20731 40 C1 6May 2000 Volume two 0- FD esigning 0< Household Survey OQ Questionnaires for n 0 Developing Countries _ Lessons from 15 years of the Living Standards Measurement Study m ca 0. Edited by Margaret Grosh and Paul Glewwe PX c The World Bank Oxford -The World Bank Volume two T es ign ing Household Survey Questionnaires for Developing Countries Lessons from 1 5 years of the Living Standards Measurement Study Edited by Margaret Grosh and Paul Glewwe U7 The World Bank "Household surveys are essential for the analysis of most policy issues. This book has carefully assessed recent experience and developed today's best-practice technique for household surveys. Indeed, much of this technique was developed and pioneered by the authors.This book is clear, systematic, and well structured. It is also wise and scholarly. It will be indispensable to anyone involved in carrying out or analyzing household surveys, and thus it is required reading for all those who wish to take evidence seriously when they think about policy." -Nicholas Stern, senior vice president, Development Economics and chief economist, the World Bank "This book is an ambitious undertaking, but it quickly exceeded my expectations. It has many strengths: ... com- prehensiveness, ...emphasis on practical application, ...and a sense of balance. For both my domestic and interna- tional survey research, this volume will serve as a valued reference tool that I will consult regularly." -David R.Williams, professor of sociology and senior research scientist, Survey Research Center, University of Michigan "This is a comprehensive guide to planning household surveys on a range of socioeconomic topics in develop- ing countries. It is authoritative, clear, and balanced. The work is a valuable addition to the library of any survey statistician or data analyst concerned with socioeconomic surveys in the developing world." -William Seltzer, former head, United Nations Statistical Office Household survey data are essential for assessing the impact of development policy on the lives of the poor.Yet for many countries household survey data are incomplete, unreliable, or out of date. This handbook is a compre- hensive treatise on the design of multitopic household surveys in developing countries. It draws on 15 years of experience from the World Bank's Living Standards Measurement Study surveys and other household surveys conducted in developing countries. The handbook covers key topics in the design of household surveys, with many suggestions for customizing sur- veys to local circumstances and improving data quality. Detailed draft questionnaires are provided in written and electronic format to help users customize surveys. This handbook serves several audiences: * Survey planners from national statistical and planning agencies, universities, think tanks, consulting firms and international organizations. * Those working on either multitopic or topic-specific surveys. * Data users, who will benefit from understanding the challenges, choices, and tradeoffs involved in data collection. Volume two tD esigning Household Survey Questionnaires for Developing Countries Lessons from 15 years of the Living Standards Measurement Study Edited by Margaret Grosh and Paul Glewwe Copyright (© 2000 The International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street, N.W Washington, D.C. 20433, U.S.A. All rights reserved Manufactured in the United States of America First printing May 2000 The findings, interpretations, and conclusions expressed in this paper are entirely those of the author(s) and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to members of its Board of Executive Directors or the countries they repre- sent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any conse- quence of their use.The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank Group any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. The material in this pubhcation is copyrighted. The World Bank encourages dissemination of its xvork and will normally grant per- mission promptly. Permission to photocopy items for internal or personal use, for the internal or personal use of specific clients, or for educational class- room use, is granted by the World Bank, provided that the appropriate fee is paid direcdy to Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, U.S.A., telephone 978-750-8400, fax 978-750-4470. Please contact the Copyright Clearance Center before photocopying items. For permission to reprint individual articles or chapters, please fax your request with complete information to the Republication Department, Copyright Clearance Center, fax 978-750-4470. All other queries on rights and licenses should be addressed to the World Bank at the address above or faxed to 202-522-2422. ISBN:0-19-521595-8 Library of Congress Cataloging-in-Publication Data has been applied for. Contents Foreword ix Acknowledgments xi Contributors xiii Volume I Part I Survey Design 1. Introduction 5 Margaret Grosh and Paul Glewwe 2. Making Decisions on the Overall Design of the Survey 21 Margaret Grosh and Paul Glewwe 3. Designing Modules and AssemblingThem into Survey Questionnaires 43 Margaret Grosh, Paul Glewwe, andJuan Muiioz Part 2 Core Modules 4. Metadata-Information about Each Interview and Questionnaire 77 Margaret Grosh andJuan Munoz 5. Consumption 91 Angus Deaton and Margaret Grosh 6. Household Roster 135 Paul Glewwe 7. Education 143 Paul Glewwe 8. Health 177 Paul J. Gertler, Elaina Rose, and Paul Glewwe 9. Employment 217 Julie Anderson Schaffner 10. Anthropometry 251 Harold Alderman v CONTENTS 11. Transfers and Other Nonlabor Income 273 Andrew McKay 12. Housing 293 Stephen Malpezzi 13. Community and Price Data 315 Elizabeth Frankenberg Volume 2 Part 3 Additional Modules 14. Environmental Issues 5 Dale Whittington IS. Fertility 31 lndu Bhushan and Raylynn Oliver 16. Migration 49 Robert E. B. Lucas 17. Should the Survey Measure Total Household Income? 83 Andrew McKay 18. Household Enterprises 105 Wim P M. Vijverberg and Donald C. Mead 19. Agriculture 139 Thomas Reardon and Paul Glewwe 20. Savings 183 Anjini Kochor 21. Credit 211 Kinnon Scott 22. Time Use 249 Andrew S. Harvey and Maria Elena Taylor Part 4 Special Topics 23. Recommendations for Collecting Panel Data 275 Paul Glewwe and Hanan Jacoby 24. Intrahousehold Analysis 3 15 Nobuhiko Fuwa, Shahidur R. Khandker, Andrew D. Mason, and Tara Vishwanath 25. Qualitative Data Collection Techniques 337 Kimberly Chung 26. Basic Economic Models and Econometric Tools 365 Jere R. Behrman and Raylynn Oliver Volume 3 Draft Questionnaire Modules Introduction I Module for Chapter 4: Metadata 5 Margaret Grosh andJuan Munoz Module for Chapter 5: Consumption 15 Angus Deaton and Margaret Grosh vi CONTENTS Module for Chapter 6: Household Roster 31 Paul Glewwe Module for Chapter 7: Education 37 Paul Glewwe Module for Chapter 8: Health 73 Paul J. Gertler, Elaina Rose, and Paul Glewwe Module for Chapter 9: Employment 147 Julie Anderson Schaffner Module for Chapter I0:Anthropometry 219 Harold Alderman Module for Chapter I I:Transfers and Other Nonlabor Income 221 Andrew McKay Module for Chapter 12: Housing 229 Stephen Malpezzi Module for Chapter 13: Community Data 247 Elizabeth Frankenberg Module for Chapter l4: Environment 285 Dale Whittington Module for Chapter 15: Fertility 325 lndu Bhushan and Raylynn Oliver Module for Chapter 16: Migration 333 Robert E B. Lucas Module for Chapter 18: Household Enterprise 349 Wim P. M. Vijverberg and Donald C. Mead Module for Chapter 19:Agriculture 407 Thomas Reardon and Paul Glewwe Module for Chapter 20: Savings 453 Anjini Kochar Module for Chapter 21: Credit 461 Kinnon Scott Module for Chapter 22:Time Use 483 Andrew S. Harvey and Maria Elena Taylor Module for Chapter 23: Panel Data 495 Paul Glewwe and Hanan Jacoby vii Foreword Multitopic household surveys have become an indis- The household surveys treated in this book truly pensable instrument for understanding development. are multitopic surveys, covering such topics as house- They are fundamental to serious microeconomic analy- hold size and composition, education, health, anthro- sis of the incentive and distributional aspects of policy, pometry, fertility, income and consumption, employ- and therefore to the analysis of most policy issues. ment, agricultural production, household enterprises, Researchers draw on them to test behavioral theories. transfers and nonlabor income, savings and credit, Policymakers need them to assess public interventions. housing, the environment, migration, and time use. The development community uses them to locate the The editors have greatly increased the value of the poor. Developing counties, without adequate house- basic approach by incorporating chapters on commu- hold survey data, are forced to make policy decisions in nity data, panel data, and the allocation of resources an environment with many blind spots, where crucial within the household. information can be seen only dimly or not at all. As the World Bank and other development Household surveys are also expensive, both in organizations increase their efforts to reduce poverty terms of money and institutional capacity. Ultimately and raise living standards in developing countries in their value depends on their design and execution. the 21st century, the need for comprehensive, reliable Errors in their design or execution are wasteful, and and up-to-date information on economic and social can lead to policies that are harmful to the poor. It is conditions in these countries will be greater than therefore important to design and implement surveys ever. The vast store of knowledge in this book will correctly from the outset. contribute significantly to meeting this need. Failure Margaret Grosh and Paul Glewwe have put to use this knowledge will consign policymakers to together one of the most comprehensive and inform- making their decisions without adequate information ative documents ever written on the design, imple- for many years to come, while systematic use of this mentation, and use of household surveys in develop- knowledge will do much more for the poor than the ing countries. If you are engaged in any of these tasks, innumerable speeches made and summits convened this book is essential reading. on their behalf. Lyn Squire Director, Global Development Network World Bank ix Acknowledgments A project of this size and scope depends on many peo- the surveys, the many agencies that provided techni- ple playing many roles. Space limitations preclude us cal assistance and funding, and the academic partici- from naming all of the hundreds of people who made pants who provided advice and criticism over the contributions along the way, but we would like to years. acknowledge some of the most important. The project as a whole was strongly supported The authors of the individual chapters deserve from original vision to final printing by our immedi- thanks for their gracious willingness to go through ate manager for most of that time, EmmanuelJimenez, many rounds of revisions, spread over a longer time who provided us with useful technical input and a than anyone originally envisioned. Producing a book great deal of enthusiasm, patience, and bureaucratic on the design of multitopic questionnaires requires support. We also greatly appreciate the support of his much more cooperation among authors and several directors, Lyn Squire and Paul Collier.The project was more iterations than does the standard edited volume. primarily financed by a grant from the World Bank We are extremely grateful for the forbearance of the Research Committee (679-61), managed by Greg authors in this difficult process.The authors themselves Ingram and administered by Clara Else. were helped by a large number of peer reviewers.They Many people reviewed the book and project as a are recognized in the individual chapters, but we whole. We gready appreciate these contributions by would like to extend our thanks to them here as well. Pat Anderson, Jere Behrman, Elisa Lustosa Caillaux, Much of the work in these volumes was based on Courtney Harold, John Hoddinott, Anna Ivanova, past practice in LSMS and other household surveys Alberto Martini, Raylynn Oliver, Prem Sangraula, including, but not limited to, the World Fertility/ Salman Zaidi, and three anonymous reviewers.To have Demographic and Health Surveys, the RAND Family input on the project as a whole from these outsiders Life surveys, the Social Dimensions of Adjustment was very helpful. In addition, participants at three surveys, and several special topic surveys such as workshops held at the World Bank, plus various train- household budget surveys, water and sanitation sur- ing events sponsored jointly by the World Bank and veys, housing surveys, and time use surveys. While the the Inter-American Development Bank, critiqued the authors pulled together the lessons from past experi- project while it was in progress. ence, it is also important to acknowledge the irre- In the course of creating the book, Diane Steele placeable contributions made by the thousands and answered questions from all authors on the details of thousands of household members who served as LSMS data sets. Fiona Mackintosh edited early drafts and respondents, the dozens of agencies that implemented helped to transform the disparate chapters into a single xi ACKNOWLEDGMENTS whole. Lyn Tsoflias provided us with valuable research Communications Development Inc. Communication assistance.Word processing and conference logistics were with the World Bank's Publications Comuittee and ably handied by Thomas Hastings, Patricia Sader, Jim with the publishers and printers was efficiently handled Schafer, and Daniel O'Connell. Questionnaire layout by Paola Scalabrin and Randi Park. was mastered by Thomas Hastings, Andrea Ramirez, and Finally, effusive and endless thanks to our families Heidi Van Schooten. Contracting support from Liliana and friends who put up with the excessively long hours Longo, Selina Khan, and Patricia Sader was timely and that we spent on this project, who cheered and calmed organized. The final editing, layout, and design were us through the frustrating times, and who helped us to handled dextrously by Meta de Coquereaumont,Wendy bring this long project to a successful conclusion with- Guyette, Kate Hull, Daphne Levitas, Heidi Manley, out completely losing track of other important aspects Laurel Morais, and Derek Thurber, all with of our personal and professional lives. xii Contributors Harold Alderman World Bank Jere R. Behrman University of Pennsylvania Indu Bhushan Asian Development Bank Kimberly Chung Michigan State University Angus Deaton Princeton University Elizabeth Frankenberg RAND Nobuhiko Fuwa World Bank Paul J. Gertler University of California, Berkeley Paul Glewwe World Bank and University of Minnesota Margaret Grosh World Bank Andrew S. Harvey St. Mary's University, Halifax, N.S. Canada Hanan Jacoby World Bank Shahidur R. Khandker World Bank Anjini Kochar Stanford University Robert E.B. Lucas Boston University Stephen Malpezzi University ofWisconsin Andrew D. Mason World Bank Andrew McKay University of Nottingham, United Kingdom Donald C. Mead Michigan State University Juan Mufioz Sistemas Integrales Raylynn Oliver Consultant,World Bank Thomas Reardon Michigan State University Elaina Rose University ofWashington Julie Anderson Schaffner Fletcher School of Law and Diplomacy,Tufts University Kinnon Scott World Bank Maria Elena Taylor St. Mary's University, Halifax, N.S. Canada Wim P.M.Vijverberg University of Texas at Dallas TaraVishwanath World Bank Dale Whittington University of North Carolina at Chapel Hill xiii Volume 2 Part 3 Additional Modules 4< j Environmental Issues 1t 4 Dale Whittington The purpose of this chapter is to examine what information is needed and can realistically be collected in a household survey (such as an LSMS survey) to support sound environmental policy analysis. Previous LSMS surveys have contained few questions specifically designed to gather household information on environmental issues and concerns, so this area is something of a clean slate. The challenge is to establish what data are most needed to allow analysts to conduct envi- ronmental policy analysis, then to establish what are the best questions to include in LSMS-type surveys to gather these data. The job of designing environmental questions to be physical and biological systems function and how included in LSMS and similar surveys is different in human interventions can alter natural and manmade two important ways from the task of designing (and processes.This understanding can be gained both from redesigning) questions on other topics. First, the num- creative theorizing and the testing of theories with ber of important policy issues that fall under the rigorous experiments and from personal experience. umbrella of "environment and natural resources poli- Survey designers should carefully consider what infor- cy" is very large. They include issues as diverse as air mation about environmental issues households might pollution, contamination of drinking water, soil degra- know that would be of policy relevance. Without the dation, rainforest loss, national parks, watershed man- resources or training to carry out scientific experi- agement, fisheries, wildlife conservation, urban sanita- ments, ordinary people may not be well informed tion, acid rain, ozone depletion, global warming, about the causes or consequences of some environ- hazardous waste disposal, misuse of pesticides, mineral mental problems, which means that some of their depletion, and malaria control-and this is by no actions may have unintended and unanticipated means an exhaustive list. No single household survey results. Thus household surveys are not the most can query respondents in depth about their knowl- appropriate way to collect the information required to edge of, attitudes toward, and practices regarding all of develop a rigorous understanding of the physical and these environmental issues or even a substantial num- biological aspects of environmental systems. However, ber of these issues. Thus the designers of LSMS-type they are generally considered a good and appropriate surveys must set priorities and ask questions about means of collecting data for the investigation of only a limited number of environmental issues. human behavior. Second, analyzing environmental policies typical- Because the level of households' scientific under- ly requires a thorough scientific understanding of how standing of environmental issues may vary greatly 5 DALE WHITTINGTON from location to location, it may be appropriate to ask capita well-being requires that environmental some questions in one location but not in another. resources-air, water, and land, and the life that Thus the approach taken by this chapter is to intro- depends on them for existence-be managed and duce a set of environment modules that illustrate the conserved in wise ways. This means that governments types and range of questions that can be included in an and individuals must think carefully about the most LSMS-type survey. These modules, presented in appropriate use of the capital stock of both nonre- Volume 3, cover important environmental issues in newable and renewable resources over time, and that urban and rural areas.The modules also collect data on policies, projects, and regulations must be chosen and households' attitudes toward the environment and per- implemented in ways that produce the desired envi- ceptions of urban air quality and on their use of water ronmental, economic, and social results (Pearce and services, sanitation services, and fuel. In addition, the others 1994, chapter 1). modules cover the willingness of households to pay for The sound selection and implementation of envi- improved water services in both urban and rural areas, ronmental policies requires that analysts design various for improved sanitation in urban areas, and for the policy alternatives and then work out how these alter- improvements in urban air quality that would result in natives will affect various groups of people (or other better health outcomes.And the environment modules affected parties) in terms of several important criteria. are designed to collect the data needed to estimate Table 14.1 presents a simple classification of issues household's rates of time preference-in other words, in terms of the spatial range of the problem (which the relative value that a household places on costs and can be anything from very local to global) and the benefits at different times. Designers of future LSMS- extent to which the resource in question is renewable type surveys can choose the submodules most relevant (like fish or forests) or nonrenewable (like oil or cop- to the circumstances in the country of the survey, then per). Renewability here refers only to the physical modify and customize them as needed to match the characteristics of the resource; all renewable resources policy goals of the survey. are ultimately exhaustible in the sense that they can be The first section of this chapter summarizes some overused and thereby extinguished. Determining the of the most important environmental policy issues and extent of the impact of the problem (whether local, the tasks involved in environmental policy analysis. regional, or global) depends not only on the location The second section discusses what data are needed to of the resource but also on its significance. For exam- analyze these policy issues and what kinds of data ple, a tropical forest can be considered a global LSMS and similar surveys are best suited to collect. resource if its value is global. The third section introduces the draft environment Households naturally know more about some modules (provided in Volume 3), and the fourth sec- kinds of environmental problems than about others. tion both explains why these modules were crafted the LSMS-type surveys should attempt to collect informa- way they were and points out important issues that are tion on the important environmental problems about likely to arise when these modules are administered. which respondents are best informed. In general, respondents are likely to be most knowledgeable about Environmental Policy Issues the damage that they suffer from the degradation of air, water, and land resources, as well as about the local, The basic message of the 1992 United Nations renewable resources that they use and depend upon for Conference on Environment and Development was their livelihood and sustenance-such as local forests, that the environment is an important factor in suc- fisheries, and groundwater (see the upper left cell in cessful economic and social development. Raising per Table 14.1). Local communities often have extensive Table 14.1 The Classification of Environmental Policy Issues Local Reg onal Global Renewable resources Local water sources and fisheries: Shared water basin systems; Ozone layer; major tropical forests; local forests; groundwater systems; airsheds genetic information fuelwood Nonrenewable resources Mineral deposits; oil, coal Shared natural gas reservoirs Source: Author's examples. 6 CHAPrER 14 ENVIRONMENTAL ISSUES experience in dealing with local pollution problems ownership of natural resources do not accrue to the and managing renewable resources. Such communities owner, because owners cannot transfer property rights may be equally experienced in dealing with local, non- to another owner in a voluntary exchange, or because renewable resources, but it is more likely either that the there are no penalties to prevent others from encroach- local population does not use these kinds of resources ing on or taking over an owner's property rights.1 or that these resources are owned or controlled by only In many places the rights to land or to a natural a small number of individuals. In either case local peo- resource (for example, the atmosphere and much of ple would have httle knowledge of these resources to the world's oceans) are not allocated or defined at all. share with the survey interviewers. Even where property rights exist, they may not be As the spatial scale of the environmental manage- enforceable so that in reality an open access situation ment problem increases, ordinary people are less like- exists-creating a risk that the resource may be over- ly to understand how the full ecological system oper- exploited. Households may be the best source of ates. This does not mean that households fail to information on the prevailing property rights to local understand the costs they bear from the degradation of renewable resources. such environmental resources as air and water. For example, upstream users of a river may not fully appre- Environmental Assets as Economic Inputs ciate the costs that they impose on downstream users Another insight afforded by environmental economics due to the flooding of the river basin that causes is that environmental assets are capital just as much as deforestation and soil erosion. However, the down- are machines, roads, or factories. Information from stream users are likely to fully understand the damages household surveys can be quite useful for establishing imposed upon them. It is even more likely that people the economic value of environmental assets such as will not fully understand how their actions may exac- drinking water supplies and forestry products. Because erbate global environment problems such as global capital stocks are needed to sustain development, the warming or the depletion of the ozone layer. Thus the overall maintenance of such stocks is an integral part of answers that respondents give to questions about such a sound environmental and economic policy. A tropical global issues are hkely to be less informed and less use- forest has a great many ecological functions, a large ful to analysts than their responses to questions about number of which have economic value. For example, environmental problems that are closer to home. the forest protects the watershed system; if the forest is removed, the watershed system will be damaged (to Property Rights and the Causes of Environmental varying degrees depending on what land-use system Degradation replaces the forest). At the global level the ozone layer Environmental economists have developed a powerful protects humans from unhealthy amounts of ultraviolet conceptual tool for identifying the causes of environ- radiation, so damage to the ozone layer is dangerous to mental degradation, with important implications for human health and productivity. Just as sound develop- the design of the environment modules of household ment planning requires an understanding of the value surveys. This tool is the analysis of property rights. of traditional capital assets, such planning also requires One potential role for the environment modules is to information on the value of environmental assets. collect information that will enable analysts to deter- mine the property rights regimes that govern impor- Taking Account of the Environment When Appraising tant natural resources and find out whether conditions Development Projects exist for the efficient allocation of property rights. The environment provides many inputs to economic A property right is an entitlement on the part of activities, and the residuals from production processes an owner to a resource or good that can be socially can affect the environment. In order to carry out enforced. In most countries an entitlement to use or sound project appraisals, analysts need to value both consume a natural resource is qualified by various legal these inputs and residuals properly and price them and customary restrictions embodied in law or in the accordingly. For example, if energy prices fail to reflect prevailing moral code. In many situations environmen- the pollution damage associated with energy produc- tal problems arise because property rights are not clear- tion and use, the goods produced using that energy ly assigned, because the benefits and costs arising from will be underpriced. 7 DALE WHITTINGTON When appraising projects, analysts must bear in state of affairs about which the public or technical mind that the prices of environmental inputs affect experts may be concerned. For example, the house- project returns.The proper pricing of inputs and out- hold survey might yield statistics on fuelwood usage puts can be seen as a way of designating property that indicates that local forestry stocks are rapidly rights or extending existing property rights, as can being depleted. The data can help decisionmakers regulatory measures such as environmental quality answer two basic questions: what is the condition that standards. Sound pricing policy requires that policy- is causing the concern and how important is it? makers know the value of environmental goods and Tabulating household survey data can show services. If the policymakers fail to include nonmar- whether or not a problem condition exists. For exam- keted environmental services in their calculation of ple, household surveys often collect information on that value, the opportunity costs of development proj- whether or not a household has access to an improved ects will be wrongly defined and measured. For exam- water source such as a private connection or a water ple, if a rural development project involves destroying pump. If members of the household have access to an a tropical forest, it is necessary to factor the forgone improved drinking water source, they are said to be benefits of the tropical forest into the calculation of "covered." Simple "coverage" statistics from household the project's economic rate of return. survey data can be tabulated to calculate the percent- In the past the techniques used to appraise devel- age of a population that has access to an improved opment projects have been criticized because of their drinking water system.2 If a significant percentage of failure to account for environmental values in the the population is not covered, the analysis will have same terms as the costs and benefits of conventional revealed a problem condition to which policymakers development. This can make the costs and benefits of should pay attention. Most environmental policy ana- conventional development seem more concrete than lysts in both industrialized and developing countries environmental gains and losses. What is quantified use household survey data in this way. appears more important than what is not quantified, Many of the questions in non-LSMS household and development costs and benefits are traditionally surveys addressing environmental issues have asked measured in monetary terms while environmental respondents about their household's use of an envi- costs and benefits have not often been valued in such ronmental resource or the damage that members of terms. the household may sustain as a result of various kinds Project appraisal techniques have also been criti- of degradation of the local environment. Analysts can cized for discriminating against the interests of future then use these data to draw inferences about problem generations by discounting the future costs and bene- conditions. However, survey planners should word fits of environment degradation or conservation. This questions on the household's use of resources so that is also a failure of valuation because it implies that responses can be used for more analytical tasks than conventional project appraisal techniques fail to ade- simply describing a problem. quately account for future values. In addition to providing information on resource use, household surveys can collect information on Specific Tasks in Environmental Policy Analysis households' knowledge of, attitudes toward, and prac- Data from household surveys can help analysts per- tices regarding a wide range of environmental condi- form three of the tasks associated with good environ- tions and natural resources. This information can also mental policy analysis: help analysts describe and diagnose problems. In many * Diagnosing and measuring environmental problems. cases very simple questions about the ownership of a * Investigating the causes of environmental problems. natural resource can provide important information to * Designing policy alternatives and evaluating the analysts that helps them explore ways of managing impact that these policies may have on the problems. natural resource problems. (This is in part because This section discusses these three tasks in turn. households' willingness to pay for capital improve- ments often depends upon the households' percep- DIAGNOSING AND MEASURING ENVIRONMENTAL tions of who holds the property rights for the facili- PROBLEMS. Data from household surveys can help ana- ties.) For example, the policy of the government of lysts explore an environmental "problem condition," a Tanzania is to transfer the ownership of most village- 8 CHAPFER 14 ENVIRONMENTAL ISSUES level capital facilities (like water supply systems) back Table 14.2 Respondents' Perceptions of Who Owns the to communities.The government conducted a house- Local Water Source (Tanzania, 1995) hold consultation survey in August 1995, which Perceived holder revealed that households were aware of the govern- of property right Percentage of respondents ment's policy. The respondents appeared to have Central government 3 accepted the notion that their water source was owned District government or council 6 accepted the nohon that thelr water source was owned .........................................l....o......w.........d.. .........o....... ..n.t.co....m. ......t.t.ee........ ............................ Village council or ward development committee 58 by the community; only 9 percent believed that it was ...................................... Those who live near the water source 7 owned by the central or district government (Table Those who use the water source 1 8 14.2). Interestingly, however, in some communities, a An ind.ivi.dual 4 few respondents seemed not to have noticed that the Source: Human Resources Deveopment Pilot Project I,World Bank AF2PH. property rights to water sources had been relinquished Systematic Client Consultation, August 1995. to the state in the first place. supply, are of concern because they are valued or dis- INVESTIGATING THE CAUSES OF THE PROBLEM. valued. Surveys can help understand what is valued Investigating the causes of environmental problems and track changes in those conditions. requires asking a more complex series of questions For example, in a recent survey in Marracuene, than the ones needed to collect descriptive informa- Mozambique (Pinheiro and Whittington 1995), an tion. For example, questions about water use in almost analysis of respondents' social and environmental pri- all household surveys ask respondents what water orities suggested that people were most concerned source their households use. This may be adequate for about the lack of drugs and supplies delivered to (pur- describing a problem, but it is not sufficient for inves- chased by) the hospital. This tells analysts what is val- tigating why a household chooses a particular water ued and suggests possible conditions to track. One way source for a specific purpose. To do this, analysts must to measure impact would be to track drug supplies in be able to model the choices that households make hospitals, which would require data from the hospitals. among existing water sources, for which they need Another possible method would be to measure the information on all of the water sources to which a change in the percentage of respondents that still household has access, not just the source(s) that they reported the supply of drugs was their top priority have chosen. after a policy intervention designed to address this When government policies are the cause of envi- problem was implemented; this method would require ronmental problems, household surveys may not seem household data. to be a particularly useful research tool because Household surveys can collect information that respondents might be reluctant to criticize govern- can be used to assess which among the proposed poli- ment policy. However, asking respondents simple cy alternatives are best. The surveys can support a vari- questions about their preferred way to receive a serv- ety of analytic approaches. For example, analysts trained ice or pay for a service may reveal some underlying in disciplines other than economics, as well as members problems. For example, respondents can be asked to of the public, often define criteria for policy evaluation say whether they would prefer a new community in terms of specific problem descriptions and these water supply system to be managed by the ministry of individuals' understanding of the causal relationships water, a community-based utility, or a private contrac- that relate policy alternatives to outcomes. Such crite- tor. If no one chooses the ministry of water, this may ria tend to be less abstract, more explicit, and, for some reveal past failures on the part of government people, easier to understand than economic criteria. providers. They may include indicators of public health, exposure to pollutants, respondents' subjective perceptions of DESIGNING POLICIES AND EVALUATING THEIR IMPACTS environmental amenities and conditions, and physical ON ENVIRONMENTAL PROBLEMS. Decisionmakers must indicators of access to services such as water supply and choose which among proposed policies are better than sanitation. Surveys can provide noneconomists and others. The criteria on which a choice is judged are members of the public with this kind of information- the valued (or disvalued) features of these policies.The information that these individuals find useful in policy conditions measured, such as coverage of the water discourse about policy alternatives. 9 DALE WHITTINGTON Surveys can also provide data that economists vidual may consider many factors including the size need for a formal economic appraisal of the costs and and age of the house, its proximity to schools, shop- benefits of different policy alternatives. Economists ping, and place of employment and possibly also the typically propose that an all-encompassing criterion of air quality in the neighborhood. The value of increased human well-being (or preference satisfac- improved air quality can be estimated by doing a care- tion) be used to evaluate different policy alternatives. ful analysis of such transactions in the housing market. The valuation task is to determine how much better This surrogate market method is also known as the or worse off individuals are (or would be) as a result of "hedonic property value model." Other surrogate a change in environmental quality or public health market methods are the travel cost model, the hedonic conditions. There are four approaches analysts can use wage model, and avertive behavior technique. to estimate economic values of environmental quality All of these surrogate market methods rely on the changes; all four require data from household surveys. behavioral trail left by individuals as they make deci- sions that affect their lives, since people are assumed to THE CONTINGENT VALUATION APPROACH. This reveal their preferences through their behavior. The approach involves simply asking people how much estimates of the value of nonmarket goods are based they would be willing to pay to ensure that a particu- on information about what people actually did and on lar improvement in environmental quality is made. a set of assumptions about why they did what they This is known as the "stated preferences" or "contin- did, rather than on what the people said they would gent valuation" method.3 It has also been called the do under a set of hypothetical conditions. "direct approach" because people are directly asked to This second approach has its own disadvantages state or reveal their preferences. This approach meas- and limitations. For example, it is not feasible to use ures precisely what the analyst wants to know-the surrogate market methods to estimate the value of a strength of an individual's preference for the proposed new good or service or of a change in environmental change-and could be used not just for nonmarket quality that has not yet been experienced. This is goods and services but also for market goods. This because there are no previous instances in which peo- approach would be ideal if analysts could be certain ple have been offered a new level of environmental that the respondents would fully understand the quality and revealed their preferences for it. Even when change in environmental quality being offered and a nonmarket good or service has been available for that they would answer the questions truthfully. some time, there may not yet have been any significant However, analysts worry whether the intentions that changes in its quality, making it impossible to infer how respondents state in contingent valuation surveys people in an area would value a change in environ- before a change will accurately describe their actual mental quality. Finally, to implement any of the surro- behavior after the change when they face no penalty gate market methods, analysts must interpret and value or cost associated with a discrepancy between the two individuals' decisions within a theoretical framework. behaviors. As a result, their estimates of value will depend upon a series of assumptions that remain largely untested. THE SURROGATE MARKET APPROACH. Because of these Information from household surveys is essential concerns about the "contingent valuation" method, for the implementation of surrogate market approach- economists have traditionally used a second approach es. Data must be collected on respondents' behavior for measuring the value of nonmarket goods and serv- and the determinants of their choices. For example, ices such as those provided by many environmental using the hedonic property value model requires data assets (for example, air quality). This is known as the on the following variables: respondents' rent or the surrogate market approach. In this technique, econo- value of their houses, the characteristics of respon- mists try to find a good or service that is sold in mar- dents' housing units (such as square footage, lot size, kets and is related to the nonmarket service. Thus the quality of construction, and access to infrastructure), individual reveals his or her preferences regarding both and neighborhood characteristics (such as crime rate, the market and nonmarket service when he or she quality of local schools, distance of respondents' purchases the market good. For example, when decid- dwellings from public facilities and environmental ing what house to buy or apartment to rent, an indi- amenities, and local air quality). I0 CHAPTER 14 ENVIRONMENTAL ISSUES THE DAMAGE FUNCTION APPROACH. In order to esti- estimates of value for the same or similar goods or mate the value of changes in environmental quality services that have already been derived in other loca- that reduce individuals' well-being, an analyst can tions, then transfer these estimates into their analysis, attempt to determine the damages an individual will possibly after adjusting them to match the prevailing suffer. A deterioration in environmental quality can circumstances in the location in question. Analysts can cause a loss of productive assets or a loss of earning transfer estimates of value that were made using any of power.An individual can be restored to his initial state the approaches described above. This approach is of well-being by being compensated in either money termed "benefit transfer." The data from an LSMS- or other goods or services, by the amount of the loss. type survey can be used in a benefit transfer calcula- This is termed the "damage function" approach. The tion, but such data are not sufficient in themselves to damage function and surrogate market techniques are conduct such calculations, and the benefit transfer both termed "indirect" valuation approaches because approach is not discussed further in this chapter. neither relies on people's direct answers to questions about how much they would be willing to pay (or Data Requirements for Environmental Policy accept) for a change in environmental quality. Analysis In the damage fuinction approach analysts seek to determine the monetary losses that an individual Household surveys cannot gather all of the informa- incurs in terms of both expenditures made to cope tion that may potentially be relevant for environmen- with the damage and residual losses after such expen- tal policy analysis.While open-ended questions can be ditures have been made. Information from household very useful for identifying environmental priorities surveys is often necessary but usually not sufficient for and attitudes, LSMS-type surveys are not the appro- implementing the damage function approach to valu- priate place for such questions. And because LSMS ation. Household surveys may not be able to provide samples are usually nationally representative with only all the information required because respondents may a few households in each of many different neighbor- not have sufficient knowledge about the health dam- hoods, watersheds, or ecological zones, such samples ages associated with different kinds of environmental are not appropriate for investigating local problems. It pollution. Therefore, LSMS-type surveys are not an can be difficult to design questionnaires that contain appropriate tool for collecting the data to implement all of the questions pertinent to each different setting, this valuation approach, although household data can and there are often not enough sample households in play a partial role in supporting such analysis. In addi- each area to yield reliable data. tion, a thorough implementation of the damage func- Nevertheless, LSMS and similar survey data can tion approach requires information on pain and dis- be used in the analysis of a wide variety of environ- comfort that is difficult to collect in a general mental issues.Thus it is important to have a clear sense household survey. For example, a case of malaria caus- of the priorities for data collection using LSMS-type es an individual to suffer three main types of losses: surveys. This section of the chapter presents conclu- monetary expenditure on medicine, lost wages or sions about how best to use LSMS-type surveys to reduced productivity from not being at work, and pain support environmental policy analysis. and discomfort. It is relatively straightforward to use In urban areas of developing countries, the main household surveys to collect information on monetary environmental policy questions are "What is the opti- expenditures for medicines and lost wages. However, mal (appropriate) level of environmental quality?" and asking a respondent to put a monetary value on pain "How stringent should environmental standards be?" and discomfort requires questions that are much more In most urban areas in developing countries, the most carefully designed. important environmental standards are those for the quality of air, water, and sanitation. THE BENEFIT TRANSFER APPROACH. The fourth In rural areas the most pressing environmental approach to obtaining estimates of the value of envi- issues are often in the water and sanitation sector, and ronmental goods and services takes a somewhat differ- are often location-specific.The main "green" problems ent tack. Rather than developing new estimates of the in developing countries-deforestation, soil erosion, value of environmental goods or services, analysts find and desertification-are found in rural areas. The I I DALE WHITrINGTON destruction of habitats and loss of biodiversity fre- both urban and rural areas it is recommended that pri- quently arise from deforestation, especially when ority be given to collecting information on water and farmers encroach on protected areas. Siltation of rivers sanitation practices and household fuel use. These and streams and loss of agricultural productivity often activities are relevant in almost all locations and cir- accompany soil erosion. In general, the study of these cumstances, and it is relatively easy to collect data on problems is best done with samples designed to study them in household surveys. Also, having information the local area only. Therefore it is not recommended on these activities is vital for analysts to develop a crit- that national LSMS-type surveys be used to study such ical understanding of environmental conditions. topics. The draft modules in Volume 3 include three In both urban and rural areas, LSMS-type surveys modules covering households' use of natural resources are most useful as a source of information about: that household survey designers can include in their households' environmental priorities and perceptions, survey if interest in the topic is significant: one on households' use of natural resources, and the monetary water use (module 4), one on sanitation practices benefits to households of pollution control and infra- (module 5), and one on fuel use (module 6). Modules structure provision. 4 and 5 are based on extensive field experience and have been well tested. Module 6, on fuel use, has not Measuring Environmental Priorities and Perceptions been field tested and is included here for purposes of LSMS and sinilar surveys can be an effective tool for illustration. Survey designers will need to carefully measuring households' perceptions and rankings of gen- revise and pretest module 6. It would be natural for eral and specific environmental problems. Many previ- survey designers to put these modules next to the ous (non-LSMS) surveys have asked people what they housing module; if very short versions of these mod- considered the most important environmental problems ules are used, survey designers might even include in their neighborhood, their province, and their country. them in the housing module. Respondents have often been asked to rank a list of Modules 4 and 5, on water and sanitation, appear problems or issues from most to least important. quite long, but in the large majority of cases it will The environmental draft modules provided in take 20 minutes or less for interviewers to complete Volume 3 include two modules that can be used in both modules. These water and sanitation modules are LSMS surveys to examine households' general envi- substantially different from the modules on the same ronmental priorities: module 1 (for fielding in urban topics that were used in previous LSMS questionnaires areas) and module 2 (for fielding in rural areas). Both and in almost all other multitopic household surveys of the modules contain a list of problems that can be that have been used in developing countries. In the found in many locations. Because the purpose of these current draft modules questions are included on each modules is to identify which environmental problems possible water source and each sanitation practice, are most important in the view of the respondents, it whether or not they are used by the household sur- does not matter if some of the items on the list are not veyed. (In fact, in many locations households often use of particular concern in a specific survey situation. two or three sources of water.) (The list of issues will generally be easier to standard- There are two principal reasons for this approach. ize for urban than for rural areas.) If certain items of First, household water use practices in developing importance in the country surveyed are missing, countries are often much more complex than such designers can easily add these items to the modules. practices in industrialized countries. In situations in Another draft module, module 3, illustrates how which a significant number of households use multi- LSMS surveys can be used to measure environmental ple water sources for different purposes in different attitudes and perceptions. This module focuses on seasons, asking questions about each water source is urban air quality but could be adapted to cover other the most systematic, accurate way to determine actual environmental issues. conditions and practices. Analysts may know little about existing water use practices, and initial assump- Measuring Households' Use of Natural Resources tions often turn out to be wrong. LSMS-type surveys can also be used to collect infor- A second reason for this systematic approach is to mation on households' use of natural resources. In better understand why different households choose 12 CHAPTER 14 ENVIRONMENTAL ISSUES different water sources for different purposes. To naire will not necessarily reflect the choices available to model households' water source choices, analysts need the survey's sample households. to know the attributes of the water sources that were It should also be noted that by placing quality not chosen as well as the attributes of the sources that questions in the community questionnaire, survey were chosen. Modules 4 and 5 collect information on designers make the implicit assumption that all house- both sets of attributes. holds' evaluation of the quality of water is the same- The designers of many LSMS-type surveys will that what looks good to one individual looks good to not be able to include such a lengthy water use mod- all and that what tastes bad to one tastes bad to all. ule due to budget limitations or due to fears that the The fourth approach to shortening the module questionnaire will become too unwieldy. However, would be to have it consist only of the questions about there are several options for shortening the water use the principal water sources (for example, private con- module. nections and public taps) and the summary section at First, if the distinction between rainy and dry sea- the end. sons is not pertinent to the country studied, the sepa- In cases in which the goal is very limited descrip- rate sets of questions for each season may be omitted. tive analysis of living standards, very limited informa- The wording may then be changed in the remaining tion on resource use can be placed in other sections of set. For instance, Questions 2-5 could become "how the questionnaire; water and sanitation questions can would you judge..." rather than "in the rainy season, be included in the housing module, and fuel use ques- how would you judge..:" and the corresponding ques- tions can be included in either the housing module or tions on the dry season (Questions 6-10) could be the consumption module. To illustrate these options, dropped. Similarly, Questions 21-23 could be reword- resource use questions are presented in all three of ed to be more generic, and Questions 24-26 could be these modules. In any real survey the questions should dropped. be coordinated. For example, questions on use of and Second, in some instances the module can be expenditure on electricity are in the housing module shortened by dropping any potential water sources introduced by Chapter 12 rather than at the end of that no one seems to use (though this might be diffi- module 6C of this chapter (which would be another cult in a national survey). In many developing coun- alternative for their placement). tries, every kind of water source is used somewhere in the country. Nevertheless, few households have access Placing a MonetaryValue on Changes in Environmental to many different sources. For example, modern, well- Quality to-do neighborhoods in cities may use only piped The problem with using LSMS-type surveys to value water, whereas urban slumdwellers may have access nonmarket environmental costs and benefits is that only to public wells, taps, or vendors and poor rural respondents in the sample are likely to be drawn from residents may have to rely on springs or surface water. throughout the country where the survey is being If only a few sources pertain to each household, the fielded, whereas many environmental problems are amount of time needed to administer the module will location-specific.Thus for an LSMS-type survey to be be much shorter than if each household uses many useful it must put a value on outcomes that are com- sources. mon to many locations. Third, there is the possibibty of moving some of On the other hand, one advantage of LSMS and the quality and price questions, such as Questions 2-10 similar surveys is that extensive data are collected on and 13-16, to the community questionnaire. This will the socioeconomic characteristics of households. work best when the community questionnaire is filled These and other potential covariates can be used to out for each primary sampling unit or community and model the determinants of the value that households when these units are geographically compact. If a unit put on environmental improvements and to model is defined as the few blocks around an urban house- how environmental considerations influence various hold, the water sources available to each household in choices that households make.4 the unit will tend to be very similar. In contrast, if the The fact that environmental problems and the way unit is defined as the entire city, the answers to ques- people react to them are often location-specific can be tions about water sources in the community question- a problem when using household survey data in 13 DALE WHITTINGTON revealed preference models (such as the travel cost and reliable or accurate estimates of economic value (see the hedonic property value models) to value changes in Box 14.1). environmental outcomes. Therefore, it is difficult to In practice there are two main ways to use a stat- design a standard set of questions on the different ways ed preference approach.The first is to ask respondents in which households adapt their behavior in accor- to value a program or policy that was designed to dance with environmental stresses that can be used uni- improve environmental quality. (What is meant by a formly for a nationwide sample of households. program is an activity or set of activities designed to However, there is one simple type of question that alleviate a problem. For example, a program to can be asked in all household interviews in a nation- improve air quality might include a variety of regula- wide sample, and the answers to questions of this type tory and investment actions such as prohibiting the use can be used to estimate the economic value of envi- of lead in gasoline, installing stack gas scrubbers on ronmental improvements. Rather than analysts certain industrial facilities, establishing a system of attempting to infer the effect on housing values of, say, tradable emission permits, and improving mass trans- improved water service or improved air quality, inter- portation.) The survey interviewer would describe the viewers can ask respondents about these matters program or policy and its consequences to the respon- directly. The following questions are examples of how dent, then ask the respondent to value the program or this could be done: policy. A variety of elicitation procedures are available * For households that rent and have a private water for survey designers to choose from in phrasing the connection: How much would this apartment/ questions. flat/house rent for if it did not have a private water The second way of using a stated preference connection? approach is to ask a respondent to value certain envi- * For households that rent and do not have a private ronmental outcomes without providing specifics water connection: How much would this about the policies or programs that will be used to apartment/flat/house rent for if it had a private achieve these outcomes. For example, a respondent water connection? may be asked to value the public health benefits of * For households that own their dwelling unit and improving the quality of drinking water without the have a private water connection: How much would interviewer explaining the treatment technologies that this apartment/flat/house sell for today if it did not would be used to do this. have a private water connection? Both approaches have advantages and disadvan- - For households that own their dwelling unit and do tages. If respondents are asked to value specific policies not have a private water connection: How much or programs, it is not clear that they will understand or would this apartment/flat/house sell for if it had a believe that the program will achieve the benefits private water connection? described. Also, the respondents' answers to such ques- However, the author believes that the most prom- tions reflect not only their willingness to pay for the ising way in which household survey data can be used benefits that they receive from the program but also for environmental valuation is to use a stated prefer- their willingness to pay for the benefits that others ence (or contingent valuation) approach to measure receive. Some policy analysts would prefer to have data both households' willingness to pay for improved on the former but not the latter. water and sanitation services and the value that house- On the other hand, questions designed to ask holds place on the improvements in health outcomes respondents to value outcomes directly (without resulting from improvements in the environment (for knowing about the policies or programs that will be example, urban air quality). From the author's per- used to achieve these results) may appear more abstract spective, the potential benefits of including contingent to respondents and thus be more difficult to answer. valuation modules would far outweigh the costs Also, this method prevents policymakers from learning involved, and this is an intriguing new avenue for sur- whether respondents perceive the specific institution- vey designers to consider. It must be emphasized, al and financing arrangements for improving environ- however, that the contingent valuation method is con- mental quality as likely to be effective and fair. troversial within the economics profession; many In fact, both ways of using a stated preference economists feel that this method is unlikely to yield approach are likely to work well in the case of 14 CHAPTER 14 ENVIRONMENTAL ISSUES Box 14.1 Criticisms of the ContingentValuation Method Over the last decade the contingent valuation method has dents will answer contingent valuation questions as if they become the most widely used nonmarket valuation tech- were faced with a real budget constraint when in fact they are nique in the world. Perhaps in part because of its popularity, not. Proponents of the method have not offered a systemat- this method has no shortage of critics. Probably the majority ic theory of how respondents answer contingent valuation of professional economists view the contingent valuation questions, so there is no convincing theoretical reason to method with considerable skepticism if not downright hostil- believe that respondents answer accuratelyThis criticism calls ityThere have been three broad types of criticism of the con- attention to the need for empirical evidence on whether tingent valuation method; I will briefly summarize each in turn. respondents answer contingent valuation questions accurate- ly and reliably. However, the fact that there is no theory to I. Estimates of economic volues based on the contingent vol- explain why respondents would answer accurately does not uation method are unreliable and inaccurate, mean that they do not do so. The most common criticism of the contingent valuation Respondents in the third category are confused by the method is that it yields unreliable and inaccurate estimates of way the words in the contingent valuation survey are used and the economic value of gains and losses (Diamond and by the context given to the good or commodity described in Hausman 1994; Diamond and others 1993). Some people the contingent valuation scenario (Kahneman and Knetsch argue that respondents do not give accurate answers to con- 1992; Kahneman, Knetsch, and Thaler 1990). Respondents tingent valuation questions, giving three main reasons: often need a frame of reference or a basis for comparison in * Respondents intentionally give inaccurate answers to val- order to value the quantity of the good or service offered in uation questions. a contingent valuation scenario. Critics of the contingent valu- * Respondents have no incentive to answer contingent val- ation method are correct that people can have considerable uation questions accurately and thus do not do so. difficulty judging the quantity of a good described without * Respondents are easily confused by the way contingent being given a context for assessing whether the amount is scenarios are crafted and the kinds of questions typically large or small.This is likely to be a particularly serious difficul- asked, and thus give inaccurate or widely varying answers ty when respondents are asked open-ended valuation ques- depending on the context. tions such as, "What is the maximum amount you would pay These reasons for inaccurate responses are not in a formal for good x?" Respondents' insensitivity to the quantity of a sense mutually exclusive, and many critics of the contingent good or service offered in a contingent valuation scenario has valuation method freely cite them all as justification for reject- become known as the "embedding problem." ing contingent valuation estimates of economic values. But it There is also considerable evidence from a wide range should be noted that each of these reasons is based on of psychological experiments that people's answers to ques- somewhat different notions of a typical respondent. tions are influenced by the initial quantities or prices offered. Respondents who intentionally give inaccurate answers There is little reason to think that contingent valuation sur- can carefully discem the purpose of the contingent valuation veys would be free of such anchoring effects, and indeed it is survey, and quickly develop a strategy for answering questions common to find such evidence. that will further their self-interest and thus foil the contingent There is little doubt that these embedding and anchor- valuation researcher Their responses are thus subject to ing problems exist in at least some contingent valuation sur- "strategic bias," which critics believe renders these responses veys, and careful contingent valuation work requires that essentially useless as measures of economic value. Despite practitioners test for the existence of such phenomena. If the widespread belief among economists that respondents they are found, such phenomena add considerable uncertain- answer contingent valuation questions strategically, there is lit- ty to an assessment of the accuracy and reliability of the data, tle empirical evidence that they do so. and certainly need to be discussed. In some cases it is possi- Respondents in the second group again perceive their ble for a contingent valuation researcher to adjust estimates self-interest, but in this case quickly determine that it is not of economic value to account for such biases in the data. worth the energy or mental effort to think carefully about the questions being asked. They thus act rationally and preserve 2. Contingent voluation researchers often ask the wrong their mental powers, waiting until a more important problem question. comes along that is worthy of their full attention.This line of The second line of criticism is that the contingent valuation criticism bears careful consideration. It is certainly true that method as practiced has been used to answer the wrong respondents do not have strong incentives to answer contin- question.There is wide recognition within the economics pro- gent valuation questions accurately Critics of the contingent fession that the conceptually correct measure of a welfare valuation method are thus quite right to ask whether respon- (Box continues on next page.) 15 DALE WHITTINGTON Box 14.1 Criticisms of the Contingent Valuation Method (continued) change may be either an individual's willingness to pay or will- includes all types of household surveys. This line of criticism ingness to accept compensation, depending upon whether comes largely from sociologists, anthropologists, and other the change is viewed by the individual as a loss or a gain in advocates of more participatory approaches to development well-being (Cohen and Knetsch 1992; Knetsch 1990; planning. It is rarely heard from economists because if one Rutherford, Knetsch, and Brown 1 998; Vatn and Bromley accepts the criticism, it cuts with even more force on other 1 994).There has not, however been a similar acceptance of nonmarket valuation techniques such the travel cost method, the overwhelming empirical evidence that has accumulated hedonic property value models, and the marginal productivi- over the past two decades that the difference between will- ty approach. Indeed, the basic thrust of this criticism applies ingness to pay and willingness to accept compensation meas- to all formal economic analyses of individuals' demand for ures is large people value losses 2-4 times more than goods and services, including cost-benefit analysis and project gains-and is not the result of income effects or transaction appraisal. Economists typically rely on survey and experimen- costs. Economists still often assume that willingness to pay tal approaches for gathering data and use theoretical models and willingness to accept compensation are (or should be) of human behavior to interpret such data rather than con- practically equivalent. sulting with people themselves about the policy and project Many contingent valuation researchers have used will- matters at hand. ingness to pay valuation questions when in fact willingness Proponents of participatory techniques also make the to accept compensation questions would have been the argument that ifthe powers that be do not want to relinquish conceptually correct approach. Some contingent valuation their authority, and want data on individuals' demand for researchers have done this because they believed there goods and services for planning purposes (as opposed to should be no difference between willingness to accept actively involving households in the planning process and compensation and willingness to pay; others have favored empowering them to make their own decisions), a variety of willingness to pay questions for reasons of practicality and participatory techniques will provide more accurate data on expediency (It is often quite difficult to obtain meaningful demand than do the contingent valuation method or other answers to questions that ask a respondent about the min- survey methods. This is in line with the second class of criti- imum levels of compensation he would accept.) However, cism above. There have been few direct empirical compar- neither of these is an adequate rationale for asking the isons of demand estimates using both the contingent valua- wrong question (and thus estimating the incorrect concept tion method and more participatory data collection of economic value), and critics of the contingent valuation techniques; the few that have been done suggest that the two method are quite right to call attention to this mistake in data collection approaches can give quite different results, the way the contingent valuation method is sometimes although they are inconclusive as to which yields the most practiced. However, this is not strictly speaking a criticism of accurate and reliable estimates (Davis and Whittington 1998; contingent valuation itself This problem could be addressed Davis 1998). if contingent valuation practitioners used willingness to pay It is important to note that the criticisms of the contin- measures where these were the conceptually appropriate gent valuation method do not all suggest that the contingent measure of economic value, if the practitioners were able valuation data will overstate or inflate the economic value of to adjust or benchmark willingness to pay measures to con- gains and losses. Critics of the widespread use of the willing- vert them to reasonable estimates of willingness to accept ness to pay structure of contingent valuation questions in fact compensation values in situations where willingness to contend that contingent valuation practice understates eco- accept compensation was the correct measure of econom- nomic values. Advocates of participatory data collection ic value, and if the practitioners were able to successfully methods make no claim about the direction or magnitude of measure willingness to accept compensation when appro- the mistakes they believe the contingent valuation method priate. will make. Most of the critics who argue that contingent val- uation will yield inaccurate and unreliable results seem to 3. The use of the contingent voluation method is ethicoaly believe that the contingent valuation method will yield over- inoppropriote. estimates of demand. But even here the direction of alleged The third type of criticism of the contingent valuation method bias is often inconclusive. For example, if respondents answer is that it is an ethically flawed method of data collection contingent valuation questions strategically, they might either because it treats respondents as subjects rather than as active understate or overstate their willingness to pay for a good or participants in the planning process. From this perspective, service, depending on how they believed their answer would contingent valuation is just an example-albeit an egregious affect the provision of the good or service by the government one-of a class of 'extractive" data collection methods that or another authority. 16 CHAPTER 14 ENVIRONMENTAL ISSUES improved water supply services, because private water improved water or sanitation service must typically be connections and reliable water services are largely pri- phrased according to the level of service that the vate goods in the sense that their benefits accrue prin- respondent already has.This means that the interview- cipally to the households that receive them. Moreover, er must determine the respondent's housing tenure these benefits are more immediate and obvious than, and water and sanitation service level in order to skip for example, the effect of some pollutants on the to the appropriate valuation question. lungs. It is likely that both kinds of contingent valua- In the first section of this chapter it was noted that tion approach will become easier and more effective as the way environmental policies and programs were they are used more often in LSMS and similar surveys. appraised in the past has been criticized on the Volume 3 contains five environmental modules grounds that it failed to take into account these poli- that illustrate the ways in which a stated preference cies and programs' future costs and benefits, thus dis- approach could be used in an LSMS-type survey. criminating against the interest of future generations. Modules 7 and 8 are designed to measure households' However, little information is currently available on willingness to pay for improved water services in individuals' own rates of time preferences. Module 11 urban and rural areas. Module 9 is designed to meas- uses a simple contingent valuation approach to ask ure households' willingness to pay for improved sani- questions that will yield the information necessary to tation services, and Module 10 measures households' impute individuals' rates of time preferences (Cropper, willingness to pay for improved health and other out- Aydete, and Portney 1994).This module has been used comes from improved urban air quality. Module 11 successfully for this purpose in Uganda, Mozambique, illustrates how a contingent valuation approach can be Indonesia, Bulgaria, Venezuela, Ethiopia, and Ukraine used to collect data for estimating individuals' rates of (Poulos and Whittington 2000). time preferences for nonmarket goods. Even more If any of these stated preference modules are than Modules 1-6, Modules 7-11 will probably have administered in a specific location within a country, to be adapted to suit local or regional conditions. analysts can use benefit transfer methods to estimate These environmental valuation modules need environmental values in other locations. However, not be used for all respondents in an LSMS-type because benefit transfer methods have only recently survey. Survey designers may use a module designed begun to be used to value nonmarket goods, the pro- to estimate the value that households place on cedures for dealing with common problems have not improved water services only in one region of a been standardized. country where water problems are known to be par- ticularly acute. Similarly, the air quality module Estimating Physical Effects of Environmental Degradation should generally be restricted to large urban areas and Physical Benefits of Pollution Control and perhaps even to specific urban locations. In this In industrialized countries household surveys have context, how urban areas are defined will depend often been used to implement a damage function upon the extent and magnitude of the environmen- approach to valuing changes in the quality of the envi- tal problem; multiple definitions might be used in a ronment, including measurement of the impact of air single survey. For example, sanitation problems can pollution on people's health. In developing countries be severe in small communities, while air quality household surveys have been used to measure the problems are most likely to be a serious concern in impact that a lack of drinking water and sanitation much larger cities. facilities has on people's health. The question arises An important complication in the design of stan- whether LSMS and similar surveys are an appropriate dardized modules for valuing improved water and san- vehicle for either of these purposes. In the opinion of itation services is that these values depend upon what the author of this chapter, the answer depends on alternative sources of supply exist and on how much which aspects of environmental health analysts wish to they cost. Therefore, the survey questions must usually study and on how the sample for the LSMS-type sur- be designed to reflect specific housing and water and vey is drawn. sanitation conditions. For example, valuation questions For example, household surveys are not an effi- must generally be phrased differently for homeowners cient technique for measuring the impact of envi- than for renters. Similarly, questions about the value of ronmental pollution on a particular health outcome, 17 DALE WHITTINGTON particularly when this health impact is a rare event (for example, mortality). The problem is that too Box 14.2 Environmental Policy Issues and Household many resources must be spent in order to find too Survey Data few cases in which the environmental pollution has Environmental poaicy issues that can be addressed using caused a health effect.When the health outcome in household survey data question is more common, two primary questions Urban and rural water and sanitation remain: whether the sample will provide enough Urban air quality variation in the environmental variable studied (for Household fuel use example, ambient air pollution levels) and whether the environmental variable can he measured at a Ioxv Information relevant for environmental policy analysis that can be collected using household surveys cost. it is not generally advisable to use householdThe damages that households suffer from environmen- Thus it is not generally advisable to use household tal degradation of air water and land resources surveys for environmental epidemiology. For measur- * Household use of local renewable resources such as ing the impact of water and sanitation interventions forests, fisheries, and groundwater on waterborne disease, for example, a case controlled Households' priorities for environmental improvements study is usually preferable to a household survey unless Households' willingness to pay for some kinds of envi- the sample size of the survey is very large. Some ronmental quality improvements household surveys have been used to study air pollu- Households'rates oftime preferences * The extent of problem conditions (such as households' tion; the most successful of these have been prospec- access to improved water supplies) tive studies in which each household kept a diary of any respiratory problems and illnesses suffered by its Environmental issues that are difficult to analyze using house- members. However, LSMS-type surveys are not gen- hold survey data erally organized in a way that permits respondents to * Global environmental issues about which households keep such diaries.Another problem is how to measure may have little understanding the ambient air quality experienced by respondents during the period when the diaries are kept. One way Environmental issues that can only be analyzed using special- that analysts could use this kind of data from a house- Property rights regmes for local environmental and hoold survey would be to perform a cross-sectional natural resources (such as ownership of natural study in which they correlate symptoms indicative of resources) chronic respiratory problems with air pollution levels. Localized natural resource management issues Such studies can be useful, but require that either indoor or outdoor air pollution be measured for all people in the sample. households' environmental attitudes and preferences Box 14.2 summarizes some ofthe main points dis- that can only be captured through the use of open- cussed thus far in this chapter. The principal environ- ended questions, and environmental health problems mental policy issues that can be addressed in household that occur only rarely in a population. surveys include urban and rural water and sanitation Box 14.3 summarizes some cautionary advice quality, urban air quality, and household use of natural about which of the draft modules presented in this resources such as fuel. Considerable information can be chapter are new and untested. Household survey collected in LSMS-type surveys that is relevant for designers are again warned that the contingent valua- environmental policy analysis, including the damages tion modules (modules 7-11) need to be carefully that households suffer from environmental degrada- adapted to country conditions and also need to be tion, households' attitudes toward and priorities for the pretested. Implementing the contingent valuation environment, households' rates of time preferences, and modules also requires more extensive training of inter- the economic value of environmental quality changes. viewers. The household fuel use module (module 6) However, Box 14.2 also points out that some impor- has not been pretested. tant environmental issues are difficult to analyze using On the other hand, the modules on general envi- household data. These include global issues about ronmental priorities (1 and 2) and on household water which households may have little understanding, and sanitation use (4 and 5) have been used extensive- 18 CHAPTER 14 ENVIRONMENTAL ISSUES Who Should Be Interviewed? Box 14.3 CautionaryAdvice It is recommended that only one respondent per household answer the questions in the environmental How many of the draft modules are new and unproven? modules.The respondent should be either the head of None of the environmental modules have yet been used as part of an LSMS survey.The household fuel household or the spouse of the head of household. use module is untested and is likely to require careful Particularly for the contingent valuation modules, the field testing and subsequent modification. Module 3 interviewer should speak to someone who has the (environmental attitudes toward and perceptions of authority over the finances of the entire household. urban air quality) has only been used once in a large It is suggested that the interviewer should simply survey and may require extensive modification in ask to speak to the head of household or the spouse of other locations. the head of household-whichever of these two people * How well have the modules worked in the past? The agrees to be interviewed becoming the selected respon- modules on general environmental priorities (modules aees t ie nterview in the seletd e n- I and 2) have been tested in numerous surveys dealing with households' water and sanitation conditions in the spouse of the head of household are interviewed many developing countries.They have worked very well together and answer the questions jointly.) In some and should present no major problems. The modules countries this approach may result in most respondents on household water use and sanitation have also been being male heads of household. However, in practice it thoroughly tested in many countries.They may need to is usually relatively easy to obtain a sizable percentage of be modified to fit a particular country situation (for female respondents. In most surveys that have used this example, in a region where the distinction between procedure there has been close to a 50/50 splt between rainy and dry seasons is not important). Nevertheless, prcdr, teehsbe ls oa5/0shewe survey plannedryseares uliky oto experi. enermanylr, male and female respondents. This has allowed analysts survey planners are unlikely to experience many prob- lems with these modules, either in their long or short to test whether the gender of respondents affects the versions. Different variations of the valuation modules answers they give in the environmental module. (modules 7-1l ) have also been used in household sur- veys, though not in LSMS surveys, in many countries. How Should Urban Areas Be Defined? * Which parts of the modules most need to be customized? This chapter has recommended that the contingent The contingent valuation scenarios and elicitation proce- valuation modules for air quality be fielded in urban dures in the contingent valuation portion of the valua- tion modules (modules 7-I I) have not been tested in LSMS-type surveys. In particular, referendum elicitation Box 14.4 List of Standard Environment Modules procedures and split-sample experiments have not been tried in LSMS-type surveys, and interviewers will need to * Module I General environmental priorities (urban) be given special training in these methods. However, sin- * Module 2 General environmental priorities (rural) gle-purpose contingent valuation surveys have become * Module 3 Environmental attitudes and perceptions about increasingly widespread in developing countries during urban air quality the past I O years.Thus there is nothing novel about ask- * Module 4 Household water use-attitudes and prac- ing respondents in developing countries such hypotheti- tices (include in housing module) cal questions about environmental goods and services. * Module 5 Household sanitation-attitudes and prac- tices (include in housing module) * Module 6 Household fuel use attitudes and practices ly all over the world and should work well with only (include in housing module) minor modifications to fit local circumstances. * Module 7 Households' willingness to pay for improved water services (urban) Questionnaire Modules * Module 8 Households' willingness to pay for improved water services (rural) Box 14.4 lists the 1 1 environment modules introduced * Module 9 Households' willingness to pay for improved Boxi 14.4nlist thisesect l of these modules are troduted isanitation services (urban) in this section; all of these modules are presented in * Module 10 Households' willingness to pay for heafth Volume 3. Two general implementation issues are also outcomes from improved urban air quality discussed in this section: who should be interviewed * Module I I Stated preference module for imputing indi- and how urban areas should be defined in the context viduals' rates of time preference for nonmarket goods of the environmental modules. 19 DALE WHTTrINGTON rather than rural areas. The definition of "urban" in recreation, tourism, and national parks (Grandstaff and this context needs to be an operational one-not a Dixon 1986; Shyamsundar, Kramer, and Sharma 1993; definition used only by a country's statistical bureau. Menkhaus 1994; Hadker and others 1995). However, Most (but not all) cities with severe air pollution are the areas of application are growing rapidly and now larger than the statistical office's standard definition of include surface water quality (Choe,Whittington, and an urban area. Survey designers need to be careful to Lauria 1996), health (Swallow and Woudyalew 1994; field the contingent valuation module on air quality Alberini and others 1995; Whittington, Pinheiro, and only in urban areas that have an air pollution problem Cropper 1996), and biodiversity conservation (Moran rather than in all urban areas of a certain standard pop- 1994).6 ulation size. Even some large cities do not have an air Some contingent valuation analysts believe that it pollution problem, and it would be a waste of is easier to administer high-quality contingent valua- resources to field this module in such cities. tion surveys in many developing countries than it is in In contrast, the urban sanitation modules will be industrialized countries. Response rates are typically pertinent in smaller urban areas. Special codes may be very high in developing countries, and respondents required on the household identification page of the will often listen to and consider the questions posed to questionnaire to designate the sorts of urban areas to them. Also, interviewers are inexpensive in developing which the different modules apply.The wording of the countries relative to their cost in industrialized coun- first questions in the modules presented here would be tries. Thus the costs of a contingent valuation survey changed to match that nomenclature. administered in a developing country are typically an order of magnitude lower than the costs of a survey Comments on the ContingentValuation with a similar sample size in an industrialized country. Modules This means that larger sample sizes and more elaborate experimental designs can be used in contingent valu- The most innovative aspect of the proposed modules ation surveys in developing countries. for the environment are the contingent valuation There are also likely to be less data on the bene- questions.5 These portions of the questionnaire are fits of different kinds of environmental policies in designed to determine the economic values that developing countries, which means that the marginal households assign to environmental quality or infra- value of data obtained from contingent valuation sur- structure changes. Ten years ago only a handful of very veys is likely to be large. Therefore it seems both fea- rudimentary contingent valuation studies had been sible and desirable to use the contingent valuation conducted in developing countries; at the time, con- method in developing countries to evaluate a wide ventional wisdom was that such studies simply could range of environmental projects. However, this does not be done. The problems associated with posing not mean that conducting contingent valuation sur- hypothetical questions to low-income, possibly illiter- veys in developing countries is easy.There are numer- ate respondents were assumed to be so overwhelming ous issues that arise in conducting contingent valua- that it was not even possible to try to pose such ques- tion surveys in developing countries that must be tions. However, these days many environmental and given careful attention to ensure that high-quality data resource economists and policy analysts working in are obtained. It may be worth considering hiring an developing countries assume that contingent valuation experienced contingent valuation consultant to help surveys are straightforward and easy to do. to prepare modules, since implementing the contin- Bilateral donor agencies and the international gent valuation method requires expertise beyond the development banks increasingly use contingent valua- usual know-how needed to formulate and administer tion techniques in project and policy appraisals as part the other modules of the questionnaire. oftheir everyday operations work.The contingent val- This section of the chapter discusses some of the uation method was initially applied in developing issues that have arisen and some of the lessons that countries primarily in two areas: water supply and san- have been learned during the past 10 years of admin- itation (Whittington and others 1988, 1990, 1993; istering contingent valuation surveys in developing McConnell and Ducci 1988; Briscoe and others 1990; countries. The discussion covers five basic issues: Altaf and others 1993; Singh and others 1993) and explaining a contingent valuation study to nonecono- 20 CHAPTER 14 ENVIRONMENTAL ISSUES mists, interpreting respondents' answers to contingent One particularly common source of confusion valuation questions, setting referendum prices, con- relates to the distinction many noneconomists make structing joint public-private contingent valuation between a household's willingness to pay and ability to scenarios; and addressing ethical problems involved in pay. It is important that interviewers clearly under- conducting contingent valuation surveys. This list is stand that the purpose of a valuation question is to not meant to be exhaustive, but it will hopefully pro- determine what respondents would do if they had to vide survey designers with insights into some of the make a real economic commitment. In other words, issues involved in including contingent valuation the objective of a valuation question is to determine modules in an LSMS-type survey. how much respondents are both willing and able to pay. Explaining a Contingent Valuation Study to Noneconomists The classification scheme presented in Table 14.3 The first difficulty that survey designers may face can be used to clarify this point. As shown, the total when fielding a contingent valuation survey in a population of respondents can be envisaged as four developing country is to explain the contingent val- groups.The respondents of cell 1 say they would make uation method to government officials and survey a real economic commitment if the consequences of interviewers. The concepts of economic value and the contingent valuation scenario could be deliv- "maximum willingness to pay" (or minimum com- ered-and have sufficient income to make such a pensation that a respondent is willing to accept) are commitment.The respondents of cell 2 are able to pay often difficult to transmit to noneconomists. In but not willing to do so-presumably because they order to include open-ended willingness-to-pay prefer to spend their money on other things. questions in the survey questionnaire, survey design- The respondents of cell 3 would like to purchase ers need to ensure that the language used captures the commodity but cannot afford to do so. It is this the notion of the maximum amount an individual is group that typically causes noneconomists the most willing to pay. confusion.The argument is often made that individuals Unfortunately, this can be particularly difficult to in this third group would like to purchase the proposed translate. For example, in a contingent valuation survey good or service if their income was higher but, in their conducted in Haiti (Whittington and others 1990), a current financial circumstances, they are not able to pay. respondent reacted to an early version of an open- Noneconomists often want to classify these respondents ended contingent valuation question by asking one of as "willing to pay," but contingent valuation researchers the interviewers, "What do you mean the maximum I must emphasize that for the purposes of the study such would be willing to pay?You mean when someone has individuals must be categorized as not willing to pay (in a gun to my head?" In fact the interviewer was trying other words, not willing and able). to determine the maximum amount that the respon- The respondents defined by cell 4 consist of indi- dent would be willing to pay for the proposed (or viduals who cannot pay and say that even if they could hypothetical) good or service in the context of the pay for the hypothetical good or service, they would existing institutional regime within which individuals not.These people should be classified as not willing to are free to allocate their personal financial resources. accept the contingent valuation scenario. Contingent valuation analysts would like to measure The important point to recognize is that demand the amount of income that the household could give for a proposed good or service is not likely to be a up after obtaining the goods and services from the function solely of income. Increases in households' project that would leave the household just as well-off income may at times have a negligible effect on the as it would have been had the project never been households' willingness to pay for a specific good or implemented. service. Table 14.3 Willingness and Ability to Pay Respondent is willing to pay Respondent is not willing to pay for the hypothetical good or service for the hypothetical good or service Respondent is able to pay for the hypothetical good or service Willing and able (Cell I) Able but not willing (Cell 2) ............a..b....................... ...f.o..... t....... ...h.. y.. p............ t......................d...............r ........ce .................n ..... ....u .......n........a..b................l.....3......... ............ ...o.... ..a........ ....a.... ....n....... w...... I.i................. l--4.).... .. Respondent is not able to pay for the hypothetical good or service Wlilling but not able (Cell 3) Not able and not wiling (Cell 4) Source Author's examples. 21 DALE WHITrINGTON Interpreting Respondents'Answers to Contingent Valuation ways in which a respondent might answer the valua- Questions tion questions by saying "yes, but ..." but essentially One of the reasons why many economists and analysts meaning "no." have been skeptical about the feasibility of conducting Table 14.4 presents this list of different ways to say contingent valuation surveys in developing countries "no" and the number of times respondents gave each is these analysts'presumption that it will be difficult to "yes, but" answer to the valuation question about understand and interpret respondents' answers to whether the respondent's household would want to be abstract (or hypothetical) questions. Such worries are connected to the new water and sewer lines if a spec- often well founded, and care is needed when drafting ified monthly tariff were charged. For example, of the these questions. 164 answers that were recorded as "no," 52 respon- Analysts had problems in interpreting preliminary dents (32 percent) answered "Yes, but I cannot afford responses to the valuation questions in a contingent it." Another 18 percent said, "I agree, but the costs are valuation survey conducted in Semarang, Indonesia too high." These "yes, but" responses-50 percent of (Whittington and others 2000).The contingent valua- the total number of"no's"-seem clearly negative and tion survey was designed to determine whether a correctly classified as "no." household would vote in favor of having water and However, another 30 percent of the respondents sewer lines installed in its neighborhood if everyone in said, "I need to know others' opinions about the pro- the community had to pay a specified assessment fee gram before I decide." The interviewers assured the (whether or not they connected to the new lines). In survey designers that this was again simply a polite way addition, the survey was designed to determine of saying "no," but the designers thought that this whether the household would choose to connect to answer seemed reasonable. The respondents might such lines if a given monthly tariff were charged. simply have needed time to think about their decision, After the first couple of days of the pretest of the and discussing the matter with their neighbors would contingent valuation questionnaire, the survey design- have been a reasonable way to analyze the pros and ers discovered that everyone was saying "yes" to every cons of the proposed project (Whittington and others question, regardless of the assessment fee or monthly 1992). Thus it was less clear that this response should tariff offered them. So they stopped pretesting and be assigned to the "no" category than it was in the case held a meeting with the team of interviewers to try to of the previous two types of answers.7 Other answers find out why this was happening. During the course listed in Table 14.4 also seemed ambiguous and uncer- of a lengthy discussion it became clear that respon- tain. Therefore, the designers came to believe that the dents were in fact answering "yes, but ...... then giving proportion of respondents they had placed in the "no" many different qualifications to their answer. The category for this valuation question was probably too interviewers informed the survey designers that, in high. Although they had followed their interviewers' Indonesia, these were all polite ways of saying "no." guidance in coding the answers, they subsequently The designers then developed a coded list of the many came to believe that their analytical results probably Table 14.4 Description and Frequency of Different "No" Responses, Semarang, Indonesia Description of response Number of times recorded Percentage of responses I cannot afford it 52 32 ................................................................................................................................................................................................................................... I need to know others' opinions about the program 49 30 i agree. hot the costs are too high 30 18 ........ ~ -e......... t's.................. 'd............................................................ *................... ................................................................................. .....7..................... Yes, if the costs are reduced I 1 7 ..........................................................................................*....................................................*.................................................................................... Ihave many expenses, chiloren. . . (and so on) 8 5 I agree, but the current situation is satisfactory 6 4 I agree, but I do not want to pay in advance 4 2 ........"t e'......... "m.................... p . ...i'd ... ..'s ... ..xt"""ded.................................................................2.........................................................................................I.............. Yes, if tne payment period is extended 21 ...................... o"n.... is......................'y.................................................................................................................................................................<...I.............. Yes, if participation is mandatory < I ...............................................*................................................................................................................................................................................... I can pay, but I want to avoid rumors about my wealth i < I .............................. ........................................................................ -4..........................................................................................0........ ...... Woral number of verboim responses t164 100 Source: Whitt ngton and others 2000. 22 CHAPTER 14 ENVIRONMENTAL ISSUES underestimated the number of households that would happening. Although the merits of referendum-type have agreed to be connected to the water and sewer questions are still being debated,8 most contingent val- lines. uation practitioners favor using only one or two such This example illustrates how careful analysts must questions in order to reduce the possibility of eliciting be in interpreting respondents' answers to valuation biased responses. questions in a cross-cultural context and how important This split-sample technique is routinely used in it is to pretest a contingent valuation questionnaire. U.S. surveys. While professional interviewers in the United States are familiar with the use of this tech- Setting Referendum Prices nique, this is not the case in many developing countries, One commonly used way of asking valuation ques- and the interviewers in such countries will want to tions in a contingent valuation survey is the referen- understand the reason for the split-sample experiment. dum elicitation procedure. In this method the ques- In the past when referendum-type elicitation pro- tions are often couched in terms of voting, as in a cedures have been used in contingent valuation surveys referendum; for example: "If the improvement in air in developing countries, the designers of these surveys quality that I have described were to cost your house- have often made the mistake of specifying too narrow hold $50 in higher taxes, would you vote for it?" To a range of prices. They have tended to set the highest use this procedure the survey sample is randomly split referendum price too low and the lowest price too into several different subgroups.The interviewers pres- high, making it difficult for analysts to estimate "good" ent the respondents in each subgroup with a different valuation functions. This tendency to use too narrow a hypothetical price for the good or service in question. price range is understandable because extremely high Thus a person in one subgroup may be told that the and extremely low prices often lack credibihty. If the price of a service would be $50, while a person in amount that an interviewer mentions to the respon- another subgroup is told that the price would be $100. dent lacks credibility, the respondent is unlikely to Analysts use the responses to construct values for the answer the question on the basis of the price asked. whole sample. In order to increase the credibility of the contin- Many contingent valuation researchers feel that gent valuation results, it is generally advisable that the the referendum approach is the best way to ask a highest price used be rejected by 90 to 95 percent of respondent for information about his or her willing- the respondents. If the frequency distribution of ness to pay because this approach presents the respon- respondents' values is known, using such a high price dent with a realistic, easy-to-answer question. is not efficient (Alberini 1995a, 1995b; Kanninen Moreover, it is not obvious how a respondent would 1995), but in developing countries it is useful to show answer a referendum question if he or she wanted to that setting prices high enough will cut off demand for give a biased answer in order to influence the results the good or service. Nevertheless, survey designers of the study (or advance personal goals). For example, tend to be reluctant to set the highest referendum if the maximum a respondent would pay for an envi- price at a high enough level to do this, partly because ronmental quality improvement were $10 per year and it is embarrassing for interviewers to have to mention the referendum question asked the respondent if he or such an unrealistically high price to respondents. she would vote for a plan that would cost households Respondents often take the contingent valuation sce- like his or hers $5 per year, the respondent would not nario very seriously, and if the interviewer implies that have any obvious incentive to give an untruthful the hypothetical good or service will cost the highest answer. If the respondent indicated an unwillingness to referendum price, they may be acutely disappointed vote for the plan, the plan's chances of being imple- that the good or service would be so expensive. mented might be reduced. It is in this sense that some This problem is exacerbated in countries with a contingent valuation researchers term the referendum highly unequal income distribution. Interviewers approach "incentive-compatible." A respondent might often complain that asking about such a high price is hope to have the plan implemented and not have to silly because everyone knows that the sample house- pay even the $5 per year, but it is not obvious why holds cannot afford to pay such a high price, which answering "no" to a referendum question that posed a makes the interviewers look insensitive or unin- price of $5 per year would increase the chances of this formed.Thus in some situations a very high price may 23 DALE WHITTINGTON not be plausible to respondents and may thus cause whether or not its members are willing to share some them to doubt the credibility of the entire scenario. of the capital costs of a project. For example, consider This problem is compounded if there is a tendency of an investment in sewer lines. If it could be assumed respondents to say "yes" to whatever question the that all households in a particular neighborhood interviewer asks ("compliance bias"). However, in would connect or could be forced to connect to new these circumstances it is even more vital to prove that sewer lines if they were installed, it would not be nec- demand will be cut off if a sufficiently high price is essary to elicit a collective decision on whether the charged. lines should be installed. However, if this could not be Survey designers often set the lowest price too assumed, a fiscally responsible sewer authority could high because the agency funding the survey often not bear the financial risk of installing such expensive wants to use the results to help it set the prices it will infrastructure without some form of payment guaran- charge and may not be interested in learning about the tee. This means that the authority would need some extent of demand for the good or service at prices assurance that, if the sewer pipe were laid in a neigh- lower than those it intends to charge. Thus, for the borhood, households would pay a predetermined funding agency, asking part of the sample about a very amount for this infrastructure improvement, whether low price may seem like a waste of resources.9 or not they agreed to be connected. From the agency's In addition, like prices set too high, prices set very financial perspective, each household in the neighbor- low can make interviewers seem uninformed and hood would be required to pay a share of the sewer undermine the credibility of the contingent valuation network installation costs (whether or not the house- scenario. One implication of this is that the survey hold obtained a connection) because the value of its needs to include questions that aim to find out why property would increase simply by having the option respondents would reject a very low price. Another to connect in the future. implication is that survey designers should explicitly Second, a household must decide whether it will recognize that many projects have negative effects on connect to such infrastructure if it is installed. Because some people; thus designers should expect some many infrastructure projects have positive externalities respondents to be unwilling to pay for the services that and public good characteristics, it is plausible that a a project will (hypothetically) provide. Some goods or household would vote in favor of a project and agree services described in contingent valuation scenarios to pay some share of the capital costs even if it decid- may have little or no value to some respondents. An ed not to use the service immediately. Because these improved water supply system will threaten the business two decisions are conceptually interrelated, the con- of water vendors; such respondents will never accept the tingent valuation scenario needs to inform the respon- contingent valuation scenario, no matter how low a dent about the terms and conditions of both public price is hypothetically charged for the service. and private components of the deal to enable the respondent to make an informed choice. In practice Constructing Joint Public-Private Contingent Valuation this means that a lot of information may need to be Scenarios conveyed to respondents-typically necessitating the Many of the contingent valuation surveys conducted use of photographs and drawings. in developing countries have been concerned with Also, respondents are likely to have numerous ques- estimating the demand for infrastructure services. In tions about the proposals. Thus it is vital to use highly one important respect, the contingent valuation sce- trained, well-informed interviewers who can easily narios required for such surveys are considerably more respond to questions from respondents. It is also gener- complex than those used in contingent valuation sur- ally inadvisable to allow interviewers to deviate from veys about environmental quality improvements in the script of the questionnaire in an ad hoc manner.The industrialized countries. In order to understand house- interviewers should be instructed to give the informa- hold demand for infrastructure services such as tion provided in the questionnaire script in a different improved sewers or piped water supply, it is often nec- form if the respondent does not initially understand it. essary to model two household decisions jointly. In some cases survey designers may need to develop First, a household must decide whether to support contingencies that help interviewers deal with particu- the collective decision of a community regarding lar inquiries that are rarely raised by respondents. 