax replacement is imposed as additional indirect taxes on com-
modities, consumer prices increase and eliminate the benefits generated from the
reduction in trade distortions. In this case, poverty rises.
The trade tax revenue is considerably lower from agricultural commodities
than from nonagricultural products. If trade liberalization is focused on agricul-
tural commodities only, the fiscal refinancing requirement is substantially less
urgent. The poverty reduction effects, although smaller, are robust to the change
Pakistan 245
in tax policy, that is, poverty is reduced under both tax replacement schemes if
only agricultural markets are liberalized.
All these results are derived using a static model. The dynamic impact of trade
reform on capital accumulation from the changes in prices has not been
accounted for. For example, if the rates of return to capital are high in sectors in
which the poor are heavily engaged, this will attract investment, thereby increas-
ing capital accumulation in these sectors and output from these sectors. This
would have favorable implications for poverty. (It is also possible that the results
would be reversed and would therefore generate negative effects on the poor.)
Furthermore, the dynamic effects would have an impact on technological
progress, the movement of the labor of farm households into nonfarm employ-
ment, factor and total productivity, and the flow of foreign direct investment.
These are all empirical issues that are relevant topics for further research.
Notes
1. Estimates of agricultural assistance in Pakistan, based on Dorosh and Salam (2009), are incorpo-
rated in the World Bank's Distortions to Agricultural Incentives Project Database (Anderson and
Valenzuela 2008). The estimates cover five decades. The representative values for CGE modeling as of
2004 that are used here are available in Valenzuela and Anderson (2008).
2. The specification of the Pakistan CGE model is based on EXTER (see Decaluw�, Dumont, and
Robichaud 2000).
3. Cororaton and Orden (2008) include a dynamic analysis in which sectoral capital adjusts over time.
4. The direct tax replacement on household income is specified as:
dyh yh(1 dtxrh[1 ndtxrh]),
where dyh is disposable income; yh is income before income taxes; dtxrh is the income tax rate at the base;
and ndtxrh is the income tax replacement. The indirect tax replacement on commodities is specified as:
pd pl(1 itxr)(1 nitx),
where pd is the domestic price; pl is the local price before indirect taxes; itxr is the indirect tax rate at
the base; and nitx is the indirect tax replacement.
5. Sectoral informal capital is also in the SAM. The returns to informal capital may be considered
primarily as payments to labor outside the formal labor market. However, instead of modeling infor-
mal capital separately, we have aggregated it with formal capital. There is no significant underestima-
tion of household incomes because informal capital is still being paid based on the returns to capital.
However, this aggregation makes the labor share in agriculture appear relatively low.
6. We have set the sectoral values of the parameter eta in the export demand function equal to the
Armington elasticities in the global model. The sectoral values of the parameter sig_e in the export
supply function and the sectoral values of the parameter sig_m in the import demand function are half
the values of eta.
7. There is no real exchange rate variable in the model. The real exchange rate is defined as the
world price, multiplied by the nominal exchange rate, divided by the local price, where the world price
is the trade-weighted world import and export prices and the local price is the sectoral output-
weighted local prices.
8. Detailed sectoral results are shown only for scenario S2A (see table 7.9). Detailed comparable
sectoral results for the other scenarios are available from the authors on request.
246 Agricultural Price Distortions, Inequality, and Poverty
9. In the model, the overall government revenue from tariffs is PRs 154 billion and from export
taxes, PRs 15 billion. Total government revenue is PRs 446 billion. The total income of the urban
nonpoor is PRs 1.73 trillion.
10. In our model, Pakistan is facing a downward-sloping world demand curve. Because the perfect
substitution assumption is imposed between exports and domestic sales in Pakistan, the export supply
curve is horizontal. The decrease in output prices raises export supply, which shifts the horizontal
export supply curve downward.
11. The total tariff revenue from agricultural imports is PRs 14.2 billion, while farm export tax
revenue is PRs 4.3 billion in the baseline.
12. Detailed sectoral results generated under this scenario are available from the authors upon request.
References
Ahmed, V., A. Abbas, and S. Ahmed. 2009. "Taxation Reforms: A CGE-Microsimulation Analysis for
Pakistan." Unpublished working paper, Modeling and Policy Impact Analysis Program, Poverty
and Economic Policy Research Network, Universit� Laval, Quebec.
Anderson, K., and E. Valenzuela. 2008. "Estimates of Global Distortions to Agricultural Incentives,
1955�2007." Data spreadsheet, October, World Bank, Washington, DC. http://go.worldbank
.org/YAO39F35E0.
Cororaton, B. C., and D. Orden. 2008. Pakistan's Cotton and Textile Economy: Intersectoral Linkages and
Effects on Rural and Urban Poverty. IFPRI Research Report 158. Washington, DC: International
Food Policy Research Institute.
Decaluw�, B., J.-C. Dumont, and V. Robichaud. 2000. Basic CGE Models.Vol. II of MIMAP Training Session
on CGE Modeling. Micro Impacts of Macroeconomic and Adjustment Policies Program, Interna-
tional Development Research Centre, Canadian Centre for International Studies and Cooperation,
and Universit� Laval, Quebec. http://www.pep-net.org/fileadmin/medias/pdf/volume2.pdf.
Dorosh, P. 2005. "Wheat Markets and Pricing in Pakistan: Political Economy and Policy Options."
Wheat Policy Note, South Asia Rural Development Unit, World Bank, Washington, DC.
Dorosh, P., M. K. Niazi, and H. Nazli. 2004. "A Social Accounting Matrix for Pakistan, 2001�02:
Methodology and Results." PIDE Working Paper 2006�9, Pakistan Institute of Development
Economics, Islamabad. http://econpapers.repec.org/paper/pidwpaper/2006_3a9.htm.
Dorosh, P., and A. Salam. 2009. "Pakistan." In Distortions to Agricultural Incentives in Asia, ed. K.
Anderson and W. Martin, 379�407. Washington, DC: World Bank.
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hies0102.html.
Foster, J., J. Greer, and E. Thorbecke. 1984. "A Class of Decomposable Poverty Measures." Econometrica
52 (3): 761�66.
Horridge, M., and F. Zhai. 2006. "Shocking a Single-Country CGE Model with Export Prices and Quan-
tities from a Global Model." In Poverty and the WTO: Impacts of the Doha Development Agenda, ed.
T. W. Hertel and L. A. Winters, 94�104. London: Palgrave Macmillan; Washington, DC: World Bank.
Ministry of Finance. 2003. Economic Survey of Pakistan 2002�03. Islamabad: Economic Advisers Wing,
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_Survey_2002-03.pdf.
Valenzuela, E., and K. Anderson. 2008. "Alternative Agricultural Price Distortions for CGE Analysis of
Developing Countries, 2004 and 1980�84." Research Memorandum 13 (December), Center for
Global Trade Analysis, Department of Agricultural Economics, Purdue University, West Lafayette,
IN. https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=2925.
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Washington, DC: Sustainable Development Unit, South Asia Region, World Bank.
8
The Philippines
Caesar B. Cororaton, Erwin Corong,
and John Cockburn*
The agricultural sector in the Philippines employed 36 percent of the labor force
and accounted for roughly 14 percent of the nation's GDP in 2004, or 26 percent
of GDP if the agriculture-based food processing sector is included.
From the 1950s to the 1970s, government policies were biased against agricul-
ture. Until the 1980s, these policies included the import-substitution policy,
which created a bias in favor of manufacturing and penalized the returns to agri-
cultural investments and exports. Export taxes and exchange rate overvaluation
greatly reduced earnings from agriculture, and government intervention through
the creation of government corporations siphoned off much of the gains from
trade (Intal and Power 1990, David 2003). Then, the trade reform program in the
1980s led to a shift from taxing to protecting agriculture relative to nonagricul-
tural sectors, and these policies became more pronounced when the country
became a member of the World Trade Organization in 1995. As a result, the cur-
rent system of protection favors agriculture through both applied tariff rates and
nominal rates of assistance (NRAs) in agriculture that are substantially higher
than the NRAs in manufacturing (Aldaba 2005; David, Intal, and Balisacan 2009).
However, two decades of protection have failed to induce competitiveness and
productivity growth in agriculture.
This chapter analyzes the poverty and inequality implications of the removal of
agricultural and nonagricultural price distortions in the domestic markets of the
*The authors are grateful to Kym Anderson, Thomas Hertel, Will Martin, Ernesto Valenzuela,
Dominique van der Mensbrugghe, and workshop participants for helpful comments.
247
248 Agricultural Price Distortions, Inequality, and Poverty
Philippines and in markets abroad. As exogenous shocks, our analysis relies on the
results of the rest-of-the-world trade liberalization simulations of the Linkage
model of the World Bank, here also called the global model (see the appendix by
van der Mensbrugghe, Valenzuela, and Anderson; see also van der Mensbrugghe
2005). We use these exogenous shocks, along with national liberalization shocks,
to derive effects based on the computable general equilibrium (CGE) model for
the Philippines by Cororaton and Corong (2009). The global model incorporates
new estimates of agricultural protection and assistance for various developing
countries, including the Philippines, and simulates scenarios involving full world
trade liberalization in all sectors and in agriculture alone.1 The global simulations
generate changes in export and import prices at the border in the Philippines, as
well as changes in world export demand for the products of the Philippines.2 We
apply these results, together with the new estimates of protection and assistance
for the Philippines by David, Intal, and Balisacan (2009), as shocks to the CGE
model for the Philippines to analyze the distributional, welfare, and poverty
impacts of various trade liberalization scenarios for the country.
We conduct our simulation analysis in stages to assess the differing impacts
that international market liberalization and domestic market liberalization may
entail. In the first stage, we extract changes in the border export and import prices
and changes in the world export demand for the products of the Philippines from
the global model, and we apply these in the model for the Philippines without
altering the existing trade protection system in the country. In the second stage,
we simulate unilateral trade liberalization in the Philippines without incorporat-
ing any changes from the global model. Finally, we combine the rest-of-the-world
liberalization shocks and the unilateral liberalization shocks to assess the total
effects.
We conduct six policy experiments using separate scenarios for trade liberal-
ization in all sectors and trade liberalization in the agricultural sector alone. We
define the agricultural sector to include primary agriculture and lightly processed
food.3 In each scenario, we generate results at the macrolevel and sectoral level, as
well as vectors of changes in household incomes, consumer prices, and sectoral
employment shares. We then use the vectors of changes as inputs in a microsimu-
lation procedure to calculate the impact on poverty and inequality. This calcula-
tion draws on data from a national household survey conducted in 2000.
The chapter is organized as follows. The next section sheds light on the degree
of price distortions and trade protection and on poverty trends in the Philippines.
The subsequent section presents the structure of the CGE model for the
Philippines, which is based on the national social accounting matrix as of 2000.
We then discuss the policy experiments and the results. The final section provides
a summary of our findings and examines some policy implications.
The Philippines 249
Trade and Assistance Policies and Poverty Trends
In 1949, the Philippines embarked on a development strategy geared toward
industrial import substitution with a lesser emphasis on the agricultural and
export sectors. The strategy provided protection to domestic producers of final
goods through high tariff rates on nonessential consumer goods and low tariff
rates on essential producer inputs. However, the policy was not effective: the
growth of manufacturing value added and industrial employment increased min-
imally. In 1970, the government shifted toward export promotion by offering tax
exemptions and fiscal incentives to capital-intensive firms located in export pro-
cessing zones. This strategy also achieved little: the highly skewed structure of the
tariff protection in favor of import-substituting manufactured goods remained.
Moreover, the imposition of export taxes, an overvalued exchange rate, and the
presence of government corporations that not only regulated domestic prices, but
also siphoned off much of the gains from domestic and international trade created
a strong bias against agriculture and exports.
The restrictive trade policies adopted between the 1950s and the late 1970s pre-
vented efficient resource allocation and the smooth functioning of markets and
penalized the domestic economy in three respects. First, import controls resulted
in an overvalued exchange rate that favored import-substituting firms. Second,
continued protection increased domestic output prices, which impeded forward
links. Third, tariff escalations and import controls weakened backward links
because the tariffs on capital and intermediate goods were maintained at low lev-
els relative to the tariffs on finished products (Austria and Medalla 1996). This
also promoted rent seeking and distorted the economic incentives for investment
in agriculture. The agricultural sector, which served as the country's backbone in
providing the necessary foreign exchange needed by the import-dependent man-
ufacturing sector, stagnated. Meanwhile, the industrial sector ventured into
import-dependent assembly operations with minimal value added content and
little or no forward and backward links.
Realizing the pitfalls of the import-substitution policy and the unsuccessful
export-promotion strategy, the government implemented a series of trade reform
programs starting in 1981.4 The Philippines has also been participating more
actively in the multilateral trading system since it joined the World Trade Organi-
zation in 1995. For example, between 1995 and 1999, the country complied with
all its multilateral commitments within the prescribed time frames (WTO 1999).
These commitments included tariff bindings at a maximum of 10 percentage
points over the 1995 applied rate on roughly 65 percent of all tariff lines, tariff
bindings on selected information technology products, and bindings on market
access in selected service sectors (Austria 2001). Nonetheless, there continues to
be a substantial tariff binding overhang, especially in agriculture.5
250 Agricultural Price Distortions, Inequality, and Poverty
By around 2000, the country had slowed the pace of trade reform (WTO 2005).
Although the average applied most favored nation tariff rate declined from 9.7 to
5.8 percent between 1999 and 2003, it then rose to 7.4 percent in 2004. This rever-
sal of the tariff adjustment process was brought about by presidential discretion
and was aimed at helping problematic domestic industries and responding to
lobbying from domestic interest groups.
Estimates of the NRA to agriculture
David, Intal, and Balisacan (2009) have estimated the NRA to key industries in the
agricultural sector. The NRA is the percentage difference between the domestic and
the border price; it thus measures the direct effect of policy-induced distortions on
producer incentives.
The NRA for coconuts (copra or dried coconuts) is negative throughout the
years shown in table 8.1, largely because of export taxes, a coconut levy, and a
copra export ban. The currency devaluation in the 1970s and the world commod-
ity boom in the middle of that decade did not translate into higher profits for
coconut farmers; rather, it translated into higher revenue for the government and
lower raw material costs for the coconut oil milling industry. Although these poli-
cies began to be eliminated in 1986, coconut farmers remain penalized owing to
the continued existence of a government corporation that controls 70�80 percent
of coconut oil milling, thereby retaining a monopsonistic command over the
domestic price of copra.
The NRA for corn has always been positive and exhibits an increasing trend.
There is not much political pressure on corn compared with rice because corn is a
subsistence crop among upland farmers in the southern part of the country.
Nonetheless, it is also a major animal feed ingredient.
Among agricultural crops, sugar has had the highest NRA since the 1960s. In
the 1960s and early 1970s, a major part of domestic production was exported to
the high-priced U.S. market through a preferential access quota allocated to
Philippine producers and sugar processors (known as the Laurel-Langley Agree-
ment). This agreement ended in 1974, resulting in a dramatic drop in sugar
exports from the Philippines to the United States.6
The NRA for chicken has been consistently high and well above the NRA for
pork. However, the government imposed the same level of in-quota and out-of-
quota tariffs for both commodities after the ratification of the World Trade
Organization agreement in 1995.
Cattle were not included on the sensitive products list for the Philippines at the
World Trade Organization. The NRA for cattle has therefore been low relative to
the NRAs for chicken and pork. In the early 1990s, the government attempted to
Table 8.1. The NRAs to Major Agricultural Commodities, the Philippines, 1960�2004
(percent)
Commodity 1960�64 1965�69 1970�74 1975�79 1980�84 1985�89 1990�94 1995�99 2000�04
Rice 6 1 10 18 16 14 21 53 51
Corn 19 38 14 24 20 60 63 79 55
Sugar 18 121 12 2 60 13 49 97 79
Domestic 4 78 39 29 14 112 45 99 75
Export 28 154 16 17 89 161 77 90 130
Coconut
Copra 12 20 25 17 27 21 15 8 14
Coconut oil 3 18 21 8 17 4 7 1 6
Beef 60 16 47 18 2 8 26 15 17
Pork 30 14 3 6 36 51 25 21 8
Chicken -- 67 29 28 38 43 57 42 52
Other 10 10 32 32 16 17 10 5 5
Source: David, Intal, and Balisacan (2009).
Note: -- no data are available.
251
252 Agricultural Price Distortions, Inequality, and Poverty
promote the fattening of cattle and allowed duty-free imports of young cattle,
while imposing more restrictive nontariff barriers on beef. Nonetheless, cattle
fattening activities did not prosper because the tariffs on beef imports fell.
Before the mid-1980s, the NRAs for agricultural inputs such as fertilizer, agri-
cultural chemicals, and farm machinery were generally higher than the NRAs for
agricultural crops, averaging well above 20 percent (David, Intal, and Balisacan
2009). This was largely caused by the government's industrial promotion policies,
which raised the domestic prices of the manufactured inputs to agriculture. How-
ever, after this period and during the trade liberalization process, there were sub-
stantial reductions in agricultural input protection, down to around 10 percent in
the late 1990s and to a uniform 3 percent in 2000�04.
Poverty trends
In rural and urban areas, over 60 percent of the expenditure of poor households
goes for food, of which almost half is cereals, primarily rice and corn (table 8.2).
Rural dwellers spend proportionately more than their urban counterparts on
food. Food consumption among nonpoor households is relatively somewhat less
(39 percent), and urban nonpoor households spend the least on food and cereals
in relative terms (8 percent).
Figure 8.1 presents the evolution of the poverty headcount index between 1985
and 2000. The national headcount index decreased from almost 50 to roughly
34 percent over the period. However, the decline was concentrated mainly in
urban areas, especially in the National Capital Region, where poverty was already
low. In contrast, the rural headcount index fell only modestly, from 56 to 49 per-
cent, compared with the fall in urban areas from 44 to 23 percent.
Table 8.2. Poverty Incidence and Food Expenditure Shares,
the Philippines, 1997 and 2000
(percent)
Rural Urban
Indicator 1997 2000 1997 2000
Poverty incidence, % of population 50.7 48.8 21.6 18.6
Poor Nonpoor Poor Nonpoor
Indicator 1997 2000 1997 2000 1997 2000 1997 2000
Expenditure shares, % of total
All food 63.6 63.6 47.6 47.6 61.4 60.8 38.8 38.7
Cereals (mostly rice) 29.5 28.8 15.4 14.6 24.5 23.0 8.6 8.2
Source: NSO (1997, 2000).
The Philippines 253
Figure 8.1. Trends in Poverty Indexes, the Philippines,
1985�2000
60
50
40
percent
30
20
10
0
1985 1988 1991 1994 1997 2000
year
all national capital region rural urban
Source: NSO (1985�).
The CGE Model
The national CGE model we use in our study is calibrated to the social accounting
matrix of the Philippines for 2000.7 There are 41 production sectors and four fac-
tors: two labor types (skilled workers with at least a college diploma and unskilled
workers), plus capital and land. Institutions include the government, firms,
households, and the rest of the world. Household categories are defined according
to income deciles. Output (X) is a composite of value added (VA) and intermedi-
ate inputs. Output is sold either to the domestic market (D), or to the export mar-
ket (E), or both. The model assumes perfect substitutability between E and D. We
assume a finite elasticity of export demand. There are two sources of domestic
market supply: domestic output and imports (M). There is substitution between
D and M depending on the changes in the relative prices of D and M and on a
constant elasticity of substitution function.
Sectoral output is a Leontief function of intermediate inputs and value added.
Value added in agriculture is a constant elasticity of substitution function of
skilled labor, unskilled labor, capital, and land. Nonagricultural value added is
also a constant elasticity of substitution function of the same factors, except land.
Capital and land are each sector specific. Skilled and unskilled labor are mobile
254 Agricultural Price Distortions, Inequality, and Poverty
across sectors, but limited within skill category, and land use is mobile within the
agricultural sector.
Households earn incomes from factors of production, transfers, foreign remit-
tances, and dividends, while paying direct income taxes to the government.
Household savings are a fixed proportion of disposable incomes, and household
demand is represented by a linear expenditure system.
Government revenue is the sum of the direct taxes on the incomes of house-
holds and firms, indirect taxes on domestic and imported goods, and other
receipts. The government spends on the consumption of goods and services,
transfers, and other payments. In the present version of the model, we assume a
fixed government balance. Since shifts in policy will result in changes in govern-
ment income and expenditure, the government balance is held fixed through a tax
replacement variable. For the present analysis, we use an indirect tax replacement
on domestic sales, but we also compare the results with the effects of a direct tax
replacement on household incomes. In either case, the tax replacement is endoge-
nously determined so as to maintain a fixed government balance.
Foreign savings are held fixed. The nominal exchange rate is the model's
num�raire. We introduce a weighted price of investment and derive total invest-
ment in real prices; total investment is held fixed by introducing an adjustment
factor in the household savings function. Equilibrium is achieved in the model if
the supply of and the demand for goods and services are equal and if investment
is equal to savings.
Table 8.3 presents the production structure in the social accounting matrix.
Generally, the agricultural and service sectors show greater shares of value added
(as a percent of output) relative to the industrial sector. In agriculture, coconut
and forestry have the largest shares of value added, almost 90 percent, while petro-
leum refining has the lowest share among the industrial sectors, at 14 percent. The
capital-output ratio is generally lower in agriculture than in industry and the ser-
vice sectors. The largest employer of labor is the service sector. More than 90 per-
cent of the labor input in agricultural production is represented by unskilled
labor. The share of skilled labor employed in the industrial sector is substantially
greater than the corresponding share in the agricultural sector. The structure of
indirect taxes reveals that tobacco and alcohol (23 percent), followed by petro-
leum (18 percent), are exposed to the highest indirect tax (table 8.3, last column).
Table 8.4 shows that almost 50 percent of total exports are electrical prod-
ucts. Almost 90 percent of the production of electrical products is exported. A
major part of this sector is the semiconductor industry. A sizable share of
exports are represented by machinery and transport equipment. This industry
has a high export-intensity ratio, 73 percent.8 It is followed in the size of the
ratio by other manufacturing, coconut oil, leather, fertilizer, other chemicals,
Table 8.3. Production Structure, the Philippines, 2000
(percent)
Value % of % of % of
added/ value % of % of Capital/ skilled unskilled Land/ Indirect
Sector output, % added output employment labor, % labor labor output, % tax rate
Agriculture
Primary agriculture
Palay (rice) 77.5 2.0 1.4 3.1 41 6.2 93.8 7.3 3.3
Corn 78.5 0.6 0.4 1.0 25 6.2 93.8 5.3 3.5
Coconuts 88.9 0.6 0.4 0.8 59 6.2 93.8 10.3 0.9
Fruits and vegetables 79.7 2.2 1.5 2.4 88 6.2 93.8 11.3 3.4
Sugar 69.7 0.3 0.2 0.3 83 6.2 93.8 11.2 1.8
Other crops 77.3 0.6 0.4 0.5 105 6.2 93.8 13.7 1.3
Hogs 63.7 1.4 1.1 1.6 84 9.5 90.5 6.8 2.2
Cattle 71.9 0.4 0.3 0.4 111 9.5 90.5 11.0 1.2
Chickens 60.7 1.3 1.1 1.4 92 9.5 90.5 8.7 2.4
Lightly processed food
Meat processing 20.5 1.1 2.8 0.8 196 25.0 75.0 n.a. 1.6
Milk and dairy 31.1 0.3 0.5 0.2 210 25.0 75.0 n.a. 1.0
Coconut and edible oil 28.7 0.5 0.9 0.2 574 25.0 75.0 n.a. 0.9
Milled rice and corn 34.8 1.4 2.1 1.2 126 25.0 75.0 n.a. 2.0
Milled sugar 22.0 0.2 0.4 0.1 191 25.0 75.0 n.a. 1.4
(Table continues on the following pages.)
255
256
Table 8.3. Production Structure, the Philippines, 2000 (continued)
(percent)
Value % of % of % of
added/ value % of % of Capital/ skilled unskilled Land/ Indirect
Sector output, % added output employment labor, % labor labor output, % tax rate
Nonagriculture
Other primary products and mining
Agricultural services 84.7 0.4 0.2 0.5 61 6.2 93.8 10.0 2.8
Forestry 89.4 0.2 0.1 0.1 217 16.9 83.1 33.0 3.9
Fishing 77.4 2.8 1.9 2.1 216 2.4 97.6 3.8 1.7
Mining 63.0 0.6 0.5 0.4 253 30.5 69.5 n.a. 2.2
Crude oil and natural gas 34.6 0.0 0.0 0.0 n.a. n.a. n.a. n.a. 0.0
Highly processed food and tobacco
Fruit processing 36.5 0.4 0.5 0.3 166 25.0 75.0 n.a. 2.2
Fish processing 28.5 0.3 0.6 0.2 355 25.0 75.0 n.a. 1.3
Other processed food 30.9 1.3 2.3 1.2 162 25.0 75.0 n.a. 1.6
Tobacco and alcohol 40.4 1.0 1.4 1.0 156 57.7 42.3 n.a. 22.9
Manufacturing
Textiles 37.3 1.0 1.4 1.0 130 6.4 93.6 n.a. 0.7
Garments and footwear 46.1 2.1 2.4 1.9 162 4.5 95.5 n.a. 0.5
Leather and rubber-wear 42.9 0.7 0.9 0.7 143 9.8 90.2 n.a. 0.4
Paper and wood products 39.3 1.7 2.3 1.5 163 23.5 76.5 n.a. 0.7
Fertilizer 39.7 0.1 0.2 0.1 140 37.8 62.2 n.a. 0.5
Other chemicals 41.1 1.9 2.4 1.5 201 37.8 62.2 n.a. 1.0
Petroleum 14.2 0.7 2.6 0.8 114 42.4 57.6 n.a. 17.7
Cement and related
products 41.7 0.7 0.9 0.6 165 29.8 70.2 n.a. 1.9
Metal and related
products 36.9 1.9 2.7 1.4 210 8.4 91.6 n.a. 1.1
Machinery and
transport equipment 40.0 3.6 4.8 1.8 368 30.4 69.6 n.a. 1.7
Electrical and related
products 45.5 8.5 9.9 7.3 171 39.5 60.5 n.a. 1.2
Other manufacturing 48.1 1.4 1.6 1.4 135 6.7 93.3 n.a. 1.8
Other industry
Construction 53.0 3.9 3.9 5.5 67 14.9 85.1 n.a. 1.4
Utilities 68.3 3.4 2.6 1.9 324 43.7 56.3 n.a. 3.2
Services
Transportation and
communications 53.6 7.0 6.9 5.3 210 18.2 81.8 n.a. 1.2
Wholesale trade 66.1 13.2 10.6 10.7 192 25.6 74.4 n.a. 1.1
Other services 63.5 20.2 16.8 17.5 171 31.5 68.5 n.a. 2.9
Public services 72.2 8.2 6.0 19.3 41 60.7 39.3 n.a. 0.0
Source: Based on the national model in Cororaton and Corong (2009).
Note: n.a. not applicable.
257
258 Agricultural Price Distortions, Inequality, and Poverty
Table 8.4. Trade Structure and Elasticity Parameters,
the Philippines, 2000
Elasticitiesa Exports, % Imports, %
b
Sector sig_va sig_m eta sig_e Share Intensity Share Intensityb
Agriculture
Primary agriculture
Palay (rice) 0.8 2.2 4.5 2.2 0.0 0.0 0.0 0.0
Corn 0.8 2.5 4.9 2.5 0.0 0.1 0.1 8.4
Coconuts 0.8 2.4 4.8 2.4 0.0 0.2 0.0 0.0
Fruits and vegetables 0.8 2.0 3.9 2.0 1.2 15.1 0.3 6.2
Sugar 0.8 3.0 5.9 3.0 0.0 0.0 0.0 0.0
Other crops 0.8 2.0 3.9 2.0 0.1 2.8 1.2 44.2
Hogs 0.8 2.0 3.9 2.0 0.0 0.0 0.0 0.0
Cattle 0.8 2.0 3.9 2.0 0.0 0.2 0.1 9.2
Chickens 0.8 2.0 3.9 2.0 0.0 0.0 0.0 0.4
Lightly processed food
Meat processing 1.5 2.0 3.9 2.0 0.0 0.0 0.4 3.4
Milk and dairy 1.5 2.0 3.9 2.0 0.0 1.7 1.0 33.6
Coconut and edible oil 1.5 2.0 3.9 2.0 1.5 32.9 0.6 19.0
Milled rice and corn 1.5 2.2 4.5 2.2 0.0 0.0 0.8 8.8
Milled sugar 1.5 3.0 5.9 3.0 0.2 8.3 0.1 8.2
Nonagriculture
Other primary products and mining
Agricultural services 0.8 2.2 4.3 2.2 0.0 0.0 0.0 0.1
Forestry 0.8 2.2 4.3 2.2 0.1 10.3 0.0 0.6
Fishing 0.8 2.2 4.3 2.2 0.8 7.9 0.0 0.3
Mining 0.8 2.2 4.3 2.2 0.4 15.8 1.4 45.8
Crude oil and natural gas 0.8 2.2 4.3 2.2 0.0 0.0 7.5 99.6
Highly processed food and tobacco
Fruit processing 1.5 2.0 3.9 2.0 0.7 24.1 0.3 13.9
Fish processing 1.5 2.0 3.9 2.0 0.7 22.0 0.2 7.4
Other processed food 1.5 2.0 3.9 2.0 0.6 4.8 0.9 9.3
Tobacco and alcohol 1.5 2.0 3.9 2.0 0.1 1.4 0.3 5.7
Manufacturing
Textiles 1.5 2.1 4.1 2.1 1.2 16.9 2.7 36.7
Garments and footwear 1.5 2.1 4.1 2.1 0.2 1.8 0.1 1.3
Leather and rubber-wear 1.5 2.0 4.1 2.0 1.3 26.6 2.3 45.6
Paper and wood products 1.5 2.0 4.1 2.0 2.3 19.7 1.8 19.3
Fertilizer 1.5 2.0 4.1 2.0 0.1 16.8 0.5 49.4
Other chemicals 1.5 2.0 4.1 2.0 0.9 7.4 5.0 35.4
Petroleum 1.5 2.0 4.1 2.0 1.6 11.8 1.8 16.6
The Philippines 259
Table 8.4. Trade Structure and Elasticity Parameters,
the Philippines, 2000 (continued )
Elasticitiesa Exports, % Imports, %
b
Sector sig_va sig_m eta sig_e Share Intensity Share Intensityb
Cement and related
products 1.5 2.0 4.1 2.0 0.4 9.5 0.5 13.8
Metal and related products 1.5 2.0 4.1 2.0 2.5 17.4 4.2 31.7
Machinery and transport
equipment 1.5 2.0 4.1 2.0 18.3 73.2 12.5 70.6
Electrical and related
products 1.5 2.0 4.1 2.0 45.9 89.0 35.2 88.9
Other manufacturing 1.5 2.0 4.1 2.0 3.7 44.3 2.0 36.1
Other industry
Construction 1.5 1.0 2.1 1.0 0.3 1.5 0.3 1.9
Utilities 1.5 1.0 2.1 1.0 0.0 0.0 0.0 0.0
Services
Transportation and
communications 1.5 1.0 2.1 1.0 3.7 10.2 8.1 24.2
Wholesale trade 1.5 1.0 2.1 1.0 2.9 5.2 0.6 1.5
Other services 1.5 1.0 2.1 1.0 8.4 9.5 6.9 10.0
Public services n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Source: Based on the national model in Cororaton and Corong (2009).
Note: n.a. not applicable.
a. sig_va substitution parameter in the constant elasticity of substitution value added function.
sig_m substitution parameter in the constant elasticity of substitution composite good function.
eta export demand elasticity.
sig_e substitution parameter in the constant elasticity of transformation.
b. Exports, divided by output; imports divided by the composite good's consumption.
garments, fruit processing, and fish processing. On the import side, electrical
products, as well as machinery and transport equipment, have high import-
intensity ratios, accounting for 35 and 12 percent of total domestic sales,
respectively. Other sectors in which imports are a major source of domestic
supply include other crops, cattle, mining and crude oil, milk and dairy, fruit
processing, fish processing, coconut oil, sugar milling, other food, textiles,
leather, paper, fertilizer, other chemicals, petroleum, cement, and transporta-
tion and communications.
Table 8.4 also shows the values of key elasticity parameters used in the model:
the import substitution elasticity (sig_m) in the constant elasticity of substitution
composite good function, the production substitution elasticity (sig_va) in the
260 Agricultural Price Distortions, Inequality, and Poverty
constant elasticity of substitution value added production function, and the
export demand elasticity (eta), which is obtained from the Armington parameter
of the global model.
The consumption structure of households is presented in table 8.5. Rice is
a significant staple among Filipinos, especially among poorer households. It
accounts for 14.3 of total expenditure among the first household decile, but the
share decreases substantially as households become richer. Fish and meat, fruits
and vegetables, and other food are the other significant items in household con-
sumption. Generally, the lower-income groups show substantial expenditure on
food and food-related products. For instance, food items account for 42.4 percent
of total expenditure among the first decile compared with 13.4 percent among the
tenth decile. Richer households spend more on services relative to poorer house-
holds. Products of special interest are corn, sugar, chicken, meat processing, milk
and dairy, processed fruit, processed fish, milled rice and corn, and milled sugar.
The shares of expenditure on these special products decline as households
become richer: they account for 25 percent of consumption among the first
decile, but only 8.6 percent among the tenth decile.
Simulations
All policy experiments reported in our study rely on an indirect tax replacement
scheme to maintain fixed government balance. We generate results at the
macrolevel and the sectoral level, as well as vectors of changes in household
incomes, consumer prices, and sectoral employment shares. We then use the
vectors of changes as inputs in a microsimulation procedure to calculate the
impacts on poverty and inequality based on a household survey in 2000. We also
undertake a sensitivity analysis with an alternative direct tax replacement scheme.
Definition of policy experiments
Table 8.6 shows the sectoral correspondence between the CGE model for the
Philippines and the global model, as well as information on the sectoral tariff rates
and export subsidies based on new estimates of nominal rates of protection and
assistance for the Philippines. The CGE model for the Philippines is initially
solved using these new estimates of protection and assistance to create the base
with which all subsequent simulations may be compared. In certain policy exper-
iments, we use the global simulation results from the global model as policy
shocks for the model for the Philippines, following the method proposed by
Horridge and Zhai (2006).
Table 8.5. Structure of Household Expenditure, by Decile, the Philippines, 2000
(percent)
Decile
Sector 1 2 3 4 5 6 7 8 9 10
Agriculture
Primary agriculture
Corn 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.2 0.1 0.1
Coconuts 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.1
Fruits and vegetables 4.1 3.8 3.6 3.4 3.1 2.8 2.5 2.2 1.9 1.3
Sugar 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Other crops 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.0
Chickens 0.8 0.9 0.9 1.0 1.1 1.1 1.1 1.1 1.0 0.7
Lightly processed food
Meat processing 4.2 4.6 4.9 5.6 6.2 6.8 7.1 6.8 6.3 4.2
Milk and dairy 1.1 1.2 1.3 1.3 1.4 1.3 1.3 1.2 1.1 0.8
Coconut and edible oil 0.7 0.6 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.2
Milled rice and corn 14.3 12.9 11.7 10.0 8.4 6.9 5.7 4.5 3.4 1.8
Milled sugar 1.2 1.1 1.0 1.0 0.9 0.8 0.7 0.6 0.5 0.3
Nonagriculture
Other primary products and mining
Forestry 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Fishing 6.8 6.4 6.1 5.5 4.9 4.2 3.6 3.1 2.5 1.5
Mining 0.1 0.1 0.1 0.0 0.0 0.0 0.1 0.1 0.1 0.1
Highly processed food, and tobacco
Fruit processing 1.2 1.1 1.0 0.9 0.9 0.8 0.7 0.6 0.5 0.4
Fish processing 2.0 1.9 1.8 1.6 1.4 1.2 1.1 0.9 0.7 0.4
261
(Table continues on the following page.)
262 Table 8.5. Structure of Household Expenditure, by Decile, the Philippines, 2000 (continued )
(percent)
Decile
Sector 1 2 3 4 5 6 7 8 9 10
Other processed food 5.1 4.8 4.7 4.3 4.0 3.7 3.3 2.9 2.5 1.6
Tobacco and alcohol 4.5 4.8 4.9 4.8 4.5 4.2 3.6 3.1 2.6 1.6
Mining and manufacturing
Textiles 0.8 0.9 1.0 1.0 1.0 1.0 0.9 0.9 0.9 0.8
Garments and footwear 1.7 1.9 2.1 2.2 2.2 2.1 2.1 2.0 2.0 1.7
Leather and rubber-wear 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.3
Paper and wood products 0.8 0.7 0.7 0.7 0.6 0.6 0.6 0.6 0.7 0.9
Fertilizer 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Other chemicals 2.7 2.4 2.2 2.1 1.9 1.8 1.8 1.9 2.2 3.1
Petroleum 1.9 1.6 1.6 1.6 1.6 1.5 1.5 1.4 1.3 0.9
Cement and related products 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Machinery and transport
equipment 0.1 0.3 0.3 0.5 0.7 0.9 1.0 1.1 1.1 1.3
Electrical and related products 0.3 0.7 0.8 1.1 1.5 1.8 1.9 2.1 2.2 2.4
Other manufacturing 0.6 0.8 0.9 0.9 1.0 1.1 1.1 1.1 1.1 1.0
Other industry
Utilities 3.4 3.0 2.9 2.9 2.9 2.8 2.8 2.6 2.3 1.7
Services
Transportation and
communications 6.0 7.0 7.3 8.2 9.4 10.1 11.5 12.9 14.7 17.4
Wholesale trade 17.8 17.5 17.1 16.7 16.3 15.9 15.7 15.5 15.3 14.6
Other services 16.5 17.5 18.8 20.8 22.2 24.8 26.9 29.3 32.0 38.7
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Source: Based on the national model in Cororaton and Corong (2009).
Note: There is no household consumption of agricultural services, crude oil and natural gas mining, and several other categories.
Table 8.6. Exogenous Demand and Price Shocks Caused by Rest-of-the-World Liberalization
(percent)
Full trade liberalization, Agricultural trade liberalization,
Trade distortions excluding Philippinesa excluding Philippinesb
Export Export Import Export Export Import
subsidy, price, price, demand price, price, Export
Tariff, % % % shifter,c % % demand
Philippine CGE model Linkage model % ( 0 tax) change change % change change shifter,c %
Agriculture
Primary agriculture
Palay (rice) Paddy rice 19.6 0.0 0.0 0.0 1.0 0.0 0.0 1.0
Corn Other grains 29.6 0.0 0.0 6.1 1.0 0.0 5.7 1.0
Coconuts Oilseeds 4.8 10.0 0.0 0.8 1.0 0.0 0.5 1.0
Fruits and vegetables Vegetables and fruits 8.7 0.0 5.7 2.4 1.1 3.8 1.7 1.2
Sugar Sugarcane and beets 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0
Other crops Other crops 3.9 0.0 5.9 1.3 1.0 3.9 1.4 1.0
Hogs Other livestock 18.7 0.0 5.6 1.0 1.0 3.6 0.1 1.0
Cattle 18.7 0.0 5.6 1.0 1.0 3.6 0.1 1.0
Chickens Cattle, sheep, other 10.0 0.0 0.0 5.6 1.0 0.0 5.5 1.0
Lightly processed food
Meat processing Beef and sheep meat 9.0 0.0 3.7 2.8 0.5 2.0 4.5 0.5
Milk and dairy Dairy products 4.1 0.0 4.9 7.0 1.1 4.2 7.4 1.1
Coconut and edible oil Vegetable oils and fats 4.4 0.0 2.6 1.1 1.0 0.9 1.7 1.0
Milled rice and corn Processed rice 29.0 0.0 5.3 4.3 0.8 3.3 1.6 0.8
Milled sugar Refined sugar 73.2 0.0 3.9 2.1 1.5 2.0 0.8 1.6
263
(Table continues on the following pages.)
264
Table 8.6. Exogenous Demand and Price Shocks Caused by Rest-of-the-World Liberalization (continued )
(percent)
Full trade liberalization, Agricultural trade liberalization,
Trade distortions excluding Philippinesa excluding Philippinesb
Export Export Import Export Export Import
subsidy, price, price, demand price, price, Export
Tariff, % % % shifter,c % % demand
Philippine CGE model Linkage model % ( 0 tax) change change % change change shifter,c %
Nonagriculture
Other primary products and mining
Agricultural services 2.7 1.0 2.8 0.6 1.1 1.0 0.9 1.0
Forestry 2.7 1.0 2.8 0.6 1.1 1.0 0.9 1.0
Fishing Other primary products 2.7 1.0 2.8 0.6 1.1 1.0 0.9 1.0
Mining 2.7 1.0 2.8 0.6 1.1 1.0 0.9 1.0
Crude oil and natural gas 2.7 1.0 2.8 0.6 1.1 1.0 0.9 1.0
Highly processed food and tobacco
Fruit processing 6.0 0.0 3.6 1.6 1.2 2.0 0.4 1.0
Fish processing Other food, beverages, 6.0 0.0 3.6 1.6 1.2 2.0 0.4 1.0
Other processed food and tobacco 6.0 0.0 3.6 1.6 1.2 2.0 0.4 1.0
Tobacco and alcohol 6.0 0.0 3.6 1.6 1.2 2.0 0.4 1.0
Manufacturing
Textiles Textiles and 8.0 1.7 2.0 0.2 1.0 1.1 0.4 1.0
Garments and footwear wearing apparel 8.0 1.7 2.0 0.2 1.0 1.1 0.4 1.0
Leather and rubber-wear 8.0 1.7 2.0 0.2 1.0 1.1 0.4 1.0
Paper and wood products 3.5 0.0 2.1 1.5 1.0 0.7 0.3 1.0
Fertilizer 3.5 0.0 2.1 1.5 1.0 0.7 0.3 1.0
Other chemicals 3.5 0.0 2.1 1.5 1.0 0.7 0.3 1.0
Petroleum 3.5 0.0 2.1 1.5 1.0 0.7 0.3 1.0
Cement and related Other manufacturing
products 3.5 0.0 2.1 1.5 1.0 0.7 0.3 1.0
Metal and related products 3.5 0.0 2.1 1.5 1.0 0.7 0.3 1.0
Machinery and
transport equipment 3.5 0.0 2.1 1.5 1.0 0.7 0.3 1.0
Electrical and
related products 3.5 0.0 2.1 1.5 1.0 0.7 0.3 1.0
Other manufacturing 3.5 0.0 2.1 1.5 1.0 0.7 0.3 1.0
Other industry
Construction Services 0.0 0.0 2.9 0.1 1.0 1.1 0.2 1.0
Utilities 0.0 0.0 2.9 0.1 1.0 1.1 0.2 1.0
Services
Transport and communications 0.0 0.0 2.9 0.1 1.0 1.1 0.2 1.0
Wholesale trade Services 0.0 0.0 2.9 0.1 1.0 1.1 0.2 1.0
Other services 0.0 0.0 2.9 0.1 1.0 1.1 0.2 1.0
Source: Linkage model simulations by van der Mensbrugghe, Valenzuela, and Anderson (see the appendix).
a. Rest-of-the-world liberalization in all sectors, excluding the Philippines.
b. Rest-of-the-world liberalization in agriculture only, excluding the Philippines.
c. Derived using (1 0.01 * p) (1 0.01 * q)^(1/ESUBM), where p is the export price change; q is the export volume change; and ESUBM is the Armington import
elasticity taken from the global (Linkage) model.
265
266 Agricultural Price Distortions, Inequality, and Poverty
The six policy experiments are as follows:
� ROW-ALL, rest-of-the-world trade liberalization in all sectors, excluding
the Philippines: This experiment uses the results of the global model under
full rest-of-the-world liberalization in table 8.6 and retains all existing trade
distortions (tariff rates and export subsidies) in the Philippines.
� ROW-AGR, rest-of-the-world trade liberalization in agriculture and lightly
processed food only: As with ROW-ALL, this experiment uses the results of the
global model, but only under rest-of-the-world liberalization in agriculture
and lightly processed food, and retains all existing trade distortions in the
Philippines.
� PHIL-ALL, unilateral trade liberalization in all sectors: All trade distortions
in the Philippines are eliminated. No changes in the sectoral border export
prices and import prices or in export demand are included in this unilateral
liberalization.
� PHIL-AGR, unilateral agriculture trade liberalization: All trade distortions in
primary agriculture and lightly processed foods in the Philippines are elimi-
nated. Similar to PHIL-ALL, there are no changes in the sectoral border export
prices and import prices or in export demand in this unilateral liberalization.
� COMB-ALL, full trade liberalization in the rest of the world and in the
Philippines: This scenario combines ROW-ALL and PHIL-ALL in a global
liberalization.
� COMB-AGR, liberalization in agriculture and lightly processed foods in the
rest of the world and in the Philippines: This scenario combines ROW-AGR
and PHIL-AGR.
Results
In this subsection, we present the modeling results of our six policy experiments
and then report the impacts on household income and welfare and on poverty
and inequality. The subsection concludes with additional results that show the
sensitivity of the core results to changes in the treatment of tax adjustments in the
model.
Rest-of-the-world trade liberalization in all sectors (ROW-ALL)
The results of the Linkage model in table 8.6 indicate that this first policy experi-
ment leads to higher export prices and greater export demand for the products of
the Philippines. Within agriculture, a significant shift in export demand is
observed in milled sugar, as well as raw fruits and vegetables (at 1.5 and 1.1 per-
cent, respectively). This is also true in fruit and fish processing (1.2 percent),
The Philippines 267
whereas slightly more modest shifts in export demand are observed in other
industrial and service sectors. Meanwhile, full rest-of-the-world liberalization
leads to higher world import prices for most Philippine goods.
Table 8.7 shows that export prices overall rise more in the agricultural sectors
than in the nonagricultural sectors (by an average of 3.6 versus 2.4 percent in local
currency). So, too, do export volumes: they expand 9.8 percent in agriculture
compared with a modest 0.3 percent rise in nonagriculture. Local import prices
also increase more in agriculture than in nonagriculture (3.0 versus 1.1 percent).
Substitution toward imported goods is observed owing to a larger rise in the
prices of domestically produced goods relative to the prices of the corresponding
imported products. Because of this, agricultural and nonagricultural import
volumes increase by 0.9 and 1.1 percent, respectively (table 8.7, second column).
The entire agricultural sector benefits from the improved international market
conditions. Agricultural output and value added prices increase by 3.5 and 3.9
percent, respectively. Thus, the returns to agriculture-specific factors, particularly
land and agricultural capital (which increase by 5.1 and 4.7 percent, respectively),
rise relative to wage rates (3 percent) and to the returns to nonagricultural capital
(2.9 percent). Unskilled wages increase slightly more than skilled wages because
unskilled workers are used more intensively in the expanding agricultural sector.
In contrast, in the nonagricultural sectors, the fall in domestic sales offsets the
export expansion. Thus, the volume of output contracts by 0.1 percent. Essen-
tially, this is traceable to the import�domestic price substitution effects (see else-
where above), the fall in the world import prices for essential consumer goods
such as garments (table 8.6), and the appreciation in the real exchange rate.
Despite falling output volumes, nonagricultural output prices still increase by
2.5 percent owing to the higher export prices. Hence, the returns to factors such
as nonagricultural capital and skilled workers, which are used intensively in
nonagriculture, increase as well.
Rest-of-the-world trade liberalization in agriculture only (ROW-AGR)
The results of the ROW-AGR scenario are similar, but smaller in magnitude rela-
tive to the results of the ROW-ALL scenario. We only focus on the results that are
different in this scenario because the mechanisms driving the model results are
essentially the same in the ROW-ALL and ROW-AGR scenarios. Agricultural
exports increase by 11 percent mainly because of a significant export demand shift
in sugar, as well as raw fruits and vegetables (1.6 and 1.2 percent, respectively; see
tables 8.6 and 8.7). A distinct feature of this scenario is that domestic agricultural
prices increase relatively less than agricultural import prices (1.7 versus 2.7 per-
cent). In the face of more expensive agricultural imports, domestic demand
expands, while imports fall (0.1 and 1.2 percent, respectively). Agricultural output
268
Table 8.7. Aggregate Simulation Results of Prospective Liberalizations in Agriculture and
Nonagriculture, the Philippines
(percent change from the baseline)
ROW-ALL ROW-AGR PHIL-ALL PHIL-AGR COMB-ALL COMB-AGR
Indicator Ag Nonag Ag Nonag Ag Nonag Ag Nonag Ag Nonag Ag Nonag
Real GDP 0.3 0.1 0.8 0.2 1.1 0.3
Prices
Real exchange rate 1.0 0.4 1.6 0.8 0.6 0.4
Export price in
local currency 3.6 2.4 2.0 0.8 1.8 1.0 1.3 0.3 1.8 1.4 0.7 0.4
Import price in
local currency 3.0 1.1 2.7 0.3 7.2 2.1 7.9 0.1 4.5 1.1 5.4 0.5
Domestic price 3.5 2.6 1.7 0.9 2.3 1.7 2.2 0.7 1.2 0.8 0.5 0.2
Output price 3.5 2.5 1.7 0.9 3.1 1.6 2.3 0.6 0.4 0.9 0.6 0.3
Value added price 3.9 2.9 2.0 1.0 4.0 1.6 2.9 0.7 0.1 1.3 0.8 0.2
Consumer price index 2.6 1.0 1.3 1.1 1.2 0.1
Volume
Imports 0.9 1.1 1.2 0.3 15.1 2.1 17.0 0.7 16.2 3.2 15.8 1.0
Exports 9.8 0.3 11.0 0.1 8.5 3.5 6.2 1.1 19.5 3.8 18.2 0.9
Domestic demand 0.2 0.2 0.1 0.1 3.1 0.6 2.8 0.2 2.9 0.8 2.6 0.0
Composite good 0.2 0.2 0.0 0.0 0.8 0.0 0.4 0.1 0.6 0.1 0.4 0.1
Output 0.6 0.1 0.6 0.1 2.5 0.3 2.4 0.3 1.9 0.3 1.7 0.2
Value added 0.7 0.1 0.7 0.1 2.3 0.3 2.2 0.3 1.6 0.2 1.5 0.2
Factor prices
Nominal wages of
skilled workers 2.9 1.0 1.6 0.9 1.3 0.1
Nominal wages of
unskilled workers 3.1 1.1 2.2 1.2 0.8 0.1
Nominal returns
to land 5.1 n.a. 3.0 n.a. 4.7 n.a. 3.5 n.a. 0.3 n.a. 0.5 n.a.
Nominal returns
to capital 4.7 2.9 2.9 0.9 5.7 1.3 4.5 0.5 1.2 1.6 1.7 0.4
Source: National CGE model simulations of the authors.
Note: Ag agriculture, which includes primary agriculture and lightly processed food. Nonag nonagriculture, which includes other primary products, highly
processed foods, manufacturing, and services. n.a. not applicable.
269
270 Agricultural Price Distortions, Inequality, and Poverty
therefore expands because domestic demand in agriculture accounts for a larger
share of domestic agricultural output.
The absence of nonagricultural liberalization results in a 0.1 percent decline in
nonagricultural exports because most nonagricultural goods show little or no
change in world export demand (table 8.6). Nonagricultural imports rise, while
domestic demand declines (0.3 versus 0.1 percent) because domestic prices
increase relatively more than import prices (0.9 versus 0.3 percent). The contrac-
tion in domestic demand, together with the 0.1 percent decline in exports, leads
to a 0.1 percent drop in nonagricultural output. Nonetheless, nonagricultural
output and value added prices still increase, owing to higher export and domestic
prices.
Full unilateral trade liberalization in the Philippines (PHIL-ALL)
This experiment eliminates all sectoral tariff rates and export subsidies in the
Philippines and assumes that there are no changes in the global model. PHIL-ALL
leads to a 7.2 and 2.1 percent decline in the local prices of imported agricultural
and nonagricultural products, respectively (table 8.7). Import prices fall more,
and import volumes correspondingly increase more in agriculture than in non-
agriculture because the initial distortions are greater in agriculture. In the face of
cheaper imports relative to domestic prices, the domestic demand declines for
local agricultural and nonagricultural producers. Meanwhile, these producers
benefit from cost savings on imported inputs, resulting in falls of 2.3 and 1.7 per-
cent in the domestic cost of production in the agricultural and nonagricultural
sectors, respectively. The real exchange rate depreciates by 1.6 percent, meaning
that products made in the Philippines are relatively cheaper in international mar-
kets. Coupled with falling domestic prices in the face of cheaper imports and
input cost savings, this encourages producers to reallocate resources toward the
export market (table 8.7).
While exports rise in both agriculture and nonagriculture, domestic demand
falls relatively more in agriculture. Because domestic demand represents a larger
share in agricultural output, agricultural output contracts, while nonagricultural
output expands. Output and value added prices in both agriculture and nonagri-
culture fall, but the fall in the former is higher than the fall in the latter. The result
of all these adjustments is a fall in all factor returns. The factors used intensively in
agriculture experience a much greater reduction: the returns to agricultural capi-
tal and land decline by 5.7 and 4.6 percent, respectively, whereas nonagricultural
capital returns fall by only 1.3 percent. The nominal wages for unskilled workers
fall relatively more than the wages for skilled workers because unskilled workers
are used more intensively in agriculture.
The Philippines 271
Unilateral trade liberalization only in agriculture in the Philippines
(PHIL-AGR)
Unilateral liberalization in agriculture and lightly processed food results in sub-
stantial expansion in agricultural imports (17 percent) owing to the significant
decline in local agricultural import prices (7.9 percent). At the same time, the
removal of domestic agricultural distortions also generates cost savings in the
export-oriented lightly processed food sector because of this sector's reliance on
primary agricultural inputs. Thus, agricultural industries, which, in the context of
our study, include lightly processed food, reorient production toward the export
market, resulting in a 6.2 percent expansion in exports and a 2.8 percent reduc-
tion in domestic sales. The net result is a contraction in agricultural output
because domestic sales comprise a larger share of total agricultural output. As a
result, agricultural value added and value added prices fall, along with the returns
to all agricultural factors. The returns to land drop 3.5 percent, and the returns to
agricultural capital decline by 4.5 percent, while the wages of unskilled workers
fall by 1.2 percent.
The results in the nonagricultural sectors are the opposite. Import prices
increase marginally by 0.1 percent, while domestic prices fall by 0.7 percent,
resulting in a 0.2 percent expansion in domestic sales. Domestic prices decrease
relatively more than world prices (0.7 versus 0.3 percent), leading to a 1.0 percent
export expansion. Together with greater domestic demand, this allows overall
nonagricultural output to expand by 0.3 percent.
A comparison of the sectoral results of PHIL-ALL and PHIL-AGR in table 8.7
confirms the heavier price burden of agricultural protection in the Philippine
economy. Indeed, the removal of agricultural distortions accounts for at least two-
thirds of the price reduction in exports, imports, domestic production, value
added, and the consumer price index.9 This is also true of factor prices: 40 to
80 percent of the fall in factor returns is traceable to the removal of agricultural
protection.
Rest-of-the-world and Philippine trade liberalization in all sectors
(COMB-ALL)
This experiment combines rest-of-the-world trade liberalization and domestic
trade liberalization. The impact of the rest-of-the-world trade liberalization dom-
inates the effects of the unilateral trade liberalization in both agricultural and the
nonagricultural sectors. Local import prices decline in the agricultural sector
despite the increase in world commodity prices, indicating the substantial level of
domestic distortions in the Philippines. The cheaper imports crowd out the corre-
sponding domestic products, leading to a contraction in the domestic sales of
domestic producers; this affects the agricultural sector more negatively than the
272 Agricultural Price Distortions, Inequality, and Poverty
nonagricultural sector. At the same time, rising world export prices, real exchange
rate depreciation, and cost savings on imported inputs allow domestic producers
to reorient a large share of their production successfully toward the more prof-
itable export market. Given the greater reliance of the agricultural sector on
domestic sales, the net impact is a contraction in agriculture and an expansion in
the nonagricultural sectors.
The output prices for both agricultural and the nonagricultural sectors
increase (0.4 and 0.9 percent, respectively). Agriculture experiences a smaller
increase owing to the substantial level of domestic agricultural distortion in the
base. With the exception of the returns to agricultural capital, all factor prices
increase. Factor prices increase especially in the nonagricultural sectors, although
they increase by less in this scenario than in the ROW-ALL scenario. The impacts
in output volumes are the opposite: the effects of unilateral liberalization domi-
nate the effects of rest-of-the-world trade liberalization. Thus, overall agricultural
output declines, while overall output in the nonagricultural sectors improves
( 1.9 and 0.3 percent, respectively).
Rest-of-the-world and Philippine trade liberalization in agriculture only
(COMB-AGR)
The unilateral agricultural trade liberalization scenario (PHIL-AGR) dominates
the global agricultural trade liberalization scenario (ROW-AGR) within the
COMB-AGR scenario. In the COMB-AGR scenario, output, prices, and volumes
in agriculture decline, as do agricultural factor prices. Local import prices in agri-
culture decline despite rising world commodity prices. This indicates that the level
of domestic distortions is substantial in the Philippines. The positive impact of
the higher world commodity prices is dominated by the negative impact of the
domestic agricultural distortions imposed by the government. Thus, the returns
to the factors used intensively in agriculture (land, agricultural capital, and
unskilled wages) fall in response to the declining agricultural output prices.
In contrast, local import prices in the nonagricultural sectors increase. This is
expected because the country's nonagricultural trade distortions are already low
relative to international standards.10 Thus, the overall output prices of nonagri-
cultural products increase by 0.2 percent, resulting in output expansion and,
consequently, higher returns to nonagricultural factors (table 8.7).
Household income, the consumer price index, and welfare
The changes in nominal household incomes, nominal consumer price indexes
(based on household-specific consumer baskets), and real incomes and welfare
are presented in table 8.8. In interpreting the changes in household-specific con-
sumer prices, recall from above that primary and processed food account for a
Table 8.8. Effects of Prospective Liberalizations on Real Household Consumption, by Income Group,
the Philippines
(percent)
ROW-ALL ROW-AGR PHIL-ALL
Change in Change in Change in Change in Change in Change in
Household nominal income consumer price EV nominal income consumer price EV nominal income consumer price EV
Decile 1 3.3 2.9 0.4 1.3 1.2 0.1 1.9 2.2 0.3
Decile 2 3.3 2.9 0.4 1.3 1.1 0.1 1.9 2.0 0.2
Decile 3 3.3 2.8 0.4 1.3 1.1 0.1 1.9 2.0 0.1
Decile 4 3.2 2.8 0.4 1.2 1.1 0.1 1.8 1.8 0.0
Decile 5 3.2 2.8 0.4 1.2 1.1 0.1 1.8 1.7 0.1
Decile 6 3.1 2.7 0.4 1.2 1.1 0.1 1.7 1.6 0.2
Decile 7 3.0 2.6 0.4 1.1 1.1 0.1 1.7 1.4 0.2
Decile 8 2.9 2.6 0.4 1.1 1.0 0.1 1.6 1.3 0.3
Decile 9 2.8 2.5 0.3 1.1 1.0 0.0 1.5 1.2 0.3
Decile 10 2.9 2.4 0.5 1.1 1.0 0.1 1.4 1.0 0.4
Overall 3.0 2.6 0.4 1.1 1.0 0.1 1.6 1.3 0.3
(Table continues on the following page.)
273
274
Table 8.8. Effects of Prospective Liberalizations on Real Household Consumption, by Income Group, the
Philippines (continued )
(percent)
ROW-ALL ROW-AGR PHIL-ALL
Change in Change in Change in Change in Change in Change in
Household nominal income consumer price EV nominal income consumer price EV nominal income consumer price EV
Decile 1 1.2 2.0 0.8 1.4 0.7 0.7 0.1 0.8 0.9
Decile 2 1.2 1.8 0.7 1.4 0.8 0.6 0.1 0.7 0.8
Decile 3 1.2 1.8 0.6 1.4 0.8 0.5 0.1 0.6 0.7
Decile 4 1.2 1.6 0.4 1.4 0.9 0.4 0.1 0.5 0.5
Decile 5 1.1 1.5 0.3 1.4 1.0 0.3 0.1 0.4 0.4
Decile 6 1.1 1.3 0.2 1.3 1.1 0.2 0.1 0.2 0.3
Decile 7 1.1 1.2 0.1 1.3 1.2 0.1 0.0 0.1 0.2
Decile 8 1.1 1.1 0.0 1.3 1.2 0.1 0.0 0.0 0.1
Decile 9 1.1 1.0 0.1 1.3 1.3 0.0 0.0 0.0 0.0
Decile 10 1.0 0.8 0.3 1.4 1.4 0.0 0.0 0.2 0.1
Overall 1.1 1.1 0.0 1.4 1.2 0.1 0.0 0.1 0.1
Source: National CGE model simulations of the authors.
Note: EV equivalent variation in income.
The Philippines 275
significant share of household expenditure, especially among the lower-income
groups, and that the initial tariff rates are higher on primary and processed food
items than on other goods (tables 8.4 and 8.5).
ROW-ALL, the scenario of global trade liberalization in all sectors, excluding
the Philippines, registers the greatest increase in nominal household incomes
because rising world prices translate into higher factor returns. For the same rea-
son, consumer prices also increase the most in this scenario. Nonetheless, the
greater nominal income growth among all households outweighs the detrimental
effects of rising consumer prices, with the result that welfare improves among all
household groups. Variations in incomes and consumer prices tend to be greater
in the poorest deciles, which are more tightly linked to the agricultural sector and
which post generally better welfare results in the scenario. The results under
ROW-AGR are similar, but less than half as large. The results are qualitatively
similar and also display a generally pro-poor effect (table 8.8).
The two domestic liberalization scenarios (PHIL-ALL and PHIL-AGR) result
in falling consumer prices, which are driven by the reduction in local import and
export prices following the elimination of trade-related distortions in the
Philippines. This price reduction is greater if the domestic agricultural and nona-
gricultural liberalizations are combined. We see that the removal of domestic
agricultural distortions reduces consumer prices by more relative to the corre-
sponding effect of the removal of nonagricultural distortions given the high share
of agriculture in household consumption and the higher initial levels of protec-
tion and, hence, consumer prices. A comparison of the changes in consumer
prices in scenarios PHIL-ALL and PHIL-AGR show that roughly 1.8 of the 2.2 per-
cent reduction in the consumer price index for the first decile is generated by the
elimination of domestic agricultural distortions alone (table 8.8).
One may also observe that nominal incomes fall under the two unilateral liber-
alization scenarios. However, the consumer price effects dominate such that,
despite falling nominal incomes, welfare and real incomes increase more under
agricultural trade liberalization. Furthermore, these welfare gains accrue propor-
tionately more to the poorer deciles owing to the greater relative agricultural
consumption among these deciles.
These welfare gains are bolstered in rest-of-the-world liberalization and unilat-
eral trade liberalization combined. Nominal incomes rise under the full rest-of-
the-world and domestic trade liberalization scenario, but this is somewhat offset
by soaring consumer prices. Overall, the combined global and domestic agricul-
tural trade liberalization scenario (COMB-AGR) provides the greatest increase in
welfare. This is because the nominal income gains from the rest-of-the-world
trade liberalization are largely conserved, and to these gains are added the con-
sumer price reductions from the domestic trade liberalization. In this case, the
276 Agricultural Price Distortions, Inequality, and Poverty
poorest deciles emerge as the winners because of both the domestic agricultural
trade liberalization and the pro-agricultural nature of the rest-of-the-world trade
liberalization.
The effects on poverty and inequality
The microsimulation process we use in our study relies on the Family Income and
Expenditure Survey of the Philippines in 2000 (NSO 2000).11 To estimate the
likely poverty and inequality impacts of the labor market conditions arising from
trade liberalization, we apply certain information from the CGE model as an
input into the microsimulation procedure in a sequential manner. In particular,
we use the vectors of changes in the total incomes of households; wage income,
capital income, and other income; household-specific consumer price indexes (to
update the nominal value of the poverty line); and sectoral employment shares.
Through a random process, we incorporate changes in the employment status
of households after the simulation. In this way, it is possible to capture household
laborers moving in and out of employment (at the microlevel) by taking into
account the changes in sectoral employment arising from a policy shift (at the
macrolevel). For instance, households that have no labor income because of
unemployment may become employed and, consequently, earn labor income.
Similarly, employed households may become unemployed and earn no labor
income at all after the policy change. Household labor income is affected by
changes in wages, as well as by the chance of becoming unemployed after the
policy shock. The microsimulation process is repeated 30 times, allowing us to
derive confidence intervals on our Foster, Greer, and Thorbecke (1984) indexes
and Gini coefficient estimates.12
To take advantage of the richness of the microsimulation procedure, we calcu-
late poverty indexes and Gini coefficients based on the demographic characteris-
tics of the household heads, especially gender, skill level, and location (urban or
rural). In total, the final Foster, Greer, and Thorbecke indexes are derived for eight
categories of household heads. Using demographic characteristics instead of
income deciles to evaluate changes in poverty and income distribution is prefer-
able because it allows for a more accurate policy evaluation and identification of
the gainers and losers from trade liberalization.
The poverty and inequality results in all experiments are summarized in
table 8.9. Inequality marginally worsens in all unilateral liberalization scenarios,
but slightly improves in the rest-of-the-world liberalization scenarios. The latter
outcome arises because of the increase in nominal incomes among poorer house-
holds, while the former outcome results from the greater decrease in the nominal
incomes of poorer households relative to richer households (table 8.8).
Table 8.9. Income Inequality and the Poverty Effects of Prospective Liberalizations, by Location, Gender,
and Skills, the Philippines
(percent change relative to the 2000 index)
Direct tax
Indirect tax replacement replacement
2000 ROW- ROW- PHIL- PHIL- COMB- COMB- COMB- COMB-
Group Variable index ALL AGR ALL AGR ALL AGR ALL AGR
All Philippines Gini 0.51 0.2 0.2 0.4 0.3 0.2 0.1 0.1 0.1
P0 33.5 0.9 0.6 1.1 0.4 0.1 0.1 0.0 0.1
P1 10.3 1.9 1.2 1.6 0.4 0.4 0.9 0.4 0.7
P2 4.3 2.8 2.0 2.0 0.4 0.8 1.5 0.9 1.3
All urban Gini 0.48 0.2 0.2 0.4 0.3 0.2 0.1 0.2 0.1
P0 18.6 1.6 0.9 2.0 0.8 0.1 0.2 0.1 0.0
P1 5.0 1.9 1.0 3.2 1.7 0.4 0.1 0.4 0.1
P2 2.0 2.0 1.1 4.7 2.7 0.8 0.0 0.9 0.5
Urban men, skilled P0 3.2 0.1 0.0 1.5 1.4 0.7 0.7 1.2 1.6
P1 0.7 1.4 0.2 2.0 3.4 2.2 2.6 3.4 3.4
P2 0.2 1.7 0.4 2.6 4.4 2.7 3.3 4.4 4.3
Urban men, unskilled P0 23.3 1.6 0.9 2.1 0.9 0.1 0.2 0.1 0.1
P1 6.4 1.8 1.0 3.2 1.7 0.5 0.1 0.5 0.2
P2 2.5 2.0 1.1 4.8 2.8 0.8 0.1 1.0 0.5
Urban women, skilled P0 0.9 0.0 0.0 2.3 1.9 1.0 1.5 1.2 1.4
P1 0.1 2.5 0.6 0.8 3.1 2.7 3.4 2.8 3.7
P2 0.0 4.1 1.0 0.2 4.4 4.1 5.2 4.4 5.5
Urban women, unskilled P0 15.2 2.1 1.2 1.8 0.6 0.1 0.4 0.1 0.2
P1 3.9 2.3 1.1 3.7 2.3 0.5 0.0 0.5 0.1
277
P2 1.6 2.4 1.3 5.1 3.4 1.0 0.1 1.0 0.6
(Table continues on the following page.)
278
Table 8.9. Income Inequality and the Poverty Effects of Prospective Liberalizations, by Location, Gender,
and Skills, the Philippines (continued )
(percent change relative to the 2000 index)
Direct tax
Indirect tax replacement replacement
2000 ROW- ROW- PHIL- PHIL- COMB- COMB- COMB- COMB-
Group Variable index ALL AGR ALL AGR ALL AGR ALL AGR
All rural Gini 0.43 0.1 0.1 0.5 0.2 0.2 0.1 0.1 0.1
P0 48.7 0.9 0.6 0.6 0.0 0.1 0.3 0.2 0.3
P1 15.9 1.4 0.8 1.6 0.4 0.1 0.6 0.2 0.5
P2 6.8 1.7 0.9 2.7 1.1 0.1 0.6 0.0 0.4
Rural men, skilled P0 12.0 0.0 0.0 1.4 2.1 1.6 3.1 1.7 2.8
P1 3.5 1.0 0.2 1.4 2.3 1.8 2.2 1.7 1.9
P2 1.4 1.2 0.3 1.5 2.7 2.2 2.6 2.0 2.3
Rural men, unskilled P0 52.4 0.9 0.6 0.6 0.0 0.1 0.3 0.2 0.3
P1 17.2 1.3 0.7 1.6 0.5 0.1 0.6 0.2 0.5
P2 7.4 1.6 0.9 2.7 1.2 0.1 0.6 0.0 0.4
Rural women, skilled P0 14.7 0.0 0.0 0.7 2.1 0.0 0.8 0.2 1.0
P1 4.1 1.0 0.2 1.1 4.4 1.1 2.5 1.2 2.5
P2 1.4 1.6 0.3 1.6 6.3 1.7 3.7 1.9 3.8
Rural women, unskilled P0 34.9 1.3 0.9 0.8 0.2 0.2 0.4 0.2 0.1
P1 10.8 1.8 1.1 2.0 0.6 0.1 0.6 0.1 0.4
P2 4.4 2.3 1.3 3.7 1.6 0.3 0.4 0.2 0.1
Source: National CGE model simulations of the authors.
Note: Gini Gini coefficient. P0 poverty headcount. P1 poverty gap. P2 poverty severity.
The Philippines 279
Rest-of-the-world liberalization reduces poverty at the national level and
favors unskilled households because rising world demand and the rising export
prices of products made in the Philippines bring about higher agricultural factor
returns (table 8.7). In contrast, unilateral liberalization favors skilled households
such that national poverty indexes worsen. This is because the removal of distor-
tions within the Philippines results in resource reallocations toward outward-
oriented and externally competitive nonagricultural sectors that employ skilled
workers intensively.
Poverty generally falls under the combined global and domestic liberalization
scenario, wherein the poverty-reducing impact of rest-of-the-world liberalization
dominates the poverty-increasing effect of unilateral liberalization. In contrast,
inequality marginally rises because the inequality-increasing effect of unilateral
liberalization dominates the inequality-reducing effect of rest-of-the-world liber-
alization. The combined global and domestic agricultural reform is the most
poverty-friendly scenario. Although the national poverty headcount decreases
marginally, all household groups, with the slight exception of urban households
headed by unskilled workers, share in the benefits of the poverty friendliness of
trade liberalization. Indeed, the poorest of the poor, particularly those poor resid-
ing in rural areas, emerge as winners because they rely on agricultural production
and the wages of unskilled labor.
