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Income, Inequality,
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during the Transition
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BRANKO MILANOVIC
Income, Inequality,
and Poverty
during the Transition
from Planned to
Market Economy
WORLD BANK
REGIONAL AND
SECTORAL STUDIES
Income, Inequality,
and Poverty
during the Transition
from Planned to
I Market Economy
BRANKO MILANOVIC
The World Bank
Washington, D.C.
� 1998 The International Bank for Reconstruction
andDevelopment/TheWorldBank
1818HStreet,N.W.,Washington,D.C.20433
All rights reserved
Manufactured in the United States ofAmerica
First printing February 1998
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The findings, interpretations, and conclusions expressedin this publication are those of
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The material in this publication is copyrighted. Requests for permission to reproduce
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Branko Milanovic is principal economist in the World Bank's Development Research
Group.
Cover design by Sam Ferro and Sherry Holmber,. The graphic shows that the
decline in Russia's gross domestic product (GDP) during the transition (1987 to
1995) was even greater than that of the United States during the Great Depres-
sion (1927 to 1935). U.S. GDP in 1927 and Russia's GDP in 1987 = 100.
Photograph: church in Tula, Russia, by Jan Pakulski.
Library of Congress Cataloging-in-Publication Data
Milanovic, Branko. 1957-
Income, inequality, and poverty during the transition from planned to
market economy / Branko Milanovic.
p. cm. - (World Bank regional and sectoral studies)
Includes bibliographical references.
ISBN 0-8213-3994-X
1. Income distribution-Europe, Eastern. 2. Income distribution-
Former Soviet republics. 3. Poverty-Europe, Eastern. 4. Poverty-
Former Soviet republics. 5. Europe, Eastern-Economic
conditions-1989- 6. Former Soviet republics-Economic conditions.
I. Title. II. Series.
HC244.Z91547 1997
339.2'0947-dc2l
96-32776
CIP
Contents
Preface xi
1. The Tectonic Changes 1
Political Developments: The New States 1
Social Costs 6
2. The Way It Was 12
Income Composition and Inequality 12
Ideological Underpinnings 19
3. Income 23
The Post-Communist Great Depression 23
The Effect on Population Incomes: How Level
and Composition of Income Changed 30
4. Inequality 40
Income Inequality 40
Distribution of Income Sources: Wages, Social Transfers
and Private Sector Income 47
Disparity among Social Groups 54
5. Poverty 60
What Happens to Poverty When Income Goes Down? 60
By How Much Has Poverty Increased? 64
How Much Is Needed to Cover the Poverty Deficit? 76
How to Explain Increases in Poverty? 85
Who Are the Poor? 92
The Incidence of Cash Social Transfers 108
v
vi Contents
6. Selected Issues in Social Policy 115
Should OECD-Like Social Assistance Be Introduced
in Transition Economies? 115
Transfer Payments to Different Groups of Recipients 120
Guaranteed Minimum Income and the Supply of Labor 122
Informal Sector and Pension Reform 126
7. A Look Ahead 132
Appendices
1. Description ot the Surveys Used and Data Problems 135
The Surveys Used 135
What Biases Are Inherent in the Data? 146
How Biases Affect Comparability between Pre-transition
and Transition Years 152
2. Decile Shares of Total Income 156
3. Change in the Poverty Deficit Due to a Uniform Slide
in Income 163
4. The Original Income Distribution Statistics 164
5. Poverty Headcount Calculations Based on the Original
Income Distribution Statistics Given in Appendix 4 183
6. Distribution of International Funds Based on Minimization
of Deprivation Function 192