24 CHAPTER 14 ENVIRONMENTAL ISSUES Ethical Problems in Conducting Contingent Valuation short time (generally two to three hours). In this way Surveys respondents would supposedly have little time to dis- Two ethical issues arise in the implementation of the cuss their interviews with one another before they had contingent valuation method; neither of these issues all been conducted. However, in one community the has received the attention it deserves.The first issue neighborhood leader dropped in on an early interview concerns the use of a referendum elicitation proce- unannounced and heard the referendum price offered dure. Because this method entails giving different sub- the respondent.This price happened to be the highest groups of respondents in the survey sample different of the four prices used, and the neighborhood leader prices for the same hypothetical good or service, it became concerned. He quickly spread word through- may sometimes cause confusion and spread misinfor- out the neighborhood to answer "no" to the valuation mation about the real costs of addressing a problem question because he felt that the improved water and that may be of great public concern. sanitation program offered in the contingent valuation For example, a referendum question was used in scenario was simply too expensive. two recent contingent valuation studies conducted for Obviously, this problem arose partly because the the World Bank (Pinheiro and Whittington 1995; field supervisor and the interviewer were unable to Whittington and others 2000). In a contingent valua- exclude the neighborhood leader from a supposedly tion survey conducted in November 1994 that was private interview (although, in fairness to them both, designed to estimate households' demand for improved this is not a easy thing to do in Indonesia). However, water services in a small town in Mozambique, five dif- it also illustrates how quickly information can spread ferent prices were randomly assigned to different sub- in a close-knit urban community, how seriously some groups of respondents. In June 1995 the study team community members may take the information pre- returned to the town where the survey was conducted sented to them in a contingent valuation scenario, and to brief a group of local government officials and com- how easily a community can be confused by the use of munity leaders on the results of the contingent valua- different prices and by other aspects of split-sample tion survey. During the ensuing group discussion, one experiments. neighborhood leader said that he had talked to many Contingent valuation experts may argue that any respondents after their interviews and that he did not such misinformation is the fault of the survey design- understand why different households were asked to pay er, who is supposed to craft language that informs different prices. He said that it did not seem fair or nec- respondents that the choice is "just" hypothetical. essary to charge one household more than another for Respondents are usually told to "suppose" or "imag- a water connection. The mistaken impression that dif- ine" the choice described, and that the choice is not ferent households in the community would be charged actually or necessarily going to be offered.This nuance different prices for a water connection seems to have is often lost in translation; in some cases the condi- been one outcome of the contingent valuation survey. tional subjunctive may not even be translatable. The Thus the use of a referendum approach with different interviewers' disclaimer may also be false in the sense prices may have increased public uncertainty and con- that a project is actually being considered and is thus fusion about the costs of improved water services in not hypothetical at all. this small Mozambique town. A good contingent valuation scenario is designed In July 1995 a contingent valuation survey of a to be realistic and taken seriously by respondents. In few hundred households was conducted for the World practice, the more seriously respondents take the Bank in three areas of Semarang, a city of 1.2 million choice put to them, the less hypothetical the scenario people on the north coast of Java, Indonesia. The sur- is likely to seem to them. This is particularly true for vey was administered in three districts of the city.The goods and services with large use values that are com- leader of each neighborhood unit had to be informed monly provided by the government, such as water about the survey by higher-level community leaders supply services. The less hypothetical the provision of before the survey could take place. After the leaders' the good or service described in the contingent valu- permission was secured, a team of interviewers and a ation scenario, the more likely the different referen- field supervisor was sent into the neighborhoods to dum prices will confuse serious public discussion of interview all of the sample households in a relatively the issue at hand. 25 DALE WHIrrINGTON Contingent valuation researchers generally scenarios used in such surveys may not be strictly assume that they will sample large populations and hypothetical. If the donors and governments that fund thus that there will be little chance that one respon- the contingent valuation surveys judge the results to dent will talk with another, so any misinformation be credible, the findings will likely be used in policy communicated to a relatively small number of respon- decisions. This movement from hypothetical to "real" dents about a hypothetical good or service will not be contingent valuation scenarios raises a host of ethical widely discussed and will not influence public debate. concerns. However, in small towns, villages, or urban neighbor- From a theoretical perspective, it is not possible to hoods in developing countries, this assumption is often value a project independent of how it is to be paid for unwarranted. Even in large capital cities, a sample of or independent of the institutional regime assumed to 1,000 to 2,000 households is not too small to avoid be in place when the project is implemented. As long the discussion of the contingent valuation survey by as people have preferences xvith regard to various many people, including some who may be knowl- aspects of how a project is carried out, such prefer- edgeable about the problem addressed in the survey or ences need to be taken into account. The fact that val- who will be influential in deciding how it should be uation estimates are context-specific has nothing to do solved. with the contingent valuation method itself, although The issue of spread of misinformation arises not the contingent valuation method does give survey only with the prices used in the referendum elicitation designers substantial control over what assumptions method but also many with other features of split- they can make about the institutional arrangements sample experiments, including the scenarios used. A for the delivery of the hypothetical good or service. contingent valuation survey in support of the State of Revealed preference valuation approaches generally Alaska's case against Exxon in the Exxon-Valdez oil accept the prevailing economic, political, and institu- spill is one of the finest, most professional contingent tional context within which the data were generated. valuation surveys conducted to date (Carson and oth- ers 1992). In this survey a contingent valuation sce- Notes nario was crafted that described an oil spill prevention program with two main components: the requirement The author would like to acknowledge Maureen Cropper at the that oil tankers be accompanied by escort ships while University of Maryland for her significant contributions to this in the Valdez Straits (to reduce the chances that the chapter. tankers inadvertently drifted onto nearby rocks) and 1. Of course, not all environmental problems are caused by the use of an oil spill containment technology called a poorly defined property rights. Many analysts have found govern- "Norwegian sea fence" that could be used in the high ment failure itself to be a pervasive force causing environmental seas of Prince William Sound. Respondents in the sur- degradation. vey were asked whether they would vote for or against 2. "Improved" water sources are the result of modifications to a rapid response oil spill containment force in Prince the natural source to increase the quahtv or quantity of water.They William Sound that would deploy these escort ships do not, however guarantee that the water is clean. Piped water, for and the Norwegian sea fence if the implementation of example, is always considered an improved source, but the wvater that the plan would cost their household a specified pipes carry may need additional treatment before it is safe to drink. amount of money. Although the "Norwegian sea 3. The contingent valuation method is a survey technique that fence" technology did exist, it was not as large or attempts to ehcit information about individuals' or households' pref- effective as was indicated by the contingent valuation erences for a good or service. Respondents in a survey are asked a scenario.The survey interviewers told respondents that question (or series of questions) about how much they value a good the Norwegian sea fence was more effective than it or service. The technique is termed "contingent" because the good actually was so that the respondents would not reject or service is not necessarily going to be provided by the interviexver the scenario as implausible. or analyst.Thus, the provision of the good or service is hypothetical. A second ethical issue concerns how honest one The contingent valuation method can be used to obtain values should be about the institutional regime contemplat- of pure public goods, goods wvith both private and public charac- ed for delivering the "hypothetical" goods or servic- teristics, and private goods. It is often used to assess households' es. In developing countries, the contingent valuation preferences for goods or services for xvhich a conventional market 26 CHAPTER 14 ENVIRONMENTAL ISSUES does not exist. For a brief introduction to the contingent valuation the benefits received by households may serve little purpose from method see Pearce and others (1994), chapter 7. For more in-depth the client's perspective and may even be deemed wasteful. presentations see Mitchell and Carson (1989) and Cummings, Brookshire, and Shultze (1986). References 4. LSMS surveys typicaDly provide a rich set of possibilites for covariates, and in practice only a few such covariates are usually need- Alberini, Anna. 1995a. "Efficiency versus Bias ofWillingness-to-Pay ed for the analysis of the contingent valuation data.The specific vari- Estimates: Bivariate and Interval-Data Models." Journal of ables used to explain a respondent's answers to contingent valuation Environmental Economics and Management 29 (2): 169-80. questions depend on the theoretical demand model employed. Most . 1995b. "Optimal Designs for Discrete Choice Contingent models would use the following minimum set of explanatory vari- Valuation Surveys: Single-Bound, Double-Bound, and ables: household income, household assets, and respondent's educa- Bivariate Models." Journal of Environmenttal Econotnics and tion, gender, and occupation (and perhaps rehgion). MVanagement 28 (3): 287-306. 5. The material in this section is drawn fromWhittington 1998. Alberini,Anna, Maureen Cropper,T.T. Fu,Alan Krupnick,J.T. Liu, 6. See Georgiou and others (1997) for additional references and D. Shaw, and Winston Harrington. 1995. "Valuing Health an annotated bibliography. Effects of Air Pollution in Developing Countries: The Case of 7. Assigning "I need to know other people's opinions" answers Taiwan." Resources for the Future Discussion Paper 95-01. to the "no" category is consistent with the recommendations of the Washington, D.C. report of the U.S. National Oceanic and Atmospheric Altaf, Mir Anjum, Dale Whittington, Haroon Jamal, and V Kerry Administration's Expert Panel on the ContinentValuation Method Smith. 1993. "Rethinking Rural Water Supply Policy in the (Arrow and others 1993). This assignation practice is followed in Punjab, Pakistan." Water Resources Researcih 29 (7): 1943-54. many large contingent valuation surveys conducted in the United Arrow, Kenneth J., Robert Solow, Edward Leamer, Paul Portney, States. For further discussion and a theoretical treatment of Roy Radner, and Howard Schuman. 1993. "Report of the ambiguous or"don't know" responses in contingent valuation stud- NOAA Panel on ContingentValuation." United States Federal ies, see Wang (1997). Register 58. 8. The principal alternatives in contingent valuation surveys Briscoe,John, Paulo F de Castro, Charles Griffin,john North, and would be to ask a single open-ended question-"How much Orjan Olsen. 1990. "Towards Equitable and Sustainable Rural would you be wiling to pay?"-or to ask a series of questions that Water Supplies: A ContingentValuation Study in Brazil." World honed in on a particular answer. For example, a respondent would Bank Economic Reviewv 4 (2): 115-34 be asked, "Would you be willing to pay $500?" If he said no, he Carson, Richard, Robert C. Mitchell,W. Michael Hanemann, Ray would be asked: "Would you be willing to pay $100?" If he said no J. Kopp, Stanley Presser, and Paul A. Ruud. 1992. "A again, a price of $50 might be mentioned. If the respondent said yes Contingent Valuation Study of Lost Passive Use Values to $100, he would be asked if he would pay $300. The questions Resulting from the Exxon-Valdez Oil Spills. A Report to the would be asked three or four times until the price the respondent Attorney General of Alaska." would pay xvas bracketed by a higher price that he would not pay Choe, KyeongAe, Dale Whittington, and Donald T. Lauria. 1996. and a lower price that he would pay "The Economic Benefits of Surface Water Quality Improve- 9. A contingent valuation expert is generally engaged by a client ments in Developing Countries: A Case Study of Davao, organization to estimate both the benefits of a project and how Philippines." Land Economics 72 (4): 519-37. these benefits would change if different prices were charged. At the Cohen, David, and Jack L. Knetsch. 1992. "Judicial Choice and time that a contingent valuation survey is undertaken, the Disparities between Measure of EconomicValues." Osgood Hall researcher typically does not know the actual cost of the project, LawvJournal 30: 737-70. either because the cost analysis is being done simultaneously or Cropper, Maureen, S. Aydete, and Paul Portney. 1994 "Preferences because several different kinds of project or levels of service are for Life Savings Program: How the Public Discounts Life being considered. Thus the results of the contingent valuation sur- Savings Programs."Journal of Risk and ULicertainty 8: 243-65. vey may be used to inform the design process. Cummings, Ronald G., David Brookshire, and Wiliam Schulze, If the client organization has already decided which specific eds., 1986. Valuing Environmental Goods: .4n Assessment of the project will be implemented, it may also have decided in general Contingent Valuation Metliod. Totowa, NJ.: Rowman & terms what it will charge. In this case the client may use the results Allanheld. of the contingent valuation survey to get an accurate prediction of Davis, Jennifer. 1998. "Assessing Conununity Preferences for revenues. Using low referendum prices to gain accurate estimates of Development Projects: Does Mode Matter?" Ph.D. diss. 27 DALE WHITTINGTON University of North Carolina, Department of Environmental National Park, Bharatpur, India." Institute of Economic Sciences and Engineering, Chapel Hill, N.C. Growth, Delhi. Davis, Jennifer, and Dale Whittington. 1998. "Participatory Mitchell, Robert C., and Richard T. Carson. 1989. Using Surveys to Research for Development Projects: A Comparison of the Value Public Goods: The Contingent Valuation Miletlsod. Community Meeting and Household Survey Techniques." Washington, D.C.: Resources for the Future. Economic Development and Cultural Change 47 (1): 73-94. Moran, Dominic. 1994. "Contingent Valuation and Biodiversitv Diamond, Peter A., and Jerry A. Hausman. 1994. "Contingent Conservation in Kenyan Protected Areas." Biodiversity and Valuation: Is Some Number Better than No Number?"Journal Conservation 3. of Economic Perspectives 8 (4): 45-64. Pearce, David, Dale Whittington, Steven Georgiou, and David Diamond, Peter A., Jerry A. Hausman, Gregory K. Leonard, and James. 1994. Project and Policy Appraisal: Integrating Economics and Mide A. Denning. 1993. "Does ContingentValuation Measure Environment. Paris: Organisation for Economic Co-operation Preferences? Experimental Evidence." In J. Hausman, ed., and Development. Contingent Valuation:A Critical Assessment. Amsterdam: Elsevier Pinheiro, Armando, and Dale Whittington. 1995. "Introducing a Science Publishers. Demand-Side Approach to Rural Water Investment in Georgiou, Stavros, Dale Whittington, David Pearce, and Dominic Mozambique: A Rapid Appraisal of Household Demand for Moran. 1997. Economic Values and the Environment in the Improved Water Services in Marracuene." A Report to the Developing World. Cheltenham, U.K.: Edward Elgar. National Program for Rural Water Supply. Government of Grandstaff, S. andJohn Dixon. 1986. "Evaluation of Lumpinee Park Mozambique, National Directorate forWater, Maputo. in Bangkok, Thailand." In John Dixon and Maynard Poulos, Christine, and Dale Whittington. 2000. "Time Preferences Hufschmidt, eds., Economic Valuation Techniques for the for Life-Savings Programs." Envirotinmental Science and Teclhnology Environment: A Case Study WVorkbook. Baltimore, Md.: Johns 34 (8): 1445-55. Hopkins University Press. Rutherford, Murray B.,Jack Knetsch, and Thomas C. Brown. 1998. Hadker, N., S. Sharma, A. David, T. T Muraleedharan, S. Geetha, and "Assessing Environmental Losses: Judgments of Importance P Babu. 1995. Are People in Developing Countries Willing to Payfor and Damage Schedules." Harvard Environmental Law Review 22: Natural Resource Preservation? Evidencefrom a Contingent Valuation of 51-101. the Borivli .National Park, Bombay Bombay, India: Indira Gandhi Shyamsundar, P, Randy Kramer, and N. Sharma. 1993. Does Institute of Development Research. Contingent Valuation Work in NVonmarket Economies? Center for Kahneman, Daniel, Jack L. Knetsch, and Richard Thaler. 1990. Resource and Environmental Policy Research. Durham, N.C.: "Experimental Tests of the Endowment Effect and the Coase Duke Universitv. Theorem."Journal ofPolitical Economy 98 (1): 1325-48. Singh, Bhanxvar, Radhika Ramasubban, Ramesh Bhatia, John Kahneman, Daniel, and Jack Knetsch. 1992."Valuing Public Goods: Briscoe, Charles Griffin, and C. Kim. 1993. "Rural Water The Purchase of Moral Satisfaction." Journal of Environmental Supply in Kerala, India: Hoxv to Emerge from a Low-level Economics and AMianagement 22: 57-70. Equilibrium Trap." Water Resources Research 29 (7): 1931-42. Kanninen, Barbara J. 1995. "Bias in Discrete Response Contingent Swallow, B.M., and M.Woudyalew. 1994. "EvaluatingWillingness to Valuation." Journal of Environmental Economics and Management Contribute to a Local Public Good: Application of Contingent 28 (1): 114-25. Valuation to Tsete Control in Ethiopia:" Ecological Economics 1 1. Knetsch, Jack L. 1990. "Environmental Policy Implications of Vatn,Arild, and DanielW Bromley 1994. "Choices Without Prices Disparities Between Willingness to Pay and Compensation Without Apologies." Journal of Environmental Econonuics and Demanded Measures of Value:" Journal of Environmental Mlanagement 26 (2): 129-48. Economics and Alanagement 18: 227-37. Wang, Hua. 1997. "Treatment of 'Don't Know Responses' in MacRae, Duncan,Jr., and Dale Whittington. 1997. Expert Advicefor Contingent Valuation Surveys: A Random Valuation Model:" Policy Choice: Analysis and Discourse. Washington, D.C.: Journal ofEnvironmental Economics and Mvanagement 32 (2): 219-32 Georgetown University Press. Whittington, Dale. 1998. "Administering Contingent Valuation McClelland, Elizabeth. 1997. "The Use ofAlutiundinal Indicators in Surveys in Developing Countries." World Development 26 (1): Contingent Valuation Research: A Test of Validity Theoretic 21-30. Compatibility." Ph.D. diss. University of North Carolina, Whittington, Dale, Armando C. Pinheiro, and Maureen Cropper. Department ofCity and Regional Planning, Chapel Hill, N.C. 1996. "The Economic Benefits of Malaria Control: A Menkhaus, S. 1994. "Measurement of Economic and Other Contingent Valuation Study in Marracuene, Mozambique." Benefits of Wildlife Preservation: A Case Study of Keoladeo World Bank Draft Discussion Paper,Washington, D.C. 28 CHAPTER 14 ENVIRONMENTAL ISSUES Whittington, Dale, John Briscoe, Xinming Mu, and William and Sanitation for Health Project Field Report 246, US Barron. 1990. "Estimating the Willingness to Pay for Water Agency for International Development,Washington, D.C. Services in Developing Countries:A Case Study of the Use of Whittington, Dale, Donald T. Lauria, Albert Wright, KyeongAe Contingent Valuation Surveys in Southern Haiti." Economic Choe, Jeffrey A. Hughes, and Venkateswarlu Swarna. 1993. Development and Cultural Change 38 (2): 293-311. "Household Demand for Improved Sanitation Services in Whittington, Dale, Jennifer Davis, Harry Miarsono, and Richard Kumasi, Ghana:A ContingentValuation Study." Water Resources Pollard. 2000. "Designing a "Neighborhood Deal" for Urban Research 29 (6): 1539-60. Sewers: A Case Study of Semarang, Indonesia." Journal of Whittington, Dale, V Kerrv Smith, Apia Okorafor, Augustine Planning and Education Research 19 (3). Okore, Jin Long Liu, and Alexander McPhail. 1992. "Giving Whittington, Dale, Mark Mujwahuzi, Gerald McMahon, and Respondents Time to Think in Contingent Valuation Studies: KyeongAe Choe. 1988. "Willingness to Pay for Water in A Developing Country Apphcation."Jourmal of Environrmental Newala District,Tanzania: Strategies for Cost Recovery."Water Economics and MlSanagement 22 (3): 205-25. 29 , - ~~Fertility 1 5) lndu Bhushan and Raylynn Oliver Data from Living Standards Measurement Study (LSMS) surveys have been used extensively to study the determinants of fertility, contraceptive use, and child mortality and to inform policy on these issues. Data on fertility and related topics are often also useful for analyzing labor market and schooling decisions. Among past LSMS surveys there is considerable vari- for empirical analysis of these issues.The third section ety in the methods used for collecting fertility data and presents short and standard draft fertility modules, and in the criteria used for selecting respondents. Most the fourth section contains annotations to those mod- LSMS surveys have included a fertility module to col- ules and guidance to survey designers on how to cus- lect information on fertility, child mortality, contra- tomize modules to the circumstances in the country ceptive use, and reproductive health.' In the LSMS where their survey is to be fielded. surveys that did not contain a fertility module, the designers often included a few questions on these Policy Issues issues in the health module.Thus the fertility data col- lected in LSMS-type surveys can vary from just a few Public policy can influence fertility and child mortal- questions-usually about the number of children ever ity outcomes by affecting prices, resource constraints, born to the respondent-to detailed information on and even individuals' preferences. Some factors that the respondent's experience of maternity, child mor- influence fertility and child mortality-the availability tality, marriage, contraceptive use, and use of repro- of health services, and health conditions in the com- ductive health services. This chapter presents short and munity-are beyond the control of the household. standard fertility modules based on the cumulative However, most fertility and child mortality outcomes experience of using data from LSMS surveys and are determined by individuals reacting to opportuni- Demographic and Health Surveys to analyze fertility ties and to changes in their environment by delaying and child mortality. marriage, using contraception, and spacing births. The chapter provides an overview of fertility and Data from an LSMS-type survey can yield nation- child mortality issues and makes recommendations al estimates on levels, trends, and distribution of fertility about how to collect the necessary data in an LSMS or and child mortality outcomes. Analyses of the data can similar multi-topic survey. The first section discusses provide policymakers with key information on the the key policy issues related to fertility and child mor- determinants of fertility and child mortality and enable tality outcomes.The second identifies the data needed comparisons of various policy options. In particular, the 31 INDU BHUSHAN AND RAYLYNN OLIVER data can be analyzed to quantify relationships between Policy Issues Conceming Fertility policy variables and important fertility, child mortality, Policymakers need to address two different types of and contraceptive use outcomes. In conjunction with problems related to fertility. The first problem is that information on the costs of various policy alternatives, women may not bear the number of children they these quantified relationships can be used to identify want. A woman may bear more children than she wants policies that will most effectively achieve desired out- if she lacks basic reproductive knowledge, if she has no comes. Also, data gathered in the fertility module can be access to modern contraceptives, or if her status within used as control or explanatory variables in the analysis the household is too weak for her to assert her will. A of other aspects of household behavior, such as labor woman may bear less children than she wants due to force participation (Behrman and Wolfe 1984) or restrictive population policies or infertility. human capital investments (Pitt and Rosenzweig 1990). The second problem is that even if a woman has Information about the levels, trends, and distribu- the number of children that she desires, that number tion of fertility and mortality variables across socioe- may be too large (or too small) from the point of view conomic, geographic, and ethnic groups can be used of society as a whole. This may happen if there are to quantify demographic outcomes and thus set poli- externalities from childbearing decisions such that cy priorities. (See Box 15.1 for a list of important fer- other members of society bear costs (or reap benefits) tility and child mortality variables.) For example, when women bear children.2 while child mortality may not be high in a given In many countries throughout the world high pop- country as a whole, it may be a problem that needs to ulation growth is a significant concern. Therefore, in be addressed among certain socioeconomic groups or recent years most discussion and research has focused on in certain geographical areas. devising effective policies to remove the barriers that There can be substantial differences in the impor- prevent a woman from having fewer children and to tance of the many issues involving fertility and child create incentives for women to reduce their fertility. For mortality variables across regions of the xvorld (Table example, policies that encourage the schooling of girls 15.1). In many Sub-Saharan African countries high and increase income-earning opportunities for women fertility and child mortality rates are a major concern. increase the cost of a woman's time-possibly leading to In many countries in East Asia a preference for sons is reduced fertility. The impact of schooling levels and an important issue. And different countries within a employment opportunities on women's fertility region may have different policy priorities. depends on the prevailing economic and social condi- tions in a country (Cochrane 1979; Ainsworth 1989; Ainsworth, Beegle, and Nyamete 1995; Pitt 1995). In many countries with high fertility rates policy- Box 15.1 Fertility and Child Mortality Variables makers are especially interested in the impact of con- traceptive use. High levels of unmet needfor contraception The following fertility and child mortality variables are important to policymakers and can be collected in LSMS- m should me more thatnt-orientednnBngaarts type surveys: ~~~~~~es should become more client-oriented (Bongaarts * Family size, and Bruce 1995; Robey and others 1996). If contra- * Total fertility rate. ceptives are expensive, of poor quality, or unavailable, * Birth rate. introducing policies that affect the price, availability, * Contraceptive use. and quality of modern contraceptive methods can * Unmet need for contraception. remove an important obstacle to reducing fertility * Age at first marriage. rates (Feyistan and Ainsworth 1994; Thomas and * Age at first childbirth. Maluccio 1995; Oliver 1995; Beegle 1995). * Abortion rate. Another aspect of fertility that often interests pol- * Use of prenatal care. Another aspectertilt thoen terestsehol- * Breastfeeding practice. icymakers is the interaction between the household's * Spacing of births, decisions about family size and its other decisions, * Infant mortality rates (deaths before one year of age). including those about human capital investments such * Child mortality rates (deaths before five years of age). as expenditures on children's health and schooling. Such interactions are the subject of studies by Pitt and 32 CHAPTER 15 FERTILITY Table 15.1 Variations in Demographic Outcomes by Region, 1995 Total Infant Life Child fertility mortality expectancy mortality Population rate rate at birth rate Areas of potential Region (millions) (per woman) (1,000 births) (years) (1,000 births) policy concern Sub-Saharan Africa 583.3 5.7 92 52 157 High fertility * Large unmet need for contraception (spacing) * High-risk births * Reproductive health (adolescents) * High chiid/infant mortality * High adult mortality due to AIDS the Pacific . Strong preference for sons South Asia 1.,243.0 3.5 75 6.1 1 06 * High fertility * Large unmet need for contraception * High-risk births * High child/infant mortality * Strong preference for sons Central Asia * Low contraceptive use Middle East and 272.4 4.2 54 66 72 * High fertiiity North Africa * Large unmet need for contraception * High-risk births * High child/infant mortality Latin America and 477.9 2.8 37 69 47 Inequalities in access to family planning the Caribbean and health care Sourme:World Bank 1984, 1993 1997a and 1997b. Rosenzweig (1990), Montgomery, Kouame, and Reducing infant and child mortality is an objec- Oliver (1994), and Benefo and Schultz (1994). tive of virtually every developing country government. One fertility issue that has been difficult to influ- The policy options for achieving such a reduction ence through public policy is the preference of some include implementing appropriate medical and public parents (mostly in parts of Asia, the Middle East, and health interventions, improving water and sanitation North Africa) for male children.A distorted gender ratio services, and disseminating accurate information both is emerging in many East Asian countries, probably due within and outside the education system. Research on to strong parental preference for sons and a decrease in the determinants of infant and child mortality at the family size. Technologies that can identify the sex of the household level can reveal other policy interventions fetus and the consequent wide prevalence of sex-selec- that would reduce infant and child mortality, such as tive abortions have contributed greatly to this trend mother's schooling (Benefo and Schultz 1994). (Park and Cho 1995).The implications of this distortion in the gender ratio are only beginning to be understood. Policy Issues' Implications for LSMS Surveys The multisectoral nature of LSMS surveys makes them Policy Issues Conceming Mortality suitable for modeling the determinants of fertility and Since 1960 infant and child mortality rates have child mortality and related behavior. (The fertility and decreased steadily in developing countries sometimes mortality issues that can be analyzed using data from even in the absence of improvements in nutrition, LSMS surveys are summarized in Box 15.2.) Many housing, and income. Much of this decrease is attrib- other demographic issues, such as the abortion rate, uted to better medicines (particularly antibiotics), female genital mutilation, and child fostering, are also public health interventions such as immunization, important areas for policy research in certain coun- diarrhea control programs, and safe motherhood ini- tries. Questions to cover these issues are not included tiatives (World Bank 1993). However, levels of infant in the draft questionnaire module presented here, but and child mortality are still high in many countries, the addition of some specific questions would enable and most developing countries have high infant and an LSMS survey to collect the data necessary to ana- child mortality rates in specific population groups. lyze these issues. However, including such questions in 33 INDU BHUSHAN AND RAYLYNN OLIVER countries where they are not appropriate or relevant household characteristics and behavior to enable ana- could upset respondents and unnecessarily lengthen lysts to study the underlying causes of these levels and the interview. trends and how policy can influence these outcomes. Some limitations of LSMS data are described in Also, few Demographic and Health Surveys collect Box 1 5.3.The relatively small sample size of LSMS sur- data on community-level variables and services, though veys (2,000-5,000 households) makes it difficult to col- the inclusion of community questionnaires in these lect enough data for analysts to explore issues that are surveys is under consideration. In contrast, LSMS-type not common throughout the population. Dis- surveys collect data on a much broader set of explana- aggregating demographic rates by region or socio- tory variables at the household level, including land economic status and measuring rare events such as holdings, productive assets, mother's height, and par- maternal mortality require samples much larger than ents' schooling. They also collect community-level those typically used in LSMS surveys. Another limita- exogenous variables that are influenced by policy, such tion of LSMS data is that they usually do not contain as the availability, quality, and price of public services. enough variation in macroeconomic and policy vari- Thus LSMS survey data are suitable for analyzing ables to analyze how those variables affect fertility. the determinants of demographic outcomes and for During the planning stage, designers should con- informing policies. Analyzing demographic outcomes sider other existing data sources. A recent national using LSMS data can provide insights into household census or demographic and health survey can often behavior that will help policymakers decide on appro- provide very precise national (and sometimes region- priate policy interventions. al) estimates of fertility outcomes. Existing data on health and family planning facilities may make the col- Data Requirements for Policy Analysis lection of such data unnecessary. Even in countries where a Demographic and Analyzing the relationship between policy variables Health Survey has been carried out, it is usually still and demographic outcomes is complicated by the fact appropriate to include a fertility module in an LSMS that those outcomes are not directly chosen by indi- survey. While demographic and health surveys yield viduals. People make decisions about contraceptive use data on levels and trends for important demographic and immunization that affect the probability of a birth indicators, they do not provide enough information on or a death, but the actual number of births and deaths Box 15.2 Fertility and Child Mortality Issues That Can Be Analyzed Using LSMS Data Determinants of fertility and contraceptive use: specifically, the Effectiveness of various components of programs-such as out- roles played by reach services-relative to clinic-based services. * Schooling. * Income. Impact of cost recovery on the use of fomily planning and repro- * Price, availability, and quality of contraceptives. ductive health services. * Labor market conditions. * Information, education, and communication programs. Determinants of infont and child mortolity: specifically, the roles * Other socioeconomic factors. played by * Parents' schooling. Determinants of age at first childbirth and age at marriage. * Income. * Other socioeconomic variables. Quality-quontity tradeoff in demand for children * Public health interventions. * Interaction between child health or mortality and family * Water and sanitation programs. size or birth interval. * Interaction between child schooling and family size or Existence of gender gops in infont and child mortality. birth interval. Prevalence of breastfeeding. Efficient targeting of government programs through identification of underserved areas and groups. Other issues: abortion, female genital mutilation, child fostenng 34 CHAPTER 15 FERTILITY Epidemiological research can throw light on the Box 15.3 IssuesThatAre Difficult to Analyze with first question, the relationship between a behavioral LSMS-Type Data choice (such as an immunization) and a demographic * The impoct of mocroeconomic policy cnanges on demo- outcome (such as child mortality). However, appropri- graphic behavior, With LSMS data it is difficult to achieve ate economic models and econometric methods are enough variation in the macroeconomic variables for a needed to help policymakers understand the determi- given sample to measure macroeconomic policy nants of behavioral choices and the policies that may impacts. affect those choices. Unfortunately, in practice, * Levels of and trends in demographic variables for small geo- research is beset with conceptual and data-related graphical or socioeconomic categories of the population. problems. Some of these problems can be avoided by LSMS samples are too small to measure these levels and trends. National census or demographic and health sur- linkin polic re ctly to demographicou- vey data may be more appropriate sources for this comes. While this relationship, known as a reduced- information. form relationship, does not show exact causal mecha- * The impact of regulatory and legal reforms on demo- nisms, it can indicate the net effect of a given policy. graphic behavior. With LSMS data it is difficult to achieve To analyze household behavior and outcomes, econ- enough variation in the variables representing legal and omists use behavioral models that assume that households regulatory reforms across a sample. make rational decisions. (See Chapter 26 on economet- * The causes and dynomics of parents' preferences for sons. rics for a detailed explanation of this economic model More qualitative and anthropological research is and reduced form relationships.) Most economic models required to analyze this issue. * How living arrangements may hove changed due to dec/in- of fertility and mortality are based on a model in which ing fertility, chong'ng inheritance patterns, and migration to households are both producers and consumers, following urban areas. Analyzing these issues would require time- the seminal work of Becker (1960). In this framework series data over a long period of time. children and health are "produced" by the household using the time of its members and other inputs such as purchased food and health services. Households are is also influenced by chance and other factors beyond assumed to allocate their limited time and economic the control of individuals or households. Infertility is resources to maximize their welfare or utility.The house- one example; high prevalence of childhood diseases in hold derives utility from children and health just as it the community is another. does from other goods or services. For a given set of pref- Analysts can inform public policy by attempting erences (in other words, a utility function), prices, and to provide data that answer the following three ques- income, household production theory explains how tions (DaVanzo and Gertler 1990): households decide the number of children they want, the * How do specific behavioral choices affect fertility level of health status they want to maintain, and the bas- and mortality outcomes? ket of inputs they will use to achieve these goals. The * What determines these behavioral choices? household's demianid for children, healtlh, and inputs * How can these choices be influenced by policy? would change if changes occurred in market prices or in To design effective policies it is important to household preferences, income, or other household vari- know the answer to each of these questions. For ables.This model guides the analyst in selecting the vari- example, to address child mortality policymakers ables required for analysis. must first understand how the use of a particular medical technology-say, child immunization- Data Needs for FertilityAnalysis affects child mortality.Then they need to know what Public policy can be informed by the answers to the makes individuals decide to take advantage of related three questions shown above. In theory it should be interventions. For example, if knowledge about the possible to answer each question separately using availability of immunization services is found to be household survey data. In practice most research significantly related to use of these services, then a involves estimating reduced-form relationships for publicity campaign may be more effective than an which the fertility variable of interest is determined by expansion of service in increasing immunization a set of explanatory variables. This approach raises the rates. following questions: 35 INDU BHUSHAN AND RAYLYNN OLIVER * What fertility variables are of interest to policy- previous years. Second, cumulative fertility may be less makers in the country being studied? sensitive to policy changes than recent fertility. * Which explanatory variables should be included in Therefore, measures of recent fertility, such as the num- the estimation of the reduced form relationship? ber of children born in the previous five years, whether This section proposes a set of variables that should be the respondent is currently pregnant, or whether she collected as part of the fertility module of LSMS and has given birth in the previous vear, are often more similar multi-topic surveys. The variables are summa- useful for policy analysis. However, collecting complete rized in Box 15.4. birth histories from respondents increases the accuracy and completeness of the data and increases the analyt- FERTILITY VARIABLES OF INTEREST. The central phe- ical possibilities of the resulting data set.4 Moreover, a nomenon of interest in fertility research is fertility few questions are usually enough to find out the num- itself Fertility can be measured in two ways: cumula- ber of children ever born to a respondent. tive fertility and recent fertility. The most frequently Many women use modern contraceptive methods used variable for cumulative fertility is the number of to control their fertility. Because the most frequently children ever born.3 Although cumulative fertility can analyzed proximate (direct) determinant of fertility is be an appropriate dependent variable for some analyt- contraceptive use, this variable is of great interest to pol- ical purposes, there are two problems with this meas- icymakers. The use of contraceptive methods is under ure. First, cumulative fertility is influenced by socioe- the control of a woman (or another household mem- conomic factors through all of the respondent's ber such as the woman's husband) and is, therefore, an reproductive years prior to the survey, and data on endogenous variable. Several measures of contracep- many explanatory variables (such as household income, tive use can be investigated, including whether the community wage rates, and the availability of health woman has ever used contraception, whether she is and contraceptive services) may not be available for currently using contraception, and whether she has Box 15.4 ImportantVariables for Fertility Analysis Fertility outcomes Explanatory individual and household characteristics Cumulative fertility Mothers schooling * Children ever born Mothers age Recent fertility Household income and other household resources * Children born in the past five years Ethnicity and religion * Current pregnancy Schooiing of other household members * Birth in the past year Explanatory community characteristics Other fertility-related endogenous variables Region and degree of urbonization Contraceptive use Prices * Current use (method-specific) Local wage rates (for adults and children) * All current and previous use (method-specific) Food and non-food prices Age at first marnoge * Formal and informal interest rates Age at first childbirth Information, public education, communications Numnber of abortions Availability ofTV, radio, and newspapers Breastfeeding practice Use of media for family planning messages * Duration of breastfeeding for last birth Family planning informational campaigns * Current breastfeeding status Health services Length of postpartum abstinence * Price, availability, quality Current pregnancy status Family planning services . Price, availability quaii y Fertility preferences Schools Desire to hove additional children Price, availability, quality Desired fertility (ideal) Unmet need for contraception 36 CHAPTER 15 FERTILITY used contraception within the past 12 months. Also, makers, but these variables should not be considered depending on the policy options being analyzed, it exogenous explanatory variables in regression analysis. may be desirable to estimate the use of a specific method, the use of each method separately. or the use FERTiLITY PREFERENCES. Many economic models of of modern methods relative to traditional methods or fertility propose that parents' desired fertility-how no method at all. many children they would like to have-is an impor- Which variable is the dependent variable depends tant determinant of fertility. It is useful to collect data on on the objective of the analysis. For example, if the a household's desired fertility so that analysts can fore- aim is to examine how the availability of family plan- cast medium-term changes in fertility and measure the ning facilities affects contraceptive use, current use prevalence of unwanted births. Recently, fertility pref- may be the most appropriate dependent variable erence data have most often been used to analyze the because accounting for all current and previous use much-debated question of how family planning pro- might include contraception used before the facility grams affect fertility, focusing on the magnitude of in question existed. In countries where a variety of unwanted fertility and the unmet need for family plan- methods are available and rates of usage are rather ning (Pritchett 1994). However, whether and how high, use of individual methods may be analyzed. On desired fertility can be measured accurately are con- the other hand, use of any modern method could be tentious issues. Early attempts to measure fertility pref- estimated to examine contraception use in countries erences used questions about ideal family size, such as: with low utilization rates. It may be of interest to "If you could start over again, how many children measure households' "knowledge of" various meth- would you like to have? How many sons and how many ods if publicity campaigns have been part of recent daughters?" Such questions have a number of theoreti- public policy. cal and empirical shortcomings.The responses may be Again, contraceptive use data can be gathered in a ambiguous, as respondents may be reluctant to name an few questions or in a complete set of questions on indi- ideal family size smaller than the size of their existing vidual methods. Gathering more data requires more family. Furthermore, the risk of child mortality is not interview time but also provides analysts with more made explicit in these questions; presumably respon- options for research. Contraceptive prevalence and the dents did not factor children's deaths into their ideal most important policy issues in the country will deter- family size, although in practice they may need to bear mine the level of detail of data needed. more children than their desired family size because of In countries facing an AIDS epidemic, it may be the high rates of child mortality in many developing worthwhile to expand the questionnaire to gather data countries. In practice, many respondents provide non- on men's and women's sexual behavior-including numerical answers such as "It's up to God," which are number of sexual partners-as well as adult mortality. not useful in empirical work. Finally, respondents may This would be a substantial expansion of the ques- have compositional preferences (for example, wanting at tionnaire and is not, therefore, discussed further here. least one boy or at least one child of each sex) that affect For more information see Ainsworth (1992), family size but are not captured in questions about Dehenesse, Carael, and Noumbissi (1996), Cleland and desired family size. Demographic and Health Surveys Ferry (1995), and Filmer (1997). See also the discus- typically ask questions about whether the respondent's sion of the health module introduced in Chapter 8. last child or current pregnancy was wanted, but the Other variables related to fertility analysis are age at answers to these questions involve many of the same first marriage, current breastfeeding practice (or the problems. Also, these questions yield no information length of time for which the last child was breastfed), and about women who have never been pregnant. Another number of abortions. Demographers identify these vari- approach is asking about the respondent's desire to have ables as proximate determinants of fertility. While it is additional children, which may yield information on tempting for many analysts to use these variables direct- potential demand for contraception or for future fertil- ly to explain fertility, they are clearly the result of house- ity. However, the responses would still be subject to hold decisions. Analysts may wish to estimate their many of the problems listed above. determinants in order to explain various aspects of fer- The unmet need for contraception is the differ- tility that may of themselves be of interest to policy- ence between women's stated fertility preferences and 37 INDU BHUSHAN AND RAYLYNN OLIVER their actual contraceptive behavior. In the demo- some LSMS surveys collect household income data, graphic literature women who are sexually active and but all LSMS surveys collect household expenditure fertile are categorized as having an unmet need if they data, and total household expenditure can serve as an are not currently pregnant or amenorrheic, do not indicator of household income. (Collecting and using want any children, and are not using contraceptives. expenditure and income data are discussed in detail in Women who are currently pregnant with an unwant- Chapters 5 and 17.) A household's income is the result ed pregnancy and amenorrheic women who did not of its decisions about the labor market participation of want their last child are also classified as having an its mem1bers, which are made jointly with fertility unmet need.To measure unmet need as defined above, decisions. To use household income as an explanatory it is necessary to find out three aspects of fertility pref- variable, it may be necessary to use data on nonlabor erences: the respondent's preference concerning her income of household members or other instrumental filture childbearing, whether her last child was want- variables that are exogenous to the fertility decision. ed, and whether her current pregnancy is wanted. (Instrumental variables and related econometric issues These questions suffer from the weaknesses listed are explained in Chapter 26.) above. In addition, it is important to stress that when a Parents' birthplace or place of residence may be woman has an unmet need for contraception, it can- correlated with their fertility desires.Variables that sig- not necessarily be assumed that she has an unmet nify ethnicity and religion can also be included to demand for contraceptive services. Even if she has a control for the effect of household preferences on the strong desire to have no more children, a woman may demand for children. It is important to collect infor- not want to use contraceptives because of financial, mation on the religious affiliation and ethnicity of religious, or other constraints. households. The schooling of household members may also influence their preferences for number of EXPLANATORY VARLABLEs. One of the most important children. policy variables in fertility analysis is mother's school- An important community-level variable is degree ing, as this variable represents the opportunity cost of of urbanization. This is an indicator of the general cost having children-in other words, the potential house- of living, availability of services, economic activity, and hold income lost during the time that the woman prevailing cultural mores. Local prices of essential spends caring for her children. In addition, years spent goods and local wage rates may also be important in school are usually years that are not spent bearing determinants offertility outcomes. However, to be use- children. Many studies suggest that schooling may also ful, the data on prices must not vary with the quantity have a socializing influence. Different rneasures of demanded by the consumer; otherwise the data will be schooling, such as years in school or highest level com- contaminated by consumers' purchasing choices pleted, may capture the different ways schooling affects (Schultz 1984). It is best to collect information about fertility decisions. Which measure of schooling to wages and prices directly in community-level surveys, choose depends on the objective of the analysis and on although if necessary wage and price information can the data available. Diplomas or literacy, rather than be obtained indirectly by aggregating household-level years in school, may make an important difference in information. (See Chapter 13, on community and people's labor market decisions and income opportu- price data, for details about how this information nities. The highest level of schooling attained by a should be collected.) woman may reflect the socializing influences of Perhaps the most important variables for policy schooling better than the number of years spent in analysis are those pertaining to the price, availability, school. (See Chapter 7 for a detailed discussion of how and quality of schools, health services, and family plan- to measure schooling.) ning services. Prices have a direct effect on how much Obviously, the number of children ever born the services are used by the household. The availabili- depends in part on mother's age. Since it is standard ty and quality of services also affect how much they practice to collect age data for all household members in are used because these factors help determine servic- LSMS surveys, this variable should always be available. es' costs and benefits to a household. Data on prices, In economic models, household income is an availability, and quality should be gathered in facility important determinant of household decisions. Only or community questionnaires. To be useful for analy- 38 CHAPTER 15 FERTILITY sis, the data must be available for all households, not use information from men. Collecting this informa- just those that use the services. (Chapter 13, on com- tion from men can be problematic for several reasons. munity and price data, describes ways of collecting this In some countries men frequently are not members of information and illuminates difficulties involved in the same household as their wives, and the fertility doing so. Chapters 7 and 8 present questionnaires preferences and contraceptive practices of a husband about schools and health care facilities, respectively.) may not be directly related to the fertility of his wife. One problem with using community-level data to Demographic and Health Surveys sometimes understand the determinants of fertility outcomes is include questions about male knowledge, attitude, and that community services may be affected by unob- use. If it is important for LSMS data to be comparable served community characteristics that also directly with Demographic and Health Survey data, it may be affect fertility outcomes. For example, communities necessary to administer the fertility preference and that emphasize the quality of their children's lives (as contraceptive use sections of the LSMS fertility mod- opposed to the quantity of children) may better attract ule to men as well as to women. This would signifi- public or private medical or family planning facilities cantly increase the length of the fertility module and because of higher demand or greater willingness to should only be considered when there are obvious pay. This would overestimate the impact that these benefits from comparability. In countries with a high facilities have on fertility; the true impact in this case prevalence of AIDS, condom use is an important issue would be the other way around-the effect of reduced independent of fertility issucs; it may be useful in thcse fertility on the attraction of such facilities. Correcting countries to gather information on condom use from for this problem is difficult and often requires panel all sexually active adults. data. (For examples of such corrections see Rosenzweig andWolpin (1986) and Pitt, Rosenzweig, Data Needs for Child Mortality Analysis and Gibbons (1993).A more detailed discussion of this The data requirements for analyzing mortality policies general issue is found in Chapter 23 on panel data.) depend on which population group is of interest to Nevertheless, LSMS-type surveys should attempt to analysts and policymakers. Some studies may focus on gather community-level data on the range and quality infant mortality while others may focus on child mor- of the available services, including the number of tality.While each of the three general questions posed hours and days during which the services are available, above about economic demography research can be the quality and type of services provided, the qualifi- answered separately using household survey data, cations of the service providers, inventories of drugs almost all relevant policy questions can be answered by and medicines, and the number of years that the serv- estimating reduced form relationships for which the ices have been operating (see the health care facility mortality variable of interest is determined by a set of questionnaire introduced in Chapter 8). exogenous explanatory variables. A final set of policy-relevant community-level data As with fertility, this approach raises two ques- that can be collected in LSMS surveys relates to infor- tions: mation, public education, and communications activi- * What mortality variables are of interest to analysts ties, including variables such as the availability of TV, and policymakers? radio, and newspapers in the community; the use of * Which explanatory variables should be included in media for fanily planning messages; and the information the reduced form relationship? available on family planning. Public information cam- This section proposes a set of variables that deter- paigns operate on several levels. Politically, they help set mine mortality-variables that should be collected as the policy agenda and elicit the commitment of politi- part of the fertility module of LSMS and similar multi- cal leaders. lnstitutionally, they can train service providers topic surveys.5 The variables are summarized in Box and provide individuals with information about available 15.5. services and changes in public attitudes and preferences. Some analysts are also interested in men's knowl- CHILD MORTALITY VARIABLES OF INTEREST. Even in edge, attitudes, and practices regarding reproduction countries with high levels of infant and child mortali- and contraception. However, most previous LSMS ty, mortality is usually a rare event.Thus it is important surveys have not gathered fertility and contraceptive to understand how to measure it accurately. It is usu- 39 INDU BHUSHAN AND RAYLYNN OLIVER ally necessary to analyze cumulative mortality over a relatively long time. As a result, child mortality experi- Box 15.5 ImportantVariables for Mortality Analysis enced in the entire lifetime of- a mother (either in Mortality outcomes terms of the total number of her children who died or Number of children thot died (under one year, under five years) the proportion of all her children who later died) is Proportion of children ever bom thot died (under one year often used as the dependent variable. Age of children under five years) at death is crucial in defining the dependent variable Age of children at death for mortality analyses.The definition of the dependent variable depends on whether neonatal, infant, or child Other mortality-related variables mortality is to be analyzed. Inaccurate recording of the High-risk births (very young or old mother short birth interval) age at death, in particular the "heaping" of observa- Low birth weight Use of and expenditure on maternal and child health care tions at one month and one year, makes it difficult to Water source, use of woter expenditure on water measure infant and child mortality. Health practices (woshing hands, storing food, use of drugs, Variables related to mortality includc: high-risk alcohol, and tobocco) births (births to very young or very old mothers and Delivery circumstonces births for which the interval between births was Child immunization short), low birth weight, use of and expenditure on maternal and child health care, delivery circumstances Explanatory individual and household characteristics (such as where the baby was delivered, what medical Household composition Age, sex, and schooling of household members personnel attended the birth, and xvhether there were Household income and other household resources any complications during the delivery), and child Ethnicity and religion immunization. High-risk births can be measured from Housing characteristics %water source, toilets, sanitary condibons) the data on mother's age and birth history that are col- lected in the fertility module. Fertility modules can Selected community characteristics also collect information on the use of prenatal health Region and level of urbanization care and breastfeeding, but only for the most recent Environment births.6 Data on respondents' use of and expenditure . Climate (rainfall, temperature, and altitude) o Prevalence of major diseases on health care are often collected in the health mod- Water supply ule, but only for the past 4 weeks or the past 12 General sanitary conditions (sewage facilities and waste months, and not for children who have died. Data on disposal) the circumstances of the delivery could also be col- Government programs lected in the fertility module, but, again, only for the * Spray for malaria most recent births. Immunization data could be col- * Purfication of water sources lected for all children in the birth history section. Information, public education, communicotions Hoxvever, again, the accuracy of data is questionable Availability ofTV, radio, and newspapers ' . ~~~* Use of media for health messages for children born more than three years prior to the UsCommuiation campaigns ; r *~~~~ Communication campaigns survev-especially for children who died young. Pnces * Local wage rates EXPLANATORY VARIABLES. Virtually all of the variables Food and non-food prices that determine fertility also determine mortality, Formal and informal interest rates including age, sex, and schooling of household mem- Heaith services bers; household income and other household * Availability, type, quality, and fees resources; and ethnicity and religion. Additional explanatory variables for mortality include health would not be difficult to include questions on both of practices in the home and households' water sources. these topics in the housing and health modules.7 While questions about sources of drinking water have In general, variables related to environment are often been collected in the housing module of previ- not under the behavioral control of household mem- ous LSMS surveys, questions about households' health bers unless the members migrate. Environmental vari- habits have rarely been included. In future surveys it ables include climate (rainfall, temperature, and alti- 40 CHAPTER 15 FERTILITY tude), water supply, prevalence of major diseases, and tial for estimating the determinants of infant and child general sanitary conditions (sewage facilities and waste mortality: disposal). These kinds of data can be gathered in the a How far an individual has to travel to obtain serv- community questionnaire, although some may have to ices (distance and travel time). be collected from other sources, such as meteorologi- * How frequently services are available (days per cal records. (For details see Chapter 13, on the com- month, hours per day). munity and price questionnaires.) * What types of services are available (primary care, Environmental variables usually do not vary across maternal and child health care). small geographical areas. However, they may vary con- * What types of procedures can be performed at the siderably over time. Therefore, environmental data service center (for example, major surgery or cae- should be collected each month or each season, and sarian sections). perhaps over more than one year. At a minimum the . Quality of service providers (qualifications and community questionnaire should ask about two differ- experience, behavior toward patients). ent seasons, such as the wet season and the dry season. * Quality of the facility (cleanliness, electricity, water However, the structure of the LSMS-type surveys and quality, refrigeration). the limits of empirical work make it difficult to * Whether emergency transport is available. include environmental data that correspond to the * Whether essential drugs and supplies are available time and place of each child's life and death.This lim- and properly stored. itation must be borne in mind when interpreting the While some of this information, such as travel estimated impact of environmental variables. times and hours that a facility is open, can be collect- Government programs that provide health servic- ed in the community questionnaire, most of the es or subsidize the prices of medicine and services information can only be collected in a health facility affect household behavior by changing the cost to questionnaire. (See Chapter 8 for a draft facility ques- households of using these services. Government- tionnaire and a detailed discussion of how to use designed public education programs can also affect information from such a questionnaire in empirical behavior, encouraging people to behave in ways that work.) are good for their health by changing perceptions of Data on public health programs (such as cam- the benefits of various actions. paigns to encourage childhood immunization, diar- Policymakers are often interested in the impact that rhea management, and safe motherhood) are also these government programs have on household behav- important policy variables for mortality analyses. ior. But measuring this impact is complicated by the Although these programs are implemented through fact that such programs are rarely located randomly existing health care facilities, data about them are best throughout the community but instead have been collected in the community questionnaire; further placed in specific areas-for example, in areas where information can be collected in a health facility ques- need appeared to be greatest. If the programs are locat- tionnaire. ed in places with the worst health problems, analysts may see a negative correlation in the general population Draft Modules between health and the existence of government pro- grams. If services are concentrated in affluent neighbor- This section provides two draft modules for collecting hoods for political reasons, a measured correlation may data on fertility and child mortality in LSMS surveys: capture the effect of high income on health and thus a standard module and a short module. Due to the overstate the impact of the health services. To accurate- multisectoral nature of the surveys, the fertility mod- ly measure the impact of government programs, analysts ule cannot be as detailed as it might be in a single pur- need to have some information about the criteria used pose survey.The recommendations in this section have to distribute government services. (For a more detailed been made with the following strictures in mind: discussion, see Chapter 8 on health, Chapter 26 on * The design of the fertility module should ensure a econometrics, and Chapter 23 on panel data.) minimum level of comparability with data from The following data on the health services provid- previous surveys in the country, especially previous ed by both the public and the private sector are essen- LSMS and Demographic and Health Surveys. 41 INDU BHUSHAN AND RAYLYNN OLIVER * The information collected should be relevant for of the questionnaire. If any of the latter three modules policy analysis. are abridged significantly, questions may need to be * Information unlikely to be used often in analysis added to the fertility module to ensure that the neces- should not be collected. sary information is collected for women of childbear- * The module and its methodology should be ing age. The reader may refer to the list of explanato- designed to ensure that the data collected are of ry variables in Boxes 15.4 and 15.5 to verify that the high qualitvy other modules in the questionnaire collect the infor- The standard module is organized in three sec- mation needed to analyze fertility and mortality. tions: maternity history, reproductive health, and con- In most previous LSMS surveys the fertility module traceptive use. The short version omits the questions has been administered to women ages 15 to 49 (inclu- about breastfeeding and abortion and the detailed sive). Survey designers can modify these criteria for the questions on methods of contraception. fertility module if conditions in the field allow only mar- The length of the questionnaire is an important ried women to be interviewed or if it is clear that there consideration in survey design. Table 15.2 presents esti- is substantial sexual activity and childbearing among girls mates of the number of questions per woman for the younger than 15. In many previous LSMS surveys the short and standard versions.8 Estimates are also present- fertility module has been administered to one randomly ed on the questions per household depending on selected woman of childbearing age in each household. whether one woman or all eligible women are surveyed. Doing this reduces interview time but also results in In most cases the module will need to be modi- women in large households being underrepresented in fied to ensure that the questions asked and the infor- the resulting data.9 Alternatively, the fertility module mation provided are appropriate to the prevailing cir- cumstances in the country where the survey is to take Box 15.6 CautionaryAdvice place. Box 15.6 presents cautionary advice on the extent to which the draft module has not been proven How much of the draft module is new and unproven? in the field and on which sections will require the Most of the draft module has been used in the major- most customization. The following section of this ity of past LSMS surveys. Part B on Reproductive chapter provides detailed comiments and explanatory Health is a slight expansion of what was usually includ- notes to gnide survey designers in modifying the draft ed (most previous LSMS surveys gathered data only on notesrtoi gwdelsurveyidesignersym module.g the draft each woman's most recent birth. The questions on fertility module. desired fertility (Questions 21, 25 and 32-34 in Part A) have not been used in previous LSMS surveys, but they Explanatory Notes on Design of the Draft have been used in many Demographic and Health Fertility Module Surveys. How well has the module worked in the past? The data The household roster of the LSMS survey is organized from the fertility module have been used widely. so that all xvomen in the household can be linked to Including all births in the past three years in the repro- the head of the household. Each woman can also be ductive health section should increase its usefulness. linkehed tof her housbandond, Eahen toancard vrsion bData on breastfeeding is difficult to use and interpret linked to her husband and, when the standard version for reasons discussed in the final section of the chapter of the household roster is used, to her children if they Which parts of the module need to be customized? are also members of the household. In addition, data Throughout the module, care must be taken to ensure about women that are gathered in the fertility section that the language used in the questions is sufficiently can be linked to data about the same women collect- precise to elicit accurate responses without making the ed in the education, health, and employment modules respondent unnecessarily uncomfortable. Questions 26 and 27 on first sexual intercourse are the most Table 15.2 Number of Questions in Fertility Module obvious example. Similarly, in some situations it may be unnecessary or impossible to ask questions on abor- Questions One woman All eligib e tion (Questions 30 and 31). The questions on contra- Version per woman per household women ceptive use will require the most customization. Standard 26620.0 28.5 Methods and sources should include only those avail- Short 16.9 12.7 8.2 Source: Authors ca culations us ng data derived from Macro Internatonal nc. able in the country. I992a and 992b. 42 CHAPTER 15 FERTILITY could be administered to all women of childbearing age questions are really "no." It is conceivable that inter- in a household. To do this, multiple copies of the fertility viewers might purposely record a negative response for modules should be included in the questionnaire or addi- these questions in order to avoid administering the sec- tional fertility modules should be made available to inter- tions that follow. Therefore, supervisors should pay spe- viewers. In practice, interviewing two or three eligible cial attention to checking these questions. women per household would address sampling consider- ations and be much simpler than including all eligible A8. The question asks for the name of the child even women. Survey designers might wish to refer to a recent if the child is dead, since the association of a name census to estimate the number of copies of the module makes it easier for the child's mother to recall the that would be required for the average household. details asked for in the questionnaire. In addition, it is The draft module presented in the previous sec- easier for the interviewer to refer to a child by name tion was designed to be administered to women. If in the later parts of the questionnaire. It is recom- data on knowledge about, attitudes toward, and use of mended that the maternity history should be record- contraceptives are also to be collected from men, this ed starting with the firstborn child. Mothers find it can be done by duplicating Part C of the module, on easier to recall various details if the maternal history is contraceptive use, and perhaps the fertility preference discussed from the first birth to the latest birth, rather questions from Part A of the module. than starting with the latest birth and going back to The rest of this section describes the different the first birth (Shyrock and Siegel 1976). parts of the draft fertility module and clarifies the design and purpose of many of the questions. All. Supervisors should instruct interviewers never to leave the question on date of birth blank. Even if the Part A: Matemity History respondent does not remember the child's exact date This section contains questions about each birth, of birth, interviewers should help them recall the year, including the child's name, sex, birth date, survival sta- and hopefully the month, by providing some impor- tus, and age at death if the child died. The section also tant reference dates and by asking them if they collects the ID code of all children living in the house- remember in what season of the year the birth took hold and the highest level of schooling for all children place. Survey designers should prepare a list of refer- who are not household members. (Schooling for chil- ence dates relevant to the country for which the sur- dren who are household members can be obtained vey is being planned.l" from the education module.) It is important to acquire a complete maternity A13. This question collects information on the age at history because this provides analysts with information which children died. In order to increase the accuracy not only about a woman's cumulative fertility but also of measurement of infant and neonatal mortality, the about her recent fertility and infant mortality. Survey time units used vary according to the age at death. designers need to ensure that: * All live births and deaths are identified. A16. This question on the highest level of schooling * The number of children ever born can be recon- attained by the child is included in the birth history to ciled with the answers given in the maternity his- ensure that this information is collected for children tory section. who do not live in the household.This information is * The date of each birth is recorded. essential for analyzing issues related to the "quality- * The age of death for each child that died is recorded. quantity tradeotf." The list of applicable schooling codes should reflect the local education system and A4-A7. These questions determine whether a woman should be the same as those used in the education should answer the maternal history and reproductive module. For children who do not live in the house- health questions. In order to ensure that all pregnancies hold, the ID code can be used to obtain the same are counted (even those that ended in an early mtiscar- information from the education module. riage) and all births are counted (even if the child lived only a short time), interviewers should be trained to A19. After recording the maternal history, interview- probe in depth to find out whether the answers to these ers should count all of the recorded births and deaths 43 INDU BHUSHAN AND RAYLYNN OLIVER and use question A20 to confirm that no birth is issues, questions about these issues have been exclud- missed. ed from the short module. In some countries, respon- dents will consider any questions on abortion inap- A21, A25, AND A32-A34. These questions collect propriate; in these countries such questions should be information on women's fertility preferences regard- excluded from the questionnaire. ing their last child, last pregnancy, and future fertility. Analysts need the answers to these questions to esti- Part B: Reproductive Health mate unmet need for contraception. However, some This section should be administered only to women or all of the questions could be excluded depending who have had at least one live birth in the previous upon the policy priorities of the country where the three years. It contains questions on prenatal care, assis- survey is to be fielded. This is a section of the ques- tance during delivery, the place of delivery, and breast- tionnaire that will require extensive field testing to feeding. In most previous LSMS surveys, such ques- vernfy that the questions are easily understood and tions were asked about the last live birth before the worded sufficiently delicately. If"I don't know" or "It's survey.The draft module presented earlier gathers data up to God" are common responses, these questions on up to three births and on the woman's utilization may not elicit useful information, and including them of prenatal and postnatal care services-allowing ana- will only frustrate both interviewers and respondents. lysts to study the effectiveness and utilization of those services. Information is collected only for births in the A23. This question deals with a woman's current preg- previous three years in order to limit the interview nancy status, which is an important piece of information time, and because it may be harder for respondents to for many types of demographic analyses. For example, in remember such details about earlier births. studying the determinants of current contraceptive use, analysts may want to exclude pregnant women. B3-B4. These questions ask about prenatal care. In order to gather the information requested in this sec- A27. This question has been included to determine the tion, interviewers should be trained in the definition age at which the woman began having sexual relations. of prenatal care so, if necessary, they can clearly explain The exact wording of this question should reflect exist- this concept to respondents. ing cultural norms in the country. In some cultures mar- riage does not take place until after the birth of the first B5-B6. For questions on the place of birth and who child, so "age at marriage" would not be the relevant assisted at the birth, the lists of responses should be question. Field testing will reveal the appropriate way to modified to reflect the full range of local options. phrase the question. It may appear inconsistent to ask questions about marital status near the end of the mod- B7-B9. These questions deal with breastfeeding, ule, after having asked about childbearing. However, which not only contributes significantly to infant experience has shown that collecting information about health but also delays the return of the mother's the respondents' age at the time of their first marriage at menses. The information collected in questions B7- the beginning of the survey may embarrass respondents B9 is useful for determining the prevalence of breast- and thus hamper the smooth flow of the interview. feeding. However, an accurate measure of the nutri- tional value of breastfeeding would require A28-A31. These questions on miscarriages and abor- information on weaning foods and more detailed tions, can yield useful insights into these aspects of fer- information on breastfeeding. Moreover, deducing tility. Questions about abortions are included because the contraceptive effect of breastfeeding is very diffi- abortions reflect women's fertility preferences. In some cult and would require much more detailed informa- countries, survey designers may wish to include more tion. And the quality of data on breastfeeding is sus- abortion-related questions, such as the type of facility pect because it may be difficult for respondents to where the abortion took place, the type of provider, recall exactly when the child was completely weaned. the length of the pregnancy before the abortion, and It may be advisable to drop these questions from the how much the procedure cost. However, because in module. This entire section has been removed from many countries these will not be the most important the short module. 44 CHAPTER 15 FERTILITY Part C: Contraceptive Use 4. Survey designers may also wish to collect sucl informsation This section should be administered to all respondents. from women who are past childbearing age. This issue is discussed It should gather information on each woman's knowl- later in this chapter. edge of, current use of, lifetime use of, source of, and 5. In general, past LSMS surveys have not included a separate payment for contraceptives. This section will almost mortalit module. Data on infant and child mortality are best col- certainly need to be modified to reflect the conditions lected in the same place as data on fertility, since analyses of fertil- in each particular country. As the draft module collects ity should include children who died at an early age. Deaths among information about contraceptive use by method, the school-age children are very rare, so rare that the relatively small list of methods included should reflect those that are sample sizes of LSMS-type surveys effectively preclude using these used and available within the country where the sur- data to analyze mortality of school-age children. vey is to be fielded. In countries where contraceptive 6. Information is collected only for the most recent births use is exceptionally low, it will not be useful to ask all because, generally, respondents' ability to recall details of all births is of the questions about each method. In some coun- very limited. tries it will suffice to ask about a woman's knowledge 7. See Chapters 8, 12, 13, and 14 for detailed discussion of the and use of modern versus traditional methods. In oth- information collected in the health, housing, community, and envi- ers it will be sufficient to ask about the woman's ronment modules of the survey. knowledge and use of any method, with a follow-up 8. Estimated number of questions is calculated using the question asking her to specify which, if any, method responses to similar questions in the Egypt and Ghana she uses or has used. If contraceptive use is widespread Demographic and Health Surveys.There are fewer questions in the and policymakers wish to know more about it, the "one women per household" column because some households survey may need to collect more detailed information have no women of reproductive age. on, for example, the length of time for which a 9. Evidence from Sub-Saharan African countries shows that woman used each method, her complete contraceptive randomly selecting one woman from each household reduces the use history, or more detailed information about her interview time by about 30 percent without biasing the descriptive source of, expenditure on, and willingness to pay for analyses. Howvever, it considerably reduces the size of the sample, contraceptives. especially in the case of young women, for whom the reduction Questions relating to a woman's knowledge of may be as large as 50 percent. Thus, if resources permit. it is best to contraceptives may require interviewers to prompt interview all eligible women in each household. respondents. In some cultures women may be embar- 1o. In AnnexV, Grosh and Munoz (1996) present an example rassed to acknowledge that they know about contra- of a list of historical events of local importance to which inter- ceptives. Also, some women may not understand ques- viewers can refer to help respondents identify the date of a partic- tions about contraceptives. The evidence from ular event. Demographic and Health Surveys is that reported knowledge of contraceptive methods is significantly References higher when respondents are prompted. Ainsworth, Martha. 1989. Socioecontomic Determinants of Fertility in Notes Cote d'lvoire. Living Standards Measurement Study Working Paper 53.Washington, D.C.: World Bank. 1. Data collected in a standard LSMS survey are not appropriate for . 1992. Mleasuring the Impact of Fatal Adult Illness in Sub- analyzing adult or adolescenit mortality See Aiisworti (1992) for a Salharan Africa: An Annotated Houselhold Questionnaire. Living description of an LSMS survey on adult mortality conducted in Africa. Standards Measurement Study Working Paper 90.Washington, 2. If high population rates put pressure on public resources, the D.C.: World Bank. number of children desired by households may be higher than is Ainsworth, Martha, Kathleen Beegle, and Andrew Nyamete. 1995. socially optimum. On the other hand, the desired number of chil- The Inmpact of Female Schooling on Fertility and Contraceptive Use: dren be less than is socially optimum. For example, pension A Study of 14 Sub-Saharatu Countries. Living Standards schemes may become insolvent if the ratio of future pensioners to Measurement Study Working Paper 110. Washington, D.C.: future workers is high. World Bank. 3. Children ever born is the number of children born live to a Beegle, Kathleen. 1995. The Quality and Availability of Fanmily wooman, including children who died after childbirth. Planning Services and Contraceptive Use in Tanzania. Living 45 INDU BHUSHAN AND RAYLYNN OLIVER Standards Measurement Study Working Paper 114. Family Planning Situational Analysis Study. New York: The Washington, D.C.: World Bank. Population Council. Becker, Gary S. 1960. "An Economic Analysis of Fertility" In Grosh, Margaret, and Paul Glewwe. 1995. A Guide to Living National Bureau of Economic Research, ed., Demographic and Standard MTeasurement Study Surveys and Their Data Sets. Living Economic Changes in Developing Countries. Princeton, NJ.: Standards Measurement Study Working Paper 120.Washington Princeton University Press. D.C.:World Bank. Benefo, Kofi D., and T. P. Schultz. 1994. Determinants of Fertility and Grosh, Margaret, and Juan Munoz. 1996. A Mlanualfor Planning and Chlild Alortality in Cdte d'Ivoire and Ghana. Living Standards Implementing the Living Standards Measurement Study Survey Measurement Study Working Paper 103. Washington D.C.: Living Standards Measurement Study Working Paper 126. World Bank. Washington D.C.:World Bank. Behrman, Jere R., and B. Wolfe. 1984. "Labor Force Participation Macro International Inc. 1992a. "Demographic and Health Survey: and Earnings Determinants for Women in the Speci3l Egypt." Conditions of Developing Countries." Journal of Development . 1992b. "Demographic and Health Surveys: Ghana." Econormics 15 (1-2-3): 259-88. Montgomery, Mark, Aka Kouame, and Raylynn Oliver. 1994. The Behrman, Jere R., Mark R. Rosenzweig, and Paul Taubman. 1994. Tradeoff between iNumbers of Children and Child Schooling: "The Endowment and the Allocation of Schooling in the Evidence from Cite d'lvoire and Ghana. Living Standards Family and in the Marriage Market: the Twin Experiment." Measurement Study Working Paper 112. Washington, D.C.: Journal of Political Economy 102 (6): 1131-74. World Bank. Bongaarts,J., and J. Bruce. 1995."The Causes of Unmet Need for Oliver,Raylynn. 1995. Contraceptive Use in Ghana:The Role of Service Contraception and the Social Content of Services." Studies in Availability, Quality, and Price. Living Standards Measurement Famiily Planning 26 (2): 57-75. Study Working Paper 11l.Washington, D.C.:World Bank. Cleland, J., and Benoit Ferrn, eds. 1995. Sexual Behaviour and AIDS Park, Chai B., and N. Cho. 1995. "Consequences of Son Preference in the Developing World. London: Taylor & Francis. in a Low-Fertility Society: Imbalance of the Sex Ratio at Birth Cochrane, Susan. 1979. "Fertility and Education: What do We in Korea." Population and Development Review 21 : 59-84 Really Knowv?" World Bank Staff Occasional Paper 26. Pitt, Mark. 1995. Women's Schooling and the Selectivity of Fertility and Washington, D.C.. Child Alortality in Sub-Saharan Africa. Living Standards DaVanzo, Julie, and Paul Gertler. 1990. "Household Production of Measurement Study Working Paper 119. Washington, D.C.: Health: A Microeconomic Perspective on Health Transitions." World Bank. A RAND Note. RAND Corporation, Santa Monica, Cal. Pitt, Mark, and Mark Rosenzweig. 1990. The Selectivity of Fertility Dehenesse,J., M. Carael, and A. Noumbissi. 1996. "Socioeconomic and the Determinants of Human Capital Investments: Parametric and Determinants of Sexulal Behaviour and Condom Use: Analysis Sem iparametric Estimates. Living Standards Measurement Study of the WHO-GPA Surveys." In Martha Ainsworth, Lieve Working Paper 72.Washington, D.C.:World Bank. Fransen, and Mead Over, eds., Confronting AIDS: Evidencefrom Pitt, Mark, Mark Rosenzweig, and Donna M. Gibbons. 1993. "The the Developinig World-Selected Background Papers for the World Determinants and Consequences of the Placement of Batik Policy Research Report Confronting AIDS: Public Priorities in Government Programs in Indonesia." The World Bank Economic a Global Epidemic. Washington, D.C.:World Bank. Review 7 (3): 319-48. Feyisetan, Bamikale J., and Martha Ainsworth. 1994. Contraceptive Pritchett, Lant H. 1994. "Desired Fertility and the Impact of Use and the Quality, Price, and Availability of Family Planning in Population Policies." Population and Development Review 20: 1-56. Nigeria. Living Standards Measurement Study Working Paper Robey, B., J. Ross, and I. Bhushan. 1996. "Meeting Unmet Need: 108. Washington, D.C.:World Bank. Newv Strategies." Population Reports Series J-43. Johns Filnier, Deon. 1997. "Socioeconomic Correlates of Risky Hopkins School of Public Health, Baltimore, Md. Behaviour: Results from the Demographic and Health Rosenzweig, Mark R., and Kenneth Wolpin. 1986. "Evaluating the Survey" In Martha Ainsxvorth, Lieve Fransen, and Mead Over, Effects of Optimally Distributed Programs!" American Economic eds., Confronting AIDS: Evidence from the Developing World- Review 76 (3): 470-82. Selected Background Papers for the World Bank Policy Research Schultz, T. Paul. 1984. "Studying the Impact of Household Report Confronting AIDS: Public Priorities in a Global Epideniic. Economic and Community Variables on Child Health." Washington, D.C.:World Bank. Population and Development Review 10: 215-35 (Supplement). Fisher, Andrewv, B. Mensch, R. Miller, I. Askex,x A. Jain, C. Ndeti, L Shyrock, H.S., and J. S. Siegel. 1976. The Methods and Materials of Ndhlovu, and P Tapsoba. 1992. Guidelines and Instruments for a Demography. New York: Academic Press. 46 CHAPTER 1 5 FERTILITY Thomas, Duncan, and John Maluccio. 1995. Contraceptive Choice, Westoff, Charles F., and A. Bankole. 1995. Unimet Need: 1990-94. Fertility, and Public Policy in Zimbabwve. Living Standards Demographic and Health Surveys Comparative Studies 16. Measurement Study Working Paper 109. Washington, D.C.: Columbia, Md.: Institute for Resource Development-Macro World Bank. International. Todaro, Michael P 1969. "A Model of Labor, Migration and Urban Westoff, Charles F, and L.H. Ochoa. 1991. Unmet lNeed and the Unemployment in Less Developed Countries." American Demandfor Family Platning. Demographic and Health Surveys Economic Review 59: 138-48 Comparative Studies 5. Columbia, Md.: Institute for Resource United Nations. 1995. "Population Trends and Population- Development-Macro International. Related Issues: The Need for International Assistance." World Bank. 1984. florld Development Report 1984. New York: Economic Commission for Europe, Population Activities Oxford University Press. Unit, New York. . 1993. 14'orld Development Report 1993: Investinig in Health . Westoff, Charles F 1991. Reproductive Preferences-A Comparative NewYork: Oxford University Press. View Demographic and Health Surveys Comparative Studies .1997a. 10orld Development Indicators 1997. Washington, D.C. 3. Columbia, Md.: Institute for Resource Development-Macro . 1997b. 14'orld Development Report 1997: The State in a International. Chtanging World. NewYork: Oxford University Press. 47 ' *t ~Migration J 6 Robert E. B. Lucas Collecting migration infornmation has not been a high priority in past LSMS surveys.Yet both internal and international migration have pervasive effects throughout most economies, with pol- icy implications for many issues that are central to LSMS and similar multi-topic surveys-issues regarding labor markets, income generation, consumption smoothing, the environment, educa- tion, and the provision of community services and facilities. Government policies affect migration flows by alter- 1985-90; this figure did not include seasonal migrants. ing the factors that people take into account when Three LSMS surveys in the past decade-in Ghana, they decide to relocate. In addition, the economic Pakistan, and Vietnam-also show a high percentage effects of government policies-including effects on of people in developing countries moving from one both efficiency and income distribution-depend on location to another. The survey in Vietnam revealed migration patterns. that 23 percent of Vietnamese adults live in a place other than the place where they were born. Equivalent Policy Issues Concerning Migration figures from surveys in Ghana and Pakistan were 35 percent and 53 percent, respectively. Several important policy issues arise from the interac- tion between policies and migration outcomes. This MIGRATION AND EMPLOYMENT. Much of economists' section scans these issues, for both internal and inter- interest in migration has focused on labor migration: national migration, and identifies the kinds of infor- reducing the supply of labor in one labor market while mation policymakers need to make well-informed increasing the supply in another. Normally, workers policy decisions. move from a place where wages are low to a place where wages are high.To the extent that higher wages Internal Migration reflect higher worker productivity, migrants who suc- Internal migration is common in many developing ceed in increasing their wages have also enhanced economies. According to the October Household their productivity. However, many factors limit the Survey conducted in 1994 by the Central Statistical geographical mobility of workers, including the Office of South Africa, 2.3 percent of South Africa's expense of moving, a preference for staying at home, adults moved from one place to another in a single and limited information about job alternatives. year. The 1990 census for Thailand showed that about How responsive are population movements to 8 percent of the country's population moved during earnings and employment opportunities? Answering 49 ROBERT E. B. LUCAS this question can provide valuable insights into a num- bers in town already or people in rural areas located ber of important issues: close to a town? And does accepting an interim job, * If the geographical dispersion of productive activi- such as casual work in the informal sector, reduce a ties changes-as a result of, say, trade liberalization person's chance of finding a good long-termjob? Data or economic growth in the absence of reform- on these largely neglected issues would be very useful will large wage increases be necessary to induce to policymakers. workers to move to the new jobs? * If a job creation program is undertaken in urban MIGRATION, POVERTY, AND INCOME DISTRIBUTION. areas, how many migrants can be expected to move Migration has the potential to alter income distribu- for every job that is created? tion in a number of ways. Over time migrants may * If a rural development program succeeds in become more socially mobile than nonmigrants. enhancing rural earnings, will this limit the number Migrants' departure from some areas and arrival in of people who move out of rural areas? Or is young others may affect the income distribution within those people's attraction to urban areas strong enough areas. The distribution of resources within a family that they will move anyway? may be altered by the departure of some members; * How far are workers willing to move to obtain a indeed, family units-among which income distribu- job in a rural works program? tion is frequently measured-may be greatly trans- • What are the important constraints on people's formed by migration. geographical mobility? Could certain policies alle- A number of key questions must be answered to viate these constraints? understand the effects of migration on poverty and If workers are unwilling to relocate elsewhere, income inequality. even for much higher wages, policymakers may wish To what extent is social mobility enhanced by to consider moving the jobs to the workers. On the migration? To what extent do migrants' earnings other hand, it is strongly suspected, at least in some rise more rapidly than the earnings of people who industries, that firms benefit greatly from being locat- do not relocate (including people native to the ed close to other firms-particularly firms in the same place of migration)? Do migrants succeed in mov- line of production. And significant economies of scale ing up the job ladder? can be achieved where infrastructure is provided in * Are migrants drawn disproportionately from low- one place to serve a group of companies. income, middle-income, or high-income families? It might be assumed that the poor are more hkely to MIGRATION AND JOB SEARCH. Workers may be able to migrate because they have few prospects at home, yet conduct a job search more effectively if they have the wealthy may be in a better position to pay the already moved to the area where they are looking for expenses of moving (and of acquiring education- a job. This simple fact can have profound implications which makes it easier to find work elsewhere). for the economic efficiency of migration.To illustrate * Do migrants send remittances to their families, or these implications it is useful to review the Harris- vice versa?' Are poor families likely to receive more Todaro (1970) framework. or fewer remittances from their migrant members Suppose urban wages are relatively high and open than rich families? Migrants from poor families urban unemployment is rife, while wages in rural areas might be expected to have an interest in supporting are low but workers can find productive work. For a their family members, but migrants from wealthy rural worker to find a more lucrative urban job, he or families may have a greater vested interest in pleas- she must generally move into town and remain unem- ing their parents, because these migrants may hope ployed while searching for a job. Any policy initiative to inherit their parents' wealth. that creates urban jobs may attract so many more * To the extent that migrants from poor families do workers into town that the unemployment rate may not send remittances home, does this leave the fam- actually rise, while total GDP may fall as workers are ily members who stay behind (and who may be pulled away from productive rural employment. elderly or incapacitated) in poverty? Does the But just how essential is relocation for finding a departure of migrants compel remaining family job? Is it less necessary for people with family mem- members to work longer, harder hours? 50 CHAPTER 16 MIGRATION • How does migration affect the wages in areas from * Do migrants send more remittances when condi- and to which migrants move? Migration generally tions in their original household are temporarily alters the incomes of people in areas of both in- worse? Conversely, do migrants receive more migration and out-migration-nonmigrants as well remittances from their original household when as migrants. However, these effects are not straight- they suffer a temporary setback? forward. The departure of skilled people presum- * Are families with members who have moved away ably boosts the earnings of locals with skills similar from home better able to smooth their consump- to those of the migrants. And in the long run, this tion during periods of adversity? trend encourages the next generation of locals to * In times of economic adversity, are remittances acquire these skills. On the other hand, the depar- smaller and less frequent when insurance is available ture of skilled people from an area can either help or other transfers (such as social security) are avail- or hurt wages of less skilled people, depending able? upon the nature of production in the area. * How is the process of trickle-down development MIGRATION AND INFRAsTRucTuRE. Migrants may be shaped by migration? The creation of industrial jobs attracted to certain places by the existence of infra- in urban areas may or may not benefit poor rural structure and facilities. However, this can cause the families, depending on who is induced to migrate to facilities to become overcrowded. In such circum- town, whether they send remittances to families in stances policymakers may build new facilities or rural areas, and how these factors affect local wage- improve existing facilities in areas with high population setting. densities; this is especially probable when economies of To understand the above questions, policymakers scale will lower the costs of providing many services. need information about social mobility, the income On the other hand, the existence of improved facilities classes from which migrants are drawn, patterns of may attract even more migrants, leading to more over- remittances and their relation to poverty, and the crowding.Thus it may be better to improve facilities in effects of the arrival and departure of migrants on local less crowded areas, encouraging migrants to move labor markets. there rather than to high-density areas. When policymakers establish a new town, expand MIGRATION AND RiSK. Migration may be one impor- a settlement, or promote a settlement scheme, they tant way for families to mitigate inherent economic make important decisions about where to locate facil- risks. By sending a family member to a place where ities.The policymakers must consider:Are these strate- times of economic misfortune do not normally coin- gies cost-effective? How much migration is there to cide with such times at home, the family can spread such areas and from which population groups? out its risk over two different sets of circumstances, To answer these questions it may be valuable to thus diminishing total risk. A household located in a find out how important the existence of public facili- frequently drought-stricken rural neighborhood ties is for people's migration decisions. If public facili- might send a migrant member to an urban area that is ties are important factors, analysts need to account for not drought-stricken but has an uncertain job market. this when evaluating the impact of the facilities on The concept of migration to mitigate risk raises a households' living standards. If sick migrants are number of questions: attracted to a village with a health clinic, and sick * Is out-migration more frequent from areas where inhabitants of this village choose not to leave, the pop- inherent risk (such as drought, flood, and disease) is ulation of the village may end up with worse health especially common? than the population of a village without such facilities. * Do migrants tend to move to locations where eco- The relationship between migration and transport nomic misfortunes are unlikely to occur at the facilities is complex and not yet well documented. It is same time as they do at home? For example, does not clear whether the existence of cheap and easy migration as a result of marriage occur especially transportation between towns and the countryside frequently between places where economic misfor- promotes or curtails out-migration from the rural sec- tunes are unlikely to occur at the same time? (See tor. The existence of reliable transportation makes it Rosenzweig and Stark 1989.) easier for rural dwellers to market their products in 51 ROBERT E. B. LUCAS town, but it also heightens competition by making may lead to a kind of "brain drain," any evaluation of goods from town available in local rural areas. It is the returns to rural education should also take into therefore not clear how easier transportation affects account the benefits to those whose education enabled the relative prices of local goods, whether such trans- them to migrate from these areas. portation encourages the production of labor-inten- sive goods, and what the consequences are for rural SOCIAL IMPLICATIONS OF MIGRATION. Social implica- employment. Moreover, the existence of reliable trans- tions of migration are often important to policymak- portation encourages migration by reducing the costs ers.Three aspects of migration's social implications are to a migrant of both moving to a town and making mentioned here. subsequent visits home. However, reliable transporta- First, migration can separate married couples, tion also allows rural dwellers to commute to urban possibly resulting in social tension and reduced fertil- areas to take advantage of urban employment oppor- ity. Separation of a husband and wife may also impov- tunities and facilities-while continuing to reside in erish a spouse left at home, although this depends the country. greatly on remittances received by the remaining Improving transportation in rural areas may also spouse and on production conditions at home. make it easier for workers to move around and change Policymakers are interested in knowing how often jobs within the rural sector.This can affect rural-urban families relocate as one unit, how often migration movements in a number of ways. If it is cheap and easy results in families being reunified, and what the eco- for workers to move from one village to a different nomic circumstances are for family members left village, the arrival of migrant workers in the second behind when household heads or other key members village may cause wages there to decline-encourag- of households migrate. ing subsequent migration to urban areas.The ability of A second set of issues arises from the role played workers to move between rural locations may offer by social and kinship networks in facilitating migra- families the opportunity to insure themselves against tion. Having kin in town may make it much easier for economic shocks by locating members in two differ- a migrant to find an urban job by providing him or ent rural locations as well as by sending some mem- her with a place to stay on arrival and generally mak- bers to urban areas. And improving transport between ing the new context less alien.Thus, when analysts are rural areas can increase returns to rural capital (notably examining the role played by other factors in promot- on trucks or other vehicles)-possibly encouraging ing or constraining migration, they should control for workers to migrate from rural areas to towns so they the existence of social and kinship networks. can save money to invest in their home area. Third, in many countries one of the principal rea- For policymakers to understand the interrelation sons for relocating is local violence-war, political of migration and transport patterns, they need com- unrest, or crime. Documenting such causes of dis- munity-level information on the availability, cost, and placement can identify the people who most need quality of transport (preferably for goods as well as relief, and lead to measures-such as enhanced law people), as well as household-level information on enforcement-that will address the underlying causes migration patterns. of this violence. Finally, the strong link between migration and educational facilities is worthy of separate mention. International Migration People may migrate in order to have access to better To a large extent, international migration and internal schools. In turn, the level (and possibly content) of the migration are driven by similar forces, forces that yield education achieved by migrants and nonmigrants may similar consequences. However, international migra- affect migration patterns, both by shaping their atti- tion raises several major policy issues not raised by tudes and by presenting them with employment internal migration. opportunities. Enhancing education in rural areas is an While immigration is subject to controls in vir- important way of enabling rural workers to land jobs tually every country in the world, the efficacy of in urban areas. But because tertiary education is remu- these controls varies enormously. Therefore, one nerated far better in towns than in rural areas, few col- major set of policy issues involves the imposition, lege graduates tend to return to rural areas.While this efficacy, and nature of immigration controls. Does 52 CHAPTER 16 MIGRATION making controls stricter simply raise the rate of ille- policy issues. Some of these data must be gathered gal immigration? Are penalties against the employers from other modules of LSMS surveys. of illegal immigrants effective or do they simply encourage discrimination against all aliens? Does Collecting Migration Histories: Methodological Issues trade protection encourage industries that employ The United Nations manual on measuring internal many immigrants, whether legal or not? Do immi- migration defines a migrant as "a person who has grants undercut the local wage? Do foreign workers changed his usual place of residence from one migra- catch up with locals in terms of their career trajecto- tion defining area to another . . . during [a given] ries, and what is the role of language in influencing migration interval" (United Nations 1970).To under- this? Do foreign students stay on to work in the stand what this means in practice, three concepts must country where they studied? Do family members of be defined: "usual place of residence," "migration immigrants work, or do they receive income from defining area," and "migration interval." Depending state aid and public expenditures? how these terms are defined, migration can mean any Fortunately, very few governments attempt to stay away from home, from a visit with a relative in the impose direct emigration controls. Indeed, some gov- same village to an irrevocable break with the migrant's ernments actively seek to export workers and have home in which he or she moves to another region of implemented programs providing training, informa- the country or world. tion, and even credit for workers interested in emi- In most contexts the "migration defining area" is grating. Several countries have offered exchange and taken to be an administrative unit such as a province, interest rate incentives, along with tax breaks, to district, county, township, or village. Anyone who, encourage emigrant workers to send remittances back within a specified time, changes his or her "usual resi- home (through legal channels). It is important to dence" across the boundary of such a unit is defined as examine the efficacy of such programs. a migrant. The key elements that help survey design- Finally, there is the issue of the "brain drain" of ers decide which administrative unit to use as the educated and skilled workers from developing coun- migration defining area in a given analysis are the het- tries to developed countries. Since the gap in earnings erogeneity of each administrative level and the focus of between most developing countries and developed the analysis. If the province were chosen as the "migra- countries is very large, it is unrealistic to expect devel- tion defining area," this would mean that any rural- oping countries to generate sufficient wage incentives urban migration within each province would not be to keep these workers from emigrating.Thus the prin- recorded. Thus, if the purpose of the analysis is a study cipal policy issue at stake is the design and financing of of rural-urban migration and if provinces are hetero- the educational system. Policymakers will likely want geneous (in other words, contain both rural and urban to know about the educational background of emi- areas), it is inappropriate to choose the province as the grants, who financed their education, and whether migration defining area. Analysts and policymakers such emigrants return home or continue to send may even be interested in knowing about changes in remittances back to their families at home. residence within the boundaries of a given city (for example, from a squatter area to a planned neighbor- Data Requirements hood) so they can analyze access to different facilities. In this case, the migration defining area would need to By no means are there straightforward answers to all be the city sector. On the other hand, village-to-vil- of the policy questions regarding migration, even lage migration may not be particularly relevant to a when ideal data exist with which to analyze them. labor market study if the rural labor market in a par- But a well-designed household and community sur- ticular province is well-integrated. vey can provide useful insights into many migration In the draft migration module it is suggested that issues. survey designers should adopt movement between vil- This section discusses the methodology involved lages, towns, or other similar units as the basis for col- in collecting migration histories in large-scale house- lecting migration information. This is often called hold surveys like LSMS surveys and outlines the data place-to-place migration. Using this definition has the that must be gathered to analyze specific migration advantage that it generally also permits analysis of 53 ROBERT E. B. LucAs movement between broader administrative units (such In this chapter, the first approach will be called the res- as districts or provinces), provided the location of each ident migration history approach and the second will place is appropriately identified in the data set made be called the absentee approach. Both approaches have available to analysts. On the other hand, using place-to- some strengths and some limitations. place migration does rule out the possibility of analyz- Perhaps the greatest limitation in using the resi- ing residential mobility within towns or villages. dent migration history approach is that it relies on The draft migration module suggests defining the respondents' knowledge and recollection of the "usual place of residence" as any place where someone households they have left. Another weakness of this lived (meaning slept and ate) for three months or more approach is that no information is collected about at one time. This avoids the complications that can emigrants even though such information can be of arise when individuals, having a sense of allegiance to great interest to analysts and policymakers. A major their original home, give that location as their "usual strength of the resident migration history approach is residence" even though they neither slept nor ate the ability to collect information about the migrant's there during the reference period. The choice of experiences directly from the migrant. On the other "three months or more at one time" is admittedly hand, using the absentee approach also relies on the arbitrary but is likely to rule out short-term visits for respondents' knowledge and recollection-in this case social, business, religious, or vacation purposes. about who has left the household. Another disadvan- A "migration interval" that is perhaps the most tage of the absentee approach is that when entire fam- commonly used in surveys is time since birth. In sur- ilies migrate, the new occupants of their old dwelling veys using this interval, respondents are asked where are unlikely to possess much information about them. they were born and are defined as migrants if this However, a major strength of the absentee approach is "migration defining area" differs from the place where that information about the household and communi- they are living when interviewed for the survey. Since ty from which the person migrated will be more accu- major moves are comparatively rare in the lives of rate than any comparable information collected using most people, using time since birth has the advantage the resident migration history approach. of maximizing the chances of observing a migration. The draft migration module presented in this On the other hand, it has the disadvantage that it does chapter is designed to allow survey designers to adopt not establish a rate of migration per unit time. Thus either (or both) of the approaches. In the absentee there are good reasons to collect information for at approach, a person who has migrated from the sam- least two points in a person's migration history-his or pled dwelling is defined using a set of people associat- her initial location and his or her location at some ed with the household. In particular, three sets ofthese fixed point in time. "associates" are defined: Note that the initial location does not necessarily * Nonresident surviving parents of each household correspond with the person's place of birth, which is member (in section B of the standard household the location used by some other surveys. When the roster) and nonresident children of household birth takes place in an urban hospital but the mother members (in section C of the standard roster). and infant soon return home to their village, the place Additional people identified on the extended ros- of birth is less relevant for most purposes than the ter, where this is applied (see Chapter 6). baby's initial residence. For this reason, the draft migra- * The head of household. In the draft standard roster tion module asks about the baby's initial residence introduced by Chapter 6, the head of household is rather than about his or her place of birth. always defined as a household member even if he or she has been absent for the whole of the previous Two WAYS TO COLLECT MIGRATION INFORMATION. 12 months. If the household head has been absent There are two different ways to approach the process for at least 6 ofthe previous 12 months and has not of collecting migration information. Either the inter- been present for the previous 7 days, then the head viewer can ask each person at the sampled dwelling is also treated as a household associate. about his or her migration history, or the interviewer If a particular associate is not present at the time can ask the resident members of the household of the interview, information about that associate whether others have migrated from their household. should be collected from his or her spouse, parent, or 54 CHAPTER 16 MIGRATION adult child or from the household head. (Note that the migrations that older people may have made in their associate may be present even if he or she is not actu- youth. ally a household member, if he or she has been living As a result, almost all surveys that include a migra- with the family intermittently.) Absentee migrants tion module have used a person's entire lifetime as the from a household are associates who at some point recall period. This raises the question of the reliability lived continuously at the dwelling for three months or of migration data recalled over a long period of time. more but who now live in another place. A recent study by Smith and Thomas (1997) addressed An associate might have migrated from another this question. The authors compared the recalled dwelling where he or she lived with the family sur- migration histories of individuals each of whom was veyed, after which time the family moved to its cur- interviewed in two rounds of the Malaysia Family Life rent residence. In such a case the associate might be Survey, with 12 years having passed between the considered a migrant from this family even though he rounds.The study did not aim to find out whether the or she has never lived in the family's current dwelling. initial information given by the respondents about A question covering this eventuality is included in the their migration histories was correct; instead it aimed draft standard migration module. to discover whether information they gave in the later round of the survey corresponded accurately with the PANEL DATA. The fact that analysts and policymakers information they had given 12 years earlier. The are often interested in information about migrants and authors concluded that "respondents tend to remember their households both before and after migration sug- salient moves, those linked with other important life gests that panel data may be appropriate. However, it events such as the start of a marriage, the birth of a can be difficult to trace individuals when-as in child, change in a job and moves that lasted for a long LSMS-type surveys-dwellings, not individuals, are time. In contrast, migrations that dim in memory as the units of study. When out-migration occurs time passes are typically short duration or local moves. between rounds of the survey, either the migrants must . . . When collecting complete lifetime histories, it be traced or data about the migrants must be collect- would seem prudent to focus on longer-term moves, ed from remaining residents to maintain the original leaving shorter-duration and circular moves to be cap- panel of individuals. tured in a supplemental module on all migrations that Although tracing is not ruled out as a possibility have taken place in, say, the last year or two" (Smith and in Chapter 23 on panel data, tracing can be an expen- Thomas 1997). Thus this study has at least two impor- sive process and is often unsuccessful. If tracing is tant implications for the design of migration surveys. rejected as a possibility in the design of a survey, it may First, the study suggests that people's memories of their be critical in later rounds of the panel survey to col- main migration events do not deteriorate substantially lect information on absentees. If individuals who do over fairly long periods of time. Second, the study sug- not appear in later rounds have migrated from the gests that it might be more reliable to gather partial household, the economic situation of the household migration histories in which data are collected on a can be compared, say, before and after the migration. person's major moves than to attempt to glean this per- Similarly, absentee information might be used to com- son's complete migration history. pare the employment of individuals before and after they migrate. However, as Chapter 23 recommends, it PARTIAL VERSUS COMPLETE MIGRATION HISTORIES. In is better to allow several years to pass between the very peripatetic societies, collecting a complete migra- rounds of the survey; otherwise very few migrations tion history can become cumbersome, even if infor- will have occurred between rounds. mation is only collected on long-term moves as Smith and Thomas suggest. This is particularly true since, RECALL PERIOD. Since major moves are comparatively although many people never move, those who move rare events for most people, using a short recall period once often move several times. In consequence, the to collect migration information is not likely to yield draft migration module is designed to collect infor- many instances of migration. Moreover, most migra- mation on only a limited number of major migrations. tion occurs among young adults, which means that The short and standard versions of the module using a short recall period would fail to capture the focus on the most recent move, the first move (if dif- 55 ROBERT E. B. LUCAS ferent), and the location of all the residents of the sam- circumstances (age, education, family background) of pled household five years previously. If a resident was migrants are contrasted with the circumstances of born abroad, this resident is asked when he or she ini- nonmigrants. Most econometric analyses of the causes tially arrived in the country of the survey.The expand- of migration use some form of discrete regression ed version of the module includes additional questions analysis (such as probit or logit) to relate whether or about whether the resident ever lived abroad and not a person has moved to a list of potential explana- where the resident lived just prior to marrying. The tory variables.2 first question of the expanded module, which asks about any past time living abroad, is designed to per- MOVERS AND STAYERS. Migration can be studied using mit analysis of the post-migration experience of partial migration histories. For example, it is possible to returned emigrants. study rural-to-urban migration by examining a sample Establishing where a person lived before he or she of people who initially lived in rural areas and distin- got married makes it possible for analysts to examine guishing those who have stayed from those who have such issues as risk-spreading through marriage (marry- moved to urban areas.3 However, an alternative distinc- ing into a family from a place where times of eco- tion is occasionally made between those who have nomic misfortune are unlikely to coincide with such stayed in a particular location and absent household times at home) or peer-group learning about contra- associates who are reported by other household mem- ception (in which a marriage partner's information bers to be in an alternative "migration defining area," and attitudes about contraception may have been say, in a town or abroad. In either case it is possible to shaped prior to marriage by people from his or her distinguish moves to various locations-abroad, to a original home). See Rosenzweig and Stark 1989, city, to a town, within the rural sector-instead of Munshi and Myaux 1997. focusing on a simple mover-stayer dichotomy (see The expanded version of the module additionally Lucas 1985, Falaris 1987, Pessino 1991, andVijverberg asks for a brief migration history of each household 1995). Other possibilities include distinguishing moves associate, using essentially the same categories of across administrative boundaries, although it is less moves as the categories used in the short version of apparent how this would be useful to policymakers. the questionnaire for household members. A person's initial location is usually interpreted to be their location at birth or shortly after birth. The Causes of Migration However, "initial" can also refer to some fixed point in There are two approaches to collecting information time-say, five years before the survey. As will be seen about why people migrate. The first and simplest in subsequent sections, specifying a short time interval approach is to ask migrants why they moved. Many has some advantages in terms of measuring explanato- surveys have used this approach, and some questions ry factors. However, choosing a short time interval along these lines are incorporated in the draft migra- means that only a small number of migrations will be tion module. However, this approach has some critical able to be studied. In less mobile societies, this small shortcomings. Migrants may have had to weigh several sample may make it impossible to perform any effec- different factors in deciding to move. Asking them to tive analysis of migration flows. identify only one (albeit the most important) may cause analysts to miss other contributing factors. On MEASuRES AFFECTING MIGRATION DECISIONS: GENERAL the other hand, migrants may find it difficult to rank CONSIDERATIONS. The theoretical and empirical litera- several contributing factors in order, let alone to ascribe ture suggest several key factors that are likely to influence weights to each reason. Moreover, why migrants people and households in deciding whether or not to moved is only part of the question; at least as important migrate.4 Some of these key components are: is why nonmigrants did not move.Yet such questions * Personal attributes, such as age or gender, that influ- are rarely, if ever, posed-partly because they require ence attitudes toward moving. respondents to be extraordinarily introspective. * Differences between earning opportunities and job Given these limitations, most economists have prospects at home and in alternative locations. preferred to rely on a second approach, known as the * Prior movement of family members (and possible "revealed preference" approach. In this approach, the reunification of a family). 56 CHAPTER 16 MIGRATION * Marriage. PERSONAL ATTRBUTES. It is well established that the * Distance and cost of relocating. propensity to migrate varies systematically with cer- * Access to information and relocation networks. tain personal attributes such as age, gender, and educa- * Ability to finance costly moves. tion. To some extent, it is likely that these patterns * Possession of assets that are difficult to transfer. reflect differences in employment opportunities. For * Family strategies to minimize economic risks. example, it is frequently supposed that young people * Availability and quality of facilities at home and in migrate more often because they have a longer lifes- alternative locations.5 pan over which they can reap the benefits of finding a * Economic inequality and relative standing in the new job and that better educated people possess supe- community. rior information about job opportunities. However, * Incidence of violence, disease, or disasters. available evidence suggests that personal attributes also * Migration controls and incentives, especially on play an independent role in influencing migration international migration. decisions. Thus it is likely that young (and single) peo- Ideally, analysts would like to have information on ple are simply more footloose and that education tends all of these components, if only because omitting some to widen people's horizons. measures might suggest patterns of association that are It should also be noted that the interpretation of actually spurious.Yet largely because of a lack of data, some of these factors depends upon which migration no existing study incorporates all of these elements.At measure is in question. If migration is measured rela- one extreme, the revealed preference approach to tive to a person's birthplace, the cumulative chance of studying the causes of migration uses a multivariate having migrated will generally rise with the person's approach to relate the migration outcome to as many age. On the other hand, if migration is measured by potential explanatory variables as are available in the any location changes in the previous five years, the data, while bearing in mind the dangers of omitted chance of migration will probably rise with the per- terms. At the opposite extreme, it is possible to do son's age until his or her late twenties, after which it much simpler cross-tabulations that relate the migra- will decline. Whatever the interpretation, these per- tion outcome to specific measures from the above list. sonal attributes are important explanatory factors that This would yield instructive insights into what moti- should generally be included in any analysis of migra- vated people to migrate, although it would not pro- tion. Data on these attributes are available from both duce results that could be safely interpreted as causal. the household roster and the education modules. The other modules in volume 3 contain many of the elements listed above, although not necessarily in EARNINGS AND EMPLOYMENT STATUS. It is necessary to an ideal form for studying migration. This is not measure job and earning opportunities at home and in because of inadequacies in the draft modules but alternative locations in order to study the role that rather because of two main problems in understanding these opportunities play in migration decisions. migration. First, when the migration occurred some- However, measuring these factors involves at least two time in the past, the relevance of information collect- fundamental difficulties. ed at the present time to understand that past outcome First, the draft employment module introduced by is debatable.6 Second, since migration often implies a Chapter 9 does not inquire into what employment change of household and always involves a change of opportunities individuals perceive will be available to community, collecting information on a migrant's cur- them if they relocate elsewhere. The reliability of rent household and community will not yield infor- responses to such questions would be very dubious so mation on any factors that prompted the move. this should not be seen as a shortcoming of the These two problems need to be borne in mind in employment module. The most common-if still the following discussion, which will cover each of the rare-technique for addressing this problem is to sim- explanatory categories listed above. In some instances ulate earnings (and, less frequently, employment questions in the draft modules will yield plausible opportunities) in alternative locations.This simulation proxies for factors that analysts would like to measure; can be done by performing a regression analysis of in other cases the data need to be collected within the earnings on personal characteristics within various migration module. categories of location such as rural areas, capital cities, 57 ROBERT E. B. LUCAS and other urban areas.7 The results of this analysis can The time dimension of this problem suggests that be used to project what earnings each person would panel data may be a solution. However, as has already have received in the alternative locations based on his been noted, it is not possible to use panel data to track or her characteristics.8 One major drawback to this respondents' employment histories unless the migrants approach is that the same personal attributes may can continue to be included in the sample in later influence not only migration decisions but also earn- rounds of the survey, perhaps through tracing. In prin- ings and employment status, making it difficult for ciple, an alternative would be to collect information analysts to distinguish among these effects. on the employment of absentee household associates The second fundamental difficulty with this in later rounds of the survey. However, it seems unlike- approach is the problem mentioned above regarding ly that interviewers could glean much reliable infor- the tinme frame for measuring employment opportuni- mation on the earnings of absentee household associ- ties. Sjaastad (1962) viewed migration as an irreversible ates from the remaining family members, although the investment decision and argued that the appropriate remaining family members may at least know whether measure of employment opportunities was the dis- or not the absentee is employed. counted stream of future lifetime earnings that migrat- In summary, how earnings differentials and the ing may bring to the migrant. At the opposite probability of finding employment (formal or infor- extreme, the decision to migrate may be seen as mal) affect the rate of migration is an important factor instantaneously reversible, in which case the only rel- in several areas of policymaking.These effects are cru- evant employment opportunities are those available at cial for evaluating the geographical integration of the the moment the decision is made.Yet neither of these labor market. And knowing the rate of migration may two approaches has been taken in the literature on help analysts study the paradoxical possibility that migration decisions. Instead, most studies have made a urban job creation programs may create unemploy- very strong, tacit assumption: that information about ment by prompting more migrants to move to the the labor markets at the time of the survey is a rea- place where the jobs are being offered than the pro- sonable proxy for the employment opportunities peo- gram can accommodate (see Harris and Todaro 1970, ple will consider when deciding whether to move. Stiglitz 1969, Corden and Findlay 1975, Smith 1983, When labor market conditions in the relevant loca- and Fields 1989). However, in practice, the technical tions are relatively stable, this assumption is somewhat problems of simulating employment alternatives mean plausible. But in most situations this assumption that it can be difficult to obtain reliable and precise becomes less reasonable as more time passes-indicat- estimates of whether and how people migrate in ing that it is preferable to analyze only recent migra- response to employment opportunities. tions in relation to current employment opportunities. LSMS-type surveys are designed to collect certain PRIOR MIGRATION OF FAMILY MEMBERS, MIGRATION pieces of information that can enrich this area of TO REUNITE A FAMILY, AND RELOCATION AFTER investigation in a couple of ways. First, the draft stan- MARRIAGE. The location of an individual's family dard migration module asks questions about a person's members may play several different roles in influenc- employment just before and after his or her most ing his or her decision to migrate. First, if other fam- recent move. Second, the recall period in the retro- ily members have already moved to a new area, they spective section of the expanded version of the draft may be able to give prospective migrants useful infor- employment module introduced by Chapter 9-five mation about what opportunities are available, help years-is the same recall period that was used in ques- them make contacts in their attempt to find a job, and tions about respondents' location in the draft migra- provide them with a cheap place to stay (Carrington, tion module. Moreover, the short version of the draft Detragiache, and Vishwanath 1996). Second, it is migration module asks respondents how long they common for family members or a spouse to move to have lived in their current place of residence. This join a migrant in a new area in order to reunite the means that it is possible to use data on time since the family or couple. Third, in many places it is common last move in a simulation of earnings, in recognition of at the time of a marriage for at least one partner to the fact that migrants may take some time to realize migrate to live with the other (Rosenzweig and Stark their full earning potential in a new location. 1989). 58 CHAPTER 16 MIGRATION It is important to control for these forces or ana- At least three elements are commonly assumed to lysts may attribute a person's decision to migrate to the be associated with distance: transport costs, the avail- prospect of employment opportunities in a new place ability of information about job opportunities, and the rather than to the real reasons for migration. However, extent to which a migrant feels estranged from home there is still little literature dealing with these factors. (in other words, the psychological costs of moving). As a first step, it may be desirable to include among the Policymakers are mainly interested in the role of trans- explanatory variables whether a spouse, spouse-to-be, port costs, which will be examined later when the role or other close relative (perhaps a child, parent, or sib- of facilities as a cause of migration is discussed. For ling) already lives in the contemplated destination of now, it may be noted that reductions in the costs of the prospective migrant.9 This information can be transport (and communication) can have an impact on gathered in two places in the draft migration module. the other two correlates of distance by giving the In the case of a person who has not yet migrated, migrant more access to information and by diminish- the issue is whether this person has family members ing the psychological costs of relocation. Isolating living elsewhere. This will be ascertained through these other two correlates of distance can be quite dif- questions about the location of household associates, ficult, and their policy implications are less obvious. In questions that are even in the short version of the some places governments have attempted to promote migration module. In the case of migrants, what mat- migration by providing information on certain desti- ters is whether a relative preceded them; this will be nations, although these initiatives appear to have had found out from the migration histories of any of the little effect.'0 The idea that the further away a person migrant's relatives who may live in his or her new is from a place, the less he or she is likely to know household.The possibility exists that a relative preced- about it has been offered as an explanation for the ed the migrant but has since moved on, or does not common phenomenon of step-migration (first from a live in the same dwelling as the migrant.Thus the stan- village to a town and later from the town to a metro- dard version of the draft migration module incorpo- pohtan area). However, this theory remains untested rates some specific questions about family and friends and its policy implications are not obvious (Pessino, who already lived in the migrant's destination prior to 1991). the migrant's move. In principle, it ought to be possible to find out To supplement this approach, migrants are asked if from respondents what information is available to they migrated because they got married or because them about potential migration destinations.The cor- their parents moved. In addition, in the standard ver- relation between this information and whether there sion of the questionnaire, migrants are asked with are family members or groups of a similar ethnic ori- whom they stayed on arrival at the migration destina- gin at these destinations might then be explored; how- tion, who helped to pay their settling-in costs, and how ever, it is not clear what policy purpose such analysis they found their initial job. The lack of comparable would meet. Another approach might be to collect measures for nonmigrants precludes the use of these information in the community questionnaire on measures as explanatory terms in regressions to study whether there has been previous migration to specific migration outcomes. It is envisioned that the answers destinations from the community studied. The prob- to these questions will simply be tabulated to present lem with this is that it is dangerous to use past migra- frequencies of the different responses that were given. tion to explain current migration; both may be shaped by the same underlying factor, in which case there is DiSTANCE, INFORMATION NETWORKS, AND THE COST OF no direct casual connection. It may be as well to col- RELOCATING. Many studies have found that long dis- lect data on both distance and the availability of trans- tances between the present location and the potential port without attempting to disentangle the other cor- migration destination are negatively correlated with relates of distance. the incidence of both internal and international migra- This raises the question of how to measure dis- tion (Schwartz 1973; Lucas 1975; Molho 1995; tance. There are several issues involved. The first issue Greenwood 1997). Thus it is important to control for is between which two points distance should be meas- distance when analyzing other causes. However, dis- ured.The solution depends on what kind of migration tance can also be a proxy for several underlying causes. is analyzed. In the case of rural to urban migration, the 59 ROBERT E. B. LUCAS distance that needs to be measured is the distance from not yet been satisfactorily proven (Cornelius and the migrant's rural place of origin to the town. For the Martin 1993; Faini and de Melo 1994; Hatton and migrants themselves, however, the key question is how Williamson 1994; Lucas 1999).What is clear is that the far they moved. To calculate this distance, analysts ide- income class of migrants is well worth studying, as it ally need information on migrants' place of origin. For has profound implications for how migration will this reason (among others), the standard version of the affect income distribution. draft migration module includes questions about the It is important to collect information on the com- migrant's specific place of origin and not just about his position of a household's assets as well as on the total or her province of origin. On the other hand, for rural value of these assets, because owning certain assets can inhabitants who have not moved to a town, the rele- sometimes give individuals a disincentive to migrate. vant distance might be the distance from their present There are several reasons why families or individuals dwelling to the nearest town of a certain size. may find it difficult either to take specific assets with The second issue to be settled is the difference them when they move, or to rent or sell these assets. between measuring the distance between two places as For example, the owner of an asset may possess specif- a straight line and measuring it in terms of the route ic information that makes the asset more valuable to that must be taken to travel from the first place to the this person than to others, yet he or she may not be second. Depending on the topography of the country, able to share this information (Manove, Papanek, and there may be a substantial difference between these Dey 1987).A landowner may know how best to oper- two measurements. Generally, it makes sense to meas- ate his own land and find it difficult to explain this ure distance in terms of the actual travel route, information to others (Rosenzweig andWolpin 1985). although it may be more difficult to measure distance It may be difficult for an owner to supervise a vulner- this way than to measure it as a straight line. able asset from far away, and the asset may also lack a A third issue will arise if confidentiality consider- rental or sale market.12 ations dictate that analysts cannot be given data on the There may also be other explanations for observed places where the interviews were conducted. In these correlations between asset composition and migration circumstances, only the staff of the statistical office will outcomes. Measurement errors in evaluating assets be able to calculate such measures as the distance to could generate such patterns. Finally, the policy impli- the nearest town or how far migrants moved from cations ofanalyzing the composition ofassets as poten- their original places of residence-since statistical tial explanatory variables for migration are not clear office staffare the only people with access to the con- other than the possibility that, for some, government fidential information on specific interview sites. policy could affect the relative price of assets or indi- cate the absence of a credit market (Morrison 1994). ASSETS AND FINANCING MIGRATION COSTS. Two key An important issue to be considered is the appro- aspects of wealth and unearned income are worth priate measure of wealth to include in an analysis of considering: the ability of migrants to finance costly the causes of migration. For most migrants the rele- moves and how people's migration decisions are vant information is likely to be the wealth of the fam- affected by owning assets that are difficult to transfer ily with whom he or she lived prior to migrating.13 In geographically. the case of nonmigrants what matters is the wealth of Clearly, well-off families find it easier to pay the their current household. The wealth of a migrant's expenses of moving than do poor families. However, if previous household is not measured elsewhere in these well-off families have high levels of unearned LSMS surveys, but the wealth of the current house- income, they may feel less pressure than other families holds of both migrants and nonmigrants is thorough- to relocate to gain access to jobs that pay more than ly measured in the agricultural, household enterprise, they earn already. Therefore, several authors have sug- and savings modules. Another alternative is to classify gested that the propensity to migrate may initially rise a household's wealth in terms of its level of consump- with the wealth (or unearned income) of a family and tion as collected in the survey's consumption module. subsequently decline at higher income levels.ii This A distinction must then be made between analyz- hypothesis has attracted particular attention in the ing respondents' migration histories and analyzing context of more costly international migration, but has information about absentee migrants. While the 60 CHAPTER 16 MIGRATION wealth and/or income of the absentee migrant's new gathered at the household level and measures that family can be well documented, it is unlikely that a should be gathered at the community level. migrant who has left his or her parents' home will be Many decisions made by households affect their able to provide much detail about his or her parents' exposure to risk. Risk may be reduced by investing in assets or income at the time when the migrant left the assets that can be liquidated if it becomes necessary to original household.'4 Even if the migrant were head smooth the household's consumption, while risk may of the household at the time he or she migrated, it is increase if a household adopts new agricultural tech- unreasonable to expect him or her to remember nologies or installs private irrigation schemes. If a details about the assets the family owned at that time, household has taken risks, it has a considerable incen- particularly if the move occurred many years previ- tive to insure itself by sending some household mem- ously. Therefore, the expanded version of the draft bers to live and work elsewhere. This can also work in module is designed to collect only some very broad reverse; sometimes households may take risks precise- indicators of family wealth at the time of migration. ly because their migrant members offer some degree These include whether the migrant's family owned of insurance. Also, some families may take less care to land or operated a household enterprise and whether avoid negative outcomes when they feel they can rely the family was relatively well-off, about average, or on their migrant members for insurance-a phenom- poor at the time the migrant left.To gather compara- enon known as moral hazard. In addition, those ble measures for nonmigrants, data from the agricul- households that adopt risky strategies may care less ture, household enterprise, and consumption modules about risk than those that do not and may, thus, be less could be used. interested in insurance. These complexities are part of the reason why FAMILY STPRATEGIES TO MINIMIZE ECONOMIC RISKS. It analysts have yet to examine family risk-taking as a used to be the case that analysts of internal migration cause of migration. Nevertheless, the various modules concentrated on studying the risks involved in trying presented in this book collect the data necessary to to find a job in town (Todaro 1969). However, these carry out this analysis. The agricultural module con- days it is increasingly recognized that in many con- tains questions on irrigation, on what agricultural texts, living in the rural sector involves even greater technologies a household has adopted, and on what economic risks (Stark and Levhari 1982). Families may agricultural assets it has that can be used to smooth attempt to spread their risk by sending some of their consumption. The household enterprise module con- members to various other locations to minimize the tains questions on other kinds of household assets. In chances that an economic downturn will happen in all some surveys the consumption module may contain of these different places at the same time. In this way, questions that make it possible to analyze actual con- the different household members can insure one sumption smoothing. (Surveys with panel data are an another against economic shocks. example.) At least in principle, examined in conjunc- There are two important policy reasons to include tion with data on the incidence of absentee migrants risk minimization in analyses of the causes of migra- from these households,'s these data allow analysts to tion. First, omitting this factor may create false impres- study whether households that have exposed them- sions of the role played by other policy-influenced selves to higher economic risk encourage more of variables. Second, the extent to which households are their members to migrate. exposed to risk can be affected by government poli- At the community level, there are also a number cies. For example, irrigation projects, crop and animal of factors that influence the economic risks faced by disease programs, and efforts to induce farmers to households and may thereby encourage household adopt risky new technologies can each alter the extent members to migrate. Section 8 of the draft communi- to which those who work in agriculture are exposed ty module introduced by Chapter 13 suggests collect- to risk. Social insurance (typically confined to towns) ing data on several of these factors, such as the inci- and relief efforts can mitigate risks. dence of floods, droughts, earthquakes, epidemics, and In order to explore such questions, it is necessary crop diseases in the previous five years. Using the data to obtain some measures of economic risk. A distinc- on absentee migrants, it should be feasible to examine tion should be made between measurements properly whether more migrants leave communities where 61 ROBERT E. B. LUCAS such incidents are common than leave other commu- ing the role that the lack of such facilities plays in nities."6 This analysis is facilitated by the fact that, in encouraging people to out-migrate, they must exam- contrast to household-level risk factors, most of these ine the data on absentee associates. In both cases, the community-level risk factors are highly unlikely to be analysts can enrich their analysis using data on the age affected by migration. of the education and health facilities from the draft In principle, analysts might also like to know the community questionnaire; such age data reveals if the incidence of risks in the various locations to which a facilities were available at the time of the migrant's potential migrant might consider moving and to know departure from the community. whether disasters occurred in these locations at the However, in neither case can analysts draw on data same time as in the potential migrant's home area.The on differences between facilities located at origin and problem with this is that the community survey would destination. Partly because of this lack of data, the draft have to be administered in communities other than migration module includes a question asking migrants those in which the sampled households were located, whether gaining access to more and/or better facilities which is not feasible. However, in some instances, use- influenced their decision to relocate. Again, secondary ful measures can be assembled from secondary sources. sources may supply useful data on the schools or For example, if meteorological records exist both for health facilities that are available in various places; pro- the communities where the sampled households are vided that specific places (as opposed to just the dis- located and for other communities, variability in trict or the region) are coded, this data can be merged recorded rainfall can be used as an alternative measure into the data set after the survey. Where place names of weather risk. are not coded, secondary data can still be merged, by using averages for the relevant district or region.These FACILITIES. Another community-level variable that can averages could be derived from the relevant commu- affect migration is the availability of various facilities nity questionnaires administered within each region. such as schools, health clinics, and transportation. To However, using such averages is clearly inferior to hav- examine this relationship, analysts need community- ing data on specific places, as the averages are a less level data on the existence of these facilities. precise measure.The averages can also be particularly Additional useful information would include the aver- misleading when the districts or regions in question age distance that people must travel to get access to are very heterogeneous. (For example, some villages these facilities, how much is charged for the use of may have secondary schools while other villages in the these facilities, and the quality of the various amenities same region have no schools at all.) These arguments that the facilities provide. Sections 3, 8, and 10 of the in favor of coding specific places (which is done in the draft community questionnaire introduced by Chapter standard version of the draft migration module) also 13 are designed to collect information on schools, apply to merging data on rainfall or other risk com- health clinics, and transportation-three kinds of facil- ponents into the data set, as was discussed above. ities that are likely to affect migration. As noted earli- er, the role that good rural transport plays in promot- ECONOMIC INEQUALITY. Several recent studies have ing or discouraging migration is complex and may emphasized the possibility that migrants' decisions to depend in part on whether improving transport relocate are affected by their relative economic stand- encourages household farms or enterprises to adopt ing in the community (Stark and Taylor 1991). cash crop production and small-scale manufacturing. Investigating this possibility requires community-level The section of the draft community questionnaire on data on prevailing economic inequality, which would employment opportunities collects information on permit analysts to establish where a potential migrant's these elements.17 household appears on the spectrum. Given a sufficient As was mentioned above, the community ques- number of household observations within each pri- tionnaire is administered only in communities where mary sampling unit, the relative standing of a given the sampled households are located.As a result, analysts household can be estimated from the household-level can only examine whether the existence of these facil- data. However, analysts must also take into account the ities attracts migrants by studying the respondents' household's likely standing in the community to which migration histories. If analysts are interested in study- it is contemplating moving."S This is not possible with 62 CHAPTER 16 MIGRATION a typical LSMS survey. At best, LSMS and similar sur- policy question is whether, all other things being veys provide analysts with data on the present standing equal, the recipients of these benefits are more likely of each household in the sample. This can be regarded to emigrate than other people. The expanded version as a factor that prompted absentees to leave or per- of the draft migration module includes a few questions suaded migrants to move in, but not as both a push and about absentee emigrants that might be useful in cir- a pull factor in any individual's decision. cumstances where such incentive schemes are impor- tant. These questions deal with whether the absent DISPLACED PERSONS. Although the number of dis- emigrants received state-funded training, how their placed persons is estimated to be very high globally, relocation costs were financed, and whether they were only a few economic studies of the causes of migration recruited by the private or the public sector.Tabulating have taken into account the factors associated with this these data should make it clear who benefits from such phenomenon (Schultz 1971; Barkley and McMillan programs where the programs exist. However, even 1994; Morrison 1993).This could easily lead analysts to with these data, it is not possible to discern how much make erroneous assumptions about other causes. In additional emigration these programs may have caused addition, if high out-migration rates are observed in since there is no information on whether people who places devastated by violence and crime, there may be did not emigrate were denied access to emigration some potential for stemming this migration by rein- incentives.20 forcing law and order. To begin to tackle this subject, analysts need information at the community level on Analyzing the Labor Market Implications of Migration the rate of crimes, violence, or incidents of civil unrest. It is crucial in any study of migration to analyze labor Invaluable information of this type is often available market implications. Important issues include from official records or from NGOs. Such secondary migrants' absorption into the labor market, the effects data can best be merged with the survey data if the spe- of migration on the labor market outcomes of non- cific place of origin-not just the region of origin-of migrants, employment patterns of immigrants, each migrant is coded. This will make it possible for migrants' skills, commuting to work, and job searches. analysts to study the extent to which migrants move from unstable places to more tranquil places. ASSIMILATING MIGRANTS INTO THE LABOR MARKET. The dynamics of migrants' absorption into the labor MIGRATION CONTROLS AND INCENTIVES. Most countries market have attracted particular attention in migration impose immigration controls. Ideally, analysts would literature. Analysts of international migration have like to know the legal status of each immigrant-both examined whether the earnings of immigrants are to check the efficacy of the controls and to examine the lower than the earnings of natives with comparable experience of undocumented immigrants in using facil- experience and education and, if so, whether the gap ities and finding jobs in the labor market. A few coun- closes over time and whether immigrants eventually tries have attempted to impose internal migration con- overtake natives.2' In countries from which many trols."9 In these countries analysts may be interested in guest workers migrate overseas, critics often express finding out how often people are granted or refused a concern about how returning emigrants can be assim- permit to relocate. However respondents may not wish ilated back into the labor market of their home coun- to answer such questions truthfully, and attempting to try and whether their sojourn abroad enhances their collect such information may compromise the remain- productivity on their return. However, these issues der of the survey by alienating respondents in the sam- have not yet been thoroughly researched. pled households.As a result, the draft migration module Analysts of internal migration have demonstrated does not address this issue at all. considerable theoretical interest in the question of On the other hand, some countries have offered whether rural-to-urban migrants tend to make a tran- incentives to their workers to encourage them to emi- sition from informal sector employment to formal sec- grate-usually as guest workers (such as migrant tor employment after working in town for a while workers in the Persian Gulf). Such incentives can (Todaro 1969; Harris and Todaro 1970; Fields 1975; include state-funded training, subsidized relocation Mazumdar 1981). Employment opportunities in the costs, and state-sponsored recruiting. An important formal sector (and workers' concomitant upward 63 ROBERT E. B. LUCAS social mobility) are a key incentive for people who live extent, wages adjust to these movements, although the in rural areas to move to the city. However, there are incidence of unemployment may change instead. Thus few empirical studies that address the issue of transi- important questions arise about what effect in- tion into the formal sector (Banerjee 1983; Lucas migration and out-migration have on the wages of 1985; Vijverberg and Zeager 1994; Marcouiller, de other workers and whether the level of unemployment Castilla, and Woodruff 1997). changes both in the place from which people migrate Issues of migrants' employment transition and and in the places to which they move.These questions assimilation can be studied using data on migrants' are relevant both to international migration (which employment histories, employment data on a cross- may or may not depress the wages of native workers) section of migrants who have arrived in the urban area and to internal migration (which, in the case of rural- or from abroad at different times, or panel data on to-urban migration, could increase the number of the employment. There are tradeoffs involved in using the urban unemployed). What kinds of workers migrate first two kinds of data. Employment histories can con- may influence the answers to these questions. Skilled tain recall errors. On the other hand, employment his- immigrants may enhance the productivity of natives tories are preferable to comparisons among a cross- and, hence, raise their earnings. And the departure of section of individuals, for which unobserved skilled people from a community may prompt other differences may be the real cause of employment dif- community members to invest in their own training. ferences. Using panel data can circumvent both of To understand how migration affects wages and these difficulties, although there is always a danger that unemployment, analysts need to understand how in a very mobile population the panel will become wages are determined in various labor markets and biased-unless migrants are traced or data on the how the demand for workers changes in response to employment status of absentees are collected in later changes in wages.They also need to know what deci- rounds. sions local workers make-decisions about the num- Whatever kind of data are collected, it is possible ber of hours they choose to work, the extent of their for analysts to relate the earnings and employment sta- participation in the labor force, and whether they will tus of migrants and the probability of them having take additional training courses in response to wage changed their employment status (unemployed to changes (Chapter 9). employed or informal to formal employment) to the A few empirical studies examine the effects of amount of time since the migrants moved, their biog- migration on changes in the labor supply and the raphical information (gender, age, education, country implications of such changes for wage formation (for or region of origin, and mother tongue), and, often, a review see Friedberg and Hunt 1995). Some of these their prior job experience.22 Regardless of whether studies use time-series data to examine the evolution analysts use data on migrants'employment histories or of mean wages as migration varies over time. compare individuals at different stages since migrating, However, such time-series analyses can suffer from the it will be necessary to collect some retrospective fact that the observed mean wages include the earn- employment information, to measure migrants' prior ings of migrants (after their arrival at their destination employment experience, and to establish whether the or before their departure from their original location). migrants moved from unemployment to employment This makes it impossible to discover what effect or from the informal sector to the formal sector. The migration has on natives' earnings (Greenwood, draft migration module collects this information; addi- Ladman, and Siegel 1981; Garcia-Ferrer 1980; tional questions on previous employment experience Salvatore 1980; Lucas 1987; Faini and Melo 1994). are included in the draft employment module Cross-sectional studies of this issue have examined the (Chapter 9). wages of natives and related them to intercity varia- tions in the number of in-migrants. However, a dis- THE IMPLICATIONS OF MIGRATION ON THE LABOR torted picture can arise in these studies simply because MARKET OuTcoMEs OF NONMIGRANTS. Much of econ- migrants presumably prefer to locate in high-wage omists' interest in migration focuses on how labor cities. A very few studies have attempted to adjust for migration reduces the supply of labor in one labor this "reverse causality" by relating present in-migration market and increases the supply in another. To some flows to in-migration flows in the past. But such cor- 64 CHAPTER 16 MIGRATION rections are not entirely satisfactory since in the past which commuting represents a viable alternative to people may have migrated to cities that today offer migration. Commuting from a village to a town is not exceptionally high wages (Altonji and Card 1991; the only kind of commuting with importance for pol- LaLonde and Topel 199 1; Pischke and Veiling 1994; icymaking; policymakers must also understand the dif- Hoddinott 1996). Another approach is to examine the ficulty of reaching work from remote urban slums. earnings of natives according to whether their occu- Moreover, understanding the dynamics of commuting pation employs a large number of in-migrants; how- within rural areas may be crucial for establishing the ever, such studies may be very sensitive to the degree extent to which rural public works projects can create of heterogeneity in each occupation (Friedberg 1997). employment. The draft employment module present- Data for cross-sectional studies (either of the ed introduced by Chapter 9 contains relevant ques- intercity or interoccupational type) can be collected in tions on commuting to work, the answers to which the kinds of surveys presented in this book. The draft will enable analysts to compile some cross-tabulations migration module in this chapter identifies individuals on who is commuting and who instead works within as migrants or natives; the remaining data necessary for their own community. It might be particularly inter- analyzing labor market issues can be collected in the esting to compare this information with data from the employment module (Chapter 9). community questionnaire on the availability of trans- port. Researchers may also be interested in exploring PATTERNS OF IMMIGRANT EMPLOYMENT. Policymakers whether a worker's decision to seek or accept a job are interested not only in understanding the dynamics outside of his or her immediate environment depends of the employment of immigrants but also in finding on whether it is possible for the worker to commute out immigrants' sectoral employment patterns. As or whether he or she will have to relocate. To date noted in the first section of this chapter, if the sectors there seems to be no well-developed framework for that employ significant numbers of immigrants receive analyzing this. effective government protection from import compe- tition or receive any form of government subsidy, this JOB SEARCH. The process of searching for a job and is tantamount to encouraging immigration.23 A simple how migration is linked to this process is poorly doc- cross-tabulation of employment by sector and by resi- umented and little understood. (For what documenta- dence status (immigrant or native) would clarify the tion exists see Banerjee 1983, 1984b, 1991; Banerjee picture of these sectoral patterns.24 and Bucci 1994, 1995; Fields 1989; Lucas 1985.) Yet the ability to conduct an effective urban job search MIGRANTS' SKILLS. The first section of this chapter while still in a rural area affects both production effi- mentions several reasons why policymakers are often ciency and income distribution-in important ways. interested in migrants' skills. How much natives earn One of the very important implications of the Harris- depends on the skills of people who migrate into their Todaro (1970) model is that urban job creation and area, and the "brain drain" effect relates directly to the wage increases may cause people from rural areas to skills of emigrants. An LSMS-type survey measures move to cities without having a job, under the several dimensions of skill, including the quantity (and assumption that it will be easier for them to look for a sometimes the quality) of migrants' education, how city job once they are already located in town. On the much and what kind of formal training they have other hand, there is a considerable amount of anec- received, their prior job experience (from their dotal evidence-if little systematic information- employment history), and-in surveys that incorpo- showing that many rural-to-urban migrants find an rate aptitude tests-their inherent skills.25 Descriptive urban job before they relocate (Banerjee 1991). In data on these measures for migrants (including inter- practice, it seems likely that some migrants find a job national as well as internal migrants) relative to non- before migrating, while others migrate to urban areas migrants could be very useful to policymakers. in search of a job. It is useful for policymakers to understand what COMMUTING TO WORK. Commuting is of special inter- differentiates these two groups. Are people with con- est in the study of migration, because policymakers tacts in town more likely to find a job before moving? may be able to influence the circumstances under Are better educated people more likely to find a job 65 ROBERT E. B. LUCAS before moving, either because they have access to more dard of living can be computed from data gathered in job opportunities or because having more education the consumption module (Chapter 5). The standard enables them to access and process relevant information version of the draft migration module presented in more effectively? Are people from villages near towns this chapter includes questions about whether each more likely to find a job before moving than people household associate ever lived with the sample house- from more distant villages (especially since they may be hold, the present location of each associate, and each able to commute into the city to search)? Do people associate's principal current activity.When these sets of who find a job before moving conduct their urban job measures are available, it is possible to cross-tabulate search while being unemployed in the village?26 the type of migration (and employment status) of peo- To help analysts study these issues, the standard ple who have left a particular family with that family's version of the draft migration module includes ques- standard of living. tions that ask inigrants how long after migrating they One weakness of this analysis is that the family's started working and whether they were already offered standard of living may have been affected by the their job before moving. It would then be possible to departure ofthe migrant. If panel data are available, the relate this information to the migrants' education, family's living standard before the person left can be whether they had family members or other acquain- measured directly using data from the first round of tances already in the toxvn, the distance to their previ- the survey. In the absence of panel data, it is useful to ous home, whether they visited the present town to find out what the family's standard of living would look for a job prior to moving, and their employment have been had the person not left.27 This requires an status (especially, whether they were unemployed) understanding of how the living standard of the prior to migrating. remaining family members is altered by the departure of migrants. Analyzing the Implications of Migration for Income Distribution THE EFFECT OF THE MIGRANT'S DEPARTURE ON THE To perform a complete analysis of migration's effects FAMILY'S LIVING STANDARD. There are several aspects to on income distribution, a wide range of links must be the question of whether the remaining members of a analyzed. While computable general equilibrium migrant's family experience a lower or higher standard models are sometimes used to simulate the interac- of living as a result of the migrant's departure. While tions arising from some of these links (Adelman and living with the family, the migrant may have consumed Robinson 1978), household survey data alone can cer- more or less than he or she contributed to the family tainly yield useful insights into a number of the key income. In addition, if the migrant sends money back elements involved. to the family, this means that not all of the migrant's income is lost to the household. Remittances from the THE ECONOMIC STATUS OF MIGRANTS' FAMILIES. Are migrant (or simply the implicit insurance offered by migrants more likely to come from rich or poor fam- migrant members) may enable the family to make ilies? Does this pattern differ depending on the type of extra investments-increasing the income-generating migration involved (such as rural-to-urban migration, potential of the remaining family members. rural-to-rural migration, or international migration)? On the other hand, the migrant's departure could How is the employment status of migrants influenced cause remaining members of the household to lose by the income class of their home family? In particu- access to certain productive assets. For example, the lar, are rural-to-urban migrants from poor households migrant might rent out some land that he or she owns. more likely to be unemployed than migrants from Alternatively, women left behind in a household may xwealthier households? The answers to these questions be denied access to communal assets because social are critical for building a picture of the effects of norms decree that only men can use them. The migration on income distribution, yet the information remaining members may be induced to work harder that has been collected to date, in previous LSMS sur- than they did before the migrant left-either to offset veys or elsewhere, is very limited. the loss of income previously generated by the So what sort of analyses are feasible using house- migrant or because the migrant's departure would hold survey data? Measures of each household's stan- otherwise leave productive assets idle. However, if the 66 CHAPTER 16 MIGRATION departure of the migrant means that a greater burden families sometimes send their members to places of child care or other non-income-earning activities is where their potential for generating income will not laid on remaining household members, they may not be affected by local fluctuations in income levels. If have time to do this extra work. this insurance strategy is effective, analysts should The various modules of LSMS surveys are expect to find that families with migrant members liv- designed to gather data that can be used to calculate ing elsewhere are better able than other families to the net outcome of these effects directly, even with a weather local fluctuations in earnings and thus main- single cross-section of data. However, having panel data tain their consumption levels during both good and would be particularly helpful in this respect. If analysts bad times. One key factor in a family's capacity to do had panel data on consumption, they would be able to this is whether the migrant member is sending them find out if consumption per person fell more in fami- money-an issue that will be discussed in the next lies from which a migrant departed between panel subsection. In the meantime, the question is how rounds than in families from which no one left.28 household survey data can be used to explore whether Indeed, it would be possible to find out in which kinds households with migrant members are better able to of families (rich or poor) this happened, and for which smooth their consumption than other households. kinds of migrant (for example, household heads or Panel data are probably the best kind of data for adult children of elderly parents).Without panel data, exploring this issue, but some analysts have used data analysts can only establish whether per capita con- from surveys that have interviewed households more sumption is lower among households that a migrant than one time during the year to examine how families has left. Considerable care is then necessary to address smooth their consumption across different seasons of the the statistical pitfalls of reverse causality-the chance year (Paxson 1993). Nevertheless, the information about that the person's migration was actually caused by the household associates that the draft migration module family's low consumption level.29 provides is sufficient for analysts to relate any consump- Even if the consumption levels of remaining family tion fluctuations to the number of migrants who have members stay the same, these members may still be departed from the family, as well as to the migrants'rela- worse off if since the migrant's departure they have had tionship to the household and current place of residence to work harder to sustain their consumption level; work- (Kochar 1995). In forming this relation, it is important load must be considered an aspect of living standards. to consider other key factors that may also affect con- The employment, household enterprise, agricul- sumption smoothing (see Chapter 5) as well as the ture, and time-use modules in this book are designed potential for reverse causality-that excessive fluctua- to collect data on both work outside the home and tions in consumption actually prompt migrants to leave. work in household enterprises and family-run agri- cultural enterprises. But for the present purpose, the Analyzing Remittances standard version of the employment module offers Remittances between migrants and their original sufficient information on the employment activities of households have several policy implications (some of household members. which were mentioned in the first section of this chap- Having panel data from the employment module ter). Policymakers need to know if the creation of high- would be very useful because it would allow analysts paying urban jobs benefits the rural poor because the to relate any changes in the amount of time that each workers send remittances to their families in rural areas family member allocates to various employment activ- (Stark, Taylor, andYitzhaki 1986, 1988). Also, they need ities to the migration of a household member. With to know whether families insure themselves against only one round of data, analysts are restricted to com- local income shocks by sending family members to live paring time allocation in families that a migrant has in areas unlikely to be affected by the same shocks left with time allocation in families that no one has left (Lucas and Stark 1985, Hoddinott 1994). Another issue (while controlling for other factors likely to affect of crucial interest to policymakers is whether the exis- these time allocation decisions).30 tence of government and private transfer programs causes migrants to send less money back to their fami- DEALING WITH A TRANSITORY INCoME Loss. The earli- ly, in the belief that the family will receive help from er discussion of the causes of migration noted that these programs instead (Cox andjimenez 1997). 67 ROBERT E. B. LUCAS Remittances sent by migrants to a household with ates (including absentee migrants). Answvering this which they used to live or from that household to question requires measuring the household's prosperi- the migrant-are only one of many different kinds of ty.The family's current consumption level presumably private interhousehold transfers (one example being depends, to some extent, on the level of transfers that remittances between individuals and families who have it receives, which means that using current consump- never lived together). A large number of these other tion as an indicator of the family's prosperity would be transfers may also need to be considered in analysis. To misleading. If household income data are available, an address this issue, it seems practical for analysts to alternative measure of the family's prosperity might be examine the transfer relationships between a household total household income minus any transfers from asso- and the entire set of people who have been identified ciates (see Chapter 17 on the relative merits of col- as household associates. Some of these household asso- lecting income data). But even this measure can be ciates (possibly including former family members) will misleading, because receiving transfers may affect deci- send nothing to the household, and the household will sions family members make about working. send nothing to some of its associates. Analysts need to A third alternative is to measure what assets the know not only what kinds of associates send transfers family has that can be used to generate income. These to a household but also what kinds do not. This is why assets can include not only physical assets such as land the transfer section of the miscellaneous income mod- or household enterprise assets but also human capital ule is designed to collect data on transfers-both in assets such as level of educational attainment of adult cash and in kind-between the family and its house- members of the household (which influences these hold associates (see Chapter 11). members' earning potential). Data on these income- generating assets are collected in the agriculture, TRANSFERS AND INDIVIDUAL HOUSEHOLD ASSOCIATES. household enterprise, and education modules.31 This Several factors influence the likelihood of associates third alternative could be extended to incorporate sending transfers to a particular household. These fac- components of household income unlikely to be tors include whether the associates ever lived with the affected by current transfers from associates, such as family and how long they have been away, how close- pensions or social security payments. Incorporating ly the associates are related to household members, these components would enable analysts to explore whether the associates have established their own fam- the hypothesis that families receiving benefits from ily elsewhere, and how much the associates earn. Data government and private transfer programs are less like- on a migrant's relationship to the members of his or ly to receive transfers from migrant relatives. her original household should be available from the roster and extended roster. However, data on the other TRANSFERS AND RISK. Analysts may also be interested in factors are not available elsewhere in typical LSMS- relating transfers from associates to whether the family type surveys. Therefore, the standard version of the has a temporary loss of income for a reason beyond its draft migration module includes some questions about own control. For example, does the family receive more household associates, such as whether they are now transfer income from migrant relatives when the major married and living with a spouse and/or children, earners in the household become ill? This can be stud- whether they ever lived at the family dwelling and ied by relating data on net transfers to data on workers when they left it, whether they are employed, what being ill and unable to work. (See the health module kind of work they do, and where they currently live. introduced by Chapter 8 and the labor force participa- These data (together with information from the roster tion section of the employment module introduced by and education modules about each associate's age, Chapter 9.) Similarly, if a community suffers from gender, and education) can be used to estimate an drought, flood, or crop disease, agricultural income may associate's earnings-making it possible to analyze decrease but transfers may increase. (See the history and transfers in relation to earnings. development section of the community questionnaire introduced by Chapter 13.) TRANSFERS RECEIVED BY RICH AND POOR FAMILIES. Policymakers may want to know whether rich or poor INVESTMENTS AND TRANSFERS. One issue that often families receive more transfers from household associ- arises regarding transfers is whether the recipient fam- 68 CHAPTER 16 MIGRATION ily invests the money it receives. However, this ques- One recent study noted that when panel data on tion is irrelevant.32 If part of the cash transferred was households are available, it is possible to find out spent on an investment item, this does not imply that whether high-income or low-income households are the transfer has caused a net increase in the family's more likely to split up-either through the migration of investments; the family may have been prepared to some members or through the establishment of a sec- make that investment anyway using an alternative ond household in the same location (Foster and source of cash. In addition, even if the migrant does Rosenzweig 1996). It is important for analysts to know not send any transfers the family may invest more or whether high-income or low-income households are adopt riskier productive activities in the knowledge more likely to split up because if some of the members that the migrant's location elsewhere provides the of rich households leave and take assets with them, these family with a financial safeguard. And if a family households may not appear to become much wealthier invests its transfer income-for example, by buying over time, but the total wealth of the original group of land-this does not imply that investment in the household members may have increased substantially. As whole economy has risen; investment in the whole important as this line of analysis may be, the methodol- economy is affected by how the seller of the land ogy for performing it is very much in its infancy. spends the money that he or she makes from selling it. Perhaps the most obvious way people join a fam- If analysts nevertheless wish to obtain cross-tabu- ily is through marriage. In many cases marriage lations of households' agricultural investments and involves the migration of one or both partners.Yet it adoption of advanced agricultural technologies against is too simple to view marriage as the cause of migra- the level of transfers received by the household, they tion. The decision to marry and the decision to can do so using LSMS-type survey data. (See Chapters migrate, while difficult to disentangle statistically, 18 and 19 on household enterprises and agriculture.) should be viewed as both discrete and interdependent (Behrman andWolfe 1985). REMITTANCE INCENTIE PROGRAMS. There are at least As mentioned in the first section of this paper, the two major difficulties involved in asking whether emi- importance of reunifying families is a concern of social grants took advantage of tax breaks or exchange and policymakers. This concern has certainly shaped interest rate incentives to send more money back to immigration policy in a number of countries. their home country. First, information may not be reli- Conjugal separation resulting from migration may be able; collecting information about taxes is notoriously of particular concern to policymakers, as may the difficult, and it is not possible to ask respondents effects of parental absence on child care. The draft whether they brought in money through the black migration module collects data on the location of market. Second, it can be difficult to analyze whether household associates. Combined with information on those who take advantage of the incentive programs family members currently living in the household, would have remitted more anyway.33 Therefore, the these data can be used to study the likelihood of mar- draft migration module does not contain questions ried couples living apart, along with such issues as the about whether absentee emigrants took advantage of likelihood of small children having absent, migrant remittance incentive programs. parents. Descriptive data on these phenomena can be informative, even though there is not yet a specific Analyzing Migration and Family Structure. framework to more comprehensively analyze these The composition of a cohabiting family can be altered factors (see, however, Banerjee 1984a). not only by births and deaths but also by the arrival and departure of members. When these arrivals and Using Household Survey Data to Address Policy Issues. departures come from migration (and not merely a The extent to which satisfactory answers can be found change of residence within a town or village), migra- to questions about migration and migration policy tion and family composition become intimately asso- varies considerably, depending in part upon the state ciated. Since almost all existing measures of income of current methodology. The extent to which data distribution and poverty are based on the cohabiting from the draft migration module can address migra- family, these measures are influenced by migration's tion issues depends heavily on whether certain data are effects on family composition. gathered in other modules in the survey. (Such data 69 ROBERT E. B. LUCAS will be discussed in the next subsection.) Box 16.1 accurate than coding specific places.The averages can summarizes questions that can be addressed using a be particularly misleading when districts or regions are well-designed LSMS-type survey as well as issues that heterogeneous with respect to the measure merged. cannot be tackled as effectively.This box has been pre- Table 16.1 also broadly indicates how well specif- pared under the assumption that necessary data from ic migration issues can be analyzed given the collec- other modules will be available. tion of appropriate data from migration and other modules. Ranking the analysis potential of these Links with Data from Other Modules. issues, from "excellent" to "not possible," is intended to Migration affects and is affected by many aspects of reflect the remaining limitations of the data, the diffi- individual and family behavior. As a result, the analysis culties involved in analyzing each issue, and how of migration is extremely dependent on links with meaningful the results will be for different areas of other modules of the surveys. Which modules are understanding. Separate rankings are provided for the included in a given survey and which are excluded short, standard, and expanded versions of the migra- determines the aspects of migration that can be ana- tion module. The rankings differ where significant lyzed with resulting data. Links with other modules additional material is incorporated into the standard are summarized in Table 16.1. In addition,Table 16.1 and/or expanded versions that is not included in the itemizes the questions within the migration module short version. that make it possible for specific issues to be analyzed. The relative importance of data from the other mod- New and Unexplored Areas of Analysis ules is categorized as follows: A number of the areas of possible analysis mentioned * Required. A link without which analysis is impossi- in this section are not yet well-developed-with some ble. still very much in their infancy. Nevertheless, collect- * Recommended. A link that is extremely desirable for ing enough data in these areas to construct cross-tab- analysis but not utterly indispensable. ulations can be very helpful to policymakers. And hav- * Other. A link that would be useful, typically for ing such data may eventually help analysts develop extended or complete analysis. more rigorous approaches to some of these little- Most of the table's references to other modules explored issues. Such issues include: should be self-explanatory. However, three references * Whether the prior migration of family members, deserve separate mention. "Roster (B+C)" refers to family reunification, and marriage are causes of sections B and C of the standard household roster; migration. these sections collect information on the nonresident * Whether information networks influence the deci- parents and children of household members. sion to migrate. "Additional roster questions for associates" refers to * Migration costs and their effect on migration deci- the questions in the draft migration module that are to sions. be included in the roster section so as to gather infor- . The link between economic risks and migration mation on household associates. And "secondary data" decisions. refers to data on communities in which the commu- * The influence of facilities on migration decisions. nity questionnaire has not been administered. * The effects of violence and displaced persons on Secondary data about such communities-data on, say, migration. available facilities or incidence of drought-must be . The role of commuting as a possible alternative to gathered from secondary sources (such as administra- migration. tive data from government ministries) after the house- * The link between migration and job search. hold survey has been conducted. * The income class of migrants' families. The key to merging secondary data with data on * The effects of out-migration on family living individuals is the coding of specific place names men- standards. tioned in the migration histories.Where place names * Consumption smoothing and migration. are not coded, secondary data can still be merged, for * The relationship between migration and invest- example using averages for the relevant district or ment and transfers. region. However, using such averages is much less * Migration and family structure. 70 CHAPTER 16 MIGRATION Box 16.1 Policy IssuesThat Can and Cannot Be Analyzed Using Household Survey Data Issues that can be addressed with household survey data Migration and economic risk Migration pattems . Is out-migration more common from areas with more sub- What are people's most recent moves? stantial inherent risks (such as drought, flood, and disease)? * What are people's five-year place-to-place histories? * Do migrants send more remittances when conditions at * What are people's lifetime place-to-place histories? their original home are temporarily worse? * Is there step-migration? What is it like? * Are families with migrant members living away from home * Is there return migration? What is it like? better able to maintain consumption levels through difficult * What are the patterns of immigration? times than families without such migrant members? * What are the patterns of emigration? * Do migrants reduce the amount of remittances they send back to a home facing economic adversity when insurance Causes of migration or other transfers (such as social security) are available? * How do earnings and employment opportunities affect population movements? Social issues and migration * What are the important factors that limit geographical mobil- * To what extent does migration resuft in conjugal separation? ity? * How important is local violence as a cause of migration? * Can constraints on mobility be addressed through policy * How is family structure affected by the arrival and depar- actions? ture of migrants? * Does having wealth enable more family members to migrate by making migration more affordable? Brain drain and emigration * Are migrants more likely to leave a community in which * What is the educational background of emigrants? their relative economic standing is low? * Who finances their education? * Are migrants attracted by good infrastructure facilities? * Do emigrants return home? If not, do they continue to * How important are local violence and unrest in inducing send transfers? departure? * Do good transport opportunities promote migration or Immigration relieve the necessity to migrate? Under what circum- * Does trade protection encourage industries that are stances? major employers of immigrants? * To what extent is the propensity to migrate less in com- * Do immigrants undercut local wages? munities far away from migration destinations? * Do foreign students stay on to work in their host countries? * How important are family reunification, the prior migra- * Are family members of immigrants employed? tion of family members and friends, and relocation upon * Do family members of immigrants receive state aid and marrying as determinants of migration? spending? * How is the decision to migrate affected by the existence of considerable economic risks in the initial location and Employment and migration limited economic risks in the destination location ? * How do immigrants and internal migrants find their first job in a new place? Social mobility and migrotion * Do immigrants and internal migrants improve on their ini- * How great are the economic retums of migration? tial employment (better pay, greater stability, full-time)? * Do the incomes of immigrants eventually catch up with * What are the effects of immigration and internal in-migra- the incomes of natives? tion on the earnings and job prospects of local nonmi- * Do the incomes of internal migrants eventually catch up grants? with the incomes of local nonmigrants? Issues for which household survey data are of limited use Poverty incidence, income distribution, and migration * What are the relative costs of moving jobs to workers * Do migrants come from richer or poorer families? rather than moving workers to jobs? * Do the remaining members of migrants' families work * Do strict controls increase the rate of illegal immigration? harder? * Are penalties on the employers of illegal immigrants effec- * Are the remaining family members enriched or impover- tive, or do they simply encourage discrimination against all ished by the departure of migrant household members? aliens? * Do migrants transfer more money to richer or to poorer * How effective are policy incentives to encourage transfers families? from abroad? 71 ROBERT E. B. LUCAS Table 16. la Migration Module: Requirements and Links with Other Modules, ShortVersion Migration Additional module roster question Links with other modules Abi ity to Aspect questions for associates Requ red Recommended Other analyze Patterns of migration individuul dota Most recent move 1-9 Roster Excellent 'l.......... ...............................................................................*......................................................................................................................................... Five year pace-to-place history 1 13 Roster Excellent Lifetime place to-place hist ory 7 Roster Excelent Generoi trends Step-migration 1-13 Good Return migratFon 13 Excellent .... ......... ....... .............................................................................................................................................................................................................. Immigration 1-4, 14 Roster Excellent Emigration -6 Roster (B+fC) Extended roster Excellent ................................................................................................................................................................................................................................... Causes of migration Methodoiogy Se f-reported causes Not possible Mover- st ay defnisons 1-13xExcellent Cause Persona attributes Roster Education Excellent Earnings and employment Roster Employment Education Good Family and marriage .6 Roste B+C Extended roster Far Distance and transport 8 9 Community Fair Wealth and finance Consumption Agriculture, Poor Household enterprise , Risks 8 9 Community Secondary data Consumption, Fair Agriculture, Household enterprise Facilities 8-9 Communty Secondary data Fair Economic inequai ty Consumption Poor Vioence 8 9 Communty Secondary data Fair Contro a Not posil Labor market implications Effects on natives 1-9,14 Roster, Employment Household enterprise Fair Assimi ation of migrants 1-4, 4 Roster, Employment Househo d enterprise Exce lent immigrant empioyment patterns i-4, 4 Roster Employment Exte lent M grant skills i- 4 Roster Educat on Employment Excei ent Brain drain i-6 Roster (B--C) Extended roster Poor ..................... ................................................................ ....... .............................................................................................. ................................ Commuting I-14 Community Em..y ment ...............Et Good job search and transtion Not........pos...........ble... Income distribution Income class of migrants' families 1-6 Consumption Miscellaneous ncome, Good Employment ................................................................................................................................................................................................................................... Effect of migration on family I 6 Roster (B+C), Employment, Good living standards Consumption Extended roster ............................................................................................................................................................................. ............ 11........ Consumption smoothing 1-6 Roster (B+C), Extended roster Good Consumption, .. .................. ................... Remittances and famniy income in6 Miscellaneous income, Extended roster Agriculture, Ea Roster (B-C), Household .....................................................*.................................................*.......................................................................................................................... Consumption 72 CHAPTER 16 MIGRATION Table 16.1 a Migration Module: Requirements and Links with Other Modules, ShortVersion (continued) Migration Additional module roster question Links with other modules Ability to Aspect questions for associates Requ red Recommended Other analyze Remittances and risk 1-6 Miscellaneous income, Extended roster Good Community, Employment. Roster (B+C) Remittances and individual migrants 1-6 Miscellaneous income, Extended roster Good Roster (B+C) Policies to promote remittances Not possible Investments and remittances Miscellaneous income, Extended roster Fair Agriculture, HH Enterprise, Roster (B+C) Marriage, migration, and family structure 1-6 Roste, Roster (B+C) Extended roster Fair Source: Authors evaluation of the migration module Table 16.1 b Migration Module: Requirements and Links with Other Modules, Standard and Expanded Versions Migration Additional Ability to analyze module roster question Links with other modules Standard Expanded Aspect questions for associates Required Recommended Other module module Patterns of migration Individual data Most recent move 1-11 Roster Excellent 1-10 Roster (B+C) Extended roster Excellent Five year place-to- 1-1 1, 36-40 Roster Excellent place history iIi0 Roster (B+C) Extended roster Excellent Lifetime place-to- 1-6 Roster Excellent place history 1-10 Roster (B+C) Extended roster Excellent ................................................................................................................................................................................................................................... General trends Step-migration 1-1 1, 36-40 Roster Good Return migration 1-1 1, 36 40 Roster Excellent ..........................................................................*................................................................................................................................................ Immigration 1-1 1, 41 Roster Excellent Emigration 1-10 Roster (B+C) Extended roster Excellent ................................................................................................................................................................................................................................... Causes of migration Methodology Self-reported 12-14 Fair ...................................................................................*............................................................................................................................................... Mover-stayer defined 1-11,36-41 Excellent ................................................................................................................................................*.................................................................................. Couse Personal attributes Roster Education Excellent Earnings and employment Roster Employment Education Good Family and marriage 5-17 1-7 Rost er (B+C) Ex ended ros er Excellent Distance and transport I0 Community Good Wealth and finance 27-35 Consumption Agriculture, Poor Good Household enterprise Risks i0 Community Secondary data Consumption, Excellent Agriculture, Household enterprise .......................... I: ......... .................................................. on... i ...........................................................................................*............................ Facilities IC Community Secondary data Excellent Economic inequality Consumption Poor Violence IC Community Secondary data Excellent ..............s...........................*..................................................................... ........................................................................................N... ot... possible................. Controls Not possible ................. *...................................................................................... .......................................................................................................................... (Table continues on next page) 73 ROBERT E. B. LUCAS Table 16.1 b Migration Module: Requirements and Links with Other Modules, Standard and Expanded Versions (continued) Migration Additional Ability to analyze module roster question Links with other modules Standard Expanded Aspect questions for associates Required Recommended Other module module Labor market implications Effects on natives I -I I, 41 Roster Employment Household enterprise Fa r Assimilation of migrants I I 1, 41 Roster Employment Household enterprise Exce lent .................................................................................................................................................................................................................................... Immigrant employment patterns I-I 1,41 Roster, Employment Excellent ....................................................................................................................................................*.............................. "'",.....................---,--......... Migrants' skills I - I1, 41 -47 Roster, Education Employment Excellent .......... ... ..u"''ng........................................................................................... ... ..u"'ity................................................*............................................""d..................... Brain dra n 12-16 Roster (B--C) Extended roster Good Excellent Commuting Community, Good Employment Job search and transition 18-35 Roster Excellent . ............................................................... ...................................................................................................................................................................................................................................................................................... Income distribution ncome class of migrants' families 1 10 Consumption Miscellaneous income, Good Excellent Employment Migration effect on family I -.IC0 Roster (B+C), Em ployment Good living standards Consumption Extended roster .....................................................................................................................................,............................................,........................................ Consumption smoothing 1 10 Roster (B+C), Extended roster Good Consumption " .. .............................................................................................................................................................................................................................. Remittances and family income 1-10 Miscellaneous income, Extended roster Agriculture, Fair Roster (B+C), Household Consumption enterprise Remittances and risk I 10 Miscellaneous ncome, Extended roster Good Excelient Community, Employment, Roster (B+C) Remittances and ndividuai migrants I -IC Miscellaneous income, Extended roster Excel ent Roster (B+C) .................................................,- - - ,- ,................................. ................ ..................................... ........................... Policies to promote rem ttances Not poss ble ..... .................................................................................. *............................................ *............................................................................................... Investments and remittances I 0 Miscellaneous income, Extended roster Fair Agriculture, HH Enterprise, Roster (B+C) ..... ................................................................................................................................................................................................................... Marriage, migration, and family structure I-10 Roster Roster (B+C) Extended roster Good Soircer Authors evaluation of the m gration module. The Migration Module * Whether the migrant had family or other contacts in the new area prior to his or her most recent Three draft versions of the migration module are pre- move. sented in this section. Notes pertaining to a few spe- * The educational attainment of household associates cific questions in these draft questionnaires and some (for brain drain analysis). general issues of definition in relation to the question- . The current principal economic activity of house- naire are contained in the notes on the migration hold associates. module (the final section of this chapter). * The family members with whom associates live. Principal additions to the standard version of the * Whether associates lived with the household in a module not included in the short version are: previous dwelling. * Job search and the employment transition associat- The main suggested additions to the expanded ed with the migrant's most recent move. version of the module not included in the standard * Coding of the migrant's place of birth and place of version are: previous residence. * Whether each household member ever lived * The reasons for the migrant's most recent move and abroad. the role of amenities in affecting the migrant's deci- * The location of a member's residence prior to mar- sion to move. rying. 74 CHAPTER 16 MIGRATION Box 16.2 CautionaryAdvice How much of the draft module is new and unproven? The * How well has the module worked in the past?The data gath- draft migration module differs substantially from the migra- ered on migration in previous LSMS surveys have not tion modules that were included in previous LSMS surveys. been studied very extensively but this may be because Even the short version of the module is designed to col- LSMS surveys to date have collected only extremely lim- lect more information on respondents' migration histories ited information on the subject. than previous surveys, which usually collected only a por- tion of a migration history (such as the migrant's province * Which parts of the module most need to be customized? of birth and previous residence). Many censuses and sur- Whether the list of household associates should include veys have collected information on migration histories, and people on an extended roster (as opposed to just non- there is at least some evidence that recall data on major resident parents and children of household members) migration moves is reasonably reliable. Collecting informa- may depend upon the specific social setting as well as tion on household associates is new to LSMS-type surveys, on how these data are likely to be used in analysis. although data on nonresident parents and children of Where analysts are planning to study interhousehold household members have frequently been collected with- transfers, the list of associates may need to include in the roster portion of previous LSMS surveys.Also, other absent spouses of household members and, in many kinds of surveys (such as the Botswana National Migration societies, members' siblings. In contexts where emigra- Survey) have successfully collected migration information tion is a comparatively rare event, the questions on on this "absentee" basis. Indeed, most of the components brain drain could easily be omitted. In places where of the standard version of the draft migration module have internally displaced persons and international refugees been successfully adopted in other contexts. However, a are of particular concern, it may be appropriate to add few aspects of the expanded version are new, including the some related questions to the draft migration module questions on the wealth of the migrant's original family and outlined here (as well as to the community the migration histories of nonresident associates. questionnaire). - The wealth of the family with which the member of guest workers overseas, survey designers may want lived prior to his or her most recent move. to include the expanded version of the emigration * More details on the migrant's employment before section. They may also wish to include the questions and after his or her most recent move. in the expanded module about whether the person - More details on emigration (for brain drain analy- ever lived abroad-making it possible for analysts to sis). study the experiences of emigrants who have * The migration history of household associates. returned. * Whether associates ever lived in the place where the interview is taking place. Notes on the Migration Module * More details on the current employment status of associates. It is important to clarify some issues about the draft The standard version is the form recommended in module, relating to both general definitions and spe- most contexts. The expanded version incorporates cific questions. some additional questions that are specific to particu- lar contexts or useful for extended analyses.The short General Definitions version may be used in surveys for which migration is A "place" normally refers to a concentration of a low analytical priority. Appended to each version is dwellings. For migration purposes an entire metropol- a set of questions to be included in the household ros- itan area, including its immediate suburbs, might be ter module (which is discussed in detail in Chapter 6). considered one place, even though this definition These questions gather data about household associ- would rule out the possibility of analyzing migration ates that can extend the analysis of migration in essen- within metropolitan boundaries. Similarly, an entire tial ways. village (and its associated land area) is normally con- Which version of the module designers include sidered a place. However, where settlement is very dif- in their survey should depend on prevailing local fuse, survey designers will need to define the concept circumstances. In countries that send large numbers of a place very carefully prior to fielding the migration 75 ROBERT E. B. LucAs module; preferably this definition will follow some Short Version notion of a community. (See Chapter 13 for a discus- The comments in this section explain questions in the sion of the concept of communities.) short, standard, and extended versions of the draft To analyze place-to-place migration effectively, module. the place of current residence must be identified as precisely as possible. At the very least, analysts need to 2. If the interviewee has lived in this place for more know a comparatively small administrative area (such than one period of time, interviewers should establish as a district or county) in which the place of inter- and record how long the person has lived there since view is located, as well as whether this is an urban or he or she last stayed in another place for three months rural place.Without such information, analysts cannot or more. observe patterns of migration among administrative areas or between urban and rural areas. Where confi- 3. The purpose of this question is to find out where the dentiality rules permit, it is desirable for the name of person first lived. This does not necessarily refer to the the town or village where the interview was con- person's place of birth; the birth may have occurred in ducted to be revealed. Knowing the town name a hospital or while the mother was traveling. allows more effective merging of secondary data, more accurate recording of distances, and more 12. It is best to reword this question so it refers to a detailed examinations of place-to-place migration. In well known national event that occurred around five most previous LSMS surveys, it has been possible to years earlier. release the names of the places where the interviews were conducted. Standard Version Throughout the draft migration module, to "live" 2 AND 3. Questions 2 and 3 are the same as questions somewhere means to eat and sleep in this place for 2 and 3 of the short version; see notes above. three months or more. In some societies people dis- tinguish between "staying" in a place-meaning eating 5. Place names will usually need to be coded after the and sleeping there-and "living" in a place-meaning initial interview. The main purpose of question 5 is to owing allegiance to that place. In these cases the more merge information about specific places, such as dis- appropriate term to use is "staying." tance to the current place of residence, amenities avail- The term "province codes" is used throughout the able, incidence of disasters, or variability of rainfall. If a draft migration module. These codes should refer to country can be divided into a small number of small areas. In some countries "county" or "district" provinces or regions and the above measures vary little may be a more appropriate term. Interviewers must be from place to place within the "province," the code for provided with guidelines about what constitutes an each province can be printed immediately below the "urban" area in a country. question, and no coding will need to be done after the "Household associates" are all the living people interview. If the place is not coded until after the inter- identified in Section B and C of the household roster: view, it is useful to obtain a set of codes that are disag- that is, all living parents and children of household gregated to the level of the district or the county. members who are not themselves members of the household. The additional questions for household 8. Interviewers should be careful to enter the person's associates should also be asked about a head of house- age, not the year in which his or her move occurred. hold who has been absent for more than three months, even if this person is still considered a household mem- 10. Question 10 is similar to question 5 of the standard ber. If an extended roster is applied-identifying spous- version; see notes above. es, siblings, or other relatives of household members who are not themselves members-these people 12. Enter the single most important reason for moving should also be included on the list of household asso- to the current place of residence. ciates.The ID number referred to in the associates sec- tion of the migration module is the ID number these 32. If the answer to question 32 is more than 60 hours or associates were given in the household roster module. less than 10 hours, the interviewer should make sure the 76 CHAPTER 16 MIGRATION respondent has understood that the time period being 4. See Lucas (1997, 1998) for surveys in the context of internal examined is one week (not one month or one day). migration in developing countries. 5. The term "facilities" is used in this chapter to refer to a set of 34. This question refers to the number of people who amenities that are available in a given place, such as elementary worked for the interviewee's firm or employer-not schools, health clinics, or services providing clean drinking water or the number of people who worked at his or her plant electric power. or job site. 6. This issue probably becomes more severe the further back in time the analysis goes, which is a good reason to analyze more 8 (QUESTIONS TO ADD TO ROSTER). The head of the recent migration decisions-assuming there are enough recent household (and sometimes even the entire household) migration outcomes to make such an analysis feasible. may have lived in another dwelling within the previ- 7. These earnings and employment opportunities should ous five years. If this is the case, question 8 of the addi- include the informal sector as well as wage employment (Fields tional questions for household associates should estab- 1975; Mazumdar 1981). This means that data from the household lish whether the associate lived with the household enterprise module are needed to estimate the labor component of (head) at that time. informal self-employment earnings (see Chapter 18). In addition, it is conceivable that personal attributes have quite different effects on Expanded Version earnings in the informal sector. If so, there is a need for a separate When the expanded migration module is used, two study of these earnings patterns as well as of the forces that affect questions need to be inserted into the standard version distribution of workers between the formal and informal sectors. of the household roster module immediately follow- 8. In the more sophisticated approaches, it is explicitly recognized ing the question on marital status. The first question, that the choice of location is endogenous to this process; hence, an "Where did you live during the three months before attempt is made to correct for positive or negative selection. In other you were first married?" should use answer codes for words, migrants may differ in ways that are not reflected in the meas- provinces and foreign countries. The second question, ured attributes alone. See Falaris (1987) and Pessino (1991). "Was the place where you lived just before you were 9. It may also be interesting to control for whether potential first married an urban area?" should use the answer migrants have other friends and family in the location to which codes "1" for yes and "2" for no. If a respondent has they are considering moving. However, collecting this information been married more than once, the second question is more complex-partly because of the difficulty of being precise refers to his or her first marriage. about "knowing" someone. Information about friends and more distant relatives is not collected in the draft module. Notes 10. See Fuller, Lightfoot, and Kamnuansilpa (1985) for descrip- tion and analysis of such an attempt in Thailand. The author is very grateful to Gary Fields, John Harris, Julie 11. Some analysts have hypothesized that precisely the opposite Schaffner, and the members of the LSMS authors' workshop for holds: that the propensity to migrate is lowest in the middle- substantive comments on earher drafts. Fiona Mackintosh provided income range. See Connell and others (1976), Baneijee and Kanbur invaluable editorial contributions. Special thanks are due to Paul (1981), and Stark and Taylor (1991). Glewwe and Margaret Grosh for the major and patient role they 12. Social assets (such as local networks) may also tie individu- played in shaping this chapter. als to a specific location, although to date this has not been tested 1. In this chapter the term "remittances" refers to private inter- in a very systematic way. See Jagannathan (1987). household transfers of money or goods. 13. This distinction has been neglected in the few empirical stud- 2. Such relationships are often referred to as micro migration ies that have attempted to examine the role of wealth in promoting equations, in contrast to macro migration studies-which (often or limiting migration. An even more complex issue arises when one using cross-tabulations of census data) analyze the proportion of a takes into account the wealth of the migrant's extended family-from population that has migrated. which migrants borrow in some societies. See Ilahi andJafarey (1995). 3. One weakness of adopting such a simple dichotomy is that 14. Note, however, that panel data on households can help some people may have moved into town and then returned to the bridge this gap by providing data on wealth in the early round and rural area prior to sampling. Note also that if migration abroad is to data on subsequent migration in later rounds. be studied on this basis, emigrants who are identified as household 15. For residents who have a migration history, the appropriate associates need to be included in the sample. measure is really the economic risk faced by the family from which 77 ROBERT E. B. LUCAS they migrated.The migration draft module does not collect data on entirely feasible if the migrant received a disproportionate share of this measure because of the likelihood that respondents will have consumption. difficulty remembering such information accurately 29. The problem of reverse causality is not entirely removed by 16. Note that this is not true for data on resident migration histo- the availability of panel data; people may be persuaded to migrate ries, where the appropriate community data xvould refer to the com- by the prospect of their consumption falling in the future. munity that the migrant left behind, on which data are not collected. 30. For references and a discussion see Chapter 9 on 17. Using data from the price questionnaire, it may be possible employment. to study the indirect effect of better transport (and hence easier 31. Indeed, there is an additional interest in relating transfers access to external food sources or markets) on prices (such as food from household associates to the availability of such assets, as it is grain prices); the resultant price changes can also affect migration. possible that people send transfers in the hope of eventually inher- 18. There is some difficulty in defining the relevant reference iting the assets, although in the long run even the possession of such group for social standing-particularly in larger setdements (Stark assets can depend on transfers too. and Taylor 1991). 32. See Lucas (1988) for a discussion in the context of guest 19. This has been the case in China and Vietnam, and was the worker emigration. case in South Africa under apartheid. 33. Note that this difficulty is not entirely removed in a panel sur- 20. In places where studying and evaluating emigration incen- vey when a change in policies occurs between rounds of the survey tive programs is important, additional questions might be inserted It is still possible that those who plan to increase their remittances the into the migration module asking non-emigrants vhether they most are more likely to take advantage of the new incentives. applied for specific forms of state emigration aid and whether these applications were successfuil. References 21. This issue has received a lot of attention in the United States, where it has played an important role in debates over immi- Abowd, J., and R. Freeman, eds. 1991. Immigration, Trade and time gration policy (Borjas 1987, 1994). Labor Mfarket. Chicago, Ill.: University of Chicago Press. 22. A remaining problem is that migrants with unsuccessful Adelman, Irma, and Sherman Robinson. 1978. "Migration, employment experiences may choose to return home or move on Demographic Change, and Income Distribution in a Model of elsewhere, vhich wvould leave analysts with a distorted impression a Developing Country" In Julian L. Simon, ed., Research in if they stadied only the remaining migrants. It may be possible to Population Economics, Vil 1. Greenwich, Conn.:JAI Press. correct for this distortion using data from the migration histories; Altonji, Joseph, and David Card. 1991. "The Effects of Immigration however, this issue has not yet been systematically addressed. on the Labor Market Outcomes of Less-Skilled Natives. In J. 23. Studies in the United States and Europe have shoNvn that Abowd and R. Freeman, eds., Immigration, Trade and the Labor heavily protected industries tend to be the major employers of Market. Chicago, Ill.: University of Chicago Press. immigrants. This is not quite the same as testing whether protec- Banerjee, Bissvajit. 1983. "The Role of the Informal Sector in the tion encourages inmnigration, but it is part of the picture. See Migration Process: A Test of Probabilistic Migration Models Abowd and Freeman (1991) and Faini andVenturini (1993). On and Labour Market Segmentation for India." Oxford Economic Malaysia see Martin (1994). Papers 35 (November): 399-422. 24. In contrast, it seems unlikely that sanctions imposed on . 1984a. "Rural-to-Urban Migration and Conjugal employers can easily be examined using household survey data, Separation: An Indian Case Study" Economic Development and partly because of a lack of variation in the coverage of the legisla- Cultural Chanige 32 Uuly): 767-80. tion but also because respondents are unlikely to answer questions . 1984b. "Information Flow, Expectations, and Job Search: about illegal employment practices honestly. Rural-to-Urban Migration Process in India." Journal of 25. See Chapter 7 for a discussion of measuring skills. Development Economics 15 (May/August): 239-57. 26. Herzog, Schlottmann, and Boehm (1993) studied data from . 1991. "The Determinants of Migrating with a Pre-Arranged industrialized countries on migration and job searches; these data Job and of the Initial Duration of Urban Unemployment: An included an analysis of the net effect of migration on the duration Analysis based on Indian Data on Rural-to-Urban Migrants." of the migrant's period of unemployment. Journal of Development Economics 36 (October): 337-51. 27. There have been some studies along these lines. See Stark Banerjee, Bissvajit, and Gabriella Bucci. 1994. "On-the Job Search and Taylor (1991). After Entering Urban Employment: An Analysis Based on 28. Note, however, that consumption per capita can fall even Indian Migrants." Oxford Bulletin of Economics and Statistics 56 though each remaining person may be consuming more. This is (February): 33-47. 78 CHAFrER 16 MIGRATION 1995. "On-the-Job Search in a Developing Country: An Activity in LDCs." Journal of Development Economics 2 June): Analysis Based on Indian Data on Migrants." Economic 165-87. Development and Cultural Change 43 (April): 566-83. . 1989. "On-the-Job Search in a Labor Market Model: Ex Banerjee, Biswajit, and S.M. Ravi Kanbur. 1981. "On the Ante Choices and Ex Post Outcomes."Journal of Development Specification and Estimation of Macro Rural-Urban Economics 30 (March): 539-58. Migration Functions: With an Application to Indian Data:' Foster, Andrew D., and Mark R. Rosenzweig. 1996. "Household Oxford Bulletin of Economics and Statistics 43 (February): 7-29. Division, Inequality, and Rural Economic Growth." University Barkley, Andrew P., and John McMillan. 1994. "Political Freedom of Pennsylvania, Philadelphia, Penn. and the Response to Economic Incentives: Labor Migration in Friedberg, Rachel. 1997. "The Impact of Mass Migration on the Africa, 1972-1987." Journal of Development Economics 45 Israeli Labor Market." Population Studies and Training (December): 393-406. Center Working Paper 97-11. Brown University, Providence, Behrman, Jere R., and Barbara L. Wolfe. 1985. "Micro R.I. Determinants of Female Migration in a Developing Friedberg, Rachel, and Jennifer Hunt. 1995. "The Impact of Country: Labor Market, Demographic Marriage Market, and Immigrants on Host Country Wages, Employment, and Economic Marriage Market Incentives." In Schultz and Growth." Journal of Econotnic Perspectives 9 (Spring): 23-44. Wolpin, eds., Research in Population Economics, Vol. 5. Fuller, Theodore D., Paul Lightfoot, and Peerasit Kamnuansilpa. Greenwich, Conn.: JAI Press. 1985. "Toward Migration Management: A Field Experiment in Boijas, George J. 1987. "Self-Selection and the Earnings of Thailand:" Economic Development and Cultural Change 33 Immigrants." American Economic Review 77 (September): (April): 601-21. 531-53. Garcia-Ferrer, Antonio. 1980. "Interactions between Internal - 1994. "The Economics of Immigration." Journal of Migration, Employment Growth, and Regional Income Economic Literature 32 (December): 1667-717. Differences in Spain."Journal of Development Economics 7 June): Carrington, William J., Enrica Detragiache, and Tara Vishwanath. 211-29. 