These results are consistent with those obtained by Cororaton, Cockburn, and
Corong (2006). However, the results of these authors suggest a worsening in both
the poverty gap and the severity of poverty, while our results show the opposite,
especially under the combined rest-of-the-world and Philippine agricultural lib-
eralization. This difference is traceable to the use of more-recent estimates of
trade protection on key food items (such as rice, corn, sugar, and processed meat),
which, if eliminated from the calculation, result in a significant fall in the con-
sumer prices faced by lower-income groups (table 8.7).
Sensitivity analysis: Indirect and direct tax replacement schemes
The results discussed above are derived using an indirect tax replacement scheme.
Are the results sensitive to the tax replacement scheme used? This subsection
compares the above results with the results obtained if a direct tax replacement
closure is adopted. We focus on an analysis of the poverty and inequality results of
COMB-ALL (full rest-of-the-world and domestic trade liberalization) and
COMB-AGR (rest-of-the-world and domestic agricultural trade liberalization).
The sensitivity analysis is summarized in table 8.9. The directions of the
changes in the poverty indexes and inequality are generally the same regardless of
the tax replacement scheme. However, the magnitudes are marginally greater
under the direct tax scenario owing to a smaller increase in consumer prices
because direct taxes are used to compensate for foregone tariff revenues.
280 Agricultural Price Distortions, Inequality, and Poverty
Summary and Policy Implications
Starting in the 1980s, the government shifted from taxing to protecting agricul-
ture relative to the nonagricultural sectors. However, two decades of protection
failed to induce competitiveness and productivity growth because agriculture
became inward looking and inefficient. This study analyzes the poverty and
inequality implications of the removal of the agricultural and nonagricultural
price distortions in the domestic markets of the Philippines as of 2004 and com-
pares these effects with the results if policies abroad are liberalized.
Rest-of-the-world liberalization reduces poverty at the national level and
favors unskilled households in the Philippines because the higher world export
prices and rising export demand for products made in the Philippines allow pro-
ducers in the Philippines to benefit from favorable international market condi-
tions. Nominal incomes improve significantly, outweighing the impact of higher
consumer prices. Rest-of-the-world trade liberalization in all sectors generates
almost uniform increases in real incomes across household types, while rest-of-
the-world trade liberalization in agriculture brings about progressive changes in
real incomes that benefit lower-income groups more.
Unilateral trade liberalization leads to a drop in consumer prices, which is
driven by the reduction in local import and export prices following the elimina-
tion of trade-related distortions induced by policies within the Philippines.
Import prices fall more, and import volumes correspondingly increase more in
agriculture relative to nonagriculture because the initial distortions are greater in
agriculture. However, unilateral liberalization favors skilled households such that
national poverty indexes and inequality worsen. This is because the removal of the
distortions leads to resource reallocation in the Philippines toward outward-
oriented and externally competitive nonagricultural sectors that employ skilled
workers intensively.
The combined global and domestic agricultural reform appears to be the most
poverty-friendly scenario for the Philippines. Although the national poverty
headcount decreases only marginally, all household groups, with the slight excep-
tion of urban households headed by unskilled workers, share in the benefits of the
poverty friendliness of the trade liberalization. The poorest of the poor, particu-
larly those poor living in rural areas, emerge as winners because of their reliance
on agricultural production and the wages from unskilled labor. Thus, it appears to
be in the best interest of the Philippines to take a proactive trade liberalization
stance by fully participating with the rest of the world in the effort at trade liberal-
ization through the inclusion of its own domestic liberalization. Our sensitivity
analysis confirms that the results are not affected by differing tax replacement
assumptions because a similar pattern of effects emerge regardless of whether
indirect or direct tax replacement schemes are used.
The Philippines 281
Notes
1. Estimates of agricultural protection and assistance in the Philippines, based on David, Intal, and
Balisacan (2009), are incorporated in the World Bank's Distortions to Agricultural Incentives Project
Database (Anderson and Valenzuela 2008). The estimates cover five decades. The representative values
for CGE modeling as of 2004 that are used here are available in Valenzuela and Anderson (2008).
2. These vectors of changes are generated by simulating the global model with no Philippine trade
liberalization.
3. This definition is maintained throughout the study. Agriculture is defined as primary agricul-
ture (excluding fishing, forestry, and agricultural services) and lightly processed food, while nonagri-
culture refers to all other sectors, including highly processed foods, tobacco, and beverages.
4. The trade reform programs were major components of the structural programs prescribed by
multilateral organizations in the 1980s, including the International Monetary Fund and the World
Bank. The Philippines is currently in the fourth phase of the trade reform programs. See Cororaton,
Cockburn, and Corong (2006) for a detailed discussion.
5. Tariff binding overhang refers to the difference between a country's bound tariffs and its
applied tariffs. Bound tariffs are tariff rates that, according to agreements with the World Trade Organ-
ization, the country will not exceed.
6. Sugar exports accounted for only 10 percent of domestic production during this period.
7. The specification of the CGE model is based on EXTER (see Decaluw�, Dumont, and
Robichaud 2000). For a complete discussion and specification of the model, see Cororaton and
Corong (2009), who also provide details on the construction of the social accounting matrix.
8. The export (import) intensity ratio is the ratio of a sector's exports (imports) to output
(domestic supply).
9. See the ratio of prices in PHIL-AGR relative to PHIL-ALL in table 8.7: exports ( 1.3 1.8 0.7),
imports ( 7.9 7.2 1.1), domestic prices ( 2.2 2.3 1.0), output ( 2.3 3.1 0.8), value
added ( 2.9 4.0 0.7), the consumer price index ( 1.1 1.3 0.8), skilled wages ( 0.9 1.6
0.4), unskilled wages ( 1.2 2.5 0.5), the returns to land ( 3.5 4.7 0.7), the returns to agricul-
tural capital ( 4.5 5.7 0.8), and the returns to nonagricultural capital ( 0.5 1.3 0.4).
10. This is because previous rounds of trade reform in the country focused primarily on reducing
nonagricultural distortions.
11. Our microsimulation is a modified version of the original approach proposed by Vos (2005).
12. Vos (2005) observes that 30 iterations are sufficient because repeating the process additionally
does not significantly alter the results. The results on confidence intervals are available from the con-
tact author, Caesar Cororaton, upon request.
References
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Paper 2005�04, Philippine Institute for Development Studies, Makati, the Philippines.
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1955�2007." Data spreadsheet, October, World Bank, Washington, DC. http://go.worldbank.org/
YAO39F35E0.
Austria, M. S. 2001. "Liberalization and Regional Integration: The Philippines' Strategy for Rest of the
World Competitiveness." Philippine Journal of Development 37 (2): 55�86.
Austria, M. S., and E. M. Medalla. 1996. A Study on the Trade and Investment Policies of Developing
Countries: The Case of the Philippines. PIDS Discussion Paper 96�03. Makati, the Philippines:
Philippine Institute for Development Studies.
Cororaton, C. B., J. Cockburn, and E. Corong. 2006. "Doha Scenarios, Trade Reforms, and Poverty in
the Philippines: A CGE Analysis." In Poverty and the WTO: Impacts of the Doha Development
Agenda, ed. T. W. Hertel and L. A. Winters, 375�402. London: Palgrave Macmillan; Washington,
DC: World Bank.
282 Agricultural Price Distortions, Inequality, and Poverty
Cororaton, C. B., and E. Corong. 2009. Philippine Agricultural and Food Policies: Implications for
Poverty and Income Distribution. IFPRI Research Report 161. Washington, DC: International Food
Policy Research Institute.
David, C. C. 2003. "Agriculture." In The Philippine Economy: Development, Policies, and Challenges, ed.
A. M. Balisacan and H. Hill, 175�218. Quezon City, the Philippines: Ateneo de Manila Press.
David, C. C., P. S. Intal Jr., and A. M. Balisacan. 2009. "The Philippines." In Distortions to Agricultural
Incentives in Asia, ed. K. Anderson and W. Martin, 223�54. Washington, DC: World Bank.
Decaluw�, B., J.-C. Dumont, and V. Robichaud. 2000. Basic CGE Models. Vol. II of MIMAP Training
Session on CGE Modeling. Micro Impacts of Macroeconomic and Adjustment Policies Program,
International Development Research Centre, Canadian Centre for International Studies and
Cooperation, and Universit� Laval, Quebec. http://www.pep-net.org/fileadmin/medias/pdf/
volume2.pdf.
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52 (3): 761�66.
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Developing Countries, 2004 and 1980�84." Research Memorandum 13 (December), Center for
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------. 2005. Trade Policy Review: The Philippines. Geneva: WTO.
9
Thailand
Peter Warr*
As Thailand has industrialized, successive Thai governments have become
increasingly interested in intervening on behalf of producers in the key declining
sector, namely, agriculture.1 Agricultural producers and processors are the
intended beneficiaries of these interventions. However, the fact that Thailand is a
major agricultural exporter has limited the scope of protection policies as a means
of influencing domestic commodity prices. Over time, the direct taxation of agri-
cultural exports has been gradually eliminated. This has been important in the
case of rice, on which the high rates of export taxation prior to the mid-1980s
were abolished in 1986. Similarly, rubber exports, taxed prior to 1990, have not
been taxed since then. Cassava exports have continued to be taxed to a minor
extent through the system of export quotas. Taxes on imports of fertilizer, a major
input into agricultural production, have been steadily phased out since the early
1990s. Maize exports have been consistently untaxed, as have chicken exports, a
commodity not covered by the present analysis because of the lack of suitable
price data. Most of this history is a tale of the elimination of the price distortions
that once acted against agricultural export industries.
Four commodities depart from this general trend of the liberalization of agricul-
tural markets. Soybeans were an export item prior to 1992 and have been a net
import item since then; the imports have been subject to quota restrictions. The
change from a net export item to a net import item coincided with a switch from
*The author has benefited greatly from the research assistance of Krisada Bamrungwong and Arief
Anshory Yusuf, from helpful discussions with Ernesto Valenzuela, and from the comments of John
Cockburn. This chapter relies on a modeling approach that is similar to the approach in the author's
chapter on Indonesia in this volume (chapter 6) and is meant to be read in conjunction with that chapter.
Methodological points that are common to the two studies are not repeated here. The discussion here
focuses on methodological points that are different and on the results of the analysis on Thailand.
283
284 Agricultural Price Distortions, Inequality, and Poverty
negative to positive nominal rates of protection. Since the early 1990s, the domestic
soybean industry has received a nominal rate of protection of between 30 and
40 percent. Sugar is an export commodity in Thailand, but the domestic sugar
industry is protected by a home price system that taxes domestic consumers and
transfers the revenue to producers. Nominal rates of protection have averaged over
60 percent. The political power of the highly capital-intensive sugar milling industry
explains this pattern of protection. The case of palm oil is qualitatively similar to the
case of sugar, but the rates of protection are somewhat lower. Finally, Thailand's
small dairy industry is protected from competition by imported milk powder. It has
not been possible to obtain sufficient data to quantify dairy protection for the
purposes of our study, but informed sources report that the rate of protection is
comparable with the rate for sugar. The prospects for additional trade liberalization
in Thailand are not encouraging unless this occurs through bilateral preferential
trading arrangements such as the scheme proposed with the United States.2
Almost all of Thailand's poor people reside in rural areas, and most are directly
involved in agricultural production (Warr 2005). The Thai public is well disposed
to finding ways to reduce rural poverty, and Thai governments have responded to
this sentiment. Interventions on behalf of rural people have been important, but
Thailand is unusual in that, except for the cases discussed above, these interven-
tions have seldom taken the form of initiatives in agricultural commodity mar-
kets. The strong export-orientation of Thai agriculture is an important reason for
this because assistance through commodity markets would require a large pro-
duction of export subsidy outlays. Instead, cash transfers to village organizations,
subsidized loan schemes not linked to agricultural production, and a generally
good system of public infrastructure have been the main instruments of interven-
tion in support of rural areas. Unfortunately, with the exception of the investment
in rural infrastructure, these transfers have seldom been directed in any systematic
way at raising the productivity of rural people or at assisting them in finding bet-
ter economic opportunities outside agriculture. The long-term contribution of
these transfers to any reduction in rural poverty will probably be small.
We use the JamlongThai general equilibrium model of the Thai economy
(Warr 2008b), in conjunction with the Linkage global economic model (van der
Mensbrugghe 2005), to analyze the effects on the incidence of poverty in Thailand
of agricultural and other policies at home and abroad. JamlongThai is structurally
similar in most respects to the Wayang model of the Indonesian economy,
described in detail in chapter 6 by Warr, and the simulations we perform using
this model are also similar to those described in the Indonesian case study.
The next section describes the JamlongThai general equilibrium model of the
Thai economy, the principal analytic tool we use in this study. The subsequent sec-
tion describes the simulations we perform with this model, in combination with
Thailand 285
the global Linkage model. The simulations involve both unilateral agricultural
and trade policy reform in Thailand and reform by the rest of the world. We do
this to assess the relative importance of own-country versus rest-of-the-world
policies to Thai households. We present the results with a focus on the implica-
tions for poverty incidence within Thailand. The final section concludes.
A General Equilibrium Model of the Thai Economy:
JamlongThai
JamlongThai is a general equilibrium model of the Thai economy based on
65 sectors and 200 households. It has been constructed for the analysis of the
effect of trade policy and other policy shocks on poverty incidence in Thailand.
Unless otherwise stated, the database of the model refers to the year 2000.
JamlongThai shares many structural features with the highly influential ORANI
general equilibrium model of the Australian economy (Dixon et al. 1982) and the
general equilibrium model of the global economy established through the Global
Trade Analysis Project (Hertel 1997), which also belong to the Johansen (1964)
category of economy-wide models. The specific structure of JamlongThai draws
on a revised version of the ORANI model, called ORANI-G (Horridge 2004), and
the PARA and Wayang general equilibrium models of the Thai and Indonesian
economies, respectively, described in detail in Warr (2001, 2005). We have adapted
this general structure of the model to reflect the specific objectives of our study
and important features of the Thai economy.
Industries
The JamlongThai model contains 65 industries, of which 24 are in agriculture.
Each industry produces a single output, and the set of commodities therefore
coincides with the set of industries. Exports are not identical with domestically
sold commodities. In each industry, the two are produced by a transformation
process with a constant elasticity of transformation.3 The core of the production
side of the model is an input-output table of 65 sectors. This table has been aggre-
gated from an input-output table of 180 sectors that has been produced by the
National Economic and Social Development Board (NESDB 2004). At the time of
our study, the latest input-output table available for Thailand related to the year
2000. We have used this data set for our study.
The cost structure of the Thai economy, with an emphasis on the agricultural
sector, is summarized in table 9.1. The agricultural sector relies on almost no
skilled (educated) labor, but it is intensive in the use of unskilled labor. Unskilled
and skilled labor are fully mobile across all industries.
286 Agricultural Price Distortions, Inequality, and Poverty
Table 9.1. Cost Shares of the Major Factors of Production, Paddy and
Other Industries, Thailand, 2000
(percent of total costs)
Other All
Cost components Paddy agriculture Nonagriculture industries
Skilled 0.0 0.0 10.5 9.9
Unskilled 24.9 15.0 4.6 5.2
Mobile capital 2.1 5.1 29.3 28.0
Land 45.7 44.1 0.0 2.3
Intermediate inputs 27.3 35.8 55.7 54.6
Total 100.0 100.0 100.0 100.0
Source: Database of the JamlongThai model, based on NESDB (2004) and agricultural cost survey data of
the Ministry of Agriculture and Cooperatives.
Table 9.2. Sources of the Factor Incomes of Broad Household
Groups, Thailand, 2000
(percent)
Total
Mobile factor
Household category Skilled Unskilled capital Land income
1. Farm, northeast 15.5 28.0 42.8 13.6 100.0
2. Farm, north 6.3 15.5 44.0 34.2 100.0
3. Farm, other 10.1 12.9 47.5 29.6 100.0
4. Farm worker, all regions 9.6 78.3 6.0 6.1 100.0
5. Entrepreneur, paid employees 2.2 0.7 97.0 0.1 100.0
6. Entrepreneur, no paid employees 1.6 1.6 96.6 0.2 100.0
7. Professional 71.2 4.8 20.1 3.9 100.0
8. Clerical employee 47.5 27.7 21.3 3.5 100.0
9. Production and construction 31.6 51.8 13.5 3.1 100.0
10. Economically inactive 7.8 4.6 38.2 50.1 100.0
Source: Database of the JamlongThai model, based on NSO (2003).
Households
The JamlongThai model contains 10 major household categories, which are based
on the socioeconomic survey classifications used by the National Statistical Office
(NSO 2003). Table 9.2 summarizes these 10 household categories, which are classi-
fied according to the occupation of the heads of household. Four are farm house-
holds: farmer, northeast; farmer, north; farmer, other region; and farm worker, all
regions. Six are nonfarm: entrepreneur with paid employees; entrepreneur without
paid employees; professional; clerical employee; production, construction, or
general laborer; and economically inactive. Each of these categories is divided into
Thailand 287
20 groups of equal population size that are sorted by household income per person.4
There are thus a total of 200 household subcategories.
The incomes of each of these 200 household subcategories depend on the
household ownership of factors of production, the returns to these factors, and
nonfactor incomes, mainly transfers from others. Since our focus is on income
distribution, the sources of the incomes of the various households are of particu-
lar interest. These differ significantly among the 10 household categories.
Our household data are extracted from the 2000 household socioeconomic sur-
vey of the National Statistical Office (NSO 2003). A social accounting matrix has
been constructed for Thailand based on data of the household socioeconomic sur-
vey, the input-output tables (NESDB 2004), the national accounts, and trade data,
all for 2000. The database for the JamlongThai model draws upon this matrix.
The consumer demand equations for the various household types are based on
a Cobb-Douglas demand system, using data on expenditure shares extracted from
the 2000 household socioeconomic survey. Within each of the 10 major cate-
gories, the 20 subcategories thus differ according to per capita expenditures,
budget shares in consumption, and sources of factor and nonfactor incomes.
Table 9.2 summarizes the sources of the factor incomes of the 10 major house-
hold categories, and table 9.3 summarizes the share of these categories in the over-
all population and in the overall incidence of poverty in Thailand based on the
official poverty line.
Table 9.3. Characteristics of the Major Household Categories,
Thailand, 2000
(number and percent)
Total population, Share of total Poverty
Household category millions population, % incidence,a %
1. Farm, northeast 8,460 13.9 40.7
2. Farm, north 3,287 5.4 27.3
3. Farm, other 4,920 8.1 14.5
4. Farm worker, all regions 4,565 7.5 29.3
5. Entrepreneur,
paid employees 1,953 3.2 0.7
6. Entrepreneur,
no paid employees 8,354 13.7 5.9
7. Professional 4,866 8.0 1.6
8. Clerical employee 7,934 13.0 2.5
9. Production and construction 8,967 14.7 7.7
10. Economically inactive 7,613 12.5 11.6
1�10. Total population 60,916 100.0 14.4
Source: Author calculations based on NSO (2003).
a. Headcount measure of poverty incidence using the national poverty line.
288 Agricultural Price Distortions, Inequality, and Poverty
Simulations
The effects of agricultural trade policy reform are simulated using the
JamlongThai model of the Thai economy, combined with the Linkage model of
the world economy (van der Mensbrugghe 2005). The simulations involve both
unilateral agricultural and trade policy reform in Thailand and reform by the rest
of the world. We do this to assess the relative importance of own-country versus
rest-of-the-world policies for Thai households. We also compare the reform of
agriculture alone with the reform of all goods markets to gauge the relative
contribution of agricultural policies to the measured impacts on households.
We consider three sets of policy reforms below: unilateral reform in Thailand
(simulation A); global reform, excluding Thailand (simulation B); and the combi-
nation of these two sets of reforms, that is, global reform, including Thailand
(simulation C).
The structure of these simulations is identical to the structure described for
the simulations on Indonesia by Warr in chapter 6; the reader should therefore
refer to chapter 6 for details. By reform, we mean the complete elimination of all
tariffs, the tariff equivalents of any nontariff barriers, export taxes and export
subsidies, and domestic agricultural policies in so far as they alter the producer
or consumer prices of farm products in various countries. Each of our three sim-
ulations is conducted twice: one time each in which the reductions to protection
in Thailand and in the rest of the world apply to all commodities (labeled simu-
lations A1, B1, and C1) and one time each in which the reductions apply only to
agricultural commodities, including lightly processed food (labeled simulations
A2, B2, and C2).
The initial rates of industry assistance in Thailand used in the simulations
are derived from the database of the Linkage model (see table 9.4). We have
reduced these rates to zero in simulations A and C. In simulations B and C,
we use the Linkage model simulations conducted by van der Mensbrugghe,
Valenzuela, and Anderson (see the appendix) to estimate the changes in the
import prices and export demand shifters that result from the liberalization in
all countries except Thailand. We then apply these results as shocks in the
JamlongThai model. The changes to border prices, derived from the Linkage
model, are shown in table 9.5.
The results of the liberalization of markets for all goods
While our emphasis in this study is on the effects on poverty and income inequality,
an understanding of these effects requires that we look first at the macroeconomic
effects.
Thailand 289
Table 9.4. Industry Assistance Rates Used in Modeling,
Thailand, 2004
(percent)
Commodity Tariff Export subsidy Output subsidy
Paddy rice 12.5 0.0 0.3
Wheat 26.8 0.0 0.2
Other grains 26.5 0.0 0.2
Vegetables and fruits 44.6 0.0 0.1
Oilseeds 32.9 0.0 0.1
Sugarcane 0.0 0.0 0.1
Plant-based fibers 5.0 0.0 0.2
Other crops 38.0 0.0 0.1
Cattle, sheep, and so on 9.6 0.0 0.1
Other livestock 13.9 0.0 0.1
Raw milk 0.0 0.0 0.1
Wool 7.2 0.0 0.1
Other primary products 2.4 0.9 2.4
Beef and sheep meat 49.3 0.0 1.2
Other meat products 40.9 0.0 1.2
Vegetable oils and fats 39.2 0.0 1.2
Dairy products 18.1 0.0 1.2
Processed rice 19.6 0.0 0.5
Refined sugar 0.0 0.0 5.0
Other food, beverages, tobacco 39.7 0.0 19.9
Textiles and wearing apparel 23.5 0.6 1.1
Other manufacturing 13.9 0.0 3.3
Services 0.0 0.0 3.7
Source: Valenzuela and Anderson (2008), based on the estimates compiled by Anderson and Valenzuela
(2008).
Macroeconomic effects
The simulated macroeconomic effects of trade reform in all goods markets are
summarized in table 9.6 (simulations A1, B1, and C1). Real GDP rises in Thailand
under all three reform scenarios. Aggregate real household consumption also
rises. The increases in real GDP and aggregate real household consumption in
simulation A1 far exceed the corresponding increases in simulation B1. Unilateral
liberalization is more beneficial to Thailand in macroeconomic terms than the
liberalization of all other countries combined.
By turning to the effects of these reforms on real factor returns, we may discern
clues to the income distributional impacts of the reforms. The real factor returns,
deflated by the consumer price index, rise in the case of unskilled labor, skilled
290 Agricultural Price Distortions, Inequality, and Poverty
Table 9.5. Exogenous Border Price Shocks Caused by
Rest-of-the-World Liberalization, Thailand
(percent deviation from base)
Export price shocksa Import price shocksb
Reform of Agricultural Reform of Agricultural
Commodity all goods reform only all goods reform only
Paddy rice 7.3 5.7 0.0 0.0
Wheat 0.0 0.0 3.3 4.0
Other grains 6.4 4.8 4.9 4.5
Oilseeds 6.0 4.7 6.0 5.6
Sugarcane and beets 0.0 0.0 0.0 0.0
Plant-based fibers 6.8 5.2 6.4 7.9
Vegetables and fruits 7.2 5.6 1.8 1.1
Other crops 7.3 5.8 1.7 1.6
Cattle, sheep, and so on 6.2 4.6 3.6 2.8
Other livestock 5.4 3.7 0.6 1.1
Raw milk 0.0 0.0 0.0 0.0
Wool 0.0 0.0 5.5 5.3
Beef and sheep meat 4.1 2.2 10.0 10.1
Other meat products 4.5 2.7 1.6 2.5
Vegetable oils and fats 2.6 0.9 0.7 1.2
Dairy products 4.4 2.6 12.2 12.5
Processed rice 6.6 4.9 1.4 0.5
Refined sugar 4.6 2.7 0.0 0.0
Other food, beverages, tobacco 3.5 1.7 0.9 1.4
Other primary products 3.3 1.2 0.5 0.8
Textiles and wearing apparel 3.2 1.4 0.4 0.5
Other manufacturing 2.9 1.1 1.3 0.3
Services 3.3 1.2 0.2 0.2
Agriculture and food 4.6 3.0 1.7 0.9
Agriculture 7.0 5.5 1.1 1.6
Processed foods 4.3 2.7 2.1 0.5
Other manufacturing 3.0 1.2 1.1 0.3
Nontradables 3.3 1.2 0.2 0.2
Total 3.3 1.4 1.0 0.4
Merchandise trade 3.3 1.5 1.2 0.4
Source: Linkage model simulations by van der Mensbrugghe, Valenzuela, and Anderson (see the
appendix).
a. Simulated as shocks to the inverse export demand equations for Thailand.
b. Simulated as shocks to the exogenous import prices for Thailand.
Thailand 291
Table 9.6. Aggregate Simulation Results of the Prospective
Liberalization of All Commodities, Thailand
Sim A1: Sim B1: rest- Sim C1:
unilateral of-the-world global
Indicator liberalization liberalization liberalization
Macroeconomic aggregates, % change from base
Real GDP, expenditure side,
GDP deflator 0.27 0.04 0.30
Real household consumption,
CPI deflator 4.32 0.16 4.43
Import volume index,
duty-paid weights 4.68 0.12 4.79
Export volume index 0.04 0.01 0.03
GDP price index,
expenditure side 8.24 2.11 10.49
Consumer price index 7.69 2.64 10.45
Nominal change, baht, millions
GDP 482,697 121,566 611,829
Consumption 397,015 90,448 493,626
Investment 7,775 19,905 28,127
Inventory 2,516 38 2,610
Government expenditure 75,390 11,175 87,466
Real returns to factors, % change from base, using CPI deflator
Unskilled labor 3.3 0.5 2.8
Skilled labor 10.0 0.8 9.1
Agricultural capital 3.3 0.3 3.5
Nonagricultural capital 9.9 0.3 9.5
Land 3.8 0.5 4.2
Real household expenditure, % change from base, using CPI deflator
1. Farm, northeast 2.6 0.8 3.3
2. Farm, north 2.9 0.7 3.5
3. Farm, other 4.4 0.6 4.9
4. Farm worker, all regions 1.1 0.5 1.6
5. Entrepreneur,
paid employees 5.7 0.1 5.8
6. Entrepreneur,
no paid employees 5.7 0.3 6.0
7. Professional 5.7 0.4 5.3
8. Clerical employee 4.3 0.1 4.1
9. Production and construction 2.7 0.1 2.7
10. Economically inactive 3.8 0.4 4.1
Source: Thailand computable general equilibrium model simulations by the author.
Note: CPI consumer price index.
292 Agricultural Price Distortions, Inequality, and Poverty
labor, and nonagricultural capital in simulation A1 (unilateral liberalization). The
returns to agricultural capital and land decline following liberalization, indicating
that the structure of protection favors industries that are intensive in the use of
agricultural capital and land. Another way of viewing this result is that the struc-
ture of protection in Thailand is such that the real returns to skilled and unskilled
labor, as well as nonagricultural capital, are lowered, while the real returns to
agricultural capital and land are raised. The removal of the protection has the
opposite effect.
Simulation C1 is approximately the sum of simulations A1 and B1. It is
dominated by the effects of unilateral liberalization.
In all three reform scenarios, real household expenditures increase among all
household categories except household group 7, the households headed by profes-
sionals. The negative effect under simulation B1 is small relative to the gains
achieved by most other household categories.
The effects on poverty incidence and inequality
The poverty effects of the reforms are summarized in table 9.7. The measured
level of the incidence of poverty depends on the poverty line used in the calcula-
tions, and this may also be true of the simulated changes in poverty incidence that
result from particular economic shocks. We present the effects on the incidence of
poverty using two poverty lines: the national poverty line and the international
US$2-a-day poverty line at purchasing power parity.5
For our calculations with each of the poverty lines, we use the same calibration
method, as follows. First, we assemble the data on the ex ante distribution of
household expenditures according to information in the model database.
Second, we use the published level of poverty incidence based on one and then
the other of the two poverty lines to find the value of the poverty line, measured in
the domestic currency, that generates the particular level of poverty incidence we
have found in the data on household expenditures in the model database. We take
these published levels of poverty incidence from data of the National Economic
and Social Development Board in the case of the national poverty line and from
PovcalNet (2008) in the case of the US$2-a-day poverty line. This then becomes
the base level of the poverty line we use in our subsequent calculations.
Third, the ex post levels of real expenditure for each household group are sim-
ulated within the model so as to reflect the effects of the shocks applied to the
model. We perform these calculations of real household expenditures using the
individual household consumer price index as the deflator so as to reflect the par-
ticular household's consumption bundle.
Fourth, we then compare these ex post real expenditures with the poverty line
described above to obtain ex post levels of poverty incidence.
Thailand 293
Table 9.7. The Poverty and Inequality Effects of the Prospective
Liberalization of All Commodities, Thailand
Change, ex post � ex ante
Ex Sim A1: Sim B1: rest- Sim C1:
ante unilateral of-the-world global
Group level liberalization liberalization liberalization
Poverty incidence at the national poverty line, %
1. Farm, northeast 40.7 6.2 2.9 9.1
2. Farm, north 27.3 2.6 1.3 4.0
3. Farm, other 14.5 12.2 5.8 18.0
4. Farm worker, all regions 29.3 3.4 0.2 3.6
5. Entrepreneur,
paid employees 0.7 0.7 0.0 0.7
6. Entrepreneur,
no paid employees 5.9 11.5 5.7 17.2
7. Professional 1.6 2.3 1.0 3.3
8. Clerical employee 2.5 3.0 1.0 4.1
9. Production and construction 7.7 9.0 6.7 15.7
10. Economically inactive 11.6 4.9 0.6 7.5
Farm households 30.1 3.5 0.9 4.4
Nonfarm households 5.9 3.3 0.6 3.9
All households 14.4 3.4 0.7 4.1
Poverty incidence at the US$2-a-day poverty line, %
Farm households 21.03 1.55 0.31 1.86
Nonfarm households 15.19 1.10 1.18 2.28
All households 25.20 1.84 1.00 2.84
Inequality, Gini coefficient
Urban households 0.152 0.006 0.006 0.012
Rural households 0.334 0.004 0.005 0.009
All households 0.339 0.005 0.007 0.012
Source: Thailand computable general equilibrium model simulations by the author.
Finally, the changes in poverty incidence reported in table 9.7 and table 9.9 (see
below) are the ex post levels of poverty incidence, minus the ex ante levels corre-
sponding to each of the two poverty lines described above. A positive number thus
indicates an increase in the simulated level of poverty incidence as a result of the
shocks identified.
In addition to the effects on poverty incidence, we also report simulated effects
on inequality in the distribution of household real expenditures. We accomplish
this using the Gini coefficient as the measure. The Gini coefficient takes values
294 Agricultural Price Distortions, Inequality, and Poverty
between zero and 1; higher values reflecting greater inequality. These coefficients
are estimated by constructing Lorenz curves from the distributions of ex ante and
ex post real expenditures and then calculating the Gini coefficients corresponding
to these distributions. These results are also presented in table 9.7.
Unilateral liberalization (simulation A1) delivers reduced poverty to all house-
hold categories and to farm and nonfarm households in aggregate. The benefits in
terms of poverty reduction in Thailand that derive from global across-the-board
liberalization (simulation C1) arise primarily from Thailand's own liberalization.
Only one-fifth of the total reduction in the incidence of poverty may be attributed
to the effects of liberalization elsewhere (simulation B1). Liberalization in
Thailand and in the rest of the world increases inequality within Thailand some-
what. Skilled labor gains proportionately more than unskilled labor. However, in
absolute terms, poor households have a strong stake in trade liberalization, espe-
cially liberalization within their own country regardless of whether or not other
countries participate.
The results of the liberalization of only agricultural markets
The macroeconomic effects of liberalization that is confined to agricultural prod-
ucts alone are summarized in table 9.8. The simulated effects are quite different
from those arising from liberalization in the markets for all goods. Unilateral agri-
cultural liberalization in Thailand raises real household consumption and mar-
ginally raises the consumer price index. It reduces the real value of unskilled labor
marginally and increases skilled wages significantly. It also reduces the returns to
agricultural capital, along with the returns to land. Relative to the rest of the econ-
omy, the agricultural industries in Thailand use much smaller shares of skilled
labor and much larger shares of unskilled labor. Reduced agricultural protection
reduces the size of the agricultural sector and raises the size of the nonagricultural
sector. In the process, it raises real skilled wages and lowers real unskilled wages,
along with the real returns to agricultural capital and land.
Unilateral agricultural liberalization in Thailand raises the incidence of
poverty among farm households (table 9.9) because it reduces the real returns to
unskilled labor (slightly)--deflated by the consumer price index--and also the
real returns to agricultural capital and land (more significantly). Agricultural lib-
eralization in the rest of the world raises the international prices of Thailand's
agricultural exports (table 9.4). This increases the real returns to skilled and
unskilled labor and the returns to land in Thailand. Farm-level poverty in
Thailand declines, along with poverty among nonfarm households. Measured solely
by the incidence of poverty in Thailand, these results suggest that Thailand has a
stake in agricultural liberalization, but only if other countries participate as well.
Thailand 295
Table 9.8. Aggregate Simulation Results of the Prospective
Liberalization of Only Agricultural Commodities,
Thailand
Sim A2: Sim B2: rest- Sim C2:
unilateral of-the-world global
Indicator liberalization liberalization liberalization
Macroeconomic aggregates, % change from base
Real GDP, expenditure side,
GDP deflator 0.01 0.03 0.04
Real household consumption,
CPI deflator 0.12 0.14 0.25
Import volume index,
duty-paid weights 0.12 0.10 0.21
Export volume index 0.00 0.01 0.01
GDP price index,
expenditure side 0.21 0.99 1.20
Consumer price index 0.12 1.25 1.37
Nominal change, baht, millions
GDP 12,633 57,660 69,886
Consumption 7,733 44,919 52,352
Investment 2,587 7,767 10,304
Inventory 80 51 131
Government expenditure 2,232 4,923 7,099
Real returns to factors, % change from base, using CPI deflator
Unskilled labor 0.07 0.25 0.18
Skilled labor 0.51 0.36 0.87
Agricultural capital 1.34 0.20 1.14
Nonagricultural capital 0.47 0.08 0.52
Land 1.46 0.42 1.04
Real household expenditure, % change from base, using CPI deflator
1. Farm, northeast 0.16 0.47 0.32
2. Farm, north 0.10 0.42 0.32
3. Farm, other 0.07 0.37 0.44
4. Farm worker, all regions 0.11 0.33 0.23
5. Entrepreneur,
paid employees 0.20 0.14 0.33
6. Entrepreneur,
no paid employees 0.24 0.26 0.48
7. Professional 0.26 0.15 0.10
8. Clerical employee 0.15 0.00 0.15
9. Production and construction 0.03 0.10 0.12
10. Economically inactive 0.05 0.26 0.30
Source: Thailand computable general equilibrium model simulations by the author.
Note: CPI consumer price index.
296 Agricultural Price Distortions, Inequality, and Poverty
Table 9.9. The Poverty and Inequality Effects of the Prospective
Liberalization of Only Agricultural Commodities,
Thailand
Change, ex post � ex ante
Ex Sim A2: Sim B2: rest- Sim C2:
ante unilateral of-the-world global
Group level liberalization liberalization liberalization
Poverty incidence at the national poverty line, %
1. Farm, northeast 40.7 0.3 0.8 0.5
2. Farm, north 27.3 0.1 0.3 0.2
3. Farm, other 14.5 0.1 1.1 1.1
4. Farm worker, all regions 29.3 1.0 4.7 3.7
5. Entrepreneur,
paid employees 0.7 0.4 0.7 1.1
6. Entrepreneur,
no paid employees 5.9 0.1 0.2 0.3
7. Professional 1.6 0.0 0.6 0.6
8. Clerical employee 2.5 0.1 1.5 1.4
9. Production and
construction 7.7 0.2 1.0 0.8
10. Economically inactive 11.6 0.1 0.5 0.4
Farm households 30.1 0.3 1.6 1.3
Nonfarm households 5.9 0.0 0.8 0.7
All households 14.4 0.1 1.1 0.8
Poverty incidence at the US$2-a-day poverty line, %
Farm households 21.03 0.09 0.31 0.24
Nonfarm households 15.19 0.03 0.14 0.17
All households 25.20 0.04 0.24 0.28
Inequality, Gini coefficient
Urban households 0.152 0.001 0.006 0.007
Rural households 0.334 0.000 0.005 0.005
All households 0.339 0.001 0.007 0.008
Source: Thailand computable general equilibrium model simulations by the author.
Conclusions and Comparison with the Study
on Indonesia
The comparative static analysis in our study indicates that across-the-board trade
liberalization is poverty reducing in Thailand, whether other countries participate
in the liberalization or not. This poverty reduction occurs among farm and non-
farm households, and the qualitative outcome is not dependent on the particular
poverty line used in the analysis. The reduction in farm and nonfarm poverty
Thailand 297
incidence occurs despite an increase in inequality in Thailand. Liberalization
raises real skilled wages relative to real unskilled wages, and this effect increases
inequality. Both Thailand's own liberalization and the liberalization of the rest of
the world reduce poverty among farm and nonfarm households, but the largest
benefits from across-the-board liberalization, measured in terms of the effects on
poverty, arise from Thailand's own liberalization.
If the trade liberalization is confined to agricultural products alone, the results
are somewhat different. A similar increase in inequality occurs, but unilateral agri-
cultural liberalization in Thailand raises the incidence of poverty among farm
households, while reducing it slightly among nonfarm households. This negative
effect on rural households arises from the reduction in real unskilled wages. If the
rest of the world also liberalizes agricultural trade, the increase in farm-level
poverty in Thailand disappears. Thailand's farm poor thus have an interest in
agricultural liberalization, but only if the rest of the world also liberalizes.
Our analyses of trade liberalization in Thailand in this chapter and the study by
Warr on Indonesia in chapter 6 use similar modeling frameworks, but the struc-
ture of agricultural trade and agricultural protection in the two countries is quite
different. A direct comparison of the simulated effects of liberalized trade policies
is therefore possible and offers insights.
The unilateral liberalization of all commodities reduces the incidence of
poverty among farm and nonfarm households in both countries. In both coun-
tries, skilled and unskilled real wages rise as a result of this liberalization. How-
ever, the magnitude of the rise in unskilled wages is greater in Thailand. This
reveals a key difference in the effects of the overall structure of the distortions to
incentives in the two countries. In Thailand, industry assistance policies act more
strongly against the interests of unskilled workers by favoring industries that are
less intensive in the use of this factor. The result is that across-the-board liberal-
ization has a much larger poverty-reducing effect in Thailand than in Indonesia,
although it reduces the incidence of poverty among all socioeconomic groups in
both countries.
Rest-of-the-world liberalization in all commodities is poverty reducing among
all socioeconomic groups in both countries, but the magnitude of this effect is
larger in Indonesia. Rest-of-the-world liberalization raises the prices of agricul-
tural products relative to the prices of manufactured goods. Indonesia's poor are
more dependent on agricultural production, and the magnitude of the poverty
reduction that occurs following rest-of-the-world liberalization is therefore
greater in Indonesia.
If liberalization occurs only in agricultural products, the returns to agricultural
capital and land decline in both countries. In Indonesia, unskilled wages also decline
significantly, whereas, in Thailand, this effect is negligible. This divergence in
298 Agricultural Price Distortions, Inequality, and Poverty
outcomes reflects a difference in the structure of agricultural assistance in the two
countries. Indonesia's agricultural distortions are more biased toward unskilled
labor�intensive agricultural industries, of which rice--highly protected in Indonesia,
but not in Thailand--is the most important example. Meanwhile, because rice is a
staple consumer good among Indonesia's poor, a reduction in agricultural assistance
benefits many poor households, rural as well as urban. The outcome of unilateral
agricultural liberalization is, in both countries, a reduction in the incidence of urban
poverty and an increase in the incidence of rural poverty. The net effect is a small
overall reduction in national poverty incidence in Indonesia and a small overall
increase in Thailand. Rest-of-the-world liberalization in agricultural products alone
is poverty reducing among all socioeconomic groups in both countries.
Overall, the key results common to both countries are as follows:
� The poor have a strong interest in across-the-board liberalization both in their
own country and in the rest of the world; this applies to the rural poor and the
urban poor.
� The urban poor have an interest in unilateral agricultural liberalization, while
the rural poor do not.
� Agricultural liberalization in the rest of the world is poverty reducing among
all socioeconomic groups in rural and urban areas.
Notes
1. Structural change in the Thai economy is reviewed by Warr (2007), and a fuller discussion of
Thailand's agricultural trade policies is provided by Warr (2008a) and Warr and Kohpaiboon (2009).
The estimates of agricultural assistance for Thailand supplied by Warr and Kohpaiboon (2009) are
incorporated in the World Bank's Distortions to Agricultural Incentives Project Database (Anderson
and Valenzuela 2008). The estimates cover four or more decades. The representative values for com-
putable general equilibrium modeling as of 2004 that are used here are available in Valenzuela and
Anderson (2008).
2. A bilateral trading arrangement with the United States was under negotiation prior to February
2006, but the negotiations are currently suspended. The protection of Thailand's soybean industry
would be an important issue in the negotiations.
3. This treatment differs from that used by us in the Indonesian case study in chapter 6, in which
the commodities exported and sold domestically are identical, but the quantity of exports in import-
competing industries is fixed exogenously.
4. The population sizes of the 10 major categories are not the same, but, within each category, the
population sizes of the 20 subcategories are the same.
5. Poverty incidence at the US$1-a-day level is extremely low in Thailand. We have been unable to
measure it with any hope of accuracy using our methods in this study.
References
Anderson, K., and E. Valenzuela. 2008. "Estimates of Global Distortions to Agricultural Incentives,
1955�2007." Data spreadsheet, October, World Bank, Washington, DC. http://go.worldbank.org/
YAO39F35E0.
Thailand 299
Dixon, P. B., B. R. Parmenter, J. Sutton, and D. P. Vincent. 1982. ORANI: A Multisectoral Model of the
Australian Economy. Amsterdam: North-Holland.
Hertel, T. W., ed. 1997. Global Trade Analysis: Modeling and Applications. New York: Cambridge
University Press.
Horridge, M. 2004. "ORANI-G: A Generic Single-Country Computable General Equilibrium Model."
Centre of Policy Studies, Monash University, Melbourne.
Johansen, L. 1964. A Multi-sectoral Study of Economic Growth. Amsterdam: North-Holland.
NESDB (Office of the National Economic and Social Development Board). 2004. Input-Output Tables
of Thailand, 2000. Bangkok: National Accounts Office, NESDB.
NSO (National Statistical Office). 2003. Report of the Household Socio-economic Survey, 2000. Bangkok:
Economic Statistics Division, NSO.
PovcalNet. World Bank, Washington, DC. http://go.worldbank.org/NT2A1XUWP0 (accessed May
2008).
Valenzuela, E., and K. Anderson. 2008. "Alternative Agricultural Price Distortions for CGE Analysis of
Developing Countries, 2004 and 1980�84." Research Memorandum 13 (December), Center for
Global Trade Analysis, Department of Agricultural Economics, Purdue University, West Lafayette,
IN. https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=2925.
van der Mensbrugghe, D. 2005. "Linkage Technical Reference Document: Version 6.0." December,
World Bank, Washington, DC. http://go.worldbank.org/7NP2KK1OH0.
Warr, P. 2001. "Welfare Effects of an Export Tax: Thailand's Rice Premium." American Journal of
Agricultural Economics 83 (4): 903�20.
------. 2005. "Food Policy and Poverty in Indonesia: A General Equilibrium Analysis." Australian
Journal of Agricultural and Resource Economics 49 (4): 429�51.
------. 2007. "Long-Term Economic Performance in Thailand." ASEAN Economic Bulletin 24 (1):
1�26.
------. 2008a. "Trade Policy and the Structure of Incentives in Thai Agriculture." ASEAN Economic
Bulletin 25 (3): 249�70.
------. 2008b. "World Food Prices and Poverty Incidence in a Food Exporting Country: A Multi-
household General Equilibrium Analysis for Thailand." Agricultural Economics 39 (3): 525�37.
Warr, P., and A. Kohpaiboon. 2009. "Thailand." In Distortions to Agricultural Incentives in Asia, ed.
K. Anderson and W. Martin, 255�79. Washington, DC: World Bank.
PART IV
NATIONAL CGE
APPROACHES:
AFRICA
10
Mozambique
Channing Arndt and James Thurlow
Mozambique has considerable agricultural potential. Only about 20 percent of its
vast tracts of decent quality land and an even smaller share of its water resources
are currently being exploited. The country's long coastline contains multiple har-
bors, which face eastward toward the dynamic markets of Asia. Regional markets
also offer promise in the short and long term.
Despite this potential, Mozambique earned the unwanted label of poorest
country in the world in the early 1990s. The country's severe poverty and poor
economic performance were the result of a number of factors, including the char-
acter of Portuguese colonization, a failed socialist experiment, and more than a
decade of vicious civil war that lasted until 1992 (Tarp et al. 2002). Since the end
of the war, Mozambique has performed much better, and most development indi-
cators have shown substantial improvement (Arndt, Jones, and Tarp 2006).
Nonetheless, its low starting point underlines the country's need for substantial
growth over an extended period if it is to reach even the average conditions
among developing countries.
So, while much improved over the past two decades, the economic situation in
Mozambique remains sobering, particularly in rural areas, where 70 percent of the
population resides. About half of the rural population is considered absolutely
poor, meaning that these households have difficulty acquiring even the most basic
necessities such as sufficient food to meet calorie requirements (Arndt and Simler
2004). Rural dwellers, particularly the poor, depend heavily on crop agriculture for
their incomes. Technology is generally rudimentary, and agricultural value added
remains concentrated in cassava, cereals (particularly maize), and beans. Only a
small minority of rural households report using improved seeds, fertilizers, and
303
304 Agricultural Price Distortions, Inequality, and Poverty
pesticides (Uaiene 2008). Rural households tend to consume most of their produc-
tion directly; they market only a relatively small share. Overall, approximately
three-quarters of the population (rural and urban) depend on agriculture for the
majority, often a large majority, of their incomes. Urban households also rely on
domestically produced agricultural goods, particularly urban households outside
Maputo, the capital city. Given the weight of agriculture in the economic life of most
Mozambicans, growth in agriculture is widely regarded as a potentially powerful
lever for reducing poverty (Thurlow 2008, Tarp et al. 2002).
Agriculture is also a potential driver of exports and economic growth. Mozam-
bique already exports a range of traditional crops, such as tobacco, sugarcane, and
cotton. These crops contribute to upstream processing in the manufacturing
sector, which has grown rapidly over the last decade. Moreover, the country's
underutilized natural resources have attracted considerable foreign investment
interest.1 However, most options for rapid growth in agriculture depend on
foreign demand through exports of primary or processed products.
The critical role of international markets in agricultural growth stems from
two facts. First, the import penetration in agriculture is relatively limited: imports
represent only about 4 percent of the total demand for agricultural products.2
While there is room to displace processed food imports, the overall scope for
growth via import substitution is narrow. Second, because of geographical factors,
even the relatively small volume of existing imports would be difficult to displace.
The large majority of agricultural imports are consumed in the south of the
country, particularly in Maputo. However, the agricultural potential in the south
is limited. The more favorable growing areas are located 1,000 to 2,000 kilometers
away in the northern and central regions of the country. Because of the large dis-
tances, inadequate infrastructure, the inefficiencies in storage, and, especially, the
high cost of capital, exporting surplus from the north and importing the same prod-
ucts in the south is generally more efficient than attempting to transport surpluses
across space (north to south) and time (from the postharvest period to the hungry
season; for example, see Arndt, Schiller, and Tarp 2001; Cruz 2006). Hence, for agri-
culture to grow substantially, an export orientation is crucial. This may be achieved
through exports of primary agriculture or processed farm products.
The links among international agricultural markets, agricultural growth, and
the prospects for overall growth and poverty reduction provide the motivation for
our study. Similar to other chapters in this volume, this chapter examines whether
distortions to agricultural markets hamper the prospects for reducing poverty and
income inequality. Two sources of distortions are considered: the distortions to
domestic agricultural markets imposed by the government of Mozambique and
the distortions imposed by other countries that influence Mozambique's export
prospects and import prices.
Mozambique 305
The chapter is structured as follows. The next section provides basic informa-
tion on the structure of the Mozambican economy and summarizes the results
of a detailed study of domestic agricultural distortions. The subsequent section
presents the modeling framework we use for our analysis. The following section
discusses our simulations and results. A final section concludes.
Economic Structure and Agricultural Distortions in
Mozambique
The structure of the Mozambican economy is summarized in table 10.1. Agricul-
ture represents about 26 percent of total GDP and 19 percent of exports. Exports
are dominated by fisheries, which generate two-thirds of raw agricultural export
earnings. The low import penetration in agriculture is reflected in the small share
of agriculture in total imports and by the small share of imports in the total
domestic demand for agricultural goods. Agriculture has strong links to agriculture-
related processing in the manufacturing sector. Sugar processing, for instance,
relies on domestically sourced sugarcane, as do cotton and tobacco processing and
grain milling. Together, these processing sectors represent about 0.6 percent of
total GDP and 3 percent of exports. Agriculture's upstream links are therefore an
important part of the sector's overall contribution to the economy.
The relatively large share of manufacturing in total exports is driven by aluminum
smelting, which accounts for around half of all export earnings and 5 percent of
total GDP. As documented by Andersson (2001), aluminum smelting is largely an
enclave sector characterized by high capital intensity, specific capital requirements,
foreign ownership, limited taxation, expatriate labor, and imported intermediates.
All aluminum production is exported, and the links between the aluminum sector
and the rest of the economy are weak. If aluminum were removed, agriculture
would represent 37 percent of exports and 28 percent of GDP.
The agricultural sector has undergone progressive liberalization and the grad-
ual elimination of government intervention since 1987. In particular, the country
shifted from central planning to a market economy. The reform program has sub-
stantially reduced government involvement in agriculture. A detailed study of the
agricultural distortions in Mozambique finds that, since the period of reform
from a centrally planned to a market economy, there has been hardly any substan-
tial government intervention in the sector, and few distortions are observed
(Alfieri, Arndt, and Cirera 2009). The government intervention that does exist
relies primarily on the use of import tariffs. There are a few exceptions, such as
the cotton, cashew, and sugar sectors, in which more complex policies have
been implemented. Nonetheless, the overall picture is one of distinctly limited
government involvement.
306
Table 10.1. The Economic Structure in 2002 and Price Distortions in 2004, Mozambique
(percent)
GDP Output Imports Exports
Sectors share subsidy Share Intensity Tariff Share Intensity Tax rate
Total 100.0 0.1 100.0 24.0 9.3 100.0 11.4 0.1
Agriculture 26.1 0.7 2.9 4.8 5.2 19.0 13.0 --
Maize 3.5 0.1 0.3 4.4 10.0 0.2 1.0 --
Sorghum 1.1 0.1 -- -- -- -- -- --
Unshelled rice 0.7 0.2 0.0 0.0 25.9 -- -- --
Wheat -- -- 1.7 100.0 2.5 -- -- --
Cassava 7.2 -- -- -- -- -- -- --
Roots and tubers 0.2 -- 0.0 2.2 10.9 0.0 0.0 --
Beans 1.0 -- 0.0 2.2 10.9 0.0 0.3 --
Groundnuts 0.9 24.6 0.1 2.1 0.8 0.0 0.0 --
Cashews 0.4 -- 0.0 0.2 10.9 1.6 43.6 --
Vegetables 1.7 0.9 0.0 1.5 19.0 0.0 0.0 --
Fruits 1.6 0.9 0.0 1.3 19.0 1.7 19.3 --
Leaf tea 0.0 -- -- -- -- -- -- --
Tobacco 0.3 -- 0.3 40.4 10.9 0.8 47.7 --
Sugarcane 0.1 8.4 -- -- -- -- -- --
Cotton 0.3 3.1 -- -- -- -- -- --
Other crops 0.8 0.8 0.2 6.5 8.2 0.0 0.1 --
Livestock 0.5 1.4 0.0 2.3 -- 0.0 0.4 --
Forestry 2.8 0.7 0.1 0.6 3.0 1.6 10.7 --
Fisheries 2.3 0.7 0.0 0.0 3.0 12.7 63.6 --
Industry 23.2 0.0 76.0 44.6 12.1 67.1 23.6 0.1
Mining 0.3 0.7 0.2 6.6 3.0 0.3 4.9 --
Manufacturing 13.7 0.0 70.3 57.9 12.4 55.5 35.7 0.1
Meat processing 1.5 -- 0.3 3.1 21.9 0.0 0.0 --
Other food products 1.4 -- 4.2 34.4 18.7 1.6 8.5 --
Grain milling 1.6 -- 8.5 45.0 18.7 0.2 0.7 --
Sugar processing 0.1 -- 0.7 49.9 98.9 0.5 15.1 --
Beverages 0.8 -- 1.3 18.6 18.7 -- -- --
Tobacco processing 0.1 -- 0.4 36.4 18.7 -- -- --
Cotton processing 0.4 0.2 2.5 66.2 18.8 2.7 64.5 2.0
Textiles and clothing 0.6 0.2 2.0 40.3 18.8 0.7 10.3 --
Wood products 0.8 0.2 3.1 39.0 18.8 0.5 4.5 --
Chemicals 0.4 -- 20.2 86.1 8.2 0.4 6.4 --
Nonmetals 0.7 -- 2.3 37.8 8.2 0.1 0.7 --
Metal products 5.2 -- 5.3 71.6 8.2 48.6 94.8 --
Machinery 0.0 -- 17.3 99.4 8.2 -- -- --
Other manufacturing 0.0 -- 2.4 96.8 8.2 0.2 46.7 --
Electricity 1.9 -- 5.5 81.0 8.2 11.3 79.9 --
Water 0.3 -- -- -- -- -- -- --
Construction 7.1 -- -- -- -- -- -- --
Services 50.7 -- 21.1 9.2 -- 13.9 3.2 --
Sources: Social accounting matrix for Mozambique in 2002 (Thurlow 2008) adjusted to reflect the developing-country distortions compiled by Anderson and
Valenzuela (2008) and harmonized with the Global Trade Analysis Project model and the Linkage model in Valenzuela and Anderson (2008).
Note: Import intensity is the share of imports in each sector's total domestic demand. Export intensity is the share of exports in each sector's total domestic
output. -- data are not available or the indicator is not applicable.
307
308 Agricultural Price Distortions, Inequality, and Poverty
The social accounting matrix we use for our analysis reflects this situation. The
only meaningful distortions in the matrix are import tariffs. These are also shown
in Table 10.1. To maintain consistency with other studies in this volume, we
impose the tariffs in the Linkage model on the Mozambican computable general
equilibrium (CGE) model.3 Similar to many other least developed countries,
Mozambique views tariffs as a tool for both raising revenue and influencing prices
and incentives within the economy (Arndt and Tarp 2008). Consistent with
the revenue-raising goal, most rates are positive, but relatively low. The exceptions
are the rates imposed on processed sugar (nearly 100 percent), rice (26 percent),
and many processed food commodities (around 20 percent). More recently, the
sugar tariff, which is implemented as a variable levy, has declined to near zero
because of increases in the world price of sugar. The tariffs on rice have also
declined. For our study, we retain the applied rates from 2004 in our estimates of
the impact of the removal of domestic price distortions.
The Modeling Framework
The CGE model used in our study contains 56 activities and commodities, including
24 agricultural and 7 food processing sectors.4 We identify five factors of production:
three types of labor (unskilled, semiskilled, and skilled), agricultural land, and capi-
tal. Factor intensities for each sector are shown in table 10.2. Rural and urban labor
markets are segmented, such that rural nonfarm and urban nonagriculture are dis-
tinguished. We assume that the factors in the model are fully employed at flexible real
wages. The only exceptions are rural and urban unskilled laborers, who are unem-
ployed at fixed nominal wages, and capital in the metals and electricity sectors, which
is immobile and earning sector-specific returns.5 The former captures the underem-
ployment of lower-skilled workers in Mozambique, while the latter reflects a depend-
ence on foreign direct investment. Using these factors, producers in the model
maximize their profits under technologies exhibiting constant returns to scale, and
the choice among factors is governed by a constant elasticity of substitution function.
We then combine factors with fixed-share intermediates using a Leontief specifica-
tion. Under profit maximization, the factors receive income such that marginal
revenue equals marginal cost based on endogenous relative prices.
The possibility of substitution exists between production for domestic and
production for foreign markets. The decision of producers is governed by a con-
stant elasticity of transformation function that distinguishes between exported
and domestic goods to capture any time or quality differences between the two
types of products. Profit maximization drives producers to sell in the markets in
which they are able to achieve the highest returns. These returns are based on
domestic and export prices (whereby the latter is determined by the world price,
multiplied by the exchange rate and adjusted for any taxes). Under the small
Table 10.2. Factor Intensities of Production, Mozambique, 2002
Labor
Sector Skilled Semiskilled Unskilled All Capital Land Total
Total 10.7 13.8 39.7 64.1 6.1 29.8 100.0
Agriculture 0.6 1.9 59.6 62.1 23.3 14.6 100.0
Maize 0.6 1.9 58.1 60.6 29.7 9.7 100.0
Sorghum 0.6 2.0 61.6 64.2 26.6 9.2 100.0
Unshelled rice 0.5 1.8 54.5 56.8 30.7 12.5 100.0
Wheat -- -- -- -- -- -- --
Cassava 0.5 1.8 56.0 58.3 30.3 11.4 100.0
Roots and tubers 0.4 1.3 39.6 41.3 42.1 16.6 100.0
Beans 0.4 1.4 42.7 44.6 38.7 16.8 100.0
Groundnuts 0.6 2.2 65.1 67.9 20.4 11.7 100.0
Cashews 0.3 1.2 35.8 37.3 42.7 20.0 100.0
Vegetables 0.6 2.0 60.2 62.8 26.8 10.5 100.0
Fruits 0.4 1.2 36.5 38.1 43.8 18.1 100.0
Leaf tea 0.4 1.3 38.5 40.1 41.2 18.6 100.0
Tobacco 0.6 1.9 56.2 58.6 29.7 11.6 100.0
Sugarcane 0.1 0.2 7.1 7.4 73.3 19.3 100.0
Cotton 0.4 1.3 38.6 40.2 41.4 18.4 100.0
Other crops 0.5 1.8 56.6 59.0 29.5 11.5 100.0
Livestock 0.8 2.5 78.0 81.3 18.7 -- 100.0
Forestry 0.6 2.0 63.2 65.8 34.2 -- 100.0
Fisheries 0.8 2.8 87.2 90.9 9.1 -- 100.0
(Table continues on the following page.)
309
310
Table 10.2. Factor Intensities of Production, Mozambique, 2002 (continued)
Labor
Sector Skilled Semiskilled Unskilled All Capital Land Total
Industry 10.7 9.7 20.8 41.2 58.8 -- 100.0
Mining 1.4 2.8 11.6 15.7 84.3 -- 100.0
Manufacturing 10.9 8.3 17.3 36.6 63.4 -- 100.0
Meat processing 18.4 14.1 29.3 61.7 38.3 -- 100.0
Other food products 18.4 14.1 29.3 61.7 38.3 -- 100.0
Grain milling 24.5 18.8 39.1 82.3 17.7 -- 100.0
Sugar processing 15.8 12.1 25.2 53.2 46.8 -- 100.0
Beverages 3.3 2.5 5.3 11.1 88.9 -- 100.0
Tobacco processing 3.3 2.5 5.3 11.1 88.9 -- 100.0
Cotton processing 23.4 17.9 37.3 78.7 21.3 -- 100.0
Textiles and clothing 24.0 18.3 38.1 80.4 19.6 -- 100.0
Wood products 24.4 18.7 38.8 81.8 18.2 -- 100.0
Chemicals 8.9 6.8 14.1 29.8 70.2 -- 100.0
Nonmetals 3.0 2.3 4.8 10.0 90.0 -- 100.0
Metal products 0.2 0.1 0.3 0.6 99.4 -- 100.0
Machinery 8.4 6.4 13.3 28.1 71.9 -- 100.0
Other manufacturing 8.2 6.3 13.0 27.4 72.6 -- 100.0
Electricity 9.0 6.9 14.3 30.2 69.8 -- 100.0
Water 6.5 7.7 17.0 31.3 68.7 -- 100.0
Construction 11.4 13.5 29.9 54.8 45.2 -- 100.0
Services 15.9 21.7 38.0 75.7 24.3 -- 100.0
Source: CGE model for Mozambique by the authors (see Thurlow 2008).
Note: -- data are not available.
Mozambique 311
country assumption, Mozambique faces perfectly elastic world demand curves at
fixed world prices (global liberalization is discussed below). The final ratio of
exports to domestic goods is determined by the endogenous interaction of the
relative prices for these two commodity types.
Additional substitution possibilities exist between imported and domestic
goods under a constant elasticity of substitution Armington specification. In this
case, substitution may take place in both final and intermediate usages. The
elasticities vary across sectors; the lower elasticities reflect greater differences
between domestic and imported goods. Under the small country assumption,
Mozambique faces an infinitely elastic world supply at fixed world prices. The
final ratio of imports to domestic goods is determined by the cost-minimizing
decision making of domestic demanders based on the relative prices of imports
and domestic goods (both of which include the relevant taxes).
In global liberalization, the results from the World Bank's model of global trade
(the Linkage model; see van der Mensbrugghe 2005) are transmitted to the
Mozambique model via changes in the import prices, export prices, and export
quantities faced by Mozambique. We simply apply the import price changes to the
exogenous import prices in the Mozambique model. We apply export price and
quantity changes derived from the Linkage model in the manner developed
by Horridge and Zhai (2006). Specifically, export demand functions of the form
Q (FP P)ES (where Q is the quantity exported; P is the export price; ES is the
elasticity of demand for exports; and FP is a shift parameter) have been added to
the Mozambique model to mimic the global Linkage model. Horridge and Zhai
(2006) show that the export price and quantity changes generated by Linkage may
be mimicked in a country model through shocks to the shifter parameter FP.
Using lowercase to indicate percentage change, one may derive the percentage
change in FP applied to the Mozambique model as follows: fp p q ES.
The model distinguishes among various institutions, including enterprises, the
government, and 10 representative household groups. Households are disaggre-
gated across rural and urban areas and national income quintiles. Households and
enterprises receive incomes in payment for the use of their factors of production
by producers. Households and enterprises pay direct taxes to the government
(based on fixed tax rates), save (based on marginal propensities to save), and make
transfers to the rest of the world. Enterprises pay their remaining incomes to
households in the form of dividends. Households use their incomes to consume
commodities under a linear expenditure system of demand.
Home consumption is important among rural households. It represents about
half of the commodity consumption among all but the top quintile of rural house-
holds; among the top quintile, the share of home consumption in total commodity
consumption is about one-quarter (table 10.3). Home consumption is driven in large
312
Table 10.3. Household Income and Expenditure Shares, Mozambique, 2002
(percent)
Household income sources
Labor
Households Skilled Semiskilled Unskilled Capital Land Other income Total
All households 11.7 15.0 43.4 22.4 6.7 0.8 100.0
Rural 0.8 5.2 68.6 7.7 17.5 0.2 100.0
Quintile 1 0.1 1.9 84.2 2.5 11.3 0.1 100.0
Quintile 2 0.2 2.4 74.5 3.2 19.6 0.2 100.0
Quintile 3 0.4 3.7 65.8 5.2 24.6 0.1 100.0
Quintile 4 0.2 4.3 69.5 5.8 20.0 0.2 100.0
Quintile 5 2.4 10.2 59.9 16.4 10.8 0.2 100.0
Urban 18.4 21.1 27.9 31.4 0.0 1.2 100.0
Quintile 1 0.1 12.4 77.9 9.5 0.0 0.1 100.0
Quintile 2 2.8 18.9 61.6 16.6 0.0 0.1 100.0
Quintile 3 1.4 15.5 69.8 13.0 0.0 0.3 100.0
Quintile 4 5.1 25.0 46.2 23.3 0.0 0.3 100.0
Quintile 5 23.5 21.3 18.0 35.8 0.0 1.5 100.0
Household expenditures
Own goods Purchased goods
Households Food Nonfood Food Nonfood Taxes Savings Total
All households 8.6 6.7 19.0 62.7 1.7 1.3 100.0
Rural 22.6 17.7 12.3 46.5 0.3 0.7 100.0
Quintile 1 36.3 15.6 14.0 34.0 0.0 0.1 100.0
Quintile 2 30.1 16.4 13.3 39.7 0.0 0.5 100.0
Quintile 3 28.2 18.9 12.4 39.8 0.3 0.4 100.0
Quintile 4 20.3 21.5 12.7 45.2 0.1 0.3 100.0
Quintile 5 10.2 14.9 10.4 62.2 0.7 1.6 100.0
Urban 0.0 0.0 23.1 72.7 2.6 1.6 100.0
Quintile 1 0.0 0.0 56.8 42.5 0.3 0.3 100.0
Quintile 2 0.0 0.0 43.1 55.5 0.6 0.9 100.0
Quintile 3 0.0 0.0 44.9 53.1 0.8 1.2 100.0
Quintile 4 0.0 0.0 30.2 66.8 1.7 1.3 100.0
Quintile 5 0.0 0.0 18.1 77.1 3.0 1.8 100.0
Source: CGE model for Mozambique by the authors (see Thurlow 2008).
Note: Other income refers to government transfers (for example, pensions) and foreign remittances received. Own goods are goods produced and consumed by a
household.
313
314 Agricultural Price Distortions, Inequality, and Poverty
measure by substantial divergences between farmgate and consumer prices because
of high transaction costs. These margins are captured in the model, wherein transac-
tions costs potentially differ among domestic, imported, and exported goods. The
modeling of home consumption and margins follows Arndt et al. (2000).
The government receives income through imposing activity, sales and direct taxes,
and import tariffs and then makes transfers to households, enterprises, and the rest of
the world. The government also purchases commodities in the form of government
consumption expenditures. The remaining income of government is (dis)saved. All
savings by households, enterprises, government, and the rest of the world (foreign
savings) are collected in a savings pool from which investment is financed.
The model includes three broad macroeconomic accounts: the government bal-
ance, the current account, and the savings and investment account. To bring about
balance among the various macroaccounts, we must specify a set of macroclosure
rules. These provide a mechanism through which macroeconomic balance may be
achieved. Consistent with other analyses in this volume, we assume a savings-driven
closure to balance the savings and investment account. Under this closure, the mar-
ginal propensities to save of households and enterprises are fixed, while investment
adjusts to changes in incomes to ensure that the level of investment and savings are
equal. For the current account, we assume that a flexible exchange rate adjusts to
maintain a fixed level of foreign savings. Thus, the external balance is held fixed in
foreign currency terms. Finally, in the government account, we assume that the fiscal
deficit remains unchanged and that government revenues and expenditures are
balanced through changes in the direct tax rates on households and enterprises.
The CGE model is calibrated to a 2003 social accounting matrix (Thurlow
2008) that was constructed using information from national accounts, trade and
tax data, and household income and expenditure data in the 2002 national house-
hold survey (INE 2004). Trade elasticities are taken from the Global Trade
Analysis Project (Dimaranan 2006). The model is calibrated so that the initial
equilibrium reproduces the values of the base year in the social accounting
matrix. The results of the CGE model are passed back down to the household
survey on which the model is based and in which the poverty measures are calcu-
lated. More specifically, the changes in the real commodity expenditures of each
representative household in the CGE model are applied to the expenditures of the
corresponding household in the survey. Total expenditures are compared to real
expenditure poverty lines, and standard poverty measures are recalculated.
The Simulations and the Results
In this section, we present the results of four simulations. The first two simulations
assess the impact of the removal of distortions in the rest of the world without any
changes in the tariffs and subsidies in Mozambique. We do this for all commodities
Mozambique 315
(simulation 1) and then only for agriculture and agriculture-related processing
(simulation 2). As with other case studies in this volume, the impacts of global
liberalization are taken from the Linkage model (see the appendix by van der
Mensbrugghe, Valenzuela, and Anderson). The impacts include changes in world
import and export prices, as well as changes in the demand for Mozambican exports
(table 10.4). The implications of global liberalization as derived from the Linkage
model are imposed as in Horridge and Zhai (2006). These two simulations therefore
model the impact on Mozambique if the rest of the world removes agricultural and
nonagricultural distortions, while Mozambique does not.
The remaining two simulations (3 and 4) assess the impact if Mozambique
removes its distortions. These include import tariffs and, to a much lesser extent,
export and output taxes and subsidies. In both of these national simulations, there
are no changes in the rest-of-the-world distortions. World prices therefore remain
unchanged because we retain the small country assumption for Mozambican
exports.
Simulation 1: The global liberalization of all commodities
The results from the Linkage model indicate that Mozambique's terms of trade
improve by 1.3 percent once the distortions on all the commodities in the rest of
the world are removed (table 10.5). World demand for Mozambique's main
exports increases, which is a strong driver in the results that follow. World import
prices decline for Mozambique's main imported goods, such as clothing and other
heavier manufactures; together, these account for more than half of total imports.
Import prices rise for some commodities, such as processed foods, but these are
less significant import commodities and show relatively low import penetration
ratios (table 10.1).
Rising export prices encourage producers to increase their production for for-
eign markets, thereby causing an appreciation of the real exchange rate of 2.8 per-
cent (table 10.5). Import demand, which is already rising because of falling world
prices, increases additionally as a result of the appreciation, while the expansion of
exports is only partially offset. The appreciation of the exchange rate also reduces
the value of foreign inflows (mostly the value of foreign assistance measured in
local currency) and, hence, lowers investment demand. However, the real appreci-
ation and cheaper imported goods drive down consumer prices, causing a
substantial rise in private consumption. Overall, the increase in exports and
consumer spending outweighs any additional import penetration, and there is an
increase in total GDP of 0.9 percent.