7. Sources for Table 1.2 193
Country Data Sheets 195
Russia 196
Ukraine 202
Poland 208
Latvia 214
References 221
Indices 233
Contents vii
Tables
1.1. Countries at War or under Economic Blockade, 1991-96 4
1.2. Speed of Reforms in Selected Countries, 1992-96 8
2.1. State Employment as a Proportion of the Labor Force, 1988 12
2.2. Composition of Income in Socialist Economies, 1988-89 13
2.3. Composition of Gross Income in Socialist, Market,
and Developing Countries, 1980s 14
2.4. Family Allowance for Two Children as Percentage of
Average Earnings, 1988 21
3.1. GDP Growth Rates in Eastern Europe and the Former
Soviet Union, 1987-96 25
3.2. Russia's Output Decline after the Dissolution of the Czarist
Empire and after the Dissolution of the U.S.S.R. 27
3.3. Ratio between Expenditures and Income Reported
in Household Budget Surveys 33
3.4. Change in Real per Capita GDP and Real per Capita
Population Income between 1988 and 1993 34
3.5. Population Income by Sources in 1987-88 and 1993-94 36
4.1. Changes in Inequality during the Transition 41
4.2. Decomposition of the Change in the Gini Coefficient
between Pre-transition and 1993-96 48
4.3. Change in Real and Relative per Capita Income of Worker,
Farmer, and Pensioner Households 57
5.1. Estimated Poverty Headcount and Poverty Deficit
in 1987-88 and 1993-95 Using HBS Income 68
5.2. Estimated Poverty Headcount and Poverty Deficit in 1993-95
Using a Higher (Macro) Income instead of HBS Income 75
5.3. Estimated Poverty Headcount and Poverty Deficit in 1993-95
Using HBS Expenditures 77
5.4. Explaining Increase in Poverty Headcount between 1987-88
and 1993-94 90
5.5. Relative Poverty Rates for Different Types of Unemployed
Households 95
5.6. Concentration Coefficients of Family Benefits before
the Transition (1988-89) and in 1993-95 109
5.7. Concentration Coefficients of Unemployment Benefits
and Social Assistance, 1993-95 110
5.8. Concentration Coefficients of Non-pension Cash Social
Transfers before the Transition (1988-89) and in 1993-95 112
viii Contents
5.9. Percentage of Social Assistance, Unemployment
Benefits, and Non-pension Cash Social Transfers
Received by the Bottom Quintile Of Population 113
6.1. The Composition of the Poor, 1993-95 117
6.2. Relationship between Poverty Lines and Wages, 1992-94 125
Figures
1.1. Number of Independent States in Europe, 1860-1995 2
1.2. A Map of Europe and Central Asia 10
2.1. Distribution of Social Cash Transfers by Income Decile 17
2.2. Concentration Curve for Pensions in Hungary,
Czechoslovakia, and the Russian Republic, 1988-89 18
3.1. Distribution of Countries' Growth Rates in Eastern
Europe, 1987-96 24
3.2. Distribution of Countries' Growth Rates in the Former
I Soviet Union, 1987-96 24
3.3. Real GDP in Poland and Russia (1987-95);
and the United States, and Germany (1927-35) 26
3.4. Real Wages in Poland and Russia (1987-96); and in the United
States, the United Kingdom, and Germany (1927-36) 29
3.5. Unemployment Rates in Hungary, Poland, and Russia
(1987-95); and in the United States, the United Kingdom,
and Germany (1927-35) 30
3.6. Real per Capita GDP and Real per Capita Population
Income in 1993-95 35
4.1. Dispersal of Gini Coefficients in Transition Economies 42
4.2. Changes in Quintile Shares in Hungary, Slovakia, and Slovenia
between 1987-88 and 1993-94: Little Change 43
4.3. Changes in Quintile Shares in Belarus, the Czech Republic,
Latvia, Poland, and Romania between 1987-88 and 1993-94:
Moderate Regressive Transfers 43
4.4. Changes in Quintile Shares in Bulgaria, Estonia, Lithuania,
Moldova, Russia, and Ukraine between 1987-88 and 1993-94:
Large Regressive Transfers 44
4.5. Relationship between Type of Adjustment and Increase