1996. "Migration with Endogenous Moving Costs:" American Greenwood, Michael J. 1997. "Internal Migration in Developed Economic Review 86 (September): 909-30. Countries." In Rosenzweig and Stark, eds., Handbook of Connell,John, B. Dasgupta, R. Laishley, and Michael Lipton. 1976. Population and Family Economics. Amsterdam: North Holland. Migration from Rural Areas: The Evidence from Village Studies. Greenwood, Michael J.,JR. Ladman, and B.S. Siegel. 1981. "Long- Delhi: Oxford University Press. Term Trends in Migratory Behavior in a Developing Country: Corden, W Max, and Ronald Findlay. 1975. "Urban The Case of Mexico." Demography 18 (August): 369-89. Unemployment, Intersectoral Capital Mobility, and Harris, John R., and Michael P. Todaro. 1970. "Migration, Development Policy." Economica 42 (February): 59-78. Unemployment, and Development: A Two-Sector Analysis." Cornelius, WA., and P.L. Martin. 1993. The Uncertain Connection: American Economic Review 60 (March): 126-42. Free Trade and Mexico--US. Migration. San Diego, Cal.: Center Hatton, T.J., and J.G. Williamson. 1994. "What Drove the Mass for US-Mexican Studies. Migrations from Europe in the Late Nineteenth Century?" Cox, Donald, and Emmanuel Jimenez. 1997. "Coping with Population and Development Review 20 (September): 533-59. Apartheid: Inter-Household Transfers over the Lifecycle in Herzog, HenryWJr.,Alan M. Schlottmann, andThomas P. Boehm. South Africa." Boston College, Chestnut Hill, Mass. 1993. "Migration as Spatial Job-Search: A Survey of Empirical Faini, R., and J. de Melo. 1994. "Trade Liberalization, Employment, Findings." Regional Studies 27, Special Issue: 327-40. and Migration: Some Simulations for Morocco." Paper pre- Hoddinott, John. 1994. "A Model of Migration and Remittances sented to the Organisation for Economic Co-operation and Applied to Western Kenya:" Oxford Economic Papers 46 July): Development Workshop on Development Strategy, 459-76. Employment, and Migration, Paris. . 1996. "Wages and Unemployment in an Urban African Faini, R., and A. Venturini. 1993. "Trade, Aid, and Migrations." Labor Market." EconomicJournal 106 (November): 1610-26. European Economic Review 37 (April): 435-42. Ilahi, Nadeem, and Saqib Jafarey. 1995. "Guest Worker Migration, Falaris, Evangelos M. 1987. "A Nested Logit Migration Model Transfers, and the Extended Family: Evidence from Pakistan." with Selectivity." International Econonmic Review 28 June): Paper presented to the Seventh World Congress of the 429-43. Econometric Society, Tokyo. Fields, Gary S. 1975. "Rural-Urban Migration, Urban Jagannathan,Vijay. 1987. Tie Logic of Unorganized Markets. Oxford: Unemployment and Underemployment, and Job-Search Oxford University Press. 79 ROBERT E. B. LUCAS Kochar, Anjini. 1995. "Explaining Household Vulnerability to . 1994. "Capital Market Imperfections. Labor Market Idiosyncratic Income Shocks." Anierican Economic Review 85 Disequilibrium, and Migration: A Theoretical and Empirical (May): 339-71. Analysis." Economic Inquiry 32 (April): 290-302. LaLonde, Robert, and Robert Topel. 1991. "Labor Market Munshi, Kaivan, and Jacques Myaux. 1997. "Social Effects in the Adjustments to Increased Immigration." In Abowd and Demographic Transition: Evidence from Matlab, Bangladesh." Freeman, eds., Immigration, Trade and the Labor l1arket. Chicago, Boston University, Economics Department, Boston, Mass. Ill.: University of Chicago Press. Paxson, Christine H. 1993. "Consumption and Income Seasonality Lucas,Robert E.B. 1975."The Supply of Immigrants' Function and inThailand."Journal of Political Economy 101 (February): 39-72. Taxation of Immigrants' Incomes: An Econometric Analysis." Pessino, Carola. 1991. "Sequential Migration: Theory and Journal of Development Economics 2 (September): 289-308. Evidence from Peru." Journal of Development Econoniics 36 - 1985. "Migration amongst the Batswana." The Economic JUly): 55-87. Journal 95 June): 358-82. Pischke, Jorn-Steffen, and Johannes Velling. 1994. "Wage and Em- 1987. "Emigration to South Africa's Mines." American ployment Effects oflmuigration to Germany:AnAnalysis Based Economic Review 77 June): 313-30. on Local Labor Markets." Massachusetts Institute of Technology - 1988. "Guest Workers, Circular Migration, and Transfers." Economics Department Working Paper 94-8. Boston, Mass. In D. Salvatore, ed., World Population Trends and their Impact oni Rosenzweig, Mark R., and Oded Stark. 1989. "Consumption Economic Development. Westport, Conn.: Greenwood Press. Smoothing, Migration, and Marriage: Evidence from Rural - 1997. "Internal Migration in Developing Countries." In India."Journal of Political Economy 97 (August): 905-26. Mark R. Rosenzxveig and Oded Stark, eds., Handbook of Rosenzweig, Mark R., and Kenneth I. Wolpin. 1985. "Specific Population and Family Economics. Amsterdam: North Holland. Experience, Household Structure, and Intergenerational - 1998. "Internal Migration and Urbanization: Recent Transfers: Farm Family Land and Labor Arrangements in Contributions and New Evidence." Background Paper for Developing Countries." Quarterly Journal of Economics 100 [Torld Developmitenit Report 1999/2000. Washington, D.C.: (Supplement): 961-87. World Bank. Salvatore, Dominick. 1980. "A Simultaneous Equations Model of - 1999. "International Trade, Capital FloNvs, and Migration: Internal Migration with Dynamic Policy Simulations and Economic Policies toward Countries of Origin as a Means of Forecasting: Italy 1952-76."Journal of Development Economics 7 Stemuning Immigration." In A. Bernstein, ed., .M1igration and June): 231-46. Refugee Policies: The International Experience. London: Cassell Schultz, T. Paul. 1971. "Rural-Urban Migration in Colombia." Academic Publishers. Review of Economics and Statistics 53 (May): 51-58. Lucas, Robert E.B., and Oded Stark. 1985. "Motivations to Remit: Schwartz, Aba. 1973. "Interpreting the Effect of Distance on Evidence from Botswana." Journal of Political Economy 93 Migration."Journal ofPolitical Economy 81 (October): 1153-69. (October): 901-18. Sjaastad, Larry A. 1962. "The Costs and Returns of Human Manove, Michael, Gustav F Papanek, and Harendra K. Dey 1987. Migration." Journal of Political Economy 70 (October), "Tied Rents and Wage Determination in Labor-Abundant Supplement: 80-93. Countries." Boston University, Boston, Mass. Smith, James P. 1983. "Income and Growth in Malaysia." Report Marcouiller, Douglas,Veronica Ruiz de Castilla, and Christopher R-2941-AID. RAND Corporation. Santa Monica, Cal. Woodruff. 1997. "Formal Measures of the Informal-Sector Smith, James P, and Duncan Thomas. 1997. "Remembrances of Wage Gap in Mexico, El Salvador, and Peru." Economic Things Past:Test-Retest Reliability of Retrospective Migration Development and Cultural Change 45 January): 367-92. Histories." RAND Corporation and University of Californina Martin, Philip L. 1994. "Augmenting the Labor Force: The Role of at Los Angeles, Santa Monica. Migration."World Bank,Washington, D.C. Stark, Oded, and David Levhari. 1982. "On Migration and Risk in Mazumdar, Dipak. 1981. The Urban Labor M11arket and Income LDCs." Economic Development and Cultural Change 31 Distribution: a Study of Mfalaysia. Oxford: Oxford University (October): 191-96. Press. Stark, Oded, and J. Edward Taylor. 1991. "Migration Incentives, Molho, Ian. 1995. "Migrant Inertia, Accessibility, and Local Migration Types: The Role of Relative Deprivation." The Unemployment." Economica 62 (February): 123-32. EcononmicJournal 101 (September): 1163-78. Morrison, Andrew R. 1993. "Violence or Economics:What Drives Stark, Oded, J. Edward Taylor, and Shlomo Yitzhaki. 1986. Internal Migration in Guatemala?" Economic Development and "Remittances and Inequality." The Economic Journal 96 Cultural Chanzge 41 July): 817-31. (September): 722-40. 80 CHAPTER 16 MIGRATION 1988. "Migration, Remittances, and Inequality: A United Nations. 1970. Methtods ofMeasuring Internal Migration. New Sensitivity Analysis Using the Extended Giri Index."Journal of York. Development Economics 28 (May): 309-22. Vijverberg,Wim PM. t995."Dual Selection Criteria with Multiple Stiglitz, Joseph E. 1969. "Rural-Urban Migration, Surplus Labour, Alternatives: Migration, Work Status, and Wages." International and Relationships between Urban and Rural Wages." Eastern Economic Revieuw 36 (February): 159-85. Africa Economic Review 1 (December): 1-27. Vijverberg, Wim P.M., and Lester A. Zeager. 1994. "Comparing Todaro, Michael P. 1969. "A Model of Labor Migration and Urban Earnings Profiles in Urban Areas of an LDC: Rural-to-Urban Unemployment in Less Developed Countries?" American Migrants versus Native Workers." Journal of Development Economic Reviewv 59 (March): 138-48. Economics 45 (December): 177-99. 81 7 Should the Survey Measure Total Household | s Income? Andrew McKay Income is clearly a variable of critical importance in the household economy, as it provides the resources to finance current consumption and to undertake any savings. Households can derive their income from many different sources, which can be classified into factor income (payments received by households or their members in return for supplying factors of production that they own, such as labor or land) and non-factor income (net transfers received from sources outside the household that do not need to be repaid).Total household income is the sum at the house- hold level of these diverse sources, and represents the total purchasing power available to a house- hold in a given time period. This chapter reviews the importance of being able to measure total household income in LSMS and similar surveys in circumstances where consumption data are also being collected (including all circumstances described by this book).The relative importance of measuring total household income clearly has substantial implications for questionnaire design. When full LSMS surveys have been fielded in the past, sumption can be measured more accurately and com- they have collected sufficiently detailed information prehensively than household income. Second, much of on the dimensions of household living standards to the analysis of household surveys has focused on living enable analysts to estimate the level and composition standards and poverty, and there are widely accepted of both total income and total consumption expendi- theoretical reasons for using consumption-based ture. In principle, having estimates of both total con- measures rather than income-based measures to ana- sumption and total income is useful for measuring and lyze living standards.The validity of these arguments is analyzing households' living conditions, both in their considered in the first section of this chapter as well as own right (for example, for studying poverty) and as in Chapter 5 on consumption. explanatory variables for other important characteris- Given that consumption data are always collected tics of households and their members (for example, in LSMS surveys, is it worthwhile to also try to esti- nutritional outcomes). In practice, analysts have used mate total household income, bearing in mind the the consumption data gathered in LSMS surveys far inevitable extra costs involved in collecting the addi- more often than the income data. There seem to be tional data? The version of the questionnaire used in two reasons for this (Lipton and Ravallion 1995; the original LSMS surveys in Cote d'Ivoire and Peru, Deaton 1997). First, most analysts believe that in which collected enough information to enable ana- developing and transition economies, household con- lysts to estimate total household income, was consid- 83 ANDREW McKAY ered too expensive or not necessary in some places, information for immediate policy purposes (including where a "stripped down" version was used instead. In officials in the statistical office and elsewhere in the these cases (which have included the surveys in government) to those who require more detailed Bolivia, the Kyrgyz Republic, and Nicaragua), the information in order to conduct careful, in-depth resulting data set is not sufficiently detailed to enable research that will later have policy implications. analysts to construct reliable (or, in some cases, any) Because users in the latter category need different types estimates of total household income.1 Although all of information than users in the former category, it is previous LSMS surveys have collected data on the important for survey designers to find out in advance income of those in wage employment, several surveys which of these groups is most likely to use the data have not gathered data on household income from from the survey; this will influence the level of detail agricultural or nonfarm enterprises. In these circum- and precise content of the data that the survey collects stances analysts have inevitably had to measure living (which will vary from country to country). For the standards using consumption data. Thus the question purposes of this chapter it is assumed that the data will arises: what is lost by not being able to estimate total be used by the research community both within and household income, given that estimates of total house- outside the country and also that the data will meet the hold consumption are available? short-term needs of government policymakers. In considering the issue of whether or not to As stated above, total household income is of measure total household income, two general points interest both in its own right and as an explanatory should be borne in mind. First, if survey planners have variable for other household- or individual-level vari- already decided to include specific modules on house- ables. In other words, total household income can be hold agriculture and nonfarm enterprises, the margin- thought of either as an output measure for the house- al costs of collecting the additional information hold (for example, in studying poverty) or as an input required to measure total household income are likely measure (for example, as one of the determinants of a to be very small. Second, even if it is judged in any sit- household's willingness to send its children to school). uation that relatively little is lost by not being able to In either case, having a measure of aggregate house- estimate total household income, it can still be very hold income is useful primarily because of its close useful for analytical purposes to collect information relationship to the household's standard of living. For on some specific components of household income. some analytical purposes knowing the composition of The following five sections consider in detail the total household income can be as useful as knowing its issue of whether or not LSMS surveys should aim to level, but the implications for collecting data are the measure the level and composition of total household same in either case. income. In the first section, the benefits of estimating There are at least three main arguments for col- total income are discussed.The second section consid- lecting the data needed to estimate total household ers what is involved in measuring total income. The income: total household income can be used to meas- third section considers experience from recent LSMS ure households' standards of living; it can be used to surveys and other sources. The fourth section consid- understand determinants of poverty; and it can be used ers the costs involved in measuring total household to estimate household savings. These arguments are income. The fifth section makes an overall evaluation considered in the following three subsections, after of the evidence and indicates what conclusions can be which other uses for income data are considered. drawn. Total Household Income Can Be Used to Measure What Are the Benefits of Being Able to Standards of Living Measure Total Household Income? When analysts have a measure of a household's total income, this provides a means of measuring the house- In assessing the benefits of being able to measure hold's standard of living. This may seem to be the most household income, the first question to address is who obvious reason for collecting data on household is likely to use this information in their analysis. In the income because a household's standard of living is case of most surveys, there is a wide range of potential closely associated with the income it receives. users ranging from those who need rapid, descriptive However, as was noted above, total household con- 84 CHAPTER 17 SHOULD THE SURVEY MEASURE TOTAL HOUSEHOLD INCOME? sumption can also be used to measure living standards, If these points are valid, and they are quite com- and this can be calculated relatively straightforwardly pelling, they constitute a strong argument in favor of from the data gathered in an LSMS-type survey. Thus using consumption-based measures of standards of liv- two questions arise. Is there any reason to prefer ing instead of measures based on income, at least in income-based standard of living measures to con- developing and transition countries. However, even sumption-based measures? If not, do income-based after accepting this, are there any reasons for analysts to measures provide sufficiently useful complementary use income-based measures of standards of living as information to the consumption-based measures to well? One reason is that analysts can use the income- justify estimating them as well? based measure to check the accuracy and validity of Regarding the first question, it is widely held that the consumption-based measure, although this is only consumption-based standard of living measures are useful if the two measures are consistent within a rea- preferable to income-based measures (Deaton 1997; sonable order of magnitude.A second reason is that for Lipton and Ravallion 1995), at least for developing some households current consumption level may not and transition countries. Two justifications are com- be an accurate measure of long-term sustainable stan- monly given for this. The first (equally valid in devel- dard of living, even if current consumption level is an oping and industrial countries) is that a household's accurate measure of current standard of living. A consumption level is more directly and closely associ- household facing a major reduction in income may ated with its current standard of living than is its cur- respond by selling some of the assets it needs to gen- rent income. Income is certainly the means of financ- erate income-a move which may be costly to reverse ing consumption, but it is consumption that provides and may reduce its future income levels. If the house- utility, the economist's measure of a person's welfare. hold's income does not return to its previous level, this According to this view, income can be thought of as sort of response is not sustainable indefinitely. In these an input while consumption is more closely associat- circumstances, the household's current income might ed with the output that is being measured (although be as good a measure of its standard of living as its cur- Sen 1985 shows that consumption also has limits as a rent (unsustainable) level of consumption. Analysts standard of living measure). Moreover, current income often wish to study such households that may not cur- is often volatile from one year to another, being sub- rently be poor but may become poor in the future, but ject to significant shocks.This is especially the case for they cannot easily identify these households unless households that are engaged predominantly in self- comparable information is available on both income employment activities or that are very reliant on trans- and consumption. fers from either public or private sources. According to A third and more complex reason for using permanent income or life-cycle models, current con- income-based standard of living measures is to distin- sumption is usually significantly more stable than cur- guish between transitory and chronic poverty-an rent income, given that it can be smoothed to some important distinction for studying the dynamics of extent by saving and dissaving/borrowing.2 As a result, poverty and directing assistance to those who most current consumption bears a closer relationship than need it. Making this distinction requires that data exist current income does to a household's permanent for two different points in time. Panel data are not income or long-term standard of living, even when always available and can be problematic to use even current income is reliably estimated. This remains true when they are available (see Chapter 23 on collecting even when households cannot borrow to smooth their panel data and Ashenfelter, Deaton, and Solon 1986), consumption because of the lack of effective credit but, to some extent, this distinction can be considered markets. using data from repeated cross-sectional surveys. Using The second justification (see, for example, Deaton panel data from South India, Chaudhuri and Ravallion 1997) is the perception that, in developing countries, (1994) argued that consumption-based standard of liv- estimates of total household consumption tend to be ing measures do not necessarily identify the chronically more accurate than estimates of total household income, poor more accurately than income-based measures. a view based both on empirical evidence and a priori They argued that although current incomes of house- assumptions.3 The empirical evidence for this view will holds tend to be more volatile over time than current be discussed in the third section of this chapter. consumption levels, if current incomes display sufficient 85 ANDREW McKAY co-movement (positive correlation) across households of a household's income and on the household's pover- in the sample then observations of current income in a ty status, they can identify which income sources are single cross-section might more accurately give the characteristic of poor households and they may be able appropriate ranking of households according to their to discover some possible reasons for this relationship. long-term standards of living.This was indeed so in the For example, they might find that poor households South Indian case studied by Chaudhuri and Ravallion; have low economic returns to the activities in which in that case current income data were more effective in they are engaged (possibly reflecting the nature of the distinguishing the long-term or chronic poor from the household or the nature of the activity) or that they are transitory poor. This evidence is no more than sugges- not engaged in any economic activity (for example, tive; the validity of the argument will certainly depend due to unemployment or nonactivity) and thus rely on on the extent and nature of any measurement error in interhousehold transfers. Without data on total house- the income estimates. hold income, it is hard for analysts to find out which of In summary, a standard of living is a complex con- these situations applies and to identify the underlying cept that cannot satisfactorily or comprehensively be reasons. Having data on household consumption alone measured by a single indicator. For most purposes, would not provide this information. Also, having data consumption-based standard of living measures are on the time household members devote to different probably superior to income-based measures, at least economic activities (available from the employment in developing countries. However, if income can be module) is not enough to inform analysts about the measured with sufficient accuracy, income-based economic returns to those activities and hence their measures offer analysts information about household importance as sources of household income. While welfare that goes beyond what is offered by the con- having these time use data is desirable, it is not a sub- sumption measures. This can be particularly important stitute for knowing the income derived from these dif- for studying the dynamics of poverty. ferent activities. That some types of activities have lower economic returns than others is a central factor Total Household Income Con Be Used to Understand the in understanding poverty. Determinants of Poverty Of course, it is possible to model the determinants In looking at living standards, analysts are interested of living standards by looking at the relationship not only in who is poor and who is not, but also why between a household's standard of living, as measured some households are poor and others are not. While through consumption, and the characteristics of that consumption data are probably the best way to answer household. A number of studies have done this. For the first question, they can only partly address the sec- example, this was done in regression-based studies of ond question. Ultimately analysts also need to know the determinants of household living standards by about the source of a household's purchasing power- Glewwe (1991) using the results of the 1985 Cote in other words, its income-and the determinants of d'Ivoire Living Standards Survey, and by Coulombe this. Thus data on total household income and its and McKay (1996) using the results of a similar Living components can be very helpful in understanding the Standards Survey in Mauritania. This approach makes determinants of poverty. Data on household income it possible to identify which factors (demographic fac- also allow analysts to gauge the likely effects on house- tors, the education level of the economic head, and so holds of policy changes, many of which affect house- on) are associated with households with a high stan- hold income directly. dard of living. However, many of the factors that influ- An important starting point is to find out from ence a household's standard of living do so by affect- where households derive their income. Many house- ing its income level. Moreover, certain factors are holds in developing or transition countries receive likely to be more important for some types of income their income from more than one source (for example, than for others; for example, a person's level of educa- from a combination of agricultural income, wage tion may have a greater influence on his or her wage income, and interhousehold transfers).This means that income than on his or her self-employment income. analysts need to know not only a household's income Also, the point remains that the relationship between level but also the sources from which this income is consumption-based standard of living measures and derived. When analysts have data on the composition the types of variables that analysts might want to con- 86 CHAPTER 17 SHOULD THE SURVEY MEASURE TOTAL HOUSEHOLD INCOME? sider as determinants of standard of living is indirect, section of this chapter for further discussion of this). operating through income. Where possible, modeling Second, calculating household savings as the difference the determinants of different categories of income (for between two large figures (household income and example, wage income and agricultural income) household consumption), each of which may be sub- would enable analysts to have a clearer understanding ject to significant measurement error, means that the of the causes of poverty. estimate of savings is liable to be affected by the meas- To examine this relationship. analysts need data on urement errors contained in both estimates. This each of the major components of household income becomes even more serious if the income and con- because the factors that influence the levels of the dif- sumption estimates are subject not just to random ferent sources of income are typically different. For measurement error but also to bias. In this case, the example, wage income is likely to be influenced by differential in the bias between the income and con- human capital variables, while income from agricul- sumption estimates will affect the estimate of savings. ture is influenced by (among other things) the use of Worse still, the magnitude of the bias and of any ran- inputs to production (including land size and quality) dom measurement error might vary systematically and the price and composition of outputs.When data according to the type of household, which would be a are available on the sources of household income, ana- problem for analysis, econometric or otherwise. lysts can make a much closer mapping of the welfare Despite these problems, modeling the determi- level of a household and the individual determinants nants of savings at the household level is a highly relevant to each of the household's income sources. attractive goal and has been attempted, for example, by This presupposes that sufficiently accurate estimates of Deaton (1992) based on data from the Cote d'lvoire household income are available. Living Standards Survey. Nevertheless, significant It is crucially important that analysts and policy- questions need to be raised about whether the esti- makers understand the determinants of a household's mates of household savings obtained in the manner standard of living to guide them in choosing the most described above are sufficiently reliable to make effective policy measures to raise the living standards econometric or other analysis of them meaningful. of particular poor or vulnerable groups and to protect The answer is likely to vary from case to case. these groups from the adverse effects of a recession. It is true that estimating total household income is not strictly necessary for estimating the flow of Total Household Income Can Be Used to Estimate household savings, as these data can be collected in Household Savings other ways-for example, by asking respondents a A reliable estimate of total household income enables direct question about how much their savings the estimation of household savings as the difference changed in the previous year. (This was done in the between household income and household consump- third round of the Ghana Living Standards Survey.) tion over a given period of time.4 This is clearly desir- Another way (which may yield more accurate infor- able, as there is relatively little reliable information in mation than asking respondents directly) is to collect many developing and transition countries about the data on the changes in assets held by the household. levels of household savings, much less their determi- This option is discussed in some detail in Chapter 20 nants. Many existing estimates of household savings on savings in this volume. However, in order to use are derived from the national accounts at an aggregate this approach to estimate household savings, the ques- level and are often of highly questionable reliability. tionnaire must be designed to collect comprehensive The critical issue here is whether estimates of the data on the household's acquisition and sales of a full flow of household savings derived in this way from range of assets. As argued in Chapter 20, it is very LSMS and similar surveys will be sufficiently reliable. unlikely that this approach will yield more accurate For instance, it must be ensured that the estimates of estimates of household savings than if this measure is both total household income and total household computed by deducting total consumption from total consumption relate to the same period of time and income. Therefore, if analysts want to be able to use that inflation does not make them incomparable (as it the survey to measure flows of household savings, it is might if the recall period were different in each case necessary for the survey to measure total household and if the inflation rate were quite high; see the third income. 87 ANDREW McKAY Data on the Income of Household Members Can Be Used analysis-for example, to estimate the contributions of to Study Intrahousehold Issues different members to household income, the different Having data on total household income is also useful economic activities in which different members are for a number of other analytical purposes. Among involved, and the different returns to these activities. these, having data on the income earned by different However, there are some limitations to what kinds of household members can be useful for analyzing intra- analysis can be done with these data. Neither children household issues. The simple neoclassical model of the nor old people within the household can be included household assumes that the household's total income in such analysis because they are unlikely to earn is shared equally among all of its members and that the much, if any, income. Also, as was mentioned above, it household makes consumption choices according to a can be difficult to attribute to any one individual the single, well-defined set of preferences, but it is widely income from household activities that involve more recognized that the reality is likely to be different (see, than one member-although this might be attempted for example, Haddad, Hoddinott, and Alderman if the analyst knows which household members are 1997). There is a class of important policy questions engaged in a given self-employment activity and the that cannot be addressed within this aggregated number of hours they devote to it. Such information household framework and that require information on is generally collected in the employment module (pre- what is happening within the household (for example, sented inVolume 3). the distribution of income and consumption). To Some analysis of this type could probably be con- address such issues it can often be highly desirable to ducted without an estimate of total household collect consumption data at the individual level, but income. However, if analysts want to look at the dis- collecting such data tends to be difficult and also very tribution of purchasing power among household expensive. For these reasons (and as argued in Chapter members (and the extent to which income pooling 5 on consumption), LSMS surveys tend not to collect takes place), they clearly need data on all of the com- extensive individual-level consumption data. ponents of the household's income, with as many of Therefore, consumption data are not very useful for these data as possible at the individual level. analyzing intrahousehold issues in this instance. Anthropometric data are commonly used to Income Data Can Be Used to Measure Economic Activities examine intrahousehold dynamics in practice. As Not Adequately Covered by Existing Statistics argued in Chapter 10 on anthropometrics, it is highly Data from LSMS surveys can be used to address statis- desirable that anthropometric data be collected as part tical issues beyond those for which they were explic- of LSMS-type surveys. These data are useful for ana- itly designed; in particular they can help improve and lyzing intrahousehold issues, even though some ana- develop existing aggregate and sectoral statistics. In lysts feel that they are only trustworthy for young chil- many low-income developing countries, a serious dren (see discussion of this issue in Chapter 10). dearth of statistical information on important facets of Irrespective of this, it can be useful to collect informa- their economy (such as the informal sector and subsis- tion on income at the individual level (as far as is pos- tence agriculture) raises questions about the quality of sible given that some household production is joint national accounts and sectoral statistics, with obvious and is not necessarily easily attributable to individual implications for analysis. In such circumstances the household members) to supplement the anthropomet- income data available in LSMS and similar surveys ric data in studying some intrahousehold issues. may be better than those currently used to measure Hoddinott and Haddad (1995) have used individual- such activities. The LSMS data may be used to level income data from the Cote d'Ivoire Living improve existing aggregate and sectoral data, as well as Standards Survey to investigate whether the share of to construct or update social accounting matrices. household income earned by female household mem- This is not by itself a sufficient argument for seek- bers affects the consumption pattern of the household. ing to measure total household income in a household Other than this, the individual-level income data survey. However, it may be a valuable side-product gathered in previous LSMS-type surveys have not where such data have been collected, one that has been widely exploited, but there is a lot of potential been underexploited in the past. One example of for using them in both descriptive and more in-depth LSMS data being used to improve government statis- 88 CHAPTER 17 SHOULD THE SURVEY MEASURE TOTAL HOUSEHOLD INCOME? tics is in measuring and studying the informal sector Summary in Ghana using data from the Ghana Living Standards A number of potentially important uses for income Survey (Coulombe, McKay, and Round 1996).While data have been set out in this section. Some of them household survey data may not be the ideal source, require reasonably reliable estimates only of the level and while it is necessary to consider the possibility of of total household income, while others require accu- income underestimation in such surveys, the resulting rate information on the composition of household estimates in this instance still appeared to be better income. Some uses are more important than others. than those that had been used previously. Comparable Ultimately, though, the most compelling argument for instances are likely to arise elsewhere. having data on the level and composition of total household income, assuming it can be estimated with Data on the Composition of Household Income Can Be sufficient reliability, is the information these data can Used to Define Socioeconomic Groups yield for understanding poverty and how poverty is In analyzing standards of living and related questions, it affected by policy changes. is invariably desirable to disaggregate households into groups according to various criteria in order to com- What Is Involved in MeasuringTotal Income? pare the different groups with one another and explore the reasons for any variations within and between each This section considers the question of what precisely group.The obvious and economically relevant criterion measuring total household income is likely to involve. by which households can be disaggregated is the main First it addresses the conceptual question of what ana- economic activity of the household, which, for conven- lysts are trying to measure. Then it discusses what ience, is referred to here as the socioeconomic group of measuring household income involves in practice and the household. There are various ways to define the what problems can arise. main economic activity of the household. One com- mon procedure is to classify households according to Conceptual Issues Involved in Measuring Household the activity of the economic head of the household Income (raising the issue of how the economic head should be A useful framework for thinking about measuring total defined). Another more direct and probably more household income (as well as household consumption) meaningful criterion is the household's main source of is offered by a system of household accounts that sum- income.While this is often the same as the activity of marizes the production, consumption, and accumula- the economic head, it is not always, and, when it is not, tion activities of households in three separate but relat- it is probably the more appropriate criterion to use. The ed accounts. (These accounts are production, current, most obvious way to find out the household's main and capital accounts, respectively; see Johnson, McKay, source of income is to collect data on a household's and Round 1990, as well as UNSD 1993 and Ruggles income by its main components (appropriately defined) and Ruggles 1986 for analogous concepts at the macro and identify which source is most important. For this and aggregate levels.)6 purpose, what matters is that the data on the composi- tion (more than the level) of the household's income is DEFINING HOUSEHOLDS. The issue of how to define a reasonably accurate. A third, less attractive alternative is household is discussed in detail in Chapter 6 on the to identify the type of activity to which household household roster. However, in the context of measur- members devote the most time, using information nor- ing household income, it is appropriate to recognize mally collected in the employment module of the ques- that in developing and transition countries, many tionnaire (see Chapter 9 on employment).5 households, such as agricultural households and Again, the fact that data on total household households that run household enterprises, may be income and its composition can be used to define production units as well as consumption units; fur- meaningful socioeconomic groups is not by itself suf- thermore, the best way to define a household as a pro- ficiently compelling to warrant collecting data on all duction unit may not be the best way to define it as a household income. Clearly, though, where these data consumption unit.This can be an important difficulty are available, it would be sensible to use them to define in practice, as Devereux 1992 shows based on field- socioeconomic groups. work in northern Ghana. It is clearly important that a 89 ANDREW McKAY consistent definition of the household is applied tant in estimating total household income, which may throughout the survey. If this definition is based on the change the analyst's perceptions of poverty and consumption unit, the income from the household's inequality among households.9 production activities needs to be related to the same However, in practice, household production is unit, which is not a trivial exercise. generally defined less broadly than Hill's definition because too many measurement problems can arise AN OPERATIONAL DEFINITION OF INCOME.7 As dis- with this ideal concept.Thus, while own-account pro- cussed above, in broad terms households and house- duction of goods within the household is usually hold members earn "income" by supplying factors of included in a practical definition of production, own- production that they own to productive activities and account production of services within the household by receiving current transfers. With regard to factor (such as childminding) is usually excluded. This is the income the fundamental issue that arises is the defini- case in the latest System of National Accounts and is tion of productive activities, which can be particularly also consistent with previous LSMS surveys. problematic in developing countries given that markets Therefore, this chapter, in accordance with the con- are often underdeveloped and so much economic sumption chapter (Chapter 5), assumes that it is activity takes place outside the market. Thus, while it is impractical to try to cover service activities conducted possible to start from Hicks' (1971) definition of pro- within the household in the estimation of total house- duction as "any activity directed to the satisfaction of hold income (or consumption) because of the practi- other people's wants through exchange:' this must be cal problems involved in valuing these activities.iO interpreted sufficiently broadly to include various Finding ways to overcome such problems is neverthe- forms of nonmonetized exchange such as barter and less an important priority for future research. wage payments in kind. Even this expanded interpreta- Factor income can be defined as the payment tion of Hicks' definition of production fails to take into received by a household for supplying factors that it account household own-account activities in which owns (such as labor, land, and capital) to a productive the household produces a good or service that is a close activity. This definition holds whether this productive substitute for one available on the market but con- activity is carried out by the household or by an insti- sumed by the household itself. The most obvious tution or individual outside the household and example is subsistence agricultural production, whether the payment is made in cash or in kind.There although some nonagricultural self-employment activ- are three main types of factor income: wage income, ities also include an element of production for the rental income, and self-employment income. (These household's own consumption-for example, the con- categories can obviously be disaggregated further in struction of furniture or sewing garments for the specific cases.) Wage income is received in return for household's own use. Natural habitat utilization- the supply of labor services; rental income is received collecting and gathering items from natural sources- in return for the supply of land, capital, and other should also be allowed for in a measure of total house- assets; and self-employment income is typically a hold income where this is important.8 Note that where return both to labor supplied by household members each of these imputations is included in a measure of and to other factors these members own, such as land total household income, they must also be included in or capital. the measure of total household consumption. Transfers can be received from various sources, An extended definition of production used by including firms, government agencies, other house- Hill (1979) considers production to be any activity holds, and nongovernmental organizations of various that can be carried out with comparable results by an types (any of which may be domestic or foreign). Only economic unit other than the one that actually carries current transfers (such as interhousehold transfers of it out. Under this definition, not only subsistence pro- cash or food) and not capital transfers (such as inheri- duction but also a whole range of additional service tance of land or receipt of a loan) should be regarded activities performed within the household can now be as income.11 In practice it can be difficult to distin- regarded as productive, including childminding, food guish between current transfers-a source of income preparation, and fetching firewood or water. for the household-and capital transfers. For example, Moreover, such activities can be quantitatively impor- when a household receives a transfer from another 90 CHAPTER 17 SHOULD THE SURVEY MEASURE TOTAL HOUSEHOLD INCOME? household, this may or may not involve an obligation production themselves. Given that this will be difficult (implicit or explicit) to repay. Also, a dowry may fall for respondents to do, they are not told which valua- into either category depending on what form it takes. tion principle to use. However, since they are also asked to quantify their own consumption, the result- VALUATION. For market transactions, goods are usually ing data set will contain the information necessary to valued in accordance with prevailing market prices- compare the implicit unit value of the household's in other words, those prices actually paid or received consumption of its own production with data from by the household.12Valuation is much more problem- the agriculture and consumption modules on the unit atic in the case of imputed transactions. When the value of sales and purchases of the corresponding household's productive output is exchanged by barter, commodity in the market. This is important because if the most appropriate way to value the goods received different respondents used different principles to value in return is presumably to use local market prices. A their consumption of their own production, estimates similar principle would hold for wage income of total household income or consumption (which received in kind. would include these data on the household's con- However, in both cases, the question arises sumption of its own production) would not be com- whether the consumer (purchasing) or producer (sell- parable without being adjusted to account for the use ing) price should be used. (The same question also of these different valuation principles. Since unit value arises in the valuation of nonmarketed household pro- information would be available in the data set, this duction such as goods produced by the household for adjustment could be made if necessary. its own consumption; see the discussion of this issue in Chapter 5 on consumption). Using the consumer SUMMARY. A theoretically valid concept of household price will typically yield a different valuation than income should include an appropriate and consistent using the producer price. Using the consumer price valuation of factor income from all productive activi- may be more appropriate for measuring welfare (given ties, whether they are carried out entirely within a that an analogous good purchased on the market household or for another institution outside the would be valued at its market price), but inconsisten- household. Such a concept should also include a valu- cies can arise. If some of the output of the household's ation of all current transfers received by the house- production were sold in the market, it would be val- hold, whether in cash or in kind. Table 17.1 reflects the ued at the producer price. Yet a higher valuation is above discussion in setting out the types of income placed on exactly the same output when it is con- components on which information should be collect- sumed by the household. Moreover, there is the prob- ed in the survey questionnaire. Some of these compo- lem that the household's own produced food is unlike- nents involve imputation; where they do, exactly anal- ly to be a perfect substitute for that purchased on the ogous imputations are required to estimate total market. household consumption. (Table 17.1 provides details Similarly, if both food that a household produces of this.) If analysts are interested in intrahousehold for its own use and food that it produces to sell on the behavior, it may be desirable to collect information on market were valued at the producer price (which which individuals are involved in each activity and to would be sensible from a production point of view), what extent, on who receives the income, and on who the household's consumption of its own production controls its use. would be valued differently from the same goods pur- chased by the household on the market, which would Estimating Total Household Income in Practice be valued at the consumer price. This appears incon- Many household surveys have aimed to collect the sistent from the point of view of measuring welfare. information necessary to estimate total household The consumer and producer prices can be thought of income, including the LSMS surveys conducted in as representing upper and lower bounds, respectively. Cote d'Ivoire, Ghana, Nepal, Pakistan, Peru, and Neither valuation is clearly superior to the other. For Vietnam. However, the difficulties involved in measur- reasons discussed in more depth in Chapter 5 on con- ing household income should not be underestimated. sumption, the proposed consumption module asks Even if respondents are willing to answer all of the respondents to value their consumption of their own questions asked and do so as honestly as possible, the 91 ANDREW McKAY Table 17.1 MeasuringTotal Household Income Income component Data that must be collected income from wage employment Wage income in cash Wage income in kind * Bonuses Household agricultural income Revenue from the sale of crops Revenue from the sale of processed crop products Revenue from the sale of animal products Consumption of self-produced food * Minus Expenditure on inputs for crop cultivation Expenditure on inputs for producing processed crop products5 Expenditure on livestock inputs Depreciation of agricultural capital equipment Nonfarm self-employment income Revenue in cash from sale of output Revenue in kind from sale of output Consumption of own produced output (where appropriate) * Minus Expenditure on inputs Depreciation of capital equipment .............................................................................................................. ....................................................................................................... .............. Imputation for commodities obtained Food commodities I from natural sources* Nonfood commodities (where not otherwise included) * ........................................................................................................................................................................ .........................................*............ .. Actual and imputed rental income Income from renting out household assets Imputed rent of owner-occupied dwellings * ............................................................................-................................................................................................................................................. ...... Income from private interhousehold transfers Income from private interhousehold transfers in cash and kind (where no repayment is expected) Other income Various miscellaneous income (income from pensions, unemployment benefits) * Elements that shou d a so be nc uded in an estimate of total household consumption a. Excepting products supplied by the househo d tself Source: Data compi ed by author myriad of income sources households can have makes (The sources excluded may not be very important it difficult to be sure that all income sources for a given overall but may be very important for a small number household have been identified. For example, some of households.) Another important issue is that where sources of income may be very casual or infrequent, respondents are asked to make imputations, such as and, therefore, the respondent might not think to placing a valuation on wage income received in kind, mention them in response to questions about wage it may be very difficult for them to do so. employment or nonfarm enterprises, either of which Estimating total household income in accordance may be taken by the respondent to refer to more for- with Table 17.1 involves collecting information on at mal or more sustained activities. least the following four elements: income from wage Respondents may genuinely not know their employment; agricultural income; nonfarm self- income from certain activities, especially self-employ- employment income; and nonlabor income. Given the ment activities for which they often do not keep very different nature of each of these income sources, accounts. Indeed, the concept of income in an the data necessary to estimate them will almost cer- accounting sense or in the sense used by economists tainly be collected in different modules within the may be quite foreign to respondents.This need not be questionnaire. The natural place to collect the data a problem, but it means that it will generally be nec- necessary for estimating income from wage employ- essary to estimate some components, such as income ment is the employment module. Data on income from self-employment activities, indirectly. For income from household agriculture and nonfarm enterprises from self-employment activities it will probably be can naturally be collected in the modules devoted to necessary to collect information on revenues and these topics, while data on nonlabor income can be input expenditure separately (as reflected in Table gathered in one or more short modules designed with 17.1). The wide variety of forms in which households the principal aim of identifying such income and esti- can receive transfer income may mean that some mating its magnitude.This is essentially the model that sources are simply not included in the questionnaire. was followed in the early LSMS questionnaires as well 92 CHAPTER 17 SHOULD THE SURVEY MEASURE TOTAL HOUSEHOLD INCOME? as in many other household surveys that have collect- hold members is very small compared with the cost of ed the information needed to estimate household adding whole modules on household agriculture and income. nonfarm enterprises, which makes this seem an attrac- When survey designers are deciding how to col- tive option in some cases. lect these income data, they need to recognize the However, how accurate can respondents be in potential complexity of the household economy, reporting their self-employment income? The infor- which is relevant to all four of the major components mal nature of most self-employment activities means of household income outlined above. Household that the vast majority of respondents probably do not members may be engaged in more than one wage keep accounts for these activities. Moreover, since employment activity, simultaneously or sequentially, most self-employment activities involve both expendi- during the reference period or periods. The house- ture on inputs and revenue from the sale of outputs, hold's agricultural activities may involve cultivating both of which may include imputed as well as cash several different crops as well as keeping livestock- elements, it is very difficult to see how a respondent and may thus involve various inputs. Households may can answer a single question about their self-employ- also run more than one nonfarm enterprise. Thus a ment earnings from a given activity in a particular ref- large and complex questionnaire is needed to ensure erence period (for example, the previous seven days). that all of this information is collected, even though The situation is even more complex when the self- large parts of the questionnaire may be irrelevant for employment activity involves more than one house- many households (for example, because they do not hold member working together and members are farm), which will make the interviewers' burden less asked individually about their earnings from the self- daunting than it appears at first sight. employment activity; in such cases it will be problem- This means that a questionnaire collecting total atic to attribute the total profit of the activity between income data must contain both an agriculture module them. However much time is saved by estimating self- and a nonfarm enterprise module.When survey plan- employment income this way, the reliability of the ners already intend to include both of these modules responses obtained must be open to serious doubt, because the information they contain is of interest in more so than most other questions in the question- its own right, the data collection implications of aim- naire. (This view is consistent with the opinions of the ing to estimate total household income are modest. authors of the employment, agriculture, and nonfarm The proposed standard versions of the employment, enterprise chapters.) The author of this chapter nonfarm enterprise, and agriculture modules (intro- strongly recommends against estimating income from duced in Chapters 9, 18, and 19) collect information self-employment this way, whether or not the ques- on income from wages, nonfarm enterprises, and agri- tionnaire includes the agriculture and nonfarm enter- culture. Therefore, measuring total household income prise modules. simply requires that a module on transfers and other Another important practical issue is the recall nonlabor income is included in the questionnaire (see period used to measure the different components. Chapter 11).13 However, if survey planners do not Because household income is complex, data should be include the standard agriculture and nonfarm enter- gathered component by component in such a way that prise modules, analysts will not be able to measure analysts will subsequently be able to add the different total household income. Another possibility is to try to income components together to compute a house- collect information on self-employment income hold's total income, to analyze the implications of the directly in the employment module. The employment composition of a household's income, or both. module in many previous LSMS surveys asked indi- However, the most appropriate reference period to use viduals about their cash earnings from the self- for each component of income is not necessarily the employment activities they reported. Combining this same. (See the specific chapters for discussion of information with data on the household's consump- appropriate recall periods for estimating each compo- tion of its own production could yield an alternate nent of income.) It is appropriate to collect wage estimate of income from self-employment activities. income data on a weekly recall basis, as the short recall The cost of adding a couple of extra questions on period will hopefully make the responses more accu- earnings from the self-employment activities of house- rate. However, a weekly recall period is clearly inap- 93 ANDREW McKAY propriate for collecting data on transfers and other purposes (see the first section of this chapter). The nonlabor income income, which may be received by problem of a lack of comparability is most likely to only a small number of households, and may also be arise in countries that are experiencing high or mod- received infrequently. For these components a longer erately high inflation or that are affected by large sea- recall period, perhaps 12 months, may be more appro- sonal variations in prices throughout the year. In these priate. A more complex issue is the appropriate recall countries, respondents would most likely value trans- period for the self-employment income components, actions that took place within a 12-month recall peri- especially for agriculture. In agriculture, revenues and od at the prices prevailing at the time of the transac- input expenditure are likely to be made at different tions, whereas they would probably value transactions points in time throughout the agricultural season. that took place within a recall period of the previous Moreover, the agricultural season is the natural refer- week at current prices.While the values of both kinds ence period during which respondents can be expect- of transactions can be expressed on a comparable basis ed to supply information about their revenues and (say, monthly or annually), there is a potential source input expenditure. This can cause a number of diffi- of error in adding them up because of the different culties in practice. The agricultural season does not valuations used due to the difference in recall periods correspond neatly with the calendar year.The agricul- from component to component. tural season may vary from one part of the country to An additional problem in comparing total income another or from one crop to another. And while it may and total consumption in a high-inflation economy is be desirable to interview all agricultural households at that the "average recall period" used for the income a similar point in the agricultural season (for example, modules is probably longer than the one used for the after the harvest), this is likely to cause problems in the consumption modules. Correcting for this problem is interview schedule. Chapter 19 on agriculture discuss- not straightforward, as it requires detailed information, es these questions in more detail. ideally by locality, on the variation of prices during the The key point is that data on different compo- period when the survey is conducted and the preced- nents of income are usually collected using different ing year (or whatever is the longest recall period used reference periods, which analysts need to take into in the survey). It may be especially difficult to make account when adding the data together to calculate this correction for self-employment income, given total household income. Of course, a similar problem that sales revenues have probably been received and arises in estimating total household consumption, input expenditures incurred by the household at given that it is desirable to use different recall periods different-and usually unknown to the analyst-inter- for collecting data on consumption of different items vals throughout the reference period. Any measure- to reflect different frequencies of consumption. ment of this kind is difficult in economies with high However, the problem is more serious in measuring inflation rates or intrayear variability in prices, and this total income, in particular because of the desirability of applies to all monetary variables, including consump- using the agricultural season as the reference period tion as well as income. for collecting data on income from this source. The agricultural season cannot easily be converted into the How Successfully Has Income Been Measured calendar month or year basis on which the other in Developing and Transition Countries? components of income can be computed, and the appropriate conversion factor may vary from situation How successfully have household surveys in develop- to situation. ing and transition countries measured total household The need to use different reference periods for income? There is a wealth of experience on this ques- gathering data on different components of both tion; this section will focus on recent experience in income and consumption can make it difficult to developing and transition countries, particularly in compare estimates of total income and total consump- instances where data from LSMS surveys have been tion. Making this comparison is absolutely essential for used to calculate total household income. measuring household savings (by subtracting total As noted above, there is a widespread perception household consumption from total household that estimates of total household income derived from income) and is necessary for many other analytical household surveys conducted in developing and tran- 94 CHAPTER 17 SHOULD THE SURVEY MEASURE TOTAL HOUSEHOLD INCOME? sition countries are often unreliable-and certainly A third point is that it may be more difficult for a less reliable than estimates of total household con- survey to identify and cover all prospective sources of sumption derived from the same source. This could be a household's income comprehensively than to cover because the questions relating to income are less accu- consumption comprehensively. This may be due to a rately answered than those relating to consumption; it reluctance on the part of respondents to report certain could also be because information on consumption is types of income or to the fact that the questionnaire more fully and more comprehensively collected than does not prompt respondents to provide information data on household income. If income information on certain sources of income, so the respondents do were not collected as comprehensively as consump- not remember or report them. Respondents may not tion information, income is likely to be underestimat- feel the need to report their most casual or infrequent ed relative to consumption. If the problem were inac- income-earning activities and will obviously be very curate answering, this might or might not lead to an reluctant to report any of their activities that may be understatement of income. of dubious legality (or downright illegal). Respondents may also be reluctant to reveal their Why Income Appears More Difficult to Calculate Than receipt of transfers and other nonlabor income. To Consumption some extent this syndrome is inevitable, but survey There are a number of reasons why this perception is planners should ensure that it does not reflect prob- so widespread. First, income is a much more sensitive lems in the design of the questionnaire or in its imple- topic to ask people about than expenditure. mentation. A poorly designed questionnaire may fail Respondents may have an incentive to understate to ask about (or fail to prompt the respondent suffi- their income in a survey interview, especially if they ciently about) a wide enough range of informal activ- fear that the information may be used for tax purpos- ities, or the interviewers may ask some of the ques- es (notwithstanding assurances to the contrary).This is tions in such a way that the respondent feels obviously a problem in developed countries as well as uncomfortable answering them. in developing and transition countries, but it may be The questionnaire cannot include questions on more acute in the developing and transition countries every conceivable source of household income, just as given the significantly greater importance in these it cannot include questions on every type of con- countries of self-employment income (which is easier sumption expenditure. However, when the sources of to understate than wage income). household income are more diverse than the cate- Second, as has previously been observed, respon- gories of household consumption, as is the case in dents may genuinely not know how much income they most developing or transition countries, it is invariably make, especially in their self-employment activities. easier to collect comprehensive consumption data Measuring self-employment income is difficult even in than comprehensive income data. Having said that, developed countries, and higher proportions of house- many key difficulties, such as the identification and holds in developing and transition countries are gener- valuation of nonmarket transactions, arise in both ally engaged in work for themselves. However (aside cases. from the above incentive to underreport income), the Consequently, the difficulties involved in calculat- situation is further complicated in developing and some ing total household income, especially self-employ- transition countries by the general absence of written ment income and transfers, should not be underesti- accounts for household production activities. As noted mated. So how much confidence can analysts have in above, it may be necessary to take an indirect approach the estimates of income derived from household sur- by asking about as many financial details as possible and veys? Are there any good practices that would increase computing an income figure from these bits of infor- the reliability of these estimates? At the outset it mation. However, this is likely to require collecting should be recognized that there are few objective tests quite a lot of information. Moreover, the accuracy of of the reliability of income (or consumption) data estimates computed as the difference between total rev- derived from household surveys. Even comparing enues and total input expenditure must be open to them with apparently similar data from national some doubt as both of these estimates may contain sig- accounts can be fraught with difficulty, as discussed in nificant measurement errors. detail in Chapter 5 on consumption. Conceptually, 95 ANDREW McKAr household income and consumption rarely have an can generally be expected to be of similar magnitudes. exact equivalent in national accounts. Even when esti- While the household sector may save (or dissave) in mates from national accounts and estimates from any given time period, the magnitude of such savings household surveys are of similar orders of magnitude, or dissavings relative to income or expenditure can be this in itself does not prove the accuracy of the house- expected to be relatively small. hold survey estimates. Clearly, when they are highly Table 17.2 provides summary information on the dissimilar, this suggests that the estimate from at least magnitude of estimates of total household income and one source is seriously inaccurate, but this does not consumption derived from a number of recent house- help analysts to identify which is the more accurate. It hold surveys in several countries. Table 17.3 provides is certainly not appropriate to assume that the nation- similar information (in a different format) for a 1995 al accounts estimates are necessarily more accurate. income and expenditure survey in Belarus (not an LSMS survey). Evidence on Accuracy of Income Estimates The information presented in these tables has Some criteria based on household survey data alone been summarized greatly, so it should be interpreted can give clues as to the possible accuracy of estimates with great care. There is a different reason for the of household income. One straightforward issue is the apparent success or failure of each survey to yield the relative consistency of estimates of household income data necessary to estimate total household income, and and consumption.While individual households save or it is impossible to do proper justice to each of these dissave in any given time period, estimates of total stories here. The surveys from which the estimates of household consumption and total household income household income and consumption have been com- Table 17.2 Comparing Estimates of Income and Consumption, Selected Household Surveys Correlation Mean total Ratio of Mean per capita between per Mean total annual per capita income annual savings capita annual annual income consumption to per capita (income minus income Country Survey per capita per capita consumption consumption) and consumption Bulgaria Integrated Household Survey 1995 * 46.02 50.32 0.915 -4.30 0.178 Cote d'lvoire Enquete Permanente aupres des menages (EPAM) 1985 * 186.5 294.8 0.633 -108.3 0.688 EPAM 1986 * 242.8 276.8 0.877 -34.0 0.849 EPAM 1987 * 236.6 286.7 0.825 -50.2 0.800 EPAM 1988 * 220.9 246.2 0.897 -25.2 0.600 Gnana Ghana Living Standards Survey .............. Round I (GLSS 1), 1987-88 * 61.0 87.0 0.701 -26.1 0.406 GLSS 2, 1988-89 69.2 107.9 0.641 -38.7 0.598 GLSS 3, 1991-92 118.8 208.9 0.569 -90. 0.540 Jamaica Jamaica Survey of Living Conditons 1993 * 26326 28308 0.930 -1982 0.412 Pakistan Pakistan Integrated Household Survey 1991 * 7682 7871 0.976 -188.8 0.151 Peru Encuesta nacional de hogares sobre medicion de niveles de vida, 1994 * 2423.4 2176.2 1.114 247.2 0.675 South A rica Sout Africa Integrated Household Survey, 1993 * 9903.8 6961.4 .423 2042.4 0.229 Venezuela, R. B. de Encuesta do Presupuestos Familiares, 1988-89 19.63 32.49 0.604 Encuesta Social, 1991-92* 96.33 69.68 1.382 - * LSMS surveys. -Not available. Note: Bulgaria: thousands of Lavy; C6te d'lvoire: thousands of CFA; Ghana: thousands of Cedis; Jamaica: Jamaican dollars; Pakistan: Rupees; Peru: Soles; South Africa: Rand. Source: Bulgaria, South Afhca: computed from estimates of household income and expenditure on LSMS Web site; C6te d'lvoire, Ghana, Peru: com- puted by author from raw data; Jamaica: based on Handa 1995; Pakistan: based on estimates of household income and expenditure constructed by the World Bank; Republica Bolivariana de Venezuela: based on Scott 1994. 96 CHAPTER 17 SHOULD THE SURVEY MEASURE TOTAL HOUSEHOLD INCOME? Table 17.3 Average Monthly Cash Income and Cash Consumption Expenditure of Households in Belarus, Ranked by Quintile of Cash Income Quintile of Average cash income Average cash consumption expenditure Ratio of cash income cash income (thousands of Belarussian rubles) (thousands of Belarussian rubles) to cash expenditure (percemt) Lowest 427 700 61 Second 780 1,064 73 ... .....................................................................................................................,....................................................................... 8...2....................... . ..... Third 1,100 1,349 82 ...... ............................................ ......................................................................5,..........................................................................9,....................... . ..... Fourth 1,527 1,725 89 ................................................................................................................................................................................................................................. Highest 2,625 2,795 94 Source: Martini, Ivanova, and Novosyolova 1996, table 3. puted are all different and not all are LSMS surveys. ding estimates of household consumption. In such The various estimates of household income and con- instances it is very unlikely that the shortfall can be sumption have been computed by different authors, explained by dissaving by the household sector because and there may be some differences in the concepts and the magnitude of the difference between income and methods used to derive them (as well as in the survey consumption is much too great. Consequently, in these information on which they are based).'4 When the cases it does appear that there has been a significant data are computed by the author from a primary underestimation of income, a significant overestimation source, the figures reported in the table are all com- of consumption, or both. As suggested above, compar- puted over the same households (those for which esti- ing these estimates with estimates of private consump- mates of both total income and total consumption can tion expenditure from the national accounts does not be computed). It is assumed that this is also the case always help analysts to tell whether income or con- where the information is derived from secondary sumption is more accurately estimated."5 The general sources. In the case of the Republica Bolivariana de arguments set out above suggest that the differences Venezuela, information was not available to compute stem more from underestimating income than from the ratio of average income to average consumption, overestimating consumption, although even if this is the average magnitude of household savings, or the true (an issue that will be considered further below) the correlation between income and consumption. explanations may differ from case to case. However, the average magnitudes of income and con- In several other surveys on which information is sumption are broadly comparable in all instances. presented in Table 17.2-Bulgaria, Jamaica, and the The extent to which the estimates of household 1987 and 1988 C6te d'Ivoire surveys-it was found income and consumption are consistent varies signifi- that, on average, households spent in excess of their cantly from country to country. It is clear that in some income for the year in question but that the implied surveys-Pakistan, Peru, South Africa-the estimates magnitudes of dissavings were within the range of of household income and consumption are broadly plausibility in general. Of course, in these kinds of consistent, which implies credible household savings cases, analysts must always test the credibility of the or dissavings rates. Of course, as noted above, the pos- implied dissavings rate by comparing the survey esti- sibility remains that both household income and mates with other evidence pertinent to the country household consumption expenditure are overestimat- and time period in question. Underestimation of ed or underestimated to similar extents, though this is income and overestimation of consumption expendi- unlikely to be substantial unless there is an overestima- ture may still have occurred in these surveys but if so tion or underestimation of a component common to they occurred to much less of an extent than in the both household income and consumption (for exam- cases of Belarus, Ghana, the 1985 C6te d'lvoire survey, ple, consumption of self-produced food). and theVenezuelan Encuesta de Presupuestos Familiares. By contrast, some of the other surveys-Belarus, In the case of the Republica Bolivariana de Ghana, the 1985 C6te d'Ivoire survey, and the 1988-89 Venezuela, the estimates of household income obtained Encuesta de Presupuestos Familiares or Income and from the Encuesta Social (Social Survey) are significantly Expenditure Survey in the Republica Bolivariana de greater than the estimates of expenditure derived from Venezuela-yielded estimates of total household the same source, even after removing outliers (Scott income that were substantially below the correspon- 1994).This is in sharp contrast to the estimates derived 97 ANDREW McKAY from the 1988-89 Encuesta de Presupuestos Familiares. explore any obvious explanations for these and make However, the questionnaires for the two surveys were consequent recommendations for future LSMS sur- very different, and in the Encuesta Social consumption, veys, it is possible to draw on the work of various ana- the data for which were collected at a relatively aggre- lysts who examined some of these surveys to see if any gated level, appears to have been significant underesti- evidence could be found to suggest that income wvas mated. Therefore, it is difficult to say how accurately less accurately estimated than consumption. Judging household income was estimated in this survey. the relative reliability of estimates of total household The discussion so far has focused entirely on the income and consumption based on information main- mean values of estimates of total household income ly from within the survey is clearly a partly subjective and consumption (or savings). However, mean values exercise. There is no clear objective basis for assessing are significantly affected by outliers and other extreme whether the underestimation of income or the over- values,16 and looking only at mean values implies los- estimation of consumption is predominantly responsi- ing a lot of information. The extent of correlation ble for the apparent underestimation of household between total household income and household con- savings in these instances. However, survey results, sumption also provides useful information, even combined with background information about the though it does not identify cases of underestimation. countries in question, can offer some clues. Household income and consumption are never per- In the case of Belarus, Martini, Ivanova, and fectly correlated across households; most theories of Novosyolova (1996) found that 67.7 percent of house- consumption (for example, the permanent income holds reported income levels below their consumption hypothesis) explicitly suggest this will not be so. levels and that 9.7 percent reported income levels that However, the two criteria should produce a broadly were less than half their reported consumption levels. similar ranking of households, implying that they are Moreover, they found that the relationship between significantly positively correlated.17 For the instances average rates of dissavings and the quintile group to in Table 17.2 where a correlation coefficient can be which a household belonged differed radically computed between total household income and con- depending on whether quintiles were defined by sumption, this indeed appears to be the case, even in income or by consumption.When income was used to cases where household savings appear to have been define quintile groups, households in the lowest quin- significantly underestimated (for example, the 1985 tile dissaved the most, and the average rate of dissaving C6te d'Lvoire survey and the Ghana surveys). fell in each higher quintile group (see Table 17.3).Yet Thus recent experience measuring total house- when consumption was used to define the quintile hold income in household surveys in developing and groups (not reported here), the average proportion of transition countries does not suggest that the objective income dissaved increased significantly with the quin- of measuring total household income using data from tile; those in the lowest quintile actually had a positive multipurpose household surveys should be aban- savings rate. Although total household income and doned. However, the experience is certainly mixed, consumption should both be legitimate measures of and in some of the surveys there is strong evidence of the standard of living, which one of them is chosen underestimation of income, overestimation of con- clearly affects the appearance of the relationship sumption, or both. As discussed above, a priori consid- between savings and the standard of living. (Of course, erations suggest that underestimation of income is this is partly because one of the two variables used in probably a much larger factor than overestimation of measuring savings is also used to define the quintile consumption, but is there any empirical evidence to groups.) back this up? If so, what might be causing this under- However, this does not indicate whether income estimation of household income? or consumption is more reliably estimated. In the case The point has already been made that the expla- of Belarus there are good reasons to assume that nations for problems of this nature may differ from income was significantly underestimated. Martini, case to case as each survey experience has its own Ivanova, and Novosyolova argued that anecdotal evi- storv So what are the possible explanations for the dence and casual empiricism suggest widespread unbelievably big gaps between household income and informal economic activity in the Belarus economy, consumption in the three cases referred to above? To yet in the survey very few people reported having sec- 98 CHAPTER 17 SHOULD THE SURVEY MEASURE TOTAL HOUSEHOLD INCOME? ond jobs, doing occasional work, or running a house- It seems clear that the underestimation of house- hold business.These informal activities may have gone hold savings in the Ghana surveys reflects an underes- unreported due to the way the questionnaire was timation of household income much more than it designed or the way survey interviewers did or did not reflects an overestimation of household consumption. prompt respondents to report such activities. A more There are a number of other arguments that support likely explanation may be that a large number of this view and explain why the underestimation of respondents were unwilling to reveal such informa- income was apparently much more of a problem in tion. If this is the case it is likely to be very difficult to Ghana than in neighbouring C6te d'Jvoire (with the measure total household income with any accuracy. exception of the 1985 survey), where a very similar The example of the surveys conducted in Ghana is questionnaire was administered. As was discussed ear- interesting because having more than one round of data lier, high rates of inflation are likely to lead to an means that the data sets can be compared with one underestimation of income relative to consumption. another, and because the third round was collected using Ghana had an average annual inflation rate of around a different questionnaire design from the one used in the 22 percent between 1988 and 1992 (the approximate earlier rounds. Making comparisons between the differ- period covered by the surveys), while rates of inflation ent rounds can yield useful clues about whether house- in Cote d'Ivoire were much more modest. Moreover, hold income or consumption is more accurately esti- self-employment income, one of the hardest compo- mated.Where similar or identical surveys are conducted nents to measure, is a more important source of close to each other in time, analysts would not expect (in household income in Ghana than in C6te d'Ivoire. the absence of a clear explaining factor) radical changes The estimates of household savings derived from in the composition of income or consumption expendi- the 1988-89 Encuesta de Presupuestos Familiares in the ture or in the nature of poverty. When the first two Republica Bolivariana deVenezuela are clearly under- rounds of data from the Ghana Living Standards Survey estimated, but it is difficult to determine whether (which were collected using identical questionnaires) income underestimation is predominantly responsible were compared, evidence emerged that that the compo- for this. For example, it is very difficult to compare the sition of income is much more unstable than the com- levels of household income and consumption in the position of consumption. More compellingly, the geo- 1988-89 Encuesta de Presupuestos Familiares with those graphical pattern of poverty, which changes gradually of the Encuesta Social conducted in 1991-92 because from one round to another when consumption data are there was a high rate of inflation between these two used to measure living standards, was seen to have dra- periods, because the questionnaires were significantly matically changed when income data were used to different, and because the Encuesta de Presupuestos measure living standards (Coulombe and McKay 1995). Familiares was conducted only in urban areas while the These sharp changes are hard to understand. Income Encuesta Social was nationwide. As in Ghana, the high data suggested that the capital city, Accra, had the high- inflation rate may suggest that income underestima- est incidence of poverty in the country (out of five local- tion is at the root of the problem.18 ities) in the first survey round but the lowest such inci- Thus, in the cases where household savings have dence in the second round (the following year). most obviously been underestimated, the underesti- Coulombe and McKay argue that the apparent geo- mation of household income seems much more likely graphical pattern of poverty based on income data was to be responsible for this than the overestimation of counterintuitive (for example, in its implication that the household consumption. The reasons for the underes- northern savannah region is one of those least affected timation of household income differ from case to case, by poverty) and contradicted most other standard of liv- although high inflation rates (where applicable) may ing measures. What this strongly suggests, therefore, is be a significant common factor. Difficulty in estimat- that the raw data used in measuring total household ing self-employment income is also likely to be a income were significantly less accurate than those used common factor; however, it is worth noting that this is to measure household consumption in this case. Indeed a problem experienced by developed countries as well in this case it appears that the nature of underestimation as developing countries (Atkinson and Micklewright of income varied from one locality to another or from 1983 for the United Kingdom; Branch 1994 for the one component to another. United States). The difference though is that such 99 ANDREW McKAY incomes are often much less important in developed module in the survey for analytical reasons, there are countries than they are in developing and transition likely to be few extra costs involved in adding the ele- countries. This is one reason why developed country ments needed to measure total household income. In experience with measuring household incomes is a survey of this kind, data on income earned from often more successful than that of developing and wage employment both in cash and kind are general- transition countries. ly collected in the employment module (introduced in Overall, however, the empirical evidence Chapter 9). Other data needed to calculate total reviewed in this section suggests that large-scale household income are collected in the standard ver- underestimation of household income is not sions of the nonfarm enterprise and agriculture mod- inevitable. Many surveys have managed to collect suf- ules (introduced in Chapters 18 and 19).19 ficiently accurate data for analysts to measure total Thus all that would need to be added to the ques- household income, which is without doubt a complex tionnaire would be a module to collect data on income variable to measure. It is indeed probable that total from transfers and other nonlabor income (see Chapter household consumption can be estimated with greater 11). The designers of the survey may have decided to accuracy than total household income. But many include a module on transfers and other nonlabor questionnaires have collected consumption data in income in the survey anyway, because these income more detail than income data anyway. Notwith- sources are of interest in their own right. Even if they standing this, there is no basis for general and univer- were not, the costs of including transfers and other non- sal pessimism about the possibility of measuring labor income would be modest. In previous LSMS income, even if doing so is a relatively complex and questionnaires, modules on transfers and other nonlabor risky business. income have tended to account for only about two pages in an approximately 70-page questionnaire. And What Costs Are Involved in Measuring Total the time taken to administer these two pages will gen- Household Income? erally have been proportionately less than the time to administer other sections, because many of the ques- The cost of a survey designed to collect the data need- tions would not have applied to respondents who did ed to measure total household income depends on the not receive a particular kind of income. Even the long amount and level of detail of the information to be versions of a transfers and other nonlabor income mod- collected. Collecting a large amount of information ule (introduced in Chapter 11), which might amount to means using a long questionnaire. A long question- four questionnaire pages, would not take much more naire demands long interviews, which lead to high time to complete; as before, many of the questions costs. And if the survey budget is fixed, a long ques- would not be applicable to a majority of households. tionnaire requires using a smaller sample, which However, when survey designers do not plan to reduces the extent to which resulting data can be dis- include one or both of the agriculture and nonfarm aggregated. An additional disadvantage of having to enterprise modules, it becomes necessary to add at conduct long interviews is that interviewees may least abbreviated versions of these modules to the become tired and bored and, thus, less careful about questionnaire to ensure that the survey yields the data the accuracy of their answers. needed to calculate total household income. This sub- These points are general and apply to all sections stantially increases the cost of fielding the survey. The of the questionnaire, and the reader should bear them short versions of these modules do not yield enough in mind throughout this volume. However, they have information to allow the estimation of income from specific implications for designing a questionnaire to these sources. However, adding the standard versions measure total household income.To illustrate this, it is of these modules simply for the purpose of estimating useful to compare a situation in which the question- income from these sources cannot be justified, given naire is designed to measure total household income the major extra costs that would be involved. When with a situation in which it is not. In broad terms, two these modules are included, it should be primarily different scenarios can be considered. because they are of interest in their own right. When survey designers have already decided to In such circumstances it is easy to see why survey include both an agriculture and a nonfarm enterprise designers may wish to include the questions on 100 CHAPTER 17 SHOULD THE SURVEY MEASURE TOTAL HOUSEHOLD INCOME? income from self-employment in agriculture and non- household consumption expenditure is already a farm activities directly in the employment module. major exercise, and it is probably more difficult to col- The costs of collecting this information are very much lect the data needed to measure a household's income. smaller. However, for reasons set out in the second sec- In some countries respondents may be very unwilling tion of this chapter, data collected in this way may be to supply information on their sources and levels of of dubious value and thus have very limited analytical income, however much assurance of confidentiality interest.Therefore, it is not recommended that data on they are given. In light of this, and since there are real- self-employment income be collected in this way ly no shortcuts in collecting income data, not all sur- despite its low costs (especially as these costs may be vey planners may choose to collect such information. even higher than expected because of the difficulty For example, those planning surveys in countries with respondents can be expected to have in answering limited household survey experience may choose not these questions or providing meaningful responses). to collect the data needed to calculate total household Thus, where survey planners have decided not to income. Alternatively, they may choose to collect such include the agriculture and nonfarm enterprise mod- data only in certain rounds of a sequence of household ules in their surveys, they should abandon the objec- surveys. tive of measuring total household income. For reasons of cost and difficulty, the most basic multipurpose household surveys are unlikely to collect Evaluation and Conclusions the information required to estimate total household income and thus will probably not include agriculture The collection of information needed to measure total and nonfarm enterprise modules and will include only household consumption is regarded as indispensable in a limited employment module. However, there are a questionnaire designed to study the living standards serious limits to the usefulness of these surveys, both of households and their members. The collection of for policy purposes and for in-depth analytical work. comprehensive data on household income is less fun- This is especially so in poorer countries where a damental because it is possible for analysts to compute majority of the population--and an even greater measures of poverty, to relate poverty to the character- majority of the poor-are likely to be engaged in istics of households, and to relate consumption-based agricultural or nonfarm self-employment activities. measures of standards of living to social variables (such Standard LSMS-type questionnaires should collect the as school attendance and use of health facilities), with- data necessary to estimate household income and do out any information on income. so properly. Where the resulting estimates of house- Yet there are serious limitations to the extent to hold income are reasonably accurate, these estimates which it is possible to understand poverty without should be used more widely in analysis than has been data on income. Understanding the reasons for pover- the case to date. ty and understanding its dynamics requires informa- tion not only on the economic activities of household Notes members (and the amount of time they devote to them) but also on the income earned from these activ- The author gratefully acknowledges the helpful comments of many ities. This is the most important reason to collect data people on the issues covered by this paper, in particular William on household income-but it is by no means the only Cavendish, Paul Glewxwe, Margaret Grosh,John Hoddinott,Alberto one. Moreover, the experience of recent household Martini, Jeffery Round, Julie Anderson Schaffner, and participants surveys demonstrates that some surveys appear to have in the World Bank wvorkshops held as part of this project in April successfully yielded the full range of data needed to 1996 and June 1997. calculate income reasonably accurately, despite the fact 1. Many of these surveys do collect information on income from that they generally devote more interview time to col- self-employment activities in their labor modules, which might lecting consumption data than to collecting income appear to be a suitable substitute for the estimation of income from data. household agriculture and nonfarm enterprise modules. However, It is of course riskier than collecting consumption the accuracy of these estimates of self-employment income is open data, as there is no guarantee of the reliability of the to serious question. As will be discussed later in this chapter and as resulting data. Collecting comprehensive data on is accepted in the specific chapters on these topics, this book rec- 101 ANDREW McKAY omimends strongly against trying to estimate household income 10. Information on the amount of time that household mem- from agriculture and nonfarm self-employment in this way. bers devote to such service activities within the household would, 2. Strictly, according to permanent income theories, consumers however, be available from the time use module (introduced in aim to smooth the marginal utility of their wealth rather than their Chapter 22). The difficulty in practice is in placing a meaningful consumption, and this can differ due to, for example, lifecycle value on this time. effects (Blundell and Preston 1994). Thus their consumption may 11. When a household inherits land, this directly and immediate- not be perfectly smoothed. However, consumption is still likely to ly increases the value of assets owvned by the household, and thus is a be a better measure of a household's permanent income and long- capital rather than a current transaction (even though it may lead to term welfare status than is current income. higher income in the future).While loans provide a household with 3. Some authors argue that in developed countries it may be purchasing power in the short term, they also establish liabilties that easier to measure household income than household consumption need to be repaid; thus, loans should not be regarded as income. expenditure (for example, Goodman and Webb 1995 make this 12. It is true that market price valuation will not necessarily rep- argument in the context of the United Kingdom). Even if this is resent "true" welfare valuations when markets fail, but in practice true for developed countries-and this claim is not beyond dis- there are no real alternatives to doing this. In any case, this distinction pute it is unhkely to be true in most developing countries given is of little importance to the household, which is interested in income the much greater importance of self-employment and nonlabor only as a means of financing its present or future consumption. income in these countries and given that their tax systems are gen- 13. In surveys whose consumption module collects information erally less developed, on natural resource utilization, this should also be included in the 4. Some past LSMS surveys have also collected information on measure of income. As explained in Chapter 5 on consumption, the stock of household savings (as opposed to the flow in any one data on natural resource utilization are not collected in the standard year). However, respondents tend to be wary of questions on such consumption module due to the fact that a nationwide, multitopic a sensitive topic, and it may be that these LSMS surveys only yield- household survey may not be suitable for collecting such informa- ed information on savings within the formal financial sector. See tion, wvhich may be highly locality-specific. Chapter 20 on savings. 14. Some of these estimates of household income may have 5. This wvas the problem faced by Coulombe and McKay been estimated using data on estimated household income from (1996) in trying to identify socioeconomic groups using data from agriculture and nonfarm enterprises that wvere yielded by direct the LSMS survey in Mauritania. Because the income data from questions on self-employment income, a procedure not recom- this survey were regarded as very unreliable and not suitable for mended in this book. The estimates for C6te d'lvoire, Ghana, and identifying socioeconomic groups, they instead used data from the Peru, which were computed from the raNv data, did not use the labor module to identify socioeconomic groups based on the eco- answers to these questions about self-employment earnings. nomic activity to which the household devoted most time. This is 15. For example, for the case of Ghana, an attempt to compute clearly not ideal for a number of reasons, but it does offer a possi- a comparable estimate of private consumption per capita from ble xvay to identify socioeconomic groups from survey data where national accounts data produced a figure halfivay between the esti- the survey designers decide not to collect comprehensive income mate of total household income and the estimate of total household data. consumption. Such a comparison is inevitably quite crude and 6. Note, though, that certain marginal differences arise bet ween approximate, given the different definitions applied in the national estimating variables at the micro level, wvhich is of interest here, and accounts and in the household survey. estimating them for macro purposes (for example, in national 16. An alternative procedure would be to look at the mean values accounts). of income and consumption having excluded the highest and loNvest, 7. This discussion draws significantly on Johnson, McKay, and say, 5 percent of values. However, such an analysis could not be con- Round (1990). ducted in all of the cases reported in Table 17.2. In the cases where it 8. For some households, natural habitat utilization can be a sig- was successfully conducted (for example, as done by Scott 1994 for nificant productive activity. Based on data collected in the Shindi the Repubhca Bolivariana deVenezuela, excluding the top and bot- ward in rural Zimbabwe, Cavendish (1999) estimates that the value tom 5 percent of the distribution), the broad conclusions have of commodities gathered from natural sources on average accounts appeared not to change markedly for 35.2 percent of household income. 17. It is true that there is a significant element of the estimates 9. For example, if fetching wvater is regarded as productive and of household income and consumption that is common to both imputed as a household income, it must also be imputed as a house- (for example, consumption of own-produced food). Repeating the hold consumption expenditure. calculations of correlation coefficients for measures of income and 102 CHAPTER 17 SHOULD THE SURVEY MEASURE TOTAL HOUSEHOLD INCOME? consumption excluding these common elements obviously gives Activity in Ghana: Proceedings of a Ghana Statistical Service/Oversears lower correlation coefficients, but the correlation remains signifi- Development Administration ConferenceJanuary 1995. Accra, Ghana. cant in most cases. Deaton, Angus 1980. The Measurement of We!fare: Theory and Practical 18. Altimir (1987) found that the estimates of household Guidelines. LSMS Working Paper 7. Washington, D.C.: World income obtained from a much earlier round of the Encuesta de Bank. Presupuestos Familiares in the Republica Bolivariana de Venezuela . 1992. "Saving and Income Smoothing in Cote d'Ivoire." corresponded quite closely to the national accounts estimates of Discussion Paper 156. Princeton University, Woodrow Wilson household income. However, it is not clear that this applies to the School of Public and International Affairs, Research Program more recent surveys and, as argued in the text, national accounts in Development Studies, Princeton, NJ. estimates are not necessarily an objective standard of accuracyv 1997. The Analysis of Household Surveys: A Microeconometric 19. Where survey designers feel it is important to include Approacih to Development Policy. Baltimore, Md.:Johns Hopkins households' use of natural resources in the estimate of total house- University Press. hold income (and consumption), they are likely to have already Delaine, Ghislaine, Lionel Demeryjean-Luc Dubois, Branko Grdjic, included these issues in the consumption module of the question- Christaan Grootaert, Christopher Hill, Timothy Marchant, naire as necessary elements in the calculation of total household Andrew McKay, Jeffery Round, and Christopher Scott. 1991. consumption. See also Chapter 5 on consumption. The Social Dimensions ofAdjustment Integrated Survey:A Survey to Measure Poverty and to Understand the Effects of Policy Change on References Households. Social Dimensions of Adjustment in Sub-Saharan Africa Working Paper 14.Washington, D.C.:World Bank. Altimir, Oscar 1987. "Income Distribution Statistics in Latin Devereux, Stephen 1992. "Observers are Worried: Learning the America and their Reliability." Revieu' of Income and Wealth 33 Language and Counting the People in Northeast Ghana." In (2): 111-55. Stephen Devereux and John Hoddinott, eds., Fieldwork in Ashenfelter, Orley,Angus Deaton, and Gary Solon. 1986. Collecting Developing Countries. Boulder, Col.: Lynne Rienner. Panel Data in Developing Countries: Does It Make Sense? LSMS Glewwe, Paul. 1991. "Investigating the Determinants of Household Working Paper 23.Washington, D.C.: World Bank. Welfare in Cote d'Ivoire."Journal of Development Economics 35 Atkinson, Anthony B., and John Micklewright. 1983. "On the (April): 307-37. Rehability of Income Data in the Family Expenditure Survey, Goodman,Alissa, and StevenWebb. 1995. "The Distribution of UK 1970-77."Journal of the Royal Statistical Society Series A, 146, Household Expenditure, 1979-92." Commentary 49. Institute Part 1: 33-53. for Fiscal Studies, London. Blundell, Richard, and Ian Preston. 1994. "Income or Grootaert, Christiaan 1983. "The Conceptual Basis of Measures of Consumption in the Measurement of Inequality and Poverty?" Household Welfare and their Implied Survey Requirements." Working PaperW94/12. Institute for Fiscal Studies, London. Review of Income and Wealth 29 (1): 1-21. Branch, E. Raphael 1994. "The Consumer Expenditure Survey: A . 1986. Measuring and Analyzing Levels of Living in Developing Comparative Analysis." Monthly Labor Review 117 (12): 47-55. Countries:AnAnnotated Questionnaire. LSMS Working Paper 26. Cavendish, William. 1999. "The Complexity of the Commons: Washington, D.C.:World Bank. Environmental Resource Demands in Rural Zimbabwe." Haddad, Lawrence, John Hoddinott, and Harold Alderman, eds. Working Paper WPS/99.8. Centre for the Study of African 1997. Intrahousehold Resource Allocation in Developing Countries: Economies, University of Oxford, U.K. Methods, Models and Policy. Baltimore, Md.: Johns Hopkins Chaudhuri, Shubham, and Martin Ravallion. 1994. "HoNv Well do University Press. Static Indicators Identify the Chronically Poor?" Journal of Handa, Ashu 1995. "Employment, Income and Labour Supply: An Public Economics 53: 367-94. Analysis of the 1993 SLC Employment Module." University of Coulombe, Harold, and Andrew McKay 1995. "An Assessment of the West Indies, Department of Economcs, Mona, Kingston, Trends in Poverty in Ghana, 1988-1992." Poverty and Social Jamaica. Policy Discussion Paper 81.World Bank,Washington, D.C. Hicks,John R. 1971. The Social Framework. 4th ed. Oxford: Oxford 1996. "Modeling the Determinants of Poverty in University Press. Mauritania." World Development 24 June): 1015-31. Hill,T. Peter. 1979. "Do ItYourself and GDP." Review of Income and Coulombe, Harold, Andrew McKay, and Jeffery I. Round. 1996. Wealth 23: 321-39. "Estimating the Contribution of the Informal Sector to the Hoddinott, John, and Lawrence Haddad. 1995. "Does Female Ghana GDP" In Ghana Statistical Service, Measuring Informal Income Share Influence Household Expenditures? Evidence 103 ANDREW McKAY from Cote d'Ivoire." Oxford Bulletin of Econonmics and Statistics Ruggles, Richard, and Nancy D. Ruggles. 1986. "The Integration 57 (February): 77-96. of Macro and Micro Data for the Household Sector." Review of Johnson, Martin, Andrew D. McKay, and Jeffery I. Round. 1990. Income and Wealth 32 (3): 245-76. "Income and Expenditure in a System of Household Sen, Amartya K. 1985. Commodities and Capabilities. Amsterdam: Accounts: Concepts and Estimation." Social Dimensions of North-Holland. Adjustment in Sub-Saharan Africa Working Paper 10. World Scott, Kinnon 1994. "Venezuela: Poverty Measurement with Bank,Washington, D.C. Multiple Data Sets." World Bank, Poverty and Human Lipton, Michael, and Martin Ravallion. 1995. "Poverty and Policy." Resources Division,Washington, D.C. In J. Behrman and T.N. Srinivasan, eds., Handbook of UNSD (United Nations Statistics Division). 1993. A System of Development Economics. Volume 3B, Amsterdam: Elsevier. NVational Accounts. NevYork: United Nations. Martini,Alberto P.,Anna Ivanova, and Svetlana Novosvolova. 1996. Vijverberg,Wim P.M. 1991. MWeasuring Incomefrom Family Enterprises "The Income and Expenditure Survey of Belarus: Design and with Household Surveys. LSMS Working Paper 84. Washington, Implementation." Statistics in Transition 2 (7). D.C.:World Bank. 104 8 o Household Enterprises Wim P. M. Vijverberg and Donald C. Mead In most Living Standards Measurement Study (LSMS) questionnaires there is a module explor- ing the dynamics and activities of nonagricultural household enterprises (which, for simplicity, are referred to in this chapter as "household enterprises"). This module gathers information on the portion of a household's income and employment derived from nonagricultural self- employment. More extensive versions of the module have also collected information on the variability of income and employment over time, the impact of the economic environment on household enterprises, and the involvement of household enterprises with credit and commodi- ty markets. The first section of this chapter explores the contri- The Role of Household Enterprises in the Development bution that household enterprises can make to eco- Process nomic development and the ways in which govern- In most developing countries, a very large number of ment policies can enhance that contribution. The people participate in household enterprises. About second links these policy issues to specific data one-half of the households sampled in previous LSMS needs. The third section translates these data needs surveys were found to operate one or more nonfarm into a model questionnaire, and the fourth section enterprises (Moock, Musgrove, and Stelcner 1990; provides some explanatory notes about the model Vijverberg 1992, 1998). Other studies in several coun- questionnaire. tries in Sub-Saharan Africa have indicated that 15-25 percent of adults in these countries are involved in The Role of Household Enterprises in such activities (Mead 1994). In most countries the Economic Development majority of household enterprises are owned and operated by women. In many countries the popula- This section provides an overview of the role of tion is growing faster than the number of new job household enterprises, detailing their prevalence, openings in the public sector and in larger enterpris- their characteristics, and the contribution that they es, so the role of household enterprises is expanding. make to economic development. The section then discusses the determinants of successful household WHAT ARE THE CHARACTERISTICS OF HOUSEHOLD enterprises and the kinds of information policy- ENTERPRISES? Most household enterprises fall into makers need to encourage the growth of these one of two major categories. Many-probably the enterprises. majority-of these enterprises generate only mini- 105 WIM P. M.VIJVERBERG AND DONALD C. MEAD mal income that is barely sufficient to enable their WHAT CONTRIBUTIONS Do HOUSEHOLD ENTERPRISES owners to survive. Such enterprises are sometimes MAKE TO HOUSEHOLD WELFARE? Household enterpris- referred to as survivalist enterprises. These enterpris- es contribute to improving household welfare in two es are often operated on a part-time basis, either year key ways: by generating income for those who work in round for only a few hours a day or full-time during the enterprise (whether they work as owner-operators only certain periods of the year. Sometimes survival- or as employees) and by creating employment. ist enterprises are only one of several income-gener- In the view of many analysts and policymakers, ating activities operated by an individual or the the principal contribution of household enterprises to household, in which case their contribution to household welfare is the generation of income for the household welfare is essentially supplementary. Often household. This includes cash income (cash receipts survivalist enterprises are run by women, who com- minus the cash expenses required to produce these bine them with their other household responsibili- receipts) and in-kind income (the net value of prod- ties. Examples of typical survivalist enterprises are ucts or services produced by the enterprise and con- food preparation, sewing, shoe shining, and street sumed within the household or of products bartered vending. with others). A few key questions need to be Other household enterprises, sometimes referred addressed: to as microenterprises, have a very different role in the * How much income does the enterprise generate development process. These enterprises generate for the household? Is this income sufficient to lift incomes that are substantially higher, and often well the household out of poverty? above the poverty level. While survivalist enterprises * How steady and reliable is this income? Income rely almost exclusively on unpaid family members flows that are highly variable over the year or that (and often consist of one person working alone), can cease unpredictably contribute less to household microenterprises are more likely to use hired workers. welfare than do flows that are regular and reliable. Microenterprises usually have more complex and * Which households receive this income? An income sophisticated production and marketing systems than flow is more or less important as a source of welfare do survivalist enterprises, and are more likely to be the depending on how well-off the recipients are and sole source of income for a household. Examples of what alternative income sources are available to microenterprises are furniture making, manufacturing, them.While survivalist enterprises often yield only and wholesaling. small returns, their contribution can be extremely Survivalist enterprises and microenterprises make important. Since survivalist entrepreneurs are fre- different potential contributions to household welfare quently very poor, even small increases in these and to the development process. As such, the two types people's incomes can contribute tremendously to of enterprises also differ in what kinds of programs their welfare.And since large numbers of people are support them and in how they respond to policy engaged in such activities, survivalist enterprises can changes. mean a great deal for welfare overall. There are many households that for one reason or * How is household enterprise income distributed, another do not operate a nonfarm enterprise. From among both employees of enterprises and different both a descriptive and analytical perspective, it is inter- household members? Does the distribution of esting to examine why some households operate household income differ depending on whether enterprises and others do not. For example, do the the entrepreneur is male or female? How is the households not currently operating an enterprise income spent, and what proportion is saved? Who engage in other, more lucrative activities? If these spends and who saves? All of these questions are households were to start up enterprises, would the important because the existence of a household enterprises earn more or less profit than the ones cur- enterprise can have a significant impact on house- rently in operation? Policies aimed at improving the hold allocation for food, schooling, health care, and performance of current enterprises may also prompt savings-and on which household members bene- other households to start up enterprises. Policymakers fit most from the way money is allocated. should not only be aware of this possibility but also be * In what ways do returns to labor vary by locality, able to quantify the magnitude of this response. sector, and the characteristics of the entrepreneur 106 CHAPTER 18 HOUSEHOLD ENTERPRISES (such as his or her gender, education, experience, working in household enterprises, how many earn an and skills)? income below the minimum wage? How many earn Is income measured per year or per day? For many an income at least twice the minimum wage? If possi- purposes it is important to account for the time ble, such questions should be asked in dynamic terms, dimension of both income received and labor input examining the patterns of income earned in jobs that required to produce this income-and thus to have come into existence during a given period-for express income in terms of hours or days worked. example, the most recent calendar year. An activity that yields high income per hour but Such time-specific analysis is particularly useful for only provides an opportunity to work a few hours exploring the impact of changes in policy or changes each month makes a contribution very different in the macro economy. Having access to this time- from that of a full-time activity that provides a specific wage information would enable analysts to lower income per hour. explore the hypothesis that many new and productive Household enterprises not only contribute to jobs are created in household enterprises at times when household welfare by providing income but also by other sectors are expanding at either the local or the providing a source of employment. The household macro level. Employment in household enterprises enterprise sector employs a substantial portion of the may also grow at times of macroeconomic stagnation, labor force. It is important to know just how many but most of these new employment openings are like- people household enterprises employ and how this ly to yield only marginal returns. Understanding the number changes over time due to sectoral growth as dynamics of household enterprises can be of consider- well as seasonality.The key questions here are: able importance in clarifying what can and cannot be * How many people work in the enterprise? How done by policymakers to promote the growth of pro- much does employment in the enterprise vary by ductive employment among household enterprises. season? Is the work part-time? If so, does this mean working only a part of each day or a part of each The Determinants of Successful Household Enterprises week? Is the work a form of moonlighting? When enough information about household enter- * What are the characteristics of the people engaged prises has been gathered, it becomes possible to pres- in the enterprise? Does the enterprise employ ent a profile of the country's household enterprises, unskilled, semiskilled, or highly skilled labor? including the number of enterprises, the income and * How has the enterprise changed over time? What employment that they generate, and the patterns of are the employment growth patterns for different change in employment. It should be possible to pres- types of enterprises? ent all this information broken down by location, sec- It is important to look not only at the number of tor, enterprise size, and gender of the owner. Such a people who work in household enterprises but also at profile constitutes the raw material for exploring to the characteristics and overall quality of their jobs. An what extent these enterprises create income and jobs. important distinction must be made between owners Using the knowledge gained from this process, policy and unpaid family workers on one hand and paid interventions can be devised that create a favorable cli- employees on the other. In most countries, more than mate for the household enterprise sector. 80 percent of those who work in household enterpris- es are either enterprise owners or unpaid family help. HOUSEHOLD- AND ENTREPRENEUR-SPECIFIC FACTORS. Policymakers need to know how many and which Two sets of factors that can influence the success of an members of households participate in the work of enterprise are the characteristics of the entrepreneur enterprises,just as they need to know who receives the and the characteristics of his or her household.The rel- income these enterprises earn. Policymakers also need evant characteristics of an entrepreneur include educa- to know how many people from outside the household tion, training, experience, and family background (for are employed in the enterprise. Once these things are example, the profession of his or her parents). Relevant known, it becomes clear who is helped and who is hurt household characteristics include what other activities by policy that impacts household enterprises. household members engage in; these activities can Another key issue is the wage levels of people in determine how the enterprise is organized and how the household enterprise labor force. Of all the people many resources household members allocate to it. 107 WIM P. M.VIJVERBERG AND DONALD C. MEAD People involved in household enterprises usually have household might specialize in only one particular other demands on their time (such as child care, cook- fuinction, such as carving backs for chairs; at the other ing, fetching water and firewood, farming, or other extreme, a household might be fully self-reliant, pro- paid jobs). In order to devote time to working for the ducing all its own inputs and making no market trans- enterprise, they must juggle these responsibilities. In actions at all. All enterprises face challenges related to some cases the household enterprise is a seasonal activ- their position along this continuum. ity undertaken only during slack times when other In surveys in several countries, household entre- demands on household labor are less pressing. preneurs have frequently indicated that the most Household enterprises can also affect the range of important problem they face is insufficient markets for other household activities. In some households an their products (Liedholm and Mead 1995). This prob- enterprise is a safe and reliable source of income that lem may arise when an enterprise serves a local mar- allows households to invest more in other activities- ket with too few customers; indeed many of the less activities that might yield higher returns but also carry successful enterprises sell only to neighbors in slow- a higher risk of failure. These kinds of decisions may growing, restricted, and localized markets. If this is the have a gender dimension. To make this point with a case, owners of the household enterprises could caricature of an example, a low-risk household enter- arrange to sell their goods in different locations or prise may be a low-productivity activity (in textiles or through different marketing channels, which is what food) performed by women that generates a low but more dynamic enterprises do. steady income. A household makes investments in Insufficient markets also arise when household riskier but more profitable household enterprises (in enterprises are not producing the exact type of goods manufacturing or wholesale) run by the men of the that consumers want to buy. In such cases owners may household. In other circumstances the household may try changing the design of the product (possibly by use profits from a household enterprise to cover cash incorporating new technologies), improving quality expenses of the household's farming activities, which control procedures, or changing the management of in turn provide food for the household. In this case the the enterprise. Policymakers can provide information household enterprise enables the operation of the about distant markets, organize meetings with distri- farm which, in turn, is critical for the household. bution networks in distant markets, ensure smooth Having data about household- and entrepreneur- operation of transportation networks (Vijverberg specific factors can help policymakers identify the 1998), provide training courses on quality control most appropriate kinds of assistance to support. This technology, and the like. assistance may include general education, vocational Research into markets for household enterprises training, or specialized management training (such as must address a few basic questions: accounting, bookkeeping, or quality control). Having * To what degree do household enterprises partici- information on these characteristics may also help pol- pate in markets? How developed are these markets? icymakers channel assistance to the clients who will * Where do household enterprises obtain capital make the best use of it. equipment, inputs, and hired labor? * Through what channels do household enterprises MARKETS. Participation in markets allows households sell their output? How reliable are these channels? to specialize. This specialization can lead to efficiency * Are enterprises that participate more extensively in and economies of scale for the economy as a whole, markets more profitable? Have the existing policies boosting productivity, income, and standards of living. regarding markets helped improve household However, such specialization is hindered when mar- enterprise performance? kets for inputs (labor, capital, land, raw materials) or outputs (the distribution system) function poorly. It is LOCATION AND INFRASTRUCTlURE. As factors influenc- difficult to operate an enterprise when supplies (either ing the success of household enterprises, location and of inputs or of consumer goods) are sporadic and access to infrastructure are related since some loca- unreliable, when demand for the enterprise's output is tional factors are heavily influenced by the availability sporadic, or when prices (of inputs, outputs, or items of roads or communications facilities. It is important for daily living) fluctuate too much. At one extreme, a to know not only where an enterprise is located but 108 CHAPTER 18 HOUSEHOLD ENTERPRISES also the degree of dynamism in the market at that funding for most household enterprises, it is also location and how effectively that location is served by important to know more about what entrepreneurs do infrastructure linking it with other markets. Access to with their savings. utilities (such as water, electricity, and telephone lines) Once again, it is vital to consider intrahousehold and to adequate workspace are also important factors. dynamics in examining the issue of finance, savings, Questions exploring these issues are normally includ- and credit. Who has responsibility for managing a ed in other modules of the LSMS survey; see Chapter household's financial assets? If profits are earned, how 13 on the community questionnaire and Chapter 12 are decisions made about how they are used? Who on housing. within a household has access to credit? The answers to these questions may depend on the division of FINANCIAL SYSTEMS, SAVINGS, AND CREDIT. There are authority between the genders or between genera- probably more programs to assist household enterpris- tions (for example, between fathers and sons). Some es in the area of credit than in all other areas com- aspects of household dynamics are explored in the bined. It is particularly important, therefore, to know household enterprise module, while others are how household enterprises interact with the financial addressed in other modules of the survey (see Chapter system and how these interactions may be strength- 24 on intrahousehold allocation). ened. Household enterprises are among the actors that exert a demand for credit. Households with accumu- REGULATORY AND LEGAL REFORMS. Some people have lated savings are among the actors that create a supply argued that instituting regulatory and legal reforms can of credit. Financial institutions (for example, banks and lead to a surge of new activity in the household enter- moneylenders) are intermediaries between the prise sector.This line of reasoning is somewhat less pop- demand and supply sides of the credit market. ular today than it was five years ago as research has led Since the LSMS is a household survey, it cannot many to question its significance. However, it remains directly explore issues relating to how financial insti- an important consideration in some countries, particu- tutions operate. For example, it cannot be used to larly in situations where enterprises seek to grow in examine lending decisions of financial institutions and both size and complexity (Mead 1995). It would be why they may choose to give priority in their lending interesting to determine to what degree enterprises to certain categories of borrowers. The household abide by existing rules and regulations and to what enterprise module can, however, throw useful light on degree entrepreneurs feel constrained by these factors. ways in which existing enterprises have financed their original and current stock of fixed and working OTHER ASPECTS OF MACROECONOMIC POLICY. Most capital-whether from an inheritance, from savings, or small enterprises pay few taxes, although they often from loans. It can also be used to find out the extent pay fees, purchase licenses, or pay indirect taxes on the of entrepreneurs' desire and need for credit. inputs they purchase. However, once enterprises grow In recent years new roles and operating proce- beyond a certain threshold, they become subject to dures have been developed for financial institutions substantially higher taxes. The foreign exchange that lend to microenterprises (Otero and Rhyne regime may make imports artificially cheap, but when 1994).When financial institutions have adopted these these imports are allocated administratively rather than new approaches, the flow of credit to household through the market, small household enterprises may enterprises has increased substantially and entrepre- find it hard to compete with larger domestic produc- neurs have had more opportunities both to borrow ers because the latter have better access to (administra- and to save. There is general agreement in the devel- tively allocated) cheap imported inputs. In addition, opment community that information about these new household enterprises may have to compete with approaches should be spread as rapidly as possible imported finished products that are artificially cheap- among financial institutions. In monitoring the effects er. The significance of these and other similar macro- of the new lending programs, it is important to find economic policy constraints appears to differ greatly out who is helped by the programs, to what extent from one country to another (Young 1993). they are helped, and what new binding constraints To analyze the impact of a change in policy, it is emerge. Since own finance is the principal source of necessary to trace the effects of the change over time, 109 WIM P M.VIJVERBERG AND DONALD C. MEAD both in the external environment and in the enter- Second, the products or services of household prise's response to the change. It is possible to trace enterprises are inputs for other activities.The expan- these effects to some extent by asking respondents ret- sion of a household enterprise increases the supply of rospective questions about when specific changes took inputs to other enterprises. If a household enterprise place in the enterprise.This approach has serious lim- sells pesticides, it increases the availability of this input itations, however, since the respondents may have for- to small farmers. Such supply effects are often called gotten relevant details. Here panel surveys, which forward linkages. return to the same set of enterprises on a regular basis Researchers must ask: to ask up-to-date questions, hold a distinct advantage. To what extent does a given household enterprise (The advantages of panel data for studying household produce demand and supply effects (backward and enterprise are elaborated later in this chapter.) forward linkages) that enable other enterprises to Policymakers must know the answers to two key prosper? questions regarding household enterprises and macro- Household surveys can shed light on this question economic policy: only if detailed information is collected about the * How has the success of different types of household nature of products and services bought and sold.' enterprises been affected by the policies and proj- Supply linkages become clearer if entrepreneurs report ects that are in place in particular localities and at in detail what their output is and to whom they sell it. particular points in time? Demand linkages require similar detail on the pur- - How do changes in the existing policies and proj- chases made by entrepreneurs. Also needed is general ects affect the success of different types of house- information about the structure of the local economy hold enterprises? and about ways in which particular household enter- A better understanding of the impact of various poli- prises might contribute to other productive activities cies could make an important contribution to improv- in the region (see Chapter 13 on the community ing program and policy design. The task of program questionnaire). Clearly, the overall data requirements design is beyond the immediate purview of LSMS sur- for analyzing this issue are substantial. The multi-topic veys. However, by throwing some light on the kinds of nature of the LSMS questionnaire, which limits the constraints facing household enterprises, LSMS sur- amount of questions that can be asked on any given veys can be of considerable help to project designers topic, may make it difficult to justify the depth of in the World Bank and other development institutions. inquiry needed to address these questions. LSMS analysis may be country-specific, time-specific, In a cruder way, LSMS surveys may help address a or both, and should take into account such factors as related key question: the dynamism of the local economy, the self-reliance To what extent and in what sectors does a house- of households, the involvement of different household hold enterprise benefit from the establishment of a members in the productive activities of the household, large company in its proximity? the market participation of household enterprises in When a large company (a corporation, parastatal enter- the region, the type of technology used by each enter- prise,joint venture, or multinational corporation) moves prise, and the social culture (in the household or com- into a particular area, it can have a significant impact on munity) within which entrepreneurs make decisions. local household enterprises. This impact can be either positive or negative.The company may displace some of Household Enterprises and Other Sectors of the Economy the demand for these enterprises' products. It may raise Household enterprises have an impact beyond the local incomes and thus increase demand for the prod- household in two key ways. First, household enter- ucts. It may cause local wages to increase. prises use as inputs the products or services of other An additional question might be raised that could enterprises. Thus the expansion of a household enter- qualify answers about a company's impact on local prise creates a new demand for other enterprises' out- enterprises: Why did the company select this particu- puts. When a household enterprise expands its use of lar location? If government officials gave the company small oil seed presses, new markets open up for farm- special incentives to pick this location, why? Was it ers growing oil seeds. Such indirect demand effects are because wages were low, because there was a local often called backward linkages. unfulfilled demand for the company's products, or 110 CHAPTER 18 HOUSEHOLD ENTERPRISES because there was already a thriving private sector? enterprises) it is possible to consider only the impact Might the success of household enterprises near a of policy on small-scale private enterprises. large company signify the company's decision to A number of the issues listed in Box 18.1 are locate near household enterprises that were already descriptive; others refer to a causal relationship. A descrip- profitable? Might the lack of success of household tive analysis of the small enterprise sector is useful enterprises near a large company signify the company's because the amount of reliable information about house- decision to locate where the household enterprises hold enterprises is so small. To understand causal phe- could easily be displaced? The point is that the exter- nomena (including interesting policy variables) goes sev- nal market environment cannot always be taken as eral steps further. Not only are good data needed, but the given. In studying the economic performance of small many causal factors must be identified, accurately meas- enterprises, it is appropriate to ask whether the pre- ured, and related to enterprise performance variables in sumed determining factors (as listed in previous sec- appropriate ways. While economic science has made tions of this chapter) are truly given or are determined progress in these areas, much remains to be learned. simultaneously alongside enterprise performance. Data Issues and Data Needs Summary Box 18.1 summarizes the major policy issues discussed A number of issues must be kept in mind when col- in this chapter. Because LSMS surveys are based on a lecting LSMS data about household enterprises.These sample of households (as opposed to a sample of issues include: Box 18.1 Policy Issues and LSMS Data Issues that can be analyzed using LSMS data Impact of other macroeconomic policies (such as tax poli- * The number of household enterprises in the economy. cies) on household enterprise performance. * Sectoral and locational characteristics of household enter- * Interactions between household enterprise performance prises. and other household activities. * Income generated by household enterprises. * Household enterprise performance over time. * Relationship between poverty and household enterprise income. Issues that are difficult to analyze with household survey data * Variability and seasonality of household enterprise * Impact of the distribution of enterprise income on con- income. sumption patterns within a household. (This requires * Income earned by hired labor in the small-scale private detailed data on the allocation of each kind of income sector among household members and on the role of each * Magnitude and structure of employment in household member in household spending decisions.) enterprises. * Links that enterprises have with other sectors of the econ- * Seasonality in household enterprise employment. omy (This would require an analysis of inter-enterprise * Patterns of employment growth in the small-scale private commodity flows in a local economy.Very detailed house- sector hold enterprise data would be useful but information on * Determinants of household entrepreneurship. the local economic structure would still be needed.) * Determinants of household enterprise income levels. *. Impact of regulatory and legal reforms. (This would require * Patterns of education, training, and experience among cross-country or time-series data to show variations in the household enterprise entrepreneurs. type of regulation across the samples observed.) * Impact of training programs provided by government and *. Impact of macroeconomic variables (such as foreign business organizations (on, for example, product design, exchange availability or market openness) on the small- quality control, marketing techniques, and management) scale private sector (This would require cross-country or on performance of household enterprises. time-series data to show variations in the macroeconom- * Marketing patterns for inputs and outputs and their ic variables across the samples observed.) impact on enterprise performance. Impact of culture, location, and level of development on * Role of locational factors and access to infrastructure in how much income and employment an enterprise gener- enterprise performance. ates. (This would require cross-country or time-series * Financing sources and impact of credit markets. data to show variations in culture and level of develop- * Impact of the regulatory and legal regime on enterprises. ment, both of which are usually constant within regions.) I I I WIM R M.VIJVERBERG AND DONALD C. MEAD * Target population. Sampling Considermtions * Fluctuations in enterprise activity over time. There are several other potential problems regarding * Panel data on enterprises. the randomness of the LSMS sample-problems * Enterprise income, sales revenue, and expenditures. which if not addressed properly could bias the statisti- * Business assets. cal results and policy recommendations derived from the data. Target Population LSMS surveys are designed to collect information SEVERAL ENTERPRISES PER HOUSEHOLD. Some house- about a random sample of households. By using a ran- holds operate more than one enterprise.Typically about dom sample of households, do LSMS surveys neces- 25 percent of the households that operate a nonagri- sarily capture a random sample of enterprises? This is cultural enterprise operate more than one and 5 percent an important question, as it is vital that an analysis of operate more than two.5 It might be tempting to reduce household enterprises be based on such a random costs by letting an interviewer determine on the spot sample. Every enterprise in the population must have which of a household's enterprises is the "most impor- an equal (or at least a priori known) likelihood of tant," and collecting information only on that enter- being selected. The LSMS survey design targets prise.This was the method used by the Peru LSMS sur- households rather than enterprises, meaning that veys of 1990 and 1991. However, if only the every household has an equal probability of being "important" enterprises in multi-enterprise households selected. If the survey collects data on every enter- are surveyed, the smallest family enterprises are likely to prise associated with the households in the survey, the be systematically excluded. To ensure a random sample survey design guarantees that each household enter- the interviewer must either survey all of the enterprises prise also has an equal chance of being selected (as operated by the household or record the number of explained below). enterprises in the household and randomly select one In LSMS surveys, a nonagricultural household or two enterprises from the full list. (Enterprise weights enterprise is defined as a household-operated busi- can be adjusted after the data are entered.) ness that performs any activity for the purpose of There is a compelling reason for surveying all of earning an income, with the exception of sale of the enterprises.To precisely measure household living agricultural crops or livestock products from a standards, all of a household's components must be household farm.2 Because public and parastatal measured, including all of the enterprises operated by enterprises, corporations, and cooperatives are not its members. associated with any one household, they are not included in LSMS samples. Thus LSMS survey JOINT OWNERSHIP OF ENTERPRISES. About 5 to 7 per- samples capture only the private, noncorporate cent of enterprises are owned jointly by two or more sector.3 households. Since the partners in such enterprises live An alternative way to acquire information about in different households, information about the enter- the private productive sector is an enterprise survey. prises could be collected at any one of those house- Because these surveys draw their enterprise samples holds. If the number of partners living in different from registration or address lists,4 these surveys households is n and the probability of selecting any (unlike LSMS surveys) do include larger enterprises household is p, the probability of selecting a particular and corporate, public/parastatal, and joint-venture jointly owned enterprise equals np. In analysis, jointly enterprises. However, they tend to overlook unregis- owned enterprises should be given a weight equal to tered and itinerant enterprises as well as the many 1/n while enterprises owned by a single household are household-based enterprises that do not appear on given a weight of 1. To make this weighting possible, any registration or address lists. LSMS survey samples, it is important that the LSMS questionnaire ask on the other hand, do tend to capture these unregis- respondents for the number of other households in tered private productive activities.Thus neither enter- which other owners of the enterprise live.6 prise nor household surveys yield complete (and hence fully random) samples of the total productive NoNRESPONSE. The sample of enterprises generated in nonagricultural sector. an LSMS survey suffers from several problems of non- 112 CHAPTER 18 HOUSEHOLD ENTERPRISES response. One form of nonresponse occurs when a ences during the recent past-say, the past month or household either cannot be found or refuses to the past 12 months. (This approach is also used in answer. A second form of nonresponse is the collec- Chapter 8 on health, Chapter 9 on employment, tion of household enterprise data from the wrong Chapter 19 on agriculture. Chapter 5 on consump- household member. To collect accurate information tion, Chapter 11 on transfers, Chapter 21 on credit, about an enterprise, an interviewer must try to address and Chapter 20 on savings.) In some cases survey the relevant questions to the member of the house- questions are used to account for fluctuations in the hold who actually operates the enterprise. status of an issue (such as the consumption of occa- How often does nonresponse happen? The 1991 sionally purchased commodities); in other cases survey Pakistan survey provides the best indication since questions are used to establish a trend (such as whether entrepreneurs were questioned in both the first and the household borrowed or saved money). second rounds of the survey. In both rounds, between The income generated by some enterprises follows 5 and 10 percent of households could not be found or seasonal patterns. LSMS surveys should seek to find out refused to answer. According to percentages from the both the level around which the income fluctuates and C6te d'Lvoire (1985-88), Ghana (1987-88), and the amount of variation. Measuring income at only one Vietnam (1992-93) surveys, for about 5 percent of (often nonrandom) point in time during the year is enterprises the respondent was someone other than insufficient because this does not allow seasonal varia- the entrepreneur. tions to be distinguished from the general level of prof- itability. An interview may even occur out of season Fluctuations in Enterprise Activity over Time when an enterprise is not in operation-and lead to the There are two ways to find out if a household is oper- incorrect conclusion that the interviewed household is ating a household enterprise. The first way is to ask not involved in any household enterprise activity. whether the household is currently involved in any It might be interesting to know what kinds of nonfarm activity on its own account with the intent households are most likely to operate enterprises-in to earn an income. This establishes the number of other words, to exhibit signs of entrepreneurship. To household enterprises currently in operation.The sec- study this question, it is necessary to survey all activi- ond way is to ask whether the household is involved ties that a household conducts on its own account, not in any such activity or has been so involved during the only at the time of the visit but also over a recent time past 12 months.This method includes not only house- period such as the past year or the past two years.The hold enterprises currently in operation but also any time factor is important because it can indicate enterprises that were in operation during the previous whether a household's failed business was once a use- year but are not operating currently-which amount- ful source of income or had always been a sinkhole. ed to between 10 and 25 percent of the sample in Either way, a trend exists that cannot be seen by doc- Vietnam, Pakistan, and Ecuador. umenting only the current involvement of a house- These two approaches generate two different hold's members in a farm, in a household enterprise, samples of enterprises, with an important difference or in wage employment. Surveying failed household between them. The second approach captures the enterprises can be as meaningful for documenting the enterprises that have gone out of business during the transitions in a household as is, for example, surveying previous 12 months as well as those that are seasonal changes in household composition. and not operating at the time of the interview. If the activities of an enterprise are seasonal, an Is it necessary to survey enterprises that are not in interviewer should ask when it carries out these activ- operation at the time of the interviewer's visit?Yes, for ities and for how long. This will help researchers and three reasons.While the living standard of a household policymakers understand the fluctuations in local can be measured by evaluating current stocks of labor markets and income and consumption patterns human and physical capital, standards of living are also during the year. When an enterprise is not in opera- measured by flows, particularly of consumption and tion for reasons other than seasonality, it is useful to income.7 Because both stock and flow variables are inquire how long ago the enterprise was last active, affected by temporal and temporary variations, it is why it is not currently in operation, and if and when reasonable to inquire about the household's experi- the entrepreneur anticipates starting it up again. 1 13 WIM P M.VIJVERBERG AND DONALD C. MEAD In any line of questions about past household can rule out all other external influences including enterprise activities it should be noted that in coun- changes in credit markets, output prices, foreign tries where inflation has been high-say, more than 30 exchange rates, and tax rates. or 40 percent per year-an entrepreneur's responses On the other hand, if each round of a panel ques- about monetary values of past revenues and expendi- tionnaire collects information about the wages that tures probably lose some accuracy. household enterprises pay hired workers (or would have to pay them if they hired any), and if these wages Panel Data on Enterprises vary between enterprises (in different areas-say, com- The issue of how enterprises change over time leads munities), one may estimate the impact of any wage to the question of how much can be learned by col- increase on household enterprise hiring practices. An lecting panel data (see also Chapter 23 on panel increase in the minimum wage is merely one example data). Panel data may be useful to track the increas- of a wage increase. ing role of household enterprises over time in many Panel data have value in both static and dynamic countries. Repeating LSMS questionnaires with economic environments. Even in static environments, unrelated samples in various consecutive years may luck or errors of judgment often cause the perform- help researchers examine the role of small enterpris- ance of enterprises to take turns for the better or for es in the economy both in recessionary times and in the worse. While some households continually invest times of rapid economic growth. Yet while using in their enterprises, others let their capital wear out unrelated samples allows one to describe an aggre- until it is no longer very useful. These choices cause gate degree of change in household entrepreneur- changes in income and employment that affect the ship, more information can be gained if the same household. Panel data help analysts see whether households are visited more than once. Researchers enterprises' lean years are temporary or permanent, can then observe which households and household how likely certain survivalist enterprises or microen- members have been more responsive to changing terprises are to succeed, and the long-term impact of external conditions. Has the response been better credit restrictions on enterprises. Panel data can also from poor or wealthy households? From men or be used to explore changes in the universe of enter- women? From better-educated or less well-educated prises-for example, to identify enterprises that have entrepreneurs (Schultz 1975)? closed since the last round of questioning. This may While there is a definite benefit to panel data, make it possible to find the former owner to ask why one must keep in mind that a panel study consisting the enterprise shut down and what the owner has of, say, four rounds generates data at only four points been doing since. And the use of panel data opens up in time. (For that matter, the same is true for repeat- new possibilities for analyzing interactions among dif- ed cross-sectional household surveys.) Between those ferent activities within a household, particularly in four points in time the economic environment terms of profit allocation among different household changes for a variety of reasons. And it is difficult to members and activities. identify which economic factors affect the perform- When panel data are collected, survey costs may ance of the enterprise: does enterprise performance be high. However, these costs can be reduced if the change because of a changing regulatory environ- entire questionnaire is not administered to the house- ment, a macroeconomic policy variable, a structural hold each round (or year).Yet because the household market phenomenon? enterprise module contributes information that is Unless there is a dominant event during the oper- essential for determining a household's living stan- ation of the panel questionnaire, the researcher is able dards, one may wish to administer at least the short to describe responses of household enterprises but- version of the questionnaire to each enterprise, along without other relevant information-not exactly to with questions that gather information about enter- what they respond. Suppose the legal minimum wage prise turnover.And given the flexibility that character- is raised. Household enterprises might be expected to izes good entrepreneurship, it is recommended to reduce their hiring of labor.Yet comparing the hiring gather the full amount of information at least every practices of enterprises before and after a minimum two years. (This will sufficiently account for the lag in wage hike does not show a clear correlation unless one the impact of any policy.) 1 14 CHAPTER 18 HOUSEHOLD ENTERPRISES Enterprise Income, Sales Revenue, and Expenditures model questionnaire in this chapter asks for many Ensuring the accuracy of the data on enterprise details. income is the most challenging part of the enterprise Still, it is useful to have some yardsticks against module. The income yielded by an enterprise can be which to evaluate revenue responses. A simple yard- measured in two ways: directly by asking the owner, or stick might be the following. After inquiring about indirectly by subtracting expenditures from sales rev- recent revenue, the interviewer asks, "Has your busi- enue. The following discussion focuses on the sales ness made more sales since my last visit than in the two revenue and expenditures variables as well as the vari- weeks before that visit?" and gives the respondent the able for enterprise income. choice of three answers-more sales, fewer sales, or about the same number of sales. This question SALES REvENUE. At a minimum, an LSMS question- appeared in theVietnam questionnaire. Indeed, it hap- naire should inquire about recent revenue and revenue pened that some entrepreneurs happily stated that they over the previous 12 months. (For enterprises not had made "more sales" but actually reported a smaller operating when a questionnaire is administered, only figure, or vice versa. This may highlight possible inac- the 12-month question is asked.) This strategy is based curacies in the reported sales, but it is also possible that on two ideas. First, an entrepreneur is likely to give the entrepreneur is not responding correctly to the more accurate answers about his or her enterprise's question itself. It is therefore imperative to incorporate recent economic performance than about its past eco- other yardsticks with which one can examine the nomic performance-so even if the recent period is accuracy of the revenue data. (The section below on not representative of the events throughout the year, enterprise income will return to this issue and propose information provided about this period will be of solutions.) higher quality and therefore be useful to researchers. The questionnaire must distinguish among differ- Second, it is useful to have information about an ent kinds of revenue: cash receipts, in-kind payments enterprise's patterns of revenue over a longer period of for goods and services, and home consumption.9 time, even if the respondent is less than perfectly accu- Typically, fewer than 10 percent of the entrepreneurs rate in his or her recall.8 indicate that they have received in-kind payments. For Comparing figures reported for recent revenues slightly more than 25 percent of the enterprises, with reported figures for 12-month revenue may respondents report some home consumption. reveal wide variations when both recent and 12- month revenue flows are expressed in monthly values. ENTERPRISE EXPENDITURES. To measure enterprise Recent revenue in the Pakistan survey was less than expenditures on inputs, a questionnaire must contain, half the 12-month revenue for 12 percent of the cur- at a minimum, a grid describing expenditures on a rently operating enterprises and more than double the specified list of inputs, with the following kinds of 12-month revenue for 4.9 percent of them. In questions for each input item: "Ql: During the past Vietnam, recent revenue xvas less than half the 12- 12 months, did you purchase ... ?" "Q2: How much month revenue for 7.9 percent of household enter- did you usually pay for ... ?""Q3: How often did you prises and more than double the 12-month revenue pay for .. .?"" These questions do not yield particu- for 18.2 percent. The two measures of revenue occa- larly good data, for three reasons. First, there is sub- sionally differ tenfold or more. stantial variation in how many items, and what types Several factors may explain the difference of items, are listed by entrepreneurs. In existing between recent and 12-month revenue flows. High LSMS data sets most entrepreneurs mention one, inflation may distort people's memory of past mone- two, or three items, and some mention none-with tary values; business cycles may cause fluctuations in wide variation in the items that are mentioned. enterprise performance; respondents (or interviewers) Certain items may be used but not purchased; may be unclear about the length of the reference peri- respondents often mention purchases of either raw od that applies to the questions asked; respondents may materials or items for resale but rarely both, regard- have difficulty recalling a 12-month revenue figure; less of whether their enterprises are in manufactur- and seasonal factors may play a role. In the hope of ing, restaurant services, or the retail trade; and few avoiding these problems, the revenue section of the enterprises report expenditures on electricity, fuel, or 1 15 WIM R M.VIJVERBERG AND DONALD C. MEAD water, even in sectors where they ought to be com- There are four sets of clues. First, the most extreme mon. Of course, differences in production techniques examples are found in Cote d'lvoire and Ghana, may explain some of the variation. where the questionnaires were administered in the The second difficulty with these data is that an mid-1980s. The results from Ecuador, Pakistan, item purchased by an enterprise may well be shared Tanzania (1994), andVietnam are more recent and are with the household and with other enterprises.'1 If more plausible, suggesting that questionnaire design sharing takes place, there is measurement error in both had improved, interviewer training was better, and household consumption expenditures and enterprise interviewers were more alert to potential misreport- expenditures (or in the value of expenditures in the ing. However, the percentage for Peru (1985) is low as two enterprises involved).The third problem with the well. data is that when an enterprise receives inputs (such as Second, Vijverberg (1992) found that in Cote electricity, water, or the use of tools) from a household d'lvoire and Ghana negative profits occurred in enter- or from other household enterprises, the entrepreneur prises regardless of the entrepreneur's education and does not report the use of these inputs because there regardless of whether the interviewer spoke with has been no purchase. For an analysis at the household someone other than the entrepreneur. Third, among level, this is problematic since both the value of house- the seven countries listed in Table 18.1, the percentage hold consumption expenditures and the value of of enterprises with negative profits appears to be lower household income are overstated by the value of the in countries with a higher general education level. It is inputs received by the enterprise. For an analysis at the indeed plausible that general numeracy among the enterprise level, this causes expenditures to be under- population improves the accuracy of household ques- stated while revenue is unaffected-making the enter- tionnaires. Fourth, while it is possible that enterprise prise seem very efficient. income is extremely variable over time, it is hardly The three problems are related, and can be solved plausible that such a large percentage of enterprises in one stroke by ensuring that the questionnaire care- would tolerate negative cash flows for long. fully accounts for the use rather than just the purchase All in all, the clues show that the income figures of inputs. The questions should reflect the fact that are probably skewed toward the negative.The lesson to usable inputs can be acquired by purchasing them, by be learned is that the questionnaire should be designed borrowing them from relatives, friends, or household so that it yields accurate information and also enables members, by picking them up if they are discarded or researchers to cross-check reported revenue and free (such as firewood or packaging materials), or by expenditure figures with the answers to some other receiving them as gifts.Accounting for input use in the direct or indirect questions. questionnaire will also yield more information on One such check is accomplished by including the each enterprise's involvement in formal markets, since following question: "After making purchases for the some of the above methods of input procurement are nonmonetary market transactions that substitute for Table 18.1 Percentage of Enterprises with Negative Profits, purchase in formal markets. Selected LSMS Surveys Country Type of Percentage of enterprises ENTERPRISE INCOME. In principle, enterprise income is of survey profits computed with negative profits defined as the difference between an enterprise's rev- C6te d'lvoirea Food commerce 63.5 Nonfood commerce 38.7 enue and expenditures. In every existing LSMS data Ecuador Last month 19.0 set, enterprise income is highly variable across enter- Ghana' Commerce 63.5 prises, with large outliers at both the positive and the Food manufacturing 56.0 negative ends of the spectrum." Table 18.1 shows the ~ ~ ...................................."t.................................................... 24...9................. negative ends of the spectrum.ii Table 18.1 shows the Pakistan Recent 24.9 percentage of enterprises in various LSMS data sets Normal 21.1 Tanzania Recent (or else normal) 29.2 to 35.5 with negative calculated profits-enterprises that lost Vita nclsvst *13 Vietnam Since last visit I13.7 money even before the values of family labor and Past 12 months 14.9 assets were taken into account. At least in some coun- a. Profits refer to profts received since interviewers' last visit if the enterprise was in operation at the time of the previous interview, during the past 12 tries, these percentages are mplausibly high. Is this a months if it was not. real phenomenon or is measurement error to blame? Source: Vijverberg 1992. 1 16 CHAPTER 18 HOUSEHOLD ENTERPRISES business, is there usually any money left? If so, how use. In terms of actual money values, only 40 percent much?" In recognition of the fact that enterprise and of the enterprises' profit and net revenue values are household monies are sometimes intermingled, it is within 25 percent of each other; for another 20 per- also useful to ask: "Do you use part of the money you cent of the enterprises, one value is less than twice the get from this business for yourself or for your house- other. While some net revenue values are large, they hold? If so, how much?"'3 "Net revenue" is defined as are not nearly as extreme as profits seem to show. And the respondent's estimate of the amount of money net revenue values correlate more closely with busi- taken from the business for household use plus the ness asset values than do profits.'4 value of the household's consumption of the output of Two conclusions may be suggested by the find- the enterprise (Vijverberg 1992). ings. One is that because of the great difficulty in pre- In an ideal data set, net revenue should be the cisely estimating enterprise income from a household same as profits calculated from other data in this mod- survey such as the LSMS, it may be better to save time ule (in other words, total revenues of the enterprise and energy by concentrating on simpler measures- minus total expenses). However, experience with past even while recognizing them to be incomplete and LSMS data is sobering.Table 18.2 shows the dispersion imprecise. A second, opposite conclusion is that of enterprise revenues for Vietnam. Enterprises are because many of the most important policy issues divided into five quintiles, first according to their net relating to household enterprises require accurate esti- revenue and then according to their calculated profits. mates of enterprise income, even more energy should The table shows the cross-tabulation of these two be devoted to collecting the most accurate possible group values. If data measures were accurate and con- estimates of income earned by these enterprises. sistent, each of the diagonal cells (those on row 1, col- If the decision is made to aim for a simpler meas- umn 1; row 2, column 2; and so on) would contain ure that is not fully precise but that is more easily one-fifth of the enterprises. Instead, the lower left and understood (and therefore more Ekely to yield mean- upper right corners show significant numbers of ingful results), this might mean paying only cursory enterprises where one income measure is high and the attention to various types of transfers between an enter- other is low. Three-quarters of enterprises in the first prise and other household activities. It would then be column would run at a loss ifjudged by their account- possible to develop the principal expense and revenue ing profit, even though some of their owners stated categories such that they could be compared during the that they have a substantial sum left over for household course of the interview and presented to respondents to Table 1 8.2a Comparing Net Revenue and Recent Profit,Vietnam Quintile rank for recent profit Rank for net revenue 1 2 3 4 5 Total .! ............. 7.71 5.93 2.71 1.87 1.78 20.00 2 4.25 8.27 4.21 1.96 1.31 20.00 3 2.90 4.44 6.92 3.36 2.48 20.09 ........................................... ..............................I...0.3................................5....6..5................................7...2.9...............................3...3..6................................. 19...5... 4 2.62 1.03 5.65 7.29 3.36 1 9.95 ......*................... .................. ....... *.................................................................1...............................5.....-1............................I.T...................................... 9. '95. ... 5 2.52 0.33 0.51 5.51 1 1.07 19.95 Total 20.00 20.00 20.00 20.00 20.00 100.00 Source: Authors' computat ons. Table 18.2b Comparing Net Revenue and 12-Month Profit,Vietnam __ ---- Q Quintile rank for 12-mnonthLrofit__ _ __ Rank for net revenue 1 2 3 4 5 Total ! 7.93 8.54 2.00 0.71 0.75 19.93 2 3.57 8.86 4.68 2.11 0.82 20.04 3 3.00 1.64 9.93 3.57 186 20.0 ..................................................................... .........................................5j................................................................................................... H~.... 4 2.46 0.54 2.93 10.64 3.46 20.04 5 3.04 0.43 0.46 2.96 13.1 1 20.00 Total 20.00 20.00 20.00 20 00 20 00 i100.00 Source: Authors' computations. 117 WIM R M.VIJVERBERG AND DONALD C. MEAD ensure that, in broad terms, their values are indeed com- an entrepreneur's responses into monthly figures of parable. The standard version of the questionnaire pre- total revenue, total expenditures, and net enterprise sented in this chapter has taken this route. income. The interviewer would immediately be able to Another approach in an abbreviated LSMS might verify these calculations with the entrepreneur and be to ask questions only about net revenue, without make any necessary corrections. However, calculating separating revenues from expenditures. (For further these figures requires a large number of data items, and discussion see Chapter 17 on measuring total house- would be difficult to do manually. To solve this prob- hold income.) While this may seem likely to yield lem, the interviewer could be provided with a laptop enterprise performance information at least as the computer loaded with software that contained a fully accurate as the information yielded by separate gross coded questionnaire. As he or she entered the respon- revenue and detailed expenditure questions, it is prob- dent's answers to the questions into the computer, ably less accurate. Why? In essence, the entrepreneur's another piece of software would automatically and response to a net revenue question is an educated immediately use the data entries to calculate revenues, guess, informed by his or her knowledge about the expenditures, and income of the enterprise in question. revenues and expenditures the enterprise incurs, by a This process is known as "computer assisted personal desired income level, or by his or her perceived con- interviewing." (The questionnaire software would con- sumption expenditures. The response ought to be tain all skip patterns, which would have the additional related to the performance of the enterprise, but there advantage of reducing skip errors as well.) Of course, is no guarantee that it is. computer assisted interviewing has costs as well as the In addition, the detailed revenue and expenditure benefit of increasing data accuracy. Using laptop com- questions provide other important insights about the puters for fieldwork is expensive, makes supervision of entrepreneur's business environment (for example, the interviewers more difficult than in the present system, degree to which the enterprise participates in the and requires interviewers who are more skilled. market) that could be influenced by public policy. Independent of the accuracy of the income estimates, Employment in the Enterprise both the gross revenue/expenditure questions and the A variety of facts are needed to explore employment- net revenue questions contribute useful information related policy issues. First, it is necessary to identify and should be retained. which household members are employed by the It may be possible to check on the accuracy of enterprise and how much work they do. This allows income estimates by asking detailed questions about analysts to relate the characteristics of household inventories.When an entrepreneur states the value of members-such as age, migration status, any illness, his or her inventory, the interviewer can ask how and level of schooling-to the operation and per- many business days this inventory will provide for. formance of the enterprise. Next, details must be col- However, it is important to note that inventories are a lected regarding any paid workers, apprentices, and more meaningful concept when an enterprise is cur- nonhousehold unpaid workers in the enterprise.What rently in operation. A seasonal enterprise or an enter- skills do these workers have? How much are they paid? prise that has ceased operating is less likely to carry an How many are male and how many female? How inventory, and even if it does carry one, the inventory much time do they devote to working in the enter- is unlikely to be reconcilable with any particular rate prise? After these details have been collected, the sur- of production or sales. vey should establish the size of the enterprise's work Another possible check could be to ask questions force over the past few (say, up to five) years-and per- about variations in business sales.The interviewer would haps also past characteristics of the work force ask the entrepreneur to describe both daily variations in (although this is very demanding on the respondents). the sales of the enterprise and how the revenues of the These data will show growth in employment, which enterprise relate to monthly expenditures. In this way can be related to the characteristics of the entrepre- the entrepreneur would be prompted to reveal his or her neur and the household. Finally, while seasonal estimate of the enterprise's cash expenditures. employment patterns are likely to follow seasonal sales The most effective method to collect more accu- patterns, it is nevertheless helpful to ascertain patterns rate data would be a quick, on-the-spot conversion of of employment in the enterprise over one year. 118 CHAPTER 18 HOUSEHOLD ENTERPRISES In order to save time and money, it may be tempt- The standard questionnaire in this chapter con- ing to collect work information about household tains employment (and labor seasonality) questions members in the employment module (see Chapter 9). within the enterprise module. This avoids any match- This strategy, followed in previous LSMS question- ing problems.And if necessary, responses that an entre- naires, has been problematic. In all but two cases sur- preneur gives in this module can be cross-checked veys collected information about hours of family labor against responses that individual household members in the employment module, during the first visit of the give in the Chapter 9 employment module (although interviewer to the household.5 To allow researchers to such a cross-check is subject to the timing and match- link data from the enterprise module with the ing problems mentioned above). The questions are responses of household members to questions about designed to have the same reference period as the their economic activities, many surveys (such as those income information in Part C of the employment in Vietnam and Ghana) have asked entrepreneurs to module. report the names of the household members who The expanded questionnaire additionally asks work in the enterprise; the interviewer recorded the whether and how much the enterprise paid to mem- ID numbers associated with these names. In principle bers of the household for their labor-a line of ques- researchers could use this information to consult the tioning that sheds light on intrahousehold income dis- employment module and extract the relevant enter- tribution. The short version of the questionnaire prise labor information. In practice this is not so easy. reverts to old practice, asking only for IDs of house- It is not immediately clear whether the researcher hold members working in the enterprise. should look at those members' main or secondary jobs during the previous week (or during the previous Business Assets year). What if two or more of a family member's jobs Business assets are an important determinant of enter- match? What if the family member claims to work in prise performance. Enterprise performance can be a different industry? What if the family member does measured not only by labor productivity or by the not report any hours of work in self-employment? If a absolute amount of income generated but also in household operates two enterprises in the same indus- terms of the percentage return to investments in the try, and both entrepreneurs claim that member as hav- enterprise. And an enterprise's start-up and subsequent ing worked for them, with which enterprise should a performance depend heavily on the entrepreneur's given family member be linked? What if other family ability to acquire the assets needed to be competitive members claim to work in self-employment in the in the sector. If one of the purposes of a particular sur- same industry as the enterprise, but the entrepreneur vey is to investigate the credit needs of small-scale pri- does not report employing them? Both conceptually vate enterprises, it is important to collect information and practically, there are likely to be numerous match- about business assets. ing problems that will be very time-consuming to sort Business assets come in two forms: fixed assets and out. inventories. Fixed assets include land, buildings, tools, The questionnaire in this chapter is inspired by the machinery, furniture, and vehicles used by the labor Pakistan and Ecuador formulations. Unlike previous force. Inventories consist of raxv materials, intermedi- LSMS surveys, the Pakistan questionnaire did not have ate goods that need to be further processed, and fin- an employment module. Instead it asked about hours of ished products ready for sale. Finished products are work at the same time that it explored wage employ- especially important for trading enterprises but can ment, farm labor, and work in family enterprises. Both also be significant for manufacturing enterprises. the Pakistan and Ecuador questionnaires required the Current enterprise performance is determined by entrepreneur to list ID numbers and working hours of the business assets in use at the moment.Therefore the all of the household members who work in his or her questionnaire must focus on the typical value of assets enterprise. This prevented many of the matching prob- in use during the reference period. Recent enterprise lems from arising.'6 The household enterprise module income can be analyzed using the current value of in the Ecuador questionnaire asked entrepreneurs for business assets. To analyze income over the past 12 the ID numbers and hours of work of the household months, more information is needed: the value of cur- members who worked for the enterprise.'7 rent business assets as well as sales and purchases dur- 119 WIM P. M.VIJVERBERG AND DONALD C. MEAD ing the past 12 months.8 While it matters when these subject to the same (national) regulations. However, if sales and purchases took place, asking for such dates is the LSMS survey is administered in a country where too burdensome in a multi-topic LSMS survey. local regulations vary across localities, these areas are Rather, assuming that sales and purchases took place particularly valuable targets for information gathering. on average a half year ago, the typical value of business In most cases it is important to know whether entre- assets in use over the past 12 months may approximat- preneurs abide by regulations that should, in principle, ed by apply to them. It may also be important to find out from entrepreneurs whether they are aware of the reg- [current value of assets] + [value of assets sold]/2 - ulations and whether they have sought to act on them. [value of assets purchased]/2. The expanded questionnaire in this chapter contains one example of such questions, regarding enterprise For land and buildings, one might also ask whether the registration. enterprise made any expenditures on improvements; It is equally difficult to measure the impact of these may be counted as assets purchased. Note, macroeconomic variables.The draft questionnaire asks though, that the usual quantity of inventories is diffi- about the incidence of taxes; this question could be cult if not impossible to measure; the questionnaires expanded to fit local conditions. Entrepreneurs could outlined in this chapter ask only for current values. also be asked about what opportunities they have to For many purposes, the most important question purchase foreign currency, their experience in buying about fixed assets is not so much what assets are owned imported commodities (inputs), and the competition by the enterprise but rather what assets it uses. An they face from imported products. entrepreneur may rent, own, or borrow assets from a The community questionnaire should contain neighbor or relative or from another enterprise oper- questions about community characteristics that affect ating in the household. Experience with previous the performance of the enterprise. Such characteristics LSMS data sets indicates that a significant proportion include roads, other infrastructure (such as railroads, (about one-fourth) of household enterprise owners waterways, telecommunication systems, market cen- report owning no assets, and those that do own assets ters, and utility services), and the presence or absence often share them with household members or with of banking and credit organizations. In addition, gov- other household enterprises; this is particularly the ernment and business associations should be asked in case with vehicles.'9 If an asset is shared, it contributes the community questionnaire about the nature of not only to the income of the enterprise that owns it their enterprise assistance programs (both direct and but also to the income of other enterprises or to gen- indirect). For specific examples of such questions see eral household welfare. In light of this fact it is neces- Chapter 13 on the community questionnaire. sary to devise a way to account for the complex Still more data are needed if all the policy ques- sources and uses of business assets; for one possible tions outlined in the first section of this chapter are to method see the expanded version of the questionnaire. be addressed.The amount of exposure that members of the household have had to entrepreneurship may be an Policy Variables important determinant of the household's decision to Variables must be collected that indicate the extent to start up an enterprise. This exposure may have come which enterprises are involved in formal and informal from parents or other kin of household members. It credit markets-both in receiving credit from sources may also have come from friends, but the influence of (such as suppliers and banks) and in extending credit a circle of friends is harder to measure in a standardized to others-and on what terms the credit is provided. manner across a sample. Moreover, causality may be In addition, the questionnaire must ask about the var- difficult to establish; does friendship with other entre- ious forms of (noncredit) professional assistance that preneurs stimulate someone to start an enterprise, or an enterprise might have received-for example, in does the operation of an enterprise cause the entrepre- product design, quality control, management tech- neur to rub shoulders with other entrepreneurs? niques, or bookkeeping. Does receiving an inheritance prompt people to It is hard to measure the impact of regulatory start up an enterprise? To answer this question, it is reform on household enterprises if all enterprises are necessary to ask a question about when a household 120 CHAPTER 18 HOUSEHOLD ENTERPRISES inherited major amounts of wealth-a question that household members. (In any case, it is unlikely that would need to be posed to all households, and not any household survey will ever measure consumption only in the context of the household enterprise mod- expenditures at the level of individual household ule. Data about the receipt of an inheritance could members.) then be analyzed together with the data from the The impact of income earned from household enterprise module about the age of the enterprise. enterprises on household consumption patterns can It would be useful to measure the way an enter- be measured by correlating household expenditures in prise has grown in the past, both in terms of income certain consumption categories with the earnings that (of both the enterprise and its employees) and in terms the entrepreneur and those members of the household of employment. Retrospective information about who are explicitly paid to work for the enterprise employment is less likely to be tainted by rccall errors bring into the houschold. Questions on thcse meas- than retrospective information about income because ures are included in the model questionnaire.To make the employment information is less detailed and easi- this analysis complete, it is necessary to have a full er to recall. However, most enterprises employ only accounting of every kind of income for each house- one or two household members; in such cases, growth hold member, including xvage earnings, farm income, is evident more in their work effort and in the income pensions, remittances, and interest income. For a more that the enterprise generates than in employment cre- elaborate discussion of these issues see Chapter 24 on ation. The draft questionnaire in this chapter asks intrahousehold transfers. about the number of people employed by the enter- prise one and two years previously and also over the Summary previous 12 months (to measure both seasonahty and Table 18.3 summarizes the data requirements implied trend growth). by the policy issues and research questions outlined in Another policy question concerns the impact of the first section of this chapter.The table refers to spe- human capital on household enterprise performance. cific questions by their number in the expanded ver- The human capital of household members is measured sion of the questionnaire. The last column of Table in the education section of the core LSMS question- 18.3 highlights the need for data collected in other naire (Chapter 7); the household enterprise module modules of the LSMS questionnaire. asks which household members work for the enter- The prospects for analysis with data generated by prise. No previous LSMS survey has asked for infor- the short, standard, and expanded versions of the mation about nonhousehold workers beyond what model questionnaire are rated from not possible to number is employed by the enterprise.The draft ques- poor to fair to good. In all cases it must be remem- tionnaire asks how many of an enterprises' workers bered that the sample is of small-scale enterprises, and have reached a certain level of schooling and how excludes corporations, public/parastatal and foreign many of them are "skilled" in the eyes of the entre- enterprises, and most joint ventures. As always the preneur. This "skill" measure is somewhat subjective, quality of the analysis depends heavily on the quality but it is impossible to measure in a more precise way of the data, which is influenced in crucial ways by how given the great diversity among household enterprises well the interviewers are trained and how well they in most developing countries. are supervised in the field. Finally, in order to measure the distributional effects of household enterprises within a household, Draft Module the questionnaire must include questions about who received money from the enterprise and how they (or The expanded version of the household enterprises the household at large) spent it. Some household module contains the most complete but also the most members may be explicitly paid by the entrepreneur; demanding list of questions. The standard version of the others may receive implicit pay when they spend a module, which abbreviates this list, does not permit part of the enterprise's revenue for consumption pur- analysis of some of the policy questions but still attempts poscs, to meet eithcr their owvn or the household's to provide good measures of important data such as needs. It is therefore difficult to get an exact account- enterprise income and employment (see Table 18.2). ing of the distribution of all enterprise income among The short version, even more abbreviated, aims to col- 121 WIM P. M.VIJVERBERG AND DONALD C. MEAD Table 18.3 Household Enterprises Module: Requirements and Links with Other Modules Short version Standard version Expanded version Prospect Prospect Module Prospect Module Data needed Issue for analysis for analysis questions for analysis questions from other sections I. Number of enterprises Good Good B: 1, 3: C:6-7 Good .......................... .... T n ............................................................................................................................................................................................................ . 2. Sectoral and locational Good Good B:3; C:4; E: 1-3 Good characteristics .....................,,................................................................................................................................................................................................... 3. Measuring enterprise income Fair Good C:5-6, 8; D:27-28, Good F: 10- 13, 36-37, 46-47, 53-55; 15-18,22-25 E:4- 13, 19-26, 29, 34, 36-44; F:2-4, 14, 19-21, 26-27, 33-34; G:22-23; H: 1-3, 6-1 1, 31-40 4. Poverty Fair Good All of issue 3 Good Al of Core: household con- issue 3 sumption expenditures S.Variability and seasonality Good Good E27 2 0,9-30, 34, Good 6:28, 31-33 in income 36-35; H:31-33 6. Earnings of hired labor None Fair D:24-28, 32-37, Good D.:29-3 i 43-47 38-40,48-50 7. Employment Fair Fair D:1-6,9-1 1, 14-17, Good D:18,29-31, 21,24-26,32-35,41-45 38-40,48-58 8~~~~~~~.... Sesoa,t ,,,n, employment, ,,, None Poor,,,,,, ,,,,,,,,, Good E:35 9. Employment growvth Poor Poor E:30, 36-39 Fair D:56-57; E:31-33, 35; G:39 ID. Entrepreneurship Poor Poor B:3; C:3-5; E:26-27, Fair A: 1-2; B:2; Community; infrastructure; 29-30 H: 14-16 Core: human capital .............................................................................I...................I.....................-.......................................I........I...................................................... 11. Determinants of income Fair Fair All of issue 3; C:3-4 Good Al of issue 3; Community: infrastructure; G: 1-3, 21-23, 29-33, G:7-20, 24-28; Core: human capitai, size 37-38; H: 1-2, 6-9 H: 17 and value of residence .............................................................................................I.......................................................................I..............................I........................... 12 Education, training, Good Good B:4; C3; D:4, 15 Good A: 1-2 Core: employment history, and experience human capital, training, apprenticeships . .. . ... ... .. .. ... ... . ....................................................... . . ... .... .. .. . .. .. .. I. ... ........................ ......... . .. I ..... ... .a 13. Impact of government Nonc Nore Fair All of Issue 3; training programs G:0- 39; H: 17 .....!....................................................................................................................................................................................................... ................ 14 Marketing patterns None Poor E:27, 30; F: -4, 19-21 Good E:33; F:5-6, 10-18,22-24; H:4-5, 12-13 iS. iocation and nfrastructure Fair Fair All of Issue 3 Fair All of issue 3; Community: infrastructure B:2; H: 14 I 6..Finance and.creditGmarkets None None Good E:14-16; F:7-9; Core: household G:34-36; credit; Community: H:23-30 private sector description; Credit interest rates. individual credit ........ .....................I............................................................."........................................I........................ ............*................................................. 17. Regulaion and enterprise Poor Fair All of issues 3 and 7; Fair All of issues 3 performance F:26-27 and 7; F:28-32; H: 14 i8. Macroeconomic policy and None Fair All of issues 3 and 7; Fair All of issues 3 and 7; enterprise performance D:53-55; F:33-34 D:5 1-52; H: 18-25 ....... ........................................................................................................I......................................... .........................................I....................... 19. Enterprise performance and Good Good All of issues 3 and 7 Good All of issues Depends on issue other household activities 3 and 7 to be studied 20. Enterprise performance Good Good All of issues 3 and 7 Good All of Issues over time 3 and 7 2.intrahousehold interactions Poor Fair All of issue 3 Good All of issue 3; Depends on issue to F: 12-14, 18-25; be studied G7-15, 24-28; H4 I .................................................................................I.........................................I................................................................................... ........*.......... 22. Extrahousehold linkages None Poor G:21-23 Poor F:17;G:16- 8 Community: private sector description ............................................................................................................................................................................................................................ 122 CHAPTER 18 HOUSEHOLD ENTERPRISES Table 18.3 Household Enterprises Module: Requirements and Links with Other Modules (continued) Short version Standard version Expanded version Prospect Prospect Module Prospect Module Data needed Issue for analysis for analysis questions for analysis questions from other sections 23. Legal reform None Poor All of issues 3 and 7; Fair All of issues D:53-55; F:26-27 3 and 7; D:5 1-52; F:28-32 ............................................................................................................................................................................................................................ 24. Macroeconomic variables None Poor All of issues 3 and 7; Fair All of issues D:53-55; F:33-34 3 and 7; D:5 1-52; H: 18-25 25. Culture, location, development Fair Fair All of issues 3 and 7: Fair All of issues Core: urban/rural C:4 3 and 7 residence, ethnicity, religion; Community: culture Source: Author's eva uation of the household enterprise modu e. lect information on enterprise income, value (as meas- mended to study the questionnaire in combination ured by assets), and employment statistics-primarily for with the annotations provided in the last section of the study of other household-related issues. this chapter. Each version of the household enterprise module As a whole, the expanded version is probably too is divided into the same eight parts, which are labeled long to be administered in one LSMS survey. While A through H. The three versions are related and have the expanded version contains questions on every many questions in common. In some cases the core issue discussed above, not all of these questions should questions are restated in the different versions of the all be posed to respondents at the same time. A careful questionnaire. selection of topics of interest should limit the length of The respondent for parts A and B of the module is the questionnaire. the head of the household. For the rest of the module- The standard and expanded versions of the ques- used if the household operates an enterprise-the tionnaire follow the same basic format. The short respondent for each enterprise should be the person in questionnaire follows a somewhat different design, charge of the enterprise or the person most informed making it difficult to generate a customized version about the enterprise. (This person generaly works in that, in length, is somewhere between the short and the enterprise, although in exceptional cases, such as ill- standard versions. ness, he or she may not.) Table 18.5 shows average numbers of questions As was mentioned in the previous section, it is asked in the household enterprise module of an LSMS essential to gather information about all the enterpris- survey.The column labeled "per enterprise" counts the es within any given household. Part B of the proposed average number of questions asked of an enterprise; questionnaire alows up to six enterprises to be listed, the column labeled"per household" shows the average along with the industries in which they operate, the Table 18.4 The Parts of the Household Enterprise Module household members most informed about and/or in charge of their day-to-day operations (often referred Part Respondent Topic to as the "entrepreneurs"), and the sequence numbers A Household head Household exposure to entrepreneurship (1, 2, . . . ) that the interviewer assigns to enterprises B Household head Existence of nonagricultural enterprises within a household. Parts C through H of the module c. Entrepreneur General information about the are used to inquire about the enterprises. These parts enterprise contain a grid for three enterprises; if a household D Entrepreneur Employment of household and nonhousehold labor operates more than three enterprises, the interviewer E ........ nonhsehed labo r must enter information about the fourth, fifth, and F Fntrepreneur ion sc expesire F......E.~n't-re-p-re-ne-u-r...... In"p"ut ..u'se ..a"nd ..e"xp"en"di'tu're ............ sixth enterprises onto another household question- G Entrep-enu; Businessassets naire form.20 H Entrepreneur Inventories: enterprise start-up; Table 18.4 summarizes the content of each part of assistance programs; exposure to th questionnaire. To get a good grasp of the purpose international markets; enterprise debt; the questionnaire. To get a good grasp of the purpose trade credit; enterprise income of questions and skip patterns, it is highly recom- Source. Authors' summary of the household enterprise module. 123 WIM R M.VIJVERBERG AND DONALD C. MEAD Table 18.5 Average Number of Questions in the Household Enterprise Module Expanded version Standard version Short version Part/Type of question Survey items Per household Per enterprise Per household Per enterprise Per household Per enterprise A Exposure Al-A2 5.40 n.a. 0.00 n.a. 0.00n.a. ... ......... .........................................................................................................................................................I................................................... ..... B Enterprise existence B l-B4 2.64 3.00 2.04 3.92 2.04 3.92 .................................................................................................................................................................................................................................. C General characteristics Cl-C9 2.94 5.65 2.94 5.65 2.94 5.65 ................................................................................................................................................ I................................................................................... D Household labor D I-D 19 4.38 8.43 3.52 6.78 1.70 3.28 ................ ..a""or.....................-0.........-0................... 2.....'......................3.....-0......................0.....5,...................... I.....4,.................... 1............................. 2...13 .. . .... Nonhousehold labor D20 D50 2.03 3.90 0.85 1.64 1.11 2.13 Minimum wage D5 I -D52 0.65 1.25 0.00 0.00 0.00 0.00 .......... .................................... ..................... ............................................... ...... .................... ...... ............................................... .... .. .... Social security D53-C55 0.83 1.60 0.83 1.60 0.00 0.00 Labor growth D56-D57 1.04 2.00 0.00 0.00 0.00 0.00 ....................................................................................................................8.93..................... 17... 8 ......................5...20 .................... 10. 22 8 . 4 1 PortC.totol 8.93 17.18 5.20 1Q.02 2.81 5.41 E Type of enterprise EI-E3, E17 2.08 4.00 1.56 3.00 0.00 0.00 T:rading enterpnise E-E 13 5.04 9.70 5.04 9.70 0.00 0.00 Credit E 148E 16 0.43 0.83 0.00 0.00 0.00 0.00 Revenue El 8 E26, E29, 5.15 9.90 5. 5 9.90 4. 8 8.05 E34, E40-E44 ............................................................................................................... .................................................................... ................................................ Interrupted operation E27-E33 0.42 0.80 0.21 0.41 0.00 0.00 . ............................................................................................................................................................. *..................................................................... Seasonal labor E35 6.76 13.00 0.00 0.00 0.00 0.00 Seasonalirevenue E36-E39 7.80 15.00 7.80 15.00 7.80 15.00 Port E toota 27.68 53.23 19.76 38.01 11.98 23.04 F Resources FI-F6, F20-25 23.53 45.25 10.47 20.13 0.96 1.85 ................................................F2"6-F32 .....................2.'22....................... 4...27 ......................0... 60 ......................I... F,..................... 0 00.........*..............0... 0 ............ Credit F7-F9 1.29 2.49 0.00 0.00 0.00 0.00 Registration F26-F32 2.22 4.27 0.60 1.15 0.00 0.00 ............................................................. ~TT4................... .....................F,..................... ....................I...25 ......................0.'0........................0... 0 ............ O)ther taxes F33-F34 0.65 1.25 0.65 1.25 0.00 0.00 .................................................................................................... *......................................... .................................................................................. Port F rotol 27.46 53?26 I (.7 22.53 0.96 1.8 G Business assets G I-G33, 28.46 54.74 18.59 35.76 7.88 15.15 G37-G39 ..... ' 't..................................................G34' G"36................... 0... 31......................0... 60.............. *.......0... 0 ...................... 0... 0 ......................0.OO........................ 0... 0 ............ Credit G34-G36 0.31 0.60 0.00 0.00 0.00 0.00 PortGtotal 28.77 55.33 1859 35.76 7.88 151.5 ........................................................................................................... I........................................................................................................................ H Inventory H I -H3, H6-H I1 2.55 4.90 2.55 4.90 1.04 2.00 ....... ..........................................-4.........'6................... 1.....'......................3.....-0......................0.....0'......................0.....0'..................... 0....0........................0... 0 .. . .... Markets H4-H5, H 12-HI 13 1.46 2.80 0.00 0.00 0.00 0.00 ....... ...... .............* ..............* ........H 17....*....................... 2.60 ......................5... 00......................0.... 00,......................0.... 00,......................0.OO.....*..................0... 0 ............ Start-up H14H H16 1.56 3.00 0.00 0.00 0.00 0.00 ..................................................................... ..................1.'i..................... F 6 .................... ' 6 .................... 6 ................... .6 *............. 6 ........... Assistance H 17 2.60 5.00 0.00 0.00 0.00 0.00 Customer credit H27-H30 1.07 2.05 0.00 0.00 0.00 0.00 ............................ ............ ............................................ ........................................................................................ .... Enterprise income H31-H49 4.52 8.70 4.52 8.70 i.66 3.20 .............. ................................ ................. ...... .......................... ................................... ................ ................. ....... Use of income H4 1 0.52 1.00 0.00 0.00 0.00 0.00 ............... ................*.............................................I 6..............................-2.....5,...................... 7.....7,...................I 3............................ 2.....-0........................5...20 .. . .... Port H totl 16.82 32.35 7.07 13.60 2.70 5.20 Totolforaiiporrs i20.64 220.00 67i3; i29i49 3i.31 60.22 n.a Not appl cable. Soarce: Authors' estimatior based on experience with previous LSMS surveys. 124 CHAPTER 18 HOUSEHOLD ENTERPRISES Box 18.2 Cautionary Advice * How much of the draft module is new and unproven? In its accuracy of measured enterprise income may be judged. basic design, the household enterprise module provided Even with these changes there is no guarantee that data here follows the same approach taken in many previous will improve.At a minimum, though, implementing the rec- LSMS surveys. The module contains parts that inquire ommended questionnaires will inform future work on the about the general operation of the enterprise, its rev- design of household enterDrise surveys. which is still a new enues and expenditures, its work force, and its assets. In field of research. many ways, however, the module attempts to collect infor- * Which parts of the module most need to be customized? mation in more precise detail than has been achieved Several parts particularly need to be customized to coun- before. This is true in particular with respect to sales, try-specific circumstances. In Part D, questions referring to expenditures, employment, and seasonality. apprenticeships may not apply, and social security systems * How well hos the module worked in the past? Past house- must be mentioned by name according to the country's hold enterprise modules have produced somewhat ques- governmental structure. In Part E, units of measurement tionable income data.The current revisions to this module for articles should be specified according to local custom, aim to improve the reliability of the enterprise variables, providing suitable codes for weight and content measures; This will be done by: dealing with seasonality and the con- also, in some countries the list of possible buyers may dif- fusion it creates for answering questions about averages fer from what Part E provides. In Part F, questions con- and 12-month totals; asking the trading enterprses a set cerning licensing problems and practices may be made of questions on sales and expenditures on raw materials more country-specific, and survey designers should that is more suitable to their context; more carefully include a specific example of a tax levied on small busi- accounting for the use of inputs and business assets; and nesses. In Part G, one question about documents of own- asking for several cross-check measures, by which the ership may need to use a specific local terminology. number of questions one household is asked in the ed in part G. Sixty percent of these assets are owned household enterprise module; this average accounts by the enterprise. for all households regardless of whether they have an It is important to note that questions E35 and enterprise. E36, which record employment and income by The proportions of respondents branching off at month, are recorded for every month (totaling 13 for each skip point (D20, D5 1, and so on) is calculated based E35 and 12 for E36) rather than as a single question on responses to similar questions in the Ecuador (1993), each.While this adds substantially to the total number Pakistan (1991), andVietnam (1992) LSMS surveys, to of questions, questions E35 and E36 are not onerous the extent that this is possible; a substantial number of to administer. questions in the standard model are new. However, the most important assumptions are the following: Annotations to the Draft Module * Of all households, 30 percent operate one house- hold enterprise, 8 percent operate two, and 2 per- This section explains the motivations behind the ques- cent operate three. If the size of a sample were tions so that researchers may better customize the 10,000 households, this would yield 5,200 nonagri- model questionnaire to fit the circumstances of the cultural enterprises. country they are studying. The explanations in this * Eighty-five percent of the household enterprises section will also assist interviewers in implementing are actively operating at the time of the interview, the survey iri the field. * On average, 1.5 family members work in each Each group of questions is described in turn. enterprise. Occasionally an alternative format is discussed. The * Fifteen percent of enterprises employ someone question numbers refer to the expanded version. from outside the household. * A typical enterprise reports using inputs from three Part A: Household Exposure to Entrepreneurship of the seven input categories listed in part F Half of A1-A2. These questions seek to establish whether these inputs are purchased. there is a pattern of entrepreneurship in the family. All * A typical enterprise reports using business assets households should answer these questions so that it from four of the eight business asset categories list- becomes clear which households are most likely to 125 WIM P M.VIJVERBERG AND DONALD C. MEAD operate a household enterprise. It might be useful to C3. This piece of information might show the level of include this part of the household enterprise module technology, amount of experience, or degree of suc- in the part of the LSMS questionnaire where general cess (longevity) of the enterprise. household background information is gathered. This would increase the response rate on the part of house- C4. Location of operation is one way to distinguish holds that do not operate household enterprises. the type of an enterprise. Is the enterprise likely to be a significant contributor to the economy, or is it a sub- Part B: Establishing the Existence of Nonagricultural sistence enterprise? Enterprises B2. For households that do not operate an enterprise, C5. If the enterprise operates from within the home, the this question asks for reasons why. (Households that do home is a business asset, although not typically reported operate an enterprise will be asked an almost identical as such in Part G. For this question to be meaningful, the question in part H: question H14.) This question value and size of the home should be measured else- allows insights into how households cope with obsta- where in the questionnaire. The value of the home as a cles to private entrepreneurship. business asset depends on the proportion of the home used for business, the amount of time that the rooms in B3-B4. At several points in the household enterprise the home are used by the business (see questions E19, module the interviewer will refer back to informa- E26, E29, and E34), and the value of the home. tion gathered in B3-B4-information concerning either the enterprise being surveyed or other enter- C6-C7. These variables help adjust the sampling prises in the household (see Cl, C3, F13, F23, G9, weight for an enterprise. G12, and G25). Because the interviewer must have ready access to the names and enterprise code num- C8. This question aims to reveal the amount of enter- bers of each enterprise in the household, it is rec- prise income flowing to the household. ommended to record the responses to B3-B4 on a fold-out piece similar to the household roster. The C9. This is a very important question; the answer is answer to C9 should also be recorded on this fold- used several times as a filter, directing the interviewer out piece. to different parts of the questionnaire later on. (See questions D2, D20, E8, Eli, and E18.) Because the B3. The interviewer (or the person coding the question is so often referred to, it is recommended that responses) must have access to the International the answer to C9 be recorded on a fold-out piece sim- Standard Industry Classification. ilar to the piece for the household roster, along with the answers to questions B3-B4. B4. This question identifies who should be the respondent for the enterprise module.The respondent Part D: Employment should be the member of the household most knowl- D2. Questions D3-D13 are addressed to currently edgeable about the enterprise and/or the person in operating enterprises and questions D14-D19 are charge of the enterprise. (In this chapter this person is addressed to enterprises not currently in operation. often referred to as the "entrepreneur.") The inter- Although these sets of questions are parallel, merging viewer must make every effort to schedule an appoint- them creates difficult skip patterns. ment with this person. D3-D4. The respondent is usually also one of the fam- Part C: General Information ily members working in the enterprise. The phrasing Cl-C2. In exceptional cases the interviewer may have of question D3 implies that the entrepreneur will to conduct the interview with a household member automatically be listed. If the entrepreneur does not other than the entrepreneur. If so, the interviewer work in the enterprise, the responses to D5 and D1O should at least report who the actual respondent is so will be "O."AIl household members should be listed in as to indicate the credibility of his or her responses D3-D4 before the interviewer proceeds with about the enterprise. D5-D13 for each person in turn. 126 CHAPTER 18 HOUSEHOLD ENTERPRISES The names are recorded here for use by the intrahousehold allocation of enterprise income will interviewer; there is no need to code them into the view them as a portion of enterprise income. computer. The IDs will be used by researchers to link the personal characteristics (for example, age, educa- D20. This question is a filter that starts the portion of tional attainment, and sex) of workers to enterprise the module dealing with nonhousehold labor. performance. Questions D21-D40 are addressed to currently oper- ating enterprises and questions D41-D50 are D7-D8. Although household members receiving these addressed to enterprises not currently in operation. payments will view them as earnings, analysts of intra- These sets of questions are parallel, but merging them household allocation of enterprise income will view creates difficult skip patterns. them as a portion of enterprise income. D22. This question is used as a check on the answers D9. The researcher must assume that during the pre- to questions D32 and D33. vious 12 months, people responding to this question have contributed the same number of hours per day D23. This question is used as a check on the more and received the same payment as they did during the detailed question D24. An important note: if the past two weeks. (It is necessary to ask about the total entrepreneur states in D23 that the enterprise did not number of weeks because it is not wise to assume that employ nonhousehold labor during the past 14 days, it all household members who ever worked for the would be awkward to ask question D24. Instead, the enterprise during the 12-month period worked for interviewer should enter "0" into D24 without asking the entire 12 months.) and follow D24's skip pattern to D33. D12-D13. Just as for questions D7-D8, household D24-D26. If there is no apprenticeship system in the members receiving these payments will view them as country, the second answer row should be dropped. If earnings, but analysts of intrahousehold allocation of the number of workers in a specified category in D24 enterprise income will view them as a portion of is 0, the interviewer should skip to D33, which asks enterprise income. about work effort over the last 12 months only. D13. After all the enterprise workers have been ques- D27-D28. If apprentices and unpaid nonhousehold tioned, the interviewer can skip to D20. (Questions workers never receive any compensation in the coun- D14-D19 refer to enterprises not in operation at the try of the study, these questions need not be asked (in time of the interview.) which case question D27 disappears altogether). D14-D15. As in question D3, the phrasing of question D29-D31. These questions are designed to yield infor- D14 implies that the entrepreneur will automatically mation about the personal characteristics of the non- be listed. If the entrepreneur does not work in the household labor force.They should help establish labor enterprise, the response to D16 will be "O."AIl house- demand patterns for various demographic groups.The hold members should be listed in D14-D15 before schooling cutoff of six years is arbitrary and should be the interviewer proceeds with D16-D19 for each per- adjusted to an appropriate level for the country of the son in turn. study. If there is no apprenticeship system in the coun- The names are recorded here for use by the try, D30 may be replaced with a suitable question interviewer; there is no need to code them into the related to the training of unskilled workers. computer. The IDs will be used by researchers to link the personal characteristics (for example, age, educa- D32. For enterprises that employed workers in the spec- tional attainment, and sex) of workers to enterprise ified category during the previous 2 weeks, this question performance. is the only measure of these workers' efforts during the previous 12 months. The assumption is that days per D18-D19. Although household members receiving week, hours per day, and payments per worker are simi- these payments will view them as earnings, analysts of lar for the 2-week and 12-month periods. If the enter- 127 WIM P M.VIJVERBERG AND DONALD C. MEAD prise employed workers in the specified category during considered more reliable than retrospective responses the previous 2 weeks, no further questions will be asked about income. about the previous 12 months; the interviewer should turn to the worker category in the next column. Part E Revenues and Operation Schedule E1-E3. These questions describe types of enterprises by D36-D37. If apprentices and unpaid nonhousehold their output. Together with E17 they provide informa- workers never receive any compensation in the coun- tion about the extent to which enterprises generate try of the study, these questions need not be asked (in demand and supply effects, and in which markets they do which case question D36 disappears altogether). so. Another option would be to specify in detail some (perhaps up to five) commodities produced by the enter- D38-D40. These questions are designed to yield infor- prise-as was done in the Ecuador 1993 survey. This mation about the personal characteristics of the non- option was not chosen because such information would household labor force.They should help establish labor be tremendously time-consuming to code and analyze. demand patterns for various demographic groups. The Note that these questions do not substitute for B3 schooling cutoff of six years is arbitrary and should be (on type of industry).While B3 allows the respondent adjusted to an appropriate level for the country of the to specify a single industry, a substantial number of study. enterprises are involved in more than one of the three economic sectors. The skip pattern associated with the D41-D50. This block of questions, about nonhouse- answer to E3 ensures that only enterprises involved in hold labor for enterprises not currently operating, is trade can respond to questions E4-E16. parallel to D21-D22 and D33-D40 taken as a block. The sets of questions are written out separately since E4-E10. This set of questions aims to compute the typ- skip patterns in a merged block of questions would be ical gross profit margin on resold items (that is, items confusing. that are not modified in any way before being resold). Since many traders sell more than just five items, the D51-D52. These questions address the effect of mini- responses to E5, E9, and ElO cannot be used to com- mum wage legislation on the wage paid in the small- pute total revenue from sales in trading. However, since scale private sector.They measure the degree to which profit margins may differ between items, the inter- the entrepreneur feels bound by the legal minimum viewer has to inquire about the five most important wage. Whether the enterprise pays minimum wage or items-computing the typical gross profit margin as a higher can be deduced from questions D26-D27, weighted average across these five items. The gross D36-D37, and D46-D47 (although these questions profit margin can be applied to expenditures on pur- include the values of in-kind benefits). chasing items for resale (questions E12-E13) to esti- mate the enterprise's revenue from sales. Later in the D53-D55. These questions collect information about questionnaire, questions E20 and E35-E38 will record the social security coverage of all workers and about total (cash) revenue from sales; these numbers should payments made by each enterprise to the social secu- correspond with the sales figures derived from rity system. If greater detail is desired, these questions E12-E13 and the gross profit margin.As such, E20 and may be merged into the household labor grid and the E35-E38 can be thought of as accuracy checks on the two nonhousehold labor grids.The precise phrasing of responses given to E4-E13, and vice versa. the questions should be adjusted according to the pre- A substantial number of enterprises are probably vailing circumstances in the country. These questions involved in both trading and production-and-sales establish the coverage of potentially important social activities. For these enterprises, E20 and E35-E38 are security legislation. a mixture of revenues-so E21 and E39 will serve as accuracy checks for E4-E13, and vice versa. D56-D57. These questions help measure how much It is important to note that the gross profit margin an enterprise has contributed to employment growth. only represents the difference between purchase and These are the only retrospective questions in the mod- sale prices of resold items. To compute enterprise ule; retrospective responses about employment are income, other cost components must still be deducted. 128 CHAPTER 18 HOUSEHOLD ENTERPRISES E4. Writing down the information requested here will credit. If this is the case, an analyst of credit markets help the next five questions go smoothly. It is not will want to know what percentage of the goods were intended to be coded. purchased on credit (E14), how the creditor was paid To ensure the best possible responses, the inter- back (E15), and what the terms of credit were (E16). viewer should list all the items first, before asking Concerning the terms of credit, one would really like questions E5-E10 for each item. The idea is that to know the (implicit) interest rate, but finding this out traders list five of their most frequently traded com- would require several more questions: were you modities. They may be reluctant to list these once they charged interest; what was the interest rate; if you had understand what questions the interviewer is going to purchased these goods with cash, would you have ask them about the commodities. been able to purchase them at a lower price; if so, how much lower? Without such questions the analyst has to E5-E6. These questions ascertain the trading margin impute the customary regional interest rates that have for the five items that the enterprise purchases for been uncovered in the credit module of the question- resale. The aim of these questions is to find out from naire (see Chapter 21 on credit). the entrepreneurs how much they spend to purchase these items and how much they think they can sell E17. The customer base helps describe the enterprise them for. and its growth potential. E7. Items for resale can be purchased in bulk (for E19. This question makes it possible to compute example, by the bag) and sold by the piece. Since each enterprise revenues on a daily basis. This can be com- entrepreneur may be working with different units (for pared to the income that waged or salaried workers example, different-size bags), it is necessary to ask him earn. It is possible to go further and ask how many or her the relationship between the unit of purchase hours per day the enterprise was open for business; the and the unit of sale. In exceptional cases, items for model questionnaire has skipped this question for rea- resale can be purchased in smaller units than they are sons of brevity. sold. In such cases the interviewer is instructed to adjust the unit recorded in question E6. E20. These are receipts from sales for cash or credit. E9. Based on this question and on questions E5-E7 it E21. The answer for pure trading enterprises will be is possible to compute sales revenue and expenditures 100 percent. The answer for pure manufacturing or on items of resale. service enterprises will be 0 percent.The target of this question is enterprises that mix trading with other El0. Based on this question and on questions E5-E7 activities. it is possible to compute sales revenue and expendi- tures on items of resale. E22-E23. Besides cash or credit sales, some 10 percent of enterprises also receive payments in the form of E11-E13. Questions about expenditures on items for goods or services. In addition, the products that the resale are relevant only for enterprises involved in entrepreneur has used to purchase inputs should be trading. As the questions at the beginning of Part E counted as in-kind sales revenue. The value of such deal with trading anyway, it is proper to ask about such products counts both as a cost and as a revenue item. expenditures here rather than in Part F. Note that traders in business assets such as dealers in cars, bicy- E26. Currently operating enterprises have not neces- cles, and furniture (see Part G) should report their sarily been in operation for all of the previous 12 expenditures on these commodities here, because the months. Measuring annual income requires this ques- commodities are used for trading rather than for oper- tion and information about income flows. See ating the trading enterprise. E37-E44 for a more detailed explanation. E14-E16. When an enterprise buys goods with intent E28-33. This should be used only for enterprises that to resell, a portion of the goods may be purchased on are not currently operating. 129 WIMP R M.VIJVERBERG AND DONALD C. MEAD E29. This question is parallel to question E26 for cur- E40. The answer for pure trading enterprises will be rently operating enterprises. Measuring annual income 100 percent. The answer for pure manufacturing or of non-operating enterprises requires this question service enterprises will be 0 percent.The target of this and information about income flows. See E37-E44 for question is enterprises that mix trading with other a more detailed explanation. activities. E30-E33. Taken together, this series of questions E41-E42. Taken together with the responses to ques- should yield insights into the dynamics of economic tions E22 and E23, these questions will measure annu- activity and employment fluctuations in the enterprise. al in-kind sales revenue. Question E42 refers to pay- ments during a month with "average" sales (as defined E34. This question is asked of both operating and non- in question E36). For the sake of brevity, the question- operating enterprises. It establishes their rate of eco- naire does not repeat this question for months with nomic activity over the past 12-month period. This "high" or "low" sales.The analyst must assume that in- makes it possible to convert annual or monthly enter- kind sales revenue either varies proportionally with prise income into a daily rate, which is then compara- cash sales revenue or remains relatively constant. ble to the income that waged or salaried workers earn. E43-E44. Taken together with questions E24 and E25, E35. This question establishes seasonality and trends in these questions will measure the annual value of home employment opportunities in an enterprise. The grid consumption. Like question E42, E44 refers to a is set up with the 12 calendar months listed over two month with "average" sales (as defined in question years. If the interview takes place in May, the inter- E36). viewer should fill in entries for January to May of the current year and May to December of the previous Part F: Input Use and Expenditures year. An alternative system would be one row of 13 Fl. This question begins a section on generic expen- cells, with the last cell referring to the current month, ditures.Trading companies may not need to report on the next-to-last cell referring to the previous month, all items here, because their major expenses may and so on until the first cell refers to 12 months earli- already have been captured in questions EPO-Ell. er. However, references to specific months seem easier Question Fl establishes whether an item is used. The to interpret. list of items does not include rental, maintenance, It is important to note that the question asks for taxes, and fees. Rental and maintenance are expenses information for the full previous 12-month period. related to tangible business assets, and taxes and fees are These data enable analysts to compute year-to-year addressed separately in F26-F34. None of these four change in employment, indicating the employment expense categories fits the set of questions posed in trend, and month-to-month change in employment, F2-F25 very well. For the same reason, some questions measuring seasonality around the trend. are blocked out for the insurance expense category. E36-E39. The sales pattern revealed by question E36 F2. This question establishes the purchase of an item, will probably resemble the pattern of employment which is different from its use. over the 12 months. Most entrepreneurs are probably better informed about employment variations than F3. Expenditures during the previous month are easi- about monthly fluctuations in sales revenue. On the est for the entrepreneur to remember. The previous other hand, many enterprises employ only one or two month is used rather than the previous two weeks (as household members, with no monthly variation. In for recent sales revenue) because expenditures fluctu- the case of these enterprises, income fluctuation will ate both with and ahead of surges in sales revenue. be the most useful piece of information about season- Measuring expenditures by month may smooth these ality (other than the enterprise shutting down out-of- fluctuations somewhat. season). The level of detail implicit in questions E36-E39 is intended to focus the entrepreneur and to F4-F6. These questions attempt to measure expendi- enhance the accuracy of his or her responses. tures on an annual basis, linking them to the level of 130 CHAPTER 18 HOUSEHOLD ENTERPRISES sales as established in questions E36-E39.This link may cost items, and "other" items. Existing LSMS surveys reveal both "lean" and "fat" months in the year-round have never before included questions about these cat- activity of the enterprise. Also, by linking expenditures egories, so it is not clear how frequently these cate- to the agricultural calendar, these questions yield addi- gories are relevant. What is known is that in many tional insights into the workings of the local economy. existing surveys enterprises have reported making rel- atively few purchases of the inputs they might logical- F7-F9. If some inputs are bought on credit, an analyst of ly be expected to use given the industry in which the credit markets will want to know what proportion of the enterprise operates. inputs are bought on credit (F7), how the creditor is paid back (F8), and the terms of credit (F9). Concerning the F18-F19. These questions establish the value of items terms of credit, one would really like to know the that were not purchased or acquired in exchange for (implicit) interest rate, but finding this out would require enterprise products. Even if this value is positive, the several more questions: were you charged interest; what direct cost to the entrepreneur was zero-although was the interest rate; if you had purchased these goods there may have been indirect costs in terms of labor with cash, would you have been able to purchase them and goodwill or direct costs to produce goods in at a lower price; if so, how much lower? Without such another enterprise of the household. The fact that questions, the analyst has to impute the customary these items cost nothing to this enterprise may regional interest rates that have been uncovered in the increase the profitability of the enterprise, but it credit module of the questionnaire (see Chapter 21 on remains to be seen whether this enterprise improves its credit). In this questionnaire questions F7-F9 have been efficiency by using them. (This is an example of the blocked out for electricity, water, insurance and other distinction between private profitability and social inputs, all of which are unlikely to have been purchased returns.) with credit. Local conditions may differ, of course, in which case modifications should be made. F20-F21. The household may use some of the items acquired by the enterprise. These questions aim for a F10-Fll. The entrepreneur may have acquired some full accounting of both the profitability of the enter- items in exchange for some of the enterprise's output. prise and the consumption of the household. The entrepreneur may not think of this action as pur- chasing the item, but it is an expense nonetheless. F22-F24. These questions ask about inter-enterprise flows of inputs, so that each enterprise's profitability F12-F13. The entrepreneur may obtain some items can be accurately measured. from another enterprise in the household. If so, the other enterprise should indicate this in its responses F25. This question verifies the responses of the entre- to F22-F23. Most households operate only one preneur regarding shared resources. enterprise, but in households where several enterpris- es are in operation, each enterprise's expenditures F26. This question begins a section on the registration must be taken into account in order to accurately of the enterprise with government authorities measure performance. (F26-F32). This may be a sensitive question for the entrepreneur, so the question is phrased to appear as if F14. The household may provide the enterprise with the interviewer is only interested in the expenses relat- some of its inputs. While in this questionnaire the ed to registration of the enterprise rather than in the value of household-provided inputs is not ascertained registration itself. separately from the value of "free" inputs in other cat- egories (F12 and F15-F17), it might be appropriate to F27. The researcher may want to cross-check the reg- insert a question in order to account fully for intra- istration expenses reported here with common regis- household flows. tration fees reported by governmental agencies. The entrepreneur's figures may be higher if he or she F15-F17. These questions refer to three remaining includes any necessary bribes in responding to this categories-"gifts" from outside the household, zero- question. 131 WIM P M.VIJVERBERG AND DONALD C. MEAD F28-F32. These questions are posed to entrepreneurs questions are motivated by the concern that in many who have not registered their enterprise. The ques- countries women are less likely to formally own the tions aim to find out how much entrepreneurs know assets of their enterprises. This may have implications about registration requirements and costs and whether when women entrepreneurs apply for credit as well as registering an enterprise has disadvantages from the implications for intrahousehold allocation patterns. household's point of view. In some cases it might make For more information see Chapter 21 on credit and sense to modify the response codes to F32 to reflect Chapter 24 on intrahousehold analysis. practices in the country of the study. It might also be appropriate to add questions about the amount of G6. Current market value is a reasonable way to value time required for registration-for registered enter- a business asset that has been purchased at one time in prises, unregistered enterprises, or both. the past. F33-F34. It is recommended that the questionnaire list G7. Ownership of an asset does not necessarily mean several examples of taxes that enterprises may have to that other enterprises do not use the asset. pay. This will depend on the tax code in the country. G8-G13. Assets may be used by another enterprise in Part G: Business Assets the household; if so, the other household enterprises Gl. The following set of questions establishes the total should also report this in G24-G25. However, it is value of the business assets in use. These assets must be quite possible that some entrepreneurs fail to mention used as part of the production process. An enterprise that assets that they borrow from other household enter- trades business assets should list the assets that it owns for prises, especially if they own one of the other enter- the purpose of trading under questions E4-E13. For prises. Responses of the entrepreneur who owns the example, a car dealer may keep six cars, five of which are asset help establish the value to the other household for sale (and should be listed under E4-E13) and one enterprises of having access to this asset. that he drives around for his own business (and should be listed here). Even if he is willing to sell this particular G14-G15. The household may borrow an asset (such car as well, one car is still a business asset because he as a vehicle). This is an intrahousehold transfer; it always uses one of his cars for his own business. reduces the opportunity for the asset to be used for the enterprise and increases household consumption. G2. The first source of asset use is ownership at the time of the survey. Questions G32-G39 deal with pur- G18-G19. The purpose of these questions is to ensure chases and sales of assets. Current ownership is not that the entrepreneur has plausibly accounted for the equivalent to use during the past 12 months. Question use of his business assets. G2 acts like a filter; if the answer is "No," the inter- viewer should skip to question G20. G21-G23. One way an enterprise can use an asset with- out owning it is to rent it. In all previous LSMS surveys, G3. If ownership of an item is shared with another rental questions were listed under expenses. This ques- enterprise, the entrepreneur of that other enterprise is tionnaire places rental expenses among business assets. likely to report this asset as well. Using question G3 To conserve space, "rental" is also interpreted to mean together with question G5, a data analyst can ensure borrowing from a neighbor or relative for free (in that business assets within the household are not dou- which case question G22 would be answered with "0"). ble-counted even if several enterprises report them. The data could be made a little more user-friendly by G24-G25. A second way an enterprise can use an asset adding a question after G3: with which other enter- without owning it is to borrow it from another enter- prise is ownership shared? prise in the household.The other enterprise will report the value of this asset and for how long it is lent out. G4-G5. For the large business assets categories, these questions establish whether a partner's ownership has G26-G28. A third way to use an asset without owning legal weight and who legally owns the assets. These it is to borrow it from the household itself. Here, the 132 CHAPTER 18 HOUSEHOLD ENTERPRISES value of the asset must be ascertained-unless this has it. Local conditions may differ, of course, in which case already been reported in the modules on housing modifications should be made. (Chapter 12) or consumption (Chapter 5). G39. Asking why assets were sold gives researchers a G29-G30. Maintenance expenses constitute some of glimpse into the dynamics of the enterprise. the costs of using tangible business assets (apart from their implicit rental cost). In previous LSMS surveys, Part H: General Business Conditions maintenance questions have always been listed under H1-H5. These questions deal with raw materials. expenses. By relating maintenance to specific assets, this questionnaire should prompt more accurate H1-H2. Questions H1-H2 measure the enterprise's responses from entrepreneurs. inventory of raw materials. Question Hi is phrased in the present tense because, as a stock, inventories ought G32-G33. If an enterprise has acquired assets during to be measured at one point in time. However, if the the previous 12 months, this implies that the value of enterprise is not in operation at the time of the inter- its business assets at the beginning of the year was view, the question is not entirely appropriate. To ask lower than it is at the time of the survey and that the for inventories one year previous to the survey pro- enterprise's ownership (and use) of assets varied dur- duces the same problem (along with causing potential ing the course of the year. To relate business assets to recall problenis) since some enterprises were not in enterprise income, it is necessary to measure business operation one year previously. Another way to phrase assets during the year rather than at the end of the year the question-"In the last month that the enterprise as is done in question G2. The questions about asset was in operation, what was the inventory of raw mate- acquisitions and sales attempt to establish the value of rials?"-is equally useless, because the enterprise was assets for a more meaningful period of time. For an winding down. Asking the question in the present even fuller accounting of asset use, a question could be tense establishes a definite time and makes recall easy. added about the date of acquisition. However, since It might then be appropriate to assume that enterpris- the asset categories are fairly aggregative, several acqui- es not currently in operation typically maintained an sitions may have occurred, making a question about inventory of a size similar to those of their competi- date of acquisition ambiguous. The model question- tors in the industry. naire assumes that any acquisitions occurred in the middle of the previous 12-month period. H3. This question is as much as check on the response to H2, about inventory, as it is a check on reported G34-G36. If the acquired business assets have been levels of sales (E20, E37-E39). Aberrations in either bought on credit, an analyst of credit markets will inventory or sales should show up in the responses to want to know how much of the purchase was financed question H3 and its parallel questions, H8 and Hi11. with credit (G34), how the creditor was paid back (G35), and what the terms of credit were (G36). H6-H8. These questions are about inventories of items Concerning the terms of credit, one would really like requiring further processing (or intermediate inputs). to know the (implicit) interest rate, but finding this out Question H8 is a check on enterprise sales. The com- would require several more questions: were you ments on questions Hl-H3 also apply here. charged interest; what was the interest rate; if you had purchased these goods with cash, would you have H9-H13. These questions refer to products ready for been able to purchase them at a lower price; if so, how sale-either products purchased by traders (as question much lower? Without such questions, the analyst has H12 implies) or products produced by the enterprise. to impute the customary regional interest rates that Question HI 1 is a check on sales revenue. The com- have been uncovered in the credit module of the ments on questions H1-H3 also apply here. questionnaire (see Chapter 21 on credit). In this ques- tionnaire, questions G34-G36 have been blocked out H14. It is important to ask about more than one prob- for furniture, tools, and other durable goods, all of lem for enterprise startup, because the primary problem which are unlikely to have been purchased with cred- may be an internal household problem and thus not 133 WIM P M.VIJVERBERG AND DONALD C. MEAD amenable to policy solutions. B2 is an almost identical and how credit obligations are fulfilled (H29-H30). question for households that do not operate enterprises. H29 and H30 are parallel to similar questions about the entrepreneur's own use of credit (El 5-E16, H15. This question illustrates how lack of start-up F8-F9, G35-G36). capital can be a barrier to entering an industry. If the Questions H26-H30 are interesting, as credit and total sum of money needed is large, few households product markets may be linked. There is little survey- will be able to afford starting a new business. The based information about the involvement of small- response to this question may need to be adjusted for scale entrepreneurs in the credit market. inflation, according to the age of the enterprise (C3). H31-H33. This set of questions asks about fluictuations H16. The source of money is always an issue for both of daily sales revenue.These questions are placed here, households and policymakers. Stimulating entrepre- away from section E of the module, in order to check neurship requires understanding how an individual the revenue responses in section E. finanices the start-up of his or her enterprise. Thus it is important to gather full information on all sources H34-H35. As xvas evident in Table 18.1, many enter- of finance. Hopefully, three sources will cover most prises seem to be losing money. This question exam- situations. ines whether the entrepreneur is aware of this or whether, instead, his or her responses to questions on H17. Does the entrepreneur receive any assistance sales and expenditures are possibly erroneous. from government or business organizations? It is diffi- cult to be more specific in these questions and yet H36. Here the entrepreneur is asked to report his maintain enough generality; to increase specificity the monthly expenses as a check on his or her responses questionnaire could be modified to incorporate local in Part F (which were used to derive monthly expens- assistance programs. The effectiveness of such pro- es). Responses here should include rental and mainte- grams could then be evaluated by relating the respons- nance expenditures and possibly purchases of business es here to the profitability of the enterprise. assets-all of which are covered in Part G. H18-H22. The list of macroeconomic variables to H37-H40. These questions are an independent check which the entrepreneur is exposed can be expanded to on enterprise income. Questions H37-H38 are asked fit the conditions of the country of the survey. in acknowledgement of the fact that the budgets of Openness of domestic markets and access to imports the enterprise and the household sometimes blend are a starting point. Depending on the industries in into each other. Questions H39-H40 let the entrepre- which they operate, entrepreneurs may or may not neur make an estimate of the (cash) profitability of his have to deal with markets or imports. or her enterprise. To derive enterprise income, it is still necessary to add consumption by the household of the H23-H25. These questions deal with the financial products of the enterprise (E25, E44). security of the enterprise. When the debt level is high and the weekly or monthly payments are large in corn- H41. This question gives an insight into how the parison to the cash flow, the enterprise is in trouble. entrepreneur uses his enterprise's income.While some These questions allow investigation of the relation operate enterprises to furnish their household with an between financial security on the one hand and seasonal income, others may operate enterprises with the goal trends, regional econotnic characteristics and macroeco- of making investments in schooling, land, or a new nornic conditions on the other. If similar information can business. Reinvesting in the present business is anoth- be extracted from the household credit module, these er form of saving. questions might be omitted (see Chapter 21 on credit). AdditionalAnnotations to the Standard Questionnaire H26-H30. The enterprise itself may also extend cred- D16-D26. Here the structure of the questionnaire dif- it. This set of questions measures how much credit is fers somewhat from that of the expanded version. extended to the enterprise's customers (H27-H28) Questions D16-D21 are to be answered by entrepre- 134 CHAPTER 18 HOUSEHOLD ENTERPRISES neurs who currently employ nonhousehold labor, and for resale, transport, electricity, water, fuel, rental, main- questions D22-D26 are to be answered by entrepre- tenance, taxes, registration fees, and insurance)?" neurs whose enterprise is currently not in operation With these changes,Table 18.5 would also change. or who have employed nonhousehold labor in the past Revenue in Part E of the table would total 4.70 ques- 12 months but not in the past 14 days. tions per household and 9.04 questions per enterprise, and Seasonal Revenue in part E would total 0.00 ques- GI. Even though the standard questionnaire does not tions per household and 0.00 questions per enterprise. attempt to distinguish use of tangible business assets from The overall number of questions would total 24.03 ownership of these assets, Gl is the best introductory questions per household and 46.22 questions per question to this section. It allows the interviewer to ask enterprise. But the tradeoff for this time saving would about all assets in use by the enterprise before launching be a loss of precision in the estimate of annual enter- into more detailed questions. Moreover, "use" covers prise income and a loss of information on seasonality. both owning and renting, details of which will be asked for specifically in questions G2-G4 and G5-G7. It also F2. Grouping all expenditure categories into a single ag- indicates whether the entrepreneur had access to assets gregate undoubtedly reduces the accuracy of the report- even if he or she ncither owned nor rented them. ed statistic. The only alternative is to collect the detailed kind of information that the standard version gathers- Additional Annotations to the Short Questionnaire although the standard version records inputs purchased D4, D9, D14. These questions record the number of rather than inputs used (as in the expanded version). family members working in an enterprise. While the standard and expanded versions of the household G1-G2. These questions aim to estimate the value of enterprise module also record the number of hours the enterprise as measured by its business assets. To worked by family members, the short version needs a shorten the questionnaire even further, these asset cat- link with the employment module for time allocated egories could be grouped into a single aggregate, but to work in nonagricultural self-employment activities. this is not advisable because the asset value of the This is a difficult link to make, as the C6te d'Ivoire enterprise represents household wealth, which ought (1985-1988), Ghana (1988-1989), andVietnam (1992) to be measured carefully in any LSMS questionnaire. surveys have shown. If the needed time allocation information is not collected in the employment mod- G3-G6. Deleting these questions would save 1.22 ule, these questions may as well be omitted. questions per household and 2.35 questions per typi- cal enterprise. The tradeoff for this time savings would E4-E7, E14-E17. These questions are retained in the be that less would be known about the dynamics of short version because nonmonetary transactions and the enterprise and the household. intrahousehold consumption constitute a significant part of the sales revenue of a small number of enterprises. Notes E10-E13. These questions make up 15 of the 60.22 The authors appreciate comments by participants of the LSMS questions asked, on average, of an enterprise. The workshops, in particular Paul Glevwe, Margaret Grosh, and Julie answers to these questions make it possible to com- Schaffner. pute an estimate of annual sales revenue and provide 1. The model questionnaire presented in this chapter does not information about the seasonal nature of the enter- provide sufficient detail to answer this question; the question can- prise. If it were necessary to shorten the questionnaire not be answered unless the list of products bought and sold is fur- even further, these questions could be replaced with ther disaggregated. the following: "During the past 12 months that your 2. Referred to in this chapter simply as a "household enter- business was in operation, how much money did it prise." This definition represents a break from tradition; in earlier receive from sales of its products, goods, or services?" LSMS questionnaires, a food-processing household business that Question F3 could be rephrased as follows: "During did not purchase any raw agricultural ingredients was not classified the past 12 months, how much have you spent in total as a household enterprise but rather as an extension of the house- on the purchase of inputs (labor, raw materials, items hold's farming activity. 135 WIM P M.VIJVERBERG AND DONALD C. MEAD 3. One question is what the smallest scale of operation is that that have been tried. The Vietnam and Pakistan questionnaires should be sampled. Should a household that butchered one animal, added the possibility of barter to question 1. Several questionnaires processed its skin and sold it for money be sampled? Should the added other family businesses as candidates for sharing the pur- survey include someone who once received some money for fix- chased inputs. The Ecuador questionnaire asked for the value of the ing a car or for transporting some commodities in his truck to a last purchase rather than the usual purchase in question 2. While neighboring toNvn? A reasonable criterion for inclusion in the sam- most questionnaires included expenditures on wages in their list of ple is xvhether the activity was purposefully intended to earn an input categories, the Ecuador, Perl (1990), and Peru (1991) ques- income rather than an incidental event of daily life. tionnaires included a question about wage payments to be asked 4. An enterprise survey can also be carried out on a door-to- immediately after the entrepreneur indicated that he paid for out- door basis, with interviewers asking at each household or place of side help.The obvious advantage of this strategy is that it eliminates business whether there is a nonfarm enterprise in operation at that the possibility of entrepreneurs reporting that they pay their svork- location, and, if so, adm-inisterinig the eniterprise questionniiaire. If the ers but failinig to report ansy wage paymiienits or reporting wage pay- sample is built on the basis of household enumeration hsts or a ments but reporting that they do not pay their workers. The complete enumeration of all activities in a random sample of local- Ecuador questionnaire listed expenditures on raw materials and ities, this type of enterprise sample will be the same as the LSMS articles for resale right after the series of revenue and home con- survey If the sample is drawn up by visiting places of business, it sumption questions instead of treating this as a regular business will include larger enterprises that are not captured by household- expense. This seems illogical and has the potential to confuse both based surveys. Using this approach carries the risk that enterprises the interviewer and respondent. The Pakistan survey distinguished with a variable location (such as taxis, fishing enterprises, and some inputs purchased weekly or more often from those purchased less vending activities) may be undersampled. frequently. It is not clear that anything substantial is gained with this 5. Estimated from LSMS surveys in Cote d'lvoire (1985-1988), information. The Pakistan questionnaire added two other ques- Ghana (1 987-1989), and Vietnam (1 992-1993). tions-one asking whether the item was purchased by cash or cred- 6. In some cases the enterprise's organizational structure may be it (and if by credit, xvhether the supplier or someone else extended such that one household owns it, perhaps by virtue of providing the credit), and the other asking wyhether the entrepreneur had ever financial start-up capital, while another operates it. The first house- encountered shortages of the item. These are interesting questions, hold would not describe itself as operating an enterprise, wvhile the although they did not yield much variation of answvers. Except for second may see itself as working for the first household rather than purchases of rawv materials and items for resale, virtually all transac- as operating the enterprise.This could lead to a nonrandom sample tions were in cash. The model questionnaire in this chapter draxvs of enterprises even though the sample of households was random- from these experiences. ly selected. This is presumed to be rare. 11. Virtually all LSMS questionnaires contain: "Q4: Does your 7. Not all tIoxvs are of living standards concepts. Some are of household use this ... ?" By itself, this question contains little infor- changes in stocks; for example, saving is a change in the asset posi- mation; the value of what is shared remains unknown. The tion of the household. researcher only knows for sure that the enterprise uses less of the 8. A few surveys have used a different set of questions.The early input than question 3 reports. Moreover, this question 4 refers only questionnaires for Cote d'lvoire, Peru (1986), and Ghana skipped to inputs purchased and shared with others.There is no mention of the 12-month revenue question if the entrepreneur gave his recent the possibility that the enterprise receives inputs (such as electrici- revenue. This meant that it was impossible to compare the revenue ty, wvater, or the use of tools) from the household or from other responses wvith one another. The Peru (1990 for Lima only and household enterprises. 1991) and Ecuador questionnaires asked only about revenue 12. Detailed tabulations show that in every data set there are received during the last month of operation, xvhich of course syas some enterprises that have expenditures in the losvest quintile and the current month if the enterprise was in operation at the time of revenues in the highest quintile, thus apparently generating huge the second visit. positive profits, and others for which the reverse is true. 9. The Pakistan questionnaire also separated out receipts from 13. The first question was in fact used in the GlianaVietnarri, subcontracting. Fexv entrepreneurs reported any such receipts, but and later Cote d'lvoire questionnaires; the second question for those xvho had some receipts, subcontracting was an important occurred in the Ghana questionnaire. Interviewers vyere not per- part of business operations. Note, though, that receipts from sub- mitted to accept negative answers to these questions. but in fact contracting should already be covered by cash sales receipts. only a few entrepreneurs answered "0." 10. These questions have been used in virtually all LSMS ques- 14. It is possible to put a positive rxvist on these figures. About tionnaires. This footnote documents additions and modifications 80 percent of the enterprises xvere either along the diagonal or in 136 CHAPTER 18 HOUSEHOLD ENTERPRISES the niext adjacenit quintile. This is riot to mininrize the fact that, in enterprises, but interviewers were instructed to fill out an extra terms of absolute values, the agreement between the two measures questionnaire form if a given household operated more than two is not good. enterprises, and to inform their supervisors that they had done so. 15. In a few questionnaires the interviewer was instructed to check the responses about the economic activities of household References members, as reported in the employment module, to determine whether the household appears to be operating a nonagricultural Liedhohm, Carl, and Donald C. Mead. 1995. "The Dynamic Role of enterprise, even if household members report that they do not. Micro and Small Enterprises in the Development Process." Presumably the interviewer will probe more vigorously if he sus- Development Alternatives, Inc., Growth and Equity through pects that an enterprise exists. Microenterprise Investments and Institutions (GEMINI) Project, 16. A remaining problem with using this approach is the time Action Research Program 1, Final Report, Bethesda, Md. gap between the first visit, xvhcn these questions are asked, and Mead, Donald C. 1994. "The Contribution of Small Enterprises to the second visit, when the rest of the enterprise information is Employment Growth in Southern and Eastern Africa." World collected. Developsnent 22 (12): 1881-94. 17. This strategy would have solved all matching problems were . 1995. "How the Legal, Regulatory, and Tax Framework it not for two other shortcomings: these questions were asked only Affects the Dynamics of Enterprise Growth." In P. English and if the enterprise employed more than one worker including the G. Henault, eds., Agents of Change: Studies on the Policy entrepreneur, and IDs and hours were ascertained for no more than Environmentfor Small Enterprises in Africa. Ottawa: International four family members. Because of the first shortcoming, the hours Develop Research Centre. that household members worked were still unknown for two-thirds Moock, Peter, Philip Musgrove, and Morton Stelcner. 1990. of the enterprises, and the second shortcoming meant that infor- Education and Earnings in Perun Informal Nonfarm Fansily mation was missing for enterprises employing five or more family Enterprises. Living Standards Measurement Study Working members. The Peru (Lima 1990; 1991) qtuestionnaires asked for the Paper 64.Washington D.C.:World Bank. total number of hours worked in the enterprise by all workers, but, Otero, Maria, and Elisabeth Rhyne, eds. 1994. The New Role of like the Ecuador questionnaire, they did so only if other members Microenterprise Finance: Building Healthy Financial Institutionsfor besides the entrepreneur worked in the enterprise. This shows how the Poor. West Hartford, Conn.: Kuoisariaii Press. carefully questionnaires must be designed in order to yield user- Schultz.Theodore W 1975. "TheValue of the Abihty to Deal with friendly data. Disequilibria."Journal of Econoniic Literature 13 (3): 827-46. 18. This is preferable to asking about the ownership of assets at Vijverberg,Wim P.M. 1992. .Mleasuring Inconiefromn Family Enterprises any time during the previous 12 months; ownership at any time uitli Household Surveys. Living Standards Measurement Study during the past year does not imply usage during the entire year, as Working Paper 84.Washington D.C.:World Bank. some assets are bought or sold during the year. . 1998. "Nonfarm Household Enterprises in Vietnam." In 19. In Pakistan, 13 percent of all assets are shared with the David Dollar, Paul Glewwe, and Jennie Litvack, eds., Household household or another enterprise; in Ecuador this figure is around Welfare and Vietnam's Transition. Washington, D.C.:World Bank. 40 percent. Young, Robert C. 1993. "Policy Biases, Small Enterprises, and 20. A similar strategy was followed by LSMS teams in Ecuador. Development." Small Enterprise Development: An International The Ecuador (1994) questionnaire allowed for responses about tvo Jouirnal 4 (1) 137 , ^ ~Agriculture J 9 Thomas Reardon and Paul Glewwe Promoting sustainable growth in agriculture can reduce rural poverty and increase employment and welfare in both rural and urban areas in developing countries, for five reasons. First, agricul- ture is a major source of household income and employment in most developing countries, both directly, through own-farm production and agricultural wage labor, and indirectly, through activi- ties that use farms' outputs or that provide products and services to farmers. Second, the poor benefit disproportionately from the welfare and employment gains brought about by agricultural growth because the majority of poor people in the developing world live in rural areas (World Bank 1995).Third, the types of crops and livestock produced by farmers and the farming tech- niques that they use affect the health, nutrition, and environmental conditions of families living both in rural areas and in nearby cities, through their ecological impact on farmland and on forests, wetlands, and rivers. For example, farmers who are unable to increase the productivity of their existing land may extend their cultivation into forests and other ecologically sensitive areas in order to maintain or increase their incomes. Fourth, the agricultural sector affects welfare and employment in urban areas because of its influence on food prices, wages, the input costs of the food and fiber processing industries, and the balance of payments. Finally, if serious problems develop in the agricultural sector (such as a drought that induces a crop failure), many rural dwellers will migrate to urban areas to seek work, which leads to overcrowding and increased unemployment in urban areas. Agriculture is usually defined in the national accounts In particular, the agriculture module presented in this of developing countries as the set of activities involved chapter includes only the activities of the farm that in the production of annual and perennial crops involve crop (annuals and perennials) and livestock (including trees for timber) and the production of live- production. It omits hunting, fishing, and gathering stock.This set of activities can be broadened to include activities as well as the processing of agricultural prod- hunting, fishing, and the gathering of wild flora and ucts.Those activities can be treated as nonfarm enter- fauna. In this chapter, the narrower definition of agri- prise activities and, therefore, should be included in culture is used for the purposes of making recommen- the household enterprise module of an LSMS or sim- dations on the design of the agriculture module in ilar multitopic survey (see Chapter 18 for further Living Standards Measurement Study (LSMS) surveys. details). 139 THOMAS REARDON AND PAUL GLEWWE In past LSMS surveys the agriculture module has Agricultural Policy Issues in Developing often been the longest module in terms of both pages Countries and interview time. The agricultural module has gen- erally had three objectives: measuring net income In many developing countries the agriculture sector is from the household's production of crops and live- changing rapidly, and in multiple ways.Yet policymak- stock; measuring the value of household agricultural ers and analysts often know very little about these assets such as land, animals, and equipment; and meas- changes because up-to-date, reliable data on agricul- uring the household's use of agricultural services such ture are scarce. This lack of information can lead poli- as extension programs, cooperatives, and veterinary cymakers to adopt inefficient or inequitable policies services (Ainsworth and van der Gaag 1988). Despite and may also cause them to miss some opportunities the length of these agricultural modules, the data col- to implement policies that can raise household wel- lected in them have been analyzed less often than the fare. data from almost all the other modules of the surveys Well-designed multitopic household surveys can containing them. Fewer than 10 percent of all of the help policymakers by providing them with accurate publications that analyze LSMS survey data have used data on agriculture and related activities, on nonagri- data from the agriculture module either directly or cultural activities, and on the characteristics of both indirectly Jolliffe 1995). One explanation for this is agricultural and nonagricultural households (and the that in almost all developing countries, researchers communities in which they live).These data can then have found it difficult to find out about the survey, to be analyzed using either "descriptive analysis" (statisti- get access to the data, and to find the time and funds cal analysis of correlation among variables, usually to study the data. Addressing these problems is beyond reported in graphs and tables) or "causal analysis" the scope of this book (see Blank and Grosh 1999). (econometric analysis to measure causal relationships), Another possible reason for the underuse of data from as explained further in the second section. previous agricultural modules is that the data collect- This section presents the most important policy ed were not useful to researchers. If this were indeed issues in the agricultural sector in developing coun- the case, it suggests that the agricultural module tries. The first subsection describes current patterns should be revised to ensure that it collect data that are and trends in agriculture in developing countries.The much more useful for policy analysis. The purpose of next subsection briefly reviews the agricultural out- this chapter is to advise survey designers on how to comes that are of greatest interest to policymakers.The accomplish such a revision. remaining subsections each discuss one of the four dif- The first section of this chapter lays out the most ferent kinds of agricultural policies, focusing on issues important agricultural policy issues in developing that are highest on the policy agenda. countries, including some links between agricultural issues and other topics on which data are usually col- Current Patterns and Trends in Agriculture in Developing lected in LSMS and other multitopic household sur- Countries veys. The second section discusses the data needed to Four broad trends have profoundly influenced agricul- analyze these issues, as well as measurement concerns ture in developing countries in recent decades and are related to those data. The third section introduces likely to prompt still more changes in the years ahead. three versions of a draft agriculture module. (The First, land in the developing world has become modules themselves are presented inVolume 3.) Each increasingly scarce as populations have grown. As a version is designed to gather information at a different result, agriculture in most developing countries has level of detail; the choice for a given survey will changed from being "extensive" (increasing produc- depend on the degree of emphasis that survey's tion by bringing more land under cultivation) to being designers wish to place on agriculture. All of these ver- "intensive" (increasing production by raising the pro- sions must be adapted to reflect the agricultural con- ductivity of the land already under cultivation).This is ditions and policy issues in the country where the sur- true even in Africa where, until recently, land scarcity vey is to be implemented. The final section of this was usually not an issue (Binswanger and Pingali chapter consists of annotated notes to specific ques- 1988). Policymakers need to know more about this tions or submodules of the draft module. inevitable move toward intensive cultivation, including 140 CHAPTER 19 AGRICULTURE whether it is leading to land degradation, water pollu- dustrial firms contract with farmers to supply crops) tion, or other environmental problems-all of which can affect the agriculture sector. can undermine long-term growth. They also need to In addition to these four trends, a key characteris- know whether the poor have access to modern tic of agriculture is that it has always been subject to inputs-such as chemical fertilizer-that enable them recurring "exogenous shocks" that affect farms and to raise the productivity of their current landholdings. farm families. Examples of such shocks are new crop Second, the agriculture sector is rapidly becoming diseases, periodic pest infestations, floods, droughts, commercialized in many countries and thus is increas- epidemics (of which AIDS is a recent example), and ingly linked to the urban and export sectors. At the civil war. Since agriculture is inherently a risky activi- same time, farmers in many countries are partially ty, it is essential for policymakers to understand how diversifying their activities by producing fewer staple farm households deal with these risks. crops and investing in dairy farming, livestock farm- ing, and the production of fruits and vegetables. Agricultural Outcomes that Interest Policymakers Policymakers need to know which farming house- As a frame of reference for the rest of this chapter, it is holds are making these changes and which households useful to identify the agricultural phenomena that are face barriers that prevent them from doing so. They of greatest interest to policymakers. In this paper they would also like to know which diversification activi- will be referred to as the "basic agricultural out- ties are most productive. comes." These outcomes are: Third, environmental degradation has become a * Production of crops, livestock, and related byproducts. major problem in most developing countries. * Use of inputs in the production processes, includ- Degradation problems include land degradation in the ing physical inputs, labor, and capital. form of soil erosion, the reduction of soil nutrient lev- * Technologies adopted and technology packages els, loss of tree and bush cover, and the salinization of used by agricultural households. soils from intensive irrigation. Another problem is the * Marketing activities that agricultural households runoff of farm chemicals into water sources, which undertake to sell their products. occurs mainly in areas where a "green revolution" has * Profits (net incomes) earned by households from taken place (in other words, where high-yielding their agricultural activities. seeds, chemical fertilizer, irrigation, and sometimes * Investments that households make in agriculture, pesticides are being used intensively). In some coun- such as the purchase of equipment and the tries a loss of biodiversity has become a problem, while improvement of land. in other areas (such as the West African Sahel) inade- * Nonincome welfare indicators of agricultural quate use of chemical fertilizers and manure have households, such as child nutrition, school enroll- exacerbated problems of soil degradation as farmers ment (of children), and household amenities. cultivate their farmiland more intensively in order to * Environmental phenomena that are affected by produce enough to survive. agricultural activities. Fourth, technological developments in recent Policymakers are interested in these basic agricul- years have greatly affected agriculture in many devel- tural outcomes for many reasons.The total production oping countries, and even more rapid change is on the of food and nonfood agricultural products has a large horizon.To the extent that new agricultural technolo- impact on the national economy and on the welfare of gies-"green revolution" technology, biotechnology the population as a whole, particularly in developing advances such as genetic modification of plants to countries where many households derive a large pro- enhance disease resistance, integrated pest manage- portion of their income from agriculture. The use of ment, and new types of agricultural equipment-are inputs is also of crucial importance, since the extent to adopted by farmers, they affect agricultural productiv- which a household uses inputs efficiently will affect its ity. It is important for policymakers to understand net income. Also, each input has an opportunity cost; what determines whether farmers adopt these new if it had not been used in agriculture, it could have technologies. Policymakers also need to know how been used elsewhere in the economy.The relationship the emergence of new institutions such as farmers' between total production and inputs depends greatly organizations and contract farming (in which agroin- on the technology used, which in turn depends on the 141 THOMAS REARDON AND PAUL GLEWWE availability of different technologies and their associat- work will be necessary to provide information useful ed inputs; in most cases policymakers would like to to policymakers.The remaining subsections review the encourage the adoption of new technologies so that current state of knowledge about how these four kinds inputs are used more efficiently. of policies affect agricultural outcomes, and emphasize Marketing activities are important for ensuring that areas where further empirical research is needed. food and nonfood products are brought to urban areas. They also affect agricultural exports, which can have a Macroeconomic Policies major impact on a country's balance of payments. Macroeconomic policies are very broad economic Agricultural profits contribute to households' incomes policies that are always implemented at the national and to their welfare as measured by nonincome indica- level and affect not only agriculture but also many tors (such as health and education outcomes), and cur- other sectors of the economy. The four macroeco- rent investments in agriculture are clearly important for nomic policies that can have strong effects on agricul- future production. Finally, the environmental conse- ture are exchange rate policies, trade policies, banking quences of agricultural activities are important policy and credit policies, and the overall size of the govern- issues in many developing countries. ment budget. Government policies affect these basic agricultur- al outcomes (as well as other more specific ones) by EXCHANGE RATE PoLIcIEs. Exchange rate policies influ- influencing their determinants. The most important ence, and in some countries completely determine, the determinants of agricultural outcomes are: value of foreign currencies in terms of domestic cur- * The prices that farmers face for both inputs and rency, directly affecting the domestic prices of all agri- products (which can be affected by taxes, subsidies, cultural inputs and products that are imported or and exchange rate policies). exported. To reduce trade deficits, governments often * Past investments that agricultural households have devalue their currency when it is thought to be over- made in their stocks of productive capital, including valued, since overvalued currencies discourage exports human capital. while stimulating imports. Such currency devaluations * The technology available to farming households. can increase the prices that farmers receive (in terms of * Farming households' access to credit. domestic currency) for their export crops and will also * Information and extension services available to tend to raise the prices of any crops produced by farm- farmers. ers that are also imported. Currency devaluations can * Large-scale investments in infrastructure, such as also have a negative effect on farm incomes if farmers transportation and irrigation networks. depend on imported inputs, since a devaluation will * The institutional environment in which households usually increase the domestic price of those inputs. operate (such as the system of land tenure, insur- These general effects of changes in the exchange rate on ance opportunities, and laws). the prices faced by farmers can be counteracted by sec- * The risks and uncertainty faced by farming house- toral policies. For example, the government can set the holds such as price variations, weather variability, domestic prices of specific agricultural inputs and prod- crop diseases, and harmful pests. ucts and not adjust these prices after a devaluation. Such * Any direct taxes, such as income taxes, for which sectoral policies are discussed further in the next sub- farm families are liable. section. A final issue regarding exchange rate policies is Four different kinds of policies-macroeconomic the extent to which exchange rates are allowed to fluc- policies, sectoral policies, policies that affect the insti- tuate over short periods of time. The government can tutional environment, and public investment pro- smooth out these fluctuations-for example, by setting grams-affect agricultural (and even nonagricultural) a fixed exchange rate in terms of U.S. dollars and main- households by altering one or more of these determi- tain the same rate for several years. This kind of policy nants of agricultural outcomes. However, the way in may reduce the risks borne by farmers through reduc- which these policies affect agricultural outcomes is ing fluctuations in the prices that they face. often ambiguous, and, even when it is clear, econom- ic theory reveals little about the magnitudes of the TRADE PoLIciEs. Trade policies usually take the form effects of specific policies. Therefore, some empirical of tariffs and various nontariff trade barriers. Examples 142 CHAPTER 19 AGRICULTURE of nontariff barriers on agricultural goods are grades government spending can mean reduced price sup- and standards related to food quality, food safety, and ports for outputs and lower price subsidies for inputs, the environmental effects of food production. as well as reductions in other kinds of government Reducing export taxes, nontariff barriers that discour- programs that benefit agricultural households, such as age exports, and tariffs imposed by importing coun- extension services and agricultural research stations. tries should make the production of export crops Similarly, policies that increase taxes can reduce farm- more profitable. In contrast, reducing import tariffs ers' income either directly (for example, through an and nontariff barriers that discourage imports have a income tax) or indirectly (through taxes that affect more ambiguous effect on agricultural households; prices). As with nationwide banking and credit poli- such reductions tend to increase competition from cies, the specific ways in which spending is cut or taxes imported agricultural products but can also reduce the are increased determines the actual effect on agricul- prices of imported agricultural inputs. Nontariffbarri- tural households. This again leads to sectoral policies. ers can also limit the technology available to farmers. For example, phytosanitary regulations-rules on SUMMARY. The discussion of macroeconomic policies importing plant matter-can limit the access of farm can be summarized in terms of the following specific households to imported seeds, and chemical standards policy questions: can limit these households' access to imports of certain * How do exchange rate policies affect the prices of chemicals, such as DDT. The standards that apply to agricultural inputs and products and the variability the goods exported by developing country producers of those prices? can also influence the use of inputs, many of which are e How do tariffs and nontariff trade barriers affect associated with technologies commonly used by farm- the availability of agricultural inputs and the prices ers in developing countries. For example, if Mexican farming households face for inputs and outputs? producers want to export to the U.S. market, they can- * How do national banking and credit policies affect not use as much of certain kinds of pesticide as they agricultural households' access to credit and the can use on goods that they produce for the Mexican terms on which credit is available? market. Finally, nontariff barriers in the household's * How do across-the-board reductions in spending own country, such as import quotas and domestic and increases in taxes affect direct taxes, prices for component regulations, can limit farmers' access to agricultural inputs and products, and the availabili- imported agricultural inputs. ty of programs that benefit farmers? There are no simple answers to these questions, BANKING AND CREDIT POLICIES. The availability of because agricultural systems vary widely across devel- credit and the terms on which credit is available can oping countries, and even within a given country the have a large impact on the activities of agricultural characteristics of farming households can vary enor- households. At the national level, policies that affect mously. For example, devaluations can increase the credit are set by finance ministries and central banks. income of producers of export crops, but may have lit- A nationwide tightening of credit can increase the tle effect on farming households that produce mainly interest rates that farmers face, while changes in bank- food crops for subsistence. Thus the answers to these ing regulations can make credit either more or less questions will vary from country to country and by accessible. Of course, the impact of national banking the different types of households within each country. and credit policies can be altered by sectoral policies In general, these effects of macro reforms on agri- such as special programs within the ministry of agri- culture have not been adequately studied. Much of the culture that provide banking services to rural areas. literature on the impact of macroeconomic policies focuses on the impact of exchange rate policies, par- SIZE OF THE GOVERNMENT BUDGET. In many develop- ticularly the effect of exchange rate devaluations. ing countries fiscal deficits are a serious problem. Another strand of the literature focuses on general Governments often seek to reduce these deficits both price liberalization-the removal of price controls and by reducing spending and by increasing taxes. In many other policies that influence prices such as subsidies cases the emphasis is on reducing public spending. and taxes on specific products. Evidence from rural From the perspective of farmers, broad reductions in areas in a variety ofAfrican and Latin American coun- 143 THOMAS REARDON AND PAUL GLEWWE tries suggests that a concentration of market power but this is mostly an issue of semantics; there is no and market entry barriers tends to produce greater need to rigidly classify each policy as one or the other. price instability when prices are liberalized, while There are many different kinds of policies in agri- devaluation has an ambiguous effect on farm prof- culture at the sectoral level.They can be grouped into itability (Reardon and others 1997). Overall, much policies that directly affect the prices of inputs and more research needs to be done on the impact of products (such as price subsidies, taxes, and price floors macroeconomic policies on rural households. and ceilings), programs that directly provide technolo- There is also a literature that focuses on the gy, information, and specialized services to farmers, macroeconomic policies that are typically included in and policies that affect the availability of credit to structural adjustment programs. The evidence in this farmers. literature tends to be quite mixed. For example, some authors have found that structural adjustment pro- PoLIcIES THAT AFFECT THE PRICE OF AGRICULTURAL grams have had positive effects (Sahn 1994), while INPUTS AND PRODUCTS. A wide variety of sectoral other authors have found that these policies have gen- policies can affect the prices for agricultural inputs and erally negative effects (Taylor 1993), and still others products. Taxes can raise (and price subsidies can have found a mixture of positive and negative effects lower) the prices of agricultural inputs. In some coun- (Commander 1989; Duncan and Howell 1992). The tries all marketing (and even production) of certain variety of results in the literature suggests that the inputs is controlled by marketing boards, which are effects of macroeconomic policies in a given country sometimes referred to as parastatal corporations. depend on the characteristics of that country, which in Agricultural products can also be taxed or subsidized, turn implies that LSMS and similar multitopic surveys and in some cases all marketing is controlled by a are useful sources of information for illuminating the national agency. Some governments decree price potential effects of these policies in specific countries. floors or ceilings, although it is not always possible to enforce such regulations.These policies can affect not Sectoral Policies only prices but also fluctuations in prices. For exam- Sectoral policies differ from macroeconomic policies ple, the government may be ready to purchase specif- in that they focus directly on a given sector, such as ic agricultural products at guaranteed minimum agriculture, and are often designed and implemented prices, which will reduce the price fluctuations faced by the appropriate ministry-in this case the ministry by farmers. Some policies that affect the prices of of agriculture. Sectoral policies for agriculture include inputs and outputs depend on aid received by the gov- taxes and price subsidies for specific agricultural inputs ernment. For example, the government may sell food and products, marketing boards that purchase agricul- or fertilizer received as aid in order to reduce food or tural outputs and sell agricultural inputs (in some cases fertilizer prices in some areas. Of course, macroeco- monopolizing these markets), regulations that govern nomic exchange rate and trade policies can also affect prices of both agricultural inputs and outputs, agricul- prices, so the overall effect will be determined, rough- tural extension services, programs that provide credit ly speaking, by the sum of the effects of macroeco- for farming households or promote new agricultural nomic and sectoral policies. technologies, and public investments in agricultural infrastructure and research. AGRICULTURAL EXTENSION SERVICES. Ministries of Sectoral policies in agriculture can be closely agriculture often provide a variety of agricultural related to macroeconomic policies. Price subsidies for extension services to farmers, such as basic agronomic certain agricultural products or inputs may be information, information on new types of technology, designed to counteract the impact of general tariffs or visits by extension agents to farms to investigate spe- an exchange rate devaluation. Alternatively, the gov- cific problems, advice on the use of pesticides and her- ernment's attempts to reduce macroeconomic budget bicides, and vaccinations and other services for farm deficits may reduce spending on price subsidies or on animals. Some ministries of agriculture are also the provision of agricultural services. At times it may involved in the production of new technology at agri- not be clear whether a given policy should be classi- cultural research stations.Agricultural extension agents fied as a macroeconomic policy or a sectoral policy, may periodically visit farming households whose 144 CHAPTER 19 AGRICULTURE members might otherwise never visit an extension * What impact do interventions that are intended to center. Many of these services are undoubtedly quite change the prices of agricultural products actually useful to farmers, but others may not be. Some of the have on prices, and what is the impact of any price advice provided may even have a negative impact on changes on basic agricultural outcomes? the welfare of agricultural households. Obviously, until * What impact do different agricultural extension policymakers understand the effects of these different services have on basic agricultural outcomes? services, they will not know which services to expand * What prices, if any, should be charged for agricul- and which ones to reduce or eliminate. tural extension services, and how are the benefits Another issue is how much to charge for agricul- and costs of those services distributed among farm tural extension services.While economic theory pro- households? vides clear reasons to subsidize some services, such as * How do credit policies and programs in the agri- the provision of information, other services may not culture sector affect the availability of credit to agri- need to be heavily subsidized and could even be made cultural households and the development of private more widely available if some element of cost recov- credit institutions? ery were introduced. A related issue is the distribution As with macroeconomic policies, there are no simple of the benefits and costs of agricultural extension serv- answers to these questions because of the enormous ices. Do they reach the poorest households? Who ulti- variation in both agricultural systems and sectoral mately pays the costs of providing these services? For policies in developing countries.The individual effects an interesting discussion of these issues and research of most sectoral policies (such as taxes, subsidies, and evidence to date see Purcell and Anderson (1996). price controls) on output or input prices are unam- biguous, and the effect of price changes on household POLICiES THAT AFFECT THE AVAILABILITY OF CREDIT. welfare and agricultural output is also well known. While macroeconomic policies clearly affect credit That is, increases in output prices raise output and markets, the ability of farmers to invest in working household welfare, while increases in input prices have capital is affected by a variety of policies implemented the opposite effect. However, the magnitude of these by ministries of agriculture at the sectoral level. In effects are usually not known, which implies that the many countries governments have directly offered size of the benefits are unknown, and the benefits may credit to agricultural households-with mixed success not be worth the costs. Moreover, when several poli- at best. For recent reviews of past experience see cies are implemented simultaneously-such as when a Besley (1994),Yaron (1994), and Zeller and Sharma structural adjustment program is implemented that (1998). In some countries access to credit has been removes many sectoral policies designed to influence increased by the development of rural bank programs prices-the overall impact on farm production and operated by nongovernmental organizations (NGOs) household welfare is uncertain. such as the Grameen Bank in Bangladesh, yet the suc- The literature on sectoral policies is large and can- cess of NGO programs has also been mixed (Morduch not be easily summarized. Nevertheless, it is clear that 1999; Rahman 1999). Economic theory shows that many questions remain unanswered. Some observers credit markets can suffer from a variety of market fail- argue that sectoral policies designed to alter market ures, including problems of moral hazard and adverse prices are inherently distortionary and inefficient, and selection in risky environments with incomplete thus should be removed (Schultz 1978).Yet the empir- information. Economists' understanding of credit mar- ical evidence has revealed several cases where the kets has increased substantially in recent years due to elimination of sector-level interventions did not lead both research and innovations in credit institutions. It to the expected outcomes. For example, in some is important for policymakers to know how credit countries the supply of fertilizer and seed from private policies and programs affect capital formation in agri- merchants increased much less than expected after the culture so they can design effective policies and create elimination of fertilizer and seed marketing boards, a policy environment that promotes the development which had depressed prices for these inputs (Rukuni of efficient credit institutions. 1996; Dembele and Savadogo 1996; Rusike and oth- This discussion of sectoral policies leads to the fol- ers 1997). Other studies have claimed that the govern- lowing specific policy questions: ment has an important role to play in developing mar- 145 THOMAS REARDON AND PAUL GLEWWE kets for agricultural outputs. For example, some econ- of law in rural areas. Examples of such policies include omists have argued that fertilizer markets in Africa are regulations specifying the acceptable range of rights plagued by a series of fundamental problems and idio- and responsibilities in contracts between agroindustri- syncrasies such as risk, seasonal demand, high transport al firms and farms, establishment and regulation of costs, underdeveloped financial markets, and cash- government-managed crop insurance and drought constrained farmers (Barrett and Carter 1999). Thus, insurance schemes, and the establishment of civil court while it is true that fertilizer subsidies and domestic systems for land disputes. fertilizer production schemes have suffered from fiscal The most important policy questions regarding unsustainability and problems of implementation in the institutional environment in developing countries Africa, it also appears that private markets in rural are: Africa may not operate in ways that some policy advi- * What impact do traditional forms of land tenure sors expected they would. It may be that governments have on basic agricultural outcomes, and what can need to invest in improving transportation infrastruc- government policies do to overcome inefficient ture before private markets can function well (Ahmed, outcomes or to change the system of tenure? Falcon, and Timmer 1989; Rusike and others 1997). If * How politically feasible is major land redistribution better agricultural data can be collected in LSMS and in countries where the distribution of land is high- similar multitopic household surveys, more light may ly unequal, and what impact will such redistribution be shed on this question. have on basic agricultural outcomes and on the dis- tribution of these outcomes across households? Policies That Affect the Institutional Environment * What policies, regulations, and enforcement mech- Institutional policies primarily concern changes in the anisms can governments implement to promote the "rules of the game," such as land tenure rules, con- rule of law, and how does the rule of law affect basic tracts, and so on. Systems of land tenure are particular- agricultural outcomes? ly important. In some countries, land rights in many * What programs can governments implement to rural areas are determined by traditional systems that provide insurance directly or to promote the provi- may discourage efficient use of the land. One example sion of insurance in the private sector, and how do of this is the designation of some land as community these insurance schemes affect basic agricultural grazing areas for livestock, which usually leads to over- outcomes? grazing of that land. As land constraints grow in many The focus of recent empirical research on the countries, there is a tendency to formalize land titling effects of institutional change on basic agricultural in response to increased competition for land. outcomes has been on changes in land institutions, In other countries the distribution of land is high- particularly land tenure policies and land redistribu- ly unequal, which may also encourage inefficient use of tion. In recent decades land redistribution from col- agricultural land. This is particularly the case in coun- lective farms to individual households has occurred in tries with "dual" agriculture sectors-where a small many socialist or formerly socialist countries, such as number of very large farms coexist with large numbers Eastern Europe, China, andVietnam. Land redistribu- of very small farms, as in Brazil, Central America, tion has been limited in other developing countries. Mexico, South Africa, and Zimbabwe. In these coun- Recent empirical evidence on the effects of land tries potential or actual land redistribution (land titling-providing more "formal," and thus more reform) has prompted heated political debate. secure, land tenure-is mixed. In some countries Policymakers need to knoxv how land reform programs researchers have found that more secure land owner- have affected or could affect the concentration of land- ship increases productive investments in land (see holdings, farmers' access to land, income distribution, Place and Hazell 1993 and Migot-Adholla, Hazell, and and the incidence ofpoverty.A particularly contentious Place 1990 for evidence from Rwanda and Ghana). issue in this context is the effect that land reform and But this was not the case for Kenya; Migot-Adholla, redistribution have had on farming productivity, capital Hazel], and Place (1990) found that the relationship to labor ratios, and the welfare of rural households. between tenure and land improvements was weak Government policies also have direct effects on because farmers already felt secure in their use rights contract enforcement and, more generally, on the rule under the traditional land use system. Overall, the 146 CHAPTER 19 AGRICULTURE impact of more formal titling appears to depend in additional person can use them at little or no cost to part on the kind of system that is being replaced and others and it is difficult to prevent people from using the kind of investment or farm practice examined. them. Others, such as education and health care, may (For example, long-term investments were more sen- have significant benefits in the form of externalities; sitive than short-term investments to land insecurity.) that is, they may provide benefits to members of soci- There is a fair amount of empirical evidence on ety beyond those that directly use the service. Some whether smaller farms are more productive, which is a large physical infrastructure projects, such as irrigation key issue concerning land redistribution policies. In and electric power grids, may have large economies of India, for example, Bardhan (1973) and Deolalikar scale, which is another reason for government involve- (1981) show that smaller farms have higher land pro- ment. A final argument in favor of government ductivity but lower labor productivity. They point to involvement is imperfect information; for example, the greater labor intensity of smallholder farms as the residents in remote rural areas may not be aware of the reason. Empirical studies tests in Africa ( Carter and benefits of education or modern medical treatments. Wiebe 1990 on Kenya; van Zyl, Binswanger, and Investments in transportation such as roads, rail- Thirtle 1995 on South Africa) also find an inverse rela- roads, water transportation, ports, and air transporta- tionship between farm size and land productivity. tion can have a dramatic impact on markets-and Another example is Barrett (1996), who shows an particularly on market prices-by linking local mar- inverse relationship for rice farmers in Madagascar. On kets more closely with regional, national, and interna- the other hand, larger farmers could in theory com- tional markets. Similarly, modern communications pensate for less family labor per hectare by using hired infrastructure (such as postal service, telephones, labor, nonlabor variable inputs, and capital to meet or radio, television, electronic mail, and even satellite surpass land productivity on small farms. Adesina and connections) also links markets more closely with Djato (1996) show this for large rice farms in Cote each other and can facilitate the flow of useful infor- d'Ivoire, and Rao and Chotigeat (1981) show it for mation to farming households, including information large farms in India. Smaller farms may also have lower on prices, new technologies, insurance opportunities, land productivity because their more intensive farm- and procedures for obtaining government assistance. ing fatigues and degrades the soil, yet a zone with bet- Finally, government investments in electric power ter soils might attract more farmers, giving rise to generation and large-scale irrigation projects can have smaller farms with better yields than in other zones. an enormous impact on households' welfare and agri- Almost no research has been done on the rule of cultural productivity. law and the agriculture sector in developing countries, Government investments in basic social services, and only a small amount has examined insurance mar- particularly schools and health facilities in rural areas, kets. Crop insurance is sometimes available for large can also affect agricultural productivity and household commercial farms, but administrative costs usually pre- welfare. There is a large literature that shows how bet- vent it from being offered to small family farms. For an ter health and higher education make agricultural introduction to crop insurance in developing coun- workers more productive (see Strauss and Thomas tries see Gudger (1990). 1995 for a recent literature review). As explained in Chapters 7 and 8, there are sound economic reasons Public Investrnents for governments to invest in these services. Of course, Public investment policies include investments in policymakers need to make decisions on the extent physical infrastructure-such as transportation, com- and nature of these investments based not only on munication systems, electric power grids, and large- their impact on agricultural outcomes but also on scale irrigation schemes-and investments in basic their impact on other outcomes. social services, particularly in schools and health clin- Thus the two specific policy questions regarding ics. From the viewpoint of economic theory there are public investments and agriculture are: many reasons why such investments should be * What impact do government investments in trans- financed (though not necessarily implemented) by the portation, communications, electric power genera- government. Many infrastructure investments, such as tion, and large-scale irrigation schemes have on roads and canals, are public goods in the sense that an basic agricultural outcomes? 147 THOMAS REARDON AND PAUL GLEWWE * What impact do government investments in outcomes of interest. But before considering in detail schools and health services (clinics, hospitals and the kinds of data needed to assess these impacts, it is public health services such as immunizations) have useful to consider how agricultural households behave on basic agricultural outcomes? and how government policies can affect their behavior. The bulk of recent empirical work on the impacts Agricultural economists and agronomists often of infrastructure development on agriculture points to think of agricultural activities in terms of a production positive effects on the rate of commercialization and process or production function. When various inputs productivity growth. For a recent review of the litera- are combined in certain ways using a certain technol- ture see Raisuddin Ahmed and Cynthia Donovan ogy, the result is the crops and animals ("outputs") that (1992). For investments in social infrastructure see agricultural households produce. These products can Strauss and Thomas (1995), who address, among other either be consumed by the household or sold to oth- things, the impact of nutrition and education on agri- ers.The overall value of these activities to each house- cultural productivity. Again, the literature usually finds hold can be measured as farm profits (net agricultural positive impacts, but in some studies the link is weak income), which include not only earnings from selling or even nonexistent. products but also the value of products that the house- hold consumes. Households can invest some or all of Analytical Approaches, Data Needs, and Data the income generated by agricultural activities (as well Collection Issues as income from other sources) in ways that will increase their agricultural production in the future. The policy questions presented in the previous section How do agricultural households decide what to cannot be resolved by appealing to economic theory. produce, what inputs to use, and related choices? They can be answered only by examining data using Economists often portray households and their mem- appropriate empirical research methods. However, col- bers as organizing their activities to maximize some lecting data is not easy, and quite often the data avail- kind of utility function.1 Their utility ("happiness") is able are insufficient for answering important policy higher when they consume more goods and services questions. LSMS and similar multitopic household and lower when they increase the amount of time they surveys can provide policymakers with detailed, accu- spend working. Given this situation, agricultural rate information that can be used to understand the households organize their crop- and livestock- impact of current trends and proposed policy changes producing activities in ways that increase farm pro- on the agriculture sector. How well they do so ductivity and economize on the amount of time that depends on the type of survey data collected and on household members spend working on these activi- the methods used to gather and analyze the data. ties. This may involve more than the organization of This section draws on the methods used by agri- agricultural activities; for some households it may cultural economists and other researchers to assess what make sense for one or more members to find employ- data are needed to answer the policy questions raised in ment in nonagricultural activities, since that may pro- the first section. The first subsection, drawing on eco- vide the household with more income than would be nomic theory, discusses the behavior of agricultural the case if those members xvorked in agriculture. households and links this to the agricultural policy Agricultural economists have developed mathe- issues. The second subsection continues the discussion matical models of the behavior of farm households by explaining what data are needed to provide answers that provide useful insights for doing empirical to the various policy questions.The third and final sub- research (see Singh, Squire and Strauss 1986). One les- section shows how the required data are collected in son from these models is that it is important to distin- practice, emphasizing the difficulties involved and rec- guish factors that are beyond the control of the house- ommending practical solutions whenever possible. hold from outcomes that can be determined, at least in part, by the household. Some examples will make this An Economic Approach to the Behavior of Agricultural distinction clear. In general, households and individu- Households als have no control over prices of agricultural inputs Ultimately, policymakers would like to know the and outputs, the technological packages available, or impacts of different policies on the basic agricultural access to credit. In contrast, households typically do 148 CHAPTER 19 AGRICULTURE have control over the crops that they plant, the * Market conditions for buying, selling, and renting amounts of inputs that they use, and the time they land. spend working in agriculture. Given the factors * Traditional land tenure arrangements. beyond their control, which economists often refer to * Institutions for enforcing contracts and settling land as exogenous variables, and family characteristics (such disputes. as amount of land owned, education levels of family . Opportunities to purchase insurance. members, and family size), farm households make Nearly all government policies affect households by decisions about the things they can control, which influencing one or more of the exogenous variables economists refer to as endogenous variables. listed above; very few government policies directly In general, all the basic agricultural outcomes pre- affect the (endogenous) basic agricultural outcomes.2 sented in the first section are endogenous: they are Consider the macroeconomic policies discussed in the determined, at least in part, by the decisions of rural first section. Exchange rate policies affect agricultural households. Rural households decide what crops they households solely through the prices that the house- will grow, what animals they will raise, and how much holds face for their inputs and outputs.Thus to under- of each input (including their own labor) to use. The stand the impact of exchange rate policies it is neces- amounts of crops and animals that households produce sary to know how a specific exchange rate policy are partially under their control; they can increase pro- affects prices and how these prices affect the different duction by bringing more land under cultivation and agricultural outcomes. using more inputs. However, other factors, such as Tariff and nontariff policies also affect farming weather, also affect production. households, primarily through the prices that the Rural households also decide what technologies households face. These policies can also restrict the to use, whether and where to market some or all of access of farm households to new technologies. And their crops, and what kinds of long-term investments trade barriers occasionally work in other ways. A to make in agriculture. These decisions affect house- quota system for an imported agricultural input, such hold profits from agriculture. In addition, rural house- as fertilizer, may be accompanied by a distribution holds decide how much time, if any, each member mechanism not governed by price, such as rationing. should work in nonagricultural activities, and rural In this situation the rationed fertilizer can be thought households make many decisions about nonwork of as a "service" provided by agricultural extension activities related to health, nutrition, education, and centers.Yet flexibility is needed; because rationing can other areas. Finally, the activities of rural households take so many different forms, other types of rationing can affect the environment, the last basic agricultural may require a different approach. outcome presented in the first section. For example, By definition, credit policies at the macroeco- intensive use of fertilizers and pesticides can have neg- nomic level affect the terms and availability of credit. ative environmental consequences. They may do so directly (for example, by establishing How do government policies affect these basic government-run credit institutions) or indirectly (for agricultural outcomes? In general, they do so by alter- example, through regulations on private banks and ing the exogenous variables that households face.The other private lending institutions). following variables are exogenous and play important Finally, general reductions in spending and roles in determining basic agricultural outcomes: increases in taxes can affect both agricultural and * Prices for agricultural products and agricultural nonagricultural prices by reducing subsidies or inputs. increasing taxes on specific items.Taxes can also affect * Weather, pest infestations, and crop diseases. rural households (and often urban households) more * The availability of different kinds of agricultural directly-for example, through a general income tax. technologies. Many sectoral policies also affect prices. In partic- * The availability of agricultural extension services ular, subsidies, commodity taxes, price controls, and and the prices charged for them. marketing board policies almost always affect rural * Physical and social infrastructure. households by altering the prices that these house- * Taxes. holds face. The pricing of agricultural extension serv- * The availability of credit and the terms of that credit. ices is another sectoral policy that works through 149 THOMAS REARDON AND PAUL GLEWWE prices. Sectoral credit programs affect both the avail- "back of the envelope" calculations. For example, they ability and terms of credit. The government can also may estimate that reducing a tariff on an imported increase the availability of extension services-and in agricultural input will reduce the domestic price by the long run the availability of technology-by estab- the same amount that the tariff was reduced. In many lishing agricultural research stations. And some tax cases this approach may be too simplistic. A better policies in the agriculture sector may also have direct approach would be to use data from sources other than effects (that is, effects that do not work through a household survey to answer this question-for prices); an example is a tax on agricultural land. example, to use macroeconomic time-series data on Policies that affect the institutional environment exchange rates and domestic prices. Neither of the and public investment policies have both direct and two approaches uses household survey data. indirect effects on the exogenous factors that agricul- In contrast, household survey data are crucial for tural households face when making decisions about answering the second question. Since this book focus- their various activities. Any policy that affects tradi- es on the design of household surveys, the rest of the tional forms of land tenure will have major implica- chapter will focus on what is needed to answer the tions on the land markets and tenure arrangements second question, which is relevant for a range of that agricultural households face.The same is true for important macroeconomic and sectoral policies. For land redistribution policies. Such policies can have example, in examining the effects of exchange rate indirect effects (for example, changing the price of policies, the discussion will focus on how changes in land) and direct effects (for example, by outlawing spe- the prices of traded inputs and outputs affect agricul- cific forms of land tenure or requiring larger landown- tural outcomes, rather than on how changes in the ers to sell some of their land). Policies that affect the exchange rate affect prices.To answer the first question rule of law, such as the establishment of a legal appara- researchers need to consult the appropriate literature, tus for enforcing private contracts, can also have direct which in general does not make use of household sur- and indirect effects.A direct effect would be that con- vey data (two examples are Krueger et al 1992 and tracts become enforceable, and an indirect effect Barrett 1999). would be a change in prices due to the increased con- The second case concerns government policies tract enforcement. Insurance policies affect the terms that directly affect households' basic agricultural out- and availability of insurance, and they may do so comes. A good example of such policies are agricul- directly or indirectly. Finally, direct investments in tural extension services provided by the ministry of either physical or social infrastructure alter the oppor- agriculture. In this case the analysis is simpler because tunities available to households either directly (for household survey data can be used to examine the example, through a new road that reduces the wear (direct) impact of such policies on the phenomena of and tear on vehicles owned by the household) or indi- interest. rectly through prices (for example, as the new road The next subsection explains two ways to use provides better access to distant markets). LSMS-type household survey data to analyze agricul- In discussing how these four kinds of policies tural policy issues and describes in detail how to col- (macroeconomic, sectoral, institutional, and public lect the data needed for both types of analysis. investment) affect the exogenous variables and how these variables in turn affect the basic agricultural out- Analytical Methods and Specific Data Needs comes, it is useful to distinguish between two cases. In Research methods for analyzing the relationship the first case the policy affects households indirectly by between the exogenous variables affected by policies influencing one or more intermediate variables, such and basic agricultural outcomes can be divided into as prices or the availability of technology. In such cases two types: descriptive analysis and causal analysis. there are two separate questions.The first is: How does the policy affect prices or the availability of technolo- DESCRIPTIVE ANALYsIs. The main objective of descrip- gy? The second is: How do changes in prices or in the tive analysis is to describe what is occurring without availability of technology affect households' basic agri- rigorously explaining why it is occurring. The meth- cultural outcomes? Data analysts sometimes answer ods used are simple calculations of various shares and the first question by making simple assumptions or levels, so the biggest challenge is not doing the analy- 150 CHAPTER 19 AGRICULTURE sis but obtaining sufficiently accurate data. Simple behavior of agricultural households. For example, data descriptive statistics can be used to answer many dif- on which households currently grow a particular crop ferent questions of interest to policymakers, such as: do not show precisely who might benefit from a * What crops are agricultural households growing, reduction in a tax on that crop (which would increase and how does this vary in different types of agri- the prices received by farmers), because households cultural households? that are not currently growing the crop may decide to * What are the net farm incomes (profits) of different do so to take advantage of the new policy. Moreover, types of agricultural households? some of the households that are currently growing the * What agricultural inputs, such as fertilizer, irriga- crop may decide to increase their production of it, tion, pesticides, and farm equipment, are used by while others may hold their production constant. Thus different types of farming households? statistics on how much each household is currently * How does access to and use of agricultural exten- producing will not fully reflect who will benefit-nor sion services vary among households? the extent of the benefit-from a tax reduction. An * Which rural households are making investments in even more difficult problem is that many policies agriculture, and what form do these investments affecting prices benefit consumers as well as produc- take? ers. For example, if the supply of a taxed crop is quite There are two reasons why simple descriptive statistics elastic while the demand is not, the market price will are useful for policymakers. First, the better informed fall, so most of the benefits of the tax cut will accrue policymakers are about the basic characteristics of the to consumers rather than to producers. agricultural sector, the better prepared they will be to Descriptive analyses of agricultural issues that are make day-to-day policy decisions. Second, descriptive of interest to policymakers require data on all of the statistics can be used to obtain estimates of who cur- basic agricultural outcomes listed above, plus data that rently benefits from specific government policies. For can be used to classify households into different socioe- example, data on which households obtain informa- conomic categories. Therefore, the agriculture module tion from (or are visited by) agricultural extension should collect the following kinds of information: agents show who benefits from agricultural extension * The amount of each crop produced and of each services. Similarly, data on who obtains loans from type of livestock produced (including any crop or government credit programs help show the distribu- animal byproducts). tion of benefits from those programs. * The amount of purchased inputs used (including Simple descriptive statistics can also show which fertilizer, herbicides, insecticides, fungicides, seeds households currently benefit from programs that affect and seedlings, and irrigation services) and the the prices of agricultural inputs and outputs. For household's use of credit and agricultural extension example, price subsidies for a particular crop benefit services. Information on the amount of labor used only the farmers who grow that crop, and the benefit (including both household members and hired any individual farmer receives is proportional to the labor) may yield a clearer picture of the agricultur- amount that he or she grows.Thus information about al sector but is not necessary for making rough esti- which households grow each crop, and how much mates of the impact of government policies. they grow, can be used to estimate the distribution of * The technologies used by each household, includ- the benefits provided by both current and proposed ing high-yielding varieties of particular crops or price subsidies (Deaton 1989). Similar calculations can new types of capital equipment. be done for price subsidies on agricultural inputs (such . The marketing activities of agricultural households, as fertilizer and insecticides) and for taxes on either including how much of their crop and livestock inputs or outputs. Other policies that affect the prices production was sold and to whom it was sold. of agricultural inputs and outputs-such as tariffs, . The net income (profits) that households earn from exchange rate devaluations, and price controls-can their agricultural activities. also be evaluated. * Households' investments in agriculture, both in While such descriptive analysis is useful, it is terms of their current stock (ownership of farm important to keep in mind that these estimates are machinery, irrigation equipment, farm tools, build- approximations because they do not account for the ings, and land), and recent additions (purchases of 151 THOMAS REARDON AND PAUL GLEWWE these items plus building construction and land CAUSAL ANALYSIS. While descriptive analysis can be quality improvements). quite useful for policy discussions, it has serious limi- No mention was made here of welfare indicators tations. In particular, it cannot be used to explain such as consumption expenditures, health status, and household behavior. This is why estimates that use school enrollment. Information on welfare should be descriptive methods to show who benefits from gov- collected in other modules of the household ques- ernment policies are only approximations. More gen- tionnaire such as the consumption, education and erally, descriptive information cannot be used to health modules. Similarly, data on how environmental examine the causes of various phenomena of interest, outcomes may be affected by agricultural activities such as the impact of additional inputs on farm pro- should be collected in the environment module. ductivity and the reasons why some households plant However, if the survey does not include an environ- certain crops, use certain inputs, or adopt certain tech- ment module, it may be necessary to collect this infor- nologies, while others do not. Understanding the mation in the agriculture module. See Chapter 14 on causal relationships between inputs and outputs and the environment for a detailed discussion of how to the determinants of household behavior requires collect such data. causal analysis, in which analysts use econometric and The definitions of most of the data listed above are statistical methods (most commonly, regression analy- quite clear. However, the calculation of net farm income sis) to explain why households make the choices that (profits) merits a brief discussion. This variable is the they do.' Ultimately this information can be used to value of agricultural outputs minus the value of the cor- estimate how existing government policies affect responding agricultural inputs.The values ofoutputs and households' activities and well-being, and to predict inputs used can be obtained directly or derived from the likely effects of any new policy options that may quantity and price information. As discussed further be under consideration. below, collecting data on quantities can lead to difficul- When analyzing agricultural issues, two kinds of ties if respondents are only able to provide answers in causal relationships can be estimated: structural rela- local units, but their responses need not be converted tionships and reduced form socioeconomic relation- into national or international units if the price data are ships.The most important example of a structural rela- also expressed in local units. The price information can tionship is the agricultural production function-the be obtained from either the household questionnaire or physical relationship between outputs (crops, animals, the community questionnaire. Two points should be and their byproducts) and inputs (including those con- kept in mind xvhen designing the survey. First, the set of trolled by households, such as labor, fertilizer, and land, outputs and inputs consists of both those that are sold for as well as random factors outside the control of house- (or purchased with) cash and those that are given (or holds, such as rainfall and pest infestations). Strictly received) in kind; market prices can be used to impute speaking, production functions do not depend in any the value of the in-kind inputs and outputs. Second, to way on the characteristics of households. Thus pro- calculate total net farm income it is not necessary to duction functions contain no information about know which inputs were used on each farm product, household behavior. although calculation of net farm income for each prod- The second kind of causal relationship, a reduced uct would require such information. form relationship, shows how exogenous variables In summary, for descriptive analysis, it is only nec- affect the different basic agricultural outcomes (all of essary to collect data on the phenomena of interest. In which are endogenous). Since these relationships all most cases there is little need to collect price data involve choices made by households, they depend on unless analysts wish to see how prices vary among dif- each household's characteristics, such as how many ferent geographical areas or socioeconomic groups. people belong to a household and the household's However, experience with past LSMS surveys has utility function. This kind of causal relationship does shown that data survey designers initially thought measure household behavior. While a production would not be useful for analysts have often later function is the same for all kinds of households, a proved to be of considerable analytical interest. Thus if reduced form relationship can vary among different possible the survey should aim to collect a broad range types of households because it depends in part on their of information. characteristics. 152 CHAPTER 19 AGRICULTURE Some examples may make the distinction or as a factor that modifies the contribution of between structural and reduced form relationships other inputs) affect the farmers' productivity, the clearer. A production function is a technological rela- crops that they grow, the inputs they use, the tech- tionship between inputs and outputs. It can be nologies they adopt, and other basic agricultural thought of as a "formula" that shows how much out- outcomes? put is produced by combining different sets of inputs. Econometric methods can be used to estimate One input may be fertilizer, or even the labor of structural relationships such as production functions, as household members, but the relationship between well as reduced form relationships. Estimates of pro- inputs and outputs is completely unaffected by duction functions can be used to answer the fifth and whether a household uses fertilizer or how it uses its eighth questions above, on the impacts of education own labor; the relationship itself is not affected by and farm size on farm productivity. Estimates of household behavior. In contrast, one example of a reduced form relationships can be used to provide reduced form relationship is the determinants of the answers to the other six questions listed above. One amount of labor that a household devotes to agricul- useful reduced form relationship is the "profit func- tural activities.This can also be thought of as a formu- tion." This shows how much profit a farm household la, but in this case the parameters of the formula may can make given certain input and output prices depend on household characteristics such as the rela- (which are clearly exogenous), household assets such tive weights on leisure and consumption in a given as farm size or capital holdings (which can be consid- household's utility function. ered exogenous in the short run), and other household Estimates of these two types of causal relationships characteristics.4 Profit functions are particularly useful can be used to answer policy questions such as: because they can be used to derive estimates of func- * How much, and how quickly, do changes in the tions that show how the supply of outputs and the prices of agricultural products and inputs affect the demand for inputs are affected by these same variables production of exported and nonexported crops, the (Sadoulet and de Janvry 1995). use of agricultural inputs, and other basic agricul- Two things must be borne in mind when consid- tural outcomes? ering causal analysis. First, more data are needed to do * How do different agricultural extension services causal analysis than to do descriptive analysis. affect farmers' output, use of inputs, incomes, and Unbiased estimation of reduced form relationships productivity? requires that the analyst have data on all of the vari- * How does the availability of credit affect the use of ables that affect the basic agricultural outcome of agricultural inputs, the adoption of new technolo- interest. For example, if an analyst is interested in find- gies, capital investments, and other basic agricultur- ing out how the price of fertilizer affects the amount al outcomes? of fertilizer used, data are needed not only on the price * How do traditional forms of land tenure and invest- of fertilizer and the quantity used but also on every- ments in physical infrastructure affect crop produc- thing else that determines the use of fertilizer, includ- tion, households' incomes from agricultural activi- ing the prices of other agricultural inputs (pesticides, ties, and other agricultural outcomes? herbicides, and hired labor), the prices of the crops on * What is the causal relationship between farm size which the fertilizer may be used, the availability of and farm productivity, and how does farm size credit, the education level of adult household mem- affect other basic agricultural outcomes? bers, the prices of other crops (and even of animals), * How do policies that promote the rule of law and rainfall and other weather conditions, the land, enforcement of contracts affect households' agri- machinery, and other types of capital owned by the cultural activities? household, and so forth. Basically, data are needed on * What impacts do different types of investments in almost all of the exogenous variables listed at the physical infrastructure have on the marketing of beginning of this section. Collecting such data is not crops, use of purchased inputs, adoption of new easy, and almost always requires a long and detailed technology, and other basic agricultural outcomes? agricultural module. * How does the educational level attained by farmers The second thing to realize about causal analysis (which can be thought of either as a distinct input is that many problems can lead to biased estimations of 153 THOMAS REARDON AND PAUL GLEWWE these relationships. If data are missing on some causal exogenous factors is always a challenge, which implies factors, the estimates are likely to suffer from omitted that a long agricultural module will be needed to variable bias (see Chapter 26 for further explanation of undertake rigorous causal analysis. this point). A classic example of omitted variable bias Table 19.1 summarizes relationships between the is the inability to observe the managerial talent of the policy issues raised in the first section and the data farmer (see Griliches 1957). Suppose analysts want to needs discussed in the second section. It also shows the estimate a production function for a certain crop and varying ability of the three different versions of the that they are particularly interested in how the use of agricultural module (which are introduced in the next fertilizer affects productivity. Suppose as well that section) to supply the data needed to answer each those farmers with more managerial talent are both question.5 As discussed above, several of the policy more productive, all else being equal, and more likely questions in the first section can be broken into two to use fertilizer. If a regression is estimated without parts: the impact of a specific policy on prices or tech- accounting for the impact of managerial talent, it will nology availability and the impact of prices or tech- overestimate the impact of fertilizer on output because nology availability on basic agricultural outcomes. In part of the impact measured is the impact of manage- Table 19.1 such questions are similarly divided. For rial talent, which is positively correlated with use of example, the table has no question on the impact of fertilizer. The "simple" solution to this bias is to exchange rate policies on basic agricultural outcomes, include all of the variables that are needed, but it is not but it does have a question on the impact of exchange always possible to collect data on some of the causal rates on prices and a question on the impact of prices factors. on basic agricultural outcomes. In addition, the table Estimates of causal relationships also suffer from includes several policy questions raised in in this sec- such other potential problems as measurement error in tion's discussion of policy questions that can be the variables (which affects both structural and answered by using descriptive analysis. reduced form relationships) and the endogeneity of the explanatory variables (which affects only structur- Some Difficulties and Some Potential Solutions al relationships such as production functions). These Collecting data on the variables discussed above is econometric issues are discussed in detail in Chapter often complicated by the complexity of the produc- 26; suffice it to say here that these problems are seri- tion process and by the great variety of agricultural ous ones that constantly plague empirical researchers. producers in developing countries. Farms vary in size However, many of the problems (including measure- from small garden plots in Russia to giant grain farms ment error and omitted variable bias) can be mini- in Argentina. Farms also vary in the extent to which mized if detailed data are collected and appropriate they are connected to markets, and in the degree to procedures are used to ensure data accuracy. The fol- which they are privately or collectively owned. lowing subsection provides advice on how to collect Moreover, within any given rural household one often such data. finds a bewildering array of common and individual In summary, the data required to undertake causal plots (the latter often controlled by different house- analysis include data on all of the basic agricultural hold members) with a variety of crops grown on each outcomes that are of interest and all of the exogenous plot. factors that determine these outcomes. These exoge- Because of these complexities, the agricultural nous factors include prices for agricultural products module of a multitopic survey must be carefully and agricultural inputs, various "shocks" (such as designed to reflect the prevailing circumstances of the weather, pest infestations, and crop diseases), the avail- agricultural sector in the country where the survey is ability of technology and of extension services (and being fielded, often within the constraint of a limited any prices associated with their use), physical and survey budget.This subsection examines several specif- social infrastructure, taxes, the availability and terms of ic issues involved in the collection of agricultural data credit and insurance, the characteristics of the local and provides practical advice on how to resolve them. land market, traditional land tenure arrangements, and It begins by broadly distinguishing between situations the country's institutional capacity for enforcing con- in which it is relatively easy to collect agricultural data tracts and settling disputes. Collecting data on these and situations in which it is relatively hard to do so. 1 54 CHAPTER 19 AGRICULTURE Table 19.1 Policy Issues, Methods of Analysis, and Household Survey Data Adequacy of data from Policy issue Data needed Short version Standard version Expanded version Policy issues that can be addressed using descriptive analysis of household survey data What crops and livestock are Quantities of crops and livestock grown (household Good Very good Very good being grown by different types questionna re) plus data from other modules for of agricultural households? classifying households. What are the net farm incomes Net income earned from agricultural activities Poor Good Very good (profits) of different types of (household quest onnaire), plus data from agricultural households? other modules for classifying households. What agricultural inputs are used Quantities of agricultural inputs used (household Fair Very good Very good by different types of farming questionnaire), plus data from other modules for households? classifying households. How does access to and use of Existence of extension services (community Very good Very good Very good agricultural extension services questionnaire) and use of those services vary among households? (household questionnaire), plus data from other modules for classifying households. Whicn rural households are Purchases of capital goods and land (household Poor Fair Good making investments in questionnaire), plus data from other modules agriculture, and what form do for classifying households. these investments take? What agricultural technologies Use of hybrid seeds, new types of capita Poor Good Good are being used by different kinds equipment and specific farming methods of agricultural households? (household questionnaire), plus data from other modules for classifying households. ... ................... ................................................................................................................................................................................................... How does access to and use Local sources of credit (community questionnaire) Poor Good Good of credit vary among and use of credit (household questionnaire), plus data households? from other modules for classifying households. How do marketing opportunities Distance to nearest local and periodic m arkets and Far V ry good Very good and activities differ across means of transportation to get to them (community different households? questionnaire), marketing activities (household questionnaire), plus data from other modules for classifying households. How are the benefits of price Amounts of outputs produced and amounts of inputzs Good Very good Very good subsidies to inputs and outputs used (household questionnaire), plus data from other distributed across different modules for c assifying households. Rough estimates households? of change in prices due to policies must be assumed or obtained from other kinds of data. ................................................................................................................................................................................................................................... Policy issues that can be addressed using causal analysis of household survey data How do changes in the prices of Reduced form estimates, which have the basic Fair Good Very good agricultural products and inputs agricultural outcome as the dependent variable.The affect production of crops, use most important explanatory variables are prices of of inputs, and other basic inputs and outputs (price questionna re and agricultural outcomes? household questionnaire): plot, farm, and household characterist cs (household questionnaire); and community characteristics such as physical infrastructure and access to extension services (community questionnaire). ............................................................................................................................................................................................................................. How do different agricultural Reduced form estimates, with the basic agr cultural Fair Good Very good extension services affect farmers' outcome as the dependent variable.The most output, use of inputs, incomes, important explanatory variables are: pr ces of inputs and productivity? and outputs (price questionnaire and household questionnaire); plot, farm, and household characteristics (household questionnaire); and community characteristics such as access to extension services (community questionnaire). ................................................................................................................................................................................................................................... (Table continues on next page.) 155 THOMAS REARDON AND PAUL GLEWWE Table 19.1 Policy Issues, Methods of Analysis, and Household Survey Data (continued) Adequacy of data from Policy ssue Data needed Short version Standard version Expanded version How does the availability of Reduced form estimates, with the bas c agricultural Fair Good Very good credit affect capital investments, outcome as the dependent variable.The most use of agricultural nputs, important explanatory variables are: prices of adoption of new technologies, inputs and outputs (price questionnaire and and other basic agricultural household questionnaire); plot, farm, and household outcomes? character stics (household questionnaire); and community characteristics such as access to, and terms of, locally available credit (community questionnaire). How do traditional forms of Reduced form estimates, with basic agricultural Fair Good Very good land tenure and investments in outcomes as the dependent variables.The most physical infrastructure affect crop important explanatory variables are: prices of production, households' incomes inputs and outputs (price questionnaire and from agriculturai activities, and household questionnaire); plot characteristics other agricultural outcomes? (including type of tenure) and household characteristics (household questionnaire); and community characteristics such as the nature of traditional tenure arrangements (community questionnaire). What is the causal relatio nship A production funct on, the estimate of which will Poor Fair Good between farm size and farm require data on all inputs, including plot size, and the productivity? output of interest (all from the household questionnaire)-and may require prices (from the household or community questionnaire) or other instrumental variables. How does farm size affect Reduced form estimates, wnich wil have the basic Pair Good Very good basic agricultural outcomes? agricultural outcome as the dependent variable.The most important explanatory variables are: prices of inputs and outputs (price questionnaire and household questionnaire); plot size, other plot characteristics, and household characteristics (household questionnaire): and such community characteristics as physical infrastructure and access to extension services (community questionnaire). -row do policies that promote Reduced form estimates, with the basic agricultural Poor Pair Pair the rule of law and enforcement outcomes as the dependent variables.The most of contracts affect households' important explanatory variables are: prices of inputs agricultural activities? and outputs (price questionnaire and household questionnaire); plot characteristics (including any relevant contract information) and other household characteristics (household questionnaire); and such community characteristics as the local legal system and its degree of enforcement (community questionnaire). What impacts do different Reduced form estimates, with the basic agricultura Fair Good Very good types of investments n physical outcome as the dependent variable.The most nfrastructure have on marketing important exp anatory variables are: prices of of crops, use of purchased inputs, inputs and outputs (price questionnaire and adoption of new technology and household questionnaire); plot characteristics and other basic agrcultural other household characteristics (household outcomes? questionnaire); and such community characteristics as physical infrastructure (community questionnaire). How do farmers' ieveis of A production function requiring diata on: all inputs, Poor Fair Good education and health status including education and health of household members, affect their productivity? and all outputs (household questionnaire) as well as prices (from household or community questionnaire) or other instrumental variables. ............................................................................................................................................................................................................................ 156 CHAPTER 19 AGRICULTURE Table 19.1 Policy Issues, Methods of Analysis, and Household Survey Data (continued) Adequacy of data from Policy issue Data needed Short version Standard version Expanded version How does the availability of Reduced form estimates, with the basic agricultural Poor Fair Good insurance affect capital outcome as the dependent variable.The most investments, the use of important explanatory variables are: prices of inputs agricultural inputs, the adoption and outputs (price questionnaire and household of new technologies, and other questionnaire); plot, farm, and household characterist cs basic agricultural outcomes? (household questionnaire); and such community characteristics as access to, and terms of, insurance opportunities (community questionnaire). How do farmers' levels of Reduced form estimates, which will have the basic Fair Good Very good education and health status agricultural outcome as the dependent variable.The affect other agricultural most important explanatory variables are: prices of outcomes? inputs and outputs (price quest onnaire and household quest onnaire); plot characteristics, health and education status of household members, and other household characteristics (household questionnaire); and such community characteristics as physical infrastructure and access to extension services (community questionnaire). ................................................................................................................................................................................................................................... Pol cy Comments Policy issues that cannot be addressed using household survey data Impact of exchange rate policies on prices of agricultural inputs and Requires times ser es data on exchange rates and local prices. outputs and on the variab lity of these prices Impact of tariff and nontariff trade barriers on prices of agricultural Requires times series data; studies of results from other countries might inputs and outputs also be useful. ................................................................................................................................................................................................................................... Impact of national and sectoral credit policies on the availability of Would require a survey of private providers of cred t, and perhaps even credit from private sector providers a series of surveys over several years. See Chapter 2 1 for further discussion. Impact of sectoral price subsidies and taxes on prices of agricultural Would require time series data on taxes, prices, and subsidies. In theory, inputs and outputs several household surveys over many years could provide the price data. Impact of government po.icies on traditional forms of land tenure Would require a special survey focusing on traditional forms of land tenure.A more sociological or anthropological approach might be useful. See Chapter 25 on collection of qualitative data. Politicai feasibility of land redistribution Could require a multidisciplinary approach. See Chapter 25. ..................................................................................................................................................*................................................................................ Impact of investments in social infrastructure (health and education) Dealt with in Chapters 7 (education) and 8 (health). on social outcomes Source: Author's summary The subsection then discusses the implementation of more disaggregated level (for example, by plot rather the survey, including issues that are important because than by entire farm and by season rather than by cal- they determine the extent to which measurement endar year) to minimize the chances that the data will errors creep into the data as the data are collected. suffer from serious measurement error. Countries (or areas within a country) where EASY-TO-SURVEY VERSUS HARD-TO-SURVEY farms tend to be easy to survey are mostly in non- AGRICULTURAL SITUATIONS. In some countries the highland, non-semiarid Asia, in non-highland Latin organization of the agriculture sector is fairly straight- America, and in the cash crop zones of Africa. With a forward, which simplifies the design of the agricultur- few exceptions, these countries are in less poor areas of al module. In contrast, the organization of the agricul- the developing world. Many of these countries also ture sector in other countries is more complex, which have a strong National Agricultural Research System can greatly complicate the design of the agricultural (NARS) and reliable national farm surveys. module. Box 19.1 presents characteristics of farms that Countries (or areas within a country) where farms tend to make them easy or hard to survey. In general, tend to be hard to survey are more commonly in those in "hard" situations it is necessary to gather data at a parts of Africa outside cash-crop areas, in highland Asia 157 THOMAS REARDON AND PAUL GLEVVWE respond to questions about all plots and crops or Box 19.1 Easy-to-Survey versus Hard-to-Survey should the individuals in charge of each plot or crop Situations respond separately to questions about the plots or Characteristics of an easy-to-survey situation crops for which they are responsible? * Farmers grow rice and wheat. A prominent feature of most hard-to-survey situ- * Farmers specialize in a few crops or types of livestock. ations is that farmers grow crops on a large number of * The farm economy is highly monetized and commer- plots. For example, in the 1996 Nepal LSMS survey cialized. the typical farm household had five or six plots.6 * Standard units (such as hectares and kilograms) are Similarly, in the 1985-86 Peru LSMS survey, 7 to 10 widely used. plots were common, and most households had 10 to * Each farm has only a few plots,.lt eecmo,admothueod a 0t The plotsfare hspaly concentr. 15 plots in the 1981-85 Burkina Faso survey by the * The plots are spatially concentrated. * There is a literate adult in the household. International Crops Research Institute for the Semi- * Agriculture is irrigated. Arid Tropics (ICRISAT).Typically such multiple plots * Where there are many livestock, they are kept in are managed by several different plot managers, many fenced-in areas. of whom manage more than one plot. The agriculture modules in most previous LSMS Characteristics ofo hard-to-survey situation surveys gathered information using the whole farm, * Farmers grow tubers, bananas, roots, and coarse grains. rather than the plot, as the unit of analysis, with the * Farmers typically produce many different products, most knowledgeable" person at the household level including both crops and livestock. * The farm economy is only partially monetized and as the sole respondent. In countries where easy-to-sur- commercialized. vey agricultural circumstances predominate, this * Nonstandard (local) units of measurement are widely approach may work reasonably well for collecting data used. that can be used to calculate basic agricultural out- * Each farm has many plots. comes (such as net farm income) for the whole house- * The plots are spatially dispersed. hold. However, this approach is unlikely to work well * There is no literate adult in the household. in countries characterized by hard-to-survey agricul- . Farmers rely solely an rainfall for irhgation. tural circumstances, where each agricultural house- * Many livestock are kept in open pastures. hold has many plots on which many different crops are Source: Authors' summary grown-with different inputs and practices used on different plots. There are several reasons why collecting data at and Latin America, and in parts of semiarid South Asia. the plot level, as opposed to the "whole farm" Because these tend to be the poorest areas of the devel- approach, is preferable in hard-to-survey situations. oping world, national farm surveys are less likely to First, using the plot as the survey unit reflects the nat- exist in those countries. Yet agriculture is often the ural flow of a conversation between an interviewer largest economic sector in such countries, and thus has and a respondent. Experience with farm management a disproportionately large impact on economic surveys has shown that farmers usually refer to each growth, nutrition, poverty, and the environment. plot as they describe the tasks undertaken and the crops produced; they refer to the whole farm only SURVEY UNIT AND CHOICE OF RESPONDENT. Once sur- when discussing the purchase of inputs (Matlon 1988). vey designers have established the extent to which the Forcing respondents and interviewers to collect data at country surveyed is an "easy" or "hard" case, they must the farm level may lead to significant errors in the choose the unit of analysis for the agriculture module. data. In most developing countries agricultural households Second, gathering data on each plot yields more work on several different plots of land, so the question observations and more variation in the data, both of regarding the unit of analysis is whether data should be which allow for more precise estimation of farm pro- collected for the farm as a whole or separately for each duction functions and other agricultural relationships. plot or field. A related issue is which household mem- Third, plot-level data are very useful for examining ber should be the respondent. Should the same person intrahousehold allocation issues (see Chapter 24 for a 158 CHAPTER 19 AGRICULTURE full discussion). A fourth reason to collect data at the ticides) are handled by a single household member plot level is that on any given farm, plots usually differ instead of by the plot managers. If the questionnaire is in terms of land quality, the degree of land degradation carefully field-tested, it should become clear where and erosion, and other characteristics such as the this is occurring; in such cases the questionnaire degree of slope or whether the plot is situated on a should be designed to ask a single respondent (the hill, a valley, or a plain. It is important to control for household member best informed on the subject) the these differences when estimating production func- questions on these topics.This was the approach taken tions, and it is more difficult to control for these dif- in the 1996 Nepal LSMS survey. ferences when the data are not disaggregated by plot. The implication of this discussion is that when A fifth and final reason to collect plot-level data is that surveys are implemented in countries with hard-to- this approach keeps all options open for future data survey agricultural sectors, they should collect data at analysis. Researchers can aggregate the plot data to the the plot level. In fact, collecting plot-level data is also level of the whole farm, whereas they would not be reasonable for easy-to-survey situations, since there are able to disaggregate whole-farm observations to the few disadvantages to collecting such data. All LSMS level of the plot. surveys that collect detailed information on outputs There are also good reasons for interviewing the and on use of labor and nonlabor inputs should do so manager of each plot rather than choosing only one for each plot, and the manager of each plot should be respondent per household. First, the operator of each the respondent for all questions concerning that plot. plot is most likely to know specific details about the This is crucial if the data are to be used in production size and quality of the plot, and about how much time function analysis. It is less essential if only rough each household member has spent working on various observations of farm income are needed, although tasks on that particular plot. Second, spreading the even here survey designers may want to collect data at burden of completing the agricultural module over the plot level in order to minimize measurement error. several different respondents avoids placing too much In contrast, in most cases the acquisition of inputs can of that burden on a single respondent. be recorded at the household level by the person best The advantages of using the plot as the unit of informed about those purchases. observation must be weighed against two possible dis- In contrast to crops, livestock are mobile.They can advantages. The first is that gathering more detailed be kept either in a specific location on the farm, such data may lengthen the time needed to complete the as in the corral or compound, or on communally interview.Yet this disadvantage may be more apparent owned pastureland. Households may move livestock than real. Respondents in farm surveys in developing from one place to another, perhaps several times, over countries often provide answers plot-by-plot even the course of a year. In addition, many of the inputs when the agricultural module is designed using a pertaining to animal husbandry are not necessarily "whole farm" approach-forcing the interviewer to linked to the plot or pasture where the animals reside. add up the responses on the spot to produce the For example, veterinary services, labor inputs, and "total" numbers required by the questionnaire.When purchased feed are not necessarily provided on the this is the case, there may be little difference in the land where livestock are usually kept. Thus there is lit- interview time, and reducing the measurement error tle reason to collect animal husbandry data at a plot generated by ad hoc aggregation on the part of the level; instead, questions about livestock husbandry interviewer would probably be worth the modest should be asked at the household level. amount of extra time needed to complete a plot-by- Nevertheless, there are cases in which the quality plot agricultural module. Moreover, the interview of livestock data would probably be higher if more time could be reduced by collecting detailed plot-level than one respondent were interviewed, particularly data only for a subsample of plots while collecting a when some household members are responsible for much smaller amount of information for the remain- herds that are separate from those of the head of ing plots, as was done in the 1995 LSMS survey in household. This is often the case in agropastoral areas, Northeast China.7 where livestock husbandry is an integral part of the The second possible disadvantage can occur when farming system. Indeed, livestock are a sign of wealth some farm activities (for example, the purchase of pes- and status, and an important form of savings. Al of this 159 THOMAS REARDON AND PAUL GLEWWE implies that the ownership of livestock can be a sensi- middle of an agricultural season, which can produce tive issue. When livestock ownership is a sensitive data that are not very useful. For example, suppose issue, serious consideration should be given to using there is only one cropping season per year and a more than one respondent to answer questions about household is interviewed a few weeks before the har- the livestock and herds of the household. Still, it may vest of that season.The output reported will be for the be difficult to elicit accurate answers if respondents previous cropping season, and nearly all of the inputs cannot be interviewed privately, since the respondents reported will be for the current cropping season. may not want other household members to know the The implication of this discussion is that the recall details of their livestock holdings. period for recording agricultural outputs and inputs should be the cropping season, not the preceding 12 RECALL PERIOD. Most previous LSMS surveys have months. More specifically, the agricultural module used a recall period of the previous 12 months in the should be administered after the end of the cropping agriculture module because this coincided with recall season; it makes little sense for the interviewer to visit periods used in other modules of the survey. Farm sur- a farm in the middle of the season and count the use veys typically use a one-vear recall period, but in con- of inputs in that season because they will not be able trast to LSMS surveys, this period cannot be a random to link these inputs to the outputs, which are not yet 12-month interval. Instead, it must correspond to known. Although this is simple enough in principle, it either the most recent "harvest year" (the 12 months may complicate the field organization of an LSMS- from one harvest until just before the next year's har- type survey (or any multitopic survey that visits house- vest) or the most recent "production year" (the 12 holds only once)-particularly by affecting the timing months from planting until right before planting of the interviewer's visit to sampled households. As begins one year later). These two ways of defining an explained in Chapter 3, in many LSMS surveys the agricultural year can incorporate one or more "crop- interviews are spread out over 12 months, which ping seasons" (also known as "agricultural seasons"), makes it almost inevitable that households are inter- which are the times from the beginning of planting viewed in the middle of a cropping season. The best until the end of the harvest. In some areas, such as the way to resolve this problem is to have interviewers ask West African semiarid tropics, there is only one agri- about the most recent 12-month period that includes cultural season, wvhich reflects the fact that there is only one or more completed cropping seasons. The follow- one rainy season per year. In other areas, such as the ing paragraphs explain how this would work for both East African highlands, there are two agricultural sea- countries with one cropping season per year and sons, since there are two rainy seasons per year. Of countries with two cropping seasons per year. course, even in areas with only a single rainy season per Consider semiarid West Africa, an area with just year, there can be two cropping seasons if irrigation is one cropping season a year. In semiarid West Africa the used in the dry season or if certain crops can be grown cropping season runs from June to October. and harvested without irrigation in the dry season. Interviewing after the end of a cropping season can be The recall periods used for agricultural activities done relatively easily. Any interview that takes place in previous LSMS surveys had several disadvantages. between November and May and uses a one-year recall First, respondents were asked to provide the total period wvill cover that one season. Interviews can be amount of outputs and inputs on their farms in the 12 done after May as long as the respondent remembers months immediately preceding the date of interview. what happened the previous May. Thus in semiarid Such an approach can lead to aggregation bias in West Africa the recall period runs from May of the pre- countries where there are two or more cropping sea- ceding year until April of the current year. More gen- sons per year (a problem discussed later in this sec- erally the recall period is the most recent 12-month tion). Second, there is evidence that the use of one period that contains a complete cropping season. long recall period, such as one year, can have a detri- Things become only slightly more complicated in mental effect on the quality of the household survey places where there are two cropping seasons, such as data (see Kelly and others 1993). Third, and perhaps the East African highlands or the Indian semiarid trop- most important, the 12-month period immediately ics. Suppose that one cropping season runs from April preceding the interview often starts and ends in the to August and the other cropping season is from 160 CHAPTER 19 AGRICULTURE November to January. If the interviewer were to visit first interview would collect data only on the first sea- the household during, say, September or October, the son and the second interview would collect data only recall period would refer to the current year's on the second season. Of course, the advantages of April-August season and the previous year's these options must be weighed against the advantages November-January season, and the module would of having the interviews spread out over a long peri- contain separate questions for each season. If the inter- od of time. (Recall from Chapter 3 that the three viewer's visit occurred sometime in February or advantages were controlling for seasonality patterns, March, the interviewer could administer the module reducing equipment costs, and reducing the for the just-completed November-January season and number-and hopefully increasing the quality-of the for the April-August season of the previous year. lf an interviewers trained.) In surveys for which agriculture interview were to fall in the middle of either season, is a lower priority, the advantages of deviating from the the reference period used would be the 12 months common LSMS interviewing scheme may be out- that contained the two most recently completed crop- weighed by the disadvantages. ping seasons. For example, if the interview were to In summary, 12-month recall periods must be take place in June, the recall period would run from used that include cropping seasons in their entirety as March of the previous year to March of the current opposed to recall periods that begin or end in the year. If the survey interviews were done continuously middle of a cropping season. For many households this over a 12-month period (as has been done in many means that the 12-month recall for agricultural activ- past LSMS surveys, for reasons given in Chapter 1), the ities will not be the 12 months immediately preceding data on the April-August season would be in one cal- the interview but will exclude the previous 2 or 3 endar year for some households and in another calen- months. One disadvantage of this is that if analysts try dar year for other households; the same would be true to calculate total household income they will be faced for the November-January season.While this is prob- with data on different sources of income that do not ably not a serious disadvantage, analysts need to keep cover the exact same time period. For example, agri- it in mind when using the data. cultural income may refer to the 12-month period In countries with two or more cropping seasons beginning 15 months before, and ending 3 months per year, data should be collected separately for each before, the date of the interview. In contrast, the data cropping season, as it is common for there to be sub- on income from wage labor may refer to the 12 stantial differences in the extent and nature (for exam- months immediately preceding the interview. ple, the crop mix or the technology) of agricultural However, this disadvantage is probably not a major activities in different seasons within one year, even problem, and it is generally preferable to forcing the when irrigation is used in the dry season. Even for agricultural module to fit the previous 12 months plants with long-growing cycles that are harvested lit- even if the interview takes place in the middle of a tle-by-little throughout the year (such as roots, tubers, cropping season (as was done in most previous LSMS bananas, and plantains), there are differences in the surveys). Of course, if agriculture is the main focus of intensity of harvesting depending on the season and the survey, the common practice of interviewing on the point in the growth cycle of the plant. Thus households over a 12-month period could be dropped respondents must be asked to provide separate answers in favor of either interviewing all households in a one- for each cropping season. or two-month period between cropping seasons or If agricultural issues are the top priority of the interviewing each household twice, once after the first survey, survey designers should seriously consider season and again after the second season. More exper- deviating from the common LSMS practice of inter- imentation in future LSMS-type surveys should pro- viewing households only once and spreading those vide useful experience on how best to collect agricul- interviews out evenly over a 12-month period. One tural data within the context of a multitopic option is to perform all interviews in a short period of household survey. time between two cropping seasons. An even better, though more expensive, option is to visit each house- SAMPLE SizE. Because of the inevitable budget con- hold twice-once soon after the first season and a sec- straints involved in any household survey, there is a ond time soon after the second season. In this case the tradeoff between using a large sample to keep sampling 161 THOMAS REARDON AND PAUL GLEWWE errors low and using a small sample to fully implement use of panel data often invoke questionable assump- all of the procedures designed to reduce nonsampling tions, as explained in Chapter 23.Thus panel data will (measurement) errors. For a given sample size, nonsam- probably prove useful, but the magnitude of the bene- pling errors xvill generally be smaller in easy-to-survey fit is difficult to gauge at first glance. situations than in hard-to-survey situations. As If panel data are collected, serious consideration explained in Chapter 1, LSMS surveys usually have rel- should be given to matching individual plots of land atively small samples (2,000-5,000 households) due to across the different surveys.The last section of Chapter their emphasis on reducing nonsampling errors. The 23 presents detailed recommendations for linking size of the whole sample for a given survey depends household members across a series of surveys that not only on the agriculture module but also on all of cover the same households. In principle, these meth- the other modules in the survey. Survey designers ods for matching household members can be adapted should bear in mind that the size of the sample for the to plots of land, but there is little experience in doing agriculture module will be smaller than that of the so for surveys separated by several years. Thus serious overall sample because many households do not have thinking and field testing need to be done to success- any agricultural activities. Also, when there is more fully link plots of land in panel surveys, using the dis- than one agroclimatic zone in the country or region cussion in the last section of Chapter 23 as a starting being surveyed, the sample for each zone will be even point. In some cases data analysts may also want to link smaller. Consider, for example, a survey with a total individual bullocks or other large animals, or individ- sample of 3,000 households. About half of that number ual pieces of capital equipment. The same general can be expected to engage in agricultural activities.5 methods may be used, but again there is very little Thus in a country with 3 distinct agroclimatic zones experience in doing this. there will be, on average, about 500 agricultural house- holds in each zone. These relatively small samples have UNITS OF MEASUREMENT. It is inherently difficult for direct implications for the design of the questionnaire. analysts to use data that are not standardized in mean- For example, if sharecropping (or orchard farming or ingful terms. When surveys are fielded in developing some other farming practice) were common only in countries, respondents frequently provide answers one of the zones and if only 10 percent of households using nonstandard units and terms. For example, in the in that zone engaged in sharecropping, it xvould not be LSMS surveys done in Ghana, fewer than 5 percent of worth asking a large number of detailed questions on the responses used standardized units (in the metric sharecropping because they would apply to just a few system or the English system).There were also a large households. In other words, a detailed analysis of share- number of nonresponses to questions concerning the cropping would not be possible due to the very small conversion of local units into kilograms and other sample (about 50 households). metric units Uolliffe 1995). One possible solution is available for local units PANEL DATA. Causal analysis of agricultural activities that are containers, such as a hollowed-out gourd of a can benefit from the collection of panel data. For certain size.The best way to determine the weight of example, panel data can be used to estimate produc- say, rice, in such a container is to fill it full of rice and tion functions that remove certain biases caused by then weigh the rice (scales are common pieces of unobserved "fixed effects" (see Chapter 23 for a equipment for collecting price data, as explained in detailed discussion).They can also be used to examine Chapter 13, and thus the survey team should have at the impact of certain kinds of government programs least one). The weight of one "gourd" of rice can be on agricultural outcomes and on the welfare of agri- recorded in the community questionnaire or the cultural households. A third use of panel data is to price questionnaire. This need not be done for every study the impact of household-specific shocks such as type of product that may be measured using such a crop disease. This issue is particularly important given container. Instead, at the national level a table can be the inherent risk of agricultural activities. Overall, prepared that shows the weights of various products there are several potential benefits of collecting panel in terms of a standard unit of volume, such as a liter. data. At the same time, survey designers should be This table can be used to determine the weight of aware that many of the estimation methods that make one "gourd" of other products without having to 162 CHAPTER 19 AGRICULTURE weigh each product described in terms of the local occasionally even no observations-for some items in unit of the community. some communities. Another disadvantage is the fre- An alternative way to resolve this problem is as quent unit conversion problems noted above. follows. For each transaction by a household, note the On the other hand, there are also problems with quantity (in local units) and total cost of the transac- prices collected in a community-level price question- tion (or, for bartered goods or consumption from own naire such as the one presented in Chapter 13. First, production, the estimated cost). In the community or these prices are retail (consumer) prices at the time of price questionnaire, ask knowledgeable people about the interview, and to calculate household income ana- the price per kilogram of that product; use that price lysts need data on producer prices at the time the and the total cost of the transaction to calculate the crops were harvested. Consumer prices at the time of weight, in kilograms, of all transactions by each house- interview can be misleading because prices vary over hold.9 If no one in the community can give a price per the year. In particular, prices may drop significantly at kilogram, weigh out one kilogram of the product harvest time, in which case the prices farmers receive using a scale and ask community members the value for crops sold immediately after the harvest may be of that amount. Unlike the method described in the lower than consumer prices prevailing at the time of previous paragraph, this method can also be used for the survey.A second problem with data from the price units of measure that are not containers, such as questionnaire is that some agricultural products may "heaps" and "bunches." not be sold in local markets throughout the year. For example, cocoa, coffee, rubber, and other export crops PRICES OF AGRICULTURAL PRODUCTS. In most previous may not be sold in the community other than at har- LSMS surveys, price questionnaires were administered vest times. In addition, subsistence crops, such as fod- in each community from which households were sam- der for animal feed, may not be sold in the communi- pled. In many surveys data on the prices of actual ty. The best way to resolve these problems is to design transactions have also been collected in the agricultur- the price questionnaire to collect data both on current al module of the household questionnaire. When col- consumer prices and on the wholesale prices that pre- lecting prices for agricultural products using either vailed during the one or two most recent harvest sea- method, two potential problems should be addressed. sons.The information on harvest prices should not be First, prices are needed not only for heavily marketed collected by visiting local markets, but instead may be crops but also for subsistence crops (including crops done as part of the interview of community leaders used solely for livestock feed) because subsistence (see Chapter 13). crops constitute-very roughly-two-thirds of crop In some countries there may also be a third alter- output in most rural areas in Africa and one-third to native: district-level price information on producer one-half of crop output in rural areas in Asia and Latin prices available from government agencies or from a America. Second, in some countries, many communi- public "market information service."This is most like- ties reported too few transactions in the household ly in countries with easy-to-survey agricultural condi- questionnaires to generate reliable community price tions. However, such official information is best used estimates. This second problem is exacerbated in situ- as a backup for the other two sources of price data ations where differences in household prices reflect rather than as a substitute for one or both of them. differences across households in the quality of the crop. ESTIMATING THE SIZE OF PLOTS. It can often be diffi- These two problems lead to the more general cult to obtain an accurate measurement of the size of issue of whether agricultural prices should be collect- plots.The most obvious option is to ask plot managers ed in a community-level price questionnaire, in the to estimate the dimensions of their plot or plots, but in household questionnaire, or both. The safest approach many countries they often find it difficult to do so, is to collect both types of agricultural price data, particularly when plots are irregularly shaped. Such a because each type has advantages and disadvantages. problem was encountered in the ICRISAT Burkina Community-level price data should be collected Faso survey (Matlon 1988). In that survey the inter- because it is often the case that data collected at the viewers had to measure the plots themselves, using a household level include few observations- compass and a measuring tape. This clearly yielded 163 THOMAS REARDON AND PAUL GLEWWE more accurate data than asking the plot managers for MEASURING SOIL QUALITY. Information on soil quali- estimates; when the interviewers' measurements were ty can be extremely useful, but data on land or soil compared to farmers' estimates of the size of their quality have not been collected in most previous plots, large differences were found.1 This problem was LSMS surveys Jolliffe 1995). The 1992-93 and also evident in a survey carried out in southern Haiti; 1997-98Vietnam LSMS surveys and the 1995 China researchers checked the accuracy of four informants' LSMS survey included questions about the quality of estimates of the size of 21 plots and found that the land but only in the form of a simple ranking (for margin of error in the respondents' estimates ranged example, from 1 to 5).While this kind of question can from 0 to 400 percent." be useful, there are ways to ascertain soil quality with Resolving this problem is not easy because meas- greater precision. One option would be to have soil uring plots is very time-consuming for interviewers, scientists from the country's NARS examine the plots especially when the number of plots to be measured is to obtain these data. This could be very expensive. A large.The standard method to measure plots is to use less precise but more practical approach would be to a tape measure and a compass. More recently, devise a series of local or folk classifications for soil Geographical Positioning System technology has quality and land configuration and then pretest them appeared that uses satellite signals to pinpoint the loca- in local languages. This has been done effectively in tion of any object on earth to within 100 feet. Yet India by Dvorak (1988), in Nigeria by Dvorak (1993), there is little experience with using this technology to and in Burkina Faso by Matlon (1988) and Prudencio measure plot size in developing country situations, and (1983). This approach is offered as one of the code such a method might be very inaccurate for small options (local or folk soil type) in the recommended plots. More experience in the use of Geographical module discussed in the next section of this chapter, Positioning System technology for this purpose would because it is less costly than soil testing but probably be extremely valuable. Another option would be for more accurate and useful than simple ranking. One the interviewer to ask all of the other questions con- disadvantage of folk classifications is that they may not cerning the plot and then send out a separate team, be very consistent across communities; however, this possibly lent by the country's National Agricultural may also be true for "official" land quality classification Research System (NARS), to measure the plots. This schemes such as those found in China andVietnam. two-step procedure has been adopted successfully in a variety of hard-to-survey situations.12 This would be LABOR. In the agriculture modules of almost all previ- justified only in places where a pilot test has shown (by ous LSMS surveys, each household member has been comparing respondents' answers with actual plot asked to give summary information on the work done measurements) that respondents do not know the size on the family farm during the previous week and in of their plots, so that relying on respondents' estimates the previous 12 months. In most cases this approach would generate very large measurement errors. If the has yielded inadequate data, for several reasons. First, pilot test shows that respondents' estimates are reason- the "past week" data are not very useful because the ably accurate, questions about plot size can be asked in week used has a 50-60 percent chance of falling out- the household questionnaire, and these direct meas- side a crop production season in areas with one rainy urement methods will not be needed. In situations in season and roughly a 20-30 percent chance of doing which the pilot test shows that the respondents cannot so in areas with more than one rainy season. Second, accurately estimate the size of their plots, it will be the type and amount of labor needed on the farm necessary to conduct an independent measurement of varies greatly throughout a given cropping season, each plot using one of the methods discussed above. because there are peaks and troughs associated with This is most likely to be the case in situations where tasks such as preparing plots (for planting), weeding, the plot shape is irregular or changes by season and and harvesting; the same holds true for livestock pro- where the operator does not measure in standard area duction. During slow periods household members units or even in commonly used local units. Of course, may need to devote only part of the day to farming, independent measurements will raise the cost of the while the rest of their time is devoted to leisure, survey, though the magnitude of this added cost will housework (including collecting firewood and making vary widely from one survey to another. repairs to the house), and off-farm employment.Third, 164 CHAPTER 19 AGRICULTURE labor input per hectare generally varies among the RAINFALL. Data on rainfall have almost never been plots on a farm depending on the crop grown, the collected in previous LSMS surveys. Since rainfall technology used, the soil type, and the operator's often differs substantially from year to year, among sea- access to labor. Fourth, analysts need to know the sons within a given year, and across communities, ana- amount of labor used per crop to estimate a produc- lysts would ideally like to have data for each commu- tion function for that crop. nity for each season. In practice, very few communities A much better approach to collecting data on have such data.Yet on a more positive note, in some labor in agriculture is for the interviewer to ask ques- countries rainfall data for each season can often be tions on a task-by-task and plot-by-plot basis.There are obtained from rainfall charts kept by district branches two ways for this to be done. One is to ask plot man- of the country's NARS or International Agricultural agers how much labor time (measured in person days) Research System (IARS).This could be useful because was spent on different tasks (plot preparation, planting, respondents at both community and household levels weeding and harvesting) for each plot. The other is for are unlikely to be able to recall rainfall levels with any each household member to be asked how much time degree of precision. If the survey has a strong focus on he or she spent doing each of these tasks for different agriculture, a generous budget, and relatively few rural plots of land.The first approach was used in the 1995 communities (less than 100), survey designers may LSMS survey in Northeast China and is used in the want to consider whether it is worth the cost to hire standard version of the agricultural module inVolume people to measure rain in each community covered by 3. The second approach was used successfully in the the survey. However, this would have to be planned ICRISAT Burkina Faso survey (see Matlon 1988) and well ahead of time-at least 12 months before the first is used in the expanded version of the draft agricultur- interviews were to begin. al module.While one might object that this approach is more time-consuming than the approach used in Recommended Questionnaires for the past LSMS surveys, this is not necessarily the case.The Agriculture Module disaggregated method may be quicker and easier than more aggregated methods in which the household This section introduces a short version, a standard ver- head struggles to estimate how much time is spent on sion, and an expanded version of the agriculture mod- given crops for the household as a whole. The two ule.The three versions are provided inVolume 3 of this approaches can be compared using a pilot test. book. On the other hand, it may be best to enumerate Each version of the agriculture module was certain tasks (for example, the maintenance of irriga- designed to address the policy questions raised in the tion infrastructure or marketing) at the level of the first section and the data needs presented in the sec- farm rather than the plot. It is generally best to distin- ond section. All three versions are divided into 6 sub- guish three types of labor: family labor, hired labor modules labeled Part A through Part F Part A collects (either tenants or permanent or casual laborers), and information on agricultural land. Part B asks the exchange labor. In addition, one may want to distin- household about its inventory of agricultural equip- guish laborers by whether they are men, women, or ment, such as tractors, threshers, and pumps. Part C children. gathers information on the amounts of each crop har- It is better to put questions about farm labor in vested, and what was done with these crops. Part D the agricultural module than in the employment collects data on inputs used in agricultural activities, module (see Chapter 9), for two reasons. First, it is best such as fertilizer and pesticides. Part E is designed to to ask all questions about tasks and use of inputs (such gather information about livestock, and Part F asks as animal traction or the application of chemicals or about the use of agricultural extension services. The manure) together, as this helps the plot manager short and the standard versions of the draft modules remember details of both. Second, respondents usually are presented in full. To avoid needless repetition, only find it easier to remember how farm labor was those parts of the expanded module that differ from deployed on individual plots rather than across the the versions in the standard module are presented. whole farm, and only the agriculture module deals The purpose of the short version is to collect data with farm plots. on agricultural assets owned by the household (land, 165 THOMAS REARDON AND PAUL GLEWWE farm equipment, and large livestock) and to record Box 19.2 CautionaryAdvice summary information on crops grown and inputs pur- chased. Data on assets are useful to obtain an approxi- *How' much of'the draft module is new and unproven? In its basic design, the agricultural module in Volume 3 follows mate measure of each household's wealth and the the approach taken in many previous LSMS surveys, in form that wealth takes. Data on crops grown can be that it contains submodules covering land, capital, output used to classify agricultural households into different and marketing, input use, livestock, and services. types, such as producers of export crops and producers However, it also contains several substantial innovations. of subsistence crops. Such crop data are also useful for First, the recommendation that the recall period be the calculating a rough measure of the incidence of any previous agricultural season (if there is only one season taxes or price subsidies for specific crops. Similarly, data per year) or the previous two agricultural seasons (if on purchased inputs can he used to estimate the ici- there are two seasons per year) differs from past prac- dence of taxes or price subsidies on agricultural inputs. tice in LSMS surveys, although it is standard in farm man- agement surveys. This is not a risky innovation and The short module also collects summary information indeed should make the survey more accurate and eas- on use of agricultural extension services, to see which ier to implement. A second innovation is collecting data households benefit from these services. This module is on outputs and inputs by plot.This has been done only considerably shorter than the agriculture module used in a few recent LSMS surveys, such as those in China, in previous LSMS surveys, and is intended for use in Nepal, and Vietnam. This approach is also standard in surveys for which agriculture issues are only of minor farm management surveys, which makes it a low-risk interest.This module does not collect the information innovation.Third, collecting data on land quality is inno- vative for LSMS surveys, but the methods suggested here required to calculate household income from agricul- are well-tried in farm management surveys. A fourth tural activities. innovation is that the respondents are the plot managers The standard version collects a large amount of rather than the single household member best informed information on agricultural activities, and can be used about agricultural activities. Fifth, the draft module col- to calculate household income from these activities. lects data on labor use by task by season, and by plot. In addition, this module can be used to estimate pro- The fourth and fifth innovations also involve little risk duction functions using the plot as the unit of obser- because they are common in farm management surveys. vation. It can also be used to estimate cost functions How well has the module worked in the post? The agri- and cultural module used in past LSMS surveys generated profe Moduleis s e t lner data that have been underused. This is not surprising than those used in past LSMS surveys, hut the time because key variables were frequently missing or per- required to administer it may be no longer than the ceived by potential users as likely to be very inaccurate. time required in those surveys because the interview- Thus it is fair to say that the agricultural module has not er and the respondent probably had to discuss the worked well in the past. additional detail in this version just to complete those Which ports of the module most need to be customized? past versions. Moreover, interview time can be The land and input use parts (Parts A and D) require by reduced by collecting detailed plot data for only a far the most pretesting and customization. Part C on crop output and disposition requires the second-most p customization. The sections of the draft questionnaires LSMS survey conducted in northeast China in 1995. pertaining to capital (Part B), livestock (Part E), and use This version of the agricultural module should be of agricultural extension services (Part F) should require used when one of the main objectives of the survey is relatively little customization. However these differences the analysis of agriculture issues. It should also be used in customization are all on a relative scale. The average when the decision has been made to collect total amount of customization required for the agricultural household income (see Chapter 17 for a full discus- module is quite large compared to the customization required in most other modules of LSMS surveys, so ftiscoc) require in mos othermodulesof LSMSsurvey, The expanded version collects all the inforimation because farming systems and agricultural and land policy the exanded version collects alle information issues differ greatly from country to countryTherefore, in the standard version and adds detailed information extensive pretests should be done, and the team doing on land transactions in the past five years. In addition, the pretesting should include specialists in conducting it collects more detailed information on labor inputs, agricultural field research in develop ng countries. both of household members and of hired laborers.This version of the agricultural module should be used 166 CHAPTER 19 AGRICULTURE when the main objective of the survey is to study agri- who should respond to certain questions, which sec- cultural issues. tions to include, and which questions to include in a All three versions of the agriculture module pre- given section. sented in this book are merely starting points for Before examining these three versions in detail, developing a module to fit any particular country; the one should review the general rules about question- great variety of agricultural systems and issues across naire formatting in Chapter 3. These rules are partic- developing countries implies that survey teams must ularly important for the agricultural module because adapt the agricultural module to their circumstances many codes, including unit codes, land area codes, and and to the issues that they want to explore in depth. In crop codes, are used to fill it out. Applying these sim- some countries substantial changes will be needed. For ple rules will greatly reduce errors in filling out the example, in transition economies where the land mar- questionnaires and should also reduce interview time. ket is privatizing, the survey team may want to collect Two formatting rules are especially important. information on land transactions during the past few First, the codes should be consistent across different years in the standard version, and perhaps even in the parts of the agricultural module (and indeed across the short version. Another example is countries where entire household questionnaire). For example, if one urban households have small farms or gardens such as developed crop codes with rice as 1, wheat as 2, and "dachas" in Russia; the survey team may want to maize as 3, these codes would need to be the same on develop a new submodule that focuses on these types every page of the module that asks about rice, wheat, of activities. Oliver (1997) provides an LSMS-type or maize. Second, the codes needed to fill out any par- questionnaire for the countries of the former Soviet ticular page of the questionnaire should appear some- Union, including a detailed agricultural module. Yet where on that page of the questionnaire or, if there is her design of the agricultural module is based on pre- not enough room, on a facing page or a laminated vious LSMS surveys and thus does not take into sheet that provides all the codes. account of some of the suggestions of this chapter, such as the collection of data on each plot of land. Short Version A final point regarding the different versions of As explained in the third section, this version of the the agricultural module is that they should be thought agricultural module is used when agriculture is only of as three points along a continuum of possible levels of minor interest and agricultural data are collected of detail. One could create a hybrid version that lies primarily for analyzing nonagricultural issues. The halfway between the short and standard versions or data obtained are not as accurate as those collected in halfway between the standard and expanded versions. standard and expanded modules because the questions The key to success is to develop very specific objec- are directed to a single household member (the mem- tives for the module, and to design the module with ber best informed about agricultural activities). A final those objectives in mind. difference between the short version and the two more extended versions is that the recall period of the Annotations to the Recommended short version is the past 12 months instead of the past Questionnaires two cropping seasons, since there is no intention of matching outputs with inputs to estimate a production This section provides detailed notes on the three ver- function or any other causal relationship. sions (short, standard, and expanded) of the agriculture The following notes provide specific information module presented in Volume 3. The notes serve three about the design of the short version of the agricul- purposes: to explain the recommended module, to tural module. point out where the survey team might introduce modifications to suit specific circumstances, and to flag PART A. potential difficulties related to survey questions, sug- A.2. The main purpose of this question is to get a gesting how interviewers can minimize these prob- name or short description of the plot for reference lems when interviewing households. In some cases during the interview. Only the code numbers for the modifications are suggested, including changes in plots, immediately to the left of the answers provided, response codes for questions, ways of posing questions, are needed for data analysis; the data entry operator 167 THOMAS REARDON AND PAUL GLEWWE need not enter the names of the plots in the electron- tion economies of Eastern Europe and the former ic files. The one exception to this rule is the case in Soviet Union, as well as for the socialist countries of which panel data on plots are collected; in this case the East and Southeast Asia. name of the plot would be useful in the electronic files for matching plots when reinterviewing the same A.10. In regions of certain countries land transactions households in a future survey. may be rare, in which case respondents would have a hard time answering this question. In such cases one A.3.The codes for land area often vary by country; the possible response could be "DON'T KNOW." appropriate codes should be obtained by consulting However, if at all possible interviewers should try to the ministry of agriculture or the local National obtain an estimate-however rough-of the value of Agricultural Research System (NARS), if there is one. the land. A.4. The different types of land will also vary by coun- PART B. The list of items for which these questions are try; again the appropriate codes should be obtained asked must be customized for each country. from the ministry of agriculture or the local NARS. Further comments on this question are provided B.3-B5. If joint ownership of these items is rare below in the notes for the same question in the stan- (which can be determined during the field test) these dard agricultural module. questions may be dropped. In this case the instruction in uppercase letters in Question 2 should be dropped A.5-A.6. If the land has been rented out for all of the and the instruction in Question 6 should be modified. past 12 months, there is probably little reason to ask what crops are grown on it. The respondent may not PART C. The answers to these questions will be even know what crops are grown on the land. approximate because they are not being asked for each plot. The list of crops must be customized for each A.6. In most countries there is a large number of dif- country; again the appropriate codes should be ferent crops; each crop can receive a different code obtained from the ministry of agriculture or the local number (as is done in Part C).The code numbers used NARS. do not fit in the space available in Question 6.Yet it is very convenient for the interviewer (and thus reduces C.2. The main purpose of this question is to obtain a errors in filling out the questionnaire) to have crop rough idea of the quantity of each crop produced by code numbers easily accessible. One way to do this is the household. This information can also be used to to have the codes printed on the facing page of the classify agricultural households by the kinds of crops questionnaire. Another possibility is to have a laminat- they grow-for example, distinguishing producers of ed sheet with crop codes (and other codes) that the export crops, such as coffee, cocoa, or rubber, from interviewer can set next to himself (or herself) during producers of domestically consumed food crops. each interview. The codes used should include a code for "FALLOW" for cases where no crop is grown on a C.3. The main purpose of this question is to obtain an field in a given season. approximate estimate of who benefits from price sub- sidies. Technically speaking, farmers only benefit from A.7. In countries where there are several different price subsidies if they sell their crops. The information kinds of irrigation systems, and differences between gathered by this question is also useful because it gives them have important implications for the productivi- a rough idea of the impact of price changes on farm- ty and value of the land, one could add a question that ing households. Households that sell few of their crops asks for the type of irrigation on the plot. will be more insulated from price changes than house- holds that sell most of their crops. A.8-A.9. These codes regarding how the land was acquired and the type of ownership rights must be PART D. customized to the circumstances in each country. D. 1. Fertilizers, kinds of manure, pesticides, herbicides, Customization is particularly important for the transi- and fungicides should all be referred to by their 168 CHAPTER 19 AGRICULTURE explicit names, either brand names or generic names. inputs used in the production process (Part D), and This will vary significantly by country. The appropri- finishing with similar questions for livestock (Part E) ate names should be obtained from the ministry of and a general discussion of access to agricultural serv- agriculture or the local NARS. ices (Part F). Activities involving the transformation of agricultural products into processed foods or other D.3. The codes for the source of purchased inputs agricultural goods are recorded in detail only in the must be modified for each country. household enterprise module (although the fact that some of the household products were used for this PART E. These purpose of these questions is to obtain purpose should be recorded in Part C of the agricul- a rough estimate of the stock of animals. Only large tural module), since such activities generally do not livestock are included. The types of livestock will vary depend on whether the raw materials were produced by country; the questionnaire must be modified by the household or purchased from some other accordingly. source. Questions regarding assets (equipment), input PART E In some countries there may be different kinds transactions, livestock, and agricultural services (Parts of agricultural extension organizations or agents. If so, B, C2, D3, E, and F) are asked of a single household the questions in Part F should be modified to distin- member-the person who is best informed. Detailed guish between the different types. questions regarding plots (characteristics, products, and inputs) are asked of each plot manager. As discussed in F.1, F.2, F.7, £8 £13,AND F.14. The difference between the second section, this should reduce recall error with what constitutes a visit by a household member to an only a small increase in interview time. agent and a visit by an agent to a household is usually The following notes provide specific information clear. However, in some countries the distinction may about the design of the standard version of the agri- be less clear, such as when an agent comes to a meet- cultural module: ing held by farmers near their homes. This distinction will depend on the nature of agriculture extension PART A. This part is divided into three different sets of services in the country; survey designers should seek questions. The first, Al, collects information on plots the advice of the ministry of agriculture and the local of land owned and farmed by the household. The sec- NARS in designing these questions. ond, A2, collects information on plots of land rented from other households, and the third, A3, gathers data F.3 AND £9. As with Question 6 of Part A, the crop on plots rented out by the household.The appropriate and animal codes should be visible to the interviewer, questions to ask on the ownership and renting of plots either on the opposing page or on a laminated code of land can vary greatly over countries, so a large sheet. amount of customization is needed. F.15. After this question, the interviewer may want to PART Al. ask what kinds of crops or animals were discussed dur- A1.2. This question obtains a name or brief descrip- ing the visits, as is done in Questions 3 and 9 for vis- tion of the plot, for reference during the interview. its to extension agents. Asking for the name of each plot should work well in farming systems with relatively few plots per house- Standard Version hold, such as Vietnam (with an average of 5)-but it The standard version of the agriculture module pro- becomes increasingly difficult in systems with more vides data that can be used to calculate net farm plots, such as China and Peru (which average 9) or income and to estimate both production functions and Burkina Faso (with an average of 10-15). Another reduced form relationships.The order of the submod- method may be needed if farmers do not have names ules is designed to follow the contours of a typical or simple brief descriptions for their plots. One possi- conversation with a farmer-first establishing the bility is to combine information on the plot manager, stock of land and equipment (Parts A and B), then ask- the location of the plot, and the primary crop grown ing about crops produced and marketed (Part C) and into a plot numbering system.13 For example, the 169 THOMAS REARDON AND PAUL GLEWWE interviewer tells the plot manager, "Let's talk about to approximate the plot size, even in local units.This is household plot 4 now, your cotton plot 100 yards from often the case in farming systems where mainly sub- the residence." It may help to have a blank page in the sistence crops are produced and there is little reason to questionnaire for the interviewer to draw a rough map have accurate measures of one's plots. In some cases that can be referred to during the interview.A second households know the area of their major plots and issue is that in some countries farmers may exclude cash crop plots, but not of minor or subsistence crop fallow or pasture plots, or plots with a failed harvest. In plots. If the pretest shows that this is the case, the sur- such cases interviewers must be trained to prompt vey team should consider adding an explicit plot each respondent (using the code list in Question 5) measurement component to the survey. This will about plots that he or she would tend to omit. A final increase survey cost, but may be the only way to get a point is that only the code number for the plot reasonably accurate measure of plot size in a rural (immediately to the left of the answers to Question 2) economy in which the majority of rural incomes are is needed for data analysis; the data entry operator from subsistence farming. One option for measuring need not enter the name of the plot in the electronic plot size would be to hire a separate team, possibly lent files. However, if panel data on plots are to be collect- by the NARS, to take the measurements. In most cases ed (see Chapter 23), including the name of the plot in the cost would be relatively low. Finally, note that the electronic files would be useful for matching plots accurate plot size data are usually not needed for fal- when reinterviewing the same households in a future low, pasture, or wood lot plots. survey. A1.5. The different types of land will also vary by Al.3. The general approach of Part Al is to ask the country, and again the appropriate codes should be best-informed household member to list the plots obtained from the ministry of agriculture or the local owned and farmed by the household and to indicate NARS. which household member manages each plot. All fur- ther questions about the characteristics of each plot are A1.6. In most countries there will be a large number addressed to the plot managers. Because plot manage- of different crops, each of which can receive a differ- ment arrangements vary widely over farming systems, ent code number (as is done in Part C). All of the this approach may have to be altered to fit the coun- code numbers cannot fit in the space available in try studied. In a "centralized" system (usually found on Question 6. Yet it is convenient for the interviewer smaller farms with fewer plots and centralized opera- (and thus reduces errors in filling out the question- tions such as irrigation), the household head manages naire) to have crop codes easily accessible. One way to operations on all plots. In this case question 3 can be do this is to have the codes printed on the facing page omitted because the household head or the household of the questionnaire. Another approach is to prepare a member "most knowledgeable about agriculture" can laminated sheet with crop codes (and other codes) answer all the questions for each plot. that the interviewer can set next to himself (or her- self) during each interview. One crop code should be A1.4. The codes for land area often vary by country; "FALLOW" indicating that no crop was grown on a the appropriate codes should be obtained by consult- field in a given season. In cases in which two or more ing the ministry of agriculture or the local National crops are cultivated on the same plot in the same sea- Agricultural Research System (NARS), if there is one. son, detailed farm management surveys often attempt It is important to pretest this area question carefully. to determine how much of the plot is assigned to The survey team should note two points in pretesting, each crop.This could be done here by asking an addi- adapting, and posing the question. First, in many tional question about the percentage of the plot countries farmers can only recall plot sizes in local devoted to each crop. If the two crops are inter- units. The pretest will easily reveal this, and local agri- cropped (planted together), a separate question asking cultural researchers usually have the conversion coeffi- about this could also be added. A final point about cients from local units to hectares. Second, in situa- Question 6 is that the interviewer should be instruct- tions in which plots are numerous, small, and ed to ask about what crops were planted, not what irregularly shaped, it may be quite difficult for farmers crops were harvested, and the interviewer should 170 CHAPTER 19 AGRICULTURE probe the respondent about this; otherwise farmers crop mix (cover), agronomic techniques (plow or may omit crops that failed. not, mulch or not, terrace/bund or not), and time since last fallow, one can compute an index of soil A1.7. In countries where there are several different erosion and degradation that is very useful in pro- kinds of irrigation systems, and differences between duction functions. (See Byiringiro and Reardon them have important implications for the productivi- 1996 for an illustration.) ty and value of the land, one could add a question that asks for the type of irrigation on the plot. Questions PART A2. Questions 2-5 are identical to Questions could even be asked about how the irrigation is man- 2-5 in Part Al, so the comments above apply here. aged. Similarly, Questions 7-8 are the same as Questions 6-7 in Part Al, and Questions 9-12 are the same as A1.8-A1.9. These codes regarding how the land was 11-14 in Part Al. acquired and the type of ownership rights must be customized to the circumstances in each country. A2.1l. A related question, useful for some types of These questions will be inadequate for farming sys- research, is the length of time that the household has tems in a socialist or "transition" economy. For two been farming this land. examples in East Asian transition economies, see the LSMS survey questionnaires used in northeast China A2.13. These codes need to be adapted to each coun- in 1995 and inVietnam in 1992-93 and 1997-98. For try. In addition, more detail could be added, such as countries of the former Soviet Union see Oliver which relative and how far away the landlord lives. (1997). A2.14. These codes also need to be modified for each A1.10. In regions of certain countries land transactions country. may be rare, in which case respondents would have a hard time answering this question. In such cases one A2.17. In some countries it may be common for live- possible response could be "DON'T KNOW." stock to be used as in-kind payments for renting. In However, if at all possible interviewers should try to such cases the "CROP CODE" column must include obtain an estimate-however rough-of the value of code numbers for livestock as well. Codes for livestock the land. One possibility in such difficult cases is to products may also be needed. For example, a common drop the question entirely and ask about land prices in arrangement in the Sahel has one pastoral family the community questionnaire. obtaining user rights to a pasture plot in the dry sea- son in return for milk provided to the landholder. A1.11-A1.14. These questions collect general infor- mation about the characteristics and quality of each PART A3. Questions 2-3 are identical to Questions plot. Other characteristics could be added depending 2-3 in Part Al, so the comments above apply here. on the nature of agriculture in the country and the Similarly, Questions 6-7 are the same as Questions issues of greatest interest to policymakers.An alterna- 4-5 in Part Al, and Questions 8-15 are the same as tive to these questions on quality is directly measur- 7-14 in Part Al. ing soil quality or other plot characteristics, but this is much more costly. Additional questions to consid- A3.4-A3.5. If the answer to Question 4 shows that the er adding are the distance to the plot from the land was rented out for only one season, presumably it household's dwelling, local or "folk" soil classification was farmed by the household members in the other codes, and, for tree crops, the fraction of the plot area season and thus was listed in Part Al. In this case there with trees that are still too young to bear fruit.. is no need to ask Questions 6-15; instead, one should Usually there is information available from agricul- simply note the plot code from Part Al and the inter- tural researchers on local farmers' "folk" soil classifi- viewer can proceed to Question 16. However, if the cation systems and how these correspond to scientif- answer in Question 4 is that the land was rented out ic classification of soils. 14 Combined with in both seasons, it presumably was not listed in Part Al information about simple categorization of slope, and the interviewer should proceed to Question 6. 171 THOMAS REARDON AND PAUL GLEWWE A3.16. These codes need to be adapted to each coun- manager for each plot is that in the relatively decen- try. In addition, more detail could be added, such as tralized farming systems commonly found in "hard- which relative the plot is rented from and how far to-survey" situations there is no single household away the landlord lives. member who knows the details of each plot. In some "easy-to-survey" situations a single person may be able A3.17. These codes need to be modified for each to respond. Part Cl could easily be modified to target country. only one respondent (by dropping the column for the name of the plot manager in Question I), but it is still A3.20. See the comment above on Part A2 Question useful to collect output data by plot and by season. In 17. contrast, there is little reason to collect disposal infor- mation by plot or by season; indeed, if the harvest from PART B. many plots is stored in a single place and then divided PART B1. The types of farm equipment must be mod- up for different uses it may be difficult, if not impossi- ified to fit the specific country. Some items, such as ble, to do so. On the other hand, in some countries rice winnowers, may pertain to specific crops. In these each plot manager may tightly control disposal of the cases the type of crop should appear in the name of products of his or her plot, in which case the questions the item. on disposal must be addressed to the plot manager for each plot, which would mean moving all of the ques- B1.3-B1.5.Joint ownership is most likely for tractors, tions in Part C2 into Part CI.This could significantly mills, and irrigation equipment. If joint ownership is increase the interview time if the same crop were rare for any item, these questions can be "blacked out" grown on many different plots. in the lines with these items. Ifjoint ownership is rare As discussed in the second section, the questions for all items, which can be determined during the field in Part C must explicitly distinguish between the dif- test, these questions can be dropped. In this case the ferent cropping seasons (as opposed to asking about instruction in uppercase letters in question 2 should be "the past 12 months"). dropped and the instruction in question 6 should be nmodified. PART Cl. There is not enough room on this page to provide all the crop codes for the interviewer. The best B1.6. It may be difficult for the respondent to state the approach is to list the codes on the facing page. This is price of each item as there may be no market for the not only more convenient for the interviewer but item, the item may be home-produced, or the item should also reduce error. An alternative is a plastic lam- may have been bought long ago and the household is inated sheet that lists crop codes and other codes as not familiar with current prices. In these cases, the sur- well. vey team may need to add a question to the commu- nity questionnaire regarding prices, but this should be C1.l. This list of plots will be used not only for Part avoided if possible because these would only be aver- C1, but also for Parts DI and D2.This being the case, age prices. A final possibility is that an item may be it may be useful to have this question as a fold-out flap obsolete and thus not sellable for any price. In this case in the questionnaire, similar to the list of names of the price should be recorded as zero. household members in the household roster.The sim- plest way to list the plots is to list the plots in Part Al PART B2. The list of hand tools needs to be modified in order, then list the plots in Part A2. In countries to fit the circumstances in each country. where households commonly own large numbers of plots, two pages may be required (which would also PART C. The general approach is to ask each plot man- imply two pages for Dl and D2). ager to recall, for each cropping season, the production of crops on each plot, then to ask a single household Cl.2. In most countries room for four crops per plot member (the one most knowledgeable about the over two seasons should be sufficient, but in some household's farming activities) about the disposition of countries room for five, six, or even more may be crops. The reason for recall of production by the plot needed. One might also ask about whether any of 172 CHAPTER 19 AGRICULTURE these crops were intercropped with each other, but in reasons for sales or to find out more about the location most cases it is more convenient to ask this in Part A. or periodicity of the sales. Another issue is that crops with long-growing cycles, such as roots, tubers, bananas, and some fruit trees, are C2.6-C2.11. Crops may be disposed of in other ways, harvested little by little throughout the year. It may be such as presenting some as gifts to relatives or neigh- hard for respondents to recall these harvests, and bors or saving some for seed. If these are common pretests will suggest the best way to resolve this prob- ways of disposing of crops, questions along those lines lem. One approach is to ask about the typical off-take should be added here. Disposal in the form of gifts per week or month during the season; for example: may be hard to recall because it often consists of many "How many cassava root pieces did you dig out of small transfers; losses are the hardest to recall because your plot per week in this season?" Finally, note that this requires retmeimibering what was harvested, sub- nonstandard units may be common in "hard-to-sur- tracting all other uses, and looking into the storage vey" situations. Methods for resolving this problem are bins or sheds to see what is left. Only in the case of discussed in the second section of this chapter. theft or loss of large amounts is it easy to recall losses. Pretests will reveal how difficult these questions are in PART C2. a specific situation and whether the questions need to C2.1. The list of crops must be adapted to each be modified. country. In some cases the list could be 60-70 items. It is good survey practice to explicitly ask for each C2.9. This includes only crops that were processed crop, since farmers may forget "minor" crops if they then sold to others. This is considered a household are simply asked to provide a list of the crops they business, and these activities are covered in detail in the cultivate. household business module (see Chapter 18). Crops that were processed then consumed by household C2.2-C2.11. Nonstandard units may be common in members should be included in Question 10. "hard-to-survey" situations. As discussed in detail in the second section, there are several methods for C2.11. In some farming systems the harvested crop is resolving this problem. stored in nonthreshed form, and in others it is stored in threshed form. The latter should be the standard, C2.4. In theory there is no need to ask for "total sales," and if the unit is cited in nonthreshed form, that form as that can be deduced from the unit price and num- should be noted. Coefficients that convert from ber of units sold. But it may be common for a respon- threshed to nonthreshed units should be provided by dent to recall easily the total cash; if this is the case, a the survey team in the documentation for the survey, column for "total sales" can be added. Another reason based on consultation with the ministry of agriculture for a total sales column is that in some situations crops and any NARS. The question can be simplified in are bartered, in which case the value of total sales can farming systems in which the respondents normally be in the form of in-kind payments. Finally, in some recall in one or the other form. situations the seller does not get full payment at once, even within the recall period. The total payment in PART D. such cases may be divided into how much has been PART Dl. This section collects plot-by-plot informa- received so far and how much is still owed. tion on the labor time of household members on dif- ferent tasks and in different seasons. An alternative, C2.5. This question establishes to whom a sale was more detailed way to collect such data is provided in made. The codes need to be adapted to each country. the expanded version of the agriculture module. In Other possible codes arc a government cereal market- contrast to the expanded module, individual house- ing agency or an export firm. Additional questions can hold members are not identified in the standard ver- be added for detail on output marketing-for exam- sion; rough estimates of the time spent working in ple, whether the sale was in response to, or part of, a agriculture can be obtained from the employment public campaign to increase commercialization of cer- module (see Chapter 9). Putting detailed agricultural tain crops. Questions can also be added to find out the labor questions in the employment module is verv dif- 173 THOMAS REARDON AND PAUL GLEWWE ficult because it would be hard to design the employ- Another issue is whether each of these questions ment module in a way to get plot-by-plot data. Also, should be asked separately for men, women, and chil- since much of the labor may be that of the plot man- dren. That would increase the length of the question- ager, it is easier for him or her to recall all the agricul- naire and presumably the interview time, but the ture information at the same time, as opposed to split- increase in interview time may not be very large. This ting it between the employment and agricultural can be evaluated in the pilot test of the questionnaire. modules (which may not be administered on the same day). Finally, as discussed in the second section, it is PART D1.6 AND DI.AL Harvesting labor may be quite best to collect all agricultural data in terms of the 12- difficult to recall for crops, such as cassava, that are har- month period that contains one or two complete agri- vested little-by-little over the season or between sea- cultural seasons. This would also be hard to do in the sons. Pretests should reveal ways of modifying the employment module, which in general collects data question to recall such labor, but it may be necessary for the past 12 months and the past 7 days. to come up with a rate and apply that to the output. In some situations, which can be discerned in For example, if it takes 1 hour to dig up a basket of pretests, it may be best to enumerate certain tasks, such potatoes, and 100 baskets of potatoes were harvested as marketing or the maintenance of irrigation infra- from the garden, about 100 hours of labor were need- structure, at the farm level. Information on such activ- ed. Dividing this by 8 hours per day yields about 12 ities could be asked of thc household member most days of labor for potato harvesting. informed about agriculture, and could be put in a set of household-level questions at the end of DI. This PART D2. Certain labor tasks may be done collective- could include time spent by household members car- ly (with other households) as "exchange labor." One ing for livestock. common variant of this is the communal work party that goes from farm to farm and performs a single D1.. As mentioned above, this question need not be task. In Part Dl there could be a separate question on filled out if Question 1 of Part Cl is written on a fold- such donations of labor on other people's farms. out flap, as is done with the names of the household Receipt of such labor should be treated as receipt of members in the household roster. "hired" labor and noted in D2. (A question could be added to distinguish such labor from "ordinary" hired D1.3 AND D1.8. In some farming systems (such those labor.) as in West Africa), animal traction rental is done as labor hire, with the laborer bringing his own plow and D2.1. As mentioned above, this question need not be team. In India, one usually rents bullocks but uses one's filled out if Question I of Part Cl is written oni a fold- own labor and one's own equipment. In some coun- out flap, as is done with the names of the household tries one can rent traction equipment and supply one's members in the household roster. own bullocks or horses, or rent a tractor and supply the diesel fuel. In general, if the arrangement is one D2.3-D2.4. An alternative for hired labor is to ask where household members rent equipment or draft about the labor days spent in each of the different tasks animals but supply their own labor, the labor time listed in question 4. This would lengthen the ques- should be noted in these questions. If the system is one tionnaire and thus this option has been left to the in which labor is hired and the laborer brings his oNvn expanded version. equipment or uses the equipment of the household, that labor time should be noted in Part D2. In some D2.13 AND D2.24. The types of fertilizer must be countries one may want to ask who supplied the adapted to the country of the survey. This list of codes equipment. (The cost of renting such equipment is should be developed in consultation with the ministry recorded in Part D3.) of agriculture and any NARS in the country. D1.3-D1.6 AND D1.8-Dl.11. These are the main tasks D2.16, D2.18, D2.27, AND D2.29. The types of manure in most countries, but in certain situations other tasks used must be adapted to the country of the survey. could be listed, or these tasks could be aggregated. This list of codes should be developed in consultation 174 CHAPrER 19 AGRICULTURE with the ministry of agriculture and any NARS in the D3.3 AND D3.6. These codes must be adapted to the country. circumstances of the country. D2.20 AND D2.31.Very specific codes for the different PART E. The information on anirmals is gathered over pesticides, herbicides, and fungicides must be provid- the t2-month period that contains the last two crop- ed. As with other agricultural inputs, the list of codes ping seasons (as opposed to the 12 months immedi- should be developed in consultation with the ministry ately preceding the day of the interview), to be con- of agriculture and any NARS in the country. sistent with the crop data in the agricultural module. Part E is designed to focus on farm households that PART D3. Prices for the different farm inputs were not hold livestock, rather than pastoral households that obtained in Part D2. The reason for this was to avoid engage in transhumance or other mobile systems. A needless repetition, since the same input could be survey of the latter type of household would require applied to many crops, and it is only necessary to ask much more detail on livestock activities. for the price once.The prices are obtained more con- cisely in Part D3. If prices vary from season to season, E.2. The types of animals listed will vary by country. one may want to make this distinction in Part D3.This In some countries it may be useful to distinguish could be done by dividing Question 2 into two ques- between mature animals and young animals. This can tions, one for each season. be done either by having separate lines for adult and young animals, or by distinguishing between the two D3.1, LINES 1-11. Lines 1-ll in this grid should be types in selected questions. replaced with explicit names of fertilizers, types of manure, pesticides, herbicides, and fungicides that are E.3. Livestock holdings questions can be very sensi- commonly used in the country. tive subjects, except in farming systems in which there are few livestock. In some farming systems, D3.1, LINES 12-15. Provision is made for recording livestock are an important store of wealth and self- different wages paid for different tasks. It is usual for insurance, a status symbol, and a means of financial there to be such variation, and such variation should freedom for family members (in relation to other be fairly easy to recall. However, there may also be dif- members of the family). In Senegal, for example, it is ferences in wage rates depending on whether men, difficult to solicit information from wives concern- women, or children are hired. In this case more lines ing the animals they own because they do not want can be added that distinguish between wage rates for the household head to know their wealth, which men, women, and children. Finally, hired labor is usu- would reduce their autonomy (Kelly and others ally paid an hourly, piece, or daily wage, perhaps with 1993). Where there is livestock taxation, households an in-kiind payment such as a meal. Rather than enter- may also be reluctant to provide accurate informa- ing into details on the latter payments, the respondent tion. In an LSMS-type survey it is best not to spend is asked simply to make a rough guess of daily wage too much effort trying to ascertain livestock holdings including in-kind payments such as meals. More very accurately. In most farming systems the data will detailed information on hired labor is obtained in part probably be reasonably accurate, and greater accura- D2 of the expanded module. If piece rate payments are cy would probably require much longer contact with common, this could be accommodated in the standard the households. In farming systems in which live- module by adding "PER HECTARE," "PER BAS- stock holdings are more complex (mainly in hard-to- KET," or similar codes into the unit code box. survey situations), it would be better to interview each household member, but this can absorb large D3.2-D3.3. One or more questions could be asked amounts of time and create tension. The person best about credit arrangements at this point, such as informed at the household level can provide a rough whether credit was obtained to purchase some or all of estimate, and where he or she does not have a good the input, and the terms on which credit was idea, it is unlikely that the interviewer can use anoth- obtained. For further discussion on collection of cred- er method to get a better estimate in a single visit to it information see Chapter 21. the household. 175 THOMAS REARDON AND PAUL GLEWWE E.6, E.11, E.14-E.17, AND E.20. Barter is often more Expanded Version common in livestock transactions than in transactions The expanded version of the agriculture module takes in other agricultural products; provision can be made the standard module as its starting point, adding Parts for this by asking about in-kind payments in a separate A4 and A5, which obtain information on land transac- question in systems in which such payments prevail. tions during the past 5 years, and replacing Part D of the standard module with a more detailed Part D. The E.15. More detailed questions can be added concern- comments below apply only to Parts A4 and A5 and ing animal health expenditures and services in coun- the expanded version of Part D. For details on the tries in which animal disease is an important policy parts of the expanded module that come from the issue, such as in the trypano zones of Africa. standard module without any modification, see the comments above. E.18-E.20. This question should exclude any animal byproducts that are used as inputs for food processing PARTS A4 AND A5. The questions in Parts A4 and A5 are that is part of a household business. Such activities are merely a point of entry for survey teams that wish to covered in the questionnaire module on household add a series of questions on the impacts of land enterprises (see Chapter 18). If such food processing reforms, decollectivizations, or other land market poli- activities are important, the use of animal byproducts cies. The general approach is to ask the best-informed for that purpose could be noted here by adding ques- household member to describe all land transactions. tions similar to 18 and 19. However, in some farming systems plot managers have a large degree of autonomy in buying and selling their .19. The types of fresh byproducts will vary by coun- plots, so they may not need to obtain the permission of try. The codes should be developed in consultation the head of household or even to inform the head. with the ministry of agriculture and the local NARS. Such situations should be ascertained in the pretest; in countries where this is the case, provision must be PART E In some countries there may be different kinds made to administer these questions to all plot managers of agricultural extension organizations or agents. If so, in the household. A final general comment is that ask- the questions in Part F should be modified to distin- ing questions about the past 5 years is rather arbitrary. guish between the different types. The length of the recall period should depend on the specific conditions and policy issues in each country. F.1-£2, F.7-F.8, AND F.13-F.14. The difference between a visit by a household member to an agent PART A4. In countries where farmers are expanding and a visit by an agent to a household is usually clear. production by bringirng new land under cultivation, However, in some countries the distinction may be one may want a separate section on such activities. The less clear, such as when an agent comes to a meeting interviewer could ask the best-informed household held by farmers near their homes. Here the distinction member to describe land clearing activities to find out will depend on the nature of agriculture extension how much land the household cleared in the past five services in the country; survey designers should seek years, the use rights the household now has on that the advice of the ministry of agriculture and the local land, and the use to which the land is being put. The NARS in designing these questions. arnount cleared may be hard to establish if the land is fragmented, partially cleared, or irregularly shaped. F.3 AND £9. As with Question 6 of Part Al, the crop This is important to ascertain in a pretest. It may also codes and the animal codes should be visible to the be hard for households to value this land in areas interviewer, either on the opposing page or on a lam- where one cannot rent or buy land (because there is inated code sheet. no land market), or if the household in question has little experience in the land market. Another issue is F.15. After this question the interviewer may want to that the household might need permission from the ask what kinds of crops or animals were discussed dur- village chief or other public authorities to clear land, ing these visits, as is done in Questions 3 and 9 for vis- and may consider that the equivalent of title. Finally, its to extension agents. questions concerning land clearing and land title may 176 CHAPTER 19 AGRICULTURE be quite sensitive because clearing may be officially A5.7. In some places, selling land does not necessarily banned, so the interviewer should be instructed to mean losing the right to produce on it. For example, assure the respondent that all answers provided will be in Burkina Faso, land can be transferred to another completely confidential. household but the original owner can still collect sheanuts from his trees on the land. Provision can be A4.2. Past land transactions involving family members made in the module in countries where this is the who now live outside the household may not be vol- case. unteered by the respondent unless the interviewer is instructed to be sensitive to this possibility and prompt A5.11. In some regions of the world it may be inter- for it.This comment also applies to Question 2 of Part esting to track "urban-based buyers" as a separate cat- A5. egory, in order to track absentee ownership. A4.3-A4.4. Because most of the plots acquired within PART D. The expanded version of Part D is intended the past five years will still be in the household's pos- to replace the Part D presented in the standard version session, information on these plots will already be avail- of the agricultural module. It collects information on able from Part Al or Part A3. When this is the case, it labor and nonlabor inputs in much more detail than is noted in Question 3 and then the plot code from does the standard version of Part D.The main purpose Part Al or Part A3 is recorded in Question 4. This of collecting such detailed data is to estimate produc- allows the interviewer to skip Questions 5, 6, and 7. tion functions and other causal relationships in much more detail. The data are also useful for detailed A4.5-A4.7. These questions are essentially the same as descriptive analysis of the agricultural activities of rural Questions 4, 5, and 8 in Part Al of the standard mod- households. ule. See the comments above on those questions. PART D t. The expanded version of Part D 1 is very dif- A4.7. It may be difficult to distinguish between long- ferent from the standard version. The main difference term lease and sale or gift. Also, in some cultures there is that each household member is asked about work may be sensitivity or shame attached to selling land done on plots farmed by the household. The informa- (just as anthropological research has shown that there tion collected is disaggregated by plot, season, and type is shame attached to selling grain within one's own of task. Each household member is expected to village in some regions), so the interviewer should be respond for himself or herself. This amount of detail instructed to be sensitive to the propensity of a will increase interview time, but such an increase respondent to describe a sale as a "gift." Finally, in areas should be expected since the expanded module with a mix of private and collective lands, allowing the assumes that agricultural policy issues are the top pri- reincorporation of land into the collective may be ority of the survey. considered neither a sale nor a permanent divestiture; it might be seen as "long-term lending" land to the Dl.l. Because the unit of observation for each line is cooperative or collective. Questions specific to such household member, as opposed to plot of land, the land systems should be added. These potential prob- fold-out list of plots in Part Cl cannot be used to lems should be explored in the pretest. replace question 1, unlike question 1 of the standard Some additional questions to consider adding are module for Part Dl. Instead, the fold-out list of house- what kind of ownership rights the household has hold members that is part of the household roster (see (similar to Question 9 in Part Al), whether the land Chapter 6) should be used. was purchased on credit and, if it was, the terms of that credit. See Chapter 21 for further details on collecting D1.2-D1.16 AND D1.18-D1.32. For each season, credit information. detailed data are gathered for the three plots on which the household member spent the most time. In addi- PART A5. These questions are mirror images of the tion, more cursory data are collected for three other questions in Part A4; thus the comments in Part A4 plots in each season (Questions 34 and 36). In some generally apply to the same questions in Part A5. countries there may be a need to increase the number 177 THOmAS REARDON AND PAUL GLEWWE of plots for which detailed data are collected, and per- D2.3-D2.10 AND D2.12-D2.19. The four main tasks list- haps even the number of plots for which cursory data ed in these sets of questions (preparing and sowing the are collected.The pilot test of the questionnaire should field, applying inputs, weeding and pruning, and harvest- reveal whether this is the case. ing) could be expanded into a more detailed list of tasks, but this would increase the time required to complete D1.2, D1.7, D1.12, D1.18,D1. 23, D1.28, D1.34, AND the questionnaire.The level of detail of the tasks depends D1.36. Plot ID codes are provided in Parts Al and A2. on the specific policy questions to be addressed and on the agriculture system prevailing in the country. D1.3-D1.6, D1.8-D1.11, D1.13-D1.16, D1.19-D1.22, D1.24-D1.27, AND D1.29-D1.32. These four D2.3, D2.5, D2.7, D2.9, D2.12, D2.14, D2.16, AND activities-preparing and sowing the field, applying D2.18. For some or all of these tasks, labor days could inputs, weeding and pruning, and harvesting-should be disaggregated into adult male, adult female, and suffice for most crops in most countries. Some data child labor days. This would lengthen the question- analysts may want a more detailed list of activities; this naire but might be xvorthwhile under some circum- can easily be provided but it will increase the inter- stances. If this were done it wvould be useful to disag- view time. Other activities may be needed for certain gregate payments for hired labor in the same way. types of crops; for example, smudge pot operations may be needed for tree crops. Labor used to maintain D2.4, D2.6, D2.8, D2.10, D2.13, D2.15, D2.17, AND or repair agricultural equipment is not explicitly men- D2.19. In countries where in-kind payments to hired tioned. In most cases respondents will include this farm laborers are common, survey designers could labor with the task for which the equipment is used- split each of these questions into two questions, one that is, maintenance of plowing equipment will be for cash payments and one for in-kind payments. included in plot preparation and repair of irrigation However, before expanding the number of questions, equipment will be included in time spent irrigating the purpose of the survey and the results of the pretest the plot. should be reviewed to see whether this is worthwhile. D1.6, D1.11, D1.16, D1.22, D1.27, AND D1.32. For PART D3. The only difference between Part D3 of the cassava and other root crops that are harvested little- expanded questionnaire and Part D3 of the standard by-little over the year, respondents may have difficulty questionnaire is that the expanded version has fewer answering questions on days spent harvesting. For sug- items for questions 1-3. Because payments to hired gestions on how to handle this, see the note on labor arc recorded in Part D2 of the expanded ques- Questions 6 and 11 of Part Dl of the standard version tionnaire, there is no need to ask about prices paid for of the agricultural module. different kinds of hiired labor in Part D3. D1.37-D1.39. These questions on hours per week Notes spent on animal husbandry activities work best for households where raising animals is not the main The authors are very grateful to Bonnie Banks and Andrea activity. In agricultural systems that where raising live- Ramirez for helping to create the questionnaire. They are also stock can be a full-time activity, much more data are thankful for comments from Harold Alderman, Christopher needed on labor inputs. Barrett, Jeanine Braithwaite, Michael Carter, Carlo del Ninno, Margaret Grosh, Courtney Harold, Juan Munioz. Scott Rozelle, PART D2. The main difference between the expanded Kinnon Scott, and the participants at two LSMS seminars. Finally, and the standard versions of Part D2 is that the expand- Reardon thanks Peter Matlon for years of training on farm surveys. ed version adds much more detail about hired labor. 1. While it is analytically convenient to think of the household as a single entity that has one utility function, it is more realistic to D2.1. Unlike Question I of Part Dl of the expanded assume that each household member has an individual utivty func- version, this question can be removed if Question 1 of tion.As explained in Chapter 25, this distinction can have impor- Part Cl is placed on a flap that is visible when the tant policy implications.Yet the discussion in this subsection is quite questions in Part D2 are being asked. general and thus applies in either case. 178 CHAPTER 19 AGRICULTURE 2. The main exception to this general statement is that in some location ("by the paved road on the south bank of the rivcr"), by socialist countries the government may stipulate that certain crops manager ("my first wife"), and often by principal crop ("a cotton should be grown. For example, in some parts of China households field"). are required to grow rice in order to meet quotas for rice produc- 14. A practical approach would be to devise a soil classification tion. However, this practice is becoming rare even in socialist and land configuration scheme and to pretest it in local languages, economies. There may also be cases where the government direct- asking the plot operator for the soil type and land configuration. ly rations scarce agricultural inputs, but this practice, too, is becom- This has been done effectively by Dvorak (t988) in India, Dvorak ing rare. (1993) in Nigeria, and Matlon (1988) and Prudencio (1983) in 3. Another use of causal analysis is to estimate parameters that Burkina Faso. researchers can use for applying optimization models. These mod- els can be used to derive what farmers should do to maximize prof- References its, or to optimize some other objective. 4. Whether farm size and capital holdings are really exogenous Adesina, A. A., and K. K. Djato. 1996. "Farm Size, Relative is a matter of debate. The answer often depends on the specific Efficiency and Agrarian Policy in Cote d'lvoire: Profit details of the data and of the agricultural system in the country Function Analysis of Rice Farms:' Agricultural Economnics 14 (2): where the data were collected. 93-102. 5. The ability of the three different versions of the agricultural Ahmed, Raisuddin, and Cynthia Donovan. 1992. "Issues of module to answver these questions will depend on whether the agri- Infrastructural Development: A Synthesis of the Literature:' cultural sector of the country in question is hard to survey or easy IFPRI Occasional Paper. International Food Policy Research to survey These two kinds of situations are discussed below. In gen- Institute, Washington, D.C. eral, the ratings in Table 19.1 are an average of the two situations. Ahmed, J., Walter Falcon, and Peter Timmer. 1989. "Fertilizer 6. Many regions of Nepal are hard-to-survey situations, but Policy for the 1990s" Harvard Institute for International some (such as the Terai region) have characteristics that make them Development Discussion Paper 293. Cambridge. Mass. easy to survey. In such cases the household questionnaire needs to Ainsworth, Martha, and Jacques van der Gaag. 1988. Guidelines for be designed to handle hard-to-survey situations. Adapting the LSMYS Living Standards Questionnaires to Local 7. Two other examples in xvhich detailed data were not gath- Conditions. Living Standards Measurement Study Working ered for "minor" plots are the 1988-90 multiround farm survey in Paper 34. Washington, D.C.:World Bank. Senegal by the International Food Policy Research Institute and Bardhan, Pranab. 1973. "Size, Productivity, and Returns to Scale:An the Senegal Agricultural Research Institute (Kelly and others 1993) Analysis of Farm-Level Data in Indian Agriculture." Journal of and the 1981-85 ICRISAT survey in Burkina Faso (Matlon 1988). Political Economy 81 (6): 1370-86. 8- Since couintries wvith hard-to-survey areas tend to he more Barrett, Christopher. 1 996. "On Price Risk and the Inverse Farm rural, this proportion will be higher in those countries. Size-Productivity Relationship." Journal of Development 9. Survey designers should resist the temptation to have inter- Economnics 51 (2): 193-215. viewers make these conversions in the field and then write down . 1999. "The Effects of Real Exchange Rate Depreciation quantities in standard units in the questionnaire. Data analysts can ois Stochastic Producer Prices in Low-Incoiise Agriculture." do the conversions much more quickly and wvith far fewer errors. .4gricultural Economics 20 (3): 215-30. 10. In the Burkina Faso survey, Matlon asked the plot managers Barrett, Christopher, and Michael Carter. 1999. whether the plot was the same size as it had been the year before. "Microeconomically Coherent Agricultural Pohcy Reform to Most said yes. For each plot said to be the same, he measured the Africa." In JoAnn Paulson, ed., .4frican Economies in Transition, plot and compared the data with measurements taken the year Volume 2: The Reform Experience. London: Macmillan. before, often finding a 100 to 200 percent difference. Besley, Timothy. 1994. "How Do Market Failures Justify 11. The authors thank Kinnon Scott for this information. Interventions in Rural Credit Markets?" World Bank Research 12. This method has been used in Burkina Faso (see Matlon Observer 9 (1): 27-47. 1988), Niger (see Hopkins and Reardon 1989), Senegal (see Fall Binswanger, Hans, and P. Pingali. 1988. 'Technological Priorities and others 1989), and Rw.anda. for Farming in Sub-Saharan Africa." World Bank Research 13. In the Burkina Faso ICRISAT survey (1981-85), the house- Observer 3 (1): 81-98. hold head named the common field or fields that he or she man- Blank, Lorraine, and Margaret Grosh. 1999. "Building Social Policy aged at the household level, then named the individual plots man- Analysis Capacity in Conjunction with Household Surveys." aged by household members, referring to them by approximate World Bank Research Observer 14 (2): 209-27. 179 THOMAs REARDON AND PAUL GLEWVVE Bviringiro, E, and Thomas Reardon. 1996. "Farm Productivity in Griliches, Zvi. 1957. "Specification Bias in Estimates of Production Rwanda: Effects of Farm Size, Erosion, and Soil Conservation Functions." Jourfnal of Farm Economics 39 (1): 8-20. Investments." Agricultural Economics 15 (2): 127-36. Grosh, Margaret, and Paul Glevvwe. 1995. A Guide to Living Carter, Michael, and K. D. Wiebe. 1990. "Access to Capital and Its Standards Mleasurement Study Surveys and Their Data Sets. Living Impact on Agrarian Structure and Productivity in Kenya." Standards Measurement Scudy Working Paper 120. AmericanJournal ofAgricultural Economics 72 (5): 1146-50. Washington, D.C.:World Bank. Clav, D., F Byiringiro, J. Kangasniemi, Thomas Reardon, B. Gudger, Michael. 1990. "Crop Insurance: Failure of the Public Sector Sibomana, L. Uwamariya. and D. Tardif-Douglin. 1995. and the Rise of the Private Sector." In D. Holden, P Hazell, and Promoting Food Security in Ru'anda Through Sustainable Agricultural A. Pritchard, eds., Risk in Agriculture: Proceedings of thle Tenth Productivity: Meeting the Challeniges of Population Pressure, land Agriculture Sector Synrposunm. Washington, D.C.: World Bank. Degradation, and Poverty. Michigan State University Jolliffe, Dean. 1995. "Reviewv of the Agricultural Activities Module International Development Paper 17. East Lansing, Mich. from the Living Standards Measurement Study (LSMS) Commander, Simon, ed. 1989. Structural Adjustment and Agriculture. Survey" World Bank, Poverty and Human Resources Division, London: Overseas Development Institute. Policy Research Department,Washington, D.C. Deaton, Angus. 1989. "Rice Prices and Income Distribution in Hopkins, J., and Thomas Reardon. 1989. 'IFPRI Survey Thailand: A Non-parametric Ar.alysis." Economic Journal 99 Methodology: Agricultural Price Policy Reform Impacts and (395): 1-37. Food Aid Targeting in Niger." Project Document 2. DembeLe, N. N., and K. Savadogo. 1996. "The Need to Link Soil International Food Policy Research Institute, Washington, Fertility Management to Input and Output Market D.C. Development: Key Issues." In S. Debrah and W Koster, eds., Kelly, V, Thomas Reardon, A. A. Fall, B. Diagana, L. McNeilly Linking Soil Fertility Management teAgricultural Iniput and Output 1993. "Final Report of IFPRI/ISRA Project on Market Development. Lome: IFDC-Africa. Consumption and Supply Impacts of Agricultural Price Deolalikar, Anil. 1981. "The Inverse Relationship Between Policies in Senegal." International Food Policy Research Productivity and Farm Size: A Test Using Regional Data from Institute and Institut Senegalais de Recherches Agricoles, India." American Journal ofAgricultural Economics 63 (2): 275-79. Washington, D.C. Donovan,WG. 1996. Agriculture and Econtomic Reform in Suib-Sahlaran Krueger, Anne, Matnrice Schiff, and Alberto Valdes, eds. 1992. Thte A4frica. Working Paper 18. World Bank, Africa Technical Political Economic ofA gricultural Pricinig Policy Baltimore, Md.: Department, Environmentally Sustainable Development Johns Hopkins University Press. Division, Washington, D.C. Matlon, Peter. 1988. 7he ICRISAT Burkeina Faso Farm-Level Studies: Duncan, A., and J. Howvell, eds. 1992. Structural Adjustmfient and the Survey Mlethods anid Data Files. International Crops Research zlfrican? Farmer. London: Overseas Development Institute. Institute for the Semi-Arid Tropics, Andhra Pradesh. Dvorak. K.A. 1988. Indigenous Soil Clcssffication in Serni-Arid Tropical Migot-Adholla, Shem, Peter Hazell, and F Place. 1990. "Land India. Economics Group Progress Report 84. Andhra Pradesh: Rights and AgricLdtural Productivity in Gharia, Keriya anid International Crops Research Institute for the Semi-AridTropics. Rwanda: A Synthesis of Findings." World Bank, Agriculture 1993. "Characterizing System Dynamics for Soil and Rural Development Department,Washington, D.C. Mcnagement Research in the Humid Tropics: Studies in Morduch, Jonathan. 1999. "The Microfinance Promise."Journal of Southeastern Nigeria:" In K.A. Dvorak, ed., Social Science Economic Literature 37 (4): 1569-614. Research fir Agricultural Technology Development: Spatial and Oliver, Raylynn. 1997. AMcdel Living Standards Measurement Study Temnporal Dimnensions. Wallingford: CAB Int'l. Survey Questionnairefor the Couintries of the Forrmer Sovirt Union. Fall, A.A., V Kelly, and Thomas. Reardon. 1989. Household-Level Living Standards Measurement Study Working Paper 130. Survey Mlethods Used in the IFPRI/ISRA Study of Consumption Washington, D.C.: World Bank. and Supply Imipacts of Agricultural Price Policies in Senegal. Place, F, and Peter Hazel. 1993. "Productivity FifectsofIndigenous Document lI.Washington, D.C. and Dakar: International Food Land Tenure Systems in Sub-Saharan Africa." American Journal Policy Research Institute and Institut Senegalais de of Agricultural Economnics 75 (February): 10-19. Recherches Agricoles Project. Prudencio, YC. 1983. "A Village Study of Soil Fertility Ferreira, M.L. and C.C. Griffin. 1995."Tanzania Human Resource Management and Food Crop Production in Upper Volta- Development Survey: Final Report."World Bank, Population Technical and Economic Analysis." Ph.D. diss., University of and Human Resources, Eastern Africa Department, Arizona, Department of Agricultural and Resource Washington, D.C. Economics, Tucson, Ariz. 180 CHAPTER 19 AGRICULTURE Purcell, Dennis, and Jock Anderson. 1996. "Achievements and Sahn, David, ed. 1994. Adjusting to Policy Failure in African Economiies. Problems in Developing National Agricultural Research Ithica, N.Y: Cornell University Press. Systems?' Report 15828.World Bank, Operations Evaluation Savadogo, K., Thomas Reardon, and K. Pietola. 1995. Department,Washington, D.C. "Mechanization and Agricultural Supply Response in the Rahman, Aminur. 1999. "Micro-Credit Initiatives for Equitable and Sahel: A Farm-level Profit Function Analysis. "Journal of African Sustainable Development: Who Pays?" World Developrment 27 Economies 4 (3): 336-77. (1): 67-82. Schultz,T.W 1978. Distortions of.4gricultural Incentives. Bloomington, Rao,V, andT. Chotigeat. 1981."The Inverse Relationship Between Ind.: Indiana University Press. Size of Land andAgricultural Productivity." AmericanJournal of Singh, Inderjit, Lyn Squire, and John Strauss. 1986. Agricultural Agricultural Economics 63 (3): 571-74. Household Models: Extensions, Applications and Policy. Baltimore, Reardon,Thomas,T.Thiombiano, and Christopher Delgado. 1988. Md.:Johns Hopkins University Press. La Substitution des Cereales Locales par les Cereales Inport&es: La Strauss, John, and Duncan Thomas. 1995. "Human Resources: Consommation Alimentaire des Menages a Ouagadougou, Burkina Empirical Modeling of Household and Family Decisions." In Faso. Serie des Resultats de Recherche 002. Research Report J. Behrman and T. N. Srinivasan, eds., Handbook of Development for International Food Policy Research Institute-Centre Ecootmics Volume 3. Amsterdam: North-Holand. d'Etudes et Recherches Economiques et Sociales Project. Taylor, Lance. 1988. Varieties of Stabilization Experience: Towards Burkina Faso: CEDRES, Universite de Ouagadougou. Sensible Mlacroeconomics in the Third World. Oxford: Clarendon Reardon, Thomas., V Kelly, E. Crawford, B. Diagana, J. Dione, K. Press. Savadogo, and D. Boughton. 1997. "Promoting Sustainable In- , ed. 1993. The Rocky Road to Reform: Adjustment, Income tensification and Productivity Growth in Sahel Agriculture After Distribution, and Grow'th in the Developing World. Cambridge, Macroeconomic Policy Reform." Food Policy 22 (4): 317-28. Mass.: MIT Press. Rukuni, M. 1996. "A Framework for Crafting Demand-driven van Zyl, J., Hans Binswanger, and C. Thirtle. 1995. "The National Agricultural Research Institutes in Southern Africa." Relationship Between Farm Size and Efficiency in South Department of Agricultural Economics Staff Paper 96-76, African Agriculture." Policy Research Working Paper 1548. Michigan State University, East Lansing, Mich. World Bank, Agriculture and Natural Resources Department, Rusike, J., Thomlas Reardon, J. Howard, anid V Kelly. 1997. Waslhington, D.C, "Developing Cereal-based Demand for Fertilizer Among Small- World Bank. 1995. World Development Report. New York: Oxford holders in Southern Africa: Lessons Learned and Implications for University Press. Other African Regions." Pohcy Synthesis 30, Food Security II Yaron, Jacob. 1994. "What Makes Rural Finance Institutions Project, Michigan State University, East Lansing, Mich. Successful?" World Bank Research Observer 9 (1): 49-70. Sadoulet, Elizabeth, and Alain de Janvry. 1995. Quantitative Zeller, Manfred, and Manohar Sharma. 1998. "Rural Finance and Development Policy Analysis. Baltimore, Md.: Johns Hopkins Poverty Alleviation." IFPRI Policy Report. International Food University Press. Policy Research Institute,Washington, D.C. 181 q ^ ~Savings 2 O Anjini Kochar The savings module is an essential part of a multitopic household survey like the LSMS surveys.This module gathers data on the value of the household's stock of financial assets. Such data are necessary to accurately estimate household wealth, a variable that is required for research on almost all aspects of household behavior. And the savings module can collect information on both the types of financial assets held by households and recent transactions in such assets during the period of the survey- information that is directly relevant for analyzing household savings, particularly financial savings. Although policymakers in most countries are interest- in detail in other chapters of this volume. Therefore, in ed in a wide range of issues relating to household sav- order to design a survey that can aid research on sav- ing, the savings modules in most multipurpose house- ings, survey designers should read this chapter in con- hold surveys (including many LSMS surveys) typically junction with the other relevant chapters of this vol- collect information only on financial assets and liabil- ume, particularly Chapters 5, 17, and 21 on ities. However, this need not limit the research on sav- consumption, income, and credit. ings that can be done using the data, for two reasons. The first section of this chapter lists the major First, the data set generally includes information on policy issues associated with household savings. The the household's nonfinancial assets in other modules of second outlines the methodologies that can be used to the survey. This is appropriate as questions about, for analyze these issues and the specific data needed for example, the capital used in farm and nonfarm enter- such research-focusing particularly on how best to prises should be asked in the same part of the ques- measure savings.The third section discusses the design tionnaire that gathers information on how these assets of the savings module and the elements required in are used. Second, while analysts need data on assets to other modules to gather data for savings research, and address some policy issues, other issues can be presents two prototype savings modules: a standard addressed using data on income and consumption. version and a short version. The fourth section pro- Given that research on savings requires data from vides explanatory notes on the two versions of the other modules of the survey, it is important to keep module presented in the third section. such data needs in mind when designing these mod- ules. Many of the data problems inherent in undertak- Policy Issues in Household Savings ing research on savings arise from difficulties in col- lecting data on income, consumption, and transactions Governments are interested in the rate of savings (in in nonfinancial assets, difficulties which are discussed other words, the fraction of a household's income that 183 ANJINI KOCHAR the household saves) and the types of assets held by rate of growth of inputs and the productivity of inputs. households for several reasons. Perhaps the most The rate of growth of inputs reflects both the econo- important reason is the relationship between the level my's savings rate and the productivity of inputs. Thus and form of household savings and the rate of growth the savings rate and the productivity of capital deter- of a nation's per capita output. And because savings mine the rate at which output grows. It is this rela- choices also affect individual incomes, concern about tionship between output growth and savings that is the the level and distribution of household income pro- main reason policymakers are interested in household vides another motive for policy interest in saving. savings. Moreover, in recent years policy interest in savings has Historically, economists have emphasized the rela- been heightened by the recognition that people's wel- tionship between the rate of savings and income fare depends not only on their level of consumption in growth rather than the relationship between output any given period but also on the stability of their con- growth and the productivity of savings. Lewis (1954) sumption over time. Since saving provides a means to maintained that the "central problem in the theory of transfer income across periods and hence smooth con- economic development is to understand the process sumption over time, policy concern about the welfare by which a community which was previously saving of households requires knowledge of whether house- ... 4 or 5 percent of its national income or less converts holds have access to the assets that allow such itself into an economy where voluntary saving is run- intertemporal income transfers, the costs of these ning at about 12 to 15 percent of national income or assets, and differences in such costs across households. more." The central importance of savings in the The interest of policymakers in predicting the groxvth process was also emphasized in the works of level of household savings and in influencing the level Harrod (1939) and Domar (1946), as well as in Solow's and distribution of household (and hence national) (1956) neoclassical growth model. income implies that savings studies must examine not However, recent empirical evidence on the deter- only the level and form of savings but also the deter- minants of economic growth in a number of minants of savings. For example, if policymakers want economies has emphasized the importance of factor to predict how anticipated changes in national demo- productivity growth (World Bank 1991). Growth in graphic structure will affect national savings, they need factor productivities is believed to explain as much as data that enable a study of which demographic vari- 50 percent of the output growth in the United States ables influence savings and how. Similarly, if policy- between 1960 and 1985. While the contribution of makers want to use interest rates to bring about desired factor productivity growth to output growth has been changes in the level and form of household savings, much smaller in Asian, African, and Latin American they need data that enable an analysis of the sensitivity economies (particularly relative to the contribution of of savings and portfolio choices to interest rates. capital growth), as much as 50 percent of the differ- The rest of this section outlines the reasons poli- ence in growth rates across economies is attributable cymakers are interested in the rate, form and determi- to dffferences in productivity growth. Moreover, a slow- nants of savings. While a number of factors determine down in the productivity of inputs is responsible for savings, policymakers often find it easiest to influence much of the fall in growth rates experienced by most savings by intervening in the financial sector.Thus this of the world's economies after 1973. section ends with a discussion of policy issues con- While much of this evidence relates to the pro- cerning the financial sector. ductivity of all inputs, not just capital, data from a number of developing economies similarly suggest The Level and Form of Household Savings that there is more reason for concern about the pro- Policy interest in the level and form of household sav- ductivity of capital than about the rate of savings. ings exists because of the effects of savings on nation- Official estimates show that in recent years the popu- al income as well as their effects on both current and lations of poor countries (including many economies fiuture household incomes and consumption. of Sub-Saharan Africa) have routinely managed to save at a rate equal to approximately 20 percent of their EFFECTS OF SAVINGS ON NATIONAL OUTPUT GROWTH. country's GDP. However, the rate of return on these Growth of output in an economy is determined by the savings appears to be very low-about 8 percent a 184 CHAPTER 20 SAVINGS year-meaning that the high savings rate has con- from future income fluctuations. However, like the tributed little to economic growth (World Bank effect of saving on household income, the usefulness of 1989). saving for smoothing consumption depends on which It is widely believed that the low return on assets assets the household has chosen to invest in. This is in developing economies partly reflects the fragment- because assets may differ in terms of their liquidity. ed nature of capital markets and, hence, the inability of Thus, to assess whether households have the means to households to hold the assets that yield the highest smooth their consumption over time, policymakers rates of return.This explains policy interest in financial need to know not just the level of a household's sav- intermediation-in the spread of financial institutions ings but also what kinds of assets it owns. and the willingness of households to entrust these institutions with their savings. Policy issues relevant to The Determinants of Savings and of Portfolio Choices the spread of financial intermediation will be discussed Several theories explain the determinants of house- in greater detail later in this section. hold savings and portfolio choices. Because these the- ories are predicated on different motives for house- EFFECTS ON INDIVIDUAL INCOMES. While the aggregate holds to save, they sometimes yield contrary savings level and the productivity of savings affect the predictions for how any given set of variables, includ- growth rate of national output, the level and especial- ing policy instruments, will affect household and ly the forms in which households save also affect national savings. This fact underscores the importance household incomes, particularly in countries where of research on the determinants of savings and portfo- agricultural or nonfarm enterprises constitute a major lio choices. source of household income. (This is the case in most developing economies; see Chapter 18 on the house- DETERMINANTS OF SAVINGS. Much of the current hold enterprise module and Chapter 19 on the agri- research on savings stems from the "life-cycle model" culture module.) Income from agricultural or non- (Modigliani and Brumberg 1954). This model assumes farm enterprises reflects, in part, the household's that people's tastes or preferences are relatively stable ownership of physical capital or "productive" assets throughout their life cycles, while their income is sta- such as the machinery and tools used in such enter- ble only during their working years, falling to zero at prises. Investment in such assets represents an act of retirement. Correspondingly, an individual's savings saving, thereby linking savings and portfolio choices to will peak in his or her prime earning years and fall as household income. the savings are drawn down to finance consumption Because of this link between a household's income during retirement years. Since this model assumes that and its stocks of productive assets, developing country the young save while the old consume in excess of governments have for a long tinme encouraged house- their income, it predicts that any changes in the holds, particularly poor households, to invest in such incomes or numbers of young people relative to old assets. Indeed, projects aimed at encouraging households people will affect national savings.Thus it predicts that to invest in productive assets have been at the heart of economies in which there are more prime-age earners many poverty reduction programs, such as the Integrated than elderly people will show positive aggregate sav- Rural Development Programs implemented in the ings, and that any increase in the relative size of the eld- 1970s and 1980s in a number of Asian economies. erly population will reduce national savings. It also pre- dicts that higher productivity growth-which increases THE IMPORTANCE OF SAVINGS AND PORTFOLIO the incomes of the young-will increase savings. CHOICES FOR SMOOTHING CONSUMPTION. Recent An alternative model of savings, the "precaution- research has shown that the welfare of households ary savings model," theorizes that even though life depends not only on the level of their consumption in cycle motives may exist, the primary motive for saving any given period but also on their ability to protect is not to guard against reductions in income in later this consumption from income fluctuations-in other life, but rather to protect consumption from annual or words, to "smooth" their consumption across periods seasonal uncertainty in incomes (Deaton 1989). This within a year or over several years. Saving provides one model implies that assets must constantly be run down means by which they can protect their consumption to protect consumption from income fluctuations. 185 ANJINI KOCHAR Thus, according to this model. household savings will members, particularly members who work, interven- as often be negative as positive and will average zero tions that improve health and sanitary conditions may over the years. If precautionary savings are important, increase household investment in productive assets. national savings are likely to be low in economies Assessing the link between health inputs and savings wvhere a significant proportion of the population is choices requires data from the health module of the employed in occupations with fluctuating short-run multitopic household survey (see Chapter 8). incomes, such as agriculture. Growth in occupations with more stable incomes may cause precautionary Financial Intermediation motives for savings to be replaced by life cycle con- In this chapter, the term "financial sector" refers to cerns-increasing national savings. The precautionary "formal" financial institutions-institutions either reg- savings model also implies that policy interventions ulated or owned by the government. Such institutions affecting the volatility or uncertainty of incomes and include banks, life insurance companies, and housing expenditures (for example, programs aimed at stabiliz- finance institutions, as well as any businesses, post ing prices or output or programs that provide unem- offices, or other government agencies that accept ployment or disability insurance) will significantly household savings (either in the form of shares or increase aggregate savings. deposits) but do not offer loans. "Informal" institu- tions, institutions not regulated by the government, DETERMINANTS OF PORTFOLIO CHOICE. In order to include moneylenders as well as relatives and friends of encourage investments in specific assets, policymakers household members who provide loans to households. need information on what determines the choices that Informal institutions also include group savings and households make about their savings portfolios. For credit associations, such as the susu men in Ghana and example, governments need to know what factors Gambia, the tontines in Senegal, and the hui in China. underlie the demand for financial assets before they Some of these associations only maintain deposits for can implement policy intended to increase deposits in their members, while others also provide their mem- financial institutions. It could he that a low demand for bers with loans. financial assets primarily reflects difficulties in with- The formal financial sector in many developing drawing money from accounts in financial institutions. economies is small, both in absolute terms and relative If so, policies that reduce such difficulties-say, by to the informal sector. (Chapter 21 on credit provides introducing passbook savings schemcs-may have a far statistics on the relative sizes of the formal and infor- greater impact on the number and amount of deposit mal sectors in a number of countries.) One common accounts than policies that increase interest rates measure of the financial depth of an economy is the offered by the accounts. percentage of the country's gross domestic product Understanding the determinants of total savings held either in currency or in bank deposits (including also helps policymakers understand households' port- currency, demand, time, and savings deposits). In 1993 folio choices. One implication of the precautionary this percentage was less than 15 percent in some savings model is that households (particularly ones that African economies, including Sierra Leone, Uganda, lack access to sources of credit for consumption as Guinea Bissau, and Ghana. In South Asian economies opposed to production) underinvest in illiquid it ranged from 33 percent in Bangladesh to 44 percent assets-such as productive capital-because they need in India (World Bank 1995). to maintain their stocks of currency, food grains, and There are several good reasons why the govern- other liquid assets in order to meet consumption ments of developing economies are keen to develop needs (Morduch 1994). In this case lack of investment the formal financial sector. The primary reason is to in productive assets primarily reflects a household's make the savings of households available to investors uncertainty about its income; thus any policy inter- so as to enable households to realize the welfare gains vention that reduces this uncertainty would increase from "trading" funds with other households over time the household's willingness to invest in productive (see Besley 1995 and Chapter 21 ofthis book).While assets. This in turn requires policymakers to under- the informal sector can serve this purpose, its geo- stand the sources of income variability. If such vari- graphic scope is relatively limited because informal ability partly results from the ill health of household sector transactions primarily occur between borrowers 186 CHAPTER 20 SAVINGS and lenders who are well acquainted with each other. In addition to being concerned about the devel- Economies with relatively large informal credit sectors opment and profitability of the financial sector, poli- thus tend to have fragmented capital markets, in which cymakers are interested in assessing whether formal investors only have access to the funds of savers they financial institutions directly benefit households either are linked to through informal institutions. In practice by providing opportunities to invest in financial assets this has meant that costs of credit and, equivalently, or by providing credit. Later sections of this chapter returns to savings, may vary tremendously across address the data requirements and research method- households. Indeed, equity concerns about the access ologies for evaluating the impact of formal financial of all households, particularly poor ones, to low-cost institutions on savings. Chapter 21 discusses how the sources of credit underlie much of government inter- availability of formal credit can benefit households. est in developing the formal financial sector, even though available evidence on formal financial institu- Data Requirements and Research tions in a number of developing countries suggests Methodologies that formal institutions do not always perform better than informal ones in this regard. This section discusses the data required to study the Policymakers in most developing countries are level, forms, and determinants of savings and other interested in knowing the level and spread of the for- issues relating to financial intermediation.This section mal financial sector across regions and among house- also discusses some of the methodologies commonly holds within a given region, both in absolute terms used in empirical savings research. and in relation to the informal sector. Concerns about The primary requirement for any research on sav- the level of financial intermediation are closely linked ings, whether it be motivated by interest in the level to concerns about the profitability of formal financial and forms of savings or in the determinants of savings, institutions, which depends on both the volume of is some measure of savings. This section starts by dis- these institutions' business and the costs of doing busi- cussing two alternative ways to measure savings: by ness relative to the returns. Net costs are determined subtracting consumption from household income and by such factors as the interest rate on deposits, the by observing changes in stocks of individual assets. interest rate at which loans are made, the ability of Since savings can be measured using income and institutions to collect on their loans, and other costs consumption data alone, is it necessary to include a of servicing deposit accounts and loans, including savings module in a multitopic household survey? In administrative and transactions costs. Many of these most cases, yes. Policymakers are interested not only factors are influenced by bank procedures and poli- in the amount of savings but also in household port- cies, which affect both borrowers' repayment incen- folio composition and issues related to the financial tives and incentives to maintain deposits. The factors sector-both of which require data on assets. Data affecting net costs are also influenced by the govern- on financial assets are best collected in the savings ment's monetary and fiscal policies, which directly or module. indirectly determine inflation and interest rates, and In this section the discussion on different ways to by education and infrastructural development poli- measure savings is followed by a discussion on types of cies, which affect transaction costs and the costs of assets for which data should be collected. Next are dis- training bank personnel (Gurgand, Pederson, and cussions of the data needed to inform financial sector Yaron 1994). Other critical inputs into the profitabil- research and of the benefits for such research of using ity of financial institutions include the willingness of panel data relative to using a single cross-section of households to maintain bank deposits and repay loans. data. The section concludes by considering whether it While household surveys may not provide all the is necessary to disaggregate the household data in the information needed to comprehensively analyze the savings module to the individual level. effects of government and bank policies on financial institution profitability, they do provide the means to Measuring Household Savings analyze the importance of several factors that may Empirical studies have measured savings by subtracting explain households' willingness to use formal financial consumption from household income and also by institutions. observing changes in household assets. Several studies 187 ANJINI KOCHAR have presented results from using both measures whereas the reference period for consumption data is (Paxson 1992;Wolpin 1982). generally a month or a week. The necessary extrapo- This subsection first discusses each method of lation of annual consumption from monthly data may measuring savings. Then the two methods are com- yield misleading estimates. This is because even if pared using data from the LSMS surveys in Pakistan households smooth their consumption against fluctu- and Ghana, to assess their relative merits and thus ations in income, consumption may vary from month determine what data are needed to measure savings. to month as a result of, for example, price fluctuations. Savings may be overestimated for households that are SAVINGS AS INCOME MINUS CONSUMPTION. Most interviewed during months in which their consump- empirical studies on household savings measure sav- tion was relatively low on account of such price fluc- ings as the difference between a household's income tuations.Thus it is crucial to follow the recommenda- and consumption (Deaton 1992a, 1992b).This meas- tion of Chapter 5 that data on consumption ure is subject to all the problems that arise when meas- expenditures should be collected by asking about a uring income and consumption, including the diffi- "usual month." culty of obtaining accurate measures of the income of A final problem relates to the separation of labor the self-employed, measures of the consumption of income and asset income. Models of savings are usual- home-produced goods, and measures of the value of ly used to test the relationship between consumption inputs that are only imperfectly marketable. Chapters (or savings) and exogenous changes in income. Such 17 and 5 of this book detail the problems in obtaining tests require that labor inconme be separated fronm asset accurate measures of income and consumption in income, since the model is testing a theory about asset household surveys. Survey designers should read these income. For households with a family farm or with a chapters carefully to understand the biases inherent in nonfarm household enterprise, it is generally not pos- measuring savings as the difference between income sible to separate labor income from asset income, since and consumption and how, to some extent, these bias- the profits realized from the business represent a return es can be overcome by improving available measures. both to the family's labor and to any fixed assets used When using income and consumption data to to produce this income. Thus, even when households measure savings, it is also necessary to be aware of do report payments to members engaged in family some issues that are not as important when the intent enterprises, it is not possible to ascertain whether this is simply to measure income or consumption. For is a return to labor or to capital. Researchers have example, when calculating income for the purpose of commonly dealt with this problem by assuming that measuring savings it is necessary to allow for the farm profits primarily reflect a return to the family's depreciation of capital inputs and the appreciation of labor. Measurement error in income and potential various stocks. In addition, the consumption measure biases caused by endogeneity of labor choices are then used should include only the value of the services pro- addressed using instrumental variable techniques (see vided by the household's current stock of consumer the statistical appendix in Chapter 26). Nevertheless, durables, while the actual investment in those durables inaccuracies remain. The assumption that farm profits should be included in the savings measure.This neces- primarily represent a return to family labor is more sitates dividing consumption goods into durables and likely to be true in countries such as Cote d'Ivoire, nondurables. While such a division is clear-cut for where labor is scarce relative to land (Deaton 1992b), goods like vehicles and heavy appliances (both than in land-scarce countries such as the South Asian durables), it is less obvious for clothing, kitchen uten- economies. sils, and even jewelry. After it has been (arbitrarily) decided which goods are durables and which are non- SAVINGS FROm DATA ON ASSET TRANSACTIONS. If data durables, a value must be imputed to the services pro- are available on a household's asset transactions or on vided by each durable good. the stock of assets at the beginning and end of a refer- A further problem arises if different reference ence period, savings can be measured as the net value periods are used for collection of income and con- of transactions in all assets or as the change in a house- sumption data, as is commonly the case.The reference hold's stock of assets over the reference period. period for income data may be a year or a season, However, it is notoriously difficult to collect informa- 188 CHAPTER 20 SAVINGS tion on all the various assets in which households that health expenses reflect consumption more than invest their savings. Researchers tend to be skeptical investment, particularly in poor agrarian economies about measuring savings based either on households' where out-of-pocket expenses for preventive treat- asset transactions or on stocks of assets at different ment are often insignificant. Nevertheless, health points in time, because data on certain asset transac- expenditures do have effects that extend beyond the tions may be missing or inaccurate. current period, and this certainly merits including Particularly difficult to collect are data on transac- health expenses in measures of savings. tions in assets such as foodgrains, fodder, building A final cautionary note relates to the means by materials, seeds, and other inputs. Possibly because of which households acquire or dispose of a particular the difficulties involved in estimating quantities and asset.The value of asset transactions should not include value of such stocks, a number of highly reputable data the (imputed) value of gifts received by the household sets, including that collected by the International Crop or given by the household. If the gift of a consumer Research Institute of the Semi-Arid Tropics durable is recorded as the purchase of an asset, the (ICRISAT) in India, contain no measures of such measure of savings derived from the asset data will not stocks. If survey designers do wish to collect these equal that calculated as the difference between income data, it is probably best to collect them at the point in and consumption. One way to ensure that the value of the agriculture module when the interviewer is asking such gifts is not included in data on asset transactions questions about the household's agricultural output is to ask explicit questions about whether any assets and its disposal. To ensure the reliability of these data were acquired as gifts (in the savings module or in it is probably best to collect them by crop. other modules recording data on asset transactions). It is also difficult to obtain accurate information This procedure was used in the Pakistan LSMS. on a household's credit transactions and stock of cur- rency.1 The direction of the bias in credit transactions COMPARING SAVINGS MEASURES TO ASSESS THE may also be hard to predict. Deaton (1992a) reported RELIAILuY OF THE DATA. Data from the LSMS sur- that there were many more creditor than debtor veys in Pakistan and Ghana illustrate the difficulties households in C6te d'Ivoire and suggested that this inherent in each of the two ways of measuring savings. may be because respondents were more willing to A lack of data on important assets is a problem when report their assets than their liabilities. The opposite measuring savings using data on asset transactions. And situation appears to prevail in Pakistan, where 1,667 the difference between income and consumption does households reported receiving loans from informal not always provide a reasonable estimate of savings, sources, primarily relatives and friends, and only 252 often because of weaknesses in the design of the households reported making loans. An apparent reluc- income and consumption modules. tance to report loans made to others has also been For the purpose ofthis exercise, data on income and noted in other South Asian economies, such as India consumption are taken from the aggregate files of the (Kochar 1997). Pakistan LSMS data set. In addition to the usual prob- If savings are to be measured using data on asset lems of measurement error, these data are subject to all transactions, a survey designer has to make judgment the assumptions that were made in arriving at these calls about how to treat certain expenditures, primari- aggregates; no additional cleaning of the data has been ly expenditures on education and health-related items. done. Data on asset transactions were obtained from sev- Typically, consumption and savings research has treat- eral different modules of the survey, primarily the ed such expenditures as items of current consumption. income modules2 and the savings module.3To the extent However, research on the economics of education has possible, the value of gifts that households received was emphasized that such expenditures represent invest- excluded from measures of income and consumption ments in future income. As Gersovitz (1988) argues, (because, as noted above, such gifts do not represent an even if education is desired only as a consumption act of saving by the household); the Pakistan LSMS good, the benefits of education are spread out over a specifically asked respondents whether any reported lifetime, so these expenditures should be regarded as a acquisition or sale of assets was in the form of gifts. consumer durable. There is less agreement on how One difficulty in estimating savings in the Pakistan health expenses should be treated. A case can be made data arises from insufficient data on transactions in jew- 189 ANJINI KOCHAR elry. While data on jewelry purchases were recorded in possibility that measuring savings as the difference the inventory of durables module, this module did not between income and consumption is just as error- provide information on the sale ofjewelry. As a result, ridden as measuring savings using asset data. Survey in this exercise jewelry purchases were considered con- experts generally believe that existing ways of measur- suniption expenditures rather than savings.4 ing income tend to underestimlate the inconrc of thie Table 20.1 shows substantial differences in the self-employed. Thus, if self-employment is more wide- Pakistan LSMS between the estimates of savings spread in rural areas, rural incomes (and hence savings) derived from asset data and the estimates derived from may be underreported relative to urban inconmes. the difference between income and consumption data. Table 20.1 also provides data across income class- Using asset transactions to measure savings yielded a es, which are created by dividing households by their lower value than the value obtained by computing median level of income. It can be seen that in the savings as the difference between income and con- Pakistan survey the difference between the measure of sumption. However, all of the assets held by house- savings derived from asset data and the measure of sav- holds may not have been enumerated in the data. In ings derived as the difference between income and order to assess this possibility and to identify which consumption was particularly evident among the rich- asset transactions are most likely to have been misre- est households. Taking high-income rural and urban ported, Table 20.2 provides details on the number of households together, there is a difference of Rs. 41,130 households reporting savings and the mean level of between income and consumption. This is probably savings by type of asset.These data show a low level of due both to the undernumeration of assets (causing savings derived from loan transactions-possibly error in asset-derived savings data) and to consump- reflecting difficulties collecting data on loan transac- tion by the wealthy (causing error in measures of sav- tions. On average, households in the Pakistan survey ings as income minus consumption). sample reported net borrowings of Rs. 19,805 (Table Official statistics from the Government of 20.2), a number that is particularly suspect given the Pakistan report a domestic savings rate of 1 1 .8 percent relatively small size of the formial sector in Pakistan. in both 1990-91 and 1991-92 (National Bank of The low level of savings measured using data on trans- Pakistan 1992).5 While use of the transactions data actions may also reflect the lack of data on stocks of reveals a negative household savings rate, measuring foodgrains and fodder. savings as the difference between income and con- Although stocks of foodgrains are more likely to sumption yields a household savings rate of 16.5 per- contribute significantly to savings in rural areas than in cent. Another source of data on household savings in urban areas, the absolute value of the difference Pakistan, the Household Income and Expenditure between income and consumption is greater, on aver- Survey, showed a household savings rate of 4.6 percent age, in urban areas than in rural areas. This raises the for the year 1987-88. Table 20.1 Alternative Measures of Mean Savings, Pakistan LSMS Survey (rupees) Savings from asset dataa Income minus consumption Mean income Mean -onsumption0 (I) (2) (3) (4) All households -3,901 8,652 52,246 43,783 poorest 50% -4,621 -21,266 13,915 36,724 richest 50% -3,088 38,463 90,577 51,743 ......................... ................................................................................................................................................................................................ Urban households -1,162 19,266 65,014 45,959 poorest 50% -3,244 -15,304 20,365 37,972 richest 50% -5,200 53,962 109,783 55.036 r.ural houseoids -3.642 - 913 39,534 4;,6- i5 poorest 50% -5,099 -24,687 9,738 35,243 chest 50% -1,998 20,778 69,463 48,791 Note: Income and consumptlor are from aggregate World Bank files. Households are divided into rich and poor by treir median level of ncome. a. Savings n this columr are calculated from asset data from different modules, as detailed nTab e 20.2. b. Consumpt on is total consumption m nus the follow ng tems: education (VEXP5240), durab es (VEXP5300), ewelry (VEXP33 2), other household effeots VEX14240), ktchen equipment (VEXP4320), fumiture and fi-tings (VEXP4330), and other durable housing expenses (VEXP4390). lewelry purchases (PV33 7) are inc uced in consumption and excluded from savings. Source Authors ca culations based on Pakisian LSMS survey. 190 CHAPTER 20 SAVINGS Table 20.2 Household Savings by AssetType, Pakistan LSMS Survey All households _ Urban Rural Mean savings Mean savings Mean savings File Number in rupees Number in rupees Number in rupees Asset number reporting (standard deviation) reporting (standard deviation) reporting (standard deviation) Agricultural land fO9a4 39 -9.626 4 16,400 35 -12,600 (77,169) (42,940) (80,027) ~~~~~~~~~~~~~~~~~~.......... " '".... ... , ,,,,,........,, ,,,,,,,,, ......... , ,,,,,,,,,,,,,,,(716,,, ...............I........ , ," ,-,"I"Ill,.94)..... " 'l............. .80 Agricultural equipment fO9dm 36 50,897 4 130,250 32 40,978 (69,900) (93,682) (61,262) .......................................................... ................ ,.......................... ................ ............. .............................................. ..... ........... 1......... . .... Livestock fO9f2 1,063 -322 200 1.683 863 -787 (8,885) (10,552) (8,390) Nonfarm assets flOcl 362 8,130 216 6,817 146 10,072 (63,442) (42,174) (85,889) Business improvement f Oc2 75 9,436 5 i 11,717 24 4,587 (36,585) (44,019) (7,615) Cash fl5di 4,559 -810 2,212 -1,349 2,347 -302 (38,334) (54,787) (S,065) ................. ................................ .................... ........................... ...................... ............................ ... .........................'........................... 8 .. ..... Residential land fl 5dm 4,030 998 1,938 2,012 2,092 58 (37,220) (51,862) (13,260) Investment land fl.5dm 565 0.0236 228 0.0008 337 0.039 (0.5219) (0.609 1) (0.4539) Shares fl5d3 261 1,787 217 598 44 7,651 (35,101) (30,054) (53,578) Deposits fi 5d4 1,159 2,856 703 3,620 456 1,676 (4 1,257) (46,59 1) (3 1,3 1 6) BSisi/savings committee fl 5d5 867 -315 670 -704 197 1,004 (12,490) (12,265) (13,174) Durable goods Aggregate 4,799 1,047 2,400 1,508 2,399 585 files (8,233) (10,870) (4,122) Education Aggregate 4,799 2909 2,400 4,527 2,399 1,290 fles ( 12,663) (17,425) (3,443) H-ome improvement Aggregate 4,799 942 2,400 1,415 2,399 469 files (10,355) (13,754) (4,984) Credit fl 5b3 2,542 -19,805 1,262 -26,711 1,280 -12,997 f 15c2 (132,689) (184,185) (37,927) Source: Author's calculations based on Pakistan LSMS survey In contrast to the data from Pakistan, the data 1987-88. Not surprisingly, calculating household sav- from the Ghana LSMS yielded higher mean savings by ings at the median (Table 20.3) considerably reduced using data on asset transactions than by subtracting the discrepancy between the two measures. However, consumption from income (Table 20.3). The data on the difference was still large. Data on asset transactions asset transactions yielded positive average savings in yielded a median level of savings close to zero in both both years of the survey (3,510 cedis in 1987-88 and years of the survey, while the income minus consump- 10,168 cedis in 1988-89), whereas the income and tion measure yielded median savings of -89,237 cedis consumption data yielded average savings far less than in 1987-88 and -100,191 cedis in 1988-89. zero (-100,490 cedis in 1987-88 and -114,851 cedis Both measures of savings are suspect. Measuring in 1988-89).6 savings as the change in the stock of households' assets The considerable discrepancy between these two was bound to provide misleading estimates in this case measures of savings in Ghana partly reflects the fact because the Ghana LSMS data set included no data that the mean net asset transactions of the sample on transactions in financial assets by the household households was skewed by exceptionally high pur- although it did include data on the value of the chases of assets by a small number of households. A household's stock of these assets. The bias introduced mere 1 percent of the survey households accounted by this lack of data is probably substantial, given that for 44 percent of the total recorded purchases of con- financial assets comprised a significant percentage of sumer durables by all of the sample households in household wealth. In 1987-88, 97 percent of sample 191 ANJINI KOCHAR Table 20.3 Comparison of Savings Measures in the Ghana LSMS Survey 1987-88 1988 89 Mean savings in cedis Mean savings in cedis (standard deviation) Median savings in ced s (standard deviation) Median savings in cedis Household income' 251,576.83 170,671.4 254,71 1.12 180,833.3 (293,219.69) (256, 143.66) Household consumption 352,066.84 292,269.6 369,561.87 309,802.4 (260,702,49) (270,024.36) ................................................................................................................................................................................................................................... Savings as change in assets0 3,509.69 -600.00 10,167.72 600.0 (99,627.49) (82,846.83) ..... .................................................................................................................................................................................................................... Savings as income 100,490.01 -89,237. -I 14,850.74 -100,191.0 minus consumption0 (260,108.76) (251,159.24) Savings as income -83,822.38 -74,622.8 92,325.03 -92,103.8 minus consumption' (284.676.95) (243,969.92) a. Co ombe, McKay, and Round; 993. b. Calculated as the va ue of sva able data on net transactions in land and build ngs vestock, farm equipment, business assets, consumer durables, and cred t transac- t ons Deta Is of these transact ons are in Tab e 20.5. c. Ca culated by the World Bank. Source Author's calculations based on Ghana LSMS survey households reported owning financial assets, the mean these transactions appear to be primarily short-term, value of such assets being 15,540.80 cedis. Much of with as much as 94 percent of outstanding debt con- this wealth xvas probably held as cash. Only a minor- tracted within the reference year. While this may ity of households reported holding financial assets in reflect a relatively low demand for long-term credit, it such forms as bank deposits, deposits in other finan- also undoubtedly reflects the limited availability of cial institutions, or stocks and bonds.7 In contrast, 84 credit for financing long-term capital investment and percent of households reported owning "other" forms investments in consumer durables. Households also of financial savings, a category that includes stocks of have the option of selling some of their other assets to cash. finance purchase of consumer durables. However, this If households finance their investments in other is unlikely to be a viable option for many households assets primarily through cash transactions, a lack of as the data indicate that most of these other assets con- data on such transactions means that any measure of sist of farm and nonfarm capital, which are relatively savings based on data on transactions is likely to be illiquid. Table 20.4 reveals that, with the exception of overestimated. Evidence on types of assets purchased livestock, households report relatively few sales of their and sold by sample households supports this conclu- other assets. sion. The disaggregated data on asset transactions in It is likely that households pay a significant share Table 20.4 reveal that the positive savings estimates of the cost of purchasing consumer durables and derived from the data on asset transactions reflect a net business assets from their accumulated stocks of cash. purchase of consumer durables and, to a lesser extent, If so, an absence of data on cash transactions in a of business assets. The disaggregated data on the con- given data set may result in an inflated estimate of the sumer durables purchased by households reveal that increase in net savings when instead it should show a the high mean level of such purchases primarily shift in the composition of households' asset portfo- reflects the purchase of high-cost indivisible items lios. It is not surprising that without data on cash such as cars and television sets.8 It is unlikely that transactions, measures of savings based on asset trans- households finance such large purchases from their actions show positive mean savings for the Ghanaian current income alone. sample. One possibility open to households is to finance Other sources of error in the transactions data for the purchase of consumer durables through loans. Data Ghana are a lack of information on the household's from the Ghana LSMS survey on credit transactions stock of foodgrains and fodder and on the loans made (Table 20.4) reveal a fairly active credit market; 39 per- by the household during the reference period. The cent of households reported borrowing and 42 per- data set does, however, provide information on the cent reported loaning to others in 1987-88. However, amount borrowed by each household during the ref- 192 CHAPrER 20 SAVINGS Table 20.4 AssetTransactions, Ghana LSMS Survey, 1987-88 and 1988-89 1987-88 1988-89 Purchases Sales Purchases Sales Land and buildingsa Mean (cedis) 138.01 196.29 177.33 444.67 Standard deviation (2,040.64) (2,508.14) (3,879.92) (1 1,268.90) ................................................................................................................................................................................................................................... Frequency (42) (25) (44) (21) ......... ......... ......... ......... ......... ......... ......... ......... ......... ...................................................... .................................... ......... ......... ......... Livestockb Mean (cedis) 875.57 3,387.89 1,393.78 3,867.01 Standard deviation (7,734.67) ( 15,818.36) (9,505.17) (14,908.44) Frequency (398) (2)(496) (774) ............................................................................................................... ............................................................................................................... Farm equipmentc Mean (cedis) 452.60 288.25 330.94 43.27 ................................................................................................................................................................................................................................... Standard deviation (1I2,261.11)i ( 10,9,,47.,61i) (6.......................,126.50) ,,, ,,(1,303.27,), Frequency (32) (3) (31) (3) ................................... ................................................................I......................... .............I............................... .~).......................................... 3 Business assetsd Mean (cedis) 4,270.42 216.97 3,420.06 464.98 Standard deviation (62,121.81) (4,134.84) (35,763.10) (16.752.39) .............c"y......................................................")...................................................................... ............................................................ Fr,uenc,y ........(...................57.1).,,5......I..,,, (.5) (657) (12) Durablese Mean (cedis) 9,093.38 1,651.14 13,688.04 1,501.45 ....... r-d................... "' 'n............................ -,1-8........... ............................................................. ......................... ................................. . .... Standard deviation (85,1 18.29) (34,690.47) (73,441.02) (25,646.65) ................................................................................................................................................................................................................................... Frequency (365) (48) (589) (6 Credit transactions' Mean (cedis) 6,173.16 9,073.60 7,852.23 8,631.21 Standard deviation (24.099.16) (80,052.52) (31,352.02) (50,297.17) ..................................................... 9- .......................................................................3-)-1111,11,11111--"I............. ............................................................. Frequency (900) (843) (1 100) (996) a. Data on land purchases are from fi e 9a of the Ghana data set. Data on land sales are from files 9a and 1 4b. b. Data are from file 9f c. Data are from file 9k1 d. Data are from file I Od. e. Data on purchases of durables are from file I c and represent the value of durab es reported as being bought during 1987-88. Data on sales of durables are from file 1 4b and include income from sales of veh cles as well as from rental of other durables. f Loans made by the household are recorded under purchases; a household's borrowing or the debt that the househo d contracted during the reference year are recorded under sa es. Source: Author's calculations based on Ghana LSMS survey erence period as well as on each household's total out- to which consumption exceeds income and the corre- standing loans and debt. Because 94 percent of these spondingly large negative savings estimated at both the loans were contracted during the reference year, the mean and the median also call into question the accu- stock data on total outstanding loans payable to the racy of the consumption and income data.9 Chapter household were used as an approximation for the loans 17 on income suggests that the problem in the income made by the household during the reference year. minus consumption measure may result from an Using the stock variable would, however, overestimate undernumeration of income. Chapter 17 also suggests households' savings. several ways in which the collection of data on income Thus in the Ghanaian data set much of the dis- can be improved-in turn increasing the accuracy of crepancy between the two measures of savings may be savings estimates. It will probably be easier to put these explained by the absence of data on transactions in improvements into effect than to attempt to collect financial assets, on foodgrains and fodder, and on loans data on households' cash holdings, stocks of food- made during the reference year. However, the extent grains, or credit transactions. 193 ANJINI KOCHAR Collecting Data on Stocks of and Transactions in Specific ductive assets to estimate the profitability and riski- Assets ness of household portfolios. Even if household savings are measured as the differ- ence between income and consumption, data on Research on Financial Intermediation stocks of and transactions in specific assets can signifi- Because of the great importance of financial sector cantly enhance the value of the survey for research on development for overall economic development, poli- savings. Data on stocks of different types of household cymakers are perhaps most interested in financial assets are of interest to policymakers because they pro- assets.This subsection outlines methodologies and data vide information on the productivity of assets and, requirements for researching the policy issues con- hence, on the contribution of savings to both house- cerning financial institutions that were identified in hold and national income. Data on transactions in spe- the first section of this chapter. The relevant policy cific assets are useful because they provide insights into issues include the level and distribution of financial issues of specific concern to policymakers, such as the intermediation, the effects of government and bank determinants of savings. For example, Rosenzweig and policies on financial intermediation, and the impact of Wolpin (1993) used regressions of sales and purchases the development of the financial sector on house- of bullocks on measures of income variability to holds. explore whether households use productive assets to Financial institutions have two distinct, though smooth consumption. Udry (1995) used data on trans- related, objectives: to maintain deposits and make prof- actions in livestock and stocks of grains and other itable loans.Their success in achieving these objectives goods to assess the responsiveness of household savings determines the financial sector's profitability and to income shocks. hence its growth. Thus researchers need to evaluate a Data on stocks of assets are also necessary to esti- country's government and bank policies in terms of mate household wealth. Experience has shown that how these policies affect both of the functions of the accuracy of estimates of household wealth can be financial institutions. Similarly, research on the impact improved if households are asked about the value of of financial institutions on households should consid- different types of assets rather than being asked to pro- er not only how socioeconomic outcomes are affect- vide an estimate of their total wealth. Since estimates ed by access to credit but also their effect on house- of household wealth are required for almost all aspects hold savings. of socioeconomic research on households, this rein- This chapter focuses on general issues that arise in forces the importance of collecting data on assets, even the context of financial sector research, issues that if income and consumption data are available in the apply as well to research on factors determining the survey. willingness of households to hold financial assets as to Much of the research on savings that has utilized the credit functions of financial institutions. However, data on assets has been conducted at a fairly high evaluating the credit functions of financial institutions degree of aggregation, analyzing, for example, the also raises a number of specific data and methodolog- determinants of transactions in livestock, liquid assets ical issues including how best to collect interest rate (such as financial assets, stocks of grains and other information and what determines households' access goods, and currency), or "productive assets," which to bank funds. Since these issues require data from the are defined as all fixed assets used in the production credit module, they are addressed in Chapter 21 on of either farm or nonfarm income (Udry 1995; credit. Therefore, this chapter's discussion of the data Kochar 1998; Alderman 1996). However, as discussed requirements and methodologies for research on in Chapters 18 and 19 on the household enterprise financial institutions should be read in conjunction and the agriculture modules, collecting accurate with the discussion of these issues in Chapter 21. information on any broad category of assets general- ly requires collecting data on narrowly defined THE DISTRIBUTION OF FINANCIAL INSTITUTIONS. groups of assets within the broad category. Having Researchers examining the spread and distribution of data at this level of detail may also facilitate savings financial institutions generally use the disaggregated research. For example, Rosenzweig and Binswanger data on stocks of financial assets collected in the sav- (1993) used details of stocks of different types of pro- ings module of household surveys.This is a major jus- 194 CHAPrER 20 SAVINGS tification for including a savings module in household interest rates on household savings have used time- surveys. On the basis of disaggregated data on stocks of series data, either for individual countries or for a financial assets, it is possible to discover which house- number of countries pooled together (VanWijnbergen holds are most likely to have accounts in financial 1982; Giovannini 1983; and Fry 1988). institutions and, hence, whether households differ by, This lack of variability in key variables also limits for example, socioeconomic status in their access to the usefulness of a single cross-section of data for ana- such institutions (Kochar 1997). lyzing the effects of various bank policies-such as The extent to which researchers can conduct such specific lending procedures and organizational innova- an analysis using data from a multitopic household tions including group lending-on various aspects of survey will vary from country to country depending financial intermediation.10 Most of the research in this on the level of development of the formal financial area (Yaron 1992; Gurgand, Pederson, andYaron 1994; sector. Analysis of data from the Ghana LSMS survey Hossain 1988) has been based oIn case studies of spe- in Deaton (1992a) revealed that only 7 percent of cific financial institutions. For example,Yaron (1992) loans to the sample households were made by formal examined four rural financial institutions in Asial 1 and sector institutions (such as private banks, government reviewed the factors underlying their success or failure banks, and cooperatives). Only 79 of 2,397 rural in a number of areas including financial self- households in the Pakistan LSMS-3 percent of the sustainability and outreach. rural sample-reported receiving loans from formal As a corollary, cross-sectional data can be used to financial institutions.This small sample limits what can analyze the effectiveness of any policy instrument that be learned about the formal sector from such multi- varies across a sample of households. For example, such topic household surveys. Thus research on the finan- data can be used to assess whether access to formal cial sector in such economies may require "stratified" financial institutions, as measured by a household's dis- surveys that identify borrowers from financial institu- tance from the nearest such institution, affects the will- tions and ensure that sufficient numbers of such ingness of the household to hold deposits, as well as households are included in the sample. the attractiveness of the formal financial sector as a source of loans relative to the informal sector. Such GOVERNMENT AND BANK PoLIcIEs. Household data information was used by Behrman, Foster, and can be used to analyze the effectiveness of any partic- Rosenzweig (1997) to assess whether the availability ular government or bank policy if the data meet two of a bank within 5 kilometers affected the savings of criteria. First, the sample needs to include a sufficient rural households in Pakistan. Household survey data number of households that are affected by the policy can also be used to analyze the extent to which inter- in question. Second, identifying the role of any partic- est rates affect the demand for deposits or credit in ular policy instrument-such as the interest rate or the economies where there is sufficient regional variation rate of return on deposits-in achieving a stated poli- in such rates. cy objective requires this instrument to display signif- icant variation across the sample of households. THE EFFECTS OF FINANCIAL INSTITUTIONS ON Data from one random cross-sectional survey will HOUSEHOLDS. Financial institutions can have an generally not be enough to evaluate government and impact on household fiduciary outcomes such as sav- bank policies relating to the financial sector, both ings and consumption and also on other aspects of because of the limited size of this sector in many well-being such as the health and education of chil- developing economies and because government poli- dren. However, it is very difficult to ascertain the cies in this area generally involve changes in variables, extent to which a household's behavior reflects its such as interest rates, that do not vary significantly transactions with the financial institution in question. across the sample.This is particularly true if the data set In particular, a researcher must address two issues. provides information on only a single cross-section of First, households that do report transactions with households, but it is also true in short panels of data financial institutions may differ in their socioeconom- that survey households over a period of two to three ic characteristics from households that do not report years.This point is also made in Chapter 23 on panel these transactions; if households with higher income data. Not surprisingly, most studies on the effects of are also more likely to borrow from such institutions, 195 ANJINI KOCHAR a positive correlation between consumption levels and in economies where the formal financial sector is loans from financial institutions may merely reflect an poorly developed. underlying correlation between consumption and While inferring causality may not always be pos- income. Second, the developers of a given financial sible, it is possible to use household surveys to assess institution or credit program may have purposely cho- the correlation between the level of development of sen to locate in a particular location because of the the financial sector and various outcomes of interest to socioeconomic characteristics of local residents or of policymakers.Thus the study by Behrman, Foster, and the agroeconomic characteristics of the region Rosenzweig (1997) was able to show how households (Rosenzweig and Wolpin 1986; Pitt, Rosenzweig, and with relatively easy access to financial institutions had Gibbons 1993). Therefore, any observed differences in a higher level of savings than households without such savings between households with access to financial access, even though this study could not explain the institutions and households without such access may factors that caused that difference in savings. merely reflect the unobserved socioeconomic or agroeconomic characteristics that motivated the place- Assessing the Determinants of Households' Savings and ment of the institution in its current location. Portfolio Choices Addressing these selection problems requires, at a Most theories of savings are based on standard models minimum, data on sufficient numbers of borrowers of intertemporal choice. In these models, households and nonborroxvers or, more generally, participants and are assumed to choose their level of consumption in nonparticipants in any particular program. Also, in any given period to maximize the present discounted order to deal with the endogeneity of program place- value of utility over the life cycle, subject to a budget ment, the nonborrowers need to be drawn from sam- constraint that equates the present value of the sum of ples of households both in areas with financial insti- the household's consumption in each period with the tutions and in areas without such institutions. For present value of its lifetime income-and also subject these reasons, studies that have analyzed the effects of to any other constraints, such as credit constraints, that financial institutions on households have generally may affect its decisionmaking over time. Given this been based on data sets that were specifically designed common framework, theories of savings differ prima- for such an analysis. For example, Pitt and Khandker rily in the importance they ascribe to the various (1997) analyzed the impact of a number of credit pro- determinants of savings, such as the variability of grams in Bangladesh, including the Grameen Bank, short-term versus long-term income, variability in on household outcomes such as consumption, labor households' preferences, uncertainty about house- supply, and the health and education of children. To holds' incomes and expenditures, and liquidity con- do so, they used a stratified random sample of house- straints. Thus, in order to distinguish among these var- holds both from villages with credit programs ("pro- ious theories, researchers need-in addition to gram" villages) and from villages without credit pro- measures of savings-data on income, on the demo- grams. The households within program and graphic variables that determine preferences and dis- nonprogram villages were further distinguished count rates, and on measures of the uncertainty in according to whether they met the eligibility criteri- income and of the liquidity constraints to which a on for participation in the credit programs. Finally, household is subject. With these data, researchers can within the program villages, households that met the use both regression analyses and simulation techniques eligibility criterion were divided into participant and (simulating savings on the basis of consumption and nonparticipant households, with 12 participants being income data and hypothesized values of other factors randomly selected for every five nonparticipants. that determine savings, such as interest rates) to under- Using this technique ensured that there were enough stand savings. Alternatively, they can use simple tech- data on a sufficient number of participants as well as niques, such as plots of income and consumption by on a "control" group against whom the outcomes for age, to assess whether savings display the "hump" shape the participants could be evaluated. In contrast, the predicted by life cycle models (Mirer 1979; Danzinger "random" survey techniques usually used in the col- and others 1983; Deaton 1992c). lection of multitopic household survey data rarely Empirical research on household savings therefore provide a sufficient sample of borrowers, particularly requires income and consumption data, as well as 196 CHAPTER 20 SAVINGS nmeasures of demographic variables and other factors is dcfined as the difference between current income that affect savings. Data on household consumption and consumption and hence must be positively corre- are required not only because such data allow lated with current income, such tests of how savings researchers to estimate savings as the difference change in response to anticipated changes in income between income and consumption but also because cannot be based on regressions of current savings on many theories of savings can be tested using data on current income. If a household's current income is consumption. For example, the hypothesis that house- used in a savings regression, it needs to be instrument- holds use savings to protect their consumption from ed by values of the household's income in previous income shocks can be tested through regressions that years to test the responsiveness of its savings to the reveal the relationship between changes in income and component of current income that was anticipated on either consumption or savings. In the past, researchers the basis of last year's value. have preferred to use consumption data to test theo- It is even more desirable to use panel data when ries of savings; this is primarily because error in con- analyzing how households' savings change in response sumption measurements is likely to be less than error to long-term changes in their income.The most con- in savings measurements, which include the measure- vincing studies of the importance of retirement sav- ment error in both consumption and income. ings, such as the Longitudinal Retirement History While LSMS surveys provide data on household Surveys in the United States, have used long panels of consumption and all the necessary demographic vari- data. The Longitudinal Retirement History Surveys ables, they do not always collect the data needed to followed over 11,000 people of retirement age for 10 estimate total income (see Chapter 17 on total years (Hurd 1987; Bernheim 1987). income). Therefore, survey designers should be aware While several studies have tested the life cycle that if savings research is an important justification for model using a single cross-section of data (Darby the survey, total income data need to be collected. 1979; Deaton and Paxson 1992), it is difficult to infer Researchers investigating the effects of liquidity con- life cycle motives from one round or even from a straints or income uncertainty on savings need either panel of data that provides information on households some measure of these variables or data allowing them over only two or three years. In order to use such data, to estimate these variables-in addition to data on the researcher has to assume that the preferences, income, consumption, and demographic variables. And prices, and constraints that influence a household's life researching the determinants of portfolio composition cycle experiences and, hence, its behavior will remain requires data on stocks of or transactions in disaggre- identical from one cohort to another. In other words, gated groups of assets. it has to be assumed that the behavior of a currently 60-year-old man is a reliable measure of how a cur- THE VALuE OF PANEL DATA. To infcr savings motives rently 40-year-old man will behave 20 years from accurately, it is generally necessary to have a panel of now. Using only one cross-section of data can also data, primarily because models of intertemporal cause sample selection problems. Good health and the choice imply that a household's savings reflect its probability of living a long life are generally positively expectations of future income and consumption. To correlated with wealth, so that the rich are over-rep- test these models, researchers need data that span a resented among the (surviving) elderly. Unless the number of years. Studies of how households change researcher controls for this bias, he or she may con- their savings in response to anticipated changes in clude that there is little decumulation of wealth with either annual or seasonal income have generally been age, even though such decumulation may in fact based on tests of the relationship between either sav- occur. ings or consumption and the change in income across Panel data are also necessary for estimating the periods (Hall 1978; Deaton 1992b; Flavin 1981, 1993; variability in individual incomes and, hence, the Alderman 1996; Kochar 1998). Deaton (1992b) importance of precautionary savings to hedge against regressed the change in income between two periods this variability. The scarcity of research on the impor- on the previous year's values of income and savings in tance of the precautionary motive in developing order to test the hypothesis that households save in economies is probably due to a lack of the long panels anticipation of changes in their income. Since savings of data required for such estimates. In contrast, consid- 197 ANJINI KOCHAR erable research on precautionary savings has been done Other researchers have used data on the number in developed countries such as the United States-in of days of illness reported by working household part because long panels of data have enabled members as a measure of income shocks (Cochrane researchers to estimate the variability in individual 1991). Reported days ofillness, however, may not rep- incomes (MaCurdy 1982; Hall and Mishkin 1982). resent a shock to the individual, not only because ill- ness is often predictable but also because there is often ENHANCING THE VALUE OF CROSS-SECTIONAL DATA systematic measurement error in self-reported meas- FOR RESEARCHING THE DETERMINANTS OF SAVINGS. ures of health. For example, there is considerable evi- While panel data can be quite valuable, collecting long dence that the number of self-reported days of illness panels of data is costly and thus not always feasible. For is correlated with household characteristics such as this reason it is likely that for the foreseeable future, income and education (see Chapter 8 on the health research on savings, particularly in developing mnodule). Nevertheless, the availability of such data economies, will have to be based on single cross- does give researchers some insights into the factors sections or short panels of data. And in spite of the that determine households' savings and portfolio deficiencies of cross-sectional data, these data can choices. reveal important information about households' The availability of data on the earnings of indi- motives for saving. vidual household members and the sources of these Since one of the merits of panel data is that they incomes can also facilitate research on particular sav- enable researchers to estimate anticipated changes in ings motives. Having data on the earnings of individ- household income, the usefulness of cross-sectional ual household members enables researchers to ascer- data can be increased if they provide measures of tain whether parents and their coresident adult expected household income. In a single cross-section children combine their incomes so that the consump- of data, Flavin (1993) used information on what tion of each individual depends not on his or her own households expected their income to be in the current income but on the combined incomes of all house- year to estimate savings responses to anticipated hold members (Hayashi 1995). Such evidence informs changes in income. Guiso, Jappelli, and Terlizzese research on life cycle savings; there may be little need (1996) used data from a single cross-sectional survey on to save for old age if the income of the young provides households' subjective perceptions of income risk and for the consumption requirements of the elderly. This on whether they had been denied credit in the past to research methodology is feasible in economies where analyze how households' choices of assets changed in individual sources of income are significant. However, response to income risk and credit constraints. it is difficult to implement in economiies where indi- Data on whether a household has experienced vidual earnings are rare, and where households instead unexpected changes in consumption expenditures or earn their incomes from family enterprises through income can also be useful in assessing whether house- the joint labor of their members. In such cases, esti- holds use savings to protect their consumption from mating the individual claims on jointly produced fam- such shocks and which of their assets they use for this ily income in order to test the hypothesis of pooled purpose. Researchers interested in this issue have family income is fraught with difficulties (see Chapter occasionally designed and administered a cross- 17 on total income). sectional survey to collect such information. Udry Having income data by source also helps (1995) designed a cross-sectional survey for rural researchers understand savings. Researchers can use Nigerian households that provided information on data on pensions or insurance payments to explore whether the households experienced unexpected whether the households receiving such income are less changes in income over the reference period of the likely to save and, hence, whether savings are a survey; he used this information to assess the respon- response to the need for annuity income or insurance. siveness of savings, by assets, to such shocks. The avail- These data also enable researchers to investigate poli- ability of disaggregated data on the types of assets held cy issues xvithout having to delve into households' by households contributed significantly to the value of motives for saving. For example, the question of this research-highlighting the benefits of collecting whether publicly funded programs such as social secu- such data. rity "crowd out" private savings can be approached 198 CHAPTER 20 SAVINGS either by examining whether savings reflect individu- children to parents and the need (or lack of need) for als' perception of their old age and insurance needs or life cycle savings, because the entire sample yielded by examining whether the receipt of social security only 99 instances of transfers from children to parents. payments causes a reduction in private transfers. Such The usefulness of cross-sectional data for savings an analysis has been conducted by Cox and Jimenez research can also be enhanced if they are supplement- (1992) using a cross-section of data from the Peruvian ed by aggregate time-series data on variables affecting LSMS. The researchers found that elderly people's household income. Paxson (1992) andWolpin (1982) receipt of social security benefits significantly reduced have used available data on rainfall statistics over a the amount of transfers they received from other number of years to predict changes in agricultural households. incomes and, hence, to separate the transitory compo- The usefulness of cross-sectional data for assessing nents of income from its permanent components- the impact of government policies depends on how without the use of panel data. many households report being affected by the policies Box 20.1 classifies savings issues according to what in question. For example, it would not be possible to kinds of data are needed to analyze them. use the Pakistan LSMS to study the effect of pensions on savings because of the very small number of house- The Relevant Unit for Analysis holds in the sample who reported receiving pensions The previous subsection suggests that individual-level (64 out of 4,799). Nor would it be possible to use these data on incomes and on interhousehold and intra- data to analyze the relationship between transfers from household transfers may help researchers understand Box 20.1 Policy Issues and multitopic Household Survey Dat Issues that can be analyzed with cross-sectional household sur- Issues that can be analyzed with I 0 years of panel data vey data * The demand for life cycle savings and how this demand * The ratio of household savings to household income. affects the composition of households' savings portfo ios. * The forms in which households save and how households' * Better estimates of both precautionary savings and savings physical assets are divided for use among self-employment for short-run consumption smoothing (due to data on enterprises, real assets, and financial assets. income and consumption profiles over the life cycle). * Variation in the rate and form of savings among house- * The effects of aggregate income shocks on savings during holds with differences in socioeconomic characteristics the survey period. such as wealth, demographic characteristics, occupation, * Analysis of the effects on household savings of policy vari- and region of residence. ables that vary over the duration of the survey, including * The importance of financial assets in household portfolios. macroeconomic factors such as inflation, interest rate * The importance of formal financial institutions relative to changes, and fiscal and monetary policy. informal ones. * A similar analysis of the impact of government policies on * The difference in the importance of financial assets across the viabilty of financial institutions and, hence, on the households of different socioeconomic status. extent and spread of financial intermediation. * How total savings and savings in particular assets change in response to income shocks and expectations of Issues for which household survey dota are not sufficient and for income, if a measure of income shocks or expected which additional data or special sample designs are needed income is available in the data. * Studying a special policy such as group lending would * Insights into households' motives for saving if data on indi- require a special survey collecting data from a sufficient vidual incomes and income by source are available. number of households affected by the policy and from a * The effect of the availability of financial institutions on control group of households not affected by the policy. financial savings. * Studying group lending would additionally require collect- ing suffic ent data on borrowers and nonborrowers, Issues that can be analyzed with two or three years of panel data including both the nonborrowers who have access to the * Whether savings are used to smooth short-run fluctua- program n question and the ones who do not. tions in income and which assets are used for this pur- pose. Issues for which household survey data are of little use * The importance of precautionary savings (even without * Analyses of the profitability of financial institutions by type of direct measures of income shocks). institution and policyThese usually require bank-level data. 199 ANJINI KOCHAR household savings. Several recent studies have argued lecting individual-level data on these measures, particu- that data on incomes and consumptions of "private" larly in the cases of Joint (or household public) goods goods, or goods for which individual consumption lev- shared among household members and income from els can easily be identified and measured, should be col- joint family activities. Similarly, assigning individual lected at the level of individuals within the household ownership to household assets-particularly consumer rather than at the level of the household. (This is dis- durables and productive assets used in the production of cussed in detail in Chapter 24 on intrahousehold issues.) income from joint activities-may be an impossible task. The authors of these studies argue that the theory of the Therefore, it is not necessary to attempt to assign unified household, which underlies much of neoclassi- individual ownership to all household assets. However, cal economics, is invalid, and that individuals within a it is worthwhile to record the ownership of assets, such household are likely to have distinct and unique prefer- as jewelry, that can easily be assigned to any given indi- ences. Empirical research generally supports this vidual in a household, and to collect data on the hypothesis. For example, available evidence suggests that wealth inherited by particular individuals in the income earned by women is spent differently from household.These data are useful inputs into tests of the income earned by men, with more of women's addi- validity of the neoclassical "unified" household model tional income spent on food and education (Thomas relative to the "individualistic" models, since they pro- 1990 and 1993; Schultz 1990; Quisumbing 1994). vide a set of variables that may be correlated with Such gender differences are likely to affect house- individual incomes. The variables can then serve as hold saving decisions, as they imply that the propensity to instruments to correct for problems caused by meas- save may vary among individuals within a household. If urement error and endogeneity in incomes. Chapter governments want to increase the savings rate they 24 discusses these and other variables that can facilitate should target individuals within a household who are research on intrahousehold issues. most likely to be responsive to savings initiatives.While a significant number of microfinance institutions, such as Two Versions of the Savings Module the Grameen Bank in Bangladesh, do target individuals wvithin the household-notablv women-this policy has This section introduces two prototype savings usually been adopted for reasons other than its potential modules-one standard length version and one short effect on savings. Evidence on the effects of such pro- version-and discusses their design. (The modules are grams on household savings is slowly accumulating (Pitt presented in Volume 3.) The primary purpose of the and Khandker 1997); however, much more research is savings module is to provide information on a house- needed in this area before firrm conclusioins can be drawn. hold's financial assets, including currency, and on its Even if programs that target individuals within a other assets such as land and buildings held for invest- household are found to increase household savings, it ment purposes. Details of this information are not vill be difficult to infer whether this is because individ- generally available in other modules.While it is possi- uals differ in their savings propensities.Why? One reason ble to provide data on stocks and flows of all house- is that testing differences in the savings propensities of hold assets in the savings module, available evidence individuals is much harder than testing the validity of the suggests that data on most assets are best collected in neoclassical model. General tests of the validity of the modules other than the savings module.Thus the stan- neoclassical model simply require identifying the dard savings module is relatively short. income earned by some individuals within the In some cases, lack of time and resources may household-an easy task to accomplish if there are wage necessitate fielding an even shorter survey-one that earners in the household. In contrast, testing differences collects just the basic information necessary to assess in the savings propensities of individuals requires assign- household welfare.This survey need not collect infor- ing the totality of household savings (or consumption) to mation on household asset transactions but must individual members, either by fully identifying the indi- include questions on the value of stocks of assets- vidual income and consumption of all of the household's including financial assets-since these data are needed members or by assigning individual ownership to all to obtain a measure of total household wealth. While assets. Chapter 17 on income and Chapter 5 on con- much of the discussion in this section pertains to the sumption both discuss the difficulties involved in col- standard-length savings module, Table 20.5 summa- 200 CHAPTER 20 SAVINGS rizes the relative merits of the short and the standard tions in assets, given the general hesitancy of house- savings modules for addressing policy issues. holds to disclose details regarding their wealth. It is necessary to keep this in mind in designing the ques- Collecting Data on Household Assets in the Savings tionnaire in general and the savings module in partic- Module ular. For example, as discussed in Chapter 3, it is gen- A recurring theme in this section is the difficulty of erally desirable to place the savings module near the collecting data on households' stocks of and transac- end of the questionnaire. This serves two purposes. Table 20.5 Summary of Policy Issues and How Multitopic Household Surveys Can Be Used to AddressThem Usefulness of questionnaire for Other modules from Issue analysis which data are needed Would need to add Short Questionnaire Rate and form of savings and differences across households Good Income and income-related modules;' Nothing Consumption from core; Socioeconomic indicators from core Lvel an srad of fnncia instittions Good Socioecooi indicatrs from core Nothing Determinants of savings Poor Income and income-related modules0- Generally requires panel data for details such as measures of income expectations and shocks Determinants of portfolio choice Poor Income and income-related modules.- Details of financial transactions for details such as measures of income from the standard questionnaire; expectations and shocks; Generally requires panel data Asset-related modulesb .............................................................. .............................. .................................... ................................................................................................................................ Effects of government ano bank policies Poor Community survey-for data on Details of transactions in financial on financial institutions distance to banks assets from standard questionnare; Generally requires special survey . ................................................................................................................................... ............................................................................................... Impact of financial institutions on Poor Details of transactions in financial households assets from standard questionnaire; Generally requires special survey .................................................................................................................................................................................. I................................................. Standard Questionnaire Rate and form of savings and Good Income and income-related modules;n Nothing dfferences across households Consumption from core; Socioeconomic indicators from core ........ ........ ........................ *............... *......................................................................................................................................................................... Level and spread of financial institutions Good Socioeconomic indicators from core Nothing Determinants of savings Poor Income and income-related modules0- Generally requires panel data for details such as measures of ncome expectations and shocks ................. *.................................................................................................................................................................................................................. Determinants of portfolio choice Fair Income and income-related modules0- Generally requires panel data for details such as measures of income expectations and shocks; Asset-related modules' Effect s of government and bank poicies Fair Commnity survey-for data on distance Generally requires special survey on financial institutions to banks .............................................. *..................................................................................................................................................................................... Impact of financial institutions Fairc Generally requires special survey on households to ensure data on sufficient numbers of borrowers . ......................................................... *...................................................................................................... *..................................................... *............. a. Estimates of annual income additionally require information on agricutural income (farming and ivestock module), labor or wage income (abor module), nonagricul- tura or enterprse income (nonfarm enterprise module), livestock income (farming and livestock module), and other income including pensions, remittances, rental and interest income (other income modu e). b Studying issues melating to portfolio composition requ res disaggregated data on the stocks of and transactions in all assets. As above, this i-format on needs to be col- ected in the modules that gather information on the different assets held by the househo d. In genera these modu es will be the housing module (for data on res den- t.a. wealth), the farming and livestock modules (for data on agricultural assets, livestock, and Inventories of foodgrains and fodder), the nonfarm enterprise module (non- farm assets and inventories), and the -onsumption module (for data on consumer durables). c. Using the standard questionna re oue can assess the mpact Of features of finaricial institutions thdt vary across a sample. Source. Authors summary. 201 ANJINI KOCHAR First, it enables the interviewer to develop a rapport questions of any kind about the financial assets of the with the respondent, hence, increasing the likelihood sample households because the survey designers that the respondent will be willing to answer questions assumed white households would not want to provide about the household's financial wealth. Second, it accurate details of their financial assets for fear that ensures that important data will already have been col- these assets would be confiscated after the imminent lected from the other modules if, upon being asked change to black majority rule.TheVietnam and Ghana about the household's financial wealth, the respondent LSMS surveys yielded only estimates of the total cur- should decide to terminate the interview. Similarly, rent value of all of the household's financial assets, within the savings module, it is best to place questions without providing any details about financial transac- about particularly sensitive assets, such as currency, tions the household may have engaged in within the toward the end of the module. previous 12 months. Conversely, the Peruvian LSMS The difficulty in collecting data on financial assets survey yielded data on the household's transactions but suggests that it may be desirable to alloxv the respon- not on the current value of its financial assets. dent to put the value of an asset that he or she owns The difficulties LSMS surveys have encountered within a broad range of values rather than citing a spe- in trying to collect accurate data on financial assets is cific figure. This approach may make the respondent an indication that it may be better to measure savings more likely to part with this information. Depending as the difference between income and consumption on the willingness of the respondent to continue with rather than to measure it on the basis of data on asset the questions, the initial value range can be successive- transactions. However, the evidence so far is only sug- ly narrowed until the value of the asset in question can gestive, and the question needs to be studied further be placed within a fairly narrow range (Juster and before any conclusive recommendation can be made. Smith 1997). However, the feasibility of this approach Until solid evidence on the reliability of one measure should be tested in a pilot survey before it is incorpo- over another becomes available, it may be desirable to rated into the questionnaire-as should the appropri- collect data on asset transactions in addition to data on ate range of values to offer the respondent as options income and consumption, so as to enable the for the value of the household's financial assets. researcher to use both measures of savings.This under- Therefore, the prototype modules presented in this scores the need to improve techniques for collecting chapter do not include such questions. income and consumption data, as well as to ensure that reliable data on all asset transactions are recorded. DATA ON HOUSEHOLDS' STOCKS AND FLOWS OF F;INANCIAL ASSETS. The standard savings module DxrA ON OTHER ASSETS IN 1IHE SAVINGS MODULE. should gather information on the household's stock of Policymakers also need information on other house- and transactions in such financial assets as stocks, hold assets, including: stock and flow of physical assets shares, bonds, other securities, and deposits in financial used in the production of farm or nonfarm income; institutions. These data not only facilitate research on real assets such as land and housing wealth other than savings but are also essential inputs into the estimation that used in the farm or nonfarm enterprise; land or of household wealth. As noted earlier in this chapter, buildings held purely for investment purposes; and an accurate measure of a household's total financial stores of foodgrains, fodder, building materials, and assets is more likely to be obtained if respondents are other inventories. As with financial assets, not all past asked to provide disaggregated information on the LSMS surveys gathered information on both the value of their financial assets such as bank deposits, stocks and flows of such assets.Those in which this was currency, and savings in informal savings institutions done differ in terms of where in the questionnaire this than if they are asked to give the sum value of all their information was collected. In the Vietnam LSMS sur- financial assets. vey, information on the stock of real assets (buildings Hoowever, households are frequently hesitant to and houses) was collected in the savings module, while provide the interviewer with details of their financial information on the sale of these assets was collected in wealth. As a result, these data are not available in many the miscellaneous income module. In the Pakistan survey data sets, including many LSMS surveys. For LSMS survey, data on the stocks of and transactions in example, the South African LSMS survey asked no real assets were gathered in the savings module, where- 202 CHAPTER 20 SAVINGS as in the South African survey these data were collect- is that there is a greater probability that some assets will ed in a separate module. inadvertently be ignored, adding to any measurement One question that survey designers must address is error in estimates of household savings based on asset the module in which to locate questions on nonfinan- transactions data. One asset that is frequently missed is cial assets so as to maximize the accuracy of responses. buildings and land held for investment purposes.13 The answer to this question varies from survey to sur- While the miscellaneous module frequently yields data vey depending on which modules are included in each on rental income from such property, it gathers no questionnaire. If the survey contains a farm and a non- information on the property's value or on transactions farm enterprise module, as do about two-thirds of in such buildings and land.The savings module may be LSMS surveys, data on the stocks and flows of land, the best place to gather this data. Separating questions buildings, and other assets used in these enterprises are on investment property from questions on residential best collectcd as part of these modules, along with property may also reduce measurement errors in the questions on the operations of the enterprise in ques- resulting data in both categories. tion. In the absence of these modules, however, data on the relevant assets should be included in the savings DATA ON STOCKS AND TRANSACTIONS OF CONSUMER module. DURABLES IN THE SAVINGS MODULE. The savings mod- Data on other real assets, such as residential land ules in several existing LSMS data sets contain infor- and housing, are frequently collected in both the sav- mation on households' stocks of and transactions in ings module and the housing module. This was the consumer durables. As with the value of property case in the Pakistan survey, where the housing module owned by the household, this information is also col- gathered data only on residential property, and the sav- lected elsewhere in the survey, most commonly in a ings module collected data on the sum of residential separate module that collects data on expenditures for and investment property.'2 This difference in method nonfood items and consumer durables and additional- led to a difference in the value of land and buildings ly provides an inventory of durable goods. Again, as in reported in these two modules. In the housing mod- the case of the value of residential and nonresidential ule 3,900 households reported owning residential property, comparing the data in this module with the land, with a mean value of Rs. 155,000, whereas in the data in the savings module suggests that the data in the savings module 3,988 households reported owning savings module are unreliable. For example, the residential land or land rented out for residential pur- Vietnam survey asked households about the current poses, with a mean value of Rs.179,000. The number value of their durable assets (such as motorbikes or of households that reported owning residential land washing machines) in the savings module. Only 22 other than land they occupied was just 88. This small households reported owning such assets, with a mean number suggests that some households did not report value per household of 8.3 million dongs.This figure their ownership of residential land, though there is no can be compared to the total value of all household means of verifying this with the data at hand. durables from the "inventory of durable goods" mod- Discrepancies of this kind suggest that respon- ule (Section 12, Part C of theVietnam questionnaire), dents may be averse to providing accurate information in which 4,663 households reported owning con- on their assets when it is clear that the intention of the sumer durables, with a mean value per household of interviewer is to collect data on the household's 2,9 million dongs. Indeed, disaggregated information wealth. It appears that respondents give far more accu- on the ownership and value of diffcrent types of assets rate information if the relevant questions are asked in reveals that the numbers of households owning just other modules in a less sensitive context, such as when washing machines and motorbikes exceed the num- the interviewer is asking about housing characteristics. bers reported in the savings module. While only 15 Whatever the reason for these discrepancies, it seems households possessed a washing machine, as many as that there may be little advantage to collecting data on 512 owned a motorbike, with a mean value of 8.8 mil- individual assets in the savings module if they can be lion dongs.Thus the data in the savings module appear collected in other modules of the survey. to bear little relation to ownership either of total assets One drawback to gathering information on asset or of the individual assets explicitly mentioned in this transactions in modules other than the savings module module. 203 ANJINI KOCHAR Because it appears that collecting data on the modules a few additional questions on any variables value of or transactions in consumer durables in the that affect savings. For example, information on savings module results in considerable measurement whether the household's total current income exceed- error, these data should, to the extent possible, be col- ed or fell short of the expected amount could be con- lected in a separate module along with details on other veniently located in the income module, and would items of expenditure. facilitate research on the responsiveness of savings to anticipated changes in income. In a similar vein, Modifing Other Modules to Facilitate Research on Savings Chapter 21 on credit suggests including in the credit Given that collecting data on real and physical assets in module questions concerning whether or not house- modules other than the savings module generally holds applied for credit over the reference period and yields more accurate data, it is important to ensure that the results of this application. The response to such these modules are designed to yield the data needed questions may provide information on the importance for doing research on savings. of liquidity constraints, information that can be used At a minimum, data are needed on the stock of to assess the effect of such constraints on savings. and transactions in all assets. Most past LSMS surveys It is important, however, to keep the overall size of have collected such data for households' agricultural the relevant module in mind when including such and business assets in the farm and nonfarm modules. questions. For relatively long modules such as the agri- However, a number of surveys have not gathered this culture module, further increases in length may com- information-compromising their value for savings- promise the quality of the data. In such cases it may be related research. For example, the Vietnam LSMS sur- best to limit the questions asked to ones that are essen- vey gathered no data on the purchase of buildings and tial for obtaining accurate measures of the stocks of lands by households for nonfarm enterprises, while and transactions in the relevant assets.Thus it may be the South Africa survey did not gather information desirable to omit questions relating to reasons for the either on the current value of household livestock or sale or purchase of any particular asset, or the timing on the value of livestock purchased by farm house- of such transactions, despite their usefulness for savings holds during the year in question. research. Survey designers need to think carefully about how The loss of data that results from collecting asset best to gathcr data on consumer durables. In most pre- information in modules other than the savings mod- vious LSMS surveys the inventory of durables module ule is counterbalanced by the fact that collecting sav- gathered data on the current value and purchases of ings-related information in these other modules yields durables. Data on sales have generally been gathered in more accurate data on the value of asset stocks and the miscellaneous income module, as in the Vietnam, transactions. China, and Ghana surveys. In all of these instances, how- ever, respondents were asked how much income their DATA ON INVENTORIES OF FOODGRAINS, FODDER, AND households received from the sale of all durable goods, OTHER MATERIAL. As noted earlier, experience has and this degree of aggregation may have increased the shown that it is notoriously difficult to gather data on measurement error in this figure. Research on savings households' stocks of foodgrains, fodder, and other also requires distinguishing between income from the materials.Yet it is widely believed that stocks of such sale of durables and income earned from their rental. liquid assets account for a significant share of house- (This distinction is recommended in Chapter 11 on hold savings in any given period. Finding ways to transers and other nonlabor income.) Similarly, as dis- study the usage of this portion of household savings is cussed in Chapter 5 on consumption, it is important to likely to yield insights into what influences household separate purchases from gifts and bequests received by decisions about savings (Chaudhuri and Paxson 1994). the household. As noted earlier, the best way to do this Some surveys, such as the Vietnam LSMS survey, may be to include an explicit question about whether an have gathered data on stocks of foodgrains (in this case, asset was received as a gift or was purchased, along with paddy and rice) in the savings module. The fact that in questions about transactions in such goods. the Vietnam survey a very low number of households The value of the data set for savings research can (337) reported owning such assets raises doubts about also sometimes be augmented by including in other the validity of the data. Other surveys provide no infor- 204 CHAPTER 20 SAVINGS mation at all on such stocks. For example, in the data which have been used successfully in past LSMS set from the Pakistan LSMS survey it is only possible to surveys. estimate the nonmarketed surplus from foodgrains pro- duction during the reference year. No data are available Explanatory Notes on the Standard and Short on any stocks carried over from previous years. Versions of the Questionnaire Practical experience suggests that such data can only be reliably collected at the point in the agricul- The Standard Questionnaire ture module when the interviewer is asking specific For each asset the survey covers, the respondent should questions about the household's crop output and its be the household member most knowledgeable about disposal (see Chapter 19 on agriculture). The China the asset. Who this person is will vary depending on LSMS survey, one of the few to follow this practice, which assets are being discussed. While the male head was designed so that data on inventories (by crop) at of household may be most knowledgeable about sav- the time of the harvest and at the time of the inter- ings in investment properties and financial assets, a view were collected in the agriculture module. To female household member may know more about sav- facilitate research on savings, a multitopic household ings held through informal savings committees. survey should include, at minimum, an extensive agri- culture module that gathers such crop-specific details. Box 20.2 Cautionary Advice While many past LSMS surveys have included such an agriculture module, questions on household stocks of * How much of the draft module is new and unproven? The and transactions in foodgrains and fodder need to be savings module presented in this chapter is similar to those used in many recent LSMS surveys. incorporated in future surveys. Since the reference * How well has the module worked in the post? While the period f taaisntrsavings modules used in past LSMS surveys have gen- previous 12 months, it would be desirable to use the erally produced reasnable data, households are typi- same reference period to record the change in inven- cally wary about providing information on their wealth tories and transactions in foodgrains and fodder. and financial savings. It is therefore necessary to follow the recommendations in the chapter regarding the SAVINGS-RELATED DATA IN THE COMMUNITY placement of the savings module toward the end of the QUESTIONNAIRE. Inserting questions on the availabili- survey, when sufficient trust has been built between the interviewer and the respondent. Some surveys have quesandncosts fcfinancalso serviceresethe comunsvit collected data on specific items (such as consumer durables or the value of residential property) in two Data on formal financial institutions may be best suit- different modules of the survey, and the wealth esti- ed for this questionnaire, because the terms of both mates derived from the different modules have some- borrowing and lending from such institutions are rel- times been significantly different from each other Such atively uniform across all households in the communi- differences appear to reflect confusion regarding ty. In economies where such terms vary across regions whether the data collected in the savings module are and hence across households, data on such variables stocks of assets at a particular point in time or changes can significantly contributc to research on houscholds' in stocks of assets dicing the reference period. These errors can be minimized with well-trained interviewers who understand the questions they must ask and who Chapter 4 on conIIIIunity and price data details can communicate these questions well to respondents. sonme of the information that can profitably be col- * Which parts of the mDdule most need to be customized? lected in the community questionnaire. This includes In countnes where the leve of financial savings is low, the kinds of financial institutions available to house- questions in the stancard questionnaire regarding details holds (such as government banks, private banks, and of different types of financial assets will not be neces- cooperatives), the kinds of savings instruments gener- sary; ir such cases only the short questionnaire may be ally used in the community, the distance of relevant feasible. In other economies, the disaggregated list of savings instruments (such as bonds, government certifi- insituion fr m he omm ni cete, andtrcates, savings accounts, and informal savings associations) age interest rates on both loans and deposits, if any. in the standard version of the questionnaire must be tai- Box 20.2 indicates which elements of the draft lored to reflect the availability of each instrument module presented here are new and unproven and .. 205 ANJINI KOCHAR PARI A. In the questions on land and property held for represent both new investments and any changes in investment purposes should be included in the savings the value of the asset over the course of the year. module only if such information is not included else- where in the survey. Thus data on income from rental PART D. The information in Part D is important in of agricultural land is perhaps best collected in the countries where local savings groups or rotating savings agriculture module. No questions on the value of associations are an important means of increasing sav- owner-occupied residential property or other land and ings. Such associations include "bisi" accounts in property used in farm and nonfarm enterprises are Pakistan, "susu" accounts in Ghana, and "tontine" included in this section, on the assumption that such accounts in C6te d'Ivoire. questions are in other modules. It is important to distinguish investments in resi- The Short Questionnaire dential land from investments in agricultural land in For each asset the survey covers, the respondent should countries where the two kinds of land are conceptu- be the household member most knowledgeable about ally distinct in terms of how they are taxed and the this asset.Who this person is will vary depending on markets in which they can be transacted. If data on which assets are being discussed.While the male head land is collected in the agriculture module, such a dis- of household may be most knowledgeable about sav- tinction is necessary to ensure that the information in ings in investment properties and financial assets, a the savings module does not overstate land ownership female household member may know more about sav- by double-counting the value of such land. ings held through informal savings committees. Questions 9 and 10 of Part A separate out receipt of gifts since not doing so will mean that the measures PART A. In this part the questions on land and prop- of savings yielded by income and consumption data erty held for investment purposes should be included will not tally with the measures of savings yielded by in the savings module only if such information is not data on asset transactions. Also, if gifts are received as included elsewhere in the survey.Thus data on income payment for previous services provided by the house- from rental of agricultural land is perhaps best collect- hold, they are more akin to credit transactions than to ed in the agriculture module. No questions on the outright gifts. value of owner-occupied residential property or other land and property used in farm and nonfarm enter- PARTS B AND C. In these two parts the lists of finan- prises are included in this section, on the assumption cial instruments are only suggestive. The actual items that such questions are in other modules. that should be included will depend on the nature of It is important to distinguish investments in resi- financial markets in the economy and the financial dential land from investments in agricultural land in instruments available to households. countries where the two kinds of land are conccptu- Question 3 of Part B collects data on interest ally distinct in terms of how they are taxed and the incomiie in the samne section wvhere data is collected on markets where they can be transacted. the household's financial assets.This will reduce meas- urement error more than will asking respondents PART C. Since the primary goal of including the value about the sum of all of the household's interest, divi- of financial assets in the short questionnaire is to dend, and profit income in some other module such as obtain a measure of household wealth, there is no the miscellaneous income module. need to collect data on transactions in financial assets. Since some of the assets in Part B may be trans- Even with such a narrow objective in mind, the broad acted several times in any given year, households may level of disaggregation in this questionnaire is useful not be able to provide accurate answers if asked about for minimizing measurement error. each separate addition/withdrawal of the particular asset in question. In such cases it is easier to ask the Notes respondent to give the value of that asset a year before the survey interview, as in question 4. The change in The author is grateful for commsents by Margaret Grosh, Paul this value during the reference year (in other words, Glewwe, Julie Schaffner, and other participants in the LSMS the difference in responses to questions 4 and 2) will authors'workshop. 206 CHAPTER 20 SAVINGS 1. One example of the difficulties in obtaining data on credit 12. The Pakistan LSMS does provide separate details of the transactions is the host of problems that can arise in trying to obtain agricultural land owned by nonfarm households. accurate details about the interest rates charged on informal loans. 13. In a few previous LSMS surveys, such as the South African (See Chapter 21 on credit.) survey, information on nonfarming land and other immovable 2. The relevant files from which the data are drawn are listed in property has been collected m a separate module. This separate Table 20.1. module provides information on only rental income from the land 3. Expenditures on education and consumer durables were and current value of the land (or property)-and not on purchases included in savings and excluded from consumption aggregates. or sales throughout the year. Consumer durables included household effects, kitchen equip- ment, furniture and fittings, and other durable housing expenses. References 4. No correction was made for the value of any jewelry received as gifts, because there were no data on this. Alderman, H. 1996. "Savings and Economic Shocks in Rural 5. Data on household savings are not available. Pakistan." Journal of Development Economncs 51 (2): 34345. 6. The estimates of aggregate income and consumption used in Behrman,Jere R., Andrew Foster, and Mark R. Rosenzweig. 1997. this exercise were generated by Coulombe, McKay, and Round "Dynamic Savings Decisions in Agricultural Environments (1993) and used by McKay in Chapter 5 of this book. Smce expen- with Incomplete Markets." Journal of Businiess and Economic ditures on health and education are included in the estimates of Statistics 15 (2): 282-92. aggregate consumption, these expeiises are excluded fromi the calcu- Beruheiins, B. Douglas. 1987. "Dissaving After Retirement: Testing lations of savings based on asset transactions. An alternative estimate the Pure Life Cycle Hypothesis." In Zvi Bodie, John B. of consumption aggregates calculated by the World Bank yielded Shoven, and David A. Wise, eds., Issues in Pension Econoniies approximately the same results. Problems encountered in estimating Chicago, Ill.: University of Chicago Press. income and consumption are detailed in Chapters 5 and 17 along Besley, Timothy. 1995. "Savings, Credit, and Insurance'' In Jere with suggestions for improving the collection of income data. Behrman and T.N. Srinivasan, eds., Hendbolok of Developnient 7. Of these financial assets, the most common was bank Economics. Vol. 34. Amsterdam: Elsevier Science, B. deposits, wvhich were held by 36 percent of the sample households. Chaudhuri, Shubham, and Christina Paxson. 1994. "Consumption Only a very small number of households reported ownin