The increase in GDP is driven by agriculture and agriculture-related process-
ing (table 10.6). Greater agricultural production occurs mainly because of
increased fisheries exports, which already dominate exports (table 10.7) and are
316 Table 10.4. The Exogenous Demand and Price Shocks Caused by Rest-of-the-World Liberalization,
Mozambique
(percent change from the baseline)
Simulation 1: All commodities Simulation 2: Agriculture only
Export Export
Sector Import price Price Quantity Import price Price Quantity
Agriculture
Maize 2.6 1.1 3.4 4.1 1.4 19.7
Sorghum 2.6 1.1 3.4 4.1 1.4 19.7
Unshelled rice 8.5 -- -- 9.7 -- --
Wheat 0.9 -- -- 0.8 -- --
Cassava 0.7 1.1 8.7 0.3 1.4 16.0
Roots and tubers 0.7 1.1 8.7 0.3 1.4 16.0
Beans 0.7 1.1 8.7 0.3 1.4 16.0
Groundnuts 1.2 1.0 9.7 0.1 1.3 2.8
Cashews 1.2 1.0 9.7 0.1 1.3 2.8
Vegetables 2.7 0.6 496.8 1.5 0.9 567.8
Fruits 2.7 0.6 496.8 1.5 0.9 567.8
Leaf tea 0.7 1.1 8.7 0.3 1.4 16.0
Tobacco 0.7 1.1 8.7 0.3 1.4 16.0
Sugarcane -- -- -- -- -- --
Cotton -- 0.7 48.4 -- 1.2 56.4
Other crops 0.7 1.1 8.7 0.3 1.4 16.0
Cattle -- -- -- -- -- --
Poultry 1.7 -- -- 0.1 -- --
Other livestock 1.7 -- -- 0.1 -- --
Forestry 0.6 0.8 38.4 0.4 1.1 3.1
Fisheries 0.6 0.8 38.4 0.4 1.1 3.1
Industry
Mining 0.6 0.8 38.4 0.4 1.1 3.1
Meat processing 1.3 -- -- 0.1 -- --
Other food products 1.9 0.4 5.8 0.3 0.7 15.7
Grain milling 1.9 0.4 5.8 0.3 0.7 15.7
Sugar processing 1.4 0.4 21.9 0.1 0.9 11.9
Beverages 1.9 0.4 5.8 0.3 0.7 15.7
Tobacco processing 0.6 0.8 38.4 0.4 1.1 3.1
Cotton processing 0.6 0.8 38.4 0.4 1.1 3.1
Textiles and clothing 1.3 0.3 22.4 0.6 0.8 3.2
Wood products 0.8 0.2 7.6 0.2 0.7 5.1
Chemicals 0.8 0.2 7.6 0.2 0.7 5.1
Nonmetals 0.8 0.2 7.6 0.2 0.7 5.1
Metal products 0.8 0.2 7.6 0.2 0.7 5.1
Machinery 0.8 0.2 7.6 0.2 0.7 5.1
Other manufacturing 0.8 0.2 7.6 0.2 0.7 5.1
Electricity 0.3 0.3 1.7 0.3 0.7 1.0
Water 0.3 0.3 1.7 0.3 0.7 1.0
Construction 0.3 0.3 1.7 0.3 0.7 1.0
Services 0.3 0.3 1.7 0.3 0.7 1.0
Source: Results from the World Bank Linkage model (see the appendix by van der Mensbrugghe, Valenzuela, and Anderson).
Note: -- data are not available or the indicator is not applicable.
317
318 Agricultural Price Distortions, Inequality, and Poverty
Table 10.5. The Macroeconomic Simulation Results of
Prospective Liberalization Abroad and Nationally,
Mozambique
(percent)
Change from the base
Rest-of-the-world Unilateral
liberalization liberalization
Indicator Base share Full Agriculture Full Agriculture
Real GDP at market prices 73.5 0.9 0.1 1.2 0.7
Consumption 59.1 1.4 0.2 1.0 1.1
Investment 18.8 0.5 0.2 1.6 0.9
Government 10.4 0.0 0.0 0.0 0.0
Exports 14.1 3.8 0.1 9.8 6.4
Agriculture 19.0 19.5 0.1 28.4 19.5
Industry 67.1 0.6 0.2 2.8 1.4
Services 13.9 2.4 1.5 18.0 12.5
Imports 28.8 2.6 0.4 4.8 3.1
Agriculture 2.9 3.5 1.2 0.7 0.1
Industry 76.0 2.6 0.3 7.2 4.9
Services 21.1 2.2 0.4 3.5 3.0
Consumer price index -- 0.5 0.0 2.4 2.3
Real exchange rate -- 2.8 0.1 5.2 4.0
World export prices -- 1.0 0.4 0.0 0.0
World import prices -- 0.3 0.2 0.0 0.0
Terms of trade -- 1.3 0.6 0.0 0.0
Source: Simulations by the authors using the CGE model for Mozambique.
Note: The domestic price index is the num�raire in the model. -- data are not available or the
indicator is not applicable.
generated by price rises and demand increases for Mozambican fish following
global liberalization (table 10.4). There is also an expansion in cotton processing
because the rice import and export prices and export demand increase. Some
other traditional exports also benefit and contribute to the greater upstream pro-
duction of processed goods. However, sectoral links work against sugarcane pro-
duction because sugar processing declines as a result of falling world prices and
greater import competition (tables 10.7 and 10.8). Finally, the overall increase in
Mozambique's international trade generates additional demand for the domestic
trade and transport sectors, which, together, drive most of the rise in service sector
GDP under this simulation.
The improved terms of trade and the stimulus to export demand for primary
and processed agricultural products resulting from global liberalization boost the
demand for unskilled workers (greater employment), as well as the returns to the
Mozambique 319
Table 10.6. The Effects of Prospective Liberalization Abroad
and Nationally on GDP, by Sector, Mozambique
(percent)
Change from the base
Rest-of-the-world Unilateral
liberalization liberalization
Sector Base share Full Agriculture Full Agriculture
Real GDP at factor cost 100.0 0.9 0.1 0.9 0.3
Agriculture 26.1 2.6 0.1 2.9 1.9
Maize 3.5 1.0 0.0 1.4 1.6
Sorghum 1.1 2.4 0.2 2.3 1.4
Unshelled rice 0.7 0.9 0.4 3.3 3.3
Wheat 0.0 0.0 0.0 0.0 0.0
Cassava 7.2 0.8 0.1 0.2 0.0
Roots and tubers 0.2 0.3 0.2 0.9 0.5
Beans 1.0 0.8 0.1 0.2 0.2
Groundnuts 0.9 0.9 0.0 21.9 22.0
Cashews 0.4 4.3 0.2 10.1 8.3
Vegetables 1.7 1.0 0.2 1.0 0.9
Fruits 1.6 0.6 0.2 0.9 0.9
Leaf tea 0.0 0.7 0.2 6.8 6.8
Tobacco 0.3 3.3 8.9 15.4 9.9
Sugarcane 0.1 0.8 0.4 2.3 2.2
Cotton 0.3 5.3 0.9 2.6 2.2
Other crops 0.8 0.5 0.4 3.5 3.6
Livestock 0.5 0.3 0.3 2.9 2.5
Forestry 2.8 2.8 0.1 0.8 0.4
Fisheries 2.3 20.2 1.0 21.5 13.8
Industry 23.2 0.1 0.1 0.7 1.3
Mining 0.3 0.1 0.0 0.1 0.1
Manufacturing 13.7 0.0 0.1 2.0 1.8
Meat processing 1.5 1.2 0.2 1.2 0.8
Other food products 1.4 0.8 0.8 1.9 2.1
Grain milling 1.6 1.2 0.3 3.5 3.8
Sugar processing 0.1 6.2 1.4 40.1 40.3
Beverages 0.8 0.5 0.2 2.3 1.8
Tobacco processing 0.1 0.6 0.1 1.8 1.6
Cotton processing 0.4 15.5 1.1 18.8 9.0
Textiles and clothing 0.6 3.0 0.6 10.5 11.1
Wood products 0.8 3.9 0.2 11.4 13.0
Chemicals 0.4 7.1 0.4 3.8 7.3
Nonmetals 0.7 3.6 0.2 1.7 2.6
(Table continues on the following page.)
320 Agricultural Price Distortions, Inequality, and Poverty
Table 10.6. The Effects of Prospective Liberalization Abroad and
Nationally on GDP, by Sector, Mozambique (continued)
(percent)
Change from the base
Rest-of-the-world Unilateral
liberalization liberalization
Sector Base share Full Agriculture Full Agriculture
Metal products 5.2 0.0 0.0 0.0 0.0
Machinery 0.0 5.8 0.5 4.8 6.4
Other manufacturing 0.0 10.7 0.9 64.6 62.3
Electricity 1.9 0.1 0.1 0.3 0.2
Water 0.3 0.5 0.3 4.4 3.2
Construction 7.1 0.5 0.1 1.5 0.8
Services 50.7 0.3 0.0 0.6 0.3
Source: Simulations by the authors using the CGE model for Mozambique.
other factors (fully employed workers). Because agricultural production relies on
unskilled rural labor and land intensively (the factor intensities are listed in
table 10.2), the employment of unskilled rural labor rises by 3.2 percent, while the
real returns to land rise by 3.0 percent (table 10.9). The increases in the demand
for urban labor and more highly skilled labor are less pronounced, thereby reduc-
ing the pressure on factor prices. In addition, skilled workers are often employed
by the government, and we assume that recurrent and wage bill expenditures are
unaffected by rest-of-the-world liberalization (see the unchanged government
spending in table 10.5). Capital returns rise slightly in nominal terms, alongside
mild increases in production in the industry and service sectors.
All households in the model benefit from full global liberalization. However,
the main beneficiaries are lower-income and rural households (measured by the
equivalent variation). This is because these households derive a larger share of
their incomes from agricultural production and processing, and these sectors are
stimulated by the rest-of-the-world liberalization. In contrast, higher-income
urban households receive a larger share of their incomes from capital earnings
and skilled labor. The improved household welfare is also reflected in changes in
the poverty headcount. The share of the population living below the US$1-a-day
poverty line falls by 1.4 percent. The decline in poverty is only slightly larger
among rural households, and there is no significant change in the inequality
measures. Thus, the removal of global distortions on all commodities increases
GDP and household welfare and reduces poverty in Mozambique, but has little
impact on national inequality.
Mozambique 321
Table 10.7. The Effects of Prospective Liberalization Abroad and
Nationally on the Real Value of Exports, Mozambique
(percent)
Change from the base
Rest-of-the-world Unilateral
liberalization liberalization
Sector Base share Full Agriculture Full Agriculture
Total exports 100.0 3.8 0.1 9.8 6.4
Agriculture 19.0 19.5 0.1 28.4 19.5
Maize 0.2 0.2 12.2 4.8 3.6
Sorghum 0.0 0.0 0.0 0.0 0.0
Unshelled rice 0.0 0.0 0.0 0.0 0.0
Wheat 0.0 0.0 0.0 0.0 0.0
Cassava 0.0 0.0 0.0 0.0 0.0
Roots and tubers 0.0 1.4 11.4 10.0 9.2
Beans 0.0 0.4 13.8 23.8 20.6
Groundnuts 0.0 402.7 504.8 169.3 162.5
Cashews 1.6 10.6 0.3 24.3 20.6
Vegetables 0.0 8.0 0.2 5.0 4.2
Fruits 1.7 10.2 0.2 13.9 12.0
Leaf tea 0.0 0.0 0.0 0.0 0.0
Tobacco 0.8 2.1 15.6 33.0 23.8
Sugarcane 0.0 0.0 0.0 0.0 0.0
Cotton 0.0 0.0 0.0 0.0 0.0
Other crops 0.0 2.1 0.1 2.0 0.9
Livestock 0.0 2.4 0.0 4.2 3.0
Forestry 1.6 20.4 0.0 17.1 13.5
Fisheries 12.7 29.5 1.3 32.6 21.2
Industry 67.1 0.6 0.2 2.8 1.4
Mining 0.3 15.9 0.2 13.7 11.6
Manufacturing 55.5 0.6 0.4 2.8 1.5
Meat processing 0.0 6.1 0.2 19.3 17.4
Other food products 1.6 6.2 8.0 6.4 5.2
Grain milling 0.2 6.7 8.7 7.3 5.1
Sugar processing 0.5 18.4 5.9 27.7 29.7
Beverages 0.0 0.0 0.0 0.0 0.0
Tobacco processing 0.0 0.0 0.0 0.0 0.0
Cotton processing 2.7 23.7 1.2 35.7 22.1
Textiles and clothing 0.7 4.7 0.3 12.2 9.2
Wood products 0.5 10.1 1.3 8.1 1.6
Chemicals 0.4 10.6 1.2 15.3 18.1
Nonmetals 0.1 9.9 1.4 15.7 14.4
(Table continues on the following page.)
322 Agricultural Price Distortions, Inequality, and Poverty
Table 10.7. The Effects of Prospective Liberalization Abroad and
Nationally on the Real Value of Exports, Mozambique
(continued)
(percent)
Change from the base
Rest-of-the-world Unilateral
liberalization liberalization
Sector Base share Full Agriculture Full Agriculture
Metal products 48.6 0.0 0.1 0.5 0.0
Machinery 0.0 0.0 0.0 0.0 0.0
Other manufacturing 0.2 12.6 1.7 107.3 92.5
Electricity 11.3 0.1 0.3 2.5 0.8
Water 0.0 0.0 0.0 0.0 0.0
Construction 0.0 0.0 0.0 0.0 0.0
Services 13.9 2.4 1.5 18.0 12.5
Source: Simulations by the authors using the CGE model for Mozambique.
Simulation 2: The global liberalization of agricultural commodities only
In the global agricultural simulation, we model the impact of the removal by
the rest of the world of only agricultural distortions, while the distortions in
Mozambique are unchanged. Unlike the full global liberalization simulation,
Mozambique now experiences a deterioration in the terms of trade of 0.6 percent.
However, the demand rises for certain agricultural exports, while the demand
declines for all processed commodities, such as processed tobacco, cotton, and sugar.
The principal macroeconomic effects of global agricultural liberalization are
small and typically opposite in sign relative to the results of the full global simula-
tion (compare table 10.5, columns 2 and 3). The changes at the sectoral level are
correspondingly small (table 10.4), reflecting the relatively mild price and quan-
tity changes that occur in the global markets for products of importance to
Mozambique as a result of the rest-of-the-world agricultural trade liberalization
(table 10.4). The impacts on factor rewards, welfare, and poverty are also relatively
small (table 10.9).
Simulation 3: The domestic liberalization of all commodities
In the third simulation, we assess the impact of the removal by Mozambique of all
its own distortions, while the distortions of the rest of the world remain unchanged.
Mozambique's largest distortions revolve around its import tariffs (table 10.1).
There is also a small export subsidy for cotton processing, as well as output taxes on
Mozambique 323
Table 10.8. The Effects of Prospective Liberalization Abroad
and Nationally on the Real Value of Imports,
Mozambique
(percent)
Change from the base
Rest-of-the-world Unilateral
liberalization liberalization
Sector Base share Full Agriculture Full Agriculture
Total imports 100.0 2.6 0.4 4.8 3.1
Agriculture 2.9 3.5 1.2 0.7 0.1
Maize 0.3 2.1 4.4 3.4 4.1
Sorghum 0.0 0.0 0.0 0.0 0.0
Unshelled rice 0.0 17.1 36.6 146.1 152.5
Wheat 1.7 0.9 0.4 3.3 3.3
Cassava 0.0 0.0 0.0 0.0 0.0
Roots and tubers 0.0 6.8 0.7 8.1 9.9
Beans 0.0 11.2 0.9 14.4 16.3
Groundnuts 0.1 11.1 2.2 26.9 25.4
Cashews 0.0 10.3 0.4 0.3 1.6
Vegetables 0.0 8.0 0.5 21.3 23.4
Fruits 0.0 7.6 0.4 16.8 18.5
Leaf tea 0.0 0.0 0.0 0.0 0.0
Tobacco 0.3 9.2 3.6 4.0 6.6
Sugarcane 0.0 0.0 0.0 0.0 0.0
Cotton 0.0 0.0 0.0 0.0 0.0
Other crops 0.2 7.3 0.4 1.1 2.0
Livestock 0.0 6.2 0.3 6.7 5.4
Forestry 0.1 4.8 1.2 5.6 3.3
Fisheries 0.0 0.2 0.5 6.6 4.2
Industry 76.0 2.6 0.3 7.2 4.9
Mining 0.2 11.0 1.2 0.3 1.3
Manufacturing 70.3 2.6 0.3 7.8 5.8
Meat processing 0.3 16.1 1.1 59.0 64.1
Other food products 4.2 2.4 0.2 15.1 17.1
Grain milling 8.5 2.1 0.1 14.8 16.4
Sugar processing 0.7 8.4 0.0 137.1 142.0
Beverages 1.3 1.5 0.0 9.0 10.9
Tobacco processing 0.4 2.9 0.6 9.6 11.0
Cotton processing 2.5 3.0 0.7 12.8 14.2
Textiles and clothing 2.0 10.1 1.6 25.7 28.0
Wood products 3.1 6.9 0.7 22.0 23.8
(Table continues on the following page.)
324 Agricultural Price Distortions, Inequality, and Poverty
Table 10.8. The Effects of Prospective Liberalization Abroad
and Nationally on the Real Value of Imports,
Mozambique (continued)
(percent)
Change from the base
Rest-of-the-world Unilateral
liberalization liberalization
Sector Base share Full Agriculture Full Agriculture
Chemicals 20.2 2.2 0.2 1.7 1.8
Nonmetals 2.3 6.9 0.7 6.2 6.0
Metal products 5.3 1.4 0.6 3.9 2.0
Machinery 17.3 1.0 0.2 1.2 1.6
Other manufacturing 2.4 2.3 0.4 1.3 4.4
Electricity 5.5 3.0 0.5 0.7 5.3
Water 0.0 0.0 0.0 0.0 0.0
Construction 0.0 0.0 0.0 0.0 0.0
Services 21.1 2.2 0.4 3.5 3.0
Source: Simulations by the authors using the CGE model for Mozambique.
groundnuts and raw sugarcane. Given the relatively few nontariff distortions, the
reduction in import tariffs dominates in the results of this simulation.
The removal of import tariffs leads to a 4.8 percent increase in import demand.
Highly protected sectors experience greater import penetration. Imports more
than double in the most highly protected industry, sugar processing (table 10.8).
However, highly protected commodities represent only a small share of total
imports (see elsewhere above). Thus, the import tariffs on manufactures are low,
and it is the increased domestic demand for these manufactured goods that drives
the overall increase in imports. Furthermore, rapidly rising import demand places
pressure on the current account, thereby inducing a 5.2 percent depreciation in
the real exchange rate.
The depreciation improves export competitiveness. Recall that the major
export, aluminum, is highly capital intensive and tied to existing manufacturing
facilities. As a result, aluminum exports effectively do not respond to exchange rate
signals, transferring the onus of the export supply response onto primary and
processed agriculture, the second-largest exporting sector. Agricultural exports
grow by 28 percent. Large increases in exports cause agricultural GDP to rise by
2.9 percent. In contrast, the manufacturing sector experiences a contraction almost
across the board, with particularly marked declines in sugar processing; the overall
decline is 2 percent. This is, however, insufficient to offset the expansion in agricul-
ture, and real GDP rises by 0.9 percent under full domestic liberalization.
Mozambique 325
Table 10.9. The Effects of Prospective Liberalization Abroad
and Nationally on Employment, Welfare, and
Poverty, Mozambique
Rest-of-the-world Unilateral
liberalization liberalization
Indicator Base value Full Agriculture Full Agriculture
a
Real factor returns Index Change from the base, %
Rural skilled labor 1.0 4.5 0.2 6.2 4.2
Rural semiskilled labor 1.0 3.2 0.2 5.2 3.0
Rural unskilled 1.0 0.5 0.0 2.4 2.3
Urban skilled labor 1.0 1.5 0.1 4.5 2.2
Urban semiskilled labor 1.0 1.9 0.2 3.9 2.0
Urban unskilled 1.0 0.5 0.0 2.4 2.3
Capital 1.0 0.6 0.5 5.7 3.2
Agricultural land 1.0 3.0 0.1 5.4 3.8
Unskilled employment Index
Rural 1.0 3.2 0.1 3.0 1.5
Urban 1.0 1.0 0.1 1.0 0.3
Equivalent variation, welfare n.a. 1.4 0.2 0.9 1.0
Rural
Quintile 1 n.a. 2.8 0.1 3.9 2.9
Quintile 2 n.a. 2.8 0.1 4.3 3.2
Quintile 3 n.a. 2.7 0.1 4.1 3.2
Quintile 4 n.a. 2.8 0.1 4.3 3.2
Quintile 5 n.a. 2.6 0.2 3.1 2.4
Urban
Quintile 1 n.a. 0.9 0.2 2.0 1.4
Quintile 2 n.a. 1.0 0.2 2.5 2.0
Quintile 3 n.a. 0.9 0.2 1.8 1.5
Quintile 4 n.a. 0.8 0.3 0.9 1.1
Quintile 5 n.a. 0.3 0.3 1.9 0.9
Poverty headcount ratio % Change from the base, percentage points
US$1-a-day poverty line 36.2 1.4 0.0 1.7 1.3
Rural 36.0 1.5 0.0 2.1 1.6
Urban 36.5 1.3 0.0 0.9 0.5
Inequality measures Value Change from the base, percentage points
Gini coefficient 0.477 0.1 0.1 1.5 1.2
Theil entropy 0.532 0.3 0.2 3.7 3.2
Source: Simulations by the authors using the CGE model for Mozambique.
Note: n.a. not applicable.
a. Real factor returns are adjusted to reflect changes in the consumer price index (hence, the change in
unskilled wages that are fixed in nominal terms).
326 Agricultural Price Distortions, Inequality, and Poverty
Cheaper imported capital goods reduce the cost of investment, while the
depreciating exchange rate raises the domestic value of foreign inflows. This
causes an increase in investment demand and explains the 1.5 percent rise in con-
struction value added. Cheaper imports also lower consumer prices. This would
typically boost consumer spending. However, to replace lost tariff revenues, the
government must raise personal and corporate tax revenues. Thus, even with the
increase in GDP, the share of direct taxes in GDP more than doubles, rising from
2.1 to 5.3 percent.
We assume that tax rates adjust proportionally such that most of the additional
tax burden falls on enterprises and higher-income urban households. This is real-
istic. Obtaining direct tax revenue from rural and urban poor households is effec-
tively impossible. The new tax burden causes the welfare outcomes to be negative
among urban households in the top quintile (table 10.9), despite the relatively
large increases in the real returns to skilled labor and capital. The growth in agri-
cultural exports and output drives a 3.0 percent gain in employment in unskilled
rural labor and increases the factor prices for rural skilled labor and land by even
larger shares. Welfare improves among rural households and all but the richest
quintile among urban households.
Our assumptions about tax incidence greatly influence the distributional
impacts of the removal of domestic distortions. If each household in Mozambique
experiences the same percentage point increase in tax rates, then the gains are dis-
tributed much more evenly across households. However, if tax rates increase pro-
portionally, as we have assumed, then most of the tax burden falls on high-income
urban households. The latter assumption represents the only feasible direct tax
policy. Sensitivity analysis reveals that other policies, such as increasing activity taxes
or sales taxes, also tend to concentrate the tax incidence on urban households
because of the significant dependence of rural households on home consumption,
which evades taxation. As a result, under all feasible revenue replacement options,
rural and lower-income urban households tend to gain more than proportionately
from own-country trade liberalization. Moreover, a larger reduction in rural house-
hold incomes and poverty relative to urban household incomes and poverty causes
national inequality to decline slightly, as reflected in the Gini coefficient.
Simulation 4: The domestic liberalization of primary
and processed agricultural commodities
In our final simulation, we consider the impact of the removal by Mozambique of
distortions only in the country's agricultural sector, including agricultural process-
ing and textiles. As in simulation 3, we assume that other countries do not alter their
Mozambique 327
own distortions. In many ways, the results of this simulation are similar to the
results of simulation 3, though they are somewhat more limited (table 10.5). Reduc-
ing tariffs causes imports to rise, the real exchange rate to depreciate, and exports to
expand. Total GDP increases by 0.7 percent, in part because of an expansion in
unskilled employment. However, there is a notable difference in the components of
GDP compared with simulation 3. The combination of the less pronounced nomi-
nal depreciation (with implications for the pool of foreign savings), the increases
in taxes on high-income urban households and enterprises, and the price rises in
the construction sector imply that real investment actually declines. This allows the
consumption aggregate to grow more relative to the result in simulation 3.
Because the largest distortions (the ones generated by import tariffs) occur in
agricultural processing (textiles also enjoyed fairly significant protection, at
19 percent), the primary impact of the liberalization is to expand the imports of
processed products, which are treated as part of industry in table 10.5. Primary
agricultural imports actually decline slightly in the aggregate, despite the reduc-
tion in protection. Because agriculture is the major source of exports at the mar-
gin, the liberalization of agriculture, processed agriculture, and textiles actually
stimulates agricultural production; the strongest production gains are registered
in fisheries and groundnuts, which had low initial protection.
Commensurate with the consumption aggregate, aggregate household welfare
improves slightly more relative to the results under full liberalization. The distrib-
utional outcomes are driven by the expansion in agriculture, which generally
favors rural labor and land, and the mechanism used to replace lost tariff rev-
enues, which affects the highest urban household quintile the most. Because the
aggregate stimulus to agriculture is less in this simulation compared with the
result in the full liberalization of all markets, the consumption gains are more
evenly distributed across factors and, hence, across households. This leads to
reductions in poverty that are slightly less pronounced than the ones in simula-
tion 3 (table 10.9). There is, however, a similar reduction in national inequality
because of the larger income gains and more significant poverty reductions
among rural households relative to urban households.
Conclusions
Agriculture is adversely affected by current global distortions, which are biased
against some of Mozambique's key export sectors, such as fisheries. Removing all
global distortions would reduce the prices for key imported commodities. It
would, however, raise the prices of imported food items, dampening the gains in
the terms of trade. Overall, Mozambique's terms of trade would improve. The
production responses to a new global environment favor agriculture because of
328 Agricultural Price Distortions, Inequality, and Poverty
the stimulation of agricultural exports and import-competing food sectors. Agri-
cultural GDP rises once all trade distortions in the rest of the world are elimi-
nated, which reduces poverty in Mozambique. In contrast, the removal of only
agricultural price distortions abroad has little effect on Mozambique's agricultural
sector. The gains in traditional export crops, such as cotton and tobacco, are offset
by heightened import competition in processed foods, which has adverse effects
on downstream food-crop farmers. Thus, the net impact is a small decline in
agricultural GDP and a small increase in national poverty if only agricultural
distortions are removed in the rest of the world.
Mozambique's own distortions are also biased against agriculture. The produc-
ers of processed agricultural products enjoy high levels of protection. The removal
of these barriers causes a significant expansion in agricultural GDP despite the
associated elimination of tariffs on primary products. The primary agricultural
sectors lose little from these tariff reductions because of the relatively low initial
protection rates and low import penetration. The rise in agricultural GDP is
driven by increased agricultural exports, especially fisheries. Full liberalization
nonetheless provides a bigger stimulus to the agricultural sector. Poverty reduc-
tion is greater in the full liberalization case because of the relatively greater pro-
poor distribution of the welfare gains to households. Inequality also declines
through the reduction in domestic distortions.
The results of the model suggest that the removal of domestic and global dis-
tortions would have positive implications for agriculture and the expansion of the
overall economy of Mozambique. It would also contribute to reducing poverty,
which is particularly severe in rural areas. Thus, while improvements in agricul-
tural productivity and rural infrastructure remain the most pressing challenges in
the effort to stimulate pro-poor agricultural growth in Mozambique, there are
also gains to be made from the elimination of the bias against agriculture caused
by existing price distortions at home and abroad.
Notes
1. For a study of the impacts of current biofuel proposals in Mozambique, see Arndt et al. (2008).
2. Fossil fuels represent more than 10 percent of total imports. The general prospects for import
substitution are therefore greater for biofuel crops.
3. Estimates of agricultural protection and assistance for Mozambique, based on Alfieri, Arndt, and
Cirera (2009), are incorporated in the World Bank's Distortions to Agricultural Incentives Project
Database (Anderson and Valenzuela 2008). The estimates cover five decades. The representative values
for developing-country agriculture as of 2004 that we use in the global CGE modeling for our study
are summarized in Valenzuela and Anderson (2008).
4. We use the International Food Policy Research Institute's static model in this study (see Lofgren,
Harris, and Robinson 2002).
5. We assume that capital is sectorally mobile and earning flexible returns, except in the metals and
electricity sectors, in which we assume capital to be fixed.
Mozambique 329
References
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11
South Africa
Nicolas H�rault and
James Thurlow
South Africa rapidly reentered global markets after apartheid ended in the early
1990s. The country had previously faced economic sanctions, which created severe
foreign exchange shortages and forced the government to restrict imports and
encourage exports through a complex system of tariffs and subsidies (Bell 1993).
The result was a heavily distorted economy designed to maintain self-sufficiency
and macroeconomic stability. Following the change in government, sanctions were
lifted, and South Africa became a member of the World Trade Organization. The
new government placed trade liberalization at the center of its export-oriented
growth strategy (Department of Finance 1996). Import tariffs were reduced; export
subsidies were eliminated; and most quantitative restrictions were replaced by
tariffs. However, despite these reforms, South Africa's system of protection remains
complex; import tariffs still favor a narrow range of sectors (Cassim, Onyango, and
van Seventer 2004).
South Africa's economic performance improved during the 1990s, and annual
growth had reached 5 percent by 2005. Evidence suggests that the removal of trade
distortions contributed positively to growth during this period without reducing
aggregate employment (Jonsson and Subramanian 2001, Edwards 2001). Studies
also find that trade-induced growth did not contribute to the rise in poverty dur-
ing the 1990s and may even have helped reduce poverty in more recent years
(H�rault 2007, Thurlow 2007). However, the past distortions were biased in favor
of lower-skilled workers, especially workers in the protected textiles and clothing
sectors (Edwards 2001). The removal of these distortions has thus exacerbated
income inequality (Thurlow 2007).
During the 1990s, a rural-urban divide also emerged and grew. The share of the
poor population living in rural areas rose from 60 to 70 percent during 1995�2000
(Hoogeveen and �zler 2005).1 Agriculture is an important sector for rural
331
332 Agricultural Price Distortions, Inequality, and Poverty
livelihoods, employing one in four rural workers. If upstream activities are included,
the share is larger. However, agriculture grew only half as quickly as nonagriculture
during 1990�2005 because the gains from export growth were more than offset
by rising import penetration (World Bank 2008; Jooste, Van Schalkwyk, and
Groenewald 2003). This poor performance is undoubtedly responsible for some of
the rise in rural poverty. Why agricultural growth was slow is unclear, as is the extent
to which global or domestic price and trade policies were biased against agriculture
and rural development. One study suggests that domestic policies may have hurt
agriculture during the late 1990s when the effective rate of protection was negative,
implying that tariffs on inputs more than offset output protection (van Seventer
2001). There is also evidence that global distortions were biased against agriculture
(Krueger, Schiff, and Vald�s 1991). However, a more recent study indicates that the
support in agriculture was greater than that in other tradable goods sectors during
1961�2006 (Kirsten, Edwards, and Vink 2009).
In this chapter, we examine the impact on the South African economy of the
removal of distortions in global and domestic prices. More specifically, we esti-
mate the size of the remaining bias against agriculture and identify the transmis-
sion channels through which trade distortions influence poverty and income
inequality. We do this using a top-down computable general equilibrium (CGE)
and microsimulation models.
The next section describes South Africa's economic structure and the current
distortions. The following two sections outline the methodology and present the
results from the model simulations. The final section offers concluding comments.
Economic Structure and Trade Distortions in
South Africa
Table 11.1 describes the South African economy in 2002, when agriculture, a small
sector, generated 4 percent of GDP. However, the sector does have strong links to
upstream processing, which represent another 5 percent of GDP. Agriculture and
processing together contribute 8.5 percent to total export earnings and account for
some of the country's more heavily traded commodities; 17 percent of agricultural
output is exported. Summer cereals (maize), fruits, cotton, tobacco, and livestock
are the main export sectors, accounting for more than 90 percent of total agricul-
tural exports. Agricultural growth during the 1990s was driven by the strong export
performance of maize, fruits, and livestock, although this was offset by rising
import demand for these commodities caused by larger declines in agricultural
tariffs. Import penetration remains low, but it doubled during the 1990s as South
Africa opened to global markets (Jooste, Van Schalkwyk, and Groenewald 2003).
Import penetration is particularly high in winter cereals (wheat), cotton, and
South Africa 333
Table 11.1. Economic Structure and Price Distortions,
South Africa, 2002
Exports, %
Imports, %
GDP Tax
Sector share, % Share Intensity Tariff Share Intensity rate Elasticity
Total 100.00 100.0 13.3 5.3 100.0 13.2 0.8 3.10
Agriculture 4.33 1.8 8.1 4.8 4.3 17.3 0.1 2.03
Summer cereals 0.54 0.2 5.0 1.0 0.3 7.9 0.0 1.30
Winter cereals 0.21 0.4 23.3 14.6 0.0 2.4 0.0 4.45
Oilseeds and
legumes 0.11 0.1 14.0 4.1 0.0 4.3 0.0 2.45
Fodder crops 0.03 0.0 2.4 2.5 0.1 28.2 0.0 3.25
Sugarcane 0.26 0.0 0.0 0.0 0.0 0.0 0.0 2.70
Cotton and
tobacco 0.06 0.4 91.8 3.9 0.3 95.0 0.0 0.50
Vegetables 0.27 0.0 0.3 2.8 0.1 7.0 0.0 1.85
Fruits 0.97 0.1 6.7 2.8 2.8 58.9 0.0 1.85
Livestock 1.58 0.3 2.8 0.5 0.4 4.5 0.0 1.53
Fishing 0.01 0.1 95.8 1.7 0.1 97.3 2.0 1.25
Forestry 0.29 0.2 15.1 1.7 0.2 10.7 2.0 2.50
Industry 33.10 86.1 22.1 6.2 80.9 22.5 0.9 3.31
Mining 8.36 11.0 43.1 0.1 31.4 67.7 1.8 0.96
Manufacturing 19.97 75.1 23.3 7.2 49.4 18.4 0.4 3.71
Meat 0.07 0.2 2.0 5.9 0.1 0.9 0.0 3.85
Fish 0.11 0.4 31.5 19.7 0.1 15.7 0.0 4.40
Fruits 0.16 0.2 12.6 14.6 1.1 44.8 0.0 3.30
Oils 0.08 1.0 39.9 24.2 0.2 12.9 0.0 3.30
Dairy 0.22 0.2 5.5 66.9 0.1 5.0 0.0 3.65
Grain milling 0.27 0.9 15.3 4.8 0.3 6.1 0.0 2.60
Animal feeds 0.08 0.2 7.5 19.9 0.0 1.3 0.0 2.00
Bakeries 0.25 0.1 2.6 47.0 0.0 1.3 0.0 2.00
Sugar 0.20 0.0 0.8 48.2 0.2 12.9 0.0 2.70
Confectionery 0.14 0.0 3.6 18.8 0.1 5.3 0.0 2.00
Other foods 0.31 0.5 16.1 22.0 0.4 13.6 0.0 2.00
Beverages and
tobacco 1.14 0.8 6.0 6.5 1.6 15.7 0.0 1.15
Textiles 0.36 1.8 21.2 22.7 0.4 6.9 2.0 3.74
Clothing and
footwear 0.57 2.4 17.3 43.8 0.5 6.4 1.9 3.92
Wood and
paper 1.91 2.4 10.1 7.7 1.3 6.0 1.9 4.05
Chemicals 4.75 14.5 18.9 4.9 11.3 16.4 0.3 3.21
(Table continues on the following page.)
334 Agricultural Price Distortions, Inequality, and Poverty
Table 11.1. Economic Structure and Price Distortions,
South Africa, 2002 (continued)
Exports, %
Imports, %
GDP Tax
Sector share, % Share Intensity Tariff Share Intensity rate Elasticity
Nonmetals 0.69 1.4 18.3 11.6 0.8 11.4 0.3 2.90
Metals 3.41 7.1 17.7 4.1 13.3 28.7 0.4 3.76
Machinery 1.59 22.6 52.8 2.9 6.0 27.4 0.3 4.22
Transport
equipment 1.92 16.7 37.4 9.7 8.7 24.6 0.3 3.88
Other
manufacturing 1.75 1.8 11.9 5.5 2.9 20.9 0.3 3.75
Other industry 4.77 0.1 0.2 0.0 0.1 0.1 0.3 2.28
Private services 47.58 12.1 4.3 0.0 14.9 4.9 0.0 1.90
Public services 14.99 0.0 0.0 0.0 0.0 0.0 0.0 1.90
Source: 2002 social accounting matrix for South Africa.
Note: Import intensity is the share of imports in total domestic demand. Export intensity is the share of
exports in total domestic output. Elasticity is the trade elasticity applied to import demand and export
supply functions.
tobacco. International trade is therefore becoming increasingly important for agri-
culture, and the sector is responsive to changing trade distortions.
Despite its small and declining share in the economy, agriculture remains an
important source of employment. In 2003, more than a million people were work-
ing in agriculture within the total employment of 11.5 million (Casale, Muller, and
Posel 2004). Around 70 percent of agricultural workers are employed as laborers on
large commercial farms, where they earn an average of one-third of the national
average wage. The 60,000 commercial farms occupy 87 percent of the total agricul-
tural land and produce 95 percent of all marketed output (Vink and Kirsten 2003).
The remaining 300,000 agricultural workers are smallholder farmers, who occupy
13 percent of the agricultural land in the more remote regions of the country. These
subsistence-oriented farmers earn less than 5 percent of the national average wage
(Casale, Muller, and Posel 2004). Thus, not only is there a growing rural-urban
divide, but there is also a divide within agriculture. Smallholders derive much of
their income from subsistence production and are thus less likely to be affected by
changes in price and trade distortions. In contrast, factors affecting commercial
farming may have large impacts on national employment and rural incomes.
Mining has historically been the cornerstone of the South African economy,
primarily as a source of export earnings. However, the mining sector also has
strong upstream links to metals processing, and, together, these sectors represent
about 10 percent of total GDP. However, these sectors are more capital intensive
South Africa 335
and generate a smaller share of total employment. Construction, textiles, and
clothing are among South Africa's more labor-intensive sectors. Food processing
is another important manufacturing sector, generating 15 percent of total
manufacturing GDP and employment. Dairy products, grain milling, and sugar
refining are key processing sectors. Nonfood processing is dominated by the
capital-intensive metals and chemicals sectors.
Most export taxes and subsidies were eliminated during the 1990s. The govern-
ment stopped intervening in input and product markets, allowing farmers to
respond more effectively to climate variability and changing market opportunities
(Van Schalkwyk, Groenewald, and Jooste 2003). Thus, the largest remaining price
distortions are import tariffs. The average tariffs in South Africa are, however, rela-
tively low, at 5.3 percent. The tariffs on agricultural commodities are especially low,
at 2 or 3 percent, with the exception of a 14.6 percent wheat tariff. Upstream food
processing enjoys far greater protection, with average tariffs of 16.8 percent. Espe-
cially high tariffs are applied to dairy products and refined sugar. Of the remaining
manufacturing sectors, considerable protection is afforded to textiles, clothing, and
motor vehicles. These are considered sensitive sectors and have received special dis-
pensation under South Africa's various trade agreements. Thus, despite far-reach-
ing reforms during the 1990s, import tariffs are not uniformly applied across sec-
tors, and efforts at rationalization are often subjected to pressure by trade unions,
especially in the metals, textiles, and clothing sectors. Indeed, tariff revenues
account for less than 6 percent of total government income, and the tariffs on agri-
culture and processed food generate less than 15 percent of these earnings. The
removal of trade distortions would therefore have direct implications for a few key
sectors, but little impact on overall government revenues.
The Modeling Approach
In our analysis, we draw on the rest-of-the-world results--that is, the results with-
out the participation of South Africa--in the global trade liberalization scenario in
the World Bank's Linkage model (see van der Mensbrugghe 2005). This model pro-
vides the estimated changes in the world import prices, world export prices, and
export quantities facing South Africa (see the appendix by van der Mensbrugghe,
Valenzuela, and Anderson). These world price and export quantity changes are
imposed exogenously on the South African CGE model, wherein the additional
impacts of domestic trade reforms are also modeled. The CGE model estimates the
impact of global and domestic reforms on domestic commodity prices and factor
employment and returns. These results are then passed down to a microsimulation
model, which estimates poverty and inequality effects. This section describes the
South African CGE and microsimulation models.
336 Agricultural Price Distortions, Inequality, and Poverty
The CGE model
The national CGE model contains 110 activities and commodities, including 17
agricultural and 12 food processing sectors.2 The model identifies four factors of
production: three types of labor (unskilled, semiskilled, and skilled) and the capital
factor.3 Agricultural land is not distinguished from other forms of capital.4 Skilled
labor and capital are assumed to be fully employed at flexible wages and returns. To
reflect South Africa's high levels of unemployment, we assume that the supply of
semiskilled and unskilled labor is perfectly elastic at a fixed nominal wage.5 In the
model, labor returns are calibrated to capture sectoral wage differentials, such that
agricultural wages are lower than the wages in most nonagricultural sectors. In addi-
tion, labor is fully mobile across sectors. Producers in the model maximize profits
under constant returns to scale, and the choice among factors is governed by a
nonnested constant elasticity of substitution function. Factors are combined by
fixed-share intermediates under a Leontief specification.
Based on a constant elasticity of transformation function, possibilities of sub-
stitution exist between production for the domestic market and production for
foreign markets. Profit maximization drives producers to sell in the markets in
which they are able to achieve the highest returns. The returns are based on
domestic and export prices; the latter are determined by the world prices, multi-
plied by the exchange rate, which is adjusted for any border taxes or subsidies.
Similar substitution possibilities also exist between imported and domestic goods
under a constant elasticity of substitution Armington specification. The final ratio
of imports to domestic goods is determined by the cost-minimizing decision
making of those responsible for domestic demand based on the relative prices of
imports and domestic goods (including relevant taxes). Under the small country
assumption, South Africa faces perfectly elastic world demand and supply at fixed
world prices. However, this small country assumption is dropped if we are model-
ing the impacts of global liberalization. In other words, world import prices are
exogenous in the South African CGE model, while world export prices and quan-
tities are determined using the approach outlined in Horridge (2004).
The model distinguishes among various types of institutions, including enter-
prises, the government, and a single representative household group. Households and
enterprises receive incomes in payment for the use by producers of household and
enterprise factors of production. Households and enterprises pay direct taxes to the
government (based on fixed tax rates), save (based on marginal propensities to save),
and make transfers to the rest of the world. Enterprises pay their remaining incomes
to households in the form of dividends. Households, unlike enterprises, use their
incomes to consume commodities under a linear expenditure system of demand. The
government receives income from imposing activity, sales and direct taxes, and
import tariffs and then makes transfers to households, enterprises, and the rest of the
South Africa 337
world. The government also purchases commodities in the form of government con-
sumption expenditure, and the remaining income of government is (dis)saved. All
savings from households, enterprises, the government, and the rest of the world (for-
eign savings) are collected in a savings pool from which investment is financed.
To balance the model's macroeconomic accounts, it is necessary to specify a set of
closure rules. A savings-driven closure is assumed to balance the savings-investment
account. Under this closure, the marginal propensities to save of households and
enterprises are fixed, while investment adjusts to changes in incomes to ensure that
the level of investment and savings are equal. For the current account, we assume
that a flexible exchange rate adjusts to maintain a fixed level of foreign savings.
Finally, for the government account, the fiscal deficit is fixed in absolute terms, and
government revenues and expenditures are balanced through uniform changes in
the direct tax rates on households and enterprises. Table 11.2 shows the current
direct tax rates for poor, nonpoor, rural, and urban households as observed in the
microsimulation model. The tax rates are highest for urban households in the top
income decile. Changes in the tax rates based on the existing tax structure mainly
affect higher-income households, while poor households are largely unaffected
because their incomes generally fall below the lowest income tax bracket. Accord-
ingly, proportional changes in income tax rates influence income inequality, leaving
the poverty effects of trade reforms largely unchanged.
The CGE model is calibrated to a 2002 social accounting matrix. We have taken
information on nonagricultural production from the 2002 supply-use tables and
national accounts (StatsSA 2004, SARB 2008). We have disaggregated agricultural
production across crops and sectors using the 2002 census of commercial agricul-
ture (StatsSA 2002). We have drawn information on labor employment and wages
from the 2000 income and expenditure survey and the labor force survey of
September 2004 (StatsSA 2000�, 2001). We have taken trade elasticities from the
Global Trade Analysis Project (Dimaranan 2006); household income elasticities
are those estimated in Case (2000). The initial price distortions, such as import
Table 11.2. Direct Tax Rates on Households, South Africa, 2000
(percent)
Taxes as a share of gross household income
Households Rural areas Urban areas All areas
Poor, US$2-a-day poverty line 0.0 0.0 0.0
Nonpoor 1.6 4.2 3.2
Richest, top income decile 11.3 18.1 17.2
Source: South Africa microsimulation model of the authors based on StatsSA (2000�, 2001).
Note: Direct taxes only concern formal labor income.
338 Agricultural Price Distortions, Inequality, and Poverty
tariff rates, are consistent with the global Linkage model; the agricultural distor-
tions in this model are based on Valenzuela and Anderson (2008).
Microsimulation model
The predicted impacts in the national CGE model are passed down to a microsim-
ulation model for South Africa. Under a top-down specification, the changes in
commodity prices, household tax rates, factor returns, and employment levels in
the CGE model are imposed on the microsimulation model, which we then use to
estimate behavioral responses at the household level for each of the 26,000 house-
holds in the income and expenditure survey of 2000 and the labor force survey.6
There are two parts to the microsimulation model. First, a selection model pre-
dicts the employment status of working-age individuals (that is, inactive, unem-
ployed, subsistence agricultural workers, informal workers, or formal workers).
The probability that an individual will have a particular status is derived from a
linear utility function based on the characteristics of individuals. Second, a regres-
sion model predicts formal and informal earnings. The regression and selection
models are econometrically estimated for four demographic groups: single
women, partnered women, single men, and partnered men.
The structure of household incomes for poor, nonpoor, and rich households in
urban and rural areas is shown in table 11.3. Since occupational choices are
Table 11.3. Household Income Shares, South Africa, 2000
(percent)
Rural Urban
Poor, Rich, top Poor, Rich, top
US$2- income US$2- income
Indicator a-day Nonpoor decile a-day Nonpoor decile
Skilled labor 0.3 1.2 9.8 0.2 5.6 35.5
Semiskilled labor 3.1 9.9 26.6 5.7 26.4 31.4
Unskilled labor 20.5 35.4 38.8 32.8 40.7 11.9
Unspecified labor 3.4 1.9 1.3 3.9 2.4 2.2
Home production 4.4 3.0 0.9 0.4 0.1 0.1
Capital income 4.3 6.9 12.3 5.0 6.7 14.4
Government
transfers 34.5 23.2 5.4 33.1 11.1 2.6
Interhousehold
transfers 29.5 18.5 4.9 19.0 7.0 2.0
Total 100.0 100.0 100.0 100.0 100.0 100.0
Source: South Africa microsimulation model of the authors based on StatsSA (2000�, 2001).
South Africa 339
endogenous, these income shares are not assumed to be fixed (and may thus be
affected by trade reforms). The labor earnings of individuals are added to other
income sources and adjusted to reflect new tax rates so as to update household
disposable incomes in the survey. This is then deflated by a household-specific
consumer price index based on the household-specific budget shares (table 11.4)
and on changes in commodity prices from the CGE model. This reestimated level
of household expenditures is then compared with various poverty lines to deter-
mine changes in poverty.
Table 11.4. Household Expenditure Shares, South Africa, 2000
(percent)
Rural Urban
Poor, Rich, top Poor, Rich, top
US$2- income US$2- income
Sector a-day Nonpoor decile a-day Nonpoor decile
Total 100.0 100.0 100.0 100.0 100.0 100.0
Agriculture 16.3 12.9 6.7 10.1 6.1 2.4
Food processing 44.4 39.7 26.7 41.1 32.5 15.4
Meat products 8.8 9.6 7.2 10.0 8.9 4.3
Fish products 1.4 1.5 0.9 1.3 1.1 0.7
Fruits and
vegetables 0.8 0.9 0.8 0.8 1.1 0.9
Oils and fats 2.3 1.7 1.0 2.0 1.2 0.4
Dairy products 3.1 2.9 2.0 3.3 2.9 1.7
Grain mill
products 9.4 6.2 2.9 6.0 3.3 1.0
Animal feeds 0.0 0.0 0.0 0.0 0.1 0.2
Bakery products 5.9 5.7 3.3 6.7 4.6 1.3
Sugar products 5.9 3.4 1.4 3.6 1.5 0.4
Confectionary
products 0.1 0.1 0.2 0.1 0.2 0.3
Other processed
foods 4.3 3.6 2.5 4.2 3.2 1.6
Beverages and
tobacco 2.5 4.1 4.5 3.1 4.1 2.6
Textiles and
clothing 7.3 8.1 8.1 6.0 7.5 4.4
Other
manufacturing 17.4 18.1 22.2 18.0 17.3 22.4
Other industry 2.7 3.5 4.5 8.9 7.2 5.9
Services 11.9 17.6 31.8 16.0 29.4 49.5
Source: South Africa microsimulation model of the authors based on StatsSA (2000�, 2001).
340 Agricultural Price Distortions, Inequality, and Poverty
The top-down approach we use to link the microsimulation model to the CGE
model is described in detail in H�rault (2006). The approach ensures that changes
in prices, direct tax rates, earnings from wages and salaries, returns to capital, and
employment levels are transmitted from the CGE model to the microsimulation
model. Thus, the microsimulation model predicts how the behavior of individuals
and household incomes are affected by the economy-wide impacts predicted by
the national CGE model.
The Model Results
We have run four simulations to examine the effects of price distortions on agri-
culture, poverty, and inequality. We begin by considering the effects of distortions
in the rest of the world on the South African economy. The first simulation
assesses the impact of the removal of all merchandise trade policy measures and
domestic agricultural policies in other countries of the world without any change
in South Africa's own distortions. The second simulation considers only the
removal of agricultural sector distortions in the rest of the world. The effects at
South Africa's borders of these liberalizations are drawn from the World Bank's
Linkage model. These include changes in the country's import and export prices,
as well as changes in the demand for South African exports (table 11.5). The
remaining two simulations examine the effect on South Africa of the removal of
only its own price distortions, first for all tradable goods sectors and then only
within agriculture. In both of these latter simulations there are no changes in the
rest of the world.
The liberalization of all commodities by the rest of the world
Full liberalization by the rest of the world results in a positive terms of trade
shock in South Africa: the weighted world price of exports rise; and the prices
of imports fall. The quantity of agricultural and food products demanded by
the rest of the world expands for most items, but contracts for horticultural
goods, which are the main source of agricultural exports (table 11.1). Demand
also expands for other primary products, which is a key export sector because
mining accounts for one-third of all export earnings. However, demand con-
tracts for nonfood manufactures (table 11.5, column 3). On the import side,
prices fall for nonfood manufactures and for most farm products (table 11.5,
column 1). Overall, the terms of trade improve by 1.4 percent, which causes
exports to rise more rapidly than imports. This reduces the trade deficit and
places pressure on the current account balance, which, by assumption, is fixed
South Africa 341
Table 11.5. Exogenous Demand and Border Price Shocks of
Rest-of-the-World Liberalization, South Africa
(percent change from the baseline)
Sim 1: Rest-of-the-world, Sim 2: Rest-of-the-world,
all commodities agriculture only
Exports Exports
Import Import
Sector price Price Quantity price Price Quantity
Primary sector
Rice 0.00 0.00 0.0 0.00 0.00 0.0
Wheat 0.73 0.46 21.6 0.72 0.35 15.9
Other grains 5.49 0.46 48.6 5.45 0.35 59.3
Oilseeds 2.28 0.47 25.5 2.02 0.34 13.8
Sugarcane 0.00 0.00 0.0 0.00 0.00 0.0
Cotton 1.21 0.44 53.2 0.11 0.33 52.9
Fruits and vegetables 0.90 0.47 38.6 0.42 0.34 33.7
Other crops 1.87 0.49 7.0 2.81 0.35 10.9
Cattle and sheep 2.21 0.50 5.7 0.44 0.33 17.1
Other livestock 0.74 0.57 10.5 1.37 0.34 1.2
Other primary products 0.19 0.55 0.3 0.71 0.41 1.0
Secondary sector
Beef and sheep meat 5.06 0.51 443.5 5.82 0.38 489.9
Other meat products 3.71 0.57 11.4 4.77 0.42 1.9
Oils and fats 1.80 0.33 1.1 2.50 0.14 0.7
Dairy products 16.58 0.57 405.9 17.47 0.46 447.6
Grain milling 5.03 0.59 47.2 4.57 0.47 41.5
Sugar refining 1.15 0.49 147.2 2.09 0.39 173.5
Other food and
beverages 4.97 0.53 58.3 0.41 0.36 10.4
Textiles and clothing 0.94 0.32 21.2 0.50 0.43 0.1
Other manufacturing 0.34 0.45 2.3 0.20 0.41 1.2
Services 0.14 0.55 2.7 0.27 0.43 0.6
Source: Results from the World Bank Linkage model (see the appendix by van der Mensbrugghe,
Valenzuela, and Anderson).
in foreign currency. This induces an appreciation of the real exchange rate by
0.8 percent, which stimulates import demand additionally, while partially
offsetting export competitiveness. The net effect is that the volume of exports
rises by only 0.2 percent, while the volume of imports rises by 2.3 percent.
Among primary agricultural goods, exports fall 13 percent and imports rise
5 percent (table 11.6).
342 Agricultural Price Distortions, Inequality, and Poverty
Table 11.6. Macroeconomic Simulation Results of Prospective
Liberalization Abroad and Nationally, South Africa
(percent)
Change from the base
Rest-of-the-
world reform Unilateral reform
Base
share of All Agriculture All Agriculture
Indicator GDP goods only goods only
Real GDP at market
prices 100.0 0.3 0.1 1.0 0.1
Consumption 61.9 1.0 0.6 1.2 0.3
Investment 16.1 1.6 1.0 1.6 0.3
Government 18.4 0.0 0.0 0.0 0.0
Consumer price index n.a. 0.4 0.1 1.0 0.3
Real exchange rate n.a. 0.8 0.6 1.9 0.3
World export prices n.a. 1.2 1.0 0.0 0.0
World import prices n.a. 0.2 0.2 0.0 0.0
Terms of trade n.a. 1.4 0.7 0.0 0.0
Volume of exports 32.6 0.2 0.0 7.5 1.0
Agriculture 4.3 13.2 11.0 10.7 1.8
Mining 31.4 0.3 0.2 0.4 0.1
Manufacturing 49.4 1.7 1.1 12.3 1.5
Other industry 0.1 0.5 0.7 5.2 0.2
Services 14.9 1.2 1.2 5.5 1.0
Volume of imports 28.9 2.3 1.6 8.4 1.1
Agriculture 1.8 5.0 2.4 6.6 8.9
Mining 11.0 1.4 1.1 3.3 0.6
Manufacturing 75.1 2.7 1.7 11.5 1.3
Other industry 0.1 3.7 3.4 1.3 0.5
Services 12.1 3.4 2.9 2.9 0.6
Source: Simulation results by the authors using their CGE model for South Africa.
Note: The domestic price index is the num�raire in the model. n.a. not applicable.
The decline in agricultural export demand causes agricultural GDP to fall by
0.2 percent mainly because of a drop in fruit production (table 11.7). There is also a
contraction in the production of oilseeds, but oilseeds represent only a small share
of the agricultural sector. Total manufacturing GDP changes only slightly because
the increased production of processed foods is offset by the falling production of
other manufactured goods, such as textiles, clothing, and machinery, in the face of
rising import competition. Dairy and processed sugar production expand substan-
tially, contributing to food processing expansion and generating positive links to
South Africa 343
Table 11.7. The Effects of Prospective Liberalization Abroad
and Nationally on GDP, by Sector at Factor Cost,
South Africa
(percent)
Change from the base
Rest-of-the-
world reform Unilateral reform
Base
share of All Agriculture All Agriculture
Sector GDP goods only goods only
Total 100.0 0.3 0.2 0.7 0.1
Agriculture 4.3 0.2 0.1 1.3 0.9
Summer cereals 0.5 3.3 3.7 0.1 0.8
Winter cereals 0.2 2.0 0.6 12.3 14.3
Oilseeds and legumes 0.1 1.4 1.8 4.9 6.0
Fodder crops 0.0 10.4 12.9 0.4 2.8
Sugarcane 0.3 8.6 8.6 0.2 0.6
Cotton and tobacco 0.1 40.5 40.2 25.2 6.1
Vegetables 0.3 1.0 1.8 0.8 1.3
Fruits 1.0 14.5 15.1 8.1 0.9
Livestock 1.6 4.2 5.3 0.8 0.6
Fishing 0.0 0.0 0.0 0.0 0.0
Forestry 0.3 1.0 0.8 2.3 1.2
Industry 33.1 0.1 0.1 0.5 0.1
Mining 8.4 0.0 0.0 0.0 0.0
Manufacturing 20.0 0.0 0.2 0.7 0.2
Meat 0.1 6.7 7.9 0.2 0.1
Fish 0.1 0.9 2.6 13.9 16.2
Fruits 0.2 0.8 0.8 9.2 0.5
Oils 0.1 2.2 3.2 17.8 21.1
Dairy 0.2 35.0 41.1 7.7 9.1
Grain milling 0.3 0.7 0.4 0.4 1.0
Animal feeds 0.1 5.0 4.7 2.5 2.7
Bakeries 0.3 1.3 0.3 0.8 1.0
Sugar refining 0.2 19.7 22.1 0.6 0.7
Confectionery 0.1 3.8 0.7 0.1 0.5
Other foods 0.3 8.9 1.0 2.2 3.6
Beverages and
tobacco 1.1 8.0 1.6 1.2 0.3
Textiles 0.4 2.8 0.5 13.4 0.6
Clothing and
footwear 0.6 1.3 0.0 13.4 0.4
(Table continues on the following page.)
344 Agricultural Price Distortions, Inequality, and Poverty
Table 11.7. The Effects of Prospective Liberalization Abroad
and Nationally on GDP, by Sector at Factor Cost,
South Africa (continued)
(percent)
Change from the base
Rest-of-the-
world reform Unilateral reform
Base
share of All Agriculture All Agriculture
Sector GDP goods only goods only
Wood and paper 1.9 0.1 0.2 0.2 0.4
Chemicals 4.8 1.2 0.8 1.4 0.5
Nonmetals 0.7 0.2 0.1 2.5 0.1
Metals 3.4 2.3 1.7 3.3 0.9
Machinery 1.6 2.1 1.5 4.6 0.8
Transport equipment 1.9 2.1 1.5 7.3 1.1
Other manufacturing 1.8 0.9 0.7 2.4 0.6
Other industry 4.8 0.7 0.5 0.8 0.0
Private services 47.6 0.6 0.4 1.0 0.2
Public services 15.0 0.0 0.0 0.0 0.0
Source: Simulation results by the authors using their CGE model for South Africa.
the dairy and raw sugar sectors. The large service sector expands the most in
absolute terms, causing national GDP to increase under full liberalization by the rest
of the world. This is because the declining production in the agricultural and non-
food processing (manufacturing) sectors reduces the employment of capital and
labor, which therefore migrate to the service sector and to construction.
The reduction in the output of farm and textile products reduces the demand for
lower-skilled workers, while the expansion in services and other parts of the manu-
facturing sector raises the demand for capital and skilled workers (table 11.8). The
real wages of lower-skilled workers thus decline, and the wages and capital returns of
skilled workers increase (table 11.9). However, under our assumption of fixed nomi-
nal wages for unskilled and semiskilled workers, the aggregate employment of both
types of labor expands by about 1 percent. The increase in the number of jobs among
lower-skilled workers more than outweighs the decline in the average real wages of
these workers, leading to larger net increases in the factor incomes of these workers
relative to either capital or the average real wages of skilled workers. The net result is
that, measured by the equivalent variation in income, national economic welfare rises
by 1.0 percent (table 11.9, final row).
Falling prices for machinery and construction reduce the cost of investment,
which rises as a result. Investment demand is additionally stimulated by the
South Africa 345
Table 11.8. Factor Intensity Structure, South Africa, 2002
Share of factor income
in total value-added, %
Skilled Semiskilled Unskilled All
Sector labor labor labor Capital factors Elasticitya
Total 11.9 21.7 16.5 50.0 100.0 1.44
Agriculture 3.3 1.2 19.4 76.1 100.0 1.50
Summer cereals 2.7 1.0 16.3 80.0 100.0 1.50
Winter cereals 3.1 1.2 18.7 77.0 100.0 1.50
Oilseeds and
legumes 2.9 1.0 16.8 79.3 100.0 1.50
Fodder crops 3.0 1.1 18.5 77.4 100.0 1.50
Sugarcane 3.4 1.3 20.0 75.4 100.0 1.50
Cotton and
tobacco 4.6 1.7 26.7 67.0 100.0 1.50
Vegetables 4.0 1.5 23.7 70.8 100.0 1.50
Fruits 3.9 1.5 23.5 71.1 100.0 1.50
Livestock 2.9 1.1 17.1 78.9 100.0 1.50
Fishing 3.7 1.5 22.4 72.4 100.0 1.50
Forestry 3.2 1.2 19.2 76.4 100.0 1.50
Industry 7.8 9.4 27.6 55.3 100.0 1.31
Mining 4.1 2.9 31.5 61.5 100.0 0.75
Manufacturing 9.1 12.4 25.2 53.2 100.0 1.50
Meat 1.2 2.5 4.2 92.2 100.0 1.50
Fish 7.1 15.1 25.8 51.9 100.0 1.50
Fruits 7.9 16.9 28.7 46.5 100.0 1.50
Oils 4.8 10.5 17.7 67.0 100.0 1.50
Dairy 8.1 17.2 29.3 45.4 100.0 1.50
Grain milling 3.9 8.4 14.2 73.5 100.0 1.50
Animal feeds 5.4 11.4 19.4 63.8 100.0 1.50
Bakeries 10.5 22.5 38.2 28.8 100.0 1.50
Sugar 6.5 13.8 23.6 56.1 100.0 1.50
Confectionery 10.2 21.8 37.0 31.1 100.0 1.50
Other foods 7.4 15.9 27.0 49.7 100.0 1.50
Beverages and
tobacco 0.0 12.3 16.0 71.7 100.0 1.50
Textiles 1.8 6.0 54.2 38.0 100.0 1.50
Clothing and
footwear 4.6 4.7 59.4 31.3 100.0 1.50
Wood and
paper 3.2 3.3 42.1 51.3 100.0 1.50
Chemicals 20.4 7.7 12.7 59.2 100.0 1.50
(Table continues on the following page.)
346 Agricultural Price Distortions, Inequality, and Poverty
Table 11.8. Factor Intensity Structure, South Africa, 2002 (continued)
Share of factor income
in total value-added, %
Skilled Semiskilled Unskilled All
Sector labor labor labor Capital factors Elasticitya
Nonmetals 1.4 8.2 24.6 65.8 100.0 1.50
Metals 2.2 17.0 23.7 57.1 100.0 1.50
Machinery 11.3 20.6 32.6 35.5 100.0 1.50
Transport
equipment 4.2 17.2 36.2 42.4 100.0 1.50
Other
manufacturing 9.2 6.3 12.3 72.2 100.0 1.50
Other industry 8.7 7.8 30.4 53.1 100.0 1.50
Private services 10.2 23.1 11.0 55.7 100.0 1.50
Public services 28.5 50.5 8.7 12.3 100.0 1.50
Source: 2002 social accounting matrix for South Africa based on StatsSA (2004).
a. Elasticity is the substitution elasticity between factors.
Table 11.9. The Effects of Prospective Liberalization Abroad
and Nationally on Factor Rewards, Employment,
and Welfare, South Africa
Change from the base, %
Rest-of-the-
world reform Unilateral reform
All Agriculture All Agriculture
Indicator Base value goods only goods only
Average real factor returns,
R 1,000 per year
Skilled labor 127 0.4 0.3 1.2 0.1
Semiskilled labor 78 0.2 0.2 0.1 0.0
Unskilled labor 49 0.5 0.2 1.6 0.3
Capital n.a. 0.6 0.4 2.3 0.2
Employment, 1,000s
Skilled labor 969 0.0 0.0 0.0 0.0
Semiskilled labor 2,910 1.2 0.7 2.0 0.2
Unskilled labor 3,519 1.0 0.6 1.6 0.0
Capital n.a. 0.0 0.0 0.0 0.0
National economic
welfare, EV n.a. 1.0 0.6 1.2 0.2
Source: Simulation results by the authors using their CGE model for South Africa.
Note: EV equivalent variation in income. n.a. not applicable.
South Africa 347
increase in private savings resulting from higher factor incomes (mainly capital
returns). Falling import prices cause consumer prices to drop and real consumer
spending to rise. Overall, the increase in real exports and in consumption and
investment spending and the rise in employment among lower-skilled labor lead
to a slight increase (0.3 percent) in national GDP, despite the large expansion in
imports (table 11.6).
The liberalization of all commodities by the rest of the world causes poverty to
fall measured according to all the reported poverty lines (table 11.10). This is
mainly because of the expansion in formal employment. This expansion is biased
toward lower-skilled workers (see above), which is particularly beneficial for poor
households because these households derive almost all their market incomes from
low-skilled labor (table 11.3). In addition, poorer households benefit more from
lower consumer prices, especially the prices for meat, sugar, and other foods,
which form a large share of their expenditures. Food and agricultural products
account for more than half the expenditures of poor households, whereas services
and the output of nonfood processing in manufacturing account for more than
half the expenditures of rich households (table 11.4). At the higher poverty lines,
the reduction in poverty tends to be smaller in rural areas than in urban areas.
Table 11.10. The Effects of Prospective Liberalization Abroad
and Nationally on Sectoral Employment, Income
Inequality, and Poverty, South Africa
Rest-of-the-
world reform Unilateral reform
Base All Agriculture All Agriculture
Indicator value goods only goods only
Change from the base, %
Real factor returns, R per yeara
Informal sector workers 12,828 2.40 1.31 6.67 0.93
Formal sector workers 50,488 0.20 0.23 0.83 0.41
Employment, 1,000s
Subsistence agriculture 736 0.10 0.07 0.16 0.01
Informal sector workers 3,357 0.25 0.18 0.15 0.10
Formal sector workers 7,307 0.94 0.58 1.54 0.06
Unemployment, 1,000s 3,806 0.66 0.40 1.17 0.07
Inactive, 1,000sb 28,032 0.12 0.07 0.22 0.02
Real per capita income, R per
yearc 10,874 0.91 0.61 1.13 0.42
(Table continues on the following page.)
348 Agricultural Price Distortions, Inequality, and Poverty
Table 11.10. The Effects of Prospective Liberalization Abroad
and Nationally on Sectoral Employment, Income
Inequality, and Poverty, South Africa (continued)
Rest-of-the-
world reform Unilateral reform
Base All Agriculture All Agriculture
Indicator value goods only goods only
% Change from the base, percentage points
Poverty headcount ratios
US$1-a-day poverty,
R 87 per month 9.8 0.28 0.27 0.78 0.21
Rural 16.7 0.35 0.34 1.09 0.33
Urban 4.0 0.22 0.21 0.51 0.10
US$2-a-day poverty,
R 177 per month 29.2 0.34 0.26 1.12 0.26
Rural 46.4 0.28 0.24 1.32 0.32
Urban 14.8 0.39 0.29 0.96 0.21
Lower poverty line,
R 322 per month 50.1 0.39 0.23 1.05 0.25
Rural 71.5 0.18 0.12 0.72 0.22
Urban 32.2 0.56 0.32 1.33 0.28
Upper poverty line,
R 593 per month 67.5 0.25 0.16 0.67 0.23
Rural 86.3 0.09 0.08 0.46 0.15
Urban 51.8 0.39 0.23 0.86 0.30
Gini coefficient 0.67 0.05 0.05 0.54 0.11
Rural 0.63 0.09 0.05 0.37 0.11
Urban 0.62 0.08 0.06 0.63 0.12
Source: Simulation results by the authors using their microsimulation model on South Africa.
a. Average real per capita earnings.
b. Includes 652,000 unspecified workers.
c. Average real disposable income per capita.
This is because rural households are more deeply rooted in poverty and because
wages are significantly lower among rural workers, so that new jobs generate less
income in rural areas than in urban areas.
The rise in average earnings in the informal sector is caused by the trickle-
down effect of formal sector development. The migration of informal workers to
lower-skilled formal jobs also raises the average skill level and earnings of the
remaining informal workers. Conversely, the rise in lower-pay, lower-skilled for-
mal employment places downward pressure on average formal earnings. Overall,
South Africa 349
however, the effects on inequality are negligible. The small reduction in income
taxes and the higher returns to capital and skilled labor contribute to a worsening
of income inequality. However, offsetting this effect is the increase in lower-skilled
employment and the drop in consumer prices. The net effect is therefore virtually
no change in national inequality, as measured by the Gini coefficient (table 11.10,
final row).
Thus, in summary, full global liberalization produces slight gains in GDP in
South Africa, but hurts the primary agricultural sector. Yet, food processing
expands, which offsets some of the decline in the competitiveness of the horticul-
tural sector. Import competition encourages farmers to shift toward more labor-
intensive production. The decrease in the real wages of semiskilled and unskilled
labor leads to a significant increase in employment among lower-skilled workers.
This is the main force driving poverty reduction in this scenario.
The liberalization only of agricultural commodities
by the rest of the world
Even if reform abroad is restricted only to liberalization in agriculture, a positive
effect on the terms of trade is still the result in South Africa. Following such liber-
alization, prices rise for South African exports by somewhat less than the corre-
sponding rise in the previous simulation (table 11.6). This is because the export
prices for most product groups except textiles and clothing rise by less following
the liberalization in agriculture alone (table 11.5). The outcome is especially
important in the production of fruits, which are South Africa's main agricultural
export. However, in contrast to the result of the simulation involving the liberal-
ization of all commodities, there is now a small overall increase in import prices
(table 11.6). This is because industrial and service sector prices rise instead of
falling, which is significant given that heavy manufactures, such as machinery and
vehicles, make up almost two-thirds of total imports. The net result in this reform
simulation of agriculture alone is a 0.7 percent improvement in the terms of trade
of South Africa, which is half the 1.4 percent improvement in the simulation
involving the liberalization by the rest of the world of all merchandise trade. The
real exchange rate appreciation is also slightly smaller, and the impact on real GDP
at market prices is only 0.1 percent instead of 0.3 percent (table 11.6).
The effects on sectoral output differ in this simulation, too (table 11.7). Agricul-
tural GDP decreases by less relative to the simulation involving the liberalization of
all commodities, although the production of vegetables and fruits falls slightly more,
and wheat production now declines. Livestock production also increases more in
this simulation. Industrial GDP now falls slightly, despite rising output in the food
processing sector. The drop in manufacturing production adds to the small fall in
350 Agricultural Price Distortions, Inequality, and Poverty
agricultural GDP, which, as before, causes a migration of workers to the service sec-
tor. However, the impact is less pronounced than in the other simulation because of
the smaller size of the shock in the terms of trade. Investment demand is bolstered
by the increase in private savings resulting from the higher national GDP and the
shift to more capital-intensive sectors outside food processing. Finally, while import
prices rise in this simulation, the real appreciation is sufficient to offset the rise, and
the consumer price index still falls, albeit by less than the case under full merchan-
dise trade reform. Falling prices cause an increase in household real incomes and
aggregate consumer spending (table 11.6, rows 1 and 2).
Household incomes also benefit from the increased employment among
lower-skilled workers and the higher returns to capital and skilled labor. The
changes produced by this more-limited reform are about two-thirds the size of the
changes produced by full merchandise trade liberalization, and the increase in
national economic welfare is about three-fifths as large (table 11.9). This is a sig-
nificant result given the relatively small size of the agricultural sector both globally
and in South Africa. It reflects the large distortions in agricultural markets in the
rest of the world.
The impacts on income inequality and household poverty are similar to the
corresponding impacts of the liberalization of all merchandise trade, but slightly
smaller in magnitude (table 11.10). The fall in consumer prices plays a more
important role in the reduction of poverty in the simulation involving only agri-
culture, and poor households particularly benefit from the falling food prices.
Thus, in summary, the removal of the agricultural distortions alone in the rest
of the world would have a positive impact on GDP, national welfare, inequality,
and poverty in South Africa. This impact is roughly two-thirds as large as the
impact of full merchandise trade reform, despite the fact that agriculture accounts
for less than one-12th of global and South African GDP and trade.
The unilateral liberalization of all commodity markets
Agricultural import tariffs are generally quite low in South Africa; duty collection
rates average 5 percent (table 11.1). Moreover, there are high tariffs on a relatively
small range of manufactured goods, including a 15 percent tariff on wheat imports
that dominates price distortions in South Africa. Import tariffs are especially high
on certain processed foods (dairy and processed sugar), textiles and clothing, and
motor vehicles and related parts (for example, tires and engines). In contrast, export
taxes are low and uniform across commodities, with the exception of agriculture,
which faces no export taxes. Output taxes and subsidies are equally negligible. So, it
is the reduction in import tariffs that drives the results in the two unilateral liberal-
ization simulations we now consider in this and the next subsection.
South Africa 351
Eliminating all South Africa's border measures and domestic agricultural sub-
sidies causes a substantial increase in import demand (table 11.6). This widens the
trade deficit and induces a 1.9 percent depreciation in the real exchange rate,
which offsets some of the rise in real imports. Export competitiveness is enhanced
by the depreciation, thus encouraging producers to boost their production for
foreign markets. Total exports rise by 7.5 percent, while farm and manufactured
exports rise by more than 10.0 percent.
The exchange rate depreciation is sufficient to offset the decline in the tariffs on
some import-competing industries, causing the related GDP to expand (table 11.7).
In contrast, in heavily protected commodities such as wheat, dairy, textiles, and cloth-
ing, imports increase and GDP falls. Thus, in line with the initial distribution of tariffs,
agricultural imports expand by less than manufactured imports. Export expansion is
more evenly distributed across both the agricultural sector and the nonagricultural
sector. Cotton, tobacco, and fruits drive the increase in exports within agriculture.
Relative to liberalization by the rest of the world, unilateral liberalization has a
larger positive impact on real consumer spending. This is because, following the
latter, falling import prices reduce the consumer price index, thereby raising real
household incomes. Rising incomes also increase the level of savings and invest-
ment in the economy. Investment is additionally supported by the expansion of
the metals and machinery sectors and the reduction in the import prices for capi-
tal goods, both of which contribute to a reduction in the cost of investment. Ulti-
mately, following unilateral reform, rising consumption, exports, and investment
lead to larger increases in total GDP: 1.0 percent compared with less than 0.3 per-
cent after rest-of-the-world reform (table 11.6).
Increased production following unilateral reform also creates more jobs for
lower-skilled workers relative to the case of rest-of-the-world reform (table 11.9).
Although most of these jobs are lower-pay jobs in agriculture, there is also an
increase in higher-paying jobs in the manufacturing sector. New jobs are created
in the metals and machinery sectors, both of which pay higher wages than the tex-
tiles and clothing sectors, where jobs are lost. Unskilled workers also migrate to
service sector jobs, not all of which are in the lower-pay trade sector. Overall, there
is a net migration into higher-pay sectors, causing average real wages and total
employment to rise among unskilled workers. There is increased demand for
skilled workers, although the shift of such workers into higher-pay sectors is less
pronounced. For instance, most skilled workers are already employed in the ser-
vice sector, where wages are typically the highest. So, it is the overall increase in
demand rather than intersectoral shifts that drives the increase in wages among
these workers. Finally, semiskilled workers are more intensively employed in the
textile sectors, and semiskilled workers thus face the largest declines in employ-
ment once the protection for these sectors is removed.
352 Agricultural Price Distortions, Inequality, and Poverty
Larger increases in factor incomes cause aggregate household welfare to
improve by more in this simulation than in the two rest-of-the-world simulations.
Poverty reduction is also larger (table 11.10). The increase in unskilled and semi-
skilled real wages, combined with the expansion in employment among the
related workers, is the main force behind the reduction in poverty. Indeed, low-
income households are the most dependent on low-skilled labor. The slight
decline in national inequality is caused by the combination of these labor market
changes and the substantial rise in income taxes needed to replace lost tariff
revenues. In this simulation, aggregate household income taxes rise by more than
10 percent.
Thus, in summary, the unilateral removal of price distortions in South Africa
would cause national GDP to expand and would boost agricultural GDP by more
than two times the rise in GDP in industry or services. This suggests that the cur-
rent domestic distortions are strongly biased against the agricultural sector. How-
ever, after the reform, much of the benefits to agriculture would accrue to specific
export-oriented crops, such as cotton, tobacco, and fruits, while other farm sectors
would be adversely affected. Manufacturing employment would also decline,
especially in the sensitive textiles and clothing sectors. This decline would be more
than offset, however, by new jobs in the heavier industrial and service sectors.
Thus, aggregate household welfare would improve; national poverty would
decline; and there would be a small decline in inequality.
The unilateral liberalization of only agricultural commodities
The removal of price distortions only in South Africa's agricultural and food pro-
cessing sectors produces macroeconomic results similar to, but much smaller than
those produced by the unilateral liberalization of all merchandise. Raising import
demand and export supply induces a small depreciation in the real exchange rate,
which enhances the competitiveness in foreign markets of domestic producers of
exports. Falling import prices also lower consumer prices, benefiting private con-
sumption and increasing national GDP, but only by one-ninth as much as in full
unilateral liberalization (table 11.6). However, while these effects are similar to those
under full domestic liberalization, their size is much smaller. This is not surprising
because South Africa's agricultural and food processing sectors are only a small part
of national GDP and are not as well protected as the manufacturing sector.
Under the unilateral liberalization of all goods, agriculture benefits from the
large depreciation in the exchange rate that is driven by the declining nonagricul-
tural tariffs and rising import penetration of nonagricultural commodities. How-
ever, in the unilateral liberalization of agriculture alone, the depreciation is
South Africa 353
smaller and is driven entirely by the decline in the supports and tariffs in agricul-
ture and food processing. Thus, only agriculture faces an increase in import com-
petition, while the nonagricultural sectors benefit from the resulting depreciation.
Accordingly, there is a decline in agricultural GDP in this simulation. This is
driven by declining wheat production, which is subject to high initial tariffs. Fruit,
cotton, and tobacco, which show substantially increased production under the full
unilateral liberalization, now expand more modestly because of the smaller depre-
ciation, while livestock declines because producers shift production toward more
export-oriented activities and because the imports of processed meats increase.
Food processing suffers the largest drop in production under the liberalization of
agriculture alone. For instance, there is now a contraction in the dried fruit and
tobacco sectors, which contributes to the decline in agriculture. Production
expands in the industrial and service sectors mainly because of greater export
demand following the depreciation. The largest increases are in metals, machin-
ery, and motor vehicles. The slowdown in investment, however, causes a slight
decline in construction, and the modest expansion of services is driven by trade,
which benefits from South Africa's increased openness. Overall, there is a much
smaller increase in national GDP under this liberalization scenario because of the
smaller size of the shock and the decline in investment demand (table 11.7).
The greater import competition in agricultural commodities and the only
modest additional demand created by nonagricultural expansion cause agricul-
tural prices to fall relatively more than nonagricultural prices. This raises the rela-
tive returns in nonagriculture, leading workers to migrate from agriculture. The
real returns to lower-skilled agricultural labor increase slightly and generate the
decline in unskilled employment resulting from the contraction in agricultural
production. The increase in nonagricultural production creates additional jobs
for semiskilled workers, but these jobs pay wages that are similar to the wages in
agriculture, leaving average wages largely unchanged and raising national
economic welfare only slightly (table 11.9).
Unilateral liberalization in agriculture alone thus has the smallest impacts on
poverty and inequality among our simulations, although the impacts are in the
same direction as the impacts of full unilateral liberalization (except in the case of
real wages among unskilled labor, which decline slightly). Unlike the other simu-
lations, poverty reduction under unilateral agricultural liberalization is driven
mainly by consumer price changes. Although the reduction in the consumer price
index is smaller in this simulation than in the full unilateral liberalization, the
changes are more beneficial for the poor because there are substantial declines in
the prices of food items that account for a large share of the expenditures of poor
households. Hence, poverty is reduced and income inequality declines slightly
even in this simulation (table 11.10).
354 Agricultural Price Distortions, Inequality, and Poverty
Thus, in summary, while the unilateral removal of the distortions in agricul-
ture and food processing has a negative impact on production in this sector, it
lowers food prices, and the net effect is a reduction in poverty and inequality in
South Africa.
Conclusions
Our model results indicate that agriculture in South Africa currently benefits
from global price distortions, but mainly because of the impact on local fruit pro-
ducers. Most of the other agricultural sectors are adversely affected by global dis-
tortions. This is especially true of traditional export crops, such as tobacco, sugar,
and cotton. The global distortions are also biased against the livestock and dairy
sectors, which are important components of food processing in South Africa.
Thus, despite a decline in agricultural GDP, the removal of global distortions
would favor the creation of new formal sector jobs for lower-skilled workers in
agriculture and food processing, including some workers who are currently
unemployed. There would need to be a period of structural adjustment in the
country as manufacturing workers migrate to the service sector. In the long run,
these workers would benefit from higher-pay jobs. The removal of global price
distortions thus improves national economic welfare, reduces poverty, and lowers
income inequality, albeit only slightly.
South Africa's own policies are also biased against agriculture. Tariff protection
is greater for nonagricultural commodities than for farm products, such that the
removal of the tariffs raises overall agricultural GDP and employment. There is
contraction in some farm industries and in textiles and clothing after unilateral
reform. The job losses in these sectors are, however, outweighed by job creation
elsewhere in agriculture and food processing, such that overall employment rises
after the domestic distortions are removed. Household welfare also improves,
especially among poorer rural households.
Our findings suggest that current own-country policies are more damaging to
welfare, poverty, and inequality in South Africa relative to the effects of distor-
tionary policies in the rest of the world. The rationalization of the country's sys-
tem of protection so that tariffs are more uniformly applied across sectors would
reduce some of the bias against agriculture and the poor. Price distortions may
thus explain some of the poor performance in the agricultural sector and in rural
development over the last decade. The complete removal of these distortions
would increase the benefits of the broader reform process in South Africa. This
reform would involve less adjustment in the South African economy if the domes-
tic reforms were accompanied by reform abroad, such as the reforms desired
under the World Trade Organization's Doha Development Agenda.
South Africa 355
Notes
1. Measured using the national household income and expenditure survey in 2000 and a US$2-a-day
poverty line (Hoogeveen and �zler 2005).
2. We use the International Food Policy Research Institute's static model for this study (Lofgren,
Harris, and Robinson 2002).
3. Table 11.8 shows the factor intensities of various aggregate sectors, as well as the factor substitu-
tion elasticities used in the sectoral production functions.
4. Agricultural land is not separated out because arable land is underutilized, and land rents are
low, at 5 percent of land values (Ortmann and Machethe 2003). Thus, we assume that agricultural
production is not so constrained by the lack of the availability of land, but rather by the lack of the
availability of other forms of capital (for example, machinery, irrigation infrastructure, and other
infrastructure). Furthermore, commercial land rents, like other forms of capital, mainly accrue to
high-income households; the poverty effects of agricultural distortions are therefore unaffected by this
assumption.
5. South Africa's unemployment rate was 32 percent in 2003 under a strict definition and 43 per-
cent if the nonsearching unemployed are included in the workforce (Casale, Muller, and Posel 2004).
While nominal wages are fixed, they may vary in real terms because of changes in consumer prices.
6. See H�rault (2006) for a detailed description of the microsimulation model.
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PART V
NATIONAL CGE
APPROACHES:
LATIN AMERICA
12
Argentina
Mart�n Cicowiez,
Carolina D�az-Bonilla,
and Eugenio D�az-Bonilla
Using the most recent estimates of agricultural price distortions, we study in this
chapter the economic, poverty, and income inequality impacts of global and
domestic agricultural liberalization and total trade liberalization in Argentina,
with a special focus on export taxes. Argentina offers an interesting case study
because it is the only large agricultural exporter that, at many times in its history,
has applied export taxes to several agricultural products. The most recent episode
started after the large devaluation of early 2002, when export taxes, which had
been abolished during the 1990s, were reinstated; the export taxes have been in
place ever since.1
Export taxes have been defended by the Argentine government on the grounds
that they are needed for poverty reduction, fiscal consolidation, the intersectoral
and intrasectoral balance of production, and other reasons. Critics have argued
that the taxes reduce growth and even increase poverty (for example, see Nogu�s
et al. 2007, Nogu�s 2008). We look at these conflicting claims using a general equi-
librium approach in an attempt to present a more integrated evaluation of
national measures. We also examine the impact on Argentina of the rest of the
world's agricultural and trade policies, with a particular focus on the effects on
poverty and inequality in Argentina.
More specifically, our analysis combines results from a global economy-wide
computable general equilibrium (CGE) model, a national CGE model, and
microsimulations. We use the World Bank's global Linkage model (see van der
Mensbrugghe 2005) to analyze the impact on the Argentine economy of trade
policy interventions in the rest of the world. The model is based on version 7 of
the Global Trade Analysis Project Database, which is calibrated to 2004
359
360 Agricultural Price Distortions, Inequality, and Poverty
(Narayanan and Walmsley 2008), while the agricultural distortions in developing
countries are taken from the new World Bank database as summarized by
Valenzuela and Anderson (2008). The impacts on the terms of trade and export
demand faced by Argentina that are generated by rest-of-the-world policies,
which are simulated using the global Linkage model (see the appendix by van der
Mensbrugghe, Valenzuela, and Anderson), are transmitted as exogenous shocks to
a national CGE model of Argentina. The results of the country CGE simulations
in regard to changes in employment, factor prices, and the prices of goods and
services are linked to a microsimulation model for Argentina, which allows us
to analyze the impacts of these various changes on household poverty and
inequality according to household survey data. We analyze and compare the
effects on Argentina's economy of the removal of rest-of-the-world distortions
and Argentina's own import tariffs and export taxes. In addition, our analysis dis-
tinguishes between agricultural reforms only and reforms that cover agricultural
and nonagricultural trade policies.
The rest of the chapter is organized into four sections. The next section pro-
vides background information, including the economic context in which export
taxes were implemented by Argentine authorities in response to the economic col-
lapse of 2001�02 and the evolution of poverty and inequality indicators. The sub-
sequent section summarizes the methodology and data for the national CGE and
microsimulation models. This is followed by the core section, which discusses the
model simulations and results. The final section offers conclusions.
Background
In 2001�02, Argentina experienced one of the most severe crises in its history.
Amid social unrest and street riots that led to more than 30 deaths and hundreds
of injuries, the country plunged into a state of institutional disarray. GDP
declined 10 percent in 2002, on top of the previous three consecutive years of
decline that involved an accumulated GDP loss of another 10 percent. Formal
unemployment jumped to more than 25 percent, and almost 60 percent of the
population was considered to be living below the national poverty line.
The collapse was the result of a combination of factors. First and foremost were
the policy rigidities of the Convertibility Plan, which was established in 1991 during
the Menem administration and linked the peso to the U.S. dollar in a (quasi)
currency board arrangement at the exchange rate of Arg$1 US$1. Inflation con-
tinued at a high rate for about two years after the exchange rate had been fixed, and
the peso became overvalued, affecting the production of tradables. The fixed peg
also forced Argentina to follow the appreciation in the U.S. dollar that started in
the mid-1990s, thereby additionally punishing production, employment, and the
fiscal accounts. Doubts about the sustainability of the peg led to a generalized
Argentina 361
dollarization of the banking system, which eliminated the possibility of counter-
cyclical monetary policy and increased the vulnerability of the domestic financial
system to runs against the peso or the banks.
Second, Menem's government had privatized a significant portion of the social
security system by the mid-1990s. This generated a substantial transitional fiscal
deficit. The deficit added to public indebtedness, which was expected to take sev-
eral decades to repay.2
Third, there was a series of negative exogenous shocks in the late 1990s and
early 2000s, including Brazil's devaluation and the decline of agricultural prices.
When the government of President De la Rua took office in late 1999, the econ-
omy was already decelerating and heading into outright recession after this com-
bination of external shocks.
Fourth, the alliance of parties that constituted the De la Rua government (the
first coalition government in many years) broke down under the economic strains
of the deepening recession. In the second half of 2000, the vice president resigned,
and the party of the vice president left the coalition, weakening the capacity of the
government to manage the difficult economic situation.
At the end of 2001, as confidence waned and a run occurred against the peso
and against the banking system, the situation became untenable. The administra-
tive constraints imposed by the government on bank withdrawals to stop the run
on the banks--because the deposits of the banks were largely in U.S. dollars, the
Central Bank, unable to print dollars, could not help the banks much--finally led
to widespread riots and the resignation of President De la Rua. A period of tur-
moil ensued, during which the country had four additional presidents within only
a few weeks through early 2002.
By late 2001 and early 2002, it had become clear that there were two separate,
but related fiscal and external problems of insolvency: a significant public debt
involving a nonfinanceable fiscal deficit and a significant external debt (private
and public) involving a nonfinanceable current account deficit. The suspension of
debt payments and the subsequent devaluation (abandoning the one-to-one peg
to the U.S. dollar) were desperate measures taken by the interim governments in
early 2002 to cope with both problems. The exchange rate jumped to almost
Arg$4 per US$1 dollar once it had been allowed to float in the first half of 2002. It
later declined to about Arg$2.50 per US$1, but, by the end of 2008, the exchange
rate was about Arg$3.30 to US$1.
In early 2002, in the middle of a deep recession, aggravated by the banking crises
and the devaluation, and needing to shore up fiscal accounts, the government
imposed export taxes on agricultural and several other products, including oil and
other energy commodities. One of the objectives of these taxes was fiscal; they were
imposed during a moment when the collection of more traditional income and
value added taxes had dropped significantly because of the recession. Not only had
362 Agricultural Price Distortions, Inequality, and Poverty
tax receipts declined, but the peso value of the public debt (most of which was
denominated in U.S. dollars) had increased significantly during the devaluation.
Under the one-to-one exchange rate, Argentina's GDP was valued at almost US$300
billion in the late 1990s, while the public debt was about US$150 billion (a ratio of
debt to GDP of 50 percent). After the devaluation, the GDP was valued at less than
US$100 billion, but the debt, yet to be renegotiated, remained the same, implying an
unsustainable ratio of debt to GDP of more than 150 percent. Through the imposi-
tion of export taxes, the negative impacts of the devaluation on public accounts
were partially offset.
Export taxes helped increase fiscal receipts by 1.5 to 2.5 percent of GDP
(figure 12.1). They were only one component in a more general effort to consol-
idate fiscal accounts through the more effective collection of all taxes. Fig-
ure 12.2 shows that overall tax receipts increased by 8 to 9 percent of GDP
beginning in 2002, compared with the 1990s; so, export taxes accounted for less
than one-third of the overall boost in taxes. There was also some adjustment in
public expenditures, which, during 2002�07, were about 0.8 percentage points
of GDP below the average for the previous decade.
A separate and important objective of the government in imposing export taxes
in the case of agricultural products, which include important food staples such as
wheat, rice, beef, fruits and vegetables, and dairy products, was to moderate the
upward impact of the currency devaluation on domestic food prices and, thereby,
on real wages and poverty. Early in the development debate in Argentina it was
argued that, because Argentina's exports are mainly wage goods (in the terminology
Figure 12.1. Export Taxes, Argentina, 2001�07
3.0
2.5
2.0
% of GDP
1.5
1.0
0.5
0
2001 2002 2003 2004 2005 2006 2007
year
Source: Informaci�n Econ�mica Database (2008).
Argentina 363
Figure 12.2. Tax Revenue, Argentina, 1991�2007
30
25
20
% of GDP
15
10
5
0
91
92
93
94
95
96
97
98
99
00
01
02
03
04
05
06
07
19
19
19
19
19
19
19
19
19
20
20
20
20
20
20
20
20
year
Source: Informaci�n Econ�mica Database (2008).
of Ricardo), a devaluation would put upward pressure on wages and influence the
competitiveness of domestic industry (D�az-Alejandro 1963, Braun and Joy 1968).
By increasing domestic food prices, devaluations would raise the poverty line, which
is based on the prices of a basket mainly consisting of food items. For a given
income, this would also increase the number of people living below the adjusted
poverty line. Because they moderated the increase in domestic food prices, export
taxes were seen as a way to reduce this impact of the devaluation.
The government has also maintained a moderate export tax differential (taxing
primary products somewhat more than processed items), presumably to compen-
sate domestic producers for the tariff escalation prevalent in the rest of the world.3
In addition, export taxes have been imposed on other important export prod-
ucts such as oil and gas, which has kept a lid on the domestic prices of energy, ben-
efiting the productive sectors in proportion to their use of energy inputs. This is
important for agricultural production because it has lowered the cost of fuel and
fertilizers among farmers.
From 2003 until about the end of 2008, the economy experienced a strong and
rapid recovery, growing at about 8.5 percent during the period. To a large extent,
the significant growth was based on reductions in the country's macrovulnerabil-
ity and high volatility, which were the main reasons for the dismal economic
performance until recently.4 The combination of an exchange rate at approxi-
mately the real level (deflated by the U.S. consumer price index) of the decades
previous to the large overvaluation of the 1990s, fiscal consolidation through
364 Agricultural Price Distortions, Inequality, and Poverty
increases in taxes and the debt renegotiation, and the accumulation of reserves in
the Central Bank has reduced the main causes of macroeconomic vulnerability by
sustaining surpluses in the fiscal and current accounts.5
The period of economic growth led to improvements in employment, poverty,
and income distribution at least through late 2007. The unemployment rate of
7.5 percent in the fourth quarter of 2007 was the lowest since the early 1990s; during
the 2001�02 crisis, it had topped 25 percent. The levels of poverty and extreme
poverty declined from 58 and 28 percent, respectively, in 2002, to 23 and 8 percent by
the end of 2007. Income distribution also improved: the ratio of the top decile to the
bottom decile fell from 32 at the beginning of 2004 to 26 in the first quarter of 2007.
What has been the role of agricultural and other trade policies, particularly
export taxes, in the positive performance? This is a policy question that may best
be addressed using an empirical general equilibrium approach.
It is useful to look, first, at the evolution of agricultural prices and production
in Argentina since the imposition of export taxes in 2002. Critics argue that the
price-distorting policy reduces farmer incentives and adversely affects growth.
The issue of farmer incentives, however, must be analyzed in a broader policy
context, along with other measures, particularly the exchange rate.6 Figure 12.3
shows that domestic producer prices in real terms have been clearly above the 1990s
Figure 12.3. Agricultural Producer Price Indexes, Argentina,
1992�2006
Arg$ deflated by the consumer price index,
4.5
4.0
3.5
3.0
2001 = 1.0
2.5
2.0
1.5
1.0
0.5
0
92
93
94
95
96
97
98
99
00
01
02
03
04
05
06
19
19
19
19
19
19
19
19
20
20
20
20
20
20
20
year
cattle meat maize rice, paddy
sorghum soybeans wheat
Sources: FAOSTAT (2008), Informaci�n Econ�mica Database (2008).
Argentina 365
values for a variety of farm products, suggesting that the negative impact of
export taxes on domestic prices has been more than offset by the devaluation or
by a rise in the international prices of farm products.7 Figure 12.4 shows another
indicator of the incentives for agricultural production in Argentina, namely, an
index of relative domestic prices for the agricultural sector and the rest of the
economy, along with an index of the real exchange rate in Argentina and an index
of real international prices (see the definitions in the note to figure 12.4). The sig-
nificant improvement in the internal terms of trade for agriculture since 2002 has
been mainly caused by the adjustment in the real exchange rate, while the changes
in world prices show a positive impact only since the second half of 2007.8
Figure 12.4. Indexes of International Agricultural Prices, Relative
Domestic Agricultural Prices, and the Real Effective
Exchange Rate, Argentina, 1996�2008
3.0
index, first quarter 2001 = 1.0
2.5
2.0
1.5
1.0
0.5
0
6
96
7
97
8
98
9
99
0
00
1
01
2
02
3
03
4
04
5
05
6
06
7
07
8
I9
I9
I9
I9
I0
I0
I0
I0
I0
I0
I0
I0
I0
III
III
III
III
III
III
III
III
III
III
III
III
quarter
REER index (CPI based against US$)
relative prices, agriculture/nonagriculture
world agricultural prices (real, deflated by the U.S. CPI)
Source: Author calculations based on data in Informaci�n Econ�mica Database (2008).
Note: All variables in the indexes are normalized to 1 for the first quarter of 2001. The index of real prices
for world agricultural products is the weighted average of International Monetary Fund indexes for food,
beverages, and agricultural raw materials, using International Monetary Fund weights, deflated by the U.S.
consumer price index (CPI). The index of relative domestic prices in the agricultural sector is the index of
the prices relative to total GDP measured as the ratio of the deflator of agricultural value added in GDP at
factor cost to the total GDP deflator at factor cost. The index of the real effective exchange rate (REER) of
the Argentine peso against the U.S. dollar is calculated using the average nominal exchange rate (Arg$ per
US$) and the consumer price indexes of Argentina and the United States.
366 Agricultural Price Distortions, Inequality, and Poverty
In addition to the devaluation, other policies in Argentina have also benefited
agriculture, such as the normalization of debt conditions for many producers
affected by the 2001�02 macroeconomic collapse, lower energy prices through
export taxes on energy products (Argentina is a net exporter of energy), and the
expansion of investments in technology and infrastructure. The combination of
these policies, plus rising international commodity prices, has contributed to a
strong supply-side response from farmers. Thus, Argentina's production of grains
and oilseeds in 2003�07 was about 10 percent above the growth trend of the pre-
vious 45 years (for details, see Cicowiez, D�az-Bonilla, and D�az-Bonilla 2009,
appendix 1).
While it is not evident from these recent data that export taxes are harming the
agricultural sector, it may be argued that, without export taxes, agricultural
growth rates would have been even higher. However, one should bear in mind that
lower or no export taxes would have affected fiscal accounts and led to higher
interest rates and possibly weaker overall macroeconomic performance. Higher
domestic prices may have also led to protests about domestic food prices,
compromising the social sustainability of the entire reform program. These
possibilities are not explicitly explored in what follows. Rather, we limit our-
selves to disentangling the effects of agricultural and trade policies at home and
abroad on poverty and income distribution in Argentina, with a special focus on
export taxes.
Methodology and Data
To explore the effects of agricultural and trade policies abroad on poverty and
income distribution in Argentina, we use the simulation results from the global
Linkage model (see the appendix by van der Mensbrugghe, Valenzuela, and
Anderson) as exogenous shocks that are fed into a national CGE model for
Argentina, and we use these results, in turn, as inputs in a microsimulation model.
We then compare the latter two sets of impacts with the impacts of own-country
agricultural and trade policies that we estimate using only the national CGE and
microsimulation models. There are no feedback effects from the microlevel to the
macrolevel, but the microsimulations are consistent with the aggregates generated
by the national CGE model.
At the macrolevel, through the national CGE model, we produce results for a
given policy change, including new levels of employment in each economic sector,
new wages, and new relative prices. At the household level, we use the microsimu-
lation model to receive the changes in the macrolevel variables and determine new
individual wages and employment, a new distribution of household per capita
income, and new poverty rates and inequality indicators.
Argentina 367
The CGE model for Argentina
The national CGE model has similarities with the models in Lofgren, Harris, and
Robinson (2002) and Lofgren and D�az-Bonilla (2007). It is based on a 2005
social accounting matrix for Argentina with 24 activities and 26 commodities
(table 12.1). This disaggregation matches as closely as possible the disaggregation
of the global Linkage model. The national CGE model includes three institu-
tions: a representative household, government, and the rest of the world. There
are eight factors, including six labor categories: men and women each divided
across unskilled, semiskilled, and skilled workers. We consider land and capital
partly sector specific and partly mobile across sectors. Land only moves within
the primary agricultural sectors. In the model and database, we consider several
Table 12.1. Components of the Social Accounting Matrix,
Argentina, 2005
Sectors (26)
Primary Processed food Other manufactures
1 Cereals 10 Meat 18 Textiles and apparel
2 Vegetables and fruits 11 Oils and fats 19 Petroleum refining
3 Oilseeds 12 Dairy products 20 Chemical products
4 Other crops 13 Sugar 21 Mineral products
5 Sugarcane and beets 14 Flour, bakery, and pasta 22 Metal products
6 Livestock, milk, and wool 15 Feed products 23 Machinery and equipment
7 Other primary nonagriculture 16 Other processed food 24 Vehicles
8 Mining 17 Beverages and tobacco 25 Other manufacturing
9 Oil 26 Services
Factors (8) Institutions (3) Taxes (9)
1 Unskilled labor, men 1 Household 1 Value added tax
2 Unskilled labor, women 2 Government 2 Fuel tax
3 Semiskilled labor, men 3 Rest of the world 3 Financial services tax
4 Semiskilled labor, women 4 Export tax
5 Skilled labor, men 5 Tariffs
6 Skilled labor, women 6 Turnover tax
7 Capital, specific and mobile 7 Taxes on products
8 Land, mobile only across agriculture 8 Income tax
9 Factor tax
Savings and investment (1)
1 Savings and investment
Source: Prepared by the authors.
368 Agricultural Price Distortions, Inequality, and Poverty
tax instruments, including export taxes, income taxes, and the value added tax.
The modeling of the value added tax incorporates rebates for intermediate
inputs and investment purchases; so, there is no cascading effect on the prices of
the taxes on intermediate goods.
While many CGE models are run under a full-employment specification, our
modeling of the labor market allows for endogenous unemployment. This is
described through a complementary slackness condition for unemployment and
wages. As the economy grows, if the factor market is below full employment, then
the unemployment rate is the clearing variable for the market, that is, unemploy-
ment decreases for the necessary labor types until a minimum unemployment
rate is reached.9 At full employment (that is, the case wherein the minimum un-
employment rate is reached), the economy-wide wage variable adjusts to clear the
market.10
Because of a modeling characteristic that results from the methodology of the
World Bank project, the national model determines export supply behavior, but,
regarding demand by the rest of the world, changes are calculated using export
demand curves based on parameters and results from the global Linkage model
(see Horridge and Zhai 2006). Therefore, we do not adopt the small country
hypothesis on the export side. Regarding imports, however, we take the price
shocks directly from the global model on the assumption that import supply
functions for Argentina are flat (the small country assumption).
The closures for the model are as follows. In the case of the government, we
assume that government consumption and savings are fixed in real terms. This
means that the level of surplus (or deficit) in the base year is maintained
(Argentina had a surplus in 2005); it also means that one or more taxes are the
equilibrating variables. Here, we keep all tax rates at the level of the base year
except for the direct tax rate, which adjusts to equilibrate fiscal accounts, compen-
sating for the revenue lost through trade liberalization. Therefore, the simulations
are fiscally neutral. For the rest of the world, foreign savings--broadly defined to
include other nontraded items--are fixed exogenously, while the exchange rate
adjusts, that is, the level of trade balance that existed in the base year is maintained
in dollar terms, and the exchange rate acts as the equilibrating variable. For the
savings-investment balance, investment is driven by savings as in the global Link-
age model. Because public savings and foreign savings are fixed, investment arises
from changes in household savings behavior; specifically, the marginal propensity
to save or consume adjusts.
An understanding of the effects of trade policy reforms is aided by an aware-
ness of the country's trade structure and price distortions in the base year. The
trade structure is summarized in table 12.2. Agricultural and agroindustrial prod-
ucts represent about 45 percent of all exports, and petroleum and related products
Argentina 369
Table 12.2. External Trade Structure, Argentina, 2005
(percent)
Sectoral Sectoral Share of Share of
share of share of exports in imports in
total goods, total goods, sectoral domestic
Sector exports imports production production
Primary
Cereals 5.9 0.0 55.0 0.4
Vegetables and fruits 2.3 0.3 29.5 4.6
Oilseeds 4.8 0.5 30.9 3.8
Other crops 0.8 0.4 9.9 4.8
Sugarcane and beets n.a. n.a. n.a. n.a.
Livestock, milk, and wool 0.5 0.1 1.8 0.2
Other primary nonagriculture 0.2 0.2 8.4 9.1
Mining 3.4 2.9 29.2 23.7
Oil 5.8 0.7 25.3 3.7
Processed food
Meat 4.1 0.2 19.0 1.0
Oils and fats 16.3 0.1 86.8 4.0
Dairy products 1.5 0.1 13.7 0.6
Sugar 0.3 n.a. 19.5 n.a.
Flour, baking, and pasta 0.7 0.1 4.1 0.7
Feed products 0.1 0.1 7.7 3.9
Other processed food 4.6 1.2 31.6 9.5
Beverages and tobacco 1.2 0.2 7.7 1.0
Other manufactures
Textiles and apparel 3.7 4.2 18.5 18.5
Petroleum refining 8.7 2.6 39.4 14.6
Chemical products 9.2 19.4 26.8 40.4
Mineral products 0.5 1.0 6.2 10.2
Metal products 7.2 7.1 21.5 19.4
Machinery and equipment 4.3 33.6 19.6 62.8
Vehicles 8.1 15.9 42.4 56.0
Other manufacturing 5.9 9.2 14.2 18.4
Total 100.0 100.0 11.8 10.8
Source: Prepared by the authors.
Note: n.a. not applicable.
add another 15 percent. Export and import taxes (or subsidies) are reported in
table 12.3 for 2005, the base year for the simulations. To put these data in histori-
cal context, Sturzenegger and Salazni (2008) show that the lowering of export
taxes in the 1990s was reversed in the next decade such that farm prices for these
products during 2000�05 were about one-sixth below international prices;
370 Agricultural Price Distortions, Inequality, and Poverty
Table 12.3. Export Taxes and Import Tariffs, Argentina, 2005
(percent)
Sector Import tariffs Export taxes
Primary
Cereals 0.0 24.0
Vegetables and fruits 0.0 7.0
Oilseeds 0.0 29.0
Other crops 0.0 6.6
Sugarcane and beets 0.0 0.0
Livestock, milk, and wool 0.0 7.8
Other primary nonagriculture 1.0 0.9
Mining 1.0 0.9
Oil 1.0 30.0
Processed food
Meat 0.0 4.4
Oils and fats 0.0 20.0
Dairy products 0.0 5.0
Sugar 0.0 2.0
Flour, bakery, and pasta 9.7 0.0
Feed products 6.4 0.0
Other processed food 7.7 0.0
Beverages and tobacco 11.7 0.0
Other manufactures
Textiles and apparel 13.2 0.3
Petroleum refining 0.9 0.9
Chemical products 7.6 0.9
Mineral products 8.1 0.9
Metal products 10.0 0.9
Machinery and equipment 11.1 0.9
Vehicles 13.8 0.9
Other manufacturing 10.4 0.9
Sources: For export taxes: Anderson and Valenzuela (2008); for import tariffs: the social accounting
matrix for Argentina in 2005 prepared by the authors.
meanwhile, nominal assistance rates for manufacturing, which were high up until
the 1970s, have since come down gradually.
The microsimulation model
We calculate the results for poverty and inequality at the microlevel by linking the
CGE model with a microsimulation model. We use the two in a sequential top-
down fashion. Thus, the CGE model communicates with the microsimulation
Argentina 371
model by generating a vector of prices, wages, and aggregate employment vari-
ables, such as labor demand by sector and the unemployment rate. The function-
ing of the labor market therefore plays an important role; the CGE determines the
changes in employment by factor type and sector and the changes in factor and
product prices that we then use for the microsimulations.
We use the Permanent Household Survey (Encuesta Permanente de Hogares)
(EPH), the main household survey in Argentina, to build the microsimulation
model. The EPH is carried out by the National Institute of Statistics and Censuses
(INDEC 2006a). It covers all 31 urban areas with more than 100,000 inhabitants.
These are home to 71 percent of the urban population in the country. Because
urban areas account for 87 percent of the total population in Argentina, the EPH
sample represents around 62 percent of the total population. The EPH contains
information on individual sociodemographic characteristics, employment status,
hours of work, wages, family incomes, types of jobs, education, and migration sta-
tus. There is no alternative source of household data in Argentina. We have made no
attempt to reconcile the household survey data with the national accounts. Instead,
the results from the CGE model are transmitted to the microsimulation model as
percentage deviations from base values. The productive sectors in the EPH are
divided into 10 categories (agriculture; mining; food, beverages, and tobacco; tex-
tiles; petrochemicals; metals; machinery; vehicles; other manufactures; and ser-
vices), and the results from the CGE simulations are adjusted to the sectors of the
EPH for the transmission of changes in the relevant variables.
INDEC (2006b) calculates the basic food basket and the total basic consump-
tion basket that determine the extreme poverty line (the "indigence line") and the
moderate poverty line (the "poverty line"), respectively. An Engel coefficient cap-
tures the relationship between the extreme and moderate poverty lines calculated
by the INDEC using the two baskets. Each CGE simulation generates a new level
of economy-wide prices for commodities, which lead to changes in the cost of the
basic food basket and therefore in the level of the extreme poverty line. The value
for the new moderate poverty line is computed by assuming that the Engel coeffi-
cient remains constant. These changes in the poverty lines are captured in the last
step of the microsimulation methodology (see below) and affect the final esti-
mated moderate poverty and extreme poverty rates.
We introduce the labor market results from the CGE model into the microsim-
ulations to produce a counterfactual labor income for each individual in the
household survey. We then recalculate household per capita income and compute
the new poverty and income inequality results.
There are two broad approaches to capturing the labor market changes
through microsimulations. In one case, the program selects at random (with mul-
tiple repetitions) from the corresponding labor groups the individuals who will
change sectors and assigns wages to new workers according to parameters for the
372 Agricultural Price Distortions, Inequality, and Poverty
average groups. This approach is an extension of the earnings inequality method-
ology developed by Almeida dos Reis and Paes de Barros (1991).
The second general approach is based on econometric techniques. In this
approach, one determines the movement of workers across sectors by econometrically
estimated probabilities in a sectoral choice model, while one determines the new
wages of workers in an econometric model of wage earnings (for example, see D�az-
Bonilla 2005, D�az-Bonilla et al. 2006). In both approaches, the new wage and employ-
ment levels for each individual result in recalculated household per capita incomes
that are then used to determine the new poverty and income distribution results.11
In this chapter, we use a combination of both approaches. The movement of work-
ers across sectors is estimated through repeated sampling as in the first approach,
while the assignment of wages depends on an econometrically estimated equation for
the base year. Because the data do not record market wages for an individual who is
not working, the human capital theory of Mincer (1962) leads to estimates of wages as
a function of human capital variables (for example, experience and education). As in
the second approach, a series of wage regressions estimates the sector-specific poten-
tial wage for each person according to his or her personal characteristics.
The labor market variables and procedures that link the CGE model with the
microsimulations are as follows. U in the tables containing the microsimulations
results (see elsewhere below) refers to the impact of changes in the unemployment
rate. This effect is simulated by changing the labor status of the active population
in the EPH sample based on the results from the CGE model. For instance, if,
according to the CGE simulations, unemployment decreases at the same time that
employment increases for, say, semiskilled men in sector A, the microsimulation
program hires randomly among the unemployed semiskilled men in the EPH
sample. Individual incomes for the newly employed are assigned based on the
characteristics of the workers (for example, educational level) by using the coeffi-
cients of a Mincer equation estimated for these individuals employed in the base
year (see above). If the CGE simulations indicate a decrease in employment for a
specific labor category and sector, the microsimulation program fires the equiva-
lent share of the workers according to the type of labor and sector, and the coun-
terfactual income for these newly unemployed is zero.
The impact of changes in the sectoral structure of employment is indicated as S
in the tables containing the results (see below). This effect is simulated by changing
the sectoral composition of employment. For those individuals who move from one
sector to another, we simulate a counterfactual labor income based on the charac-
teristics of the individuals and on their new sector of employment by drawing on a
Mincer equation estimated for the base year with sectoral dummies.
The impact of changes in relative wages is indicated by W1. Wages in a sector
are adjusted according to the changes from the CGE simulations, but the aggregate
average wage for the economy is held constant. The impact of the change in the
Argentina 373
aggregate average wage for the economy, W2, is simulated by changing all labor
incomes in all sectors by the same proportion based on the changes from the CGE
simulations. All the above steps are then repeated several times and averaged.
PL is the impact of changes in the poverty line. The official extreme poverty (or
indigence) line is recalculated for each scenario based on the changes in the CGE
simulations for the prices of the commodities used to compute the extreme
poverty line (only food items). The value for the official moderate poverty line is
then computed on the assumption that the Engel coefficient stays fixed at base
year values.12
Model Simulations and Results
This section describes our main simulations. We then present, first, the overall
economic effects of liberalization and, second, the impacts of liberalization on
poverty and inequality. Unless otherwise stated (for specific simulations), the clo-
sure rules for the government, the rest of the world, and the savings and invest-
ment balance are the same across all simulations.
Simulations
As with the other chapters in this volume, our interest in this case study is in
achieving a better understanding of the impact of agricultural and trade policies
on poverty and inequality in a specific country. In the base year, no global or
domestic liberalization is imposed; rather, the model is calibrated to the actual sit-
uation of the country, Argentina, in 2005.
A model scenario involving the global liberalization of all goods markets may
provide estimates of bottom-line impacts. On its own, however, such a simulation
does not reveal the relative importance of various policies at home and abroad in
generating these estimated impacts. Such differentiated analysis requires that the
results be disaggregated in various ways. We thus also separately analyze the con-
tribution of agricultural reform alone because, in trade negotiations, this contri-
bution is negotiated separately.
Currently, export taxes are not subject to negotiation in the World Trade
Organization (WTO); so, we study a reform in all goods, as well as in agriculture
alone, but without reforming export taxes. (We label the related simulations WTO
for this reason only, not because they represent the specific negotiating alterna-
tives we discuss.)
We next look separately at rest-of-the-world versus own-country policy reform
with and without nonagricultural reform.
Finally, because, in the case of Argentina, the country's agricultural (and min-
eral and energy) policies are dominated by export taxes, we examine the effects of
374 Agricultural Price Distortions, Inequality, and Poverty
the unilateral removal by the country of its export tax regime for all goods, as well
as for farm products only. We first calculate a base scenario for 2005 and then run
the following 10 simulations:
� Sim 1, GLOB: This involves the removal of all agricultural subsidies and all import
and export taxes on goods trade in the rest of the world and in Argentina.
� Sim 2, GLOBag: This is the same as Sim 1 except that only agricultural reform
is involved, that is, domestic, import, and export taxes and subsidies are
removed only on agricultural products.
� Sim 3, WTO: This is the same as Sim 1 except that export taxes are not removed.
� Sim 4, WTOag: This is the same as Sim 3 except that only agricultural reform is
involved.
� Sim 5, ROW: This is the same as Sim 1 except that it covers only the rest of the
world, that is, without Argentina. Because it does not include Argentina, export
taxes are still imposed in Argentina in this simulation.
� Sim 6, ROWag: This is the same as Sim 5 except that it covers agricultural
reform alone.
� Sim 7, ARG: This is the same as Sim 1 except that it covers Argentina alone.
� Sim 8, ARGag: This is the same as Sim 7 except that it covers only agricultural
reform.
� Sim 9, ARG-ex: This is the same as Sim 7 except that only export taxes are
removed in all categories of goods and services.
� Sim 10, ARGag-ex: This is the same as Sim 9 except that it covers only agricul-
tural reform.
Overall economic results
We first look at the impact of the reforms on macroeconomic variables and factor
markets. Various indicators are summarized in table 12.4 for the baseline and for
our 10 simulations. In particular, it is important to examine the interaction of the
changes in the poverty line and in unemployment because these may have oppos-
ing impacts on poverty.
The results show a negative effect on the poverty line in all simulations, that is,
the poverty line increases relative to the base year because of inflation in con-
sumer prices. Under a scenario of global liberalization that removes all import
and export taxes and agricultural subsidies (GLOB and GLOBag), the poverty line
increases by more than 6 percent over the base year. The impact on the poverty
line is strongest in this simulation because of the combination of increases in the
world prices of agricultural goods (from global liberalization) and the elimination
of export taxes, which boosts domestic food prices. Although the real exchange
rate, which is defined in the model as the price of tradables over nontradables,
Table 12.4. Aggregate Simulation Results of Prospective Liberalization, Argentina
Indicator Base GLOB GLOBag WTO WTOag ROW ROWag ARG ARGag ARG-ex ARGag-ex
a
GDP factor cost, LCU, billions 4,395.0 0.2 0.8 1.5 0.2 0.3 0.2 0.4 0.9 1.7 0.9
Household consumption, LCU, billionsa 3,263.0 0.5 0.0 2.1 0.9 1.0 0.9 0.5 0.8 1.7 0.8
Poverty linea n.a. 6.3 6.0 2.1 1.2 1.3 1.2 4.9 4.8 4.1 4.8
Real exchange ratea 1.0 4.6 6.3 0.5 2.9 2.0 2.9 2.7 3.5 5.3 3.5
Terms of tradea n.a. 0.1 1.6 1.0 2.0 2.3 2.0 2.2 0.6 1.3 0.6
Unemployment rate, %b 12.3 0.4 0.8 2.6 0.4 0.6 0.4 0.1 1.1 2.4 1.1
Unskilled labor, women 29.4 0.4 0.7 2.4 0.3 0.6 0.3 0.1 0.9 1.9 0.9
Semiskilled labor, women 18.8 0.1 1.0 2.4 0.2 0.4 0.2 0.4 1.1 2.4 1.1
Skilled labor, women 5.1 0.4 1.0 2.6 0.1 0.4 0.1 0.1 1.1 2.4 1.1
Unskilled labor, men 14.3 0.4 0.6 3.0 0.6 0.8 0.6 0.2 1.1 2.5 1.1
Semiskilled labor, men 9.4 0.0 1.0 2.8 0.4 0.5 0.4 0.4 1.3 2.8 1.3
Skilled labor, men 4.1 1.1 0.5 1.6 0.5 0.5 0.5 0.7 0.9 2.0 0.9
Returns to factorsa
Unskilled labor, women n.a. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Semiskilled labor, women n.a. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Skilled labor, women n.a. 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Unskilled labor, men n.a. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Semiskilled labor, men n.a. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Skilled labor, men n.a. 0.0 0.0 1.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Land n.a. 62.4 59.3 20.6 12.1 12.9 12.1 46.2 44.6 37.3 44.6
Capital, specific, agriculture n.a. 79.1 76.3 18.4 10.7 11.2 10.7 63.8 62.2 53.9 62.2
Capital, specific, nonagriculture n.a. 2.6 3.1 1.6 0.7 0.6 0.7 3.5 2.4 0.9 2.4
Capital, mobile n.a. 5.4 1.4 2.7 0.1 0.2 0.1 5.1 1.2 2.4 1.2
Source: CGE model simulations on Argentina by the authors.
Note: LCU local currency unit. n.a. not applicable.
375
a. Percent change from the base.
b. Percentage point difference from the base.
376 Agricultural Price Distortions, Inequality, and Poverty
declines significantly, this is not sufficient to compensate for the elimination of
export taxes, and the domestic prices of food therefore increase. However, the
results also show that the impact on the poverty line is smallest in those simula-
tions in which the export taxes in Argentina are not eliminated (WTO, WTOag,
ROW, and ROWag).
The GLOB and GLOBag simulations also show negative impacts on GDP, as do
the simulations of unilateral liberalization by Argentina (ARG, ARGag, ARG-ex,
ARGag-ex). The negative impact on GDP is more significant in the unilateral sim-
ulations. This is especially the case if export taxes are eliminated, but not import
taxes.13 In addition, GDP decreases by 1.7 percent in the case of the liberalization
of all export taxes in Argentina (ARG-ex) and by 0.9 percent in the case of the
liberalization of only agricultural export taxes in Argentina (ARGag-ex). This
is partly a result of the assumption that Argentina has some degree of monopoly
power in its export markets, but also a consequence of our treatment of unem-
ployment in the model. The simulations without changes in export taxes, but that
include the elimination of import taxes and rest-of-the-world liberalization, all
appear to increase GDP, particularly the two simulations with more traditional
scenarios of national and world liberalization focusing on import taxes alone
(WTO and WTOag). In all cases, the liberalization of all goods, rather than the
liberalization of only agricultural products, generates a larger GDP increase (or a
smaller decrease).
To help us understand the sectoral changes, table 12.5 disaggregates GDP,
exports, and imports into agricultural and nonagricultural groups and also pres-
ents the value of food consumption by households.14
The declines in GDP generated by the elimination of export taxes result from
the negative impact on nonagricultural GDP, which outweighs the positive impact
on agricultural value added following the change in relative prices. The shift of
incentives toward agriculture affects production in other sectors through the
usual general equilibrium effect: it extracts capital and labor away from these
other sectors once the pool of employable, but unemployed workers has been
exhausted. The largest positive impact on agriculture occurs when domestic prices
are increased because of higher world prices through liberalization in the rest of
the world and because of the elimination of export taxes. Likewise, agricultural
exports expand and nonagricultural exports decline except when import tariffs
are eliminated for all products (when nonagricultural exports also increase, that
is, GLOB, WTO, and ARG). A consequence of this export orientation of agricul-
tural products is that household food consumption declines in all simulations in
which export taxes are eliminated (table 12.5, bottom row).
Table 12.4 also shows that unemployment increases in most of the simulations
that include the elimination of export taxes, with the exception of the GLOB
Table 12.5. Sectoral Simulation Results of Prospective Liberalization, Argentina
(percent change from the base)
Base, LCU,
Indicator billions GLOB GLOBag WTO WTOag ROW ROWag ARG ARGag ARG-ex ARGag-ex
GDP, agriculture 434 16.5 16.1 9.6 6.6 6.8 6.6 10.4 10.4 8.0 10.4
GDP, nonagriculture 3,961 2.0 2.7 0.6 0.5 0.4 0.5 1.6 2.1 2.8 2.1
Exports, agriculture 470 44.0 43.8 15.3 11.4 10.9 11.4 34.4 34.6 30.4 34.6
Imports, agriculture 15 113.5 104.2 0.2 3.0 1.0 3.0 115.5 116.5 113.0 116.5
Exports, nonagriculture 894 0.6 17.1 4.5 7.1 6.7 7.1 7.4 10.6 4.0 10.6
Imports, nonagriculture 1,025 19.4 6.3 12.6 2.2 2.3 2.2 17.1 3.9 7.0 3.9
Food consumption, households 641 0.7 1.1 1.5 0.6 0.7 0.6 1.3 1.6 2.3 1.6
Source: CGE model simulations on Argentina by the authors.
Note: LCU local currency unit.
377
378 Agricultural Price Distortions, Inequality, and Poverty
simulation, which combines complete liberalization in Argentina (that is, the
elimination of export and import taxes) with liberalization in the rest of the world
for all products (but not if the simulation covers only agricultural products, that
is, GLOBag). The worst impact on employment arises from the unilateral elimina-
tion of all export taxes in Argentina.
The negative impact on employment of the elimination of export taxes appears
more pronounced in the case of unskilled and semiskilled labor and is also more
pronounced in the case of women workers rather than men workers. In general,
these results are explained by the factor intensities of the sectors that expand and
contract after a given policy change (see the discussion below).
Meanwhile, total world liberalization that does not include export taxes (WTO
and WTOag) and total rest-of-the-world liberalization that does not eliminate
export taxes in Argentina (ROW and ROWag) reduce unemployment; this is so
particularly of the simulation WTO. Rest-of-the-world liberalization benefits
unskilled labor more and benefits workers who are men more than workers who
are women.
To understand the negative results on employment that occur because of the
elimination of export taxes, one needs to look at the sectoral composition of
export taxes (table 12.3) and the supply-side response. In terms of sectoral com-
position, the largest export taxes in Argentina are those levied on grains, oilseeds,
and oil. The elimination of these taxes increases the supply of products that have
three characteristics: first, they are less labor intensive than alternative activities;
second, they are inputs into other sectors; and, third, they have an outward orienta-
tion of sales that increases significantly after the removal of export taxes. Through
various and cumulative channels (discussed below), the consequence of these three
characteristics is less employment generally.
Without a full employment specification, the first characteristic (low labor
intensity) leads directly to declines in employment. In the case of agriculture in
particular, land is shifted from livestock, industrial crops, and other products
that tend to be more labor intensive to grains and oilseeds that are less labor inten-
sive. This negative employment effect at the primary level is reinforced because,
given that the commodities from these sectors are inputs into other production
activities, the increase in prices also affects other activities, in which production
and employment then tend to shrink as a result of the higher input prices.15
Primary products that used to be processed locally are now exported as raw mate-
rials, and the domestic industry declines. Oil, in particular, once it has been
processed and refined, is an input in most other activities, which are negatively
affected by higher oil prices. The outward orientation of the expanding activities
also appreciates the real exchange rate (see table 12.4), which generates a form of
Dutch Disease among the rest of the tradables.
Argentina 379
The positive impact of rest-of-the-world liberalization on GDP and employ-
ment is obvious. It creates more markets for Argentina's exports, and, because of
the specification of the labor market and the high unemployment that prevailed
in the base year, the expansion in aggregate demand leads to more production and
employment. In turn, the elimination of import taxes triggers a different mecha-
nism. It leads to more imports that, because of the closure assumption of fixed
foreign savings, also require more exports. Therefore, the real exchange rate
depreciates, which increases the production of tradables and expands employ-
ment. In general, the elimination of import taxes increases imports and moder-
ates the appreciation of the domestic currency in most simulations in which it
occurs and, in the WTO simulation for all products, even leads to a small overall
depreciation in the real exchange rate (table 12.4).
Note that the terms of trade tend to disfavor Argentina if export taxes are elimi-
nated (table 12.4). This is particularly the case if export taxes are eliminated unilat-
erally. The specification we follow in this project involves treating countries as
though they have a degree of market power (see elsewhere above). The elimination
of export taxes therefore depresses the world prices of the products the supply of
which expands significantly after such a policy change.16
The results in poverty and income distribution
The main results in poverty are shown in table 12.6, which is based on the national
extreme poverty line (the indigence line) and the national moderate poverty line
(the poverty line). Table 12.7 presents the results on the Gini coefficient of house-
hold per capita income and of labor income. Each of the simulation results shows
the observed base year poverty (or inequality) in the first row, followed by the
poverty and inequality results under each of the steps discussed elsewhere above in
the explanation of our microsimulation model. The second-to-last row shows the
cumulative effects of all of the changes (U, S, W1, W2, and, in the case of the poverty
line, PL) on the poverty and inequality results. The last row shows the results as the
percent change with respect to the observed base year value.
In terms of poverty, world trade liberalization for all goods (excluding export
taxes; simulation WTO) diminishes both moderate and extreme poverty in
Argentina. As we have indicated, there are two opposite effects at work, but the end
result here is a reduction in poverty. The increase in the poverty line (table 12.4)
affects the poor negatively. This increase is also the result of opposing forces:
although the elimination of import taxes lowers domestic prices, the rise in world
food prices and the devaluation of the domestic currency show effects in the opposite
direction. The negative impact of a higher poverty line, however, is more than offset
by a strong employment effect. Table 12.4 shows that the WTO trade simulation is
380
Table 12.6. Poverty Effects of Prospective Liberalization, Argentina
(percent of the headcount)
Effect Base GLOB GLOBag WTO WTOag ROW ROWag ARG ARGag ARG-ex ARGag-ex
a. Extreme poverty line
Observed 12.7 12.7 12.7 12.7 12.7 12.7 12.7 12.7 12.7 12.7 12.7
U 12.7 12.6 13.0 11.9 12.6 12.5 12.6 12.7 13.1 13.7 13.1
U S 12.7 12.6 13.0 11.9 12.6 12.5 12.6 12.7 13.1 13.7 13.1
U S W1 12.7 12.6 13.0 11.9 12.6 12.5 12.6 12.7 13.1 13.7 13.1
U S W1 W2 12.7 12.6 13.0 11.9 12.6 12.5 12.6 12.7 13.1 13.7 13.1
U S W1 W2 PL 12.7 13.7 14.2 12.2 12.8 12.7 12.8 13.6 14.0 14.4 14.0
Change from the base, % n.a. 8.5 11.8 3.4 1.1 0.6 1.1 7.5 10.9 14.0 10.9
b. Moderate poverty line
Observed 34.2 34.2 34.2 34.2 34.2 34.2 34.2 34.2 34.2 34.2 34.2
U 34.2 34.1 34.7 33.0 34.1 34.0 34.1 34.3 34.8 35.6 34.8
U S 34.2 34.1 34.8 33.1 34.1 34.0 34.1 34.3 34.8 35.6 34.8
U S W1 34.2 34.1 34.8 33.1 34.1 34.0 34.1 34.3 34.8 35.6 34.8
U S W1 W2 34.2 34.1 34.8 33.1 34.1 34.0 34.1 34.3 34.8 35.6 34.8
U S W1 W2 PL 34.2 36.5 37.1 33.8 34.5 34.5 34.5 36.2 36.7 37.1 36.7
Change from the base, % n.a. 6.6 8.5 1.3 0.9 0.9 0.9 5.7 7.3 8.5 7.3
Source: CGE model simulations on Argentina by the authors.
Note: The net impact is the sum of the impacts of changes in five variables. U refers to the impacts arising from changes in the unemployment rate, S to impacts related to changes
in the sectoral structure of employment, W1 to changes in relative wages, W2 to changes in the aggregate average wage in the economy, and PL to changes in the poverty line.
n.a. not applicable.
Table 12.7. Income Inequality Effects of Prospective Liberalization, Argentina
(Gini coefficient)
Effect Base GLOB GLOBag WTO WTOag ROW ROWag ARG ARGag ARG-ex ARGag-ex
a. Household per capita income
Observed 0.499 0.499 0.499 0.499 0.499 0.499 0.499 0.499 0.499 0.499 0.499
U 0.499 0.499 0.500 0.495 0.498 0.498 0.498 0.499 0.500 0.502 0.500
U S 0.499 0.499 0.500 0.495 0.498 0.498 0.498 0.499 0.500 0.502 0.500
U S W1 0.499 0.499 0.500 0.495 0.498 0.498 0.498 0.499 0.500 0.502 0.500
U S W1 W2 0.499 0.499 0.500 0.495 0.498 0.498 0.498 0.499 0.500 0.502 0.500
Change from the base, % n.a. 0.000 0.200 0.700 0.100 0.200 0.100 0.100 0.300 0.600 0.300
b. Employed labor income
Observed 0.472 0.472 0.472 0.472 0.472 0.472 0.472 0.472 0.472 0.472 0.472
U 0.472 0.472 0.472 0.471 0.472 0.472 0.472 0.472 0.472 0.473 0.472
U S 0.472 0.472 0.472 0.471 0.472 0.472 0.472 0.472 0.472 0.473 0.472
U S W1 0.472 0.472 0.472 0.471 0.472 0.472 0.472 0.472 0.472 0.473 0.472
U S W1 W2 0.472 0.472 0.472 0.471 0.472 0.472 0.472 0.472 0.472 0.473 0.472
Change from the base, % n.a. 0.000 0.100 0.200 0.000 0.100 0.000 0.100 0.100 0.300 0.100
Source: CGE model simulations on Argentina by the authors.
Note: For an explanation of the symbols, see the note to table 12.6.
381
382 Agricultural Price Distortions, Inequality, and Poverty
the scenario that generates the largest decline in unemployment (2.6 percentage
points). Poverty therefore declines in this trade liberalization simulation. Meanwhile,
the same scenario of liberalization, but only in agriculture, does not reduce poverty.
In that case, the employment effect is far weaker and is more than offset by the
negative impact on poverty of the increase in the poverty line.
Liberalization in the rest of the world (ROW and ROWag) initially reduces
poverty slightly through the employment effect, but this is more than offset by the
increase in the poverty line because of higher world food prices, resulting in a small
increase in poverty overall (which is worse if only agriculture is liberalized,
ROWag).17 This suggests that the positive impact on poverty from the simulation
that includes the rest of the world and Argentina (WTO) is driven in good measure
by the domestic, not international, liberalization in nonagricultural goods.
If both own liberalization (exports and imports) and liberalization in the rest
of the world are considered (GLOB and GLOBag), poverty increases. In the case of
the liberalization of all products (GLOB), there is a positive employment effect on
poverty (though not if only agricultural products are considered), which is more
than offset by the increase in the poverty line.
The main difference in policies in GLOB and GLOBag compared with WTO
and ROW is that the export taxes in Argentina are also eliminated. In fact, all sim-
ulations that include the elimination of export taxes either on all goods or only for
agricultural goods show an increase in the poverty headcount. Particularly nega-
tive for the poverty headcount is the unilateral elimination of export taxes by
Argentina. The first negative impact arises from the unemployment effect, which,
as we have already indicated, results from a combination of a shift from the pro-
duction of greater value added goods to primary products that are less labor
intensive and a form of Dutch Disease in the nonagricultural sectors. A second
negative effect, which, in the simulations, is generally stronger than the unem-
ployment effect, results from the increase in the poverty line because of higher
food prices (see the line U S W1 W2 PL in all poverty simulations). In
general, the sectoral and wage effects do not make much of a difference in poverty
terms.
Table 12.7 shows the results in income distribution. Simulations that include
the elimination of export taxes increase income disparities slightly at the house-
hold level and also if only the labor incomes of the employed are considered.
Meanwhile, liberalization in the rest of the world and in Argentina that does not
involve changing export taxes (WTO and WTOag) and liberalization only in the
rest of the world (ROW) produce small, but positive effects that reduce inequality.
Land receives important increments in factor incomes (not shown here) in all
simulations involving the elimination of export taxes, which should increase
income inequality (see footnote 13 elsewhere above).
Argentina 383
At least within the context of a static framework, the simulation results suggest
that export taxes help reduce poverty and inequality, generate additional employ-
ment opportunities that the production and export of raw materials would not
have provided, and help support a more competitive exchange rate. The simula-
tions also imply that, under the large country assumption, the elimination of
export taxes negatively affects the country's terms of trade.18
The simulations reported here differ from the results in Nogu�s et al. (2007)
and Nogu�s (2008), who argue that poverty increases with the use of export taxes.
The discrepancies in the results are the consequence of the differences in the
methodological frameworks (see the discussion in Cicowiez, D�az-Bonilla, and
D�az-Bonilla 2009, appendix 2).
Fiscal and Growth Implications
Two other important issues that the simulations raise are the fiscal and growth
effects of the various liberalization scenarios. Regarding fiscal accounts, we run
our model with a balanced budget, and the increases in direct tax rates compen-
sate for any change in trade tax revenues. Panel a in table 12.8 shows the revenue
from taxes as a share of GDP in the various simulations, with a focus on trade and
direct (income) taxes. The simulations involving the elimination only of agricul-
tural export taxes still collect some export tax revenue from other products (about
0.5 percent of GDP, mainly from oil and energy commodities). The increase in
direct taxes needed to close the fiscal gap is somewhat more than the direct rev-
enues lost (2.2 percent of GDP) because government consumption is fixed in real
terms, which means that the nominal cost of running the government and, there-
fore, the potential need to collect additional taxes also changes across the simula-
tions. Moreover, because GDP declines, taxes increase somewhat as a share of
GDP (and vice-versa in the simulations in which GDP increases).19
It has also been suggested that, to counter the poverty effects of export taxes,
value added taxes might be reduced to compensate the poor for the higher food
prices. In addition, it has been argued that more targeted safety nets, such as con-
ditional cash transfers (such as the Familias Program in Argentina) or food
stamps of some sort, might be utilized to counter the negative impacts on poverty
of the elimination of export taxes. However, both suggestions would entail even
larger losses in fiscal revenue and the negative macroeconomic implications of
these losses. Moreover, while these remedies would address the poverty line (PL)
segment of the impact on poverty, they would not solve the employment issue
(U). An alternative involving raising the indirect (value added) tax rate (perhaps
only on nonfood items so as not to hurt the poor) may also adversely affect
employment.
384
Table 12.8. Fiscal and Real Investment Effects of Prospective Liberalization, Argentina
Indicator Base GLOB GLOBag WTO WTOag ROW ROWag ARG ARGag ARG-ex ARGag-ex
a. Trade and income taxesa
Import tax 1.5 0.0 1.5 0.0 1.5 1.5 1.5 0.0 1.5 1.6 1.5
Export tax 2.2 0.0 0.5 2.4 2.3 2.3 2.3 0.0 0.5 0.0 0.5
Direct (income) tax 7.5 11.2 9.2 8.4 7.3 7.2 7.3 11.3 9.3 10.1 9.3
b. Real investment
Change from the base, % 1,114.0b 4.2 2.4 3.5 1.6 1.6 1.6 2.6 0.8 0.6 0.8
As % of GDP at market prices 20.8 21.4 21.3 21.2 21.1 21.0 21.1 21.1 21.0 21.0 21.0
Source: CGE model simulations on Argentina by the authors.
a. As a percent of GDP. The total tax revenue is kept constant at 29 percent of GDP.
b. In billions of local currency units.
Argentina 385
Another important issue is the growth effects of export taxes. It may be argued
that, even if the actual effects are the ones reported here in the short term, the
elimination of export taxes would generate stronger growth in the medium to
longer term that may more than compensate for the immediate negative impact
on employment, the poverty line, and inequality. The simulations presented here
may offer some indication of the dynamics as time passes via the change in real
investment in the various simulations relative to the base year (table 12.8, panel b).
The simulations involving the unilateral elimination of export taxes alone (ARG-ex
and ARGag-ex) do increase real investment. Although the increase is less than
1 percent, this would accumulate over time and might become significant. In
counterargument, one might say that a more-diversified productive structure,
in which investments flow to nonagricultural sectors, may have a greater payoff in
higher economic growth and less poverty and inequality. These are all legitimate
claims worthy of empirical analysis, and they raise the issue of the nature of the
development path that would be more pro-poor in Argentina: one based mostly
on agriculture, or one with a more diversified economic structure.20 This has long
been a matter of policy debate in Argentina, with implications not only for
poverty, but also for income distribution given that the country's agrarian struc-
ture shows a greater presence of large holdings relative to many other developing
countries.
Final Comments
In this chapter, we have analyzed several simulations of trade liberalization at
home and abroad both for all products and only in agriculture, and we have esti-
mated the impacts of these liberalizations on poverty and inequality in Argentina.
Global trade liberalization of all products, but not including the elimination of
export taxes (the WTO simulation) reduces poverty and inequality. This result is
generated by the strong employment effects in this scenario, which are not
negated by the increases in the poverty line that also take place. However, if the
same liberalization is applied only to agricultural products, poverty and inequality
do not improve, but even deteriorate somewhat, mostly because the smaller (but
still positive) employment effect is now more than offset by the increase in the
poverty line. All other simulations, particularly those involving the elimination of
export taxes, affect poverty and inequality more negatively not only because the
poverty line increases as food prices rise, but also because the employment effects
are small or negative.
Should one then conclude that export taxes, especially on agricultural
products, are always good for poverty reduction? Such a conclusion would be pre-
mature. First, any statements in this regard must be tempered by the knowledge
386 Agricultural Price Distortions, Inequality, and Poverty
that the coverage of the EPH household survey is tilted toward urban areas.
Although the results from the CGE model, which obviously includes all sectors
and populations, show declines in overall employment and increases in the value
of the poverty line (all negative effects for the poor), it is not possible to glean
from these results what may happen if the rural population not covered by the
survey were to be included.
Second, the simulations do not say anything about the impact on the rest of the
world of higher agricultural prices or about the impact on poverty outside
Argentina that would arise from export taxes.21
Third, the model we use in our chapter has other limitations. Thus, it does not
include the medium- to long-term dynamic effects on production and technolog-
ical innovation that may result from policy reforms. Clearly, there remains much
scope for additional empirical economic analysis.
Notes
1. Thus, this policy was unrelated to the sharp increase in world prices in 2007�08, when numerous
developing countries that were exporters of agricultural products introduced temporary export con-
trols to reduce domestic food price increases.
2. Recently, the Argentine Congress approved a law that returned the retirement system to a pay-as-
you-go scheme, thus eliminating the middle-term transition deficit, but absorbing the longer-term lia-
bilities of the system.
3. Tariff escalation has been discussed at least since the Kennedy Round. The practice in high-
income countries of imposing high import taxes on processed goods and lower or no tariffs on pri-
mary products (thus granting a higher effective rate of protection to value added in the importing
country) reduces significantly the processing capability of developing countries. This places agroin-
dustrial production in developing countries at a disadvantage, tilting the export profile of developing
countries toward raw materials (Balassa and Michalopoulos 1986, D�az-Bonilla and Reca 2000).
4. The country has been in recession about 40 percent of the last half century because of recurrent
macroeconomic crises linked to overvalued exchange rates and fiscal and current account deficits that
have led to the accumulation of public and external debt.
5. The overall fiscal surplus for the consolidated public sector (central government and provinces)
averaged 1.6 percent of GDP during 2003�07 compared with an average annual deficit of 4.2 percent of
GDP during 1961�2002. The surplus of the current account of the balance of payments in 2007 was
2.8 percent of GDP, while, during 1976�2002, the country experienced an average annual deficit of
1.7 percent of GDP. The trade surplus for 2007 was more than 4 percent of GDP, against 1.4 percent dur-
ing 1976�2002. Boosted by high commodity prices in international markets, the value of exports in 2007
increased to about US$56 billion, nearly triple the 1990s average, while the value of imports jumped to
around US$43 billion, double the 1990s average. One of the consequences has been that the Central Bank
reserves exceeded US$50 billion at the end of the first quarter of 2008, or 16 percent of GDP.
6. After all, domestic producer prices (PD) result from the combination of export tax rates (TX), the
nominal exchange rate (ER), world prices (PW), and marketing costs and margins (MCM), as follows:
PD PW * ER * (1 TX MCM).
7. These are producer prices in pesos (as reported in FAOSTAT 2008), deflated by the consumer
price index in Argentina. It should be noted that current controversies about the changes in the calcu-
lation of the Argentine consumer price index do not affect figure 12.3, considering that the data
reported in FAOSTAT currently end in 2006.
Argentina 387
8. The behavior of relative prices is different now with respect to the period before 2002 when the
Convertibility Plan maintained a fixed one-to-one exchange rate between the peso and the U.S. dollar
and the internal terms of trade in agriculture basically followed a downward trend in international
prices to the lows of 2001. Real international agricultural prices recovered somewhat thereafter and
improved significantly in the second half of 2007. By early 2008, they were about 60 percent above the
levels in 2001; however, this means only that they were back to the levels of the mid-1990s. There is also
a debate now in Argentina over the real level of inflation. The real exchange rate shown in figure 12.4 is
based on official statistics. However, even if we use the more extreme estimates of inflation by private
analysts, the real exchange rate would still be clearly above the level of 2001 by about 60 percent,
instead of around 80 percent using official data.
9. We have run the model using two values for full employment, which is defined by the level of
unemployment below which any additional labor demand results in increases in wages rather than in
employment. These values are 2.5 percent and 5 percent. The results reported here correspond to the
lower value of unemployment (2.5 percent). The direction of our results does not differ much,
although the employment multiplier effects are somewhat stronger under the lower value for full
employment (2.5 percent) than under the alternative (5 percent) threshold.
10. A reviewer has argued that using employment as the adjustment variable is as extreme as the
full employment assumption whereby wages adjust. The key issue, however, is determining which
labor closure reflects the economic situation in Argentina more accurately considering that, during the
base year, unemployment was clearly above the frictional level. Also, we must note that the comple-
mentary slackness condition allows the labor closure to switch toward wage adjustments once the
frictional unemployment rate has been reached. We have also run the model with a full-employment
specification, and the results are qualitatively similar, such that poverty is affected by lower salaries
(instead of increases in unemployment) and higher food prices. Still, the two labor market closures are
not completely symmetrical given that the unemployment closure allows for larger changes in GDP.
Finally, the model may also be run with a wage curve, but the simulations, which do not differ much,
are not shown here.
11. The microsimulation model in D�az-Bonilla (2005) and D�az-Bonilla et al. (2006) has three
main components: a sectoral choice model, a model of wage earnings, and a summation of the new
wage and employment results for each household (from which the new poverty and income distribu-
tion results follow). Rather than on random selection, the sectoral choice component is based on the
estimation of a multinomial logit model that determines an individual's probability, given certain
characteristics, of working in each of the productive sectors; the multinomial logit model therefore
ranks who will move first into a growing sector. The second component of the econometric microsim-
ulation model estimates a wage regression model that determines the labor income received by a new
worker. If, instead, the macromodel determines that employment should decrease in a given sector,
then those individuals with the lowest probability of working in that sector exit first, and the newly
unemployed lose their wages.
12. We have also run the microsimulations with an additional effect resulting from other factor
incomes (land and nonhuman capital) that we do not report here. The nonlabor income data from the
EPH are weak; they cannot be considered to represent appropriately the distribution of nonlabor
incomes and factors across households. In any case, given that the poor do not have much nonlabor
income, the introduction of this adjustment does not change the impact on poverty, although it does
have an impact on the results on income distribution.
13. In the protection database used in the global model and applied to the Argentina model, the
agricultural import tariffs in Argentina are negligible, and the results on the elimination of agricultural
import taxes only are therefore also small and are not reported. However, the direction of the results on
the elimination of agricultural import taxes alone may be inferred by comparing the simulations in
which only export taxes are changed (ARGag-ex) with those in which export taxes and import tariffs
are considered jointly (ARGag).
14. As defined in the World Bank project, agricultural activities include agriculture and lightly
processed food, but exclude highly processed food, beverages, and tobacco, which are Global Trade
388 Agricultural Price Distortions, Inequality, and Poverty
Analysis Project sectors 25 (food products, not elsewhere classified) and 26 (beverages and tobacco
products). In our model, they correspond to the following sectors in table 12.1: (1) cereals; (2) vegeta-
bles and fruits; (3) oilseeds; (4) other crops; (5) sugarcane and beets; (6) livestock, milk, and wool; (10)
meat; (11) oils and fats; (12) dairy products; and (13) sugar.
15. For instance, cereals and oilseeds are inputs in flour mills, oilseed processors, beef and poultry
producers and processors, bakeries, and so on, and oil is an input in the refining industry, transporta-
tion, and a variety of energy and chemical industries that, in turn, are inputs in other downstream
activities.
16. We have also run the simulations with exogenously fixed world prices, that is, with the small
country assumption. The effects on unemployment and poverty are comparable in direction and
interpretation with the effects presented here. According to a tentative conclusion, which may require
additional analysis, the structure of the economy and the development pattern that ensues from the
elimination of export taxes--and not necessarily the large country assumption--drive the results in
poverty and income distribution discussed here.
17. In any case, given that Argentina does not change agriculture-related policies except for the
policies on export taxes in these simulations, the results of WTOag and ROWag are basically the same.
18. We have also run an optimal trade tax simulation based on the notion that, if a country has
market power internationally, there must be a positive trade tax rate that maximizes some indicator
of welfare for that country. Based on real GDP, the rates in the base year should be increased by about
80 percent, which would mean that, for example, the export tax rate on cereals should rise from 24 to
43 percent, on oilseeds from 29 to 52 percent, and on oil products from 20 to 36 percent. If the objec-
tive is to maximize employment, export taxes should be somewhat larger in this case than in the case
of GDP maximization. These numbers must be viewed with extreme caution, however, because of the
uncertainties about the shape of the demand curves assumed in the World Bank global model.
Additional work needs to be completed on the impact of the large country assumption, which may be
forcing a more extreme agricultural specialization than is warranted.
19. This result is in contrast to the result estimated by Nogu�s et al. (2007), who argue that a
nontrivial portion of these revenues may be recovered by maintaining the rates of other taxes at the
baseline levels. The partial equilibrium analysis of these authors still estimates a net loss in fiscal
revenues, but a far smaller one than is suggested by our simulations using a general equilibrium model.
Given the high debt-to-GDP ratio and the high public debt payments in Argentina, the negative
macroeconomic effects of a drop in tax revenue might be considerable.
20. G�mez Galvarriato and Williamson (2008) and Williamson (2008) provide historical perspec-
tives on the wisdom of countries exploiting the positive terms of trade in primary products and
specializing in these products versus maintaining a more diversified production structure.
21. In the case of Argentina, the devaluation and normalization of the real exchange rate within the
context of this country's history, although offset in part by export taxes, have delivered a strong
production and export response, as we argue (see also Cicowiez, D�az-Bonilla, and D�az-Bonilla 2009,
appendix 1). All else being equal, the increase in agricultural output that resulted from all the policy
reforms followed in Argentina during this decade (notwithstanding the inclusion of new export taxes)
should have reduced the upward pressures on agricultural prices in international markets.
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13
Brazil
Joaquim Bento de Souza Ferreira Filho
and Mark Horridge*
Brazil exhibits a high degree of income concentration, and this inequality has per-
sisted throughout the dramatic economic and political changes in the country in
the past 20 years. The resilience of this problem in income distribution has
attracted the attention of researchers inside and outside Brazil. Although increased
world trade offers many opportunities for the Brazilian economy to grow, the ques-
tion addressed in this study is: how much would growth induced by such trade
reform benefit the poor?
To answer this question, we provide a quantitative ex ante assessment using a
computable general equilibrium (CGE) model of Brazil tailored for income dis-
tribution and poverty analysis. The model also has a regional dimension, allowing
the comparison of effects across Brazil's 27 states. It builds on earlier studies by the
authors, for example, Ferreira Filho and Horridge (2006), that link national CGE
and microsimulation (MS) models to analyze the income distribution effects of
changes in trade policy. There are two distinctive features of the present analysis.
First, the World Bank global Linkage model provides an estimate of the external
terms of trade shock that would be caused by rest-of-the-world trade liberalization
(see van der Mensbrugghe 2005). Second, we use a full interregional (bottom-up)
CGE model of Brazil's 27 states based on the 2001 input-output table.1 We also
bring to bear new farm price distortion estimates for other developing countries
to assess the impact of rest-of-the-world trade reform on poverty and inequality
in Brazil.2
*The authors are grateful to Xiang Tao for research assistance; Harry de Gorter, Alessandro Olper, and
Gordon Rausser for discussions and insights on the issues; and Kym Anderson for excellent collabora-
tion and encouragement throughout the project.
391
392 Agricultural Price Distortions, Inequality, and Poverty
The next section offers background on previous, similar analyses, as well as
data on poverty and income distribution in Brazil. We then describe our method-
ology, discuss the relevant literature on the many approaches to the issues, and
present the model and the database. A discussion of our results follows. The
chapter ends with concluding remarks.
The Evolution of Poverty and Income
Distribution in Brazil
Although Brazil has many poor people, it is not, on average, a poor country. As many
as 77 percent of the world's people and 64 percent of nations have average incomes
lower than the average in Brazil. However, because of the particularly uneven distri-
bution of income, about 30 percent of Brazilians are poor. The share would be only
8 percent if the incomes were distributed as evenly in Brazil as they are in other coun-
tries at similar per capita income, according to Barros, Henriques, and Mendon�a
(2001). These authors show that, in 1999, about 53 million people, one-third of the
Brazilian population, were living in households with incomes below the poverty line
(down from 40 percent in 1977), while 14 percent were living in extreme poverty.
Whether measured as a share of the population or in terms of a poverty gap, Brazil's
poverty stabilized between the second half of the 1980s and 2001 at a lower level than
previously. The situation then began to change again, as we see below.
Barros and Mendon�a (1997) analyze the impact on poverty of the relationship
between economic growth and reductions in inequality in Brazil. They conclude
that an improvement in the distribution of income would be more effective for
poverty reduction than an economic growth that maintained the current pattern
of inequality. According to these authors, because of the high level of income
inequality in Brazil, it is possible to reduce poverty in the country dramatically
even without economic growth if the level of inequality in Brazil were to become
closer to the levels observed in a typical Latin American country.
Brazilian poverty also has an important regional dimension. According to a study
by Rocha (1998) on the 1981�95 period, the richer southeast of the country, which
accounted for 44 percent of the total population in 1995, had only 33 percent of the
poor. The corresponding figures were 15.4 percent for the south (8.2 percent of the
poor), and 6.8 percent for the central-west (5.2 percent of the poor). In the poorer
regions, in contrast, the share of the population is less than the share of the poor:
4.6 percent (9.3 percent of the poor) in the north and 29 percent (44 percent of the
poor) in the northeast, which is the poorest region in the country.
The behavior of wages and the allocation of labor throughout the trade liberal-
ization period in Brazil (1980�99) are analyzed by Green, Dickerson, and Arbache
(2001). They point out that wage inequality remained fairly constant in the 1980s
and 1990s, with a small peak in the mid-1980s. Trade liberalization had little
Brazil 393
egalitarian consequence in Brazil during that period, but the authors note the low
trade exposure of the Brazilian economy (around 13 percent in 1997), as well as
the low 8 percent share of workers who had completed tertiary studies.
The pattern of poverty in Brazil started to change in 2001. Barros et al. (2007a)
show that, while there was a 0.9 percent annual increase in national income dur-
ing 2001�05, the income of the richest fell: the annual increase in income of the
top decile (the richest 10 percent) and the top two deciles was 0.3 and 0.1 per-
cent, respectively, while income among the poorest decile grew at 8 percent a year.
There was thus a significant decline in the Gini coefficient, 4.6 percent, and a cor-
responding drop in poverty incidence (4.5 percent). The latter was caused mainly
by the decline in inequality and not to the rise in incomes. This is contrary to what
has been observed historically in Brazil.
This unusual pattern of poverty reduction has attracted the attention of many
experts and uncovered an important aspect of the problem in income distribution.
In dealing with this issue, Hoffmann (2006) finds that transfers from the federal gov-
ernment were one of the main determinants of the observed fall in poverty. Accord-
ing to Hoffmann, 31 percent of the drop in the Gini coefficient nationally in 2002�04
(87 percent of the drop in the northeast) and 86 percent of the reduction in poverty
were associated with the share of household income accounted for by transfers
through the Bolsa Familia Program (family grants), the main federal government
income transfer program.3 Thus, the recent improvements in poverty incidence in
Brazil are related to transfer programs and, so, may be regarded as a short-run rather
than permanent initiative. This highlights the importance of assessing, as we do in
this chapter, the role that might be played by market effects, such as through trade
reform, as a source of permanent gains in poverty reduction.
Methodology and Data
Although CGE models have long been used in poverty analysis, many have relied on a
single representative household in examining consumer behavior. However, this lim-
its the scope of the analysis of income distribution and poverty because there are no
observations of changes in intragroup income distribution. Recent CGE models take
account of several household types, often distinguishing them by income level. For
example, Harrison et al. (2003) use a multicountry model drawing on data from the
Global Trade Analysis Project and additional data on Brazil in which they recognize
10 urban and 10 rural household types according to income. Because they exhibit
varying expenditure shares and differences in incomes and sources of income, the
households are affected differently by economic changes. However, income or other
differences within a distinct group of households are ignored by Harrison et al.
Other approaches draw on MS techniques. In this case, a CGE model generates
aggregate changes that are used to update a large unit record database such as a
394 Agricultural Price Distortions, Inequality, and Poverty
household survey. This approach allows the model to take into account the full
detail in the household data and avoids prejudgments about the aggregation of
households into categories. Changes in the distribution of real income are com-
puted by comparing the unit record data before and after updating. Savard (2003)
points out that, in this approach, the causality usually runs from the CGE model
to the MS model and that there is no feedback between the models. Our method-
ology addresses this difficulty by constraining certain aggregate results (for
example, the aggregate household use of each good) from the MS model to equal,
corresponding variables in the CGE model.4 The main advantages of the two-
model approach (CGE and MS) are that it avoids the need to scale the microeco-
nomic data to match the aggregated macrodata; it may accommodate more
households in the MS model; and the MS model may incorporate discrete-choice
or integer behavior that might be difficult to incorporate in the CGE model alone.
The CGE model we use here, TERM-BR, is a static interregional model of
Brazil's economy. It is based on the TERM model of Australia (Horridge, Madden,
and Wittwer 2005).5 It consists of 27 separate CGE models (one for each Brazilian
state) that are linked by the markets for goods and factors. For each state, the
structure of the CGE model is fairly standard. Each industry and final demander
combines Brazilian and imported versions of each commodity to produce a user-
specific constant elasticity of substitution composite good. The household con-
sumption of these domestic-imported composites is modeled through the linear
expenditure system, while intermediate demand is Leontief (fixed input-output
coefficients). The industry demand for primary factors follows a constant elastic-
ity of substitution pattern, while labor is a constant elasticity of substitution func-
tion of 10 types of labor. The model distinguishes 41 single-product industries,
and the agricultural industry (called agriculture) distributes output between
11 agricultural commodities according to a constant elasticity of transformation
constraint. Export volumes are determined by constant-elasticity foreign demand
schedules.6
The state CGE models are linked by the trade in goods, which is underpinned
by large arrays of interstate trade that record, for each commodity, the source state
and the destination state, the values of the Brazilian and foreign goods trans-
ported, and the associated transport or trade margins.7 Thus consumers of, say,
vegetables in S�o Paulo substitute between vegetables produced in the 27 states
according to the relative prices of the vegetables under a constant elasticity of
substitution demand system.8
A variety of labor market closures are possible. For the simulations we report
here, we assume that the employment of each of the 10 occupational groups is
fixed nationally, but that labor migrates to states where real wages grow more
(based on a constant elasticity of transformation formulation).
Brazil 395
There are 27 states, 42 industries, 52 commodities, and 10 labor types, and the
model thus represents around 1.5 million nonlinear equations. We have solved it
using Gempack software. The CGE model is calibrated with data from two main
sources: the 2001 Brazilian input-output matrix (IBGE 2001a) and shares derived
from the Brazilian census of agriculture (IBGE 1996a) and the Brazilian survey of
municipal agricultural production (IBGE 2001b).9
On the income-generation side of the model, workers are divided into 10 occu-
pation or skill categories according to wages. The wage groups are then assigned to
each state industry in the model. Together with revenues from other endowments
(capital and land rents), these wages generate household incomes. Each activity
relies on a particular mix of the 10 labor categories. Any shift in the activity level
alters employment by sector and state. This drives changes in poverty and income
distribution. Using data of the consumer expenditure survey (IBGE 1996b), we
extend the CGE model to cover 270 different expenditure patterns composed of
10 different income classes in 27 states. In this way, the expenditure side of the MS
data set is incorporated entirely within the main CGE model.
There are two main sources of information for the household MS model: the
national household sample survey (IBGE 2001a) and the consumer expenditure
survey (IBGE 1996b). The household sample survey contains information about
households and persons and has produced a total of 331,263 records. As the main
output, the survey extracts information on wages by industry and state, as well as
other personal characteristics such as years of schooling, gender, age, position in
the family, and other socioeconomic details. The consumer expenditure survey
covers 11 major metropolitan areas in Brazil, that is, only urban areas. The main
information drawn from this survey, which provides the structure of the con-
sumption bundle in 1996 among 16,014 households, revolves around expenditure
patterns among 10 separate income groups in the 11 metropolitan areas. We
assign one such pattern to each household in the household sample survey
according to our income groups in the CGE model. We also map the 11 metropol-
itan areas in the consumer expenditure survey to the larger set of 27 CGE states.
The procedure for running the models
The model consists of two main parts: a CGE model and a household MS model
(see elsewhere above). We run the models sequentially. We assure consistency
between the two models by constraining the MS model to agree with the CGE
model. The CGE model is sufficiently detailed, and its categories and data are suf-
ficiently similar to the categories and data of the MS model so that the CGE model
closely predicts the MS aggregate behavior that is also included in the CGE model
(such as household demand or labor supply). The role of the MS model is to
396 Agricultural Price Distortions, Inequality, and Poverty
provide extra information about, for example, the variance in income within
income groups or the incidence of price and wage changes among groups not
identified by the CGE model, such as groups defined by ethnicity, educational
level, or family status. To conform with the structure of the global Linkage model,
labor supplies are fixed. Furthermore, we assign one of the 270 expenditure pat-
terns identified in the main CGE model to each household in the microdata set.
There is thus little scope for the MS model to disagree with the CGE model.
We begin our simulation with a set of trade shocks generated by simulations
from the World Bank's Linkage model involving the elimination of distortions to
agricultural trade or all merchandise trade outside Brazil. These shocks consist of
changes in import prices and in export demand. The changes in export demand
are implemented in the Brazil CGE model via vertical shifts in the export demand
curves facing Brazil. The results of the applied trade shocks are generated for
52 commodities, 42 industries, 10 households, and 10 labor occupations, all of
which vary across the 27 states.
Next, we use the results from the CGE model to update the MS model.
Initially, this involves updating data on the wages of and the hours worked by the
263,938 workers in the sample. These changes have geographical and sectoral indus-
try dimensions (27 states, 42 industries). The model then relocates jobs according to
changes in labor demand.10 We accomplish this by changing the weight of each
worker in the household sample survey to mimic the change in employment (see
the appendix in Ferreira Filho and Horridge 2009 for details). In this approach, job
relocation occurs through a process that may have considerable impact on the vari-
ance in incomes within the groups, even though it may have little effect on the
distribution of wages among the 270 household groups identified in the CGE model.
Although the changes in the labor market are simulated for each adult in the
labor force, the changes in expenditures and in poverty are tracked back to the
households. A key in the household sample survey links persons to households
that contain dependents and one or more adults who are employed in a particular
sector and occupation or who are unemployed. In the model, it is possible to
recompose changes in household incomes from changes in the wages of individu-
als. This is an important aspect of the model because it is likely that variations in
family incomes are cushioned by this procedure. For example, if one person in a
household loses his job, but another in the same household obtains a job, house-
hold income may change little or even increase. Because households are the
expenditure units in the model, we would expect household spending variations
to be smoothed by this income pooling effect. However, the loss of a job will
increase poverty more if the displaced worker is the sole earner in the household.
National employment in each skill type is fixed, but shifting industry outputs
redistribute the jobs among households.
Brazil 397
The base year picture
Our description of poverty and income inequality in Brazil is extended in this section.
The base year for the analysis is 2001. The aggregate indicators of poverty and income
inequality for that year are summarized in table 13.1. The rows correspond to house-
hold income groups, which are joined according to the consumer expenditure survey
definitions in a footnote to the table, such that POF[1] is the lowest income group and
POF[10], the highest. The first five income groups, while accounting for 53 percent of
the total population of Brazil, receive only 17 percent of total income. Meanwhile, the
highest income group accounts for 11 percent of the population and 46 percent of total
income.The Gini coefficient associated with income distribution in Brazil in 2001,cal-
culated on an equivalent household basis, is 0.58.11 This means that income distribu-
tion in Brazil is among the most unequal income distributions in the world.
The unemployment rate is also higher among the poor. This is relevant in the
modeling. Obtaining a job is probably the main element in lifting people out of
poverty; hence, the importance of allowing the poverty model to capture the exis-
tence of a switching regime (from unemployment to employment), as well as the
information on changes in wages. The unemployment rate is 33 percent among per-
sons above 15 years of age in the lowest income group, but only 4 percent among the
richest income group (table 13.1, column 4). The share of white people in the
groups also increases considerably as household income rises, while the share of
children decreases markedly (table 13.1, columns 5 and 6). Although our analysis
does not focus on these aspects, the MS approach allows us to measure the effects of
a policy change on groups not distinguished in the main CGE model.
The poverty line we use in our study is set at one-third of the average house-
hold income.12 According to this criterion, 31 percent of Brazilian households
were poor in 2001.13 This share consists of 96, 77, and 54 percent, respectively,
of the households in the first three income groups (table 13.1, column 7) or
35 million of the 112 million households in 2001.14
Table 13.1 also shows the contribution of each consumer expenditure survey
group to two indicators of poverty stressed by Foster, Greer, and Thorbecke
(1984). One indicator is the share of households living below the poverty line. In
this case, the two lowest income groups account for more than half the 31 percent
of households living below the poverty line (column 8). The other indicator is the
poverty gap, which is the share by which a group's average household income falls
below the poverty line. Among the first (poorest) income group, this share is
73 percent (column 10), which means that there must be large income increases
among the poor if the number of poor is to change significantly.
The poverty and inequality picture also has an important geographical dimen-
sion in Brazil. Economic activity, particularly manufacturing, is located mainly in
the southeast, while agriculture is more dispersed among regions outside the
398
Table 13.1. Poverty Indicators, by Poverty Group, Brazil, 2001
(percent)
Average Share of Share in all
Share in Share in household Unemploy- Share of Share of households households Share in
POF total total income relative ment whites in population below below Poverty poverty
groupa population income to POF[5] rate population under 15 poverty line poverty lineb gapc gapc
POF[1] 10.7 0.9 0.1 32.6 35.2 46.2 96.2 11.22 73.3 8.56
POF[2] 8.0 1.8 0.4 17.3 38.3 37.2 76.6 7.16 30.5 2.85
POF[3] 16.0 5.2 0.6 10.4 42.0 35.1 53.6 8.77 15.0 2.45
POF[4] 7.3 3.1 0.8 8.8 45.1 32.5 28.4 2.02 5.4 0.38
POF[5] 11.0 5.8 1.0 7.5 49.2 28.7 11.4 1.22 1.9 0.20
POF[6] 7.9 5.1 1.2 7.4 53.4 26.4 3.9 0.29 0.5 0.04
POF[7] 12.9 11.1 1.7 6.8 60.3 24.5 0.8 0.10 0.1 0.01
POF[8] 7.5 8.7 2.3 6.1 66.3 21.5 0.1 0.01 0.0 0.00
POF[9] 7.7 12.7 3.1 5.9 71.2 20.5 0.0 0.00 0.0 0.00
POF[10] 10.9 45.7 7.9 4.2 81.6 17.7 0.0 0.00 0.0 0.00
Sources: IBGE (1996b, 2001a).
a. POF consumer expenditure survey (see IBGE 1996b). POF[1] is the poorest household group; POF[10] is the richest group. POF[1] ranges from 0 to 2 times the
minimum wage, POF[2] from 2 to 3 times, POF[3] from 3 to 5 times, POF[4] from 5 to 6 times, POF[5] from 6 to 8 times, POF[6] from 8 to 10 times,
POF[7] from 10 to 15 times, POF[8] from 15 to 20 times, POF[9] from 20 to 30 times, and POF[10] more than 30 times the minimum wage. The minimum
wage in Brazil in 2001 was US$76 per month.
b. The national average share of households below the poverty line is 30.8 percent, the sum of column 8.
c. The poverty gap is the share by which the average household income of the group falls below the poverty line. The national average is 14.6 percent, which is the
sum of column 10.
Brazil 399
two big city-states (Rio de Janeiro and S�o Paulo). The map (figure 13.1) shows the
location of the states of Brazil, which are shaded according to share of households
living in poverty. The states in the north account for 8 percent of the total popula-
tion compared with 24 percent in the northeast, 45 percent in the southeast,
16 percent in the south, and 7 percent in the central-west (table 13.2). In the south-
east, the state of S�o Paulo alone accounts for 23 percent of the total population.
Table 13.2, column 5 shows the share of households in each state that are living
below the poverty line. The states in the northeast (states 8�16 in the table), plus the
states of Par� and Tocantins in the north have the largest shares, indicating that these
states are the poorest. However, if state population is taken into account, column 6
shows that the populous states of Bahia, Cear�, Minas Gerais, Pernambuco, and
Figure 13.1. The Share of the Poor, by State, Brazil, 2001
Roraima
Amap�
Rio Grande
do Norte
Amazonas Maranh�o Cear�
Par�
Para�ba
Piau�
Acre Pernambuco
Rond�nia Mato Tocantins Bahia Alagoas
Grosso
Goias Sergipe
Distrito
Federal Minas
Mato Gerais
Grosso Espirito
do Sul S�o Santo
Paulo Rio de
proportion living below
Janeiro
the poverty line Parana
0.14 (minimum Santa
0.24 Catarina
0.35 (median) Rio Grande
0.51
do Sul
0.58 (maximum)
Source: Author compilation.
Note: The states of Minas Gerais, Parana, Rio de Janeiro, Rio Grande do Sul, Santa Catarina, and S�o
Paulo account for 78 percent of GDP, 58 percent of the total population, and 37 percent of the poor.
400
Table 13.2. Poverty and Income Inequality by State, Brazil, 2001
Share in Share in total Share of Share of poor Contribution Average
total agricultural agriculture in households in to national poverty
State Regiona population GDP state GDP state population poverty gapb gapb
1. Rond�nia N 0.005 0.010 0.29 0.338 0.001 0.147
2. Acre N 0.002 0.004 0.30 0.356 0.000 0.176
3. Amazonas N 0.011 0.003 0.06 0.396 0.002 0.196
4. Roraima N 0.001 0.001 0.16 0.347 0.000 0.152
5. Par� N 0.023 0.031 0.25 0.425 0.005 0.194
6. Amap� N 0.003 0.002 0.13 0.151 0.000 0.069
7. Tocantins N 0.006 0.007 0.26 0.429 0.001 0.180
8. Maranh�o NE 0.029 0.014 0.23 0.579 0.008 0.288
9. Piau� NE 0.015 0.005 0.15 0.564 0.005 0.304
10. Cear� NE 0.042 0.010 0.07 0.540 0.011 0.267
11. Rio Grande do Norte NE 0.016 0.006 0.07 0.471 0.004 0.218
12. Para�ba NE 0.019 0.009 0.14 0.550 0.005 0.257
13. Pernambuco NE 0.045 0.016 0.09 0.512 0.011 0.248
14. Alagoas NE 0.015 0.012 0.24 0.577 0.004 0.289
15. Sergipe NE 0.010 0.002 0.05 0.503 0.002 0.239
16. Bahia NE 0.073 0.050 0.14 0.520 0.019 0.256
17. Minas Gerais SE 0.108 0.141 0.18 0.301 0.014 0.133
18. Esp�rito Santo SE 0.019 0.025 0.17 0.324 0.003 0.144
19. Rio de Janeiro SE 0.095 0.019 0.01 0.202 0.009 0.095
20. S�o Paulo SE 0.229 0.219 0.06 0.166 0.019 0.083
21. Paran� S 0.059 0.112 0.19 0.237 0.006 0.100
22. Santa Catarina S 0.034 0.076 0.18 0.136 0.002 0.055
23. Rio Grande do Sul S 0.067 0.084 0.12 0.179 0.005 0.073
24. Mato Grosso do Sul CW 0.013 0.034 0.41 0.289 0.002 0.120
25. Mato Grosso CW 0.015 0.053 0.46 0.251 0.002 0.106
26. Goi�s CW 0.031 0.046 0.23 0.300 0.004 0.126
27. Distrito Federal CW 0.013 0.006 0.02 0.219 0.001 0.106
Total All 1.000 1.000 0.10 0.308 0.145 0.145
Source: IBGE (2001a).
a. N north, NE northeast, SE southeast, S south, CW central-west.
b. The poverty gap is the share by which the average household income of the group falls below the poverty line.
401
402 Agricultural Price Distortions, Inequality, and Poverty
S�o Paulo contribute most to the Foster, Greer, and Thorbecke (1984) poverty gap
index.15 The last column in table 13.2 shows the proportion by which a state's aver-
age household income falls below the poverty line (the state poverty gap). The states
in the northeast, plus the states of Par� and Tocantins show the largest poverty gaps,
while two states in the south, Rio Grande do Sul and Santa Catarina, show the lowest
poverty gaps, followed closely by S�o Paulo.16
Table 13.3 contains information on the labor structure of the economy. In this
table, the sectoral wage bill is split into the model's 10 occupational groups. The
occupational groups are defined in terms of a unit wage ranking. More (less) skilled
Table 13.3. Share of Occupations in the Activity Labor Bill,
by Wage Group, Brazil, 2001
(percent)
Occupation, wage group
Sector 1 2 3 4 5 6 7 8 9 10 Total
Agriculture 40.5 30.2 5.8 6.0 5.2 3.3 3.7 1.8 1.9 1.6 100
Mineral extraction 12.0 19.4 6.8 6.9 8.4 6.1 12.8 9.9 10.8 6.9 100
Petroleum and gas
extraction, charcoal 0.0 0.0 0.0 0.9 0.9 6.1 16.1 12.1 22.8 41.1 100
Nonmetallic minerals 7.1 18.8 7.4 8.9 11.5 11.8 14.1 7.6 7.4 5.3 100
Iron products 1.9 6.8 4.0 6.3 10.2 9.7 22.7 14.0 15.4 9.1 100
Nonferrous metals 1.9 6.8 4.0 6.3 10.2 9.7 22.7 14.0 15.4 9.1 100
Other metals 1.9 6.8 4.0 6.3 10.2 9.7 22.7 14.0 15.4 9.1 100
Machines,
tractors, parts 0.5 4.6 1.9 4.8 6.8 9.0 19.6 17.2 16.8 18.8 100
Electric material 0.4 3.8 2.6 3.3 10.3 11.6 20.4 15.5 17.0 15.1 100
Electronic equipment 0.4 3.8 2.6 3.3 10.3 11.6 20.4 15.5 17.0 15.1 100
Automobiles 0.3 2.5 1.0 2.4 7.7 8.6 19.6 15.7 22.4 19.8 100
Other vehicles,
spare parts 0.3 2.5 1.0 2.4 7.7 8.6 19.6 15.7 22.4 19.8 100
Wood and furniture 8.2 11.7 6.6 8.8 12.4 11.9 16.6 9.3 9.6 5.0 100
Paper and graphics 2.3 7.8 3.7 6.2 8.4 8.1 18.7 13.0 16.7 15.1 100
Rubber industry 0.8 4.7 3.2 4.6 14.4 5.5 24.0 13.6 16.6 12.5 100
Chemical elements 2.1 7.8 3.0 4.2 9.1 11.8 14.2 15.6 16.4 15.8 100
Petroleum refining 0.5 1.5 2.7 0.3 9.0 5.7 13.1 7.2 10.5 49.5 100
Various chemicals 0.0 6.8 9.6 13.4 25.3 0.0 14.5 2.8 7.9 19.7 100
Pharmaceutical
performance 1.7 5.7 3.1 6.8 4.1 7.5 13.5 11.3 18.7 27.4 100
Plastics 1.6 6.3 2.3 8.5 12.8 12.1 24.6 10.3 9.0 12.6 100
Textiles 14.7 9.0 4.9 7.2 12.5 11.0 17.6 11.3 6.2 5.5 100
Apparel 3.2 17.3 7.5 15.1 16.1 9.7 15.7 5.4 4.5 5.5 100
Shoes and leather
products 4.1 16.2 6.5 13.5 18.2 13.0 14.4 5.7 4.8 3.6 100
Brazil 403
Table 13.3. Share of Occupations in the Activity Labor Bill,
by Wage Group, Brazil, 2001 (continued )
(percent)
Occupation, wage group
Sector 1 2 3 4 5 6 7 8 9 10 Total
Industrialized coffee 8.6 14.3 6.1 9.6 13.2 11.3 15.1 8.3 7.4 6.0 100
Vegetable processing 8.6 14.3 6.1 9.6 13.2 11.3 15.1 8.3 7.4 6.0 100
Meats 8.6 14.3 6.1 9.6 13.2 11.3 15.1 8.3 7.4 6.0 100
Dairy products 8.6 14.3 6.1 9.6 13.2 11.3 15.1 8.3 7.4 6.0 100
Sugar 8.6 14.3 6.1 9.6 13.2 11.3 15.1 8.3 7.4 6.0 100
Vegetable oils 8.6 14.3 6.1 9.6 13.2 11.3 15.1 8.3 7.4 6.0 100
Other agriculture 8.6 14.3 6.1 9.6 13.2 11.3 15.1 8.3 7.4 6.0 100
Other industrial
products 16.8 13.4 6.6 6.2 11.4 7.4 13.1 7.8 10.7 6.5 100
Public utilities
and services 1.7 17.5 5.3 8.6 7.1 6.0 12.9 12.2 14.2 14.5 100
Civil construction 6.3 13.4 8.6 10.1 12.5 9.0 20.2 9.6 6.9 3.4 100
Trade 10.0 14.2 6.6 8.2 10.7 8.2 15.1 8.3 10.0 8.7 100
Transport 4.6 7.0 4.4 4.7 7.5 7.1 19.0 16.1 18.1 11.6 100
Communication
services 1.4 4.6 2.4 5.1 7.9 9.4 18.6 13.9 17.2 19.4 100
Financial services 0.9 3.5 1.3 3.5 6.6 4.2 10.0 11.8 23.3 34.9 100
Household services 16.4 20.3 7.4 8.4 9.6 6.8 12.1 6.5 7.2 5.4 100
Enterprise services 2.9 8.1 4.3 5.7 8.1 6.4 13.0 8.6 15.7 27.2 100
Dwellings 2.0 4.3 2.7 4.8 9.9 6.3 17.1 8.8 18.4 25.7 100
Public administration 1.7 13.1 3.6 7.2 7.6 6.8 13.0 12.1 19.3 15.6 100
Nonmerchandise
private services 7.6 16.6 6.0 9.2 9.3 10.9 13.7 8.2 11.6 6.9 100
Source: IBGE (2001a).
workers are those in the highest (lowest) income groups. Agriculture is the activity
most dependent on unskilled labor, which absorbs 41 percent of the sector's labor
bill, while petroleum and gas extraction, as well as petroleum refining, is the most
intensive user of skilled labor. The 10th labor group accounts for more than 40 per-
cent of the wage payments for these activities; financial services follow, at 35 percent.
If labor inputs are measured in hours rather than in values, the concentration of
low-skill labor in agriculture would be even more pronounced.
Agriculture is also the sector that hires the largest share of unskilled labor in
Brazil, around 41 percent of total workers in wage group 1. The trade sector is the
second largest employer of this type of labor. The financial and other services
sectors hire the largest numbers of higher-wage workers. As a general rule, the
wage earnings of higher-wage occupations are concentrated in higher-income
households, while the wage earnings of lower-wage occupations are concentrated
404 Agricultural Price Distortions, Inequality, and Poverty
in lower-income households. Thus, most of the wages earned by workers in wage
group 1 accrue to the three poorest household groups, while all the workers in the
highest wage group are located in households in the eighth wage group or higher,
that is, the household income groups are positively correlated at a high level with
the occupational wage earning groups.
The Model Simulations and Results
In this section, we present the results from a liberalization scenario in which the rest
of the world, in addition to Brazil, removes all distortions in merchandise trade. In
the case of agriculture and lightly processed agriculture, the reforms involve the
removal of all trade taxes (import and export) and subsidies, the removal of all out-
put taxes and subsidies, and the removal of all farm input subsidies.17 Among other
nonagricultural goods, the reforms involve only the removal of all import tariffs and
export taxes. We analyze the results with the aid of a decomposition algorithm sug-
gested by Harrison, Horridge, and Pearson (2000) that allows the presentation of
the results of the total shocks and the decomposition of the results according to the
correspondent partial shocks, that is, liberalization in farm products in the rest of
the world (ROW ag), liberalization in nonagricultural goods in the rest of the world
(ROW nonag), liberalization in farm products in Brazil only (Brazil ag), and liberal-
ization in nonfarm goods in Brazil only (Brazil nonag).
Model closure
The closure we adopt for the CGE model of Brazil's economy aims to mimic the clo-
sure of the World Bank Linkage model that generated the foreign price and export
demand shocks. On the supply side, national employment by occupation is fixed.18
Real wage differentials drive labor migration among states.19 The model allows
industries to substitute between occupations according to relative wages. Similarly,
capital is fixed nationally, but is mobile across sectors and states (all rates of return
shift as one). The land stock in each state--land is used only in the agricultural and
mining sectors--is fixed.20 However, in the mining sectors (mineral extraction, as
well as petroleum and gas extraction), this stock is treated as a natural resources stock
and does not affect the price of agricultural land, which is restricted to agriculture.
Since agriculture is an activity that produces 11 products, land is allocated to these
competing products through relative prices, allowing the crop mix to change.
On the demand side, real government demands are fixed, while investment in
each state and sector follows the growth of the corresponding capital stock.21 A
fixed (nominal trade balance�GDP) ratio enforces the national budget balance,
which is accommodated by changes in real consumption. The trade balance drives
the level of absorption. The national consumer price index is the model's
Brazil 405
num�raire. A tax replacement mechanism is in force, allowing the direct tax rate
to adjust endogenously to maintain total (indirect, plus direct) government tax
collection unchanged after the elimination of trade taxes and subsidies. This
mechanism is the same as the one used in the Linkage model.
The CGE model results
The Brazilian economy has limited exposure to external trade. The shares of
exports and imports in total GDP were, respectively, 13.8 and 14.7 percent in the
2001 base year (up from 7.0 and 8.9 percent, respectively, in 1996). The signifi-
cance and structure of Brazilian external trade are summarized in table 13.4. The
border price and export demand shocks applied to the national CGE model,
Table 13.4. The Structure of External Trade, Brazil, 2001
(percent)
Sectoral Share of Share of Sectoral
share of total sectoral local market share Capital-
total output supplied by of total labor
Sector exports exported imports imports ratio
Coffee 0 0 0 0 0.72
Sugarcane 0 0 0 0 0.72
Paddy rice 0 0 2 0 0.72
Wheat 0 0 72 1 0.72
Soybeans 3 38 3 0 0.72
Cotton 0 0 0 0 0.72
Corn 1 16 2 0 0.72
Livestock 0 0 0 0 0.72
Raw milk 0 0 0 0 0.72
Poultry 0 0 1 0 0.72
Other agricultural
products 2 3 2 1 0.72
Mineral extraction 4 56 7 1 0.25
Petroleum and gas
extraction, charcoal 1 5 24 6 6.59
Nonmetallic minerals 1 7 4 1 0.71
Iron products 4 16 5 1 7.18
Nonferrous metals 3 19 12 2 3.80
Other metals 2 7 8 2 0.26
Machines, tractors,
parts 3 10 22 8 1.93
Electric material 2 14 29 5 0.68
Electronic equipment 3 36 56 10 2.15
(Table continues on the following page.)
406 Agricultural Price Distortions, Inequality, and Poverty
Table 13.4. The Structure of External Trade, Brazil, 2001 (continued )
(percent)
Sectoral Share of Share of Sectoral
share of total sectoral local market share Capital-
total output supplied by of total labor
Sector exports exported imports imports ratio
Automobiles 5 23 14 3 2.03
Other vehicles,
spare parts 9 41 25 7 0.75
Wood and furniture 3 21 3 0 0.53
Paper and graphics 3 11 5 1 1.20
Rubber industry 1 12 13 1 3.31
Chemical elements 1 10 18 3 6.84
Petroleum refining 5 7 13 10 21.68
Various chemicals 1 6 17 4 1.22
Pharmaceutical
performance 1 5 25 4 1.65
Plastics 1 6 11 1 0.51
Textiles 2 10 10 2 0.56
Apparel 0 2 2 0 0.39
Shoes and leather
products 4 63 7 0 1.31
Industrialized coffee 2 22 0 0 3.77
Vegetable processing 3 14 4 1 0.95
Meats 4 16 1 0 1.36
Dairy products 0 1 3 0 2.17
Sugar 3 37 0 0 3.50
Vegetable oils 4 29 2 0 5.53
Other agriculture 2 8 5 1 0.88
Other industrial
products 1 12 23 2 1.89
Public utilities and
services 0 0 3 1 1.77
Civil construction 0 0 0 0 4.09
Trade 1 3 4 1 0.16
Transport 6 14 10 4 0.04
Communication services 0 1 1 0 1.90
Financial services 1 1 2 1 0.38
Household services 3 4 7 5 0.10
Enterprise services 6 15 18 9 0.44
Dwellings 0 0 0 0 46.46
Public administration 1 1 1 2 0.00
Nonmerchandise
private services 0 0 0 0 0.00
Source: IBGE (2001c).
Brazil 407
shown in table 13.5, have been generated through a previous run of the World
Bank global Linkage model (see the appendix by van der Mensbrugghe,
Valenzuela, and Anderson). The border price effects in the Brazilian economy
have been transmitted to the Brazil CGE model through import price changes and
shifts in the demand schedules for Brazilian exports.22
Table 13.5. Exogenous Demand and Border Price Shocks Caused
by Prospective Global Trade Liberalization, Brazil
(percent change)
Vertical
Armington shift in the
elasticities, Import Export export
Linkage Import CIF FOB demand
Sector model tariffs prices prices schedule
Coffee 6.5 0.0 0.0 9.0 10.1
Sugarcane 5.4 0.0 0.0 0.0 0.0
Paddy rice 10.1 0.0 4.8 0.0 0.0
Wheat 8.9 0.3 2.8 7.1 9.3
Soybeans 4.9 0.0 1.5 8.1 2.2
Cotton 5.0 0.0 14.4 8.7 8.2
Corn 2.6 0.0 3.3 8.4 16.2
Livestock 3.9 0.0 1.1 8.9 9.3
Raw milk 7.3 0.0 0.0 0.0 0.0
Poultry 2.6 0.0 0.0 8.8 11.5
Other agricultural
products 3.7 2.5 2.2 8.7 4.5
Mineral extraction 1.8 1.7 2.6 5.6 10.2
Petroleum and gas
extraction, charcoal 10.4 0.0 2.6 5.6 10.2
Nonmetallic minerals 5.8 5.5 0.6 5.1 0.7
Iron products 5.9 5.2 0.6 5.1 0.7
Nonferrous metals 8.4 4.5 0.6 5.1 0.7
Other metals 7.5 8.4 0.6 5.1 0.7
Machines, tractors,
parts 8.6 7.4 0.6 5.1 0.7
Electric material 8.1 7.5 0.6 5.1 0.7
Electronic equipment 8.8 6.4 0.6 5.1 0.7
Automobiles 5.6 7.8 0.6 5.1 0.7
Other vehicles,
spare parts 8.6 5.5 0.6 5.1 0.7
Wood and furniture 6.8 7.4 0.6 5.1 0.7
(Table continues on the following page.)
408 Agricultural Price Distortions, Inequality, and Poverty
Table 13.5. Exogenous Demand and Border Price Shocks Caused by
Prospective Global Trade Liberalization, Brazil (continued )
(percent change)
Vertical
Armington shift in the
elasticities, Import Export export
Linkage Import CIF FOB demand
Sector model tariffs prices prices schedule
Paper and graphics 5.9 3.6 0.6 5.1 0.7
Rubber industry 6.6 8.4 0.6 5.1 0.7
Chemical elements 6.6 4.9 0.6 5.1 0.7
Petroleum refining 4.2 3.0 0.6 5.1 0.7
Various chemicals 6.6 5.8 0.6 5.1 0.7
Pharmaceutical
performance 6.6 4.5 0.6 5.1 0.7
Plastics 6.6 9.5 0.6 5.1 0.7
Textiles 7.5 11.4 0.1 5.4 1.2
Apparel 7.4 12.4 0.1 5.4 1.2
Shoes and leather
products 8.1 6.1 0.1 5.4 1.2
Industrialized coffee 2.3 1.5 7.3 6.1 25.5
Vegetable processing 4.0 2.8 5.9 6.1 25.4
Meats 8.4 1.8 3.7 7.7 25.4
Dairy products 7.3 2.7 10.5 7.0 38.9
Sugar 5.4 0.7 0.0 6.5 25.3
Vegetable oils 6.6 4.5 0.8 6.8 1.3
Other agriculture 3.8 5.1 7.3 6.1 25.5
Other industrial products 7.5 7.2 0.6 5.1 0.7
Public utilities and
services 5.6 0.0 0.2 5.9 0.7
Civil construction 3.8 0.0 0.2 5.9 0.7
Trade 3.8 1.8 0.2 5.9 0.7
Transport 3.8 0.0 0.2 5.9 0.7
Communication
services 3.8 1.2 0.2 5.9 0.7
Financial services 3.8 0.0 0.2 5.9 0.7
Household services 3.8 0.1 0.2 5.9 0.7
Enterprise services 3.8 0.0 0.2 5.9 0.7
Dwellings 3.8 0.0 0.2 5.9 0.7
Public administration 3.8 1.5 0.2 5.9 0.7
Nonmerchandise
private services 3.8 0.0 0.2 5.9 0.7
Source: Linkage model simulations (see the appendix by van der Mensbrugghe, Valenzuela, and Anderson).
Note: CIF cost, insurance, and freight. FOB free on board.
Brazil 409
An inspection of tables 13.4 and 13.5 gives an idea of the importance of these
shocks, combined with a view on the importance of each commodity in Brazil's
external trade. The exports are spread across many different commodities, and
there is no strong specialization. Primary agricultural products have a small share
(mostly soybeans) in total exports. Nonetheless, processed agricultural and agri-
cultural based exports, including wood and furniture, rubber, paper, textiles, and
apparel, account for a significant 30 percent share of total exports in the base year,
highlighting the importance of agriculture in the broader sense in the economy.
The significant products in terms of the shares of domestic production imported
are wheat, petroleum, machinery, electric materials, electronic equipment, and
chemical products. In terms of shares in total imports, the highest ranking sectors
are petroleum products (raw and refined), machinery, electric materials,
electronic equipment, and chemical products.
The agricultural sector is modeled as a multiproduction sector; it produces
11 commodities. Thus, the value-based ratio of capital to labor in table 13.4 is the
same for every agricultural product. The value of land is not included in the value
of capital, but, if it were, the value of the ratio of capital to labor in agriculture
would rise to 1.11.23
Agriculture--primary agriculture and livestock production--is the only sector
with a negative production tax in the database: 0.7 percent or 0.007 points in
the levels. To eliminate this tax, the scenario includes an increase of 0.007 points
in this tax rate. In the lightly processed sectors, there are production subsidies, and
the shocks on these sectors are meats ( 0.046), dairy products ( 0.047), sugar
( 0.048), and vegetable oils ( 0.046).
The national macroeconomic effects of this global liberalization shock are
shown in table 13.6. Because the closure fixes the total supply of all primary
factors (land, the 10 categories of labor, and capital), GDP shows only a slight
increase of 0.1 percent. The real exchange rate rises as a result of the shocks
(a revaluation), with corresponding gains in the external terms of trade. The
table also shows the subtotals associated with each partial shock. As one may
see, the liberalization of agricultural product markets in the rest of the world
generates the largest increase in real GDP and real household consumption in
Brazil.
Recall that land is used only by agriculture, while capital and the 10 types of
labor are fixed nationally, but mobile across sectors. As a result of the simulation,
the average (aggregated) rental return on capital rent increases by 0.7 percent.
Because total capital stocks and labor are fixed, expanding industries attract
capital and labor from contracting ones, driving up real wages by 1.3 percent, on
average. The increase is 21 percent for the lowest wage group, less for medium
skills, and negative, at around 3 percent, for higher skills. In those industries
410 Agricultural Price Distortions, Inequality, and Poverty
Table 13.6. The Macroeconomic Impacts of Prospective Trade
Liberalizations, Brazil
(percent change from the baseline)
ROW ROW Brazil Brazil Global ag
Indicator ag nonag ag nonag nonag
Real household consumption 1.05 0.20 0.03 0.15 0.66
Real investment 0.10 0.02 0.02 0.04 0.14
Real government expenditure 0.00 0.00 0.00 0.00 0.00
Export volume 0.71 0.91 0.63 4.85 5.29
Import volume 5.12 1.61 0.57 3.84 7.92
Real GDP 0.08 0.01 0.00 0.03 0.10
Aggregate employment 0.00 0.00 0.00 0.00 0.00
Average real wage 0.43 0.10 0.34 0.62 1.28
Average real return to farmland 22.43 0.94 3.72 0.86 28.00
Average real return to capital 0.11 0.04 0.34 0.22 0.71
Aggregate capital stock 0.00 0.00 0.00 0.00 0.00
GDP price index 0.23 0.10 0.06 0.06 0.13
Consumer price index 0.00 0.00 0.00 0.00 0.00
Export price index 0.10 0.13 0.25 0.19 0.68
Import price index 4.28 0.59 0.20 0.53 3.37
Terms of trade 4.31 0.73 0.05 0.74 2.78
Real exchange rate 4.66 0.71 0.27 0.61 3.62
Nominal GDP 0.31 0.11 0.06 0.03 0.22
Nominal land price 22.43 0.94 3.72 0.86 28.00
Gini coefficienta 1.4 0.1 0.2 0.1 1.7
Poverty headcountb 2.3 0.1 0.5 0.4 3.5
Source: CGE model simulations for Brazil by the authors.
a. The baseline value is 0.58.
b. The baseline value is 31 percent.
with falling capital-labor ratios, the marginal productivity of capital increases,
and, hence, so do capital returns. The real price of agricultural land also shows a
28 percent rise nationally, reflecting the boost in the demand for land in every
state as a consequence of the expansion in production activities relying on this
factor, that is, in agriculture. The bulk of this effect is generated by the liberaliza-
tion of agricultural markets in the rest of the world.
The increase in export volume (5.3 percent) is generated mainly by the liberal-
ization in nonagricultural markets in Brazil. This is because the trade balance is
fixed as a share of GDP in the closure. The trade shocks on the import side are
generated by the liberalization in imported manufactured goods, on which the
bulk of protection is concentrated in Brazil (table 13.5). Because GDP does not
Brazil 411
change much, the increase in the import volume (7.9 percent) must be matched
by an increase in the export volume (5.3 percent), which is facilitated by an
exchange rate devaluation ( 0.61).
National-level changes in industry output are shown in table 13.7. Agricul-
ture and agricultural related industries expand, and there is a general drop in
manufacturing output following the trade liberalization. This suggests that
regions specializing in manufacturing would fare worse, which is, indeed, the
case. (See table 13.8, in which the states are grouped according to geographical
regions.) For each of the 10 labor types, total employment is fixed; so, labor
demand is redistributed across regions according to changes in regional indus-
try output. Employment falls in the most populous and most industrialized
states, Rio de Janeiro and S�o Paulo in the southeast, and also in Amazonas and
Rio Grande do Norte, where there are free export zones (table 13.8). Thus,
Table 13.7. The Effects of Prospective Global Trade Liberalization
on Sectoral Outputs, Exports, and Imports, Brazil
(percent change from the baseline)
Sector Output Exports Imports
Coffee 12.2 0.0 0.0
Sugarcane 18.2 0.0 0.0
Paddy rice 12.6 82.8 116.6
Wheat 2.2 105.7 16.5
Soybeans 3.2 0.5 5.8
Cotton 2.3 107.8 16.5
Corn 8.2 10.0 28.3
Livestock 18.0 76.8 71.6
Raw milk 6.3 0.0 0.0
Poultry 12.8 55.0 34.3
Other agricultural products 3.5 6.9 4.9
Mineral extraction 12.1 16.5 5.9
Petroleum and gas extraction, charcoal 4.2 57.1 2.7
Nonmetallic minerals 2.6 15.0 15.9
Iron products 9.4 14.3 11.6
Nonferrous metals 11.8 16.2 7.5
Other metals 6.9 18.4 27.0
Machines, tractors, parts 6.6 23.7 10.8
Electric material 5.8 14.3 9.0
Electronic equipment 4.8 13.1 1.6
(Table continues on the following page.)
412 Agricultural Price Distortions, Inequality, and Poverty
Table 13.7. The Effects of Prospective Global Trade Liberalization
on Sectoral Outputs, Exports, and Imports, Brazil
(continued )
(percent change from the baseline)
Sector Output Exports Imports
Automobiles 0.8 5.4 2.6
Other vehicles, spare parts 11.7 16.5 10.1
Wood and furniture 5.5 23.7 17.8
Paper and graphics 2.8 17.1 10.4
Rubber industry 8.6 12.9 23.6
Chemical elements 12.0 46.4 27.1
Petroleum refining 1.3 5.2 3.3
Various chemicals 1.6 11.6 14.5
Pharmaceutical performance 0.2 15.7 3.4
Plastics 5.0 13.3 30.3
Textiles 2.9 9.3 35.8
Apparel 0.4 8.0 21.1
Shoes and leather products 12.8 17.5 14.1
Industrialized coffee 14.3 34.3 11.1
Vegetable processing 15.1 102.8 1.3
Meats 19.0 132.4 45.0
Dairy products 7.9 960.3 20.9
Sugar 59.6 137.1 22.1
Vegetable oils 4.5 7.2 4.7
Other agriculture 8.6 104.3 0.1
Other industrial products 7.8 23.7 13.7
Public utilities and services 1.1 19.8 8.2
Civil construction 0.0 13.3 6.0
Trade 1.2 15.5 8.2
Transport 0.2 7.7 3.2
Communication services 0.2 11.1 5.1
Financial services 0.3 10.2 4.7
Household services 1.5 18.9 9.3
Enterprise services 2.9 12.3 4.6
Dwellings 0.2 0.0 0.0
Public administration 0.2 13.5 6.8
Nonmerchandise private services 0.8 0.0 0.0
Source: CGE model simulations for Brazil by the authors.
Brazil 413
Table 13.8. The Effects of Prospective Global Trade Liberalization
on Output, by State, Brazil
(percent change from the baseline)
Real GDP Aggregate
employment
ROW ROW Brazil Brazil Global ag Global ag
State Regiona ag nonag ag nonag nonag nonag
1. Rond�nia N 2.8 0.0 0.3 0.1 3.2 1.5
2. Acre N 2.5 0.0 0.3 0.1 2.9 1.3
3. Amazonas N 0.7 0.1 0.2 0.5 0.5 0.5
4. Roraima N 1.7 0.0 0.1 0.1 1.8 0.8
5. Par� N 1.8 0.3 0.5 0.0 2.1 1.1
6. Amap� N 1.4 0.2 0.3 0.0 1.5 0.7
7. Tocantins N 3.3 0.0 0.3 0.0 3.6 2.3
8. Maranh�o NE 3.3 0.0 0.4 0.1 3.6 2.2
9. Piau� NE 2.1 0.0 0.3 0.1 2.3 1.3
10. Cear� NE 0.1 0.3 0.1 0.0 0.2 0.0
11. Rio Grande
do Norte NE 0.3 0.2 0.0 0.2 0.6 0.2
12. Para�ba NE 1.2 0.1 0.3 0.0 1.6 0.7
13. Pernambuco NE 1.0 0.0 0.2 0.1 1.1 0.5
14. Alagoas NE 4.6 0.1 1.1 0.0 5.9 2.9
15. Sergipe NE 0.3 0.3 0.1 0.2 0.4 0.2
16. Bahia NE 0.4 0.0 0.0 0.2 0.3 0.2
17. Minas Gerais SE 0.5 0.1 0.1 0.0 0.5 0.2
18. Esp�rito Santo SE 0.8 0.4 0.0 0.0 0.4 0.1
19. Rio de Janeiro SE 0.9 0.1 0.2 0.2 1.4 1.0
20. S�o Paulo SE 0.6 0.0 0.1 0.1 0.5 0.5
21. Paran� S 1.5 0.0 0.2 0.1 1.8 1.0
22. Santa
Catarina S 0.6 0.3 0.1 0.1 0.5 0.7
23. Rio Grande
do Sul S 0.3 0.3 0.1 0.1 0.1 0.1
24. Mato Grosso
do Sul CW 4.4 0.1 0.6 0.1 5.2 3.1
25. Mato
Grosso CW 4.2 0.1 0.4 0.1 4.8 3.0
26. Goi�s CW 2.4 0.1 0.4 0.0 2.9 1.8
27. Distrito
Federal CW 0.3 0.1 0.1 0.1 0.0 0.0
Source: CGE model simulations for Brazil by the authors.
a. N north, NE northeast, SE southeast, S south, CW central-west.
414 Agricultural Price Distortions, Inequality, and Poverty
trade liberalization redistributes economic activity toward the poorer regions
of Brazil, a result mainly driven by the liberalization in agricultural markets in
the rest of the world.
The results in poverty and income distribution
Because of the differences in the results among states, regions, and industries,
there are marked changes in income inequality and poverty as a result of trade
liberalization. The changes in the consumer price index specific to income
groups are presented in table 13.9. The Gini coefficient of inequality falls by a
nonnegligible 1.7 percent as a result of the reallocation effects that change wages
and the labor demand structure in expanding and contracting sectors.
Table 13.9. The Effects of Prospective Global Trade Liberalization
on Poverty and Income Inequality, by Household
Income Group, Brazil
(percent change from the baseline)
Share of
households
Average Consumer living below
Household nominal price the poverty Poverty
income groupa income index line gapb
POF[1] 34.5 0.48 2.7 8.3
POF[2] 7.7 0.42 3.1 9.4
POF[3] 4.8 0.35 5.6 9.4
POF[4] 2.7 0.24 6.7 3.5
POF[5] 1.6 0.22 4.5 9.6
POF[6] 0.5 0.19 7.3 53.9
POF[7] 0.4 0.10 56.5 313.5
POF[8] 1.2 0.03 470.4 2,032.7
POF[9] 1.7 0.12 0.0 0.0
POF[10] 2.4 0.36 0.0 0.0
Original value, base year n.a. n.a. 30.8 14.5
% change n.a. n.a. 3.45 7.59
Gini coefficient 1.7
Source: CGE model simulations for Brazil by the authors.
Note: n.a. not applicable.
a. See note a in table 13.1 for definitions of the income groups.
b. The poverty gap is the share by which the average household income of the group falls below the
poverty line.
Brazil 415
The literature on poverty recognizes the importance that a change in
inequality may have in growth. Barros et al. (2007a) have estimated the equiva-
lent growth for Brazil, which they define as the growth rate that would produce
a reduction in poverty equal to the reduction caused by a particular fall in
inequality. According to their estimates, the 4.6 percent decline in inequality
observed in Brazil in 2001�05 was equivalent, from a poverty point of view, to a
balanced growth rate of 11 percent with no change in inequality. This leads to
the conclusion that a 1 percent decline in inequality is equivalent to an increase
in income of 2.4 percent. Thus, one might say, the poor in Brazil would be indif-
ferent whether there was a 1 percent drop in the Gini coefficient or a 2.4 percent
balanced increase in per capita income. The simulation result of a 1.7 percent
decline in the Gini coefficient would therefore be the equivalent, in terms of
poverty reduction, of a 4.1 percent GDP increase above trend between the old
and the new static equilibrium.
Although the results on the consumer price index differ less across house-
holds than the income results, the trend is the same: living costs go up more for
the poor, who spend relatively more of their budgets on food. There is a signif-
icant increase in the agricultural prices for some products, such as meats,
which is driven mainly by the liberalization in the rest of the world. This con-
trasts with the expectation of Rocha (1998), who posited that opening the
Brazilian economy to external markets--a unilateral liberalization--would
help reduce inequality in Brazil by reducing prices in the poorest regions. Our
results suggest that the consumer price index would actually go up more
among the lowest income groups, but that this would be more than offset by
increases in income (table 13.10). The strong positive real income effect on
the poorest households is caused mainly by the liberalization in agricultural
markets in the rest of the world.
The largest positive changes in household incomes are concentrated in the low-
est income households, and the changes decrease monotonically as household
income increases. Indeed, as one may see in table 13.9, the reduction in the num-
ber of poor households is concentrated in the poorest groups. The high positive
numbers among groups 6, 7, and 8 in the consumer expenditure survey represent
percent changes in low numbers because, as shown in table 13.1, there are few
poor households in these income groups.24
The headcount ratio index (table 13.9, third column) captures only the exten-
sion of poverty, not the intensity of poverty. The change in the intensity of poverty
may be seen in the fourth column, which shows the change in the poverty gap. A
reduction in the poverty gap means that there has been a reduction in the severity
of poverty within each household income group. The poverty gap is reduced
416 Agricultural Price Distortions, Inequality, and Poverty
Table 13.10. Decomposition of the Effects of Liberalization on
Real Incomes, by Household Income Group, Brazil
(percent change from the baseline)
Household ROW Brazil Brazil Global ag
income groupa ROW ag nonag ag nonag nonag
POF[1] 27.38 2.60 3.80 2.66 34.02
POF[2] 5.56 0.21 0.97 0.46 7.28
POF[3] 3.14 0.21 0.66 0.47 4.45
POF[4] 1.57 0.00 0.42 0.47 2.46
POF[5] 0.69 0.00 0.42 0.48 1.38
POF[6] 0.25 0.10 0.20 0.48 0.31
POF[7] 0.86 0.10 0.07 0.49 0.50
POF[8] 1.45 0.20 0.06 0.49 1.23
POF[9] 1.74 0.21 0.04 0.51 1.58
POF[10] 2.11 0.23 0.09 0.56 2.04
Source: CGE model simulations for Brazil by the authors.
a. See note a in table 13.1 for definitions of the income groups.
relatively more than the headcount ratio among the poorest three household
income groups, thus reducing income inequality, but not sufficiently to drive a
large number of individuals (or households) out of poverty because of the high
value of these indictors during the base year.
The breakdown in the changes in poverty by state is reported in table 13.11.
Only in the large, industrialized states of Rio de Janeiro and S�o Paulo and in
Amazonas, where there is a free trade processing zone specialized in electronic
products, would there be an increase in the number of households living below
the poverty line. This result is generated because of the high concentration of
manufacturing industries in Rio de Janeiro and S�o Paulo, mainly automobiles,
machinery and tractors, electric materials, electronic equipment, other vehicles
and spare parts, and chemicals. The poverty gap increases in Rio de Janeiro, but
not in Amazonas or S�o Paulo. This is because the share of agriculture and lightly
processed food in GDP is larger in Amazonas than in Rio de Janiero or S�o Paulo
(see table 13.2, column 3). The higher wages and employment in agriculture
reduce the poverty gaps in these states, even though the fall in manufacturing
activities causes the number of poor to increase. Rio de Janeiro, meanwhile, is less
agricultural so that rising agricultural wages and employment do not compensate
for the fall in manufacturing industries.
Brazil 417
Table 13.11. The Effects of Prospective Global Trade Liberalization
on Poverty, by State, Brazil
(percent change from the baseline)
Share of households living
State below the poverty line Poverty gapa
1. Rond�nia 6.3 7.9
2. Acre 4.5 8.5
3. Amazonas 0.1 0.9
4. Roraima 5.4 7.4
5. Par� 4.3 7.5
6. Amap� 0.6 2.3
7. Tocantins 9.5 15.6
8. Maranh�o 5.4 14.2
9. Piau� 4.2 8.4
10. Cear� 2.6 6.5
11. Rio Grande do Norte 3.3 6.1
12. Para�ba 4.5 9.3
13. Pernambuco 4.5 9.2
14. Alagoas 6.9 14.8
15. Sergipe 3.6 6.9
16. Bahia 2.8 7.7
17. Minas Gerais 5.1 9.2
18. Esp�rito Santo 4.5 10.7
19. Rio de Janeiro 2.9 1.9
20. S�o Paulo 1.9 0.8
21. Paran� 7.1 11.1
22. Santa Catarina 3.9 6.9
23. Rio Grande do Sul 6.2 10.3
24. Mato Grosso do Sul 14.3 19.4
25. Mato Grosso 10.1 21.3
26. Goi�s 8.6 13.9
27. Distrito Federal 0.3 1.7
Change in the total number of poor
households, millions 0.53
Change in the total number of the
poor, millions 1.94
Source: CGE model simulations for Brazil by the authors.
a. The percent by which the average household income of the group falls below the poverty line.
418 Agricultural Price Distortions, Inequality, and Poverty
Conclusions
Our simulated global trade liberalization scenario shows positive impacts on
poverty in Brazil, a result mainly driven by the liberalization in the agricultural
markets of the rest of the world. Although the country is not particularly oriented
toward external trade, the strong push in border prices and external demand gen-
erated by trade liberalization causes agriculture to expand considerably, with pos-
itive effects on poverty. This highlights the importance that agriculture still has
among the poorest households in Brazil. Despite the steady decline in agriculture's
share in GDP over the years, the sector still employs most of the nation's poorest.
The agricultural sector is thus uniquely important to the poorest workers.
This means that, in Brazil's manufacturing states, particularly Rio de Janeiro
and S�o Paulo, but also in Amazonas, though on a much smaller scale, the number
of poor households increases. This occurs because the protection of import-
competing manufacturing is reduced by own-country liberalization, and agricul-
ture expands (because of the rest-of-the-world reform), while manufacturing
contracts. This is important. Like most of the other countries in Latin America,
Brazil once pursued import-substitution policies that benefited the manufactur-
ing sector. Our model results show that the trade liberalization scenario would
reverse this outcome somewhat, benefiting agriculture at the expense of the
industrial sectors.
Another important point arising from our analysis is the fall in inequality,
which is even more dramatic than the fall in the number of people living in
poverty. This improvement in inequality would be equivalent, in terms of poverty
reduction, to a significant boost in GDP per capita. Furthermore, the biggest nar-
rowing in the poverty gap occurs among the poorest household groups, suggest-
ing that the poorest households in Brazil would tend to benefit most from global
trade liberalization. In fact, this result holds for every state in Brazil except Rio de
Janeiro.
In this chapter, we have avoided a rural versus urban analysis because of the
difficulties of this sort of approach in the case of Brazil. However, the composition
of our household groups takes into account the occupational diversity in the
economy. It also captures the multiactivity phenomenon, that is, many house-
holds include workers in both agriculture and manufacturing. This phenomenon
has been intensely researched in Brazil.25 Approaching poverty through the prism
of households and tracking the changes in the labor market from individual
workers to households are important modeling issues. In the 2001 data of the
national household sample survey we use here, the incomes of household heads
account for only 65 percent of household incomes. Using head-of-household
income as a proxy for household income may therefore poorly predict the effect of
Brazil 419
policy changes, as convincingly argued by Bourguignon, Robilliard, and Robinson
(2003). The more that spending (and welfare) is a household phenomena, the
more appropriate is the method we use here.
Giambiagi and Franco (2007) note that one of the strategies used by the federal
government to fight poverty, namely, increases in the minimum wage, seems to be
close to reaching the limits in terms of effectiveness, especially in the poorest
region, the northeast. This region would be among the regions to benefit the most
from global trade liberalization. Thus, a global freeing of trade would provide an
alternative channel for helping the poor in Brazil.
Notes
1. This approach follows closely that of Ferreira Filho, Santos, and Lima (2007). Previous studies
have used a simpler top-down or interregional model involving regional differentiation in quantity
changes, but not price changes, and reliance on the 1996 input-output table.
2. Estimates of agricultural protection and assistance in Brazil, based on Lopes et al. (2008), are
incorporated in the World Bank's Distortions to Agricultural Incentives Project Database (Anderson
and Valenzuela 2008). These estimates cover five decades. The representative values for developing-
country agriculture as of 2004 that are used in the global CGE modeling for our study are summarized
in Valenzuela and Anderson (2008).
3. Barros et al. (2007b) find an even larger effect. According to these authors, federal government
transfers were responsible for about 50 percent of the observed fall in inequality in Brazil in 2001�05.
4. Following Savard (2003), another approach involves iteration, whereby the CGE simulation is
rerun with adjustments to make it consistent with the previous results from the MS model. The
process may be repeated until the results converge.
5. Versions of TERM have been prepared for Australia, Brazil, China, Finland, Indonesia, and
Japan. Related material may be found at http://www.monash.edu.au/policy/term.htm.
6. For the simulations reported here, we set the export demand elasticities to values derived from
the Linkage model so as to increase consistency between the results of the global model and the Brazil
model.
7. The dimensions of this margins matrix are 52 * 2 * 2 * 27 * 27.
8. For most goods, the interstate elasticity of substitution is fairly high. To ease the computa-
tional burden, we assume that all users of good G in state R draw the same share of their demands
from state Z.
9. The 2001 Brazilian Input-Output Database we use in this study has been generated by Ferreira
Filho, Santos, and Lima (2007) based on the Brazilian national accounting system tables because the
last official input-output table published by the IBGE, the Brazilian statistical agency, ends in 1996.
10. This quantum method is described in more detail by Ferreira Filho and Horridge (2005; 2009,
annex).
11. The concept of the equivalent household measures the subsistence needs of a household by
attributing weights to household members: 1 to the head, 0.75 to each other adult, and 0.5 to each
child. Because we define poverty on an equivalent basis, a few (large) families in the middle-income
groups fall below the poverty line.
12. This poverty line was equivalent to US$48 in 2001.
13. Working with a poverty line that takes nutritional needs into account, Barros, Henriques, and
Mendon�a (2001) find that 34 percent of households were poor in 1999.
14. The shares of households living below the poverty line in the other income groups are
0.284 percent in the fourth group, 0.14 percent in the fifth, 0.04 percent in the sixth, 0.008 percent in
420 Agricultural Price Distortions, Inequality, and Poverty
the seventh, and 0.001 percent in the eighth. There are no households living below the poverty line in
the two highest (richest) income groups.
15. The values of the poverty gap and poverty line are constructed based on adult equivalent per
capita household income.
16. While Amap�, in the north, shows a poverty gap in line with the gaps in the richer states of the
south and southeast, this should be viewed with caution because Amap� has only a small share in
the total population and the result may be generated by a sampling bias. The national household
sample survey (an urban survey) does not cover the rural areas in the north, in which poverty is
usually concentrated.
17. Highly processed agriculture, beverages, and tobacco (sectors 25 and 26 in the database of
the Global Trade Analysis Project) are included as part of manufacturing rather than agriculture.
In the classification system in the model for Brazil, these sectors correspond to industrialized coffee
(CoffeeInd), vegetable processing (VegetProcess), and other agricultural products (OthAg).
18. There is a tension between this labor closure and the Brazilian reality. The microdata show
substantial unemployment among less-skilled groups in all states. An alternate scenario, whereby
fixed real wages replace national labor constraints, yields results similar to those we report here.
19. For a particular occupation and state, the intersectoral wage variation has been fixed. In the
MS, it is assumed that jobs created (or lost) in a state are allotted to (or taken from) the households in
that state.
20. The factor market closure causes the model to generate percent changes in the factor prices for
the 10 labor groups, capital, and land; the price changes vary across states. In addition, the percent
changes in the demand for each of the 12 factors vary by sector and state. Each adult in the national
household sample survey microdata is identified by state and labor group; the employed are also iden-
tified by sector. Changes in poverty levels in the microdata are driven by wage changes and by the
redistribution of jobs among sectors and states (and, hence, among households).
21. Thus, investment-capital ratios are fixed. Because national capital stock is fixed, changes
in aggregate investment are also limited, but do arise from intersectoral variations in the initial
investment-capital ratios, although the model is static.
22. We have calculated the shifts in the demand schedules for Brazilian exports based on the export
price and quantity results (and export demand elasticities) from the World Bank Linkage model. We
have used the method of Horridge and Zhai (2006). The Armington elasticities, reported in table 13.5,
are borrowed from the Linkage model. The export demand elasticities (not shown in the table) are
equal to the Global Trade Analysis Project state-generic elasticity of substitution among imports in the
Armington structure.
23. The primary factor shares in agriculture are land (0.19), labor (0.47), and capital (0.34). The
labor bill in agriculture has been adjusted to take wage income from self-employment into account.
24. Some middle-income households have many family members. Because of the low per capita
income, they fall below the poverty line.
25. See, for example, Del Grossi and Graziano da Silva (1998), Graziano da Silva and Del Grossi
(2001), and Nascimento (2004).
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14
Nicaragua
Marco V. S�nchez and Rob Vos
The Doha Round of multilateral trade negotiations stalled in 2008 owing in no
small measure to a lack of agreement on the terms of a substantial reduction in
trade-distorting support for agricultural products. The round aims to address the
needs of developing countries, but there is controversy regarding the extent to
which a reduction in trade barriers and domestic supports for agricultural com-
modities would, in fact, be beneficial to developing countries, and, in particular,
whether it would unequivocally reduce poverty. Nicaragua presents an interesting
case in point. It is one of the poorest economies in Latin America and still has a
relatively large agricultural sector and significant rural poverty.
At the end of 2005, Nicaragua joined the Dominican Republic�Central
America Free Trade Agreement (DR-CAFTA), a regional free trade agreement
between the Central American countries, the Dominican Republic, and the
United States. Before Nicaragua signed the agreement, there was substantial
debate in the country, especially over concerns regarding the repercussions on
poverty. Some feared poverty would become more widespread, particularly in
rural areas. An ex ante impact assessment of DR-CAFTA showed that the open-
ness in trade resulting from the agreement would yield positive overall welfare
gains and poverty reduction effects, but that these, at best, would be small and
that traditional agriculture and the rural poor would be among the likely losers
(S�nchez and Vos 2006a, 2006b). Most welfare gains accruing to Nicaragua
because of DR-CAFTA would arise from an export quota that would provide
greater access to textile and clothing markets in the United States, while tariff
cuts and expanded agroindustrial export quotas would contribute relatively lit-
tle. However, the agreement would liberalize trade only with the United States.
While the United States is the most important trading partner of Nicaragua, it
was unclear if multilateral trade liberalization involving all the trading partners
423
424 Agricultural Price Distortions, Inequality, and Poverty
of the country would yield better outcomes in national welfare, poverty, and
income inequality.
In this chapter, we provide a quantitative analysis addressing this question in
the debate about the DR-CAFTA agreement, now signed. We do so using a com-
putable general equilibrium (CGE) model for Nicaragua, coupled with a microsim-
ulation methodology. The next section supplies background information on trade
reform policies and macroeconomic trends in Nicaragua with special reference to
the agricultural sector and rural poverty. The subsequent section describes the
main features of the CGE model and the microsimulation methodology we use to
assess the impact on poverty and inequality. We then lay out the model simula-
tions we consider, which include the liberalization of agricultural trade and the
liberalization of all merchandise goods trade by the rest of the world and by
Nicaragua. In the following section, we provide a summary analysis of the results.
This analysis includes tests for the sensitivity of the results with respect to assump-
tions regarding the responsiveness of trade to price liberalization, as identified
through the relevant trade elasticities. The final section concludes and offers a dis-
cussion of possible policy implications.
Trade Reform, Agricultural Development,
and Poverty in Nicaragua
In this section, we describe the role of trade and agriculture in the economy of
Nicaragua, the recent liberalization associated with DR-CAFTA, and the remain-
ing national distortions to agricultural incentives. We then add a brief discussion
on the nexus between agriculture and poverty in Nicaragua.
Trade and agriculture
Agriculture is a mainstay of the economy of Nicaragua. Its share in GDP is larger in
Nicaragua than in other Central and South American countries. The sector's growth
in Nicaragua averaged a little over 4 percent per year between 1994 and 2006, keep-
ing pace with the rest of the economy and thereby maintaining its contribution to
real GDP at around 20 percent. The nominal share declined from 32 to 18 percent
over the period, however, because factor costs in agriculture increased at a slower
rate than the average for the economy as a whole (tables 14.1 and 14.2).
Agricultural activity is concentrated in the production of basic staple grains,
which dominate in land use and among export crops. About 80 percent of the culti-
vated land is rainfed and planted with corn, beans, rice, and sorghum. Export crops
such as coffee, sesame, sugar, tobacco, and peanuts occupy the other 20 percent
of the arable land. Yet, basic staple grains contributed no more than 30 percent of
agricultural GDP in 2006, while export crops contributed 50 percent, and other
crops and livestock the remaining 20 percent.
Nicaragua 425
Table 14.1. Macroeconomic Indicators, Nicaragua, 1990�2005
(annual averages)
Indicator 1990�94 1995�99 2000�05
a
Average real wage growth rate, % 19.2 2.3 3.1
Annual consumer price inflation, % 2,096.3 11.2 7.7
GDP growth rate, % 0.6 5.4 3.2
Agriculture -- 4.6 4.0
Industry -- 6.2 3.9
Services -- 5.5 3.5
Employment growth rate, % 2.1 5.6 3.7
Exports, plus imports, % of GDP 66.4 66.1 76.6
Exports of goods and services, % of GDP 20.0 21.4 24.8
Traditional exportsb
% of merchandise exports 73.9 52.7 37.2
Annual growth rate, % 1.8 10.8 6.9
Nontraditional exports
% of merchandise exports 26.1 47.3 62.8
Annual growth rate, % 28.6 24.6 20.1
Nontraditional exports, excluding maquilac
% of merchandise exports 24.7 33.0 40.0
Annual growth rate, % 16.6 17.5 12.1
Sources: World Development Indicators Database (2008) for GDP, exports of goods and services,
inflation, and trade. All other data are from the Central Bank of Nicaragua.
Note: -- no data are available.
a. Data for the first period cover only 1991�94.
b. Traditional exports include coffee, bananas, sugar, bovine meat, cattle, seafood products (shrimp and
lobster), sesame seeds, gold, and silver.
c. A maquila is a factory that imports raw materials free of tariffs for assembly or manufacturing and then
exports the output, usually to the country that has supplied the raw materials.
Nicaragua's agricultural sector reached a high, but volatile rate of growth
during the 1990s. Most of this growth was achieved by bringing more land under
cultivation rather than through productivity gains, which occurred mostly in
large-scale export agriculture (World Bank 2003). Between 1990 and 2000, the
share of land under cultivation increased from 51 to 57 percent of the total land
area, but the share has not expanded since then.
Agricultural exports account for 50 to 70 percent of Nicaragua's total merchan-
dise export earnings. Coffee exports alone make up 25 to 30 percent of the total
value of exports, depending on the price of coffee. Coffee has been a major engine
of growth in Nicaragua. It contributed an average 5.3 percent of GDP and gener-
ated 32 percent of rural employment during the 1990s. About 30,000 households
grow coffee, and another 150,000�200,000 households receive some part of their
426 Agricultural Price Distortions, Inequality, and Poverty
Table 14.2. Structure of Value Added by Sector at Factor Cost,
Nicaragua, 1995, 2000, and 2004
(percent)
Sector 1995 2000 2004
Agriculture 31.5 27.6 26.1
Agriculturea 23.4 20.9 17.8
Light food processingb 8.2 6.7 8.3
Mining and quarrying 0.7 0.8 1.4
Manufacturing 10.6 10.3 9.9
Electricity, gas, and water supply 2.0 3.3 3.1
Construction 5.9 7.0 5.9
Services 49.2 50.9 53.7
Total 100.0 100.0 100.0
Sources: Personal communication, Central Bank of Nicaragua.
a. Including livestock, forestry, and fishing.
b. Including processed food and excluding beverages and tobacco.
incomes through full- or part-time labor in coffee production, processing, or mar-
keting. Together, coffee and fish have contributed about 40 percent of total
exports since 2000.
Agricultural exports increased by only 20 percent between 1990 and 2006,
much less than the increase elsewhere in Latin America. Traditional agricultural
exports grew relatively well during the first half of the 1990s, but, since then, most
growth has been dominated by nontraditional products, which now comprise
almost two-thirds of total merchandise exports (table 14.1). Exports of all types
recovered notably in the early 1990s following the end of the trade embargo by the
United States and after the period of macroeconomic instability. Nontraditional
export growth was stimulated by special export promotion measures, including
the creation of an export processing zone. The zone has favored the development
of the maquila-type production of textiles and wearing apparel, which is highly
import dependent and, yet, has created many new jobs.1 Nearly 40 percent of the
nontraditional export growth during 2000�05 was accounted for by the maquila
industry. Owing to its high import content, the maquila industry has only weak
links with the rest of the economy.2
Trade reform and DR-CAFTA
Nicaragua joined the Central American Common Market in 1960 with the aim of
pursuing deeper regional economic integration. Trade with the rest of the world
was liberalized in the early 1990s following a decade of civil strife in Nicaragua
Nicaragua 427
and an international boycott. Around 1990, the pacification process was initiated,
and financial and commercial relations with the United States and multilateral
financial institutions were restored. Unilateral trade liberalization was part of a
broader set of market-oriented reforms. Tariffs on imports were reduced, includ-
ing in agricultural commodities and processed food products. Quantitative
restrictions on imports and exports, import surcharges, and all state monopolies
trading in food staples were phased out, and customs procedures were simplified.
Most export taxes were eliminated in 1993, and the agricultural sector began
benefiting from tax exemptions for imports of raw materials and capital goods.
Temporary tax-credit certificates, used extensively in the 1990s to promote non-
traditional exports, were subsequently eliminated. These reforms increased the
degree of trade openness--the total share of exports and imports in GDP--from
66 to 77 percent of GDP between 1990�94 and 2000�05 (table 14.1). The greater
trade openness has not boosted agricultural productivity, however. Empirical
evidence suggests that the gains in productivity growth have been modest at best
and are concentrated in the large-scale farm production of export crops, while pro-
ductivity growth has been stagnant in smallholder farming (Deininger, Zegarra, and
Lavadenz 2003; Bravo-Ortega and Lederman 2004; World Bank 2003).
The importance of the United States as Nicaragua's main trading partner is
likely to increase through DR-CAFTA. In 2000�05, Nicaragua's exports to and
imports from the United States represented 42 percent of total exports and 29 per-
cent of total imports. Under DR-CAFTA, 92.5 percent of Nicaragua's trade with
the United States will be fully liberalized over a period of 20 years, and, for many
of the remaining products, the country's access to the markets of the United States
will be enhanced through tariff rate quotas and other preferential access quotas. In
return, Nicaragua is offering the United States greater access to its domestic mar-
kets. The weighted average tariff rate for imports from the United States in 2003,
before the signing of DR-CAFTA, was around 6.2 percent; it is projected to fall to
0.2 percent by 2020. The agricultural sectors would initially benefit from greater
protection against agricultural imports from the United States. The reduction in
the tariffs on agricultural goods will be gradual, especially for sensitive products
such as rice, beans, corn, meats, dairy products, and sugarcane. For some of these
products, DR-CAFTA includes safeguard measures in the event of massive
imports, and some of these products (such as white corn) have been excluded
from the tariff reduction program. Even so, the cuts in the tariffs on agricultural
imports from the United States would be ample: by 2020, the weighted average
tariff rate should have dropped to 2.1 percent (figure 14.1). Domestic agricultural
producers in Nicaragua fear that this trade opening will put many of them out of
business and induce more rural poverty, especially if farmers in the United States
continue to receive subsidies.
428 Agricultural Price Distortions, Inequality, and Poverty
Figure 14.1. Tariff Rates on Agricultural and Total Imports
from the United States under DR-CAFTA,
Nicaragua, 2003�20
14
12
10
tariff rate, %
8
6
4
2
0
2003 2005 2010 2015 2020
year
all goods agricultural goods
Source: Author construction based on data in S�nchez and Vos (2006b).
Note: The tariff rates are weighted average nominal rates. DR-CAFTA was implemented immediately
after it was ratified on October 10, 2005.
The United States has also agreed to open its markets to Nicaraguan exporters
under DR-CAFTA. Reductions in the tariffs on imports from Nicaragua are likely to
have a minor impact because the tariffs are already fairly low and even nonexistent
in some cases. This is because of unilateral preferences granted by the United States
through the Generalized System of Preferences and the Caribbean Basin Initiative
and its extensions.3 Nearly 80 percent of Central America's exports to the United
States are already subject to duty-free access owing to unilateral preferential pro-
grams of the United States (USTR 2005). It may therefore be expected that exports
from Nicaragua to the United States will not increase notably through additional
tariff cuts. However, DR-CAFTA makes previously unilateral preferential access per-
manent. The unilateral preferences of the United States may be revoked at any time
for countries that do not have a trade agreement with the United States. Exports
from Nicaragua to the United States are expected to increase more notably if tariff
rate quotas granted by the United States are fully implemented. Nicaragua has also
obtained temporary preferential access quotas, or tariff preference levels, that allow
the use of third-country yarn and cloth if equal amounts of U.S. cloth are imported.
In the case of Nicaragua, up to the equivalent of 100 million square meters of cloth
would be allowed to enter the United States annually free from the restrictions of
Nicaragua 429
rules of origin for the first 10 years, a benefit arising from Nicaragua's status as a
heavily indebted poor country.
The distortions to farmer incentives
Domestically produced farm inputs are not subsidized in Nicaragua, but the agricul-
tural sector (including the light processing of food) historically receives some output
price support. Agricultural activities are generally untaxed.4 Furthermore, imported
inputs for agricultural production and some agroindustries are exempt from duties.
Export taxes, which were nontrivial in the past, have generally been eliminated.
Export subsidies are limited and are being phased out to comply with World Trade
Organization commitments. Taken together, the average overall nominal rate of
assistance (NRA) for farmers in Nicaragua has been close to zero since the early
1990s; import-competing assistance, which increased until 2000, but has since fallen
to near zero, has been slightly more than offset by export taxes, which are levied on a
production-weighted basis (figure 14.2). The slightly negative NRA for tradable agri-
cultural products contrasts with the average NRA for nonfarm tradables, which has
been around 10 percent; so, the relative rate of assistance has fluctuated between 5
and 15 percent since the early 1990s (figure 14.3).5 Since 2004, the direct influence
Figure 14.2. The NRAs for Exportable, Importable, and All
Covered Farm Products, Nicaragua, 1991�2004
60
50
40
30
20
NRA, %
10
0
10
20
30
40
91
92
93
94
95
96
97
98
99
00
01
02
03
04
19
19
19
19
19
19
19
19
19
20
20
20
20
20
year
Import competing exportables Total
Source: Berthelon, Kruger, and Saavedra (2008).
430 Agricultural Price Distortions, Inequality, and Poverty
Figure 14.3. The NRAs for Agricultural and Nonagricultural
Tradable Sectors and the RRAs for Farmers,
Nicaragua, 1991�2004
25
20
15
rate of assistance, %
10
5
0
5
10
15
20
91
92
93
94
95
96
97
98
99
00
01
02
03
04
19
19
19
19
19
19
19
19
19
20
20
20
20
20
year
NRA agricultural tradables RRA
NRA nonagricultural tradables
Source: Berthelon, Kruger, and Saavedra (2008).
Note: The relative rate of assistance (RRA) is defined as 100 * [(100 NRAagt ) (100 NRAnonagt ) 1],
where NRAagt and NRAnonagt are the percentage NRAs for the tradables part of the agricultural and
nonagricultural sectors, respectively.
of trade and agricultural policies on farm prices in Nicaragua has thus been rather
modest. This is important for the simulation analysis we present below; it leads one
to expect that the estimated impact on farm output and the economy generally from
the elimination of agricultural taxes and subsidies would be small.
Agriculture and poverty
Agricultural growth may be effective in reducing poverty because the vast majority of
Nicaraguan farmers are small-scale producers, and many of them are poor. The agri-
cultural sector's rapid, broadbased growth in the 1990s may have represented the sin-
gle most important cause of the significant reduction in poverty that occurred
between 1993 and 2001 (World Bank 2003; see table 14.3). However, the sources of this
growth--high export commodity prices, the availability of unoccupied land, and the
return to normalcy after a decade of civil war--were temporary. None of these factors
should be expected to continue as pillars of sustained growth in the years ahead.
Nicaragua 431
Table 14.3. The Poverty Headcount Ratio and the Gini Coefficient
of Income Inequality, Nicaragua, 1993�2005
Indicator 1993 1998 2001 2005
a
Poverty headcount ratio at US$1 a day 44.0 42.2 43.0 39.4
Urban 26.0 24.9 27.5 22.3
Rural 69.2 62.8 64.7 60.7
Poverty headcount ratio at US$2 a daya 74.0 77.8 78.0 75.8
Urban 61.2 66.1 67.0 63.0
Rural 92.0 91.6 93.3 91.9
Inequality in consumption, Gini coefficientb 0.49 0.44 0.43 0.40
Urban 0.45 0.43 0.41 0.38
Rural 0.43 0.36 0.35 0.34
Income inequality, Gini coefficientc 0.58 0.58 0.58 --
Urban 0.55 0.55 0.56 --
Rural 0.54 0.56 0.51 --
Sources: Data of the National Institute of Statistics and the Census, except for the Gini coefficient for
income inequality, which is from ECLAC (2008).
Note: -- no data are available.
a. The share of the population whose per capita consumption is below the respective poverty line.
b. Inequality in the distribution of per capita consumption.
c. Inequality in the distribution of per capita household income.
Indeed, the world prices for Nicaragua's main export crops were subject to declines
and substantial volatility in 1998�2001 such that poverty increased in some rural
areas, especially in the principal coffee-growing areas of the central region, where the
farmers are most vulnerable to price shocks. National-level poverty continued to
decline though, mainly because of the reconstruction boom, which followed
Hurricane Mitch (late 1998) and came to an end in 2001.After 2001, the buoyant com-
modity prices in world markets, including coffee and basic grain prices, helped boost
agricultural incomes; this led to another period of declining rural poverty. The
enhanced living conditions were clearly not underpinned by any substantial improve-
ments in farm productivity, but rather by more favorable world market conditions.
Modeling the Macro-Micro Impact
of Trade Liberalization
We now examine the impact on national economic welfare, inequality, and
poverty of price-distorting agricultural and trade policies at home and abroad
in 2004, the most recent year for which there is a complete data set of such
policy measures for the world. (The data have been compiled by Anderson and
432 Agricultural Price Distortions, Inequality, and Poverty
Valenzuela 2008 and made available for modelers by Valenzuela and Anderson
2008.) To provide a quantitative ex ante assessment of the effects of the
removal of these policies, we use a CGE model of Nicaragua tailored for the
analysis of income distribution and poverty. The model builds on our earlier
analyses (for example, S�nchez and Vos 2006a, 2006b) that link a country-
specific CGE model and a microsimulation methodology. A distinctive feature
of the analysis we present in this chapter is the use we make of the World Bank's
global Linkage model to provide the external shocks on the terms of trade
caused by rest-of-the-world trade liberalization. We also use, for the first time,
the new estimates compiled by Anderson and Valenzuela (2008) on farm price
distortions in developing countries, including Nicaragua, to assess the impact
of rest-of-the-world trade reform on the country's income inequality and
poverty. The World Bank global Linkage model is already well documented (see
van der Mensbrugghe 2005). We therefore now describe only the CGE model
for Nicaragua.
Theoretical foundations of the national CGE model
To analyze the impact on Nicaragua of agricultural and total trade liberaliza-
tion, we use a CGE framework that consists of a static block and a dynamic
block. The static part of the model provides within-period equilibrium solu-
tions, starting from the base year, and it shares most of the features of the
generic CGE model developed at the International Food Policy Research Insti-
tute and documented in Lofgren, Harris, and Robinson (2002).6 The main
changes we have made to the generic model are the inclusion of bilateral trade
in import and export functions and in an export demand function, a decompo-
sition of foreign savings into capital flows and foreign direct investment, and a
wage function to deal with sector-specific conditions regarding wage indexation
and labor market adjustment.7
The dynamic part of the model is recursive in the sense that it connects the
within-period equilibrium solutions over time through lagged variables and
through updates of stock variables (particularly factors of production) and
selected parameters that are either fixed or absent in the first within-period solu-
tion. We consistently link all within-period equilibrium solutions for the desired
number of simulation periods (years) to generate the baseline scenario.8
We model trade using a standard Armington constant elasticity of substitution
function that defines optimal combinations of domestically produced and
imported commodities. The model also includes an export supply function of the
standard constant elasticity of transformation format, although exports are effec-
tively driven in the model by world demand (see below). Both the export and the
Nicaragua 433
import functions are adapted to account for bilateral agreements with trading
partners (see S�nchez and Vos 2009, appendix, equations 21�2 and 25�6). Tariff
reductions may be simulated by adjusting the tariff parameter in the equation that
determines the domestic prices of imports by commodity and trading partner
(see S�nchez and Vos 2009, appendix, equation 1). Likewise, changes in export
taxes and subsidies are transmitted through an equation that defines the domestic
price of exports by commodity and trading partner. The world import prices
involved in determining domestic prices may be manipulated to impose shocks in
the terms of trade.
The model also specifies an export demand function by commodity and by
trading partner (see above). The function serves to link Nicaragua's CGE model to
a global trade model and facilitates simulations of the worldwide liberalization of
trade and domestic agricultural prices. The global Linkage model of the World
Bank is used to simulate the liberalization of trade and agricultural policies in the
world, excluding Nicaragua. These simulation results provide information on the
expected changes in world import and export prices and in the demand for
Nicaraguan exports; these changes are subsequently imposed on the Nicaraguan
CGE model.9 Unlike typical country models, global trade models generally do not
use constant elasticity of transformation functions to define the export supply
behavior of individual countries. Instead, they assume downward-sloping export
demand schedules derived from the Armington assumption applied to the import
behavior of trading partners. To match information from both types of models,
we did not remove the constant elasticity of transformation function from the
Nicaraguan CGE model, but, instead, gave the high value of 20 to the elasticities
of transformation such that the constant elasticity of transformation function
no longer has any major influence on export supply behavior and exports become,
in effect, driven by demand shifts.10 By implication, the small country assumption
no longer holds.
The factor market closure rules
In the base year solution, we have assumed that capital is underutilized and fixed
and may not be reallocated across sectors. Activity-specific rents assure that fixed
activity utilization levels are consistent with profit maximization through a flexi-
ble adjustment of activity-specific wage-distortion factors.11 Firms are allowed to
increase their capacity utilization rate in response to an increase in the demand for
capital in the subsequent period solutions, however, such that capital is no longer
fixed. The capital market begins to clear through a flexible adjustment in the
economy-wide rent, while the activity-specific wage-distortion factor remains
fixed. Aggregate real investment generated at the end of each period is reallocated
434 Agricultural Price Distortions, Inequality, and Poverty
for use as capital in the next period. We do this by allowing activities with higher
past profitability to gain larger shares of investable funds, as suggested in Dervis,
de Melo, and Robinson (1982). The ratio of aggregate real investment at the end of
the past period to the aggregate capital stock at the beginning of the past period
determines the growth in capital stock in sectors and in the economy. The initial
economy-wide and sectoral stocks of capital in any given period (excluding the
base year) are defined as the capital stocks of the past period, adjusted by the
growth rate of the capital stocks, less the rate of depreciation.
Labor is classified by occupational category (wage and nonwage), skill level
(skilled and unskilled), and gender (men and women). We assume that the quantity
of the labor demand for each type is endogenous in all period solutions and for all
activities except in fishing and mining. We assume that labor supply increases at
constant population growth rates for each category. We specify wage adjustment
rules as much as possible in accordance with the existing institutional setting of
labor markets in Nicaragua. The market clears in all segments (with a few excep-
tions) through adjustments in the level of employment, implying that there is
unemployment in the economy, and activity-specific wages are fixed. Wages are not
fully fixed for unskilled or skilled workers in government services and public utility
sectors, but are indexed to consumer prices following trade-union bargaining.
Another exception is workers in the fishing and mining sectors; in the case of
Nicaragua, these workers tend to face difficulties in finding jobs in other sectors.
Hence, labor is assumed to be specific to these two sectors, that is, the demand for
workers in these sectors is fixed, and the activity-specific wage equilibrates the labor
market (through changes in the activity-specific wage-distortion factor).
The macroeconomic closure rules
The macroclosure rules are the same in the base year and all other period solu-
tions. In the fiscal adjustment process, we assume that government savings are
fixed (at the base year level) and that direct tax rates adjust to maintain real gov-
ernment spending and fiscal balance. For the purposes of our analysis, we keep the
distributional effects of the changes in direct tax rates neutral across domestic
institutions. This government closure rule enables us to calculate the amount of
direct taxes that would need to be collected to compensate for any loss of revenue
from the taxes on trade (net of any subsidy change) in each trade liberalization
simulation. The exchange rate adjusts to maintain a fixed current account balance
in nominal terms (that is, foreign savings are fixed). Investment is driven by sav-
ings; thus, we use fixed marginal propensities to save so that the private savings of
domestic nongovernmental institutions are determined endogenously. Aggregate
investment passively adjusts to match aggregate savings.
Nicaragua 435
Model calibration and the baseline
We implement the base year calibration of the Nicaraguan CGE model using a
social accounting matrix for 2000. We have compiled this matrix in collaboration
with government experts in Nicaragua.12 The key economic activities and com-
modities relevant for the analysis of trade liberalization, including special entries
for the commodities produced in the export processing zone, are all captured in
the matrix. Nicaragua's social accounting matrix (and CGE model) has 40 pro-
ductive sectors and the same number of commodities. In addition, the matrix has
external accounts for trade and other current account flows, which are disaggre-
gated by main trading partner.
Because 2000 is the base year of the social accounting matrix, the model is
solved recursively up to 2004 to enable us to conduct counterfactual simulations of
what would happen if the protection structure were fully dismantled in 2004. Prior
to conducting the counterfactual simulations, the matrix was complemented with
other data to generate two alternative baseline scenarios for the period from 2000
to 2004. In the first baseline scenario, we have solved the dynamic recursive CGE
model using two types of Armington elasticities that we have borrowed from the
World Bank global Linkage model. At the top level of the nested function are the
elasticities of substitution between domestic goods and aggregate imports. We have
used these in the Nicaraguan CGE model to calibrate the Armington function
exponent. At the second level are the elasticities of substitution across imports,
which, in general, are defined as the top-level elasticities, times two. In the
Nicaraguan CGE model, we have used these to calibrate the export demand func-
tion exponent. In the second baseline scenario, the Armington elasticities are
parameter values that we have estimated using country-specific data and sensitivity
analysis.13 The country-specific Armington elasticities range from 0.46 to 1.42 for
the top-level elasticities and from 0.83 to 2.83 for the second-level elasticities, while
those in the global Linkage model are between 2.08 and 5.91 and between 4.16 and
11.82, respectively. Hence, trade liberalization policies would likely produce weaker
trade effects in Nicaragua if country-specific elasticities were used. Consequently,
different assumptions regarding the Armington elasticities may also yield different
welfare effects.14
The two baseline scenarios differ solely in the Armington elasticities. We have
estimated all other model parameters and elasticities based on country-specific
data, as explained in more detail in S�nchez and Vos (2006b), where the data
sources and the estimation methods are listed and described. In addition, we have
taken base year employment and population data from the employment and
wages survey produced by the National Institute of Statistics and the Census of
Nicaragua for November 2000. Both the population and the labor force grow
according to estimations provided to us by the institute for 2001�04.
436 Agricultural Price Distortions, Inequality, and Poverty
The two baseline scenarios also account for an exogenous update in several
parameters. We have updated trade taxes and subsidies for 2001�04 using data of
the Customs Office of the Ministry of Finance of Nicaragua. We have additionally
adjusted the agricultural protection structure to make it compatible with the cor-
responding structure generated for 2004 by Berthelon, Kruger, and Saavedra
(2008), which is also the structure used for Nicaragua in the global Linkage model.
For this purpose, the global Linkage model was calibrated using version 7 of the
Global Trade Analysis Project protection database for 2004 once this had been
amended to incorporate new estimates of distortions to agricultural and food
markets in developing countries.15 To complete the recursive calibration of
Nicaragua's CGE model, we have also updated world export and import prices to
2004 using trade price deflators provided by the Central Bank of Nicaragua. We
have also updated autonomous foreign direct investment (and, implicitly, foreign
savings) for 2001�04 using data from the Central Bank of Nicaragua. Lastly, we
have exogenously updated total factor productivity to enable the reproduction of
actual economic growth during 2001�04.
Modeling the impact on poverty
Because CGE models typically only specify a limited number of representative
households, they provide insufficient detail regarding changes in income distribu-
tion and expenditures to allow one to make robust statements regarding poverty
outcomes. In consequence, the CGE analysis needs to be supplemented by certain
assumptions (such as fixed within-group distributions) or, as we have done for the
empirical analysis reported here, by a microsimulation method that applies the
labor market outcomes (relative remunerations, employment, changes in skill lev-
els) from the CGE model for different types of workers to a microdata set (based
on a household survey) to obtain the required detail about income distribution
for the poverty analysis. Bourguignon, Robilliard, and Robinson (2002), Ganuza,
Barros, and Vos (2002), and Vos et al. (2006) offer a discussion and application of
such methods in conjunction with CGE model analysis. The approach we follow
here is that of Ganuza, Barros, and Vos (2002) and Vos et al. (2006), which was
designed for application in the context of a static CGE model. This method
adjusts the original labor market structure (l) as observed in a household survey
to simulate the sequential effects of a new labor market structure (l*) with conse-
quent changes in employment, household income, and employment and income
distribution. The original labor market structure, that is,
l = f(Pj, Uj, Sjk, Ojk, W1jk, W2, Mjk), (14.1)
Nicaragua 437
is adjusted using simulated CGE labor market outcomes to obtain the new labor
market structure, that is,
l* = f(P* U* S* , O* , W1* , W2*, M* ),
j , j , jk jk jk jk (14.2)
where P and U, respectively, are the participation and unemployment rates for
labor type j; S and O represent the structure of employment by, respectively, sector
and occupational category for labor type j in segment k; W1 is the relative remu-
neration (that is, relative to the mean) for labor type j in segment k; W2 is the aver-
age consumption wage per worker; and M is the structure of employment by the
skill (education) level of workers of type j in segment k.
In the microsimulation procedure, we assume that workers move among occu-
pational situations and economic sectors according to a random process in a nor-
mal distribution. We generate confidence intervals using a Monte Carlo proce-
dure. A more elaborate exposition of this procedure and the related assumptions
is found in Ganuza, Barros, and Vos (2002) and Vos et al. (2006). Meanwhile, for
the application of this microsimulation methodology in a dynamic setting, a
number of additional assumptions are required because observed survey data are
only available for the base year (and a few subsequent years). In essence, we
assume that no demographic shifts (such as migration or population aging) take
place during the simulation period. This is an obvious limitation of the method-
ology, but justifiable to the extent that the CGE model does not model or consider
such demographic change either. Thus, we essentially use the labor market
outcomes in the CGE model scenarios to generate labor market structures for t
periods (lt and l*) and apply them to a single microdata set (for a given t).16
t
We have implemented the microsimulation methodology using the 2001
round of the Living Standards Measurement Study of the National Institute of
Statistics and the Census. The CGE model provides baseline and simulation
results for the parameters of the labor market structure for 2000�04.17 We have
imposed the changes in the labor market structure with respect to 2001 on the
Living Standards Measurement Study, and this has enabled us to generate poverty
and inequality indicators for the baseline and the simulated scenarios. Before
implementing this method, we have adjusted per capita household incomes to be
able to reproduce the official poverty figures of the National Institute of Statistics
and the Census for 2001. We have produced these by comparing per capita con-
sumption rather than per capita income with respect to different total and
extreme poverty lines. We have followed two steps. First, we have matched per
capita household income with per capita consumption among nonpoor families
the per capita incomes of which were lower than their per capita consumption
and among poor families the per capita incomes of which were above their per
438 Agricultural Price Distortions, Inequality, and Poverty
capita consumption. Second, for some families, we have detected that their labor
incomes are larger than their total incomes; we have imputed the difference to
total family income.
Our adjusted per capita household income is defined as follows:
1 nh
c yp + yqh d,
nh ia hi
ypch = (14.3)
=1
where nh is the size of household h; yphi is the labor income of member i of house-
hold h; and yqh is the sum of all nonlabor incomes of the household, defined as:
nh
yqh = a yqphi + yqth, (14.4)
i =1
where yqphi is the individual nonlabor income of member i of household h, and
yqth is other household incomes. In the simulations, yphi is altered for some indi-
viduals i of household h as a result of changes in the labor market parameters.
We have used the endogenous poverty lines produced through the CGE
model to generate the poverty results and to generate the poverty effect of trade
liberalization through the cost of basic consumption. We have first calibrated
the US$1-a-day and US$2-a-day poverty lines (at purchasing power parity)
using the 2001 round of the Living Standards Measurement Study to replicate
the official poverty numbers for 2001 of the National Institute of Statistics and
the Census.18 We have transformed the calibrated real poverty lines for 2001
into monetary poverty lines for all years in the simulation period (that is, 2000�
04). For this purpose, we have used the composite (consumption) price for each
commodity from the CGE model, which, for all commodities, has been indexed
to unity in 2001. For all other years, the composite price of each commodity dif-
fers from 1. We have measured the influence of the composite price of each
commodity in the computation of the monetary poverty lines for all years of the
simulation period through commodity-based weights (that is, using the gamma
parameter in the linear expenditure system of the CGE model; see S�nchez and
Vos 2009, appendix, equation 34).
Liberalization Simulations
We are interested in assessing the impact, particularly on the poor in Nicaragua,
arising from the removal of all forms of trade protection and farm price support
measures in Nicaragua and in the rest of the world. Because most of the policies that
were distorting agricultural incentives in Nicaragua were eliminated during the
1990s, we expect additional own-country liberalization to have only a limited impact.
The elimination of the much more substantial distortions in the rest of the world,
Nicaragua 439
especially in agriculture, may be more important for farmers in Nicaragua because of
the impact this would have on the border prices for agricultural products.
To assess the welfare implications of the various levels of trade and domestic
price liberalization in Nicaragua that would result from unilateral and rest-of-the-
world reforms, we have performed four static simulations and assessed their
impacts through comparisons with the two alternative baselines. We have simu-
lated the liberalization of trade through the removal of trade (import and export)
taxes and subsidies for all tradable commodities, as well as all agricultural domes-
tic supports.19 The four simulations are as follows:
� trdlib1: the unilateral liberalization of trade in agricultural commodities
and the domestic markets for agriculture
� trdlib2: the unilateral liberalization of trade in all tradable commodities and
the domestic markets for agriculture
� trdlib3: the worldwide liberalization of trade in agricultural commodities and
the domestic markets for agriculture, that is, simulation trdlib1, plus the
changes in export and import prices (terms of trade shocks) resulting from
the rest-of-the-world elimination of agricultural support measures
� trdlib4: the worldwide liberalization of trade in all tradable commodities and
the domestic prices for agriculture, that is, simulation trdlib2, plus the changes
in export and import prices (terms of trade shocks) resulting from the rest-of-
the-world liberalization of all trade, including the elimination of agricultural
support measures
Because Nicaragua has already liberalized most of its domestic agricultural mar-
kets, simulations trdlib1 and trdlib2 reflect mainly the impact of the removal of the
remaining import tariffs. The worldwide trade liberalization scenarios (trdlib3 and
trdlib4) reflect, in addition, the effects on export and import prices in Nicaragua that
such reform is expected to generate. We have performed all four simulations as static
simulations as of 2004 because the global Linkage model is calibrated with data for
2004. We have calibrated the base run of the model so as to reproduce an agricultural
protection structure consistent with that of the global Linkage model.
The Effects of Agricultural and
Trade Liberalization
The changes in the export and import prices of Nicaragua in the global Linkage
model after worldwide agricultural and nonagricultural trade liberalization
through simulations trdlib3 and trdlib4 are shown in table 14.4. The border price
changes are shown for the commodity breakdown in the global Linkage model.
For our present analysis, we have reweighted these changes to fit the commodity
440 Agricultural Price Distortions, Inequality, and Poverty
Table 14.4. Trade Structure and World Price Shocks Imposed
in the Global Trade Liberalization Simulations,
Nicaragua, 2004
(percent)
World export World import
price, % change price, % change
Shares over 2004 Shares over 2004
in in
Sector exports trdlib3 trdlib4 imports trdlib3 trdlib4
Paddy rice 0.00 0.00 0.00 1.39 9.66 8.52
Wheat 0.00 0.00 0.00 1.72 3.13 2.06
Other grains 0.00 0.00 0.00 0.48 17.64 16.42
Oilseeds 2.81 7.18 6.11 0.00 0.00 0.00
Vegetables and fruits 2.62 0.70 0.18 0.31 4.55 2.62
Other crops 7.91 0.92 0.12 0.26 9.15 7.17
Sugarcane and beets 0.00 0.00 0.00 0.00 0.00 0.00
Plant-based fibers 0.00 0.00 0.00 0.00 0.00 0.00
Other primary products 0.98 0.97 2.21 4.46 2.09 0.47
Cattle, sheep, and so on 2.09 6.91 5.36 0.06 21.03 18.65
Other livestock 0.07 0.11 1.62 0.43 3.72 1.70
Raw milk 0.00 0.00 0.00 0.00 0.00 0.00
Wool 0.00 0.00 0.00 0.00 0.00 0.00
Beef and sheep meat 6.88 6.04 4.97 0.34 3.12 2.02
Other meat products 0.14 10.21 8.98 0.17 7.74 5.92
Vegetable oils and fats 0.59 1.19 1.77 2.58 1.29 0.08
Processed rice 0.00 0.00 0.00 0.16 1.11 0.07
Dairy products 1.86 2.39 0.99 0.30 7.72 5.82
Refined sugar 2.24 6.44 5.21 0.00 0.00 0.00
Other food, beverages,
and tobacco 8.97 0.22 2.85 6.74 1.42 1.15
Textiles and wearing
apparel 35.59 0.75 1.94 13.87 0.63 0.21
Other manufactured
goods 13.07 0.48 2.11 56.30 1.02 0.01
Services 14.18 0.22 0.20 10.42 0.16 0.36
Source: Linkage model simulations of van der Mensbrugghe, Valenzuela, and Anderson (see the
appendix).
Note: See the text for an explanation of the simulations.
classification of the Nicaragua CGE model.20 The table shows that export and
import prices would increase among most commodity groups. In the reclassified
commodity groupings of the Nicaragua model, there would be a decline only in
other manufactures in the simulation of the worldwide trade liberalization of all
goods (trdlib4). For virtually all other industries, the simulated worldwide liberal-
ization would result in higher export and import prices in Nicaragua.
Nicaragua 441
In aggregate, the terms of trade of Nicaragua would improve by a slight 0.3 and 1.2
percent in the global simulations used as inputs for trdlib3 and trdlib4, respectively.
Table 14.5 shows that export prices would increase an average of 1.8 and 1.5 percent,
respectively, in the two simulations, while import prices would rise by, respectively,
Table 14.5. The Impact of Prospective Trade Liberalization
on the Macroeconomy, Nicaragua, 2004
(percent deviation from the baseline)
Indicator trdlib1 trdlib2 trdlib3 trdlib4
GDP, factor cost
Simulations with global Linkage model elasticities 0.3 0.5 1.2 1.5
Simulations with country-specific elasticities 0.4 0.8 0.7 1.1
Private consumption
Simulations with global Linkage model elasticities 1.1 1.6 1.7 2.7
Simulations with country-specific elasticities 0.8 1.5 0.9 2.0
Fixed investment
Simulations with global Linkage model elasticities 1.1 3.4 1.4 4.7
Simulations with country-specific elasticities 0.9 3.1 0.9 3.8
Exports
Simulations with global Linkage model elasticities 6.9 10.0 11.6 14.1
Simulations with country-specific elasticities 2.1 4.0 3.8 5.0
Imports
Simulations with global Linkage model elasticities 4.5 7.5 6.4 10.0
Simulations with country-specific elasticities 1.9 4.3 2.6 5.2
Real exchange rate
Simulations with global Linkage model elasticities 1.4 2.8 3.6 4.6
Simulations with country-specific elasticities 1.2 3.1 3.2 4.4
World export price
Simulations with global Linkage model elasticities 0.0 0.0 1.8 1.5
Simulations with country-specific elasticities 0.0 0.0 1.8 1.5
World import price
Simulations with global Linkage model elasticities 0.0 0.0 1.3 0.3
Simulations with country-specific elasticities 0.0 0.0 1.3 0.3
Terms of trade
Simulations with global Linkage model elasticities 0.0 0.0 0.3 1.2
Simulations with country-specific elasticities 0.0 0.0 0.3 1.2
Consumer price index
Simulations with global Linkage model elasticities 0.8 1.5 0.9 1.7
Simulations with country-specific elasticities 0.7 1.4 0.8 1.5
Source: Nicaraguan CGE model simulations by the authors.
Note: See the text for an explanation of the simulations. Real government consumption is assumed to be
fixed in the model such that this variable is not expected to change with respect to the baseline in the
trade liberalization simulations.
442 Agricultural Price Distortions, Inequality, and Poverty
1.3 and 0.3 percent. The resource allocation effects depend critically on the impact of
the trade liberalization on the real exchange rate and domestic (consumer) prices.
The real exchange rate appreciates in all simulations; the more comprehensive the
trade liberalization, the more significant the appreciation. The initial relative price
shock favors exports more than import demand in all simulations. Consequently, the
(nominal) trade deficit narrows. Given the external closure rule that keeps foreign
savings fixed, the exchange rate adjusts. The real exchange rate appreciation of 1 to 4
percent causes positive second-round effects on import demand and negative effects
on exports, weakening the final impact on the real trade balance. However, only in the
simulations using the lower country-specific Armington elasticities for the combined
agricultural and nonagricultural price liberalization (trdlib2 and trdlib4) does real
import demand grow more than exports.
Domestic consumer prices unambiguously fall with respect to the baseline in
all the simulations. As expected, the decline is greater if agricultural and nonagri-
cultural commodities are liberalized together and if the liberalization is global
rather than only unilateral. The trade opening would also allow private consump-
tion to grow because of the simulated decline in consumer prices. Private invest-
ment expands in all simulations (table 14.5).
Real exchange rate appreciation tends to stimulate economic activity in
Nicaragua. This outcome, which is embedded in the empirical structure of the
country CGE model, occurs because production costs fall in the highly import-
dependent Nicaraguan economy and because of the real wage effects under
conditions of unemployment. While such stimulus is at work in our simulations, the
relative price shifts resulting from trade liberalization appear equally important.
Thus, traditional exports (especially coffee and livestock), some manufacturing
sectors, construction, and services expand in almost all simulations, although to
varying degrees (table 14.6). Farmers cultivating basic grains lose under all simula-
tions because their protection is fully dismantled. Important manufacturing sectors
(sugar processing and other food processing) also suffer from the trade opening in
most simulations, though less so if we assume the lower country-specific Armington
elasticities. Output in the export processing zone suffers heavily in the simulations
involving nonagricultural trade liberalization because producers face rising costs for
imported inputs that are exacerbated by the exchange rate appreciation and by the
full exposure to global competition in the markets for textiles and garments. Thus,
much of the maquila industry would lose its competitive edge without preferential
market access and support measures. The relatively small industrial sectors with
links to livestock production and fishery (dairy products and processed meats and
fish) are among the winners in these liberalization simulations.
In aggregate, the national economic welfare gains are modest, but they are
somewhat larger if we assume the higher Armington elasticities in the global
Linkage model. In the latter case, compared with the baseline, GDP increases
Nicaragua 443
Table 14.6. The Impact of Prospective Trade Liberalization
on Real Sectoral GDP, Nicaragua
(percent)
Deviation from
Share in the baseline
real base
Sector year GDP trdlib1 trdlib2 trdlib3 trdlib4
Coffee
Simulations with global Linkage model
elasticities 3.2 8.6 17.8 28.0 41.4
Simulations with country-specific elasticities 3.2 2.6 7.1 8.3 12.9
Sugarcane
Simulations with global Linkage model
elasticities 0.9 0.3 1.3 1.9 0.4
Simulations with country-specific elasticities 0.9 0.0 0.1 0.7 0.5
Basic grains
Simulations with global Linkage model
elasticities 3.9 7.4 7.7 5.6 5.9
Simulations with country-specific elasticities 3.9 2.4 2.5 1.7 1.7
Other agricultural production
Simulations with global Linkage model
elasticities 4.0 1.0 2.6 5.6 7.2
Simulations with country-specific elasticities 4.0 0.4 1.0 1.1 1.8
Livestock farming
Simulations with global Linkage model
elasticities 6.0 2.4 6.9 1.4 6.5
Simulations with country-specific elasticities 6.0 0.6 2.6 0.2 1.8
Forestry, logging, and related service activities
Simulations with global Linkage model
elasticities 1.1 0.0 0.6 0.7 0.5
Simulations with country-specific elasticities 1.1 0.2 0.4 0.4 1.0
Fishing
Simulations with global Linkage model
elasticities 1.5 2.7 7.2 1.6 6.6
Simulations with country-specific elasticities 1.5 0.7 2.7 0.0 2.0
Mining and quarrying
Simulations with global Linkage model
elasticities 0.8 0.7 1.4 1.6 3.0
Simulations with country-specific elasticities 0.8 0.7 0.9 0.3 0.5
Production, processing, and preservation of meat and fish
Simulations with global Linkage model
elasticities 1.8 4.8 13.6 2.7 12.1
Simulations with country-specific elasticities 1.8 1.2 4.7 0.3 3.0
(Table continues on the following page.)
444 Agricultural Price Distortions, Inequality, and Poverty
Table 14.6. The Impact of Prospective Trade Liberalization
on Real Sectoral GDP, Nicaragua (continued)
(percent)
Deviation from
Share in the baseline
real base
Sector year GDP trdlib1 trdlib2 trdlib3 trdlib4
Production, processing, and preservation of sugar
Simulations with global Linkage model
elasticities 1.0 0.2 0.3 2.8 2.0
Simulations with country-specific elasticities 1.0 0.3 0.8 1.3 1.7
Manufacture of dairy products
Simulations with global Linkage model
elasticities 1.0 0.8 0.5 1.1 1.8
Simulations with country-specific elasticities 1.0 0.2 0.4 0.3 0.6
Manufacture of other food products
Simulations with global Linkage model
elasticities 2.2 6.3 7.0 6.6 7.6
Simulations with country-specific elasticities 2.2 0.5 0.6 0.5 0.4
Zona franca, export processing zone
Simulations with global Linkage model
elasticities 1.4 3.6 23.8 10.6 24.4
Simulations with country-specific elasticities 1.4 0.9 10.3 2.9 11.1
Other manufacturing
Simulations with global Linkage model
elasticities 8.1 0.0 0.0 0.0 0.0
Simulations with country-specific elasticities 8.1 0.0 0.0 0.0 0.0
Electricity, gas, and water supply
Simulations with global Linkage model
elasticities 3.1 0.1 0.4 0.2 0.3
Simulations with country-specific elasticities 3.1 0.1 0.1 0.2 0.0
Construction
Simulations with global Linkage model
elasticities 7.8 0.4 1.7 0.5 2.3
Simulations with country-specific elasticities 7.8 0.4 1.5 0.3 1.8
Services
Simulations with global Linkage model
elasticities 52.3 0.0 0.3 0.6 0.4
Simulations with country-specific elasticities 52.3 0.0 0.3 0.3 0.6
Source: Nicaraguan CGE model simulations by the authors.
Note: See the text for an explanation of the simulations. The base year is 2000.
by 0.3 percent after the unilateral liberalization in agricultural trade (trdlib1) and by
0.5 percent after the unilateral removal of all price distortions under trdlib2
(table 14.5). If we use the lower country-specific Armington elasticities, the out-
put gains are slightly higher: 0.4 and 0.8 percent, respectively. The difference is
Nicaragua 445
explained by the fact that, at lower Armington elasticities, the domestic consumer
response to the cheaper imports of basic grains and some manufactured food
products is weaker than in the case of the Armington elasticities in the global
Linkage model. The output in basic grains and other manufacturing consequently
suffers less (table 14.6). The direction of the change is generally the same under
the simulations involving worldwide trade liberalization, but the effects are more
significant because the global Linkage model results suggest that the associated
Armington elasticities would generate positive effects on the terms of trade of
Nicaragua. Compared with the baseline, aggregate GDP would be 1.2 percent
higher under trdlib3 and 1.5 percent higher under trdlib4 (table 14.5). Greater
import competition continues to affect the farmers producing basic grains, as well
as some of the food processing, but the impact is less unfavorable compared with
the outcomes of unilateral trade and domestic price liberalization (table 14.6).
The welfare effects of worldwide trade liberalization are much less significant, yet
still positive, if we use the lower country-specific Armington elasticities. This is
because the responsiveness of domestic producers to the larger world demand for
Nicaraguan exports and higher world market prices is weaker.
The fiscal cost of trade liberalization
The government closure rule of the model assumes a fixed fiscal deficit. Conse-
quently, to maintain the baseline fiscal balance, domestic tax rates need to adjust
for any possible gains or losses in trade tax revenue. In the model, we make direct
taxes adjust (neutrally) to accommodate. Total government revenue falls initially
in all trade liberalization simulations.21 As shown in figure 14.4, the fiscal costs are
not trivial and would be almost 2 percent of GDP under trdlib2 and trdlib4. In the
model simulations, we assume that the government is able to raise sufficient extra
direct taxes in this way. A reduction in trade barriers only gradually would render
this more feasible, such as in the case of DR-CAFTA, and might also allow time to
raise funds through public borrowing if this is needed over the short term.
The labor market effects
The potential productivity gains from trade openness are treated exogenously in
the CGE model for Nicaragua. Hence, the simulated output gains from trade
liberalization are realized through the greater use of factors of production, espe-
cially labor. Employment and real labor incomes generally follow the output
effects. Aggregate employment would increase moderately in the unilateral trade
liberalization simulations (trdlib1 and trdlib2) and slightly more under the
446 Agricultural Price Distortions, Inequality, and Poverty
Figure 14.4. The Fiscal Cost of Trade Liberalization, Nicaragua
2
1.8
deviation of tax revenue from the
1.7
1.5
1.4
baseline, % of GDP
1
0.8
0.7 0.7
0.5
0
tradlib1 tradlib2 tradlib3 tradlib4
simulations with global Linkage model elasticities
simulations with country-specific elasticities
Source: Nicaraguan CGE model simulations by the authors.
assumption of lower trade elasticities. If we use the higher Armington elasticities,
around 20,000 jobs are lost on farms producing basic grains, and about 3,000
more jobs are lost in manufacturing in the case of unilateral agricultural liberal-
ization (tables 14.7 and 14.8). Job losses in manufacturing and services are more
significant, at around 23,000, if nonagricultural trade is also unilaterally liberal-
ized, especially because of the lower labor demand in the maquila export process-
ing zone. The unemployed workers find new employment opportunities in tradi-
tional export farming (coffee and livestock). Although this is not captured by the
CGE model, it is unlikely to be a smooth adjustment. In the short run at least, fric-
tional unemployment is likely to emerge given the differences in skill require-
ments and locations between the lost jobs and the new jobs. Under the assump-
tion of lower country-specific Armington elasticities, the employment effects
(both positive and negative) are more substantial (table 14.7, first rows).
Worldwide trade liberalization (trdlib3 and trdlib4) produces more significant
employment effects in parallel with the more substantial output effects. In
essence, the employment effects would magnify the effects observed under the
unilateral trade liberalization simulation, but leave a proportionally larger net
employment gain because the job losses in the farming of basic grains are some-
what less under the worldwide scenarios.
Unskilled workers see somewhat greater improvements in their employment
opportunities in most simulations, especially unskilled wage laborers in traditional
agriculture and particularly if the liberalization is global (tables 14.7 and 14.8).
Table 14.7. The Impact of Prospective Trade Liberalization on Employment and Real Wages, Nicaragua
(percent deviation from the baseline)
Employment Real consumption wage per worker
Indicator trdlib1 trdlib2 trdlib3 trdlib4 trdlib1 trdlib2 trdlib3 trdlib4
Total
Simulations with global Linkage model elasticities 0.1 0.7 1.8 2.7 1.4 2.2 0.8 1.6
Simulations with country-specific elasticities 0.4 1.2 1.1 1.9 0.9 1.8 0.8 1.7
Skilled workersa
Simulations with global Linkage model elasticities 0.4 0.8 1.6 2.1 1.0 1.8 0.8 1.6
Simulations with country-specific elasticities 0.4 1.0 0.8 1.4 0.9 1.7 0.9 1.8
Unskilled workersb
Simulations with global Linkage model elasticities 0.0 0.7 2.0 3.0 1.6 2.5 0.9 1.8
Simulations with country-specific elasticities 0.4 1.3 1.2 2.2 1.0 1.9 0.8 1.8
Wage labor
Simulations with global Linkage model elasticities 0.7 0.2 2.3 1.9 0.8 1.7 0.3 1.1
Simulations with country-specific elasticities 0.6 1.0 1.3 1.7 0.7 1.5 0.6 1.5
Nonwage labor c
Simulations with global Linkage model elasticities 0.3 1.1 1.5 3.2 1.8 2.9 1.2 2.4
Simulations with country-specific elasticities 0.3 1.4 0.9 2.1 1.1 2.1 0.9 2.0
Source: Nicaraguan CGE model simulations by the authors.
Note: See the text for an explanation of the simulations.
a. Skilled workers have completed nine or more years of formal education and may be employed in wage or nonwage segments of the labor market.
b. Unskilled workers have completed eight or fewer years of formal education and may be employed in wage or nonwage segments of the labor market.
447
c. Nonwage labor represents self-employed workers.
448 Agricultural Price Distortions, Inequality, and Poverty
Table 14.8. The Impact of Prospective Trade Liberalization on
Employment, by Sector, Nicaragua
(number and percent)
Base % deviation
employment from the baseline
level, 1,000s,
Sector 2004a trdlib1 trdlib2 trdlib3 trdlib4
Coffee
Simulations with global Linkage model
elasticities 139 8.2 16.9 27.3 40.0
Simulations with country-specific elasticities 139 2.3 6.4 7.9 12.0
Sugarcane
Simulations with global Linkage model
elasticities 18 0.0 0.7 2.4 1.3
Simulations with country-specific elasticities 18 0.2 0.4 1.0 1.2
Basic grains
Simulations with global Linkage model
elasticities 255 7.5 7.9 5.8 6.2
Simulations with country-specific elasticities 255 2.5 2.7 1.8 1.9
Other agricultural production
Simulations with global Linkage model
elasticities 195 0.6 1.9 5.0 6.2
Simulations with country-specific elasticities 195 0.1 0.4 0.8 1.1
Livestock farming
Simulations with global Linkage model
elasticities 231 1.9 6.0 0.8 5.3
Simulations with country-specific elasticities 231 0.3 1.9 0.6 0.9
Forestry, logging, and related service activities
Simulations with global Linkage model
elasticities 47 0.4 1.3 0.2 0.5
Simulations with country-specific elasticities 47 0.1 0.2 0.1 0.3
Fishing
Simulations with global Linkage model
elasticities 25 4.8 13.5 2.7 12.4
Simulations with country-specific elasticities 25 1.4 5.2 0.0 3.9
Mining and quarrying
Simulations with global Linkage model
elasticities 17 2.1 4.1 4.8 8.6
Simulations with country-specific elasticities 17 1.3 1.6 0.5 0.9
Production, processing, and preservation of meat and fish
Simulations with global Linkage model
elasticities 34 4.4 12.6 2.0 10.7
Simulations with country-specific elasticities 34 0.8 3.9 0.8 2.0
Nicaragua 449
Table 14.8. The Impact of Prospective Trade Liberalization on
Employment, by Sector, Nicaragua (continued)
(number and percent)
Base % deviation
employment from the baseline
level, 1,000s,
Sector 2004a trdlib1 trdlib2 trdlib3 trdlib4
Production, processing, and preservation of sugar
Simulations with global Linkage model
elasticities 5 0.4 0.1 3.1 2.5
Simulations with country-specific elasticities 5 0.5 1.2 1.5 2.1
Manufacture of dairy products
Simulations with global Linkage model
elasticities 24 1.3 1.5 0.4 0.4
Simulations with country-specific elasticities 24 0.1 0.5 0.2 0.4
Manufacture of other food products
Simulations with global Linkage model
elasticities 68 6.8 7.8 7.2 8.8
Simulations with country-specific elasticities 68 0.9 1.4 0.9 1.4
Zona franca, export processing zone
Simulations with global Linkage model
elasticities 41 0.0 0.2 0.1 0.2
Simulations with country-specific elasticities 41 0.0 0.1 0.0 0.1
Other manufacturing
Simulations with global Linkage model
elasticities 88 0.5 4.2 0.1 4.2
Simulations with country-specific elasticities 88 0.2 1.3 0.1 1.3
Electricity, gas, and water supply
Simulations with global Linkage model
elasticities 14 0.2 0.9 0.2 1.1
Simulations with country-specific elasticities 14 0.1 0.5 0.1 0.6
Construction
Simulations with global Linkage model
elasticities 99 0.1 0.6 0.3 0.7
Simulations with country-specific elasticities 99 0.0 0.5 0.2 0.6
Services
Simulations with global Linkage model
elasticities 824 0.0 0.7 0.3 0.5
Simulations with country-specific elasticities 824 0.2 0.0 0.3 0.2
Source: Nicaraguan CGE model simulations by the authors.
Note: See the text for an explanation of the simulations.
a. The number employed.
450 Agricultural Price Distortions, Inequality, and Poverty
We assume the nominal wage adjustment is rather rigid in Nicaragua, given the
prevailing institutional wage-setting mechanisms in most sectors of the economy.
For some types of workers in the formal (wage-based) segment of the labor mar-
ket in the CGE model, wages are partially indexed to year-to-year changes in con-
sumer prices. Labor demand pressures influence the movements in real wages, but
only moderately and without clearing labor markets. Real consumption wages are
otherwise strongly influenced by changes in consumer prices. As domestic prices
fall for consumers relative to the baseline under all the trade liberalization simula-
tions, real wages (labor incomes) increase. This is consistent with the simulated
appreciation of the real exchange rate. Real wage increases are similar across types
of workers, although the increases are slightly greater among unskilled workers
because these workers are in greater demand following the additional trade open-
ing. Consistent with our results above, the real wage effects are more significant
under economy-wide liberalization than under agricultural liberalization alone.
Growth in the real wage of each worker is somewhat weaker under worldwide
trade liberalization compared with the unilateral liberalization because employ-
ment grows more markedly under global liberalization.
The inequality and poverty effects
Although employment growth favors unskilled workers in the trade opening simu-
lations, the estimated effect on inequality in the distribution of labor incomes and
per capita household incomes is minimal (table 14.9). The Gini coefficient drops by
a slight 0.003 points in the case of the full-blown global liberalization of agricultural
and nonagricultural commodities (trdlib4) and in a context of high Armington elas-
ticities. This is consistent with the more significant employment effects under
trdlib4. These inequality-reducing outcomes vanish, however, if we use the lower
country-specific Armington elasticities, and, in contrast, inequality drops--again
only slightly--under the unilateral trade liberalization simulations. The employ-
ment effects are more substantial in the model runs in which we use the higher
Armington elasticities, and the job losses in the low-productivity smallholder farm-
ing of basic grains and in higher-productivity, more skill-intensive industrial sectors
are offset by the growth of jobs in higher-productivity export agriculture. On the
whole, though, the compounded effects of shifts in labor demand across skills and
sectors produce small distributional effects.
In consequence, the poverty effects are mainly driven by the average wage and
employment effects. As table 14.9 shows, the incidence of poverty falls in all simula-
tions and model runs, but typically only by 1 percentage point or less (apart from
one exception, under trdlib2 if we use the lower Armington elasticities). Such a small
impact on poverty is to be expected given the modest output and employment
effects of trade liberalization. If we assume the lower country-specific elasticities,
Table 14.9. Microsimulation Results for Poverty and Inequality, Nicaragua
Poverty headcount ratio, %
Gini coefficient
At US$1 a day At US$2 a day
Labor Per capita
Simulation Total Urban Rural Total Urban Rural income household income
Simulations with global Linkage model elasticities
baseline, 2001a 42.9 27.5 64.4 77.9 67.7 92.3 0.569 0.536
baseline, 2004 41.4 26.0 63.0 72.1 59.7 89.6 0.568 0.531
trdlib1, 2004 41.3 26.3 62.3 71.8 59.6 88.9 0.568 0.530
Deviation from the baselineb 0.1 0.3 0.7 0.4 0.1 0.7 0.000 0.001
trdlib2, 2004 40.5 25.3 61.7 71.5 59.0 89.0 0.566 0.529
Deviation from the baselineb 1.0 0.7 1.3 0.6 0.7 0.6 0.002 0.002
trdlib3, 2004 41.2 25.8 62.6 71.7 59.2 89.2 0.567 0.532
Deviation from the baselineb 0.3 0.2 0.4 0.4 0.4 0.4 0.001 0.000
trdlib4, 2004 41.0 26.3 61.7 71.6 59.2 89.0 0.565 0.529
Deviation from the baselineb 0.4 0.2 1.3 0.5 0.4 0.6 0.003 0.003
Simulations with country-specific elasticities
baseline, 2001a 42.9 27.5 64.4 77.9 67.7 92.3 0.569 0.536
baseline, 2004 42.0 27.2 62.8 72.0 59.7 89.2 0.571 0.534
trdlib1, 2004 41.1 26.0 62.3 71.3 58.5 89.2 0.567 0.531
Deviation from the baselineb 0.8 1.1 0.5 0.7 1.2 0.1 0.003 0.003
trdlib2, 2004 40.5 25.7 61.1 71.4 59.0 88.7 0.568 0.531
Deviation from the baselineb 1.5 1.4 1.6 0.6 0.7 0.5 0.003 0.003
trdlib3, 2004 41.7 27.0 62.2 72.0 59.6 89.3 0.571 0.533
Deviation from the baselineb 0.3 0.2 0.6 0.1 0.1 0.1 0.000 0.000
trdlib4, 2004 41.1 26.7 61.2 71.1 58.4 88.9 0.571 0.534
Deviation from the baselineb 0.9 0.5 1.5 0.9 1.3 0.3 0.001 0.000
Source: Nicaraguan CGE model results and microsimulations by the authors.
Note: See the text for an explanation of the simulations.
451
a. The use of different Armington elasticities does not affect the results for 2001 in the baseline scenario because, in the application of the microsimulations, all changes
in the labor market are seen relative to 2001, the year on which the Living Standards Measurement Study that was used was conducted.
b. The differences are expressed as absolute deviations (points or percentage points) from the baseline.
452 Agricultural Price Distortions, Inequality, and Poverty
rural poverty reduction is somewhat more substantial among the extreme poor
(that is, using the US$1-a-day poverty line). This may be explained by the lower
employment and real labor income losses among the poorest in traditional small-
holder farming and the income gains among the poorest in other agricultural activ-
ities. The poverty reduction in urban areas is somewhat more significant among the
moderately poor (people living below the US$2-a-day poverty line) who benefit
more from the decline in consumer prices and the related increase in real consump-
tion wages (relative to the baseline).
Along with the output and employment effects discussed above, the poverty
reduction tends to be slightly greater if both agricultural and nonagricultural
trade are liberalized. Yet, at a drop in the incidence of poverty by 1 percentage
point or less, only around 22,000 extreme poor and 17,000 moderately poor are
lifted out of poverty. Clearly, an enormous challenge remains in reaching the goal
of poverty eradication.22
Conclusions and Policy Implications
Nicaragua's agricultural sector is already close to free of import protection and
price interventions; few agricultural and agroindustrial products are still highly
protected from import competition. Under DR-CAFTA, much of the country's
trade with its major trading partner, the United States, is nearly fully liberalized. In
an ex ante impact analysis, S�nchez and Vos (2006a, 2006b) have shown that
openness under DR-CAFTA would yield positive overall welfare gains and poverty
reduction effects, but that these, at best, would be small and that traditional agri-
culture and the rural poor are among the likely losers in the process. This analysis
demonstrates that only small welfare gains would be obtained through the elimi-
nation of taxes on agricultural trade or through enhanced agroindustrial export
quotas in trade with the United States; most of the gains for Nicaragua would be
obtained if the economy were able to use the full extent of the greater market
access granted for textile and clothing exports to the United States.
Our study addresses the question whether the additional liberalization of trade
with all trading partners would yield greater welfare outcomes and the question
whether full worldwide liberalization of policy barriers to the free flow of agricul-
tural trade would contribute to poverty reduction in Nicaragua. Our analysis con-
firms that small gains in output and in poverty reduction in Nicaragua may be
expected under the various simulations of trade opening we consider. The esti-
mated effects are somewhat greater than those recorded in the 2006a S�nchez and
Vos study, but, at best, aggregate output would increase by 1.5 percent, and the
reallocation of resources and the labor market adjustments would have only a
small impact on income inequality. Modest aggregate employment and real wage
growth would contribute to poverty reduction, but by only 1 percentage point
Nicaragua 453
from still high levels of extreme and moderate poverty. The extreme rural poor
would likely gain somewhat more, however, because much of the employment
gains would occur in the rural sector.
It is hardly surprising that we find such modest output gains and poverty reduc-
tion, given that import tariffs are already low, most export taxes have been eliminated,
and no direct farm-input subsidies exist. Furthermore, the gains in some sectors, espe-
cially traditional export agriculture (coffee and livestock) and the meat processing
industry, would arise at the expense of incomes and jobs among smallholder farmers.
The export taxes that, in 2004, were still being levied on commodities such as vegeta-
bles and fruits, cattle and sheep, and meats have since been eliminated; hence, the wel-
fare gains have already materialized. Additional gains in output growth and poverty
reduction from more trade liberalization in Nicaragua would likely be even more
modest than the gains we report in this chapter.
The fall in government revenue caused by the elimination of import duties and
export taxes would be significant, ranging between 0.5 and 1.8 percent of GDP. The
broadening of the tax base because of higher aggregate output following trade
liberalization is unlikely to offset this loss, not least because it would be politically
controversial. An alternative might involve financing the fiscal loss through increased
public borrowing or aid inflows, but this, too, would be difficult for the government
to accomplish given the country's significant indebtedness and reliance on official
development assistance. Thus, a gradual approach to trade reform would be more
desirable for fiscal reasons, but also to avoid labor market adjustment problems.
We must make an additional cautionary remark about our simulation results:
they are sensitive to the trade elasticity values chosen. The higher trade respon-
siveness assumed in the global Linkage model of the World Bank tends to magnify
output and employment gains (and losses) from trade liberalization relative to the
outcome using the relatively lower country-specific elasticities that we have esti-
mated for Nicaragua. Should one consider the estimated elasticities more realistic,
then the overall outcomes are even more modest.
Overall, the simulations indicate that agricultural and nonagricultural trade lib-
eralization may be viewed as a mixed blessing for Nicaragua's poor. If history is a
guide, such measures, if taken in isolation, may not have a lasting impact on farm
output growth or agricultural efficiency; past liberalizations, for instance, have had
only a weak impact on the productivity and dynamism of the agricultural sector.
The attainment of larger welfare gains would depend on complementary domestic
policies directed at strengthening productivity growth and dynamic diversification
in the agricultural sector and in other sectors of the economy. Such policies might
aim at improving rural infrastructure, enhancing access to credit and to modern
farm inputs, building up better marketing and distribution systems, and boosting
the investment in human capital in rural areas. Achieving greater welfare gains
would also require prudent macroeconomic policies, including keeping the
454 Agricultural Price Distortions, Inequality, and Poverty
exchange rate competitive and sustaining countercyclical fiscal and monetary
stances. The domestic and international liberalization of the markets for agricul-
tural goods and other goods clearly does not represent a quick fix for Nicaragua's
structural problems in the development of a dynamic and diversified agricultural
sector and the reduction of widespread poverty, particularly in rural areas.
Notes
1. A maquila is a factory that imports raw materials free of tariffs for assembly or manufacturing
and then exports the output, usually to the country that has supplied the raw materials.
2. According to data of the Central Bank of Nicaragua, the import-export ratio of the maquila
export processing zone averaged 68 percent per year in 2000�05 and has been above 70 percent since
2004.
3. The trade preferences of the Caribbean Basin Initiative were granted to the countries of the
region by the Caribbean Basin Economic Recovery Act, which was enacted by the United States in 1983
and went into effect on January 1, 1984. The benefits of the initiative were expanded in 2000 through
the enactment of the Caribbean Basin Trade Partnership Act, which allows duty-free and quota-free
treatment for selected items of apparel assembled in qualified initiative countries and applies reduced
tariffs to certain other previously excluded products.
4. Agricultural producers only pay arbitrary municipal taxes on sales and services, real state, and
registrations and licenses. They also pay a tax under the administration of the Instituto Nacional
Tecnol�gico aimed at the collection of a mandatory 2 percent contribution from payrolls in the formal
sector.
5. The relative rate of assistance is defined as 100 * [(100 NRAagt ) (100 NRAnonagt ) 1],
where NRAagt and NRAnonagt are the percentage NRAs for the tradables parts of the agricultural and
nonagricultural sectors, respectively.
6. This model belongs to the family of structuralist neoclassical general equilibrium models devel-
oped for trade policy analysis. The theoretical foundations are described by Dervis, de Melo, and
Robinson (1982) and Robinson (1989).
7. We made the first two extensions because the model was initially used to assess the impact of
DR-CAFTA on Nicaragua's economy (S�nchez and Vos 2006a, 2006b).
8. A more-detailed description of the dynamic recursive CGE framework for Nicaragua may be
found in S�nchez and Vos (2006a, 2006b), while a summary of the model equations may be found in
the appendix by van der Mensbrugghe, Valenzuela, and Anderson.
9. For these purposes, we have followed the procedure that is spelled out in Horridge and Zhai
(2006) and that is designed to connect the Global Trade Analysis Project model and a country model.
10. S�nchez and Vos (2006a, 2006b) include the export demand function for various practical pur-
poses in their modeling of the impact of DR-CAFTA in Nicaragua. They use it to impose changes in
export quotas by exogenously changing the base year quantity of exports (that is, the shift parameter in
the export demand function).
11. The activity-specific wage of each factor is the product of the economy-wide wage of the factor
(that is, the average wage by factor type) and an activity-specific wage-distortion factor. The latter
measures the extent to which base year activity-specific wages deviate from the economy-wide wage by
factor type.
12. S�nchez and Vos (2006b, appendix A3) provide a detailed description of the construction of
Nicaragua's social accounting matrix.
13. The Armington elasticities in the global Linkage model are not country specific, but tend to be
equal across countries.
14. Indeed, it has been found that, in a context of weak trade responsiveness, the gains from trade
may even be reversed. See, among others, Vos (2007) for a review.
Nicaragua 455
15. Unlike previous versions of the Global Trade Analysis Project Database, version 7 merges an
input-output table for Nicaragua with world trade flows and protection data. See Narayanan and
Walmsley (2008).
16. S�nchez (2004) and S�nchez and Vos (2005, 2006b) present a more detailed discussion of the
implementation of the methodology in a dynamic setting, as well as a discussion of the limitations for
analysis.
17. The participation rate is a constant in our CGE model and thus does not play a role in the
microsimulation analysis. The base year unemployment rate by labor type is a constant in the model,
too, but we have changed it inversely (and proportionately) in response to changes in the employment
rate by labor type to implement the microsimulations.
18. We have used the international comparable poverty lines as previously defined by the World
Bank. At the time we wrote this chapter, the new international poverty line estimates based on new
purchasing power parity weights were not yet publicly available. See Chen and Ravallion (2008) for a
discussion of the new poverty line estimates and the implications for trends in global poverty. We
assume here that the directions of change in the poverty incidence in Nicaragua are not affected by the
revised poverty line definition.
19. Nonagricultural commodities include highly processed food products (specifically, beverages
and tobacco, which are Global Trade Analysis Project sectors 25 and 26) and all other manufactures
and nonfarm primary goods.
20. Because the Nicaragua CGE model disaggregates trade by trading partner, we have adapted the
terms of trade shocks additionally using the weighted participation of trading partners in exports and
imports during the base year that is indicated in this model.
21. An alternative closure rule for the government would allow savings to fluctuate to balance the
fiscal accounts, while direct tax rates would be fixed at base year levels. Under such a closure rule,
government savings are found to increase significantly to offset the elimination of revenues from
import duties and taxes. In our analysis, however, we assume that there is some sort of fiscal discipline
and that the government may increase the tax burden without limit to keep government accounts in
balance if trade taxes no longer generate revenue.
22. Our findings here are not dissimilar to the findings obtained on the expected impact of DR-
CAFTA, the regional trade agreement with the United States, as analyzed by S�nchez and Vos (2006a,
2006b).
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Appendix
Border Price and Export
Demand Shocks in
Developing Countries
from Rest-of-the-World
Trade Liberalization:
The Linkage Model
Dominique van der Mensbrugghe,
Ernesto Valenzuela, and Kym Anderson
A global study by Anderson, Valenzuela, and van der Mensbrugghe--chapter 2 in
this volume--uses the World Bank's global Linkage model to examine the eco-
nomic impacts of agricultural and trade policies on various countries, regions,
and the world as a whole as of 2004. It does this by applying shocks to the global
Linkage model through the removal of all agricultural price-distorting domestic
and border policies both with and without the removal of the price-distorting
trade policies affecting all other goods. (The application of the two sets of shocks
helps us identify the relative contribution to various indicators that is supplied by
agricultural policies and by trade policies directed at other merchandise.)
The sets of shocks are also used in another global study in this volume--
chapter 3 by Bussolo, De Hoyos, and Medvedev--to examine, in this case, the
implications of price-distorting policies for inequality and poverty in more than
100 countries (by applying the Global Income Distribution Dynamics microsim-
ulation tool, the GIDD Database, of the authors).
The Linkage model is also used by the authors of the 10 national case studies
reported in this volume to provide exogenous shocks to the national economy-
wide models of these authors on the developing countries in the studies.1 The
effects of the shocks on the national economies are then compared with the effects
457
458 Agricultural Price Distortions, Inequality, and Poverty
of own-country liberalization by using the same national models, as well as the
agricultural protection rates for these countries that are used in the global Linkage
model.
In this appendix, we describe the main assumptions that have been adopted to
generate the border price and export demand shocks arising from agricultural and
trade policy reforms by the rest of the world. We also describe the way these
shocks are communicated to the national models.
We use the comparative static version of the World Bank's Linkage model (van
der Mensbrugghe 2005, 2006) with a baseline--the base year is 2004--calibrated
to prerelease 5 of beta version 7 of the Global Trade Analysis Project (GTAP) data
set. The new version of the GTAP global protection database, version 7, has some-
what greater regional coverage than version 6, which was calibrated to a baseline
of 2001 (Dimaranan 2006). The sectoral and regional aggregations and concor-
dances are shown in tables A.1 and A.2. We first amend the distortions in version 7
of the GTAP Database (see Narayanan and Walmsley 2008) by replacing the
applied tariffs with distortion rates that reproduce the nominal rates of assistance
estimated by the authors of the developing-country case studies in the World
Bank's research project, Distortions to Agricultural Incentives, as presented for
modelers by Valenzuela and Anderson (2008) using the sectors described in the
GTAP Database.2
Global Linkage Model Border Shocks
to the Single-Country Models
It is important to ensure uniformity in the simulations used in the global and
national modeling. Therefore, the results communicated from the Linkage model
to the single-country models are rest-of-the-world reforms, that is, they represent
global liberalization without liberalization in the country of interest. The shocks
imposed are as follows:
� In agricultural and lightly processed food sectors (excluding highly processed
food, beverages, and tobacco):
� The removal of all trade (import and export) taxes and subsidies
� The removal of all farm input and output taxes and subsidies
� In other, nonagricultural sectors:
� The removal of all trade (import and export) taxes
Border Price and Export Demand Shocks in Developing Countries 459
Table A.1. Sectoral Concordance of the Linkage Model and the
GTAP Version 7 Database
Number Code Description
1 pdr Paddy rice
2 wht Wheat
3 gro Other grains
4 osd Oilseeds
5 c_b Sugarcane and beets
6 pfb Plant-based fibers
7 v_f Vegetables and fruits
8 ocr Other crops
9 ctl Cattle, sheep, and so on
10 oap Other livestock
11 rmk Raw milk
12 wol Wool
13 cmt Beef and sheep meat
14 omt Other meat products
15 vol Vegetable oils and fats
16 mil Dairy products
17 pcr Processed rice
18 sgr Refined sugar
19 ofb Other food, beverages, and tobacco
Food products n.e.s. (ofd), beverages and tobacco products (b_t)
20 prm Other primary products
Forestry (frs), fishing (fsh), coal (coa), oil (oil), gas (gas),
minerals n.e.s. (omn)
21 twp Textiles and wearing apparel
Textiles (tex), wearing apparel (wap), leather products (lea)
22 omx Other manufacturing
Wood products (lum); paper products, publishing (ppp); petroleum,
coal products (p_c); chemical, rubber, plastic products (crp);
mineral products n.e.s. (nmm); ferrous metals (i_s); metals n.e.s.
(nfm); metal products (fmp); motor vehicles and parts (mvh);
transport equipment n.e.s. (otn); electronic equipment (ele);
machinery and equipment n.e.s. (ome); manufactures n.e.s. (omf)
23 srv Services
Electricity (ely); gas manufacture, distribution (gdt); water (wtr);
construction (cns); trade (trd); transport n.e.s. (otp); sea transport
(wtp); air transport (atp); communications (cmn); financial
services n.e.s. (ofi); insurance (isr); business services n.e.s. (obs);
recreation and other services (ros); public administration and
defense, education, health services (osg); dwellings (dwe)
Sources: van der Mensbrugghe (2005), Narayanan and Walmsley (2008).
Note: n.e.s. not elsewhere specified.
460 Agricultural Price Distortions, Inequality, and Poverty
Table A.2. Regional Concordance of the Linkage Model
and the GTAP Version 7 Database
Number Code Description
1 aus Australia
2 nzl New Zealand
3 jpn Japan
4 kor Korea, Rep.
5 twn Taiwan, China
6 hyc Hong Kong, China (hkg); Singapore (sgp)
7 can Canada
8 usa United States
9 e15 EU15 (see next)
Austria (aut), Belgium (bel), Denmark (dnk), Finland (fin),
France (fra), Germany (deu), Greece (grc), Ireland (irl),
Italy (ita), Luxembourg (lux), the Netherlands (nld),
Portugal (prt), Spain (esp), Sweden (swe),
United Kingdom (gbr)
10 xer Rest of Western Europe
Cyprus (cyp), Malta (mlt), Switzerland (che), rest of the
European Free Trade Association (xef), rest of Europe (xer)
11 bgr Bulgaria
12 cze Czech Republic
13 est Estonia
14 hun Hungary
15 lva Latvia
16 ltu Lithuania
17 kaz Kazakhstan
18 kgz Kyrgyzstan
19 pol Poland
20 rom Romania
21 svk Slovak Republic
22 svn Slovenia
23 rus Russian Federation
24 tur Turkey
25 xca Rest of Europe and Central Asia
Albania (alb), Croatia (hrv), Ukraine (ukr), rest of Eastern
Europe (xee), Armenia (arm), Azerbaijan (aze),
Georgia (geo), rest of former Soviet Union (xsu)
26 mde Middle East
Iran, Islamic Rep. (irn); rest of Western Asia (xws)
27 egy Egypt, Arab Rep.
28 mar Morocco
29 xnf Rest of North Africa
Tunisia (tun), rest of North Africa (xnf)
30 zaf South Africa
Border Price and Export Demand Shocks in Developing Countries 461
Table A.2. Regional Concordance of the Linkage Model
and the GTAP Version 7 Database (continued)
Number Code Description
31 mdg Madagascar
32 moz Mozambique
33 zmb Zambia
34 zwe Zimbabwe
35 wcf Rest of West and Central Africa
Rest of West Africa (xwf), Central Africa (xcf),
south-central Africa (xac)
36 uga Uganda
37 tza Tanzania
38 nga Nigeria
39 sen Senegal
40 xss Rest of Sub-Saharan Africa
Malawi (mwi), Mauritius (mus), rest of East Africa (xec),
Botswana (bwa), rest of Southern African Customs
Union (xsc)
41 chn China
42 idn Indonesia
43 mys Malaysia
44 phl Philippines
45 tha Thailand
46 vnm Vietnam
47 xea Rest of East Asia
Rest of Oceania (xoc), rest of East Asia (xea),
Cambodia (khm), rest of Southeast Asia (xse)
48 bgd Bangladesh
49 ind India
50 pak Pakistan
51 lka Sri Lanka
52 xsa Other South Asia
53 arg Argentina
54 bra Brazil
55 chl Chile
56 col Colombia
57 ecu Ecuador
58 mex Mexico
59 nic Nicaragua
60 xlc Rest of Latin America and the Caribbean
Rest of North America (xna); Bolivia (bol); Paraguay (pry);
Peru (per); Uruguay (ury); Venezuela, R.B. de (ven);
rest of South America (xsm); rest of Central America (xca);
Caribbean (xcb)
Sources: van der Mensbrugghe (2005), Narayanan and Walmsley (2008).
462 Agricultural Price Distortions, Inequality, and Poverty
Macroclosure in the Linkage Model
The macroclosure of the global Linkage model involves the following:
� Fixing the balance of trade
� Fixing regional investment
� Fixing real government spending
� Implementing a tax replacement to offset the lost revenue in trade and produc-
tion (if present) taxes, net of subsidies that would no longer be paid after the
reforms; specifically, a tax on factors of production is implemented to ensure
that the share of net tax receipts in net national incomes remains unchanged in
the face of the elimination of distortions
Implementing Global Results
in the Single-Country Models
As indicated by Horridge and Zhai (2006), the aim is to allow the single-
country models to determine export supply behavior and to use the changes in
demand by the rest of the world as presented in the global model. In calculat-
ing the export demand curve, one should obtain from the global model the
slope--approximately equal to the elasticity of substitution among imports--
and the shift (fp) of the world demand schedule, where fp is computed as
follows:
fp p q (Elasticity of substitution among imports), (A.1)
while p is the percentage change in export prices, and q is the percentage change in
export quantities.3
On the import side, one takes the import price shocks directly from the global
model. In global models such as Linkage, the import supply curves for a small
country are flat; in single-country models, the import demand curves are compar-
atively steep because they typically rely on the import-domestic elasticities sug-
gested by Armington (1969). Hence, vertical shifts in import supply are readily
proxied by exogenous price changes.4
The Linkage Model
In a key specification, the Linkage model contains three production technologies
for agricultural activities: crops, livestock, and all else.5 The first technology, crops,
attempts to capture the trade-offs between intensive and extensive farming. The
Border Price and Export Demand Shocks in Developing Countries 463
second, livestock, captures the trade-offs between extensive grazing and intensive
ranch-fed farming by allowing substitution between feedgrains and land. The
third is the more traditional capital-labor substitution, albeit with a special, addi-
tional nesting for energy use.
Another key model specification is the treatment of land. In the Linkage
model, land supply is variable according to region-specific land supply elasticities.
A listing of these and other key model elasticities is provided in table A.3. For the
purposes of the simulations in this volume, the vintage capital structure elastici-
ties in the Linkage model are imposed in all production functions so that the
functions emulate the long-run substitution possibilities.
All runs use the following closure rules:
� Fixed real government expenditures and fiscal balance: To meet ex ante rev-
enue changes (for example, a loss in tariff revenues), the rate of lump sum
direct taxation on the single representative household is endogenous. While
perhaps unrealistic in some countries in the short run, this specification has
the advantage of not adding to existing distortions in the economy.
� Private savings are endogenous and determined by the extended linear expen-
diture system of demand. Investment is determined as the sum of all available
savings: private, public (fixed in real terms), and foreign (fixed in real terms
relative to the model num�raire). Because all three forms of savings are more
or less invariant (in nominal terms), major changes in the volume of invest-
ment are largely driven by changes in the unit cost of investment (for example,
a reduction in tariffs on imported capital goods). In the comparative static
model, this has only minor impacts that may be attributed to the final compo-
sition of demand because investment has no effect on the capital stock in the
comparative static version of the model.
� The capital account and, hence, the current account are fixed. The real
exchange rate adjusts to clear ex ante changes in the current account. A reduc-
tion in import tariffs, for example, leads to an increase in import demand. The
demand for additional imports needs to be financed through an increase
in exports, most often generated by a fall in the real exchange rate, that is, a
depreciation.
Some of the other key features of the model include the following:
� The model num�raire is defined as the export price index of manufactured
exports from high-income countries. This is set to 1 in the base year and in
subsequent shocks.
464 Agricultural Price Distortions, Inequality, and Poverty
Table A.3. Key Elasticities in the Linkage Model
a. Production elasticities
Elasticity Long-run parameter
Across inputs, excluding sector-specific and energy inputs 0.0
Between other inputs and value added, including energy 0.0
Between labor and the capital and energy bundle 1.0
Across labor inputs 0.5
Between capital and energy 0.8
Between capital and sector-specific factors 0.0
Across fuel inputs 2.0
Between chemicals and land-capital inputs 0.5
Between feed and land 0.5
Across agricultural chemicals 0.5
Across livestock feed 0.5
b. Armington elasticitiesa
Elasticity Value
b
pdr Paddy rice 4.45
wht Wheat 5.85
gro Other grains 4.93
v_f Vegetables and fruits 3.94
osd Oilseeds 4.75
c_b Sugarcane and beets 5.91
pfb Plant-based fibers 3.94
ocr Other crops 3.94
ctl Cattle, sheep, and so on 3.94
oap Other livestock 3.94
rmk Raw milk 3.94
wol Wool 3.94
cmt Beef and sheep meat 3.94
omt Other meat products 3.94
vol Vegetable oils and fats 3.94
mil Dairy products 3.94
pcr Processed riceb 4.45
sgr Refined sugar 5.91
ofb Other food, beverages, and tobacco 3.94
prm Other primary products 4.31
twp Textiles and wearing apparel 4.11
omx Other manufacturing 4.09
srv Services 2.08
Border Price and Export Demand Shocks in Developing Countries 465
Table A.3. Key Elasticities in the Linkage Model (continued)
c. Land supply elasticitiesc
Elasticityc Value
aus Australia 1.00
nzl New Zealand 1.00
jpn Japan 0.25
kor Korea, Rep. 0.25
twn Taiwan, China 0.25
hyc Hong Kong, China; Singapore 0.25
can Canada 1.00
usa United States 1.00
e15 EU15d 0.25
xer Rest of Western Europe 0.25
bgr Bulgaria 0.25
cze Czech Republic 0.25
est Estonia 0.25
hun Hungary 0.25
lva Latvia 0.25
ltu Lithuania 0.25
kaz Kazakhstan 1.00
kgz Kyrgyzstan 1.00
pol Poland 0.25
rom Romania 0.25
svk Slovak Republic 0.25
svn Slovenia 0.25
rus Russian Federation 1.00
tur Turkey 1.00
xca Rest of Europe and Central Asia 0.25
mde Middle East 0.25
egy Egypt, Arab Rep. 0.25
mar Morocco 0.25
xnf Rest of North Africa 0.25
zaf South Africa 1.00
mdg Madagascar 1.00
moz Mozambique 1.00
zmb Zambia 1.00
zwe Zimbabwe 1.00
wcf Rest of West and Central Africa 1.00
uga Uganda 1.00
tza Tanzania 1.00
nga Nigeria 1.00
(Table continues on the following page.)
466 Agricultural Price Distortions, Inequality, and Poverty
Table A.3. Key Elasticities in the Linkage Model (continued)
c. Land supply elasticitiesc
Elasticityc Value
sen Senegal 1.00
xss Rest of Sub-Saharan Africa 1.00
chn China 0.25
idn Indonesia 1.00
mys Malaysia 1.00
phl Philippines 1.00
tha Thailand 1.00
vnm Vietnam 1.00
xea Rest of East Asia 1.00
bgd Bangladesh 0.25
ind India 0.25
pak Pakistan 0.25
lka Sri Lanka 0.25
xsa Other South Asia 0.25
arg Argentina 1.00
bra Brazil 1.00
chl Chile 1.00
col Colombia 1.00
ecu Ecuador 1.00
mex Mexico 0.25
nic Nicaragua 0.25
xlc Rest of Latin America and the Caribbean 1.00
Source: van der Mensbrugghe (2005).
a. The elasticities in this table represent the top-level trade elasticities, that is, the substitution between
the aggregate domestic good and aggregate imports. The second-level elasticity, that is, the substitu-
tion across imports by region of origin, is double the top-level elasticity using the rule of 2.
b. Because of convergence, the rice trade elasticities for Japan, the Republic of Korea, and Taiwan, China
have been set at 1.5, 1.0, and 1.5, respectively, for both levels of the elasticities and for paddy rice
and processed rice.
c. Countries and regions are classified as either land abundant (with an elasticity of 1) or land scarce
(with an elasticity of 0.25).
d. The 15 members of the European Union prior to 2004; see table A.2.
Border Price and Export Demand Shocks in Developing Countries 467
� Aggregate labor supply is fixed and fully mobile across all sectors. Note that in
the version of the model used in the present study, no exogenous assumptions
are imposed on the relative rural-urban wage ratio, and wages are assumed to
be uniform across sectors. Assuming intersectoral wage differentials (with or
without perfect mobility) may lead to different results depending on whether,
in aggregate, demand in the high-wage sectors is greater than demand in
the low-wage sectors.6 Skilled labor is a substitute for unskilled labor, and the
composite labor bundle may be substituted for capital.
� Aggregate capital supply is fixed and fully mobile across all sectors.7
� Agricultural land supply is endogenous, and land is specified as either scarce or
abundant. Land is perfectly mobile across agricultural industries.
The price distortion structure in the GTAP Version 7p5 database for 2004 is
shown in table A.4, for comparison with the distortion rates drawn from the
World Bank project's database for use in the present study in place of the standard
GTAP database numbers.
Table A.5 provides the export price, import price, and export demand shocks
from rest-of-the-world agricultural and trade policy reform for our selected
developing countries. These come from the Linkage model, for use in the other
models employed in the present study. In terms of those Linkage model results, we
note the following:
� Changes in value added are computed with and without taxes and subsidies on
the factors of production. The exception is wage taxes, which are not used in
this version of the model. The first definition reflects the cost of value added,
inclusive of total taxes and subsidies. The second definition reflects final pay-
ments to the owners of the factors of production. In the output provided to
the authors, total value added at factor cost (that is, inclusive of taxes and
subsidies) is provided.
� Welfare is measured using the Hicksian equivalent variation in income. In the
base year, the expenditure function is exactly equal to disposable income and
reflects the expenditures on goods, services, and savings. After the shock, the
expenditure function reflects the value of the expenditures required to achieve
the new level of utility at the base year prices. It is closely approximated by the
value of nominal disposable income, deflated by the consumer price index. In
some countries, the increase in the income (lump sum) tax may not be fully
offset by a decline in consumer prices, even taking into account changes in
factor incomes.
Tables A.4 and A.5 provide additional summary material on our data.
Table A.4. Price Distortion Structure in GTAP Version 7p5 and the Distortion Rates Drawn from the
468 World Bank Project, 2004
(percent)
GTAP version 7p5 Amended rates
Primary Agriculture and lightly Other Primary Agriculture and lightly Other
agriculture processed food goods agriculture processed food goods
Domestic Export Import Import Domestic Export Import Import
Country, region support subsidy tariff tariff support subsidy tariff tariff
Australia 0.0 0.0 0.7 3.3 0.0 0.0 0.5 3.3
New Zealand 0.0 0.0 2.8 3.3 0.0 0.2 0.7 3.3
EU15a 1.0 10.8 7.1 0.7 1.2 12.8 6.9 0.7
Rest of Western Europe 2.6 8.6 52.9 2.2 2.6 13.4 53.9 2.2
Russian Federation 1.7 0.1 7.5 7.4 1.7 0.9 18.9 7.4
Kazakhstan 0.9 0.0 2.9 2.7 0.9 0.0 3.4 2.7
Kyrgyzstan 1.0 0.1 3.1 5.0 1.0 0.1 3.8 5.0
Turkey 0.8 0.0 29.0 3.1 0.8 0.0 33.3 3.1
Rest of Europe and
Central Asia 1.1 0.0 9.8 5.7 1.1 0.9 9.9 5.7
Bulgaria 0.6 0.0 17.0 11.5 0.6 0.0 14.8 11.5
Czech Republic 0.6 10.2 3.1 0.5 0.6 0.0 3.0 0.5
Estonia 0.0 9.7 6.2 0.9 0.0 0.0 5.0 0.9
Hungary 3.1 9.7 6.6 0.5 3.1 0.0 6.2 0.5
Latvia 13.1 9.9 3.7 0.9 13.3 0.0 3.3 0.9
Lithuania 0.5 9.4 13.1 1.0 0.5 0.0 12.1 1.0
Poland 0.4 8.3 6.1 0.8 0.4 0.0 6.2 0.8
Romania 1.3 0.0 19.8 9.8 1.3 0.0 18.0 9.8
Slovak Republic 0.0 10.4 5.5 0.4 0.0 0.0 5.2 0.4
Slovenia 0.0 10.5 6.3 0.4 0.0 0.0 7.8 0.4
United States 4.0 0.5 2.5 1.3 5.2 0.6 6.1 1.3
Canada 1.6 2.0 23.1 1.4 1.6 3.6 18.9 1.4
Japan 2.0 0.0 141.1 1.7 2.0 0.0 151.7 1.7
Korea, Rep. 0.0 0.0 172.7 5.9 0.0 0.0 319.4 5.9
Taiwan, China 0.4 0.0 77.4 3.9 0.4 0.0 84.2 3.9
Hong Kong, China;
Singapore 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
China 0.0 0.0 12.6 7.1 0.0 0.2 6.5 7.1
Indonesia 0.0 0.0 6.4 4.9 0.0 1.6 7.3 4.9
Malaysia 0.0 0.0 2.4 5.9 0.0 0.2 5.0 5.9
Philippines 4.7 0.0 20.0 3.4 4.7 0.0 7.1 3.4
Thailand 0.2 0.0 22.1 12.9 0.2 0.0 26.2 12.9
Vietnam 3.6 0.0 15.5 18.5 3.6 0.5 21.5 18.5
Bangladesh 1.0 0.0 16.3 22.5 1.0 0.0 9.9 22.5
India 3.9 0.0 29.8 20.9 10.1 2.5 2.9 20.8
Pakistan 0.0 0.0 10.8 18.5 0.0 0.2 19.4 18.5
Sri Lanka 0.6 0.2 24.3 5.8 0.6 0.3 23.8 5.8
Rest of South Asia 0.5 0.0 5.0 15.6 0.5 0.0 6.9 15.6
Rest of East Asia 0.7 0.0 2.8 2.3 0.7 0.0 3.2 2.3
Rest of Middle East 12.4 0.0 9.0 5.7 12.4 0.0 7.5 5.7
Egypt, Arab Rep. 0.0 0.0 4.0 13.5 0.0 0.0 5.0 13.5
Morocco 0.0 0.3 33.3 20.0 0.0 0.4 28.4 20.0
Rest of North Africa 3.9 0.5 24.9 13.1 3.9 1.3 30.7 13.1
South Africa 0.0 0.0 9.7 6.5 0.0 0.0 10.2 6.5
Madagascar 0.0 0.0 3.9 2.7 0.0 4.4 3.4 2.7
Mozambique 0.2 0.0 12.5 10.9 0.2 0.0 14.5 10.9
Zambia 0.8 0.0 5.6 9.0 0.8 0.0 7.0 9.0
469
Zimbabwe 3.2 0.0 13.6 15.4 3.2 0.0 8.9 15.4
(Table continues on the following page.)
470 Table A.4. Price Distortion Structure in GTAP Version 7p5 and the Distortion Rates Drawn from the
World Bank Project, 2004 (continued)
(percent)
GTAP version 7p5 Amended rates
Primary Agriculture and lightly Other Primary Agriculture and lightly Other
agriculture processed food goods agriculture processed food goods
Domestic Export Import Import Domestic Export Import Import
Country, region support subsidy tariff tariff support subsidy tariff tariff
Uganda 0.0 0.0 9.5 5.5 0.0 2.6 9.2 5.5
Tanzania 0.3 0.0 11.6 13.7 0.3 0.0 11.8 13.7
Nigeria 0.1 0.0 74.0 17.2 0.1 0.0 76.1 17.2
Senegal 0.0 0.0 8.4 8.9 0.0 1.1 6.2 8.9
Rest of West and
Central Africa 0.2 0.0 10.5 8.9 0.2 0.0 10.8 8.9
Rest of Africa 0.4 0.0 10.4 14.1 0.4 0.0 10.6 14.1
Argentina 4.9 0.0 2.9 5.7 0.0 14.8 0.0 5.8
Brazil 0.0 0.0 4.5 8.9 0.0 0.0 4.8 8.9
Chile 1.7 0.0 1.3 1.8 0.0 0.0 2.4 1.8
Colombia 0.0 0.0 12.9 9.8 0.0 0.0 21.6 9.8
Ecuador 0.0 0.0 6.8 10.4 0.0 0.0 13.4 10.4
Mexico 1.3 0.0 8.6 3.4 1.2 0.0 6.2 3.4
Nicaragua 0.0 0.0 8.0 3.9 0.0 2.8 9.6 3.9
Rest of Latin America
and the Caribbean 1.7 0.6 9.8 9.9 1.7 0.3 9.9 9.9
Sources: The GTAP version 7p5 rates, which have since been amended, by Narayanan and Walmsley (2008), to create the GTAP Database, version 7; the new rates in the World
Bank Agricultural Distortions Global Database compiled by Valenzuela and Anderson (2008).
Note: The price distortion structure is based on the value of production using undistorted prices as weights.
a. The 15 members of the European Union prior to 2004; see table A.2.
Table A.5. Export Price, Import Price, and Export Demand Shocks to National Models from Rest-of-the-World
Agricultural and Trade Policy Reform, Selected Developing Countries
(percent deviation from the baseline)
a. Argentina (global results, excluding Argentina)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Paddy rice 4.8 5.6 98.7 98.8 n.a. n.a.
Wheat 5.0 5.7 21.7 17.4 n.a. n.a.
Other grains 4.7 5.5 30.8 33.6 4.8 6.5
Oilseeds 4.8 5.6 31.1 30.1 2.8 5.0
Sugarcane and beets n.a. n.a. n.a. n.a. n.a. n.a.
Plant-based fibers 4.0 4.9 30.9 36.3 7.8 9.2
Vegetables and fruits 5.1 5.9 34.3 31.9 6.4 7.7
Other crops 5.6 6.4 24.3 22.5 3.9 4.9
Cattle, sheep, and so on 5.5 6.2 36.2 33.4 1.7 2.9
Other livestock 4.7 5.5 28.9 33.3 1.5 2.7
Raw milk n.a. n.a. n.a. n.a. n.a. n.a.
Wool 5.4 6.2 34.1 30.2 2.7 2.6
Beef and sheep meat 4.5 5.3 105.2 113.8 5.7 7.4
Other meat products 4.0 4.8 148.7 154.5 5.7 6.7
Vegetable oils and fats 4.2 5.0 16.2 15.9 0.4 1.6
Dairy products 3.8 4.7 426.9 439.4 7.1 9.2
Processed rice 4.1 4.9 90.2 91.5 0.6 1.9
Refined sugar 3.9 4.7 67.4 86.3 n.a. n.a.
471
Other food, beverages, and tobacco 3.3 4.1 12.3 26.5 3.5 2.1
(Table continues on the following pages.)
Table A.5. Export Price, Import Price, and Export Demand Shocks to National Models from Rest-of-the-World
Agricultural and Trade Policy Reform, Selected Developing Countries (continued)
472
(percent deviation from the baseline)
a. Argentina (global results, excluding Argentina)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Other primary products 3.0 3.8 21.0 19.0 2.1 2.9
Textiles and wearing apparel 2.8 3.7 2.4 20.5 0.8 2.6
Other manufacturing 2.6 3.5 25.8 18.3 0.4 1.8
Services 3.0 3.8 12.0 13.4 0.3 0.3
Agriculture and food 4.3 5.1 21.2 17.3 4.0 4.1
Agriculture 4.9 5.7 14.7 14.7 4.5 5.9
Processed foods 4.0 4.8 25.2 18.9 3.7 3.1
Other manufacturing 2.7 3.6 22.7 18.6 0.5 1.9
Nontradables 3.0 3.8 12.0 13.4 0.3 0.3
Total 3.6 4.4 0.5 0.9 0.4 1.6
Merchandise trade 3.7 4.5 1.1 0.9 0.6 1.9
b. Brazil (global results, excluding Brazil)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Paddy rice n.a. n.a. n.a. n.a. 4.8 6.4
Wheat 7.1 7.2 19.6 33.7 2.8 3.5
Other grains 8.4 8.6 19.9 21.8 3.3 3.1
Oilseeds 8.1 8.3 23.9 21.8 1.5 3.2
Sugarcane and beets n.a. n.a. n.a. n.a. n.a. n.a.
Plant-based fibers 8.7 8.9 2.4 1.0 14.4 16.7
Vegetables and fruits 8.6 8.9 54.8 51.7 7.1 7.2
Other crops 9.0 9.3 6.4 14.3 0.0 0.7
Cattle, sheep, and so on 8.9 9.1 51.9 47.8 1.1 2.3
Other livestock 8.8 9.0 41.5 38.4 0.0 1.0
Raw milk n.a. n.a. n.a. n.a. n.a. n.a.
Wool 10.8 11.1 55.5 47.7 n.a. n.a.
Beef and sheep meat 7.6 7.7 918.9 972.6 5.1 6.6
Other meat products 7.8 8.0 76.2 84.7 3.4 2.7
Vegetable oils and fats 6.8 6.9 40.6 40.8 0.8 0.3
Dairy products 7.0 7.1 572.8 626.0 10.5 11.1
Processed rice 6.7 6.8 27.5 28.0 4.0 5.3
Refined sugar 6.5 6.6 140.7 164.1 n.a. n.a.
Other food, beverages, and tobacco 6.1 6.1 84.5 38.1 7.3 2.4
Other primary products 5.6 5.7 30.5 33.5 2.6 1.4
Textiles and wearing apparel 5.4 5.5 27.6 30.3 0.1 1.2
Other manufacturing 5.1 5.2 32.4 29.5 0.6 0.7
Services 5.9 5.9 21.7 20.5 0.2 0.3
Agriculture and food 7.5 7.7 92.8 87.0 3.4 1.9
Agriculture 8.5 8.7 12.7 8.5 0.9 1.0
Processed foods 7.1 7.3 166.2 153.4 6.0 2.9
Other manufacturing 5.2 5.3 31.8 30.1 0.3 0.8
Nontradables 5.9 5.9 21.7 20.5 0.2 0.3
Total 6.1 6.3 2.2 1.9 0.3 0.7
Merchandise trade 6.1 6.3 4.9 4.4 0.5 0.8
473
(Table continues on the following pages.)
474
Table A.5. Export Price, Import Price, and Export Demand Shocks to National Models from Rest-of-the-World
Agricultural and Trade Policy Reform, Selected Developing Countries (continued)
(percent deviation from the baseline)
c. China (global results, excluding China)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Paddy rice 4.2 1.8 94.9 123.6 n.a. n.a.
Wheat 3.5 1.4 15.5 45.8 2.9 3.6
Other grains 3.9 1.6 105.1 157.7 6.5 6.5
Oilseeds 4.0 1.7 10.3 42.9 2.8 2.3
Sugarcane and beets n.a. n.a. n.a. n.a. n.a. n.a.
Plant-based fibers 3.3 1.3 30.0 51.4 10.0 11.5
Vegetables and fruits 4.2 1.8 185.5 232.9 1.9 1.6
Other crops 4.5 2.0 12.7 8.4 1.3 1.5
Cattle, sheep, and so on 4.4 1.9 18.6 3.1 6.5 6.6
Other livestock 3.8 1.6 20.8 0.2 0.7 1.6
Raw milk 4.1 1.7 48.3 31.7 1.8 0.7
Wool 3.8 1.6 13.1 10.1 4.9 4.9
Beef and sheep meat 3.4 1.3 1.8 21.0 7.0 7.6
Other meat products 3.5 1.3 30.7 58.1 1.9 2.8
Vegetable oils and fats 1.8 0.3 6.4 5.7 0.2 0.9
Dairy products 2.9 0.8 191.2 251.6 10.9 11.4
Processed rice 3.0 0.9 148.8 192.1 4.2 3.4
Refined sugar 3.0 0.8 410.2 560.4 1.4 2.0
Other food, beverages, and tobacco 2.9 0.8 66.4 16.0 0.0 1.0
Other primary products 2.7 0.6 7.8 2.0 0.5 1.1
Textiles and wearing apparel 2.6 0.8 13.7 2.1 0.2 0.4
Other manufacturing 2.2 0.5 3.3 1.6 0.7 0.3
Services 2.5 0.5 10.5 0.9 0.1 0.2
Agriculture and food 3.3 1.2 66.6 33.8 1.4 1.5
Agriculture 4.1 1.8 72.2 106.3 1.8 2.4
Processed foods 3.0 0.9 63.9 0.4 0.9 0.3
Other manufacturing 2.3 0.5 0.6 1.6 0.6 0.3
Nontradables 2.5 0.5 10.5 0.9 0.1 0.2
Total 2.4 0.6 2.2 0.3 0.6 0.4
Merchandise trade 2.4 0.6 3.2 0.3 0.7 0.4
d. Indonesia (global results, excluding Indonesia)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Paddy rice n.a. n.a. n.a. n.a. 4.3 2.7
Wheat n.a. n.a. n.a. n.a. 6.8 7.1
Other grains 3.1 1.4 50.8 69.4 2.7 2.8
Oilseeds 3.0 1.3 13.6 7.6 1.8 1.3
Sugarcane and beets n.a. n.a. n.a. n.a. n.a. n.a.
Plant-based fibers 3.1 1.4 2.8 38.2 7.6 8.7
Vegetables and fruits 3.1 1.4 5.4 20.9 2.6 1.9
Other crops 3.0 1.3 13.4 2.6 1.5 1.8
Cattle, sheep, and so on 3.0 1.3 1.1 24.3 5.6 5.5
475
Other livestock 2.7 1.1 18.3 5.2 1.9 0.6
(Table continues on the following pages.)
476 Table A.5. Export Price, Import Price, and Export Demand Shocks to National Models from Rest-of-the-World
Agricultural and Trade Policy Reform, Selected Developing Countries (continued)
(percent deviation from the baseline)
d. Indonesia (global results, excluding Indonesia)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Raw milk n.a. n.a. n.a. n.a. n.a. n.a.
Wool n.a. n.a. n.a. n.a. 9.8 10.0
Beef and sheep meat 3.0 1.4 61.2 50.8 5.6 5.7
Other meat products 2.5 0.8 185.5 265.8 3.3 3.6
Vegetable oils and fats 2.5 0.9 8.9 22.5 0.4 1.1
Dairy products 2.7 1.1 419.3 514.3 8.6 8.8
Processed rice 3.0 1.3 38.1 28.7 3.7 2.8
Refined sugar 2.6 1.0 445.7 502.2 2.9 2.5
Other food, beverages, and tobacco 2.4 0.9 106.0 12.7 0.8 0.7
Other primary products 2.1 0.6 8.6 2.3 1.3 0.8
Textiles and wearing apparel 2.1 1.0 11.4 3.9 0.3 0.4
Other manufacturing 1.9 0.6 4.3 2.6 0.4 0.4
Services 2.1 0.6 9.0 1.4 0.2 0.2
Agriculture and food 2.5 1.0 39.0 12.9 3.0 3.2
Agriculture 3.0 1.3 11.2 4.4 4.1 4.5
Processed foods 2.5 0.9 51.4 15.0 1.7 1.8
Other manufacturing 2.0 0.6 2.4 2.0 0.4 0.4
Nontradables 2.1 0.6 9.0 1.4 0.2 0.2
Total 2.1 0.7 2.2 0.1 0.5 0.6
Merchandise trade 2.1 0.7 3.1 0.0 0.7 0.8
e. Mozambique (global results, excluding Mozambique)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Paddy rice n.a. n.a. n.a. n.a. 8.5 9.7
Wheat n.a. n.a. n.a. n.a. 0.9 0.8
Other grains 1.1 1.4 3.4 19.7 2.6 4.1
Oilseeds 0.6 0.9 496.8 567.8 2.7 1.5
Sugarcane and beets n.a. n.a. n.a. n.a. n.a. n.a.
Plant-based fibers 0.7 1.2 48.4 56.4 n.a. n.a.
Vegetables and fruits 1.0 1.3 9.7 2.8 1.2 0.1
Other crops 1.1 1.4 8.7 16.0 0.7 0.3
Cattle, sheep, and so on n.a. n.a. n.a. n.a. n.a. n.a.
Other livestock n.a. n.a. n.a. n.a. 1.7 0.1
Raw milk n.a. n.a. n.a. n.a. n.a. n.a.
Wool n.a. n.a. n.a. n.a. n.a. n.a.
Beef and sheep meat n.a. n.a. n.a. n.a. 1.3 0.1
Other meat products n.a. n.a. n.a. n.a. 1.6 3.1
Vegetable oils and fats 0.3 0.7 13.1 10.8 0.3 0.8
Dairy products n.a. n.a. n.a. n.a. 0.9 0.4
Processed rice n.a. n.a. n.a. n.a. 3.3 3.2
Refined sugar 0.4 0.9 21.9 11.9 1.4 0.1
Other food, beverages, and tobacco 0.4 0.7 5.8 15.7 1.9 0.3
Other primary products 0.8 1.1 38.4 3.1 0.6 0.4
Textiles and wearing apparel 0.3 0.8 22.3 3.2 1.3 0.6
477
Other manufacturing 0.2 0.7 7.6 5.1 0.8 0.2
(Table continues on the following pages.)
478
Table A.5. Export Price, Import Price, and Export Demand Shocks to National Models from Rest-of-the-World
Agricultural and Trade Policy Reform, Selected Developing Countries (continued)
(percent deviation from the baseline)
e. Mozambique (global results, excluding Mozambique)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Services 0.3 0.7 1.7 1.0 0.3 0.3
Agriculture and food 0.7 1.0 15.7 18.0 0.5 0.3
Agriculture 0.9 1.3 37.7 48.9 0.5 0.0
Processed foods 0.4 0.7 6.9 13.6 1.1 0.5
Other manufacturing 0.2 0.7 5.2 5.0 0.8 0.3
Nontradables 0.3 0.7 1.7 1.0 0.3 0.3
Total 0.3 0.7 0.3 0.0 0.5 0.3
Merchandise trade 0.3 0.8 1.1 0.5 0.5 0.3
f. Nicaragua (global results, excluding Nicaragua)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Paddy rice n.a. n.a. n.a. n.a. 8.5 9.7
Wheat n.a. n.a. n.a. n.a. 2.1 3.1
Other grains n.a. n.a. n.a. n.a. 16.4 17.6
Oilseeds 2.3 2.7 41.2 49.4 n.a. n.a.
Sugarcane and beets n.a. n.a. n.a. n.a. n.a. n.a.
Plant-based fibers n.a. n.a. n.a. n.a. n.a. n.a.
Vegetables and fruits 2.4 2.8 18.0 15.2 2.6 4.6
Other crops 2.5 2.9 18.2 14.2 7.2 9.1
Cattle, sheep, and so on 2.8 3.3 21.7 31.3 18.7 21.0
Other livestock 3.0 3.5 30.5 24.7 1.7 3.7
Raw milk n.a. n.a. n.a. n.a. n.a. n.a.
Wool n.a. n.a. n.a. n.a. n.a. n.a.
Beef and sheep meat 1.5 2.0 30.3 36.2 2.0 3.1
Other meat products 2.3 2.8 64.1 72.7 5.9 7.7
Vegetable oils and fats 1.8 2.1 24.2 22.5 0.1 1.3
Dairy products 2.2 2.5 8.7 0.6 5.8 7.7
Processed rice n.a. n.a. n.a. n.a. 0.1 1.1
Refined sugar 1.7 2.0 49.3 64.8 n.a. n.a.
Other food, beverages, and tobacco 1.8 2.1 8.5 13.3 1.1 1.4
Other primary products 1.5 1.6 27.5 4.9 0.5 2.1
Textiles and wearing apparel 1.3 1.4 5.6 4.8 0.2 0.6
Other manufacturing 1.1 1.4 23.1 7.1 0.0 1.0
Services 1.4 1.4 6.2 4.8 0.4 0.2
Agriculture and food 2.0 2.4 9.4 8.9 2.4 3.3
Agriculture 2.5 2.9 2.1 3.2 5.6 6.9
Processed foods 1.7 2.1 18.1 13.1 1.0 1.7
Other manufacturing 1.2 1.4 2.6 5.4 0.0 1.0
Nontradables 1.4 1.4 6.2 4.8 0.4 0.2
Total 1.5 1.8 1.2 0.1 0.3 1.3
Merchandise trade 1.6 1.8 2.5 0.6 0.4 1.4
479
(Table continues on the following pages.)
480 Table A.5. Export Price, Import Price, and Export Demand Shocks to National Models from Rest-of-the-World
Agricultural and Trade Policy Reform, Selected Developing Countries (continued)
(percent deviation from the baseline)
g. Pakistan (global results, excluding Pakistan)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Paddy rice 1.4 1.0 117.4 138.0 n.a. n.a.
Wheat n.a. n.a. n.a. n.a. 2.4 3.1
Other grains n.a. n.a. n.a. n.a. 13.6 14.6
Oilseeds 1.6 1.2 76.7 103.1 1.5 1.9
Sugarcane and beets n.a. n.a. n.a. n.a. n.a. n.a.
Plant-based fibers 1.3 1.0 57.8 59.2 4.4 6.7
Vegetables and fruits 1.4 1.0 20.9 13.7 2.9 2.6
Other crops 1.5 1.1 17.7 1.6 1.9 0.0
Cattle, sheep, and so on 1.4 1.0 42.1 28.1 n.a. n.a.
Other livestock 1.4 1.0 20.5 9.1 8.0 6.8
Raw milk 1.4 1.0 36.5 27.6 n.a. n.a.
Wool n.a. n.a. n.a. n.a. 10.5 8.8
Beef and sheep meat 1.1 0.6 39.4 31.0 2.2 3.2
Other meat products n.a. n.a. n.a. n.a. 0.2 1.4
Vegetable oils and fats 1.1 0.7 28.0 26.7 1.8 0.4
Dairy products 1.2 0.7 22.0 49.6 16.8 17.8
Processed rice 1.2 0.7 25.8 34.0 8.2 10.2
Refined sugar 1.1 0.6 26.3 5.3 1.6 3.4
Other food, beverages, and tobacco 1.1 0.6 3.6 14.0 0.0 1.7
Other primary products 1.0 0.4 20.0 1.8 0.1 0.8
Textiles and wearing apparel 1.0 0.7 3.1 2.1 0.7 0.5
Other manufacturing 0.7 0.5 2.4 1.9 0.4 0.4
Services 0.9 0.5 4.5 0.6 0.2 0.2
Agriculture and food 1.2 0.8 10.8 15.0 1.6 2.0
Agriculture 1.4 1.0 15.7 25.6 1.5 3.0
Processed foods 1.1 0.7 9.1 11.4 1.7 0.5
Other manufacturing 0.9 0.7 2.3 2.0 0.4 0.4
Nontradables 0.9 0.5 4.5 0.6 0.2 0.2
Total 1.0 0.6 2.0 0.2 0.1 0.5
Merchandise trade 1.0 0.7 3.3 0.1 0.1 0.6
h. The Philippines (global results, excluding the Philippines)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Paddy rice n.a. n.a. n.a. n.a. n.a. n.a.
Wheat n.a. n.a. n.a. n.a. 3.0 3.9
Other grains n.a. n.a. n.a. n.a. 6.1 5.7
Oilseeds n.a. n.a. n.a. n.a. 0.8 0.5
Sugarcane and beets n.a. n.a. n.a. n.a. n.a. n.a.
Plant-based fibers 5.5 3.7 4.0 32.7 13.8 15.6
Vegetables and fruits 5.7 3.8 35.8 51.0 2.4 1.7
Other crops 5.9 3.9 30.3 10.6 1.3 1.4
Cattle, sheep, and so on n.a. n.a. n.a. n.a. 5.6 5.5
Other livestock 5.6 3.6 30.0 12.2 1.0 0.1
481
Raw milk n.a. n.a. n.a. n.a. n.a. n.a.
(Table continues on the following pages.)
482
Table A.5. Export Price, Import Price, and Export Demand Shocks to National Models from Rest-of-the-World
Agricultural and Trade Policy Reform, Selected Developing Countries (continued)
(percent deviation from the baseline)
h. The Philippines (global results, excluding the Philippines)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Wool n.a. n.a. n.a. n.a. n.a. n.a.
Beef and sheep meat 3.7 2.0 95.3 94.0 2.8 4.5
Other meat products 4.7 2.7 14.0 42.0 0.2 0.3
Vegetable oils and fats 2.6 0.9 10.3 5.9 1.1 1.7
Dairy products 4.9 4.2 13.0 19.6 7.0 7.4
Processed rice 5.3 3.3 65.7 59.7 4.3 1.6
Refined sugar 3.9 2.0 885.2 1,238.4 2.1 0.8
Other food, beverages, and tobacco 3.6 2.0 73.0 20.0 1.6 0.4
Other primary products 2.8 1.0 12.6 1.9 0.6 0.9
Textiles and wearing apparel 2.0 1.1 12.4 4.0 0.2 0.4
Other manufacturing 2.1 0.7 5.1 3.7 1.5 0.3
Services 2.9 1.1 11.9 3.6 0.1 0.2
Agriculture and food 4.3 2.5 64.2 46.6 2.5 1.7
Agriculture 5.7 3.8 32.6 48.1 2.6 3.0
Processed foods 3.5 1.8 82.6 45.7 2.4 1.3
Other manufacturing 2.1 0.7 3.8 3.7 1.3 0.3
Nontradables 2.9 1.1 11.9 3.6 0.1 0.2
Total 2.3 0.9 0.4 0.9 1.3 0.4
Merchandise trade 2.3 0.9 0.3 0.7 1.4 0.5
i. South Africa (global results, excluding South Africa)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Paddy rice n.a. n.a. n.a. n.a. n.a. n.a.
Wheat 0.5 0.4 21.6 15.9 0.7 0.7
Other grains 0.5 0.3 48.6 59.3 5.5 5.5
Oilseeds 0.5 0.3 25.5 13.7 2.3 2.0
Sugarcane and beets n.a. n.a. n.a. n.a. n.a. n.a.
Plant-based fibers 0.4 0.3 53.2 52.9 1.2 0.1
Vegetables and fruits 0.5 0.3 38.6 33.7 0.9 0.4
Other crops 0.5 0.3 7.0 10.9 1.9 2.8
Cattle, sheep, and so on 0.5 0.3 5.7 17.1 2.2 0.4
Other livestock 0.6 0.3 10.5 1.2 0.7 1.4
Raw milk 0.5 0.3 32.0 23.8 n.a. n.a.
Wool 0.4 0.4 4.1 11.0 19.5 19.8
Beef and sheep meat 0.5 0.4 443.5 489.9 5.1 5.8
Other meat products 0.6 0.4 11.4 1.9 3.7 4.8
Vegetable oils and fats 0.3 0.1 1.1 0.7 1.8 2.5
Dairy products 0.6 0.5 405.9 447.6 16.6 17.5
Processed rice 0.6 0.5 47.2 41.5 5.0 4.6
Refined sugar 0.5 0.4 147.2 173.5 1.2 2.1
Other food, beverages, and tobacco 0.5 0.4 58.3 10.4 5.0 0.4
Other primary products 0.5 0.4 0.3 1.0 0.2 0.7
Textiles and wearing apparel 0.3 0.4 21.2 0.1 0.9 0.5
483
Other manufacturing 0.4 0.4 2.3 1.2 0.3 0.2
(Table continues on the following pages.)
484 Table A.5. Export Price, Import Price, and Export Demand Shocks to National Models from Rest-of-the-World
Agricultural and Trade Policy Reform, Selected Developing Countries (continued)
(percent deviation from the baseline)
i. South Africa (global results, excluding South Africa)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Services 0.6 0.4 2.7 0.6 0.1 0.3
Agriculture and food 0.5 0.4 31.3 7.7 2.2 0.6
Agriculture 0.5 0.3 23.7 18.6 0.3 0.3
Processed foods 0.5 0.4 80.2 31.0 3.4 0.8
Other manufacturing 0.5 0.4 2.4 0.8 0.3 0.3
Nontradables 0.6 0.4 2.7 0.6 0.1 0.3
Total 0.5 0.4 0.8 0.1 0.2 0.3
Merchandise trade 0.5 0.4 1.3 0.2 0.2 0.3
j. Thailand (global results, excluding Thailand)
Export price impact Export quantity impact Import price impact
All Agriculture All Agriculture All Agriculture
Sector goods only goods only goods only
Paddy rice 7.3 5.7 82.5 118.1 n.a. n.a.
Wheat n.a. n.a. n.a. n.a. 3.3 4.0
Other grains 6.4 4.8 30.1 48.7 4.9 4.5
Oilseeds 6.0 4.7 30.9 18.4 6.0 5.6
Sugarcane and beets n.a. n.a. n.a. n.a. n.a. n.a.
Plant-based fibers 6.8 5.2 7.2 13.5 6.4 7.9
Vegetables and fruits 7.2 5.6 5.3 11.9 1.8 1.1
Other crops 7.3 5.8 33.4 21.5 1.7 1.6
Cattle, sheep, and so on 6.2 4.6 17.2 3.8 3.6 2.8
Other livestock 5.4 3.7 25.0 12.2 0.6 1.1
Raw milk n.a. n.a. n.a. n.a. n.a. n.a.
Wool n.a. n.a. n.a. n.a. 5.5 5.3
Beef and sheep meat 4.1 2.2 91.7 89.2 10.0 10.1
Other meat products 4.5 2.7 48.9 79.7 1.6 2.5
Vegetable oils and fats 2.6 0.9 5.6 20.1 0.7 1.2
Dairy products 4.4 2.6 70.3 93.1 12.2 12.5
Processed rice 6.6 4.9 48.9 64.4 1.4 0.5
Refined sugar 4.6 2.7 506.3 563.6 n.a. n.a.
Other food, beverages, and tobacco 3.5 1.7 47.6 17.2 0.9 1.4
Other primary products 3.3 1.2 10.5 5.1 0.5 0.8
Textiles and wearing apparel 3.2 1.4 0.9 7.6 0.4 0.5
Other manufacturing 2.9 1.1 8.1 6.9 1.3 0.3
Services 3.3 1.2 13.0 3.7 0.2 0.2
Agriculture and food 4.6 3.0 70.3 45.6 1.7 0.9
Agriculture 7.0 5.5 5.9 17.4 1.1 1.6
Processed foods 4.3 2.7 80.0 49.8 2.1 0.5
Other manufacturing 3.0 1.2 7.4 7.0 1.1 0.3
Nontradables 3.3 1.2 13.0 3.7 0.2 0.2
Total 3.3 1.4 1.0 0.4 1.0 0.4
Merchandise trade 3.3 1.5 3.1 0.1 1.2 0.4
Source: World Bank Linkage model simulations by the authors.
485
Note: n.a. not applicable.
486 Distortions to Agricultural Incentives in Latin America
Notes
1. The 10 national studies cover Argentina (Cicowiez, D�az-Bonilla, and D�az-Bonilla; chapter 12),
Brazil (Ferreira Filho and Horridge; chapter 13), China (Zhai and Hertel; chapter 5), Indonesia (Warr;
chapter 6), Mozambique (Arndt and Thurlow; chapter 10), Nicaragua (S�nchez and Vos; chapter 14),
Pakistan (Cororaton and Orden; chapter 7), the Philippines (Cororaton, Corong, and Cockburn;
chapter 8), South Africa (H�rault and Thurlow; chapter 11), and Thailand (Warr; chapter 9).
2. The distortions database of the World Bank research project is documented fully in Anderson
and Valenzuela (2008); it is based on the methodology summarized in Anderson et al. (2008a, 2008b).
3. Such a calculation is pertinent for models in which exports and domestic goods are perfect sub-
stitutes; see Horridge and Zhai (2006) for details on models that assume imperfect substitution.
4. In this study, the average sectoral trade volumes in the Linkage model are derived using Divisia
indexes. These are computed using weights that change over the course of the simulations. In effect,
they are an average of before and after shares. In earlier studies, the results from the Linkage model
were derived using the Paasche export price index (that is, based on trade volumes after the shocks).
5. There is a special version of the GTAP model known as GTAP-AGR that has an agricultural focus
(Keeney and Hertel 2005).
6. A discussion on this and other labor market specification issues is available in van der
Mensbrugghe (2007).
7. The fixed factor in the natural resource sectors is aggregated with capital.
References
Anderson, K., M. Kurzweil, W. Martin, D. Sandri, and E. Valenzuela. 2008a. "Methodology for Measur-
ing Distortions to Agricultural Incentives." In Distortions to Agricultural Incentives: A Global
Perspective, 1955�2007, ed. K. Anderson, Appendix A. London: Palgrave Macmillan; Washington,
DC: World Bank.
------. 2008b. "Measuring Distortions to Agricultural Incentives, Revisited." World Trade Review
7 (4): 1�30.
Anderson, K., and E. Valenzuela. 2008. "Estimates of Global Distortions to Agricultural Incentives,
1955�2007." Data spreadsheet, October, World Bank, Washington, DC. http://go.worldbank.org/
YAO39F35E0.
Armington, P. S. 1969. "A Theory of Demand for Products Distinguished by Place of Production."
IMF Staff Papers 16 (1): 159�78.
Dimaranan, B. D., ed. 2006. Global Trade, Assistance, and Protection: The GTAP 6 Data Base. West
Lafayette, IN: Center for Global Trade Analysis, Department of Agricultural Economics, Purdue
University. https://www.gtap.agecon.purdue.edu/databases/v6/v6_doco.asp.
GIDD Database (Global Income Distribution Dynamics Database). World Bank. http://go
.worldbank.org/YADEAFEJ30.
Horridge, M., and F. Zhai. 2006. "Shocking a Single-Country CGE Model with Export Prices and
Quantities from a Global Model." In Poverty and the WTO: Impacts of the Doha Development
Agenda, ed. T. W. Hertel and L. A. Winters, 94�104. London: Palgrave Macmillan; Washington, DC:
World Bank.
Keeney, R., and T. W. Hertel. 2005. "GTAP-AGR: A Framework for Assessing the Implications of Mul-
tilateral Changes in Agricultural Policies." GTAP Technical Paper 24 (August), Center for Global
Trade Analysis, Department of Agricultural Economics, Purdue University, West Lafayette, IN.
https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=1869.
Narayanan, B. G., and T. L. Walmsley, eds. 2008. Global Trade, Assistance, and Production: The GTAP 7
Data Base. West Lafayette, IN: Center for Global Trade Analysis, Department of Agricultural Eco-
nomics, Purdue University. https://www.gtap.agecon.purdue.edu/databases/v7/v7_doco.asp.
Border Price and Export Demand Shocks in Developing Countries 487
Valenzuela, E., and K. Anderson. 2008. "Alternative Agricultural Price Distortions for CGE Analysis of
Developing Countries, 2004 and 1980�84." Research Memorandum 13 (December), Center for
Global Trade Analysis, Department of Agricultural Economics, Purdue University, West Lafayette,
IN. https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=2925.
van der Mensbrugghe, D. 2005. "Linkage Technical Reference Document: Version 6.0." December,
World Bank, Washington, DC. http://go.worldbank.org/7NP2KK1OH0.
------. 2006. "Linkage Technical Reference Document: Version 6.1." Draft. World Bank, Washington,
DC.
------. 2007. "Modeling the Impact of Trade Liberalization: A Structuralist Perspective?" Paper pre-
sented at the 10th Annual Conference on Global Economic Analysis, Purdue University, West
Lafayette, IN, June 7�9. https://www.gtap.agecon.purdue.edu/resources/download/3332.pdf.
Index
Figures, notes, and tables are indicated by f, n, and t, respectively.
A Anderson, Kym
Africa. See Middle East and North Africa; Sub- acknowledged, 247n, 391n
Saharan Africa; specific countries biographical information, xxi
agricultural price distortions, poverty, and as chapter author, 3, 49, 457
inequality, xvii�xviii, 3�45 cited, 6, 12, 15, 25, 38, 42n7, 50, 51, 52, 60, 96,
analytical framework for, 10�14 99, 128, 132, 192, 210, 248, 335, 366,
defining poverty, 9, 106 407, 431�32, 458
further research required into, 38�40 as editor of Distortions to Agricultural
global CGE models, empirical results of, Incentives: A Global Perspective, xvii
15�25. See also Global Income Arab Republic of Egypt. See Egypt, Arab
Distribution Dynamics; Global Trade Republic of
Analysis Project; Linkage model of Arbache, J. S., 392
global economy Argentina, 359�90
growth-trade-poverty nexus, 39 border price and export demand shocks,
levels of global poverty and inequality, 3�5, 4t 471�72t
liberalization, effects of. See liberalization characteristics of national CGE model, 26t
measuring inequality, 9 conclusions regarding, 385�86
national CGE models, empirical results of, Convertibility Plan, 360
25�35. See also national CGE models, Dutch Disease, 378, 382
and specific countries and regions economic crisis of 2001-02, 360�62
NRAs in developing versus high-income economic recovery in, 363�66
countries, 6�8, 7t export taxes on farm products, 359
policy implications of empirical findings, agricultural growth rates and, 366
40�41 agricultural producer price indexes since
probability of falling into poverty, 39 introduction of, 364�65, 364f
reasons for focusing on, 8�10 growth effects of, 385
Ahmed, S. A., 39 liberalization effects of removing, 376, 378,
AIDADS demand system, 121, 129 379, 382, 383
Albania in Linkage model, 83n10
in GIDD model, 93f, 95f, 114f objectives of imposing, 362�63f
in Linkage model, 460t poverty and inequality affected by, 35, 37,
Almeida dos Reis, J. G., 372 379, 382, 383
Altertax procedure, 52 in simulations, 373�74
analytical framework, 10�14 table of, 369, 370t
489
490 Index
Argentina (continued) in Linkage model, 56, 464t
external trade structure, 368�70, 368t in Mozambique national CGE model, 311
GDP in, 27, 362, 376, 383 in Nicaragua model, 432, 433, 435, 442,
GIDD model, 100f, 102f 444�46, 450
index of agricultural prices, 365f in Pakistan national CGE model, 214,
labor market results, 371�73 219, 245n6
liberalization effects, 374�85 in Philippines national CGE model, 260
aggregate simulation results, 275t, 374�79 in South African national CGE model, 336
different simulations affecting poverty and Arndt, Channing, xxi, 303
inequality, 374�76, 379�83, 380�81t Asia. See East Asia and Pacific; Eastern Europe
export taxes, removing, 376, 378, 379, and Central Asia; South Asia;
382, 383 specific countries
fiscal and growth effects, 383�85, 384t Australia
GDP, 376, 383 GIDD model, 95f, 100f, 102f
global liberalization's effects on poverty and Linkage model
inequality, 28�30t, 31, 32�34t, 35, 37 agricultural outputs, imports, and
macroeconomic effects, 374�76 exports, 64t
sectoral changes, 376, 377t economic welfare, global and national, 59t
unemployment, 375t, 376�79, 382, 383 factor prices, 69, 72t
Linkage model land supply elasticities, 465t
agricultural outputs, imports, and price distortion structure, 54t, 468t
exports, 64t regional concordance, 460t
border price and export demand shocks to sectoral value added, 75t
national model from, 471�72t ORANI general equilibrium model, 182
economic welfare, global and national, 58t TERM CGE model, 394
factor prices, 71t Austria
land supply elasticities, 466t in GIDD model, 95f
national CGE model, use with, 359�60, 366, in Linkage model, 460t
367, 368 Azerbaijan
price distortion structure, 53t, 470t in GIDD model, 93f, 95f, 112t, 114f
regional concordance, 461t in Linkage model, 460t
sectoral value added, 69, 74t
Menem and De la Rua governments, 360�61 B
methodology and data, 366�73 Balisacan, A. M., 248, 250
microsimulations, use of, 359�60, 370�73 Baltic states (Estonia, Latvia, Lithuania)
national CGE model, 359�60, 367�70 GIDD model, 93f, 95f, 100f, 101, 102f, 114f
poverty and inequality Linkage model
different simulations affecting, 374�76, agricultural outputs, imports, and
379�83, 380�81t exports, 64t
global liberalization's effects on, 28�30t, 31, economic welfare, global and national, 59t
32�34t, 35, 37 factor prices, 71t
rural versus urban, 28�29t, 32�33t, 42n16 land supply elasticities, 465t
rural versus urban poverty and inequality in, price distortion structure, 54t, 468t
28�29t, 32�33t, 42n16 regional concordance, 460t
safety nets for the poor in, 383 sectoral value added, 74t
SAM, 367t Bamrungwong, Krisada, 283n
simulations used for, 373�74 Bangladesh
tax replacement schemes, 383 GIDD model
unemployment in, 12, 14, 364, 368, 375t, empirical results of, 23, 24t
376�79, 382, 383 factor prices, 102f
VAT proposal, 383 inequality variations in agricultural and
Armenia nonagricultural households, 95f
in GIDD model, 93f, 95f, 112t, 114f poverty, effects of global liberalization on,
in Linkage model, 460t 110f, 111t
Armington elasticities real national consumption, effects of global
in Chinese CGE model, 153 liberalization on, 100f
in Indonesian Wayang model, 183, share of population in agriculture and of
188�90, 207n4 agriculture in total income, 93f
Index 491
Theil Index, income distributional Philippines, 263�65t, 481�82t
changes, 114f South Africa, 340, 341t, 483�84t
GTAP model, 121 Thailand, 288, 291t, 484�85t
agricultural versus nonagricultural trade Botswana, in Linkage model, 461t
reforms, 136�38t, 138 Bourguignon, F., 8, 96, 419, 436
commodity-specific support, effects of, Brazil, 391�422
140t, 141t Bolsa Familia Program, 393
contribution of earnings to total poverty border price and export demand shocks,
response, 127t 405�9, 407�8t, 472�73t
elasticities approach to poverty characteristics of national CGE model, 26t
headcount, 122t conclusions regarding, 418�19
percentage change in national poverty external trade structure, 405�6t, 409
headcount, 131t factor market closure rules, 420n20
poverty population, stratum contributions geographic issues
to, 124t output, 411�14, 413t
preference erosion resulting from global of poverty and income inequality
liberalization, 134 in base year picture, 398�99, 399f,
price distortion structure, 469t 400�401f
stratum- and earnings-specific elasticities, liberalization effects on, 416, 417t
125, 126t GIDD model
tax replacement, 131t empirical results of, 23, 24t
Linkage model factor prices, 102f, 103
agricultural outputs, imports, and GICs, 108f, 113
exports, 63t inequality variations in agricultural and
economic welfare, global and national, 58t nonagricultural households, 95f
factor prices, 71t poverty, effects of global liberalization on,
land supply elasticities, 466t 110f, 111t, 113
price distortion structure, 53t, 469t real national consumption, effects of global
regional concordance, 461t liberalization on, 100f, 101
sectoral value added, 74t share of employment in agriculture and
Barros, R. P. de, 302, 393, 415, 436, 437 income levels, relationship between, 92f
Belarus, in GIDD model, 93f, 95f share of population in agriculture and of
Belgium, in Linkage model, 460t agriculture in total income, 93f
Benin, in GIDD model, 93f, 95f, 111t, 114f Theil Index, income distributional
Bolivia changes, 114f
in GIDD model, 93f, 95f, 111t, 114f GTAP model, 121
in Linkage model, 461t agricultural versus nonagricultural trade
Bolsa Familia Program, Brazil, 393 reforms, 136�38t
border price and export demand shocks in commodity-specific support, effects of,
Linkage model, 457�87 140t, 141t
export price, import price, and export demand contribution of earnings to total poverty
shocks by country, 467, 471�85t response, 127t
global results, implementing, 462 elasticities approach to poverty headcount,
key specifications, 462�67 122t, 123
macroeconomic closure rules, 462 percentage change in national poverty
price distortion structure, 468�70t headcount, 131t, 132
regional concordance, 460�61t poverty population, stratum contributions
sectoral concordance, 458, 459t to, 124t
shocks imposed, 458, 459t price distortion structure, 470t
border price and export demand shocks in tax replacement, 131t
national models labor market
Argentina, 471�72t in base year picture, 402�3t, 402�4
Brazil, 405�9, 407�8t, 472�73t in national model, 394�95
China, 474�75t unemployment rates, 397
Indonesia, 190�92, 191t, 193t, 475�76t liberalization effects, 405�16
Mozambique, 315, 316�17t, 477�78t border price and export demand shocks,
Nicaragua, 439�41, 440t, 478�79t 405�9, 407�8t
Pakistan, 219, 220�21t, 480�81t external trade structure, 405�6t, 409
492 Index
Brazil (continued) price distortion structure, 54t, 468t
GDP, 409�11, 418 regional concordance, 460t
geographic outputs, 411�14, 413t sectoral value added, 74t
global liberalization's effects on poverty and Burkina Faso, in GIDD model, 93f, 95f, 111t, 114f
inequality, 28�30t, 31, 32�34t, 36t Burundi, in GIDD model, 93f, 95f, 111t, 114f
macroeconomic effects, 409�10, 410t Bussolo, Maurizio, xxi, 8, 19, 23, 24, 31, 80, 87,
national model simulations, poverty and 96, 457
inequality effects, 414�16, 414t,
416t, 417t
sectoral outputs, 411�12t C
Linkage model Cai, J., 22
agricultural outputs, imports, and Cambodia
exports, 64t in GIDD model, 93f, 95f, 111t, 114f
border price and export demand shocks to in Linkage model, 461t
national model from, 472�73t Cameroon, in GIDD model, 93f, 95f, 111t, 114f
economic welfare, global and national, 58t Canada
factor prices, 71t GIDD model, 95f, 100f, 102f
land supply elasticities, 466t Linkage model
national model, use in, 396, 404�5, 407, agricultural outputs, imports, and
420n22 exports, 64t
price distortion structure, 53t, 470t economic welfare, global and national, 59t
regional concordance, 461t factor prices, 69, 72t
sectoral value added, 69, 74t land supply elasticities, 465t
national model price distortion structure, 54t, 469t
base year picture, 397�404, 398t, 399f, regional concordance, 460t
400�403t sectoral value added, 75t
CGE and microsimulation, use of, Caribbean. See Latin America and Caribbean;
393�96 specific countries
households in, 395 Caribbean Basin Initiative, 428, 454n3
labor market in, 394�95 cattle. See livestock and meat products
Linkage model, use of, 396, 404�5, 407, Central Asia. See Eastern Europe and Central
420n22 Asia; specific countries
macroeconomic closure rules, 404�5 CGE. See computable general equilibrium
procedure for running, 395�96 (CGE) modeling
simulations, 404 Chen, S., 96, 147, 148
TERM-BR, 394, 419n5 Chile
poverty and income inequality GIDD model
in base year picture, 397, 398t empirical results of, 23, 24t
geographic aspects of factor prices, 102f
in base year picture, 398�99, 399f, inequality variations in agricultural and
400�401f nonagricultural households, 95f
liberalization effects on, 416, 417t poverty, effects of global liberalization on,
global liberalization's effects on, 28�30t, 31, 109, 111t
32�34t, 36t real national consumption, effects of global
incidence of, 391, 392�93 liberalization on, 100f
national model simulations, 414�16, 414t, share of population in agriculture and of
416t, 417t agriculture in total income, 93f
rural versus urban, 28�29t, 32�33t Theil Index, income distributional
rural versus urban poverty and inequality in, changes, 114f
28�29t, 32�33t global liberalization, effects on incidence of
transfer payments (remittances), importance poverty and inequality in, 36t
of, 127t, 128 GTAP model, 121
Bulgaria agricultural versus nonagricultural trade
GIDD model, 93f, 95f, 100f, 102f, 114f reforms, 131t
Linkage model commodity-specific support, effects of,
economic welfare, global and national, 59t 140t, 141t
factor prices, 71t concentration of poverty in agricultural
land supply elasticities, 465t households, 121
Index 493
contribution of earnings to total poverty land market rental assumption,
response, 127t 173�75, 174t
elasticities approach to poverty macroeconomic effects, 160�63, 161t
headcount, 122t national model impacts on poverty and
percentage change in national poverty inequality, 163�66, 164t
headcount, 131t sectoral impacts, 171�73
poverty population, stratum contributions Linkage model
to, 124t agricultural outputs, imports, and
price distortion structure, 470t exports, 63t
tax replacement, 131t border price and export demand shocks to
Linkage model national model from, 474�75t
agricultural outputs, imports, and economic welfare, global and national, 58t
exports, 64t empirical results of, 15t, 17, 18t
economic welfare, global and national, 58t factor prices, 70t
factor prices, 71t land supply elasticities, 466t
land supply elasticities, 466t poverty, effects on, 76�80, 77�79t
price distortion structure, 53t, 470t price distortion structure, 53t, 469t
regional concordance, 461t regional concordance, 461t
sectoral value added, 74t sectoral value added, 69, 73t
transfer payments (remittances), importance NRAs in developing versus high-income
of, 127t, 128 countries, 7t
China, 147�78 policy implications for, 175�76
benchmark data, social accounting matrix poverty and inequality
used for, 153�56, 154�55t global model effects on, 28�30t, 31, 32�34t,
border price and export demand shocks, 35, 36t, 37
474�75t levels of, 3, 4t, 147
CGE model and data, 149�56 national model impacts on, 163�66, 164t
characteristics of national CGE model, 26t rural versus urban, 28�29t, 32�33t, 147
Doha Round trade liberalizations, effects of, 149 production, modeling, 152�53
factor prices, 161t, 162 rural versus urban
GIDD model current market reforms, impact of, 148
factor prices, 101, 102f impact of global liberalization in poverty
inequality variations in agricultural and and inequality, 164t, 165
nonagricultural households, 95f incidence of poverty and inequality, 28�29t,
poverty, effects of global liberalization 32�33t, 147
on, 111t migration from country to city, 151�52,
real national consumption, effects of global 160�62, 161t, 163, 173
liberalization on, 100f, 101 sectoral impacts of liberalization, 171�73
share of employment in agriculture and sectoral structure of benchmark data,
income levels, relationship 154�55t, 154�56
between, 92f simulation design for, 156�59, 157�58t
Theil Index, income distributional TERM CGE model, 419n5
changes, 114f trade, modeling, 152�53
household behavior in, 149�51 WTO accession, 52, 99, 128,
household impact of liberalization, 166�71, 147�48, 149
167�69f Cicowiez, Martin, xxi, 35, 359
labor market Cirera, X., 94
hukou system, 6, 35, 152, 165 closure. See factor market closure rules;
reform/relaxation of hukou system, effects macroeconomic closure rules
of (LABOR), 39, 157t, 159, 161�66, Cockburn, John, xxi, 3, 247, 279, 283n
161t, 164t, 169f, 170�71, 175�76 Colombia
land market rental assumption, 173�75, 174t GIDD model
land reform in (LAND), 157t, 159, 161t, 162�66, empirical results of, 23, 24t
164t, 169f, 170�71, 171t, 175�76 factor prices, 102f
liberalization effects, 160�75 inequality variations in agricultural and
global model effects on poverty and nonagricultural households, 95f
inequality,28�30t, 31,32�34t, 35,36t, 37 poverty, effects of global liberalization on,
household impacts, 166�71, 167�69f 110f, 111t
494 Index
Colombia (continued) cotton. See textiles and textile raw materials
real national consumption, effects of global Croatia, in Linkage model, 460t
liberalization on, 100f Croser, Johanna, 49n
share of population in agriculture and of Cyprus, in Linkage model, 460t
agriculture in total income, 93f Czech Republic
Theil Index, income distributional GIDD model, 95f, 100f, 102f
changes, 114f Linkage model
global liberalization, effects on incidence of agricultural outputs, imports, and
poverty and inequality in, 36t exports, 64t
GTAP model, 121 economic welfare, global and national, 59t
agricultural versus nonagricultural trade factor prices, 71t
reforms, 136�38t land supply elasticities, 465t
commodity-specific support, effects of, price distortion structure, 54t, 468t
140t, 141t regional concordance, 460t
concentration of poverty in agricultural sectoral value added, 74t
households, 121
contribution of earnings to total poverty D
response, 127t dairy
elasticities approach to poverty headcount, in Argentina, 367t, 369t, 370t, 388n14, 471t
122t border price and export demand shocks, 459t,
percentage change in national poverty 464t, 471�85t
headcount, 131t, 132 in Brazil, 403t, 405�8t, 411�12t, 473t
poverty population, stratum contributions in China, 154t, 158t, 474t
to, 124t GTAP model, commodity-specific support
price distortion structure, 470t in, 140t
self-employed nonagricultural households in Indonesia, 191t, 193t, 476t
as share of poor, 124 in Linkage model, 62t, 65, 66�67t, 68t, 69
tax replacement, 131t in Mozambique, 477t
Linkage model in Nicaragua, 440t, 479t
agricultural outputs, imports, and in Pakistan, 212, 215t, 239, 480t
exports, 64t in Philippines, 255t, 258t, 261t, 263t, 481�82t
economic welfare, global and national, 58t in South Africa, 250, 342�44, 483t
factor prices, 71t in Thailand, 284, 290t, 485t
land supply elasticities, 466t David, C. C., 248, 250
price distortion structure, 53t, 470t De la Rua government, Argentina, 361
regional concordance, 461t Denmark
sectoral value added, 69, 74t in GIDD model, 95f
transfer payments (remittances), importance in Linkage model, 460t
of, 127t, 128 Dervis, K., 434
commodity-specific support in GTAP model, D�az-Bonilla, Carolina, xxii, 35, 359, 367
120�21, 139�40, 140t, 141t D�az-Bonilla, Eugenio, xxii, 35, 359
computable general equilibrium (CGE) Dickerson, A., 392
modeling Diffenbaugh, N. S., 39
in analytical framework, 11�13 distortions to agricultural incentives. See
global models. See global CGE models agricultural price distortions, poverty,
microsimulation modeling, combined with, 8, and inequality
13, 19. See also microsimulation Distortions to Agricultural Incentives Project
modeling Database, 176n1, 196, 209, 245n1,
national models. See national CGE models 281n1, 298n1, 328n3
Convertibility Plan, Argentina, 360 Divisia indexes, 486n4
corn. See grains and oilseeds Doha Development Agenda, 120, 132, 142n2, 354
Corong, Erwin, xxii, 247, 248, 279 Doha Round
Cororaton, Caesar B., xxii, 209, 212, 247, agricultural policy reform as contentious issue
248, 279 in, 8, 49, 87, 423
Costa Rica, in GIDD model, 93f, 95f, China, trade liberalization effects on, 149
111t, 114f merchandise trade barriers and subsidies,
C�te d'Ivoire, in GIDD model, 93f, 95f, agricultural price distortions
111t, 114f contributing to cost of, 6
Index 495
poverty reduction potential of, 119 factor prices, 71t
prospective agreement, poverty consequences land supply elasticities, 465t
of, 11 poverty, effects on, 77�79t
reasons for participation in, 9 price distortion structure, 54t, 468t
Dominican Republic, in GIDD model, 93f, 95f, regional concordance, 460t
112t, 114f sectoral value added, 74�75t
Dominican Republic�Central America Free Ecuador
Trade Agreement (DR-CAFTA), banana market in EU, discrimination in, 16, 57
423�24, 426�29, 428f, 445, 452 GIDD model
Dorosh, P., 209, 212 factor prices, 102f
Dutch Disease, 378, 382 inequality variations in agricultural and
nonagricultural households, 95f
E poverty, effects of global liberalization on,
East Asia and Pacific. See also specific countries 110f, 112t, 113
GIDD model real national consumption, effects of global
empirical results of, 20t, 21, 22t, 23, 24t, 25 liberalization on, 100f, 101
factor prices, 101, 102f share of population in agriculture and of
global liberalization, effects of, 107t agriculture in total income, 93f
population represented in, 89 Theil Index, income distributional
poverty, effects of global liberalization on, changes, 114f
109, 111t Linkage model
real national consumption, effects of global agricultural outputs, imports, and
liberalization on, 100f exports, 64t
share of population in agriculture and of economic welfare, global and national,
agriculture in total income, 94 57, 58t
Theil Index, income distributional changes empirical results of, 16
following global liberalization, 113 factor prices, 71t
global liberalization, effects on incidence of land supply elasticities, 466t
poverty and inequality in, 37 price distortion structure, 53t, 470t
GTAP model, 121, 469t regional concordance, 461t
Linkage model sectoral value added, 69, 74t
agricultural outputs, imports, and exports, Egypt, Arab Republic of
63t, 65t GIDD model, 100f, 102f
economic welfare, global and national, 58t Linkage model
empirical results of, 15t, 17t, 18t agricultural outputs, imports, and
factor prices, 70�71t exports, 63t
land supply elasticities, 466t economic welfare, global and national, 58t
poverty, effects on, 77�79t factor prices, 70t
price distortion structure, 53t, 469t price distortion structure, 53t, 469t
regional concordance, 461t regional concordance, 460t
sectoral value added, 69, 73�74t sectoral value added, 73t
NRAs in developing versus high-income El Salvador, in GIDD model, 93f, 95f, 112t, 114f
countries, 7t elasticities. See Armington elasticities, and under
poverty and inequality specific global and national models
in GIDD model, 109, 111t Estonia. See Baltic states
levels of, 4t Ethiopia, in GIDD model, 93f, 95f, 111t, 114f
in Linkage model, 77�79t European Free Trade Association, 460t
Theil Index, income distributional changes European Union/Western Europe. See also
following global liberalization, 113 Eastern Europe and Central Asia;
Eastern Europe and Central Asia. See also specific specific countries
countries banana market, discrimination in, 16, 57
developing-country region, treatment as, 52 GIDD model, 92f, 95f, 99, 100f, 102f
GIDD model, 89, 94, 100f, 102f, 112t GTAP model, 128, 468t
Linkage model Linkage model
agricultural outputs, imports, and exports, agricultural outputs, imports, and exports,
64t, 65t 60, 64t
economic welfare, global and national, 59t economic welfare, global and national, 59t
empirical results of, 15t, 17t, 18t factor prices, 69, 72t
496 Index
European Union/Western Europe (continued) Gini coefficient of income distribution, as
land supply elasticities, 465t measure of inequality, 9, 83n12
price distortion structure, 54t, 468t global CGE models, 14�25. See also Global
regional concordance, 460t Income Distribution Dynamics;
sectoral value added, 75t Global Trade Analysis Project; Linkage
preference erosion resulting from global model of global economy
liberalization, 134 in analytical framework, 12�14
ex ante simulation analysis used in GIDD, 88 characteristics of national and global models
exogenous border price shocks. See entries at compared, 26t
border price and export demand shocks Global Income Distribution Dynamics (GIDD),
export demand shocks. See entries at border 8�9, 87�118. See also under specific
price and export demand shocks countries
exports agricultural versus nonagricultural
Argentina, export taxes in. See under households
Argentina characteristics of poor in, 91t
Linkage model, prospective effects of global global liberalization, global effects of,
liberalization in, 60�68, 62�67t 105�6, 105t
subsidies. See subsidies income distributions among,
89�92, 90f
F inequality variations, 94, 95f
factor market closure rules in analytic framework, 11
Brazil, 420n20 characteristics of national and global models
Nicaragua, 433�34 compared, 26t
factor prices conclusions regarding, 115�16
in China, 161t, 162 empirical findings, 19�23, 20t, 21t, 22t
in GIDD model, 101�3, 102f ex ante simulation analysis used in, 88
in Linkage model, 68�69, 70�72t initial conditions in data set, 89�96, 90f, 91t,
FAOSTAT Database, 42n9 92f, 93f, 95f
Ferreira, F. H. G., 96 labor market effects, accounting for, 98,
Ferreira Filho, Joaquim Bento de Souza, xxii, 391 117n13
Finland liberalization effects, 37, 98�115
in GIDD model, 95f macroeconomic general equilibrium
in Linkage model, 460t effects, 99�103, 100f, 102f
TERM CGE model for, 419n5 microsimulation results, 103�15
foreign investment distortions, 82n3 GICs, 104�5, 104f, 107�9, 108f, 113,
Foster, J., 276, 397 117n20
France global poverty and inequality, 105�9,
in GIDD model, 95f 105t, 107t, 108f
in Linkage model, 460t regions and countries, poverty and
Franco, S., 419 inequality effects within, 102f, 109�15,
fruit. See vegetables, fruits, and vegetable oils 111�12t, 114f
Theil Index, income distributional
G changes, 113�15, 114f
The Gambia, in GIDD model, 93f, 95f, 111t, 114f Linkage model, use of, 88, 97, 117n13
Ganuza, E., 436, 437 methodology of model, 96�98, 97f
GDP. See gross domestic product microsimulation modeling combined with
Gempack Software, 128, 395 CGE, 88, 96
Generalized System of Preferences, 428 poverty and inequality
Georgia agricultural versus nonagricultural
in GIDD model, 93f, 95f, 112t, 114f households, 91t, 94, 95f
in Linkage model, 460t global, 105�9, 105t, 107t, 108f
Germany, in GIDD model, 95f in regions and countries, 102f, 109�15,
Ghana, in GIDD model, 93f, 95f, 111t, 114f 111�12t, 114f
Giambiagi, F., 419 share of employment in agriculture
GICs (growth incidence curves), GIDD model, income levels, relationship to, 92f
104�5, 104f, 107�9, 108f, 113, 117n20 share of agriculture in total income and,
GIDD. See Global Income Distribution 92�94, 93f
Dynamics global inequality, 4t, 5
Index 497
Global Trade Analysis Project (GTAP), 9, Indonesia
119�43. See also under exogenous border price shocks, 193t,
specific countries 475�76t
adjustments to database, 50, 52, 128�29 industry assistance rates, 191t
agricultural and nonagricultural households protection of sugar and rice industries in,
contribution of earnings to total poverty 179�81, 202�5
response, 125�28, 127t in Linkage model, 62t, 65, 66�67t, 68t
elasticities approach, 122�23, 122t in Mozambique, 306�7t, 308, 309�10t,
poverty population, stratum contributions 316�17t, 318, 319t, 321t, 323t, 477t
to, 124t in Nicaragua, 424, 440t, 446, 478�79t
stratum- and earnings-specific elasticities, in Pakistan, 211, 212, 215t, 218, 220t, 232, 234,
125, 126t 236t, 239, 480t
stratum classification of, 121�22 in Philippines, 250, 251t, 255t, 258t, 260, 261t,
in analytic framework, 12 263t, 481�82t
characteristics of national and global models in South Africa, 332, 333t, 339t, 341t, 343t,
compared, 26t 345t, 483t
Chinese benchmark data and, 154 in Thailand, 283, 290t, 484�85t
commodity-specific support, effects of, Greece
120�21, 139�40, 140t, 141t in GIDD model, 95f
conclusions regarding, 140�42 in Linkage model, 460t
countries studied in, 121 Green, F., 392
empirical findings, 23�25, 24t Greer, J., 276, 397
GTAP-AGR, 82n2, 486n5 gross domestic product (GDP)
international cross-section approach, 120 agricultural GDP and farm household
liberalization effects, 130�40 welfare, relationship between, 8
agricultural versus nonagricultural trade agricultural share in, 6
reforms, 134�39, 136�38t in Argentina, 27, 362, 376, 383
percentage changes in national poverty Brazil, liberalization effects in, 409�11, 418
headcounts, 130�32, 131t Mozambique, liberalization effects in, 315�18,
on poverty and inequality, 37, 38, 131t, 319�20t, 324�26, 327
133�34 in Nicaragua. See under Nicaragua
preference erosion, 134 South Africa, liberalization effects in, 342,
tax replacement, 130, 131t, 132�33 343�44t, 347, 349�54
Linkage model, use of database for, 51, 52, 56, growth incidence curves (GICs), GIDD model,
458, 467, 468�70t 104�5, 104f, 107�9, 108f,
macroeconomic closure rules, 130 113, 117n20
methodology of model, 128�30 growth-trade-poverty nexus, 39
modifications to model, 129�30 GTAP. See Global Trade Analysis Project
ORANI general equilibrium model for Guatemala, in GIDD model, 93f, 95f, 112t, 114f
Australia, 182 Guinea, in GIDD model, 93f, 95f, 111t, 114f
poverty and inequality Guyana, in GIDD model, 93f, 94, 95f,
analytical approach to poverty modeling, 112t, 114f
121�28, 122t, 124t, 126t, 127t
liberalization effects on, 37, 38, 131t, H
133�34 Haiti, in GIDD model, 93f, 94, 95f,
utility function and associated consumer 112t, 114f
demand system, 121, 129 Harris, R. L., 367, 432
Golub, A., 121 Harrison, G. W., 393
Gorter, Harry de, 391n Harrison, W. J., 130
grains and oilseeds Heckman, J. J., 153
in Argentina, 366, 367t, 369t, 370t, Henriques, R., 392
388n14�15, 471t H�rault, Nicolas, xxii, 331, 340
border price and export demand shocks in Hertel, Thomas W.
Linkage model, 459t, 464t, 471�85t acknowledged, 247n
in Brazil, 403t, 405t, 407t, 411t, 472�73t biographical information, xxii
in China, 154�55t, 158t, 474t as chapter author, 119, 147
GTAP model, commodity-specific support in, cited, 8, 11, 12, 13, 23�25, 27, 39, 120, 121,
139�40, 140t, 141t 122, 129, 148
498 Index
high-income countries. See also specific countries income distribution/income inequality. See
Linkage model agricultural price distortions, poverty,
agricultural outputs, imports, and exports, and inequality, and under specific
64t, 65t countries and regions
economic welfare, global and national, 59t India
factor prices, 72t domestic policy and trade liberalization in,
price distortion structure, 54t 39�40
sectoral value added, 75t GIDD model
NRAs in developing countries versus, 6�8, 7t empirical results of, 22t
Honduras, in GIDD model, 93f, 95f, 112t, 114f factor prices, 102f, 103
Hong Kong, China GICs, 108f, 113
in GIDD model, 95f, 100f, 102f inequality variations in agricultural and
Linkage model nonagricultural households, 95f
agricultural outputs, imports, and poverty, effects of global liberalization on,
exports, 64t 109, 110f, 111t, 113
economic welfare, global and national, 59t real national consumption, effects of global
factor prices, 72t liberalization on, 100f
land supply elasticities, 465t share of employment in agriculture and
negligible influence on, 83n8 income levels, relationship between, 92f
price distortion structure, 469t share of population in agriculture and of
regional concordance, 460t agriculture in total income, 93f
sectoral value added, 75t Theil Index, income distributional
Horridge, Mark changes, 114f
biographical information, xxii global liberalization, effects on incidence of
as chapter author, 391 poverty and inequality in, 36t
cited, 130, 213, 214, 260, 311, 315, 336, 462 levels of poverty and inequality in, 3�5, 4t
De Hoyos, Rafael, xxiii, 19, 23, 24, 31, 80, 87, 457 Linkage model
Huang, J., 150 agricultural outputs, imports, and
hukou system, China. See subhead "labor exports, 63t
market," under China economic welfare, global and national, 58t
human capital theory, 372 empirical results of, 15t, 17�19, 18t
Hungary factor prices, 71t
GIDD model land supply elasticities, 466t
factor prices, 102f poverty, effects on, 77�79t, 80
inequality variations in agricultural and price distortion structure, 53t, 469t
nonagricultural households, 94, 95f regional concordance, 461t
real national consumption, effects of global sectoral value added, 74t, 76
liberalization on, 100f Indonesia, 179�208
share of population in agriculture and of border price and export demand shocks,
agriculture in total income, 93f 190�92, 191t, 193t, 475�76t
Theil Index, income distributional characteristics of national CGE model, 26t
changes, 114f conclusions regarding, 207
Linkage model GIDD model
agricultural outputs, imports, and empirical results of, 23, 24t, 42n11
exports, 64t factor prices, 102f
economic welfare, global and national, 59t inequality variations in agricultural and
factor prices, 71t nonagricultural households, 95f
land supply elasticities, 465t poverty, effects of global liberalization on,
price distortion structure, 54t, 468t 110f, 111t
regional concordance, 460t real national consumption, effects of global
sectoral value added, 75t liberalization on, 100f
share of population in agriculture and of
I agriculture in total income, 93f
imports Theil Index, income distributional
Linkage model, prospective effects of global changes, 114f
liberalization in, 60�68, 62�67t GTAP model, 121
Philippines import-substitution policy, 247, 249 agricultural versus nonagricultural trade
tariffs on. See tariffs reforms, 135�38, 136�38t
Index 499
commodity-specific support, effects of, international cross-section approach in GTAP
140t, 141t model, 120
concentration of poverty in agricultural International Food Policy Research Institute, 432
households, 121 international inequality, 5
contribution of earnings to total poverty Iran, Islamic Republic of, in Linkage model, 460t
response, 127t Ireland
elasticities approach to poverty in GIDD model, 95f
headcount, 122t in Linkage model, 460t
percentage change in national poverty Islamic Republic of Iran, in Linkage model, 460t
headcount, 131t Israel, in GIDD model, 95f
poverty population, stratum contributions Italy
to, 124t in GIDD model, 95f
price distortion structure, 469t in Linkage model, 460t
tax replacement, 131t Ivanic, M., 120, 133
liberalization effects, 194�207
agricultural products, liberalization only of, J
202�7, 203�6t Jamaica, in GIDD model, 93f, 95f, 112t, 114f
global liberalization's effects on poverty and JamlongThai general equilibrium model. See
inequality, 28�30t, 32�34t, 35, 36t under Thailand
incidence of poverty and income inequality, Janvry, A. de, 22
197�202, 198�99t, 201f, 202t Japan
macroeconomic effects, 194�97, 195t GIDD model, 100f, 102f
Linkage model Linkage model
agricultural outputs, imports, and agricultural outputs, imports, and
exports, 63t exports, 64t
border price and export demand shocks to economic welfare, global and national, 59t
national model from, 475�76t factor prices, 69, 72t
economic welfare, global and national, 58t land supply elasticities, 465t
factor prices, 70t price distortion structure, 54t, 469t
land supply elasticities, 466t regional concordance, 460t
price distortion structure, 53t, 469t sectoral value added, 75t
regional concordance, 461t TERM CGE model for, 419n5
sectoral value added, 73t Johansen category, 182
rice and sugar industries, protection of, Jordan, in GIDD model, 93f, 95f, 114f
179�81, 202�5
rural versus urban poverty and inequality in, K
28�29t, 32�33t Katz, L. F., 153
SAM, 181�82, 185�86, 189 Kazakhstan
TERM CGE model, 419n5 GIDD model
Thai study, comparison with, 296�98 factor prices, 102f
Wayang general equilibrium model, inequality variations in agricultural and
181�90 nonagricultural households, 95f
assumed rates of industry assistance, 191t poverty, effects of global liberalization on,
border price and export demand shocks, 112t, 113
190�92, 191t, 193t real national consumption, effects of global
commodities, 183 liberalization on, 100f
database, 189 share of population in agriculture and of
elasticities, 190 agriculture in total income, 93f
factors of production, 183�85, 184t Theil Index, income distributional
households, 185�88, 187t changes, 114f
industries, 182�83 Linkage model
macroeconomic closure, 192�94 agricultural outputs, imports, and exports, 64t
theoretical/analytical structure, 188 economic welfare, global and national, 59t
inequality. See agricultural price distortions, factor prices, 71t
poverty, and inequality, and under land supply elasticities, 465t
specific countries and regions price distortion structure, 54t, 468t
Intal, P. S., 248, 250 regional concordance, 460t
intercountry inequality, 5 sectoral value added, 75t
500 Index
Keeney, Roman, xxiii, 23�25, 27, 119, 129 Linkage model
Kennedy Round, WTO, 386n3 agricultural outputs, imports, and exports,
Kenya, in GIDD model, 93f, 95f, 110f, 111t, 114f 64t, 65t
Korea, Republic of economic welfare, global and national, 58t
as developing country, 56 empirical results of, 15t, 17t, 18t
GIDD model, 95f, 100f, 102f factor prices, 71t
Linkage model land supply elasticities, 466t
agricultural outputs, imports, and poverty, effects on, 77�79t
exports, 63t price distortion structure, 53t, 470t
economic welfare, global and regional concordance, 461t
national, 58t sectoral value added, 69, 74t
factor prices, 70t NRAs in developing versus high-income
land supply elasticities, 465t countries, 7t
price distortion structure, 53t, 469t poverty and inequality
regional concordance, 460t in GIDD model, 109, 111t
sectoral value added, 73t global liberalization, effects of, 37
Kurzweil, Marianne, 49n levels of, 4t
Kyrgyz Republic, in GIDD model in Linkage model, 77�79t
factor prices, 102f Theil Index, income distributional changes
inequality variations in agricultural and following global liberalization, 113
nonagricultural households, 95f Latvia. See Baltic states
poverty, effects of global liberalization Laurel-Langley Agreement, 250
on, 112t Lay, J., 96
real national consumption, effects of global Leite, P. G., 96
liberalization on, 100f Leontief specifications, 188, 253, 308, 336
share of population in agriculture and of Lessem, Rebecca, 87n
agriculture in total income, 93f liberalization
Theil Index, income distributional benefits of, 36�38, 36t
changes, 114f further research required into effects of, 38�40
Kyrgyzstan, in Linkage model, 460t, growth-trade-poverty nexus, 39
465t, 468t national and global CGE models, prospective
effects according to. See under specific
L models and countries
labor market. See under specific countries policy implications of, 40�41
land market rental assumption, China, redistributive effects of, 87�88
173�75, 174t reduction of poverty and inequality by,
Lao PDR, in GIDD model, 93f, 95f 28�30t, 31�35, 32�34t
Latin America and Caribbean. See also specific services, trade in, 39, 82n3
countries Lin, Justin Yifu, xviii
Caribbean Basin Initiative, 428, 454n3 Linkage model of global economy, 8�9, 49�85.
GIDD model See also under specific countries
empirical results of, 20t, 22t, 23, 24t, 25 in analytical framework, 12, 13
factor prices, 101, 102f border price and export demand shocks. See
GICs, 107, 108f border price and export demand
global liberalization, effects of, 107t, shocks in Linkage model
109, 115 caveats regarding, 80�81
population represented in, 89 characteristics of national and global models
poverty, effects of global liberalization on, compared, 26t
109, 111t conclusions regarding, 81�82
real national consumption, effects of global description of, 55�57
liberalization on, 100f elasticities, 463, 464�66t
share of population in agriculture and of empirical results, 15�19, 15t, 17t, 18t
agriculture in total income, 94 GIDD's use of, 88, 97, 117n13
Theil Index, income distributional changes GTAP database, use of, 51, 52, 56, 458, 467,
following global liberalization, 113 468�70t
global liberalization, effects on incidence of key distortions and their effects in 2004,
poverty and inequality in, 37 52�55, 53�54t
GTAP model, 121, 470t key specifications, 462�67
Index 501
liberalization effects, 37�38, 57�60 Madagascar
agricultural output, import, and export, GIDD model, 93f, 95f, 100f, 102f, 111t
60�68, 62�67t Linkage model
economic welfare, global and national, agricultural outputs, imports, and
57�60, 58�59t exports, 63t
poverty, effects on, 76�80, 77�79t economic welfare, global and national, 58t
product and factor prices, effect on, 68�69, factor prices, 70t
68t, 70�72t land supply elasticities, 465t
regional and sectoral distribution of, 60, 61t price distortion structure, 53t, 469t
sectoral value added, 69�76, 73�75t regional concordance, 461t
simulations used, 37�38, 57�60 sectoral value added, 73t
macroeconomic closure rules, 462 maize. See grains and oilseeds
Lithuania. See Baltic states Malawi
Liu, X., 151 GIDD model, empirical results of, 23, 24t
livestock and meat products GTAP model, 121
in Argentina, 367t, 369t, 370t, 388n14�15, 471t agricultural versus nonagricultural trade
border price and export demand shocks in reforms, 136�38t
Linkage model, 459t, 464t, 471�85t commodity-specific support, effects of,
in Brazil, 403t, 405�8t, 411�12t, 473t 140t, 141t
in China, 154�55t, 156, 158t, 474t concentration of poverty in agricultural
in Indonesia, 191t, 193t, 475�76t households, 121
in Linkage model, 62t, 66�67t, 68t contribution of earnings to total poverty
in Mozambique, 306�7t, 309�10t, 316�17t, response, 127t
319t, 321t, 323t, 477t elasticities approach to poverty headcount,
in Nicaragua, 440t, 479t 122t
in Pakistan, 215t, 220t, 232, 236t, 239, 480t percentage change in national poverty
in Philippines, 250�52, 251t, 255t, 258t, 260, headcount, 131t
261t, 263t, 481�82t poverty population, stratum contributions
in South Africa, 332, 333t, 339t, 341t, 343t, to, 124t
345t, 483t tax replacement, 131t
in Thailand, 283, 290t, 485t Linkage model, 461t
Lochner, L., 153 Malaysia
Lofgren, H., 367, 432 GIDD model, 100f, 102f
Luxembourg Linkage model
in GIDD model, 95f agricultural outputs, imports, and
in Linkage model, 460t exports, 63t
economic welfare, global and
M national, 58t
Macedonia, FYR, in GIDD model, 93f, 95f factor prices, 70t
MacMaps, 52 land supply elasticities, 466t
macroeconomic closure rules, 14 price distortion structure, 53t, 469t
Brazilian national model, 404�5 regional concordance, 461t
GTAP model, 130 sectoral value added, 73t
Indonesia, Wayang model, 192�94 Mali, in GIDD model, 93f, 95f, 111t, 114f
Linkage model, 462 Malta, in Linkage model, 460t
Mozambique national CGE model, 314 maquila industry, Nicaragua, 425t, 426, 442, 446,
Nicaraguan national model, 434 454n1�2
South African national model, 337 Martin, Will, xxiii, 3, 60, 87n, 96, 247n
macroeconomic effects Mauritania, in GIDD model, 93f, 95f, 111t, 114f
Argentine national model, 374�76 Mauritius, in Linkage model, 461t
Brazilian national model, 409�10, 410t McCulloch, N., 94
Chinese national model, 160�63, 161t MDG (Millennium Development Goals), 41, 50
GIDD model, 99�103, 100f, 102f meat. See livestock and meat products
Indonesia, Wayang model, 194�97, 195t Medvedev, Denis, xxiii, 19, 23, 24, 31, 80, 87, 457
Nicaraguan national model, 441�42, 441t Melitz, M., 80
South African national model, 341, 342t Melo, J. de, 434
in Thai CGE model, 284, 291t, 295t, Mendon�a, R., 392
298�92 Menem government, Argentina, 360�61
502 Index
Mexico Linkage model
GIDD model agricultural outputs, imports, and exports,
empirical results of, 23, 24t 63t, 65t
factor prices, 102f, 103 economic welfare, global and national, 58t
inequality variations in agricultural and empirical results of, 15t, 17t, 18t
nonagricultural households, 95f factor prices, 70t
poverty, effects of global liberalization on, land supply elasticities, 465t
109, 110f, 112t new distortion estimates, lack of, 82n4
real national consumption, effects of global poverty, effects on, 77�79t
liberalization on, 100f, 101 price distortion structure, 53t, 469t
share of population in agriculture and of regional concordance, 460t
agriculture in total income, 93f sectoral value added, 69, 73t
Theil Index, income distributional migration, rural to urban, in China, 151�52,
changes, 114f 160�62, 161t, 163, 173
global liberalization, effects on incidence of Milanovic, B., 5, 57
poverty and inequality in, 36t milk. See dairy
GTAP model, 121 Millennium Development Goals (MDG), 41, 50
agricultural versus nonagricultural trade Mincer, J. A., 372
reforms, 135�38, 136�38t, 139 Moldova, in GIDD model, 93f, 95f, 112t, 114f
commodity-specific support, effects of, 139, Mongolia, in GIDD model, 95f, 114f
140t, 141t Morocco
contribution of earnings to total poverty in GIDD model, 93f, 95f, 100f, 102f, 110f
response, 127t in Linkage model, 461t, 465t, 469t
elasticities approach to poverty Mozambique, 303�29
headcount, 122t agricultural potential of, 303�4
percentage change in national poverty agricultural price distortions in, 305�8
headcount, 131t border price and export demand shocks, 315,
poverty population, stratum contributions 316�17t, 477�78t
to, 124t characteristics of national CGE model, 26t
preference erosion resulting from global conclusions regarding, 327�28
liberalization, 134 economic structure of, 305, 306�7t
price distortion structure, 470t factor intensities of production, 308, 309�10t
tax replacement, 131t GIDD model, 23, 24t, 100f, 102f
Linkage model government income and expenditure, 314
agricultural outputs, imports, and exports, 64t GTAP model, 121
economic welfare, global and national, 58t agricultural versus nonagricultural trade
factor prices, 71t reforms, 136�38t
land supply elasticities, 466t commodity-specific support, effects of,
price distortion structure, 53t, 470t 140t, 141t
regional concordance, 461t concentration of poverty in agricultural
sectoral value added, 74t households, 121
transfer payments (remittances), importance contribution of earnings to total poverty
of, 127t, 128 response, 127t
microsimulation modeling elasticities approach to poverty
in Argentina, 359�60, 370�73 headcount, 122t
CGE, combined with, 8, 13, 19 percentage change in national poverty
in GTAP model, 88, 96, 122 headcount, 131t
in Nicaragua, 432, 436, 437 poverty population, stratum contributions
in South Africa, 338�39t, 338�40 to, 124t
Middle East and North Africa. See also specific price distortion structure, 469t
countries tax replacement, 131t
GIDD model household income and expenditure, 311�14,
factor prices, 101, 102f 312�13t
population represented in, 89 liberalization effects, 314�27
real national consumption, effects of global border price and export demand shocks,
liberalization on, 100f, 101 315, 316�17t
share of population in agriculture and of domestic liberalization of agricultural
agriculture in total income, 94 products, 326�27
Index 503
domestic liberalization of all commodities, agricultural outputs, imports, and
322�26, 325t exports, 64t
GDP, 315�18, 319�20t, 324�26, 327 economic welfare, global and national, 59t
global liberalization's effects on poverty and factor prices, 69, 72t
inequality, 28�30t, 32�34t, 36t land supply elasticities, 465t
rest-of-the-world agricultural liberalization, price distortion structure, 54t, 468t
316�18t, 322, 325t regional concordance, 460t
rest-of-the-world liberalization of all sectoral value added, 75t
commodities, 315�20, 316�25t Niazi, M. K., 212
simulations used, 314�15 Nicaragua, 423�56
Linkage model agricultural growth affecting poverty and
agricultural outputs, imports, and inequality in, 430�31, 431t
exports, 63t agricultural price distortions in, 429�30f
border price and export demand shocks to agriculture and agricultural trade in, 424�26,
national model from, 477�78t 425�26t
economic welfare, global and national, 58t base year calibration of national model,
factor prices, 70t 435�36
land supply elasticities, 465t border price and export demand shocks,
national CGE model, use with, 311, 315 439�41, 440t, 478�79t
price distortion structure, 53t, 469t CGE, use of, 432
regional concordance, 461t characteristics of national model, 26t
sectoral value added, 69, 73t DR-CAFTA, 423�24, 426�29, 428f, 445, 452
macroeconomic closure rules, 314 factor market closure rules, 433�34
national CGE modeling framework, 308�14 GDP
rural versus urban poverty and inequality in, agricultural share of, 424, 425t
28�29t, 32�33t exports and imports as share of, 427
SAM, 308, 314 liberalization effects, 441t, 443�44t, 443�45
simulations, 314�15 GIDD model
tax replacement schemes in, 326 factor prices, 102f
transfer payments (remittances), importance inequality variations in agricultural and
of, 127t, 128 nonagricultural households, 95f
Murphy, K. M., 153 poverty, effects of global liberalization
on, 112t
N real national consumption, effects of global
national CGE models, 25�35. See also specific liberalization on, 100f
countries share of population in agriculture and of
in analytical framework, 11�14 agriculture in total income, 93f
border price and export demand shocks. See Theil Index, income distributional
border price and export demand changes, 114f
shocks in national models GTAP database, 436, 454n9, 455n15,
characteristics of, 25, 26t 455n19, 470t
incidence of extreme poverty, 27, 28�30t labor market
incidence of income inequality, 27, 32�34t liberalization effects, 445�50, 447�49t
reduction of poverty and inequality by global structure, 434, 436�38
liberalization, 28�30t, 31�35, 32�34t liberalization effects, 439�52
unweighted averages for poverty results, border price and export demand shocks,
28�31, 30t 439�41, 440t
Nazli, H., 212 fiscal costs, 445, 446f
Nelgen, Signe, 49n GDP, 441t, 443�44t, 443�45
Nepal, in GIDD model, 93f, 95f, 114f on labor market, 445�50, 447�49t
poverty, effects of global liberalization macroeconomic effects, 441�42, 441t
on, 111t on poverty and inequality
Netherlands in global liberalization models, 28�30t,
in GIDD model, 95f 32�34t, 36t
in Linkage model, 460t in national model, 450�52, 451t
New Zealand Linkage model
GIDD model, 95f, 100f, 101, 102f agricultural outputs, imports, and
Linkage model exports, 64t
504 Index
Nicaragua (continued) Philippines, estimates of NRA to agriculture
border price and export demand shocks to in, 250�52, 251t
national model from, 478�79t North Africa. See Middle East and North Africa;
economic welfare, global and national, 58t specific countries
factor prices, 71t NRAs. See nominal rates of assistance
land supply elasticities, 466t numerical open-economy growth model, 41n6
national model's use of, 432, 433, 439
price distortion structure, 53t, 470t O
regional concordance, 461t OECD (Organisation for Economic Co-
sectoral value added, 74t operation and Development), 119,
macroeconomic closure rules, 434 128�29, 142n7, 157
macroeconomic indicators in, 424, 425t oils, vegetable. See vegetables, fruits, and
maquila industry, 425t, 426, 442, 446, vegetable oils
454n1�2 oilseeds. See grains and oilseeds
microsimulation, use of, 432, 436, 437 Olper, Alessandro, 391n
national model, 26t, 431�39 ORANI general equilibrium model for
NRAs in, 429�30f Australia, 182
policy implications for, 452�54 Orden, David, xxiii, 209, 212
poverty and inequality Organisation for Economic Co-operation and
agricultural growth affecting, 430�31, 431t Development (OECD), 119, 128�29,
under GIDD model, 112t 142n7, 157
global liberalization's effects on, 28�30t,
32�34t, 36t P
modeling methodology, 436�38 Paasche price index, 117n14
national model's liberalization effects on, Pacific Region. See East Asia and Pacific, and
450�52, 451t specific countries
rural versus urban, 28�29t, 32�33t Paes de Barros, R., 372
rural versus urban poverty and inequality in, Pakistan, 209�46
28�29t, 32�33t agricultural price distortions in, 210�12
simulations used, 438�39 border price and export demand shocks, 219,
tariffs in, 427, 428f 220�21t, 480�81t
unemployment in, 12, 14 characteristics of national CGE model, 26t
Nigeria GIDD model
GIDD model factor prices, 102f
factor prices, 102f inequality variations in agricultural and
inequality variations in agricultural and nonagricultural households, 95f
nonagricultural households, 95f poverty, effects of global liberalization on,
poverty, effects of global liberalization on, 110f, 111t
110f, 111t real national consumption, effects of global
real national consumption, effects of global liberalization on, 100f
liberalization on, 100f, 101 share of population in agriculture and of
share of population in agriculture and of agriculture in total income, 93f
agriculture in total income, 93f Theil Index, income distributional
Theil Index, income distributional changes, 114f
changes, 114f indicators and incidence of poverty, 223,
Linkage model 224f, 224t
agricultural outputs, imports, and liberalization effects, 226�43
exports, 63t agricultural liberalization
economic welfare, global and national, 58t global, 240�41
factor prices, 70t by Pakistan unilaterally, 239�40
land supply elasticities, 465t by rest of the world, 232�35
price distortion structure, 53t, 470t border price and export demand shocks,
regional concordance, 461t 219, 220�21t
sectoral value added, 73t empirical results, 28�30t, 31, 32�34t, 36t
nominal rates of assistance (NRAs) trade liberalization
in developing versus high-income countries, global, 240
6�8, 7t by Pakistan unilaterally, 235�39,
in Nicaragua, 429�30f 236�37t
Index 505
by rest of the world, 226�31, 227�29t, GTAP model, 121
232�33t agricultural versus nonagricultural trade
Linkage model reforms, 136�38t
agricultural outputs, imports, and commodity-specific support, effects of,
exports, 63t 140t, 141t
border price and export demand shocks to contribution of earnings to total poverty
national model from, 480�81t response, 127t
economic welfare, global and national, 58t elasticities approach to poverty
factor prices, 71t headcount, 122t
land supply elasticities, 466t percentage change in national poverty
price distortion structure, 53t, 469t headcount, 131t
regional concordance, 461t poverty population, stratum contributions
sectoral value added, 74t to, 124t
used in national CGE model, 209, 213�14 self-employed nonagricultural households
national CGE model, 212�23 as share of poor, 124
border price and export demand shocks, tax replacement, 131t
219, 220�21t Linkage model, 461t
consumption structure, 218 Philippines, 247�82
household income sources and taxes, agricultural price distortions in, 247, 249�52,
219�23, 222t 251�52t, 253f
households, 214, 218t border price and export demand shocks,
Linkage model and, 209, 213�14 263�65t, 481�82t
SAM, economic structure in, 214, 215�18t GIDD model
simulations used, 209�10, 225�26 empirical results of, 23, 24t
structure of, 212�13 factor prices, 102f
tariffs, 219 inequality variations in agricultural and
policy implications in, 244�45 nonagricultural households, 95f
poverty and inequality poverty, effects of global liberalization on,
in GIDD model, 110f, 111t 110f, 111t, 113
rural versus urban, 28�29t, 32�33t, 42n19 real national consumption, effects of global
Theil Index, income distributional liberalization on, 100f
changes, 114f share of population in agriculture and of
rural versus urban poverty and inequality in, agriculture in total income, 93f
28�29t, 32�33t, 42n19 Theil Index, income distributional
SAM, 210, 212, 214, 215�18t, 223, 241 changes, 114f
tax replacement schemes, 209, 210, 241�43, GTAP model, 121
242�43t, 245n4 agricultural versus nonagricultural trade
palm oil. See vegetables, fruits, and vegetable oils reforms, 136�38t, 138
Panama, in GIDD model, 93f, 95f, 112t, 114f commodity-specific support, effects of,
Paraguay 140t, 141t
in GIDD model, 93f, 95f, 112t, 114f contribution of earnings to total poverty
in Linkage model, 461t response, 127t
Park, A., 151 elasticities approach to poverty
Pearson, K. R., 130 headcount, 122t
Pereira da Silva, L. A., 8, 96 percentage change in national poverty
Peru headcount, 131t
GIDD model poverty population, stratum contributions
empirical results of, 23, 24t to, 124t
inequality variations in agricultural and price distortion structure, 469t
nonagricultural households, 95f tax replacement, 131t
poverty, effects of global liberalization on, import-substitution policy, 247, 249
110f, 112t, 113 liberalization effects, 266�79
share of population in agriculture and of agricultural liberalization
agriculture in total income, 93f global, 272
Theil Index, income distributional by rest of the world, 267�70
changes, 114f unilaterally by Philippines, 271
global liberalization, effects on incidence of border price and export demand shocks,
poverty and inequality in, 36t 263�65t
506 Index
Philippines (continued) economic welfare, global and national, 59t
consumer price index, 274t, 275 factor prices, 71t
global liberalization's effects on poverty and land supply elasticities, 465t
inequality, 28�30t, 31, 32�34t, 35, 36t price distortion structure, 54t, 468t
household incomes, 272�75, 273t regional concordance, 460t
national model's effects on poverty and sectoral value added, 75t
inequality, 276�79, 277�78t policy implications of empirical findings, 40�41
trade liberalization Portugal
global, 271�72 in GIDD model, 95f
by rest of the world, 266�67, 268�69t in Linkage model, 460t
unilaterally by Philippines, 270 poverty. See agricultural price distortions,
welfare gains, 275�76 poverty, and inequality, and under
Linkage model specific countries and regions
agricultural outputs, imports, and Powell, A., 121
exports, 63t PPP (purchasing power parity) prices, in GIDD
border price and export demand shocks to data set, 89
national model from, 481�82t preference erosion resulting from global
economic welfare, global and national, 58t liberalization, 134
factor prices, 70t probability of falling into poverty, 39
land supply elasticities, 466t product prices, in Linkage model, 68�69, 68t
national CGE model and, 248, 260, 262�65t purchasing power parity (PPP) prices, in GIDD
price distortion structure, 53t, 469t data set, 89
regional concordance, 461t
sectoral value added, 73t R
national CGE model, 253�60 Rausser, Gordon, 391n
characteristics of, 26t Ravallion, M., 96, 147, 148
consumption structure of households, 260, regional and sectoral distribution of welfare
261�62t effects, in Linkage model, 60, 61t
Linkage model and, 248, 260, 262�65t remittances (transfer payments), 125�28,
production structure, 254�59, 255�57t 127t, 136t
simulations, 260�66 replacement of tariff revenue. See tax
trade structure and elasticity parameters, replacement schemes
258�59t, 259�60 Republic of Korea. See Korea, Republic of
NRA to agriculture, estimates of, 250�52, 251t Republic of Yemen, in GIDD model, 93f, 95f,
policy implications, 280 114f, 1110f
poverty and inequality Rep�blica Bolivariana de Venezuela. See
in GIDD model, 110f, 111t, 113 Venezuela, Rep�blica Bolivariana de
global liberalization's effects on, 28�30t, 31, rice. See grains and oilseeds
32�34t, 35, 36t Rimmer, M., 121
in GTAP model, 124t Robilliard, A.-S., 419, 436
incidence of, 252, 252t, 253f Robinson, S., 367, 419, 432, 434, 436
national model's effects on, 276�79, Rocha, S., 392, 415
277�78t Rodrik, D., 87
rural versus urban, 28�29t, 32�33t Romania
Theil Index, income distributional GIDD model
changes, 114f factor prices, 102f
rural versus urban poverty and inequality in, inequality variations in agricultural and
28�29t, 32�33t nonagricultural households, 84, 95f
SAM, 248, 253 real national consumption, effects of global
tariff binding overhang, 249, 281n5 liberalization on, 100f
tax replacement schemes, 279 share of population in agriculture and of
plant-based fibers. See textiles and textile raw agriculture in total income, 93f
materials Theil Index, income distributional changes,
Poland 114f
GIDD model, 93f, 95f, 100f, 102f, 114f Linkage model
Linkage model agricultural outputs, imports, and
agricultural outputs, imports, and exports, 64t
exports, 64t economic welfare, global and national, 59t
Index 507
factor prices, 71t real national consumption, effects of global
land supply elasticities, 465t liberalization on, 100f, 101
price distortion structure, 54t, 468t share of population in agriculture and of
regional concordance, 460t agriculture in total income, 93f
sectoral value added, 75t Theil Index, income distributional
Rozelle, S., 150 changes, 114f
rural versus urban. See also under specific Linkage model
countries agricultural outputs, imports, and
incidence of extreme poverty, 28�29t exports, 63t
levels of inequality, 5, 32�33t economic welfare, global and national, 58t
redistributive effects of trade liberalization, 88 factor prices, 70t
Russian Federation land supply elasticities, 466t
GIDD model price distortion structure, 53t, 470t
factor prices, 102f regional concordance, 461t
inequality variations in agricultural and sectoral value added, 73t
nonagricultural households, 95f services, trade in, 39, 82n3
poverty, effects of global liberalization sheep. See livestock and meat products
on, 112t Shi, X., 152
real national consumption, effects of global Sicular, T., 150, 152
liberalization on, 100f Singapore
share of employment in agriculture and GIDD model, 95f, 100f, 102f
income levels, relationship between, 92f Linkage model
share of population in agriculture and of agricultural outputs, imports, and
agriculture in total income, 93f exports, 64t
Theil Index, income distributional economic welfare, global and national, 59t
changes, 114f factor prices, 72t
Linkage model land supply elasticities, 465t
agricultural outputs, imports, and negligible influence on, 83n8
exports, 64t price distortion structure, 469t
economic welfare, global and regional concordance, 460t
national, 59t sectoral value added, 75t
factor prices, 71t Slovak Republic
land supply elasticities, 465t GIDD model, 100f, 102f
price distortion structure, 54t, 468t Linkage model
regional concordance, 460t agricultural outputs, imports, and
sectoral value added, 75t exports, 64t
Rutherford, T. F., 41n6 economic welfare, global and national, 59t
factor prices, 71t
S land supply elasticities, 465t
Sadoulet, E., 22 price distortion structure, 54t, 468t
Salam, A., 209 regional concordance, 460t
Salazni, M., 369 sectoral value added, 75t
SAMs. See social accounting matrices Slovenia
S�nchez, Marco V., xxiii, 423, 432 GIDD model, 95f, 100f, 102f
sectoral and regional distribution of welfare Linkage model
effects, in Linkage model, 60, 61t agricultural outputs, imports, and
sectoral concordance of border price and export exports, 64t
demand shocks in Linkage model, economic welfare, global and national, 59t
458, 459t factor prices, 71t
sectoral value added, in Linkage model, 69�76, land supply elasticities, 465t
73�75t price distortion structure, 54t, 468t
Senegal regional concordance, 460t
GIDD model sectoral value added, 75t
factor prices, 102f social accounting matrices (SAMs), 13
inequality variations in agricultural and Argentinian CGE model, 367t
nonagricultural households, 95f China, benchmark data for, 153�56, 154�55t
poverty, effects of global liberalization Indonesian Wayang model, 181�82,
on, 111t 185�86, 189
508 Index
social accounting matrices (continued) land supply elasticities, 465t
Mozambique CGE model, 308, 314 national CGE model's use of, 335, 338, 340
Nicaraguan model, 435 price distortion structure, 53t, 469t
Pakistan's CGE model, 210, 212, 214, 215�18t, regional concordance, 460t
223, 241 sectoral value added, 73t
Philippines CGE model, 248, 253 microsimulation model, 338�39t, 338�40
South African CGE model, 337 national model, 335�40
Thai CGE model, 287 poverty and inequality
South Africa, 331�56 in GIDD model, 109, 110f, 111t
agricultural price distortions in, 335 global liberalization's effects on, 28�30t, 31,
apartheid, end of, 331 32�34t, 36t
border price and export demand shocks, 340, national model's effects on, 347�48t,
341t, 483�84t 347�49
CGE model, 336�38, 337t rural versus urban, 28�29t, 32�33t,
characteristics of national CGE model, 26t 331�32, 334
conclusions regarding, 354 Theil Index, income distributional
direct tax rates in, 337t changes, 114f
economic structure of, 332�35, 333�34t rural versus urban poverty and inequality in,
factor intensity structure, 344, 345�46t 28�29t, 32�33t, 331�32, 334
GIDD model SAM, 337
factor prices, 102f simulations, 340
inequality variations in agricultural and unemployment in, 6, 12, 14, 344, 346�47t,
nonagricultural households, 94, 95f 354, 355n5
poverty, effects of global liberalization on, WTO accession, 331
109, 110f, 111t South Asia. See also specific countries
real national consumption, effects of global GIDD model
liberalization on, 100f empirical results of, 20t, 22t, 23, 24t, 25
share of population in agriculture and of factor prices, 102f
agriculture in total income, 93f GICs, 107, 108f
Theil Index, income distributional global liberalization, effects of, 107t, 109, 115
changes, 114f population represented in, 89
household income and expenditure, 338�39t poverty, effects of global liberalization on,
liberalization effects, 340�54 109, 111t
border price and export demand shocks, real national consumption, effects of global
340, 341t liberalization on, 100f
GDP after global liberalization, 342, share of population in agriculture and of
343�44t, 347, 349�54 agriculture in total income, 94
global liberalization's effects on poverty and global liberalization, effects on incidence of
inequality, 28�30t, 31, 32�34t, 36t poverty and inequality in, 37
macroeconomic effects of global GTAP model, 121, 469t
liberalization, 341, 342t levels of poverty and inequality in, 4t
national model's effects on poverty and Linkage model
income inequality, 347�48t, 347�49 agricultural outputs, imports, and exports,
rest-of-the-world agricultural 63t, 65t
liberalization, 349�50 economic welfare, global and national,
rest-of-the-world liberalization of all 57, 58t
commodities, 340�49, 341�48t empirical results of, 15t, 16, 17t, 18t
unilateral liberalization of agricultural factor prices, 70�71t
commodities, 352�54 land supply elasticities, 466t
unilateral liberalization of all commodities, poverty, effects on, 77�79t, 80
350�52 price distortion structure, 53t, 469t
Linkage model regional concordance, 461t
agricultural outputs, imports, and sectoral value added, 69�76, 73�74t
exports, 63t NRAs in developing versus high-income
border price and export demand shocks to countries, 7t
national model from, 483�84t poverty and inequality
economic welfare, global and national, 58t in GIDD model, 109, 111t
factor prices, 70t in Linkage model, 77�79t, 80
Index 509
South Korea. See Korea, Republic of NRAs in developing versus high-income
Southern Africa Customs Union, 461t countries, 7t
soybeans. See vegetables, fruits, and poverty and inequality
vegetable oils in GIDD model, 111t
Spain global liberalization, effects of, 37
in GIDD model, 95f levels of, 3, 4t
in Linkage model, 460t in Linkage model, 77�79t, 80
Sri Lanka subsidies. See also agricultural price distortions,
GIDD model poverty, and inequality
factor prices, 102f China, benchmark data for, 154�55t
inequality variations in agricultural and commodity-specific support, effects of,
nonagricultural households, 95f 120�21, 139�40, 140t, 141t
poverty, effects of global liberalization on, GIDD model simulations based on removal
110f, 111t of, 99
real national consumption, effects of global Indonesia, simulated shocks in Wayang
liberalization on, 100f model, 191t
share of population in agriculture and of Linkage model, price distortion structure,
agriculture in total income, 93f 52�55, 53�54t
Theil Index, income distributional in South Africa, 335
changes, 114f sugar
Linkage model in Argentina, 367t, 369t, 370t, 388n14, 471t
agricultural outputs, imports, and border price and export demand shocks in
exports, 63t Linkage model, 459t, 464t, 471�85t
economic welfare, global and national, 58t in Brazil, 403t, 405�8t, 405t, 407t,
factor prices, 71t 411�12t, 473t
land supply elasticities, 466t in China, 154�55t, 158t, 474t
price distortion structure, 53t, 469t GTAP model, commodity-specific support
regional concordance, 461t in, 140t
sectoral value added, 74t in Indonesia, 179�81, 191t, 193t, 202�5,
Sturzenegger, A. C., 369 475�76t
Sub-Saharan Africa. See also specific countries in Linkage model, 62t, 65, 66�67t, 68t
GIDD model in Mozambique, 306�7t, 308, 309�10t,
empirical results of, 20t, 22t, 23, 24t, 25 316�17t, 318, 319t, 321t, 322, 323t,
factor prices, 102f 324, 477t
global liberalization, effects of, 107t in Nicaragua, 424, 440t, 479t
population represented in, 89 in Pakistan, 211�12, 215t, 219, 220t, 232, 236t,
poverty, effects of global liberalization 239, 480t
on, 111t in Philippines, 250, 251t, 255t, 258t, 260, 261t,
real national consumption, effects of global 263t, 266, 481�82t
liberalization on, 100f in South Africa, 333t, 339t, 341t, 342�44, 343t,
share of population in agriculture and of 345t, 350, 483t
agriculture in total income, 94 in Thailand, 284, 290t, 484�85t
global liberalization, effects on incidence of Sweden
poverty and inequality in, 37 in GIDD model, 95f
GTAP model, 121, 469�70t in Linkage model, 460t, 465t
Linkage model Switzerland, in GIDD model, 95f
agricultural outputs, imports, and exports,
63t, 65t T
economic welfare, global and national, Taber, C., 153
57, 58t Taiwan, China
empirical results of, 15t, 16, 17t, 18t as developing country, 56
factor prices, 70t GIDD model, 100f, 102f
land supply elasticities, 465�66t Linkage model
poverty, effects on, 77�79t, 80 agricultural outputs, imports, and
price distortion structure, 53t, exports, 63t
469�70t economic welfare, global and national, 58t
regional concordance, 461t factor prices, 70t
sectoral value added, 69, 73t land supply elasticities, 465t
510 Index
Taiwan (continued) border price and export demand shocks in
price distortion structure, 53t, 469t Linkage model, 459t, 464t, 471�85t
regional concordance, 460t in Brazil, 402t, 473t
sectoral value added, 73t in China, 154�55t, 156, 158t, 474�75t
Tajikistan, in GIDD model, 93f, 95f, GTAP model, commodity-specific support
112t, 114f in, 140t
Tanzania in Indonesia, 191t, 193t, 475�76t
GIDD model in Linkage model, 62t, 66�67t, 68t
factor prices, 102f maquila industry, 425t, 426, 442, 446,
inequality variations in agricultural and 454n1�2
nonagricultural households, 95f in Mozambique, 306�7t, 316�17t, 318, 319t,
poverty, effects of global liberalization 321t, 322, 323t, 477t
on, 111t in Nicaragua, 425t, 426, 428�29, 440t, 442,
real national consumption, effects of global 446, 454n1, 479t
liberalization on, 100f, 101 in Pakistan, 211, 215�16t, 218, 219, 220�21t,
share of population in agriculture and of 232, 236�37t, 238, 239, 480�81t
agriculture in total income, 93f in Philippines, 256t, 258t, 262t, 264t, 481�82t
Theil Index, income distributional in South Africa, 332, 333t, 339t, 341t, 343t,
changes, 114f 345t, 350, 351, 483t
Linkage model in Thailand, 290t, 485t
agricultural outputs, imports, and Thailand, 283�99
exports, 63t border price and export demand shocks, 288,
economic welfare, global and national, 58t 291t, 484�85t
factor prices, 70t characteristics of national CGE model, 26t
land supply elasticities, 465t conclusions regarding, 296�98
price distortion structure, 53t, 470t GIDD model
regional concordance, 461t empirical results of, 23, 24t, 25
sectoral value added, 73t factor prices, 102f
tariff binding overhang, 249, 281n5 inequality variations in agricultural and
tariffs. See also agricultural price distortions, nonagricultural households, 95f
poverty, and inequality poverty, effects of global liberalization on,
aggregate gains following elimination of, 87 110f, 111t, 113
Argentina, table of export taxes and import real national consumption, effects of global
tariffs for, 369, 370t liberalization on, 100f
China, benchmark data for, 154�55t share of population in agriculture and of
developing-country cuts in, 119 agriculture in total income, 93f
GIDD model simulations based on removal Theil Index, income distributional
of, 99 changes, 114f
Indonesia, simulated shocks in Wayang GTAP model, 121
model, 191t agricultural versus nonagricultural trade
Linkage model, price distortion structure, reforms, 136�38t
52�55, 53�54t commodity-specific support, effects of,
in Nicaragua, 427, 428f 140t, 141t
in Pakistan, 219 contribution of earnings to total poverty
in Philippines, 249�50 response, 127t
in South Africa, 335 elasticities approach to poverty
tax replacement. See tax replacement schemes headcount, 122t
Tarr, D. G., 41n6 percentage change in national poverty
tax replacement schemes headcount, 131t
Argentina, 383 poverty population, stratum contributions
GTAP model, 130, 131t, 132�33 to, 124t
Mozambique, 326 price distortion structure, 469t
Pakistan, 209, 210, 241�43, 242�43t, 245n4 tax replacement, 131t, 132
Philippines, 279 Indonesian study, comparison with, 296�98
TERM CGE model, 394, 419n5 JamlongThai general equilibrium model,
textiles and textile raw materials 285�88
in Argentina, 367t, 369t, 370t, 388n14�15, border price and export demand shocks,
471�72t 288, 291t
Index 511
households in, 286�87t trade taxes. See tariffs
industries in, 285, 286t transfer payments (remittances), 125�28,
industry assistance rates used in modeling, 127t, 136t
288, 289t Tunisia, in Linkage model, 460t
Linkage model, used with, 284 Turkey
simulations, 288 as developing country, 52
liberalization effects GIDD model
agricultural liberalization, 294�95, factor prices, 102f
295�96t, 297 inequality variations in agricultural and
border price and export demand shocks, nonagricultural households, 95f
288, 291t poverty, effects of global liberalization
global liberalization, 288�94, 291t, 293t on, 110f
global models' effects on poverty and real national consumption, effects of global
inequality, 28�30t, 31, 32�34t, 35, 36t liberalization on, 100f
macroeconomic effects, 284, 291t, 295t, share of population in agriculture and of
298�92 agriculture in total income, 93f
national model's effects on poverty and Theil Index, income distributional
inequality, 292�94, 293t, 296t changes, 114f
rest-of-the-world liberalization, 290�91t, Linkage model
292, 293t, 294, 297 agricultural outputs, imports, and
trade liberalization, 296�97 exports, 59t
unilateral liberalization, 290�91t, 292, 293t, economic welfare, global and national, 59t
294, 297 factor prices, 71t
Linkage model land supply elasticities, 465t
agricultural outputs, imports, and price distortion structure, 54t, 468t
exports, 63t regional concordance, 460t
border price and export demand shocks to sectoral value added, 75t
national model from, 484�85t
economic welfare, global and national, 58t U
factor prices, 70t Uganda
land supply elasticities, 466t GIDD model
national CGE model used with, 284 empirical results of, 23, 24t
price distortion structure, 469t factor prices, 102f
regional concordance, 461t inequality variations in agricultural and
sectoral value added, 74t nonagricultural households, 95f
poverty and inequality poverty, effects of global liberalization
in GIDD model, 110f, 111t, 113 on, 111t
global models' effects on, 28�30t, 31, real national consumption, effects of global
32�34t, 35, 36t liberalization on, 100f
in GTAP model, 122t, 124t, 131t share of population in agriculture and of
national model's effects on, 292�94, agriculture in total income, 93f
293t, 296t Theil Index, income distributional
rural versus urban, 28�29t, 32�33t, 284 changes, 114f
Theil Index, income distributional global liberalization, effects on incidence of
changes, 114f poverty and inequality in, 36t
rural versus urban poverty and inequality in, GTAP model, 121
28�29t, 32�33t, 284 agricultural versus nonagricultural trade
SAM, 287 reforms, 136�38t
transfer payments (remittances), importance commodity-specific support, effects of,
of, 127t, 128 140t, 141t
Theil Index, income distributional changes contribution of earnings to total poverty
following global liberalization, response, 127t
113�15, 114f elasticities approach to poverty
Thorbecke, E., 276, 397 headcount, 122t
Thurlow, James, xxiii, 303, 331 percentage change in national poverty
Timmer, Hans, 87n headcount, 131t
trade in services, 39, 82n3 poverty population, stratum contributions
trade liberalization. See liberalization to, 124t
512 Index
Uganda (continued) van der Mensbrugghe, Dominique
price distortion structure, 470t acknowledged, 87n, 247n
tax replacement, 131t biographical information, xxiv
Linkage model as chapter author, 49, 457
agricultural outputs, imports, and cited, 6, 15, 25, 38, 42n7, 60, 96, 98, 132, 192,
exports, 63t 209, 210, 248, 335, 366, 407
economic welfare, global and national, 58t VAT. See value added tax
factor prices, 70t vegetables, fruits, and vegetable oils
land supply elasticities, 465t in Argentina, 367t, 369t, 370t, 388n14, 471t
price distortion structure, 53t, 470t border price and export demand shocks in
regional concordance, 461t Linkage model, 459t, 464t, 471�85t
sectoral value added, 73t in Brazil, 403t, 405�8t, 411�12t, 420n17, 473t
Ukraine in China, 154�55t, 156, 158t, 474t
in GIDD model, 93f, 94, 95f, 112t, 114f in Indonesia, 191t, 193t, 475�76t
in Linkage model, 460t in Linkage model, 62t, 66�67t, 68t
unemployment. See under specific countries in Mozambique, 306t, 309t, 316t, 319t, 321t,
United Kingdom 323t, 477t
in GIDD model, 95f in Nicaragua, 440t, 479t
in Linkage model, 460t in Pakistan, 211�12, 215t, 218, 220t, 236t,
United States 239, 480t
Caribbean Basin Initiative, 428, 454n3 in Philippines, 250, 251t, 255t, 258t, 260, 261t,
GIDD model, 92f, 95f, 100f, 102f 263t, 266, 481�82t
Linkage model in South Africa, 332, 333t, 339t, 341t, 343t,
agricultural outputs, imports, and 345t, 351, 483t
exports, 64t in Thailand, 283�84, 290t, 485t
economic welfare, global and national, 59t Venezuela, Rep�blica Bolivariana de
factor prices, 69, 72t GIDD model
land supply elasticities, 465t empirical results of, 23, 24t
price distortion structure, 54t, 469t inequality variations in agricultural and
regional concordance, 460t nonagricultural households, 95f
sectoral value added, 75t poverty, effects of global liberalization
Mexican agricultural imports purchased on, 112t
from, 103 share of population in agriculture and of
Nicaragua, trade relations with, 426, agriculture in total income, 93f
427�29 Theil Index, income distributional
Philippines, sugar exports from, 250 changes, 114f
preference erosion resulting from global global liberalization, effects on incidence of
liberalization, 134 poverty and inequality in, 36t
Thailand, bilateral trade agreement on dairy GTAP model, 121
products with, 284 agricultural versus nonagricultural trade
urban versus rural. See rural versus urban reforms, 136�38t
Uruguay, in Linkage model, 461t commodity-specific support, effects of, 139,
Uruguay Round 140t, 141t
GIDD database and, 99 contribution of earnings to total poverty
GTAP database affected by, 52, 128 response, 127t
Uzbekistan, in GIDD model, 93f, 95f, 112t, 114f elasticities approach to poverty
headcount, 122t
V percentage change in national poverty
Valenzuela, Ernesto headcount, 131t
acknowledged, 183n, 247n poverty population, stratum contributions
biographical information, xxiii to, 124t
as chapter author, 49, 457 self-employed nonagricultural households
cited, 6, 12, 15, 25, 38, 42n7, 50, 51, 52, 96, 98, as share of poor, 124
128, 132, 192, 210, 248, 335, 366, 407, tax replacement, 131t
431�32, 458 Linkage model, 461t
value added tax (VAT) Vietnam
in Argentina, 383 GIDD model
as tariff revenue replacement, 131t, 133 empirical results of, 23, 24t, 25
Index 513
factor prices, 102f World Bank
inequality variations in agricultural and data sets, 8�9, 119. See also global CGE
nonagricultural households, 95f models
poverty, effects of global liberalization on, Distortions to Agricultural Incentives Project
110f, 111t, 113 Database, 176n1, 196, 209, 245n1,
real national consumption, effects of global 281n1, 298n1, 328n3
liberalization on, 100f World Trade Organization (WTO)
share of population in agriculture and of China, accession of, 52, 99, 128,
agriculture in total income, 93f 147�48, 149
global liberalization, effects on incidence of Doha Round. See Doha Round
poverty and inequality in, 36t export taxes not subject to negotiation in,
GTAP model, 121 373, 386n3
agricultural versus nonagricultural trade future rounds of reforms, anticipating results
reforms, 135�38, 136�38t, 139 of, 51
commodity-specific support, effects of, 139, Kennedy Round, 386n3
140t, 141t South Africa, accession of, 331
contribution of earnings to total poverty Uruguay Round. See Uruguay Round
response, 127t
elasticities approach to poverty X
headcount, 122t Xiang Tao, 391n
percentage change in national poverty
headcount, 131t Y
poverty population, stratum contributions Yemen, Republic of, in GIDD model, 93f, 95f,
to, 124t 114f, 1110f
price distortion structure, 469t Yusuf, Arief Anshory, 283n
tax replacement, 131t, 132
Linkage model
agricultural outputs, imports, and Z
exports, 63t Zambia
economic welfare, global and GIDD model, 23, 24t, 100f, 102f
national, 58t GTAP model, 121
factor prices, 71t agricultural versus nonagricultural trade
land supply elasticities, 466t reforms, 136�38t, 138
price distortion structure, 53t, 469t commodity-specific support, effects of,
regional concordance, 461t 140t, 141t
sectoral value added, 74t concentration of poverty in agricultural
Vos, Rob, xiv, 423, 432, 436, 437 households, 121
contribution of earnings to total poverty
W response, 127t
Wang, Z., 148 elasticities approach to poverty headcount,
Warr, Peter, xiv, 179, 283, 297 122�23, 122t
Wayang general equilibrium model. See under percentage change in national poverty
Indonesia headcount, 131t
welfare poverty population, stratum contributions
agricultural GDP and farm household to, 124t
welfare, relationship between, 8 price distortion structure, 469t
global and national economic welfare, tax replacement, 131t
in Linkage model, 57�60, Linkage model
58�59t agricultural outputs, imports, and
regional and sectoral distribution exports, 63t
of welfare effects, in Linkage economic welfare, global and
model, 60, 61t national, 58t
Western Europe. See European Union/Western factor prices, 70t
Europe; specific countries land supply elasticities, 465t
wheat. See grains and oilseeds price distortion structure,
Winters, L. A., 8, 11, 12, 13, 94, 120 53t, 469t
wool. See textiles and textile raw regional concordance, 461t
materials sectoral value added, 69, 73t
514 Index
Zhai, Fan Linkage model
biographical information, xiv agricultural outputs, imports, and
as chapter author, 147 exports, 63t
cited, 148, 213, 214, 260, 311, economic welfare, global and national, 58t
315, 462 factor prices, 70t
Zhang, L., 150 land supply elasticities, 465t
Zhao, Y., 150, 151, 152 price distortion structure, 53t, 469t
Zimbabwe regional concordance, 461t
GIDD model, 100f, 101, 102f sectoral value added, 69, 73t
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The prices of farm products are crucial determinants of the extent of poverty and
inequality in the world.
The vast majority of the world's poorest households depend to a considerable
extent on farming for their incomes, while food represents a large component of the
consumption of all poor households. For generations, food prices have been heavily
distorted by government policies in high-income and developing countries. Many
countries began to reform their agricultural price and trade policies in the 1980s, but
government policy intervention is still considerable and still favors farmers in high-
income countries at the expense of many farmers in developing countries.
What would be the poverty and inequality consequences of the removal of the
remaining distortions to agricultural incentives? This question is of great relevance
to governments in evaluating ways to engage in multilateral and regional trade
negotiations or to improve their own policies unilaterally.
Agricultural Price Distortions, Inequality, and Poverty analyzes the effects of agricultural
and trade policies around the world on national and regional economic welfare, on
income inequality among and within countries, and on the level and incidence of
poverty in developing countries. The studies include economy-wide analyses of the
inequality and poverty effects of own-country policies, compared with rest-of-the-
world policies, for 10 individual developing countries in three continents. This book
also includes three chapters that each use a separate global economic model to examine
the effects of policies on aggregate poverty and the distribution of poverty across many
identified developing countries.
This study is motivated by two policy issues: first, the World Trade Organization's
struggle to conclude the Doha Round of multilateral trade negotiations, in which
agricultural policy reform is, again, one of the most contentious topics in the talks
and, second, the struggle of the developing countries to achieve their Millennium
Development Goals by 2015--notably the alleviation of hunger and poverty--which
depends crucially on policies that affect agricultural incentives.
ISBN 978-0-8213-8184-7
SKU 18184