in Gini Coefficient 46
4.6. Composition of Disposable Income in Bulgaria, 1987-95 50
4.7. Concentration Coefficients of Wages, Cash Social Transfers,
and Non-wage Private Sector Income in Bulgaria, 1989-95 50
Contents ix
4.8. Composition of Disposable Income in Poland, 1987-95 51
4.9. Concentration Coefficients of Wages, Cash Social Transfers,
and Non-wage Private Sector Income in Poland, 1987-95 51
4.10. Composition of Disposable Income in Slovenia, 1987-95 52
4.11. Concentration Coefficients of Wages, Cash Social Transfers,
and Non-wage Private Sector Income in Slovenia, 1987-95 52
4.12. Composition of Disposable Income in Hungary, 1987-93 53
4.13. Concentration Coefficients of Wages, Cash Social Transfers,
and Non-wage Private Sector Income in Hungary, 1987-93 53
4.14. Composition of Disposable Income in Russia, 1989-94 55
4.15. Concentration Coefficients of Wages, Cash Social Transfers,
and Non-wage Private Sector Income in Russia, 1989-94 55
4.16. Composition of Disposable Income in Latvia, 1989-96 56
4.17. Concentration Coefficients of Wages, Cash Social Transfers,
and Non-wage Private Sector Income in Latvia, 1989-96 56
5.1. Income Density Function and the Poverty Line 63
5.2. Average Monthly per Capita $PPP Incomes, 1993-95 70
5.3. Income Distributions in Moldova and Slovakia, 1992 72
5.4. Estimated Number of Poor before the Transition and in 1993-95 76
5.5. Estimated Annual Costs of Poverty Elimination Assuming
Perfect Targeting, 1993-95 78
5.6. Estimated Monthly Spending Required to Lift an Average
Poor Person out of Poverty, 1993-95 78
5.7. Optimal Allocation of Funds by Countries if the Objective
Function Is to Minimize Deprivation of the Poor 83
5.8. Distribution of Income in the Czech Republic, 1988 and 1993 86
5.9. Decomposing the Change in the Poverty Rate: Parts
Due to Change in Income and Change in Distribution 87
5.10. Breakdown of the Increase in Poverty Headcount between
Growth and Distribution Effects in Poland, 1990-94 89
5.11. Breakdown of the Increase in Poverty Headcount between
Growth and Distribution Effects in Russia, 1990-94 89
5.12. Social Group and Relative Poverty Rates, 1992-95 93
5.13. Poverty Headcount by Socioeconomic Group in Poland,
1987-94 94
5.14. Average Wage, Pension, and per Capita Income as a Ratio
of the Poverty Line in Russia, 1987-96 97
5.15. Average Wage, Pension, and per Capita Income as a Ratio
of the Poverty Line in Ukraine, 1987-95 97
5.16. Cumulative Wage Distribution in Ukraine (1992)
and Poland (1993) 98
x Contents
5.17. Average Wage, Pension, and per Capita Income as a Ratio
of the Poverty Line in Poland, 1987-96 100
5.18. Household Size and Relative Poverty Rates, 1993-95 101
5.19. Relative Poverty Rates for Single-parent Households, 1993-95 102
5.20. Age and Relative Poverty Rates, 1992-95 103
5.21. Age and Relative Poverty Rates in Eastern Germany,
1990 and 1992 104
5.22. Education of Household Head and Relative Poverty
Rates, 1993-95 105
5.23. Size of Locality and Relative Poverty Rates, 1993-95 106
5.24. Sex, Age, and Relative Poverty Rates in Bulgaria
and Russia, 1992-93 107
5.25. Poverty Rates for Male- and Female-headed Households
in Poland, 1993 108
6.1. The Effect of Taxes on Supply of Labor of the Working
Population 121
6.2. Labor Supply of the Poor (with and without Transfers) 122
6.3. Gross and Net Income if There Is a Minimum Income
Guarantee 123
6.4. Marginal Tax Rate in the Presence of a Minimum Income
Guarantee 124
6.5. Relationship between the Expected Future Tax Rate
and Current Value of Contributions 128
6.6. Shift of Formal Sector Labor Supply Curve as a Function
of Current Discount of Contributions 129
7.1. The Year When the Countries Are Expected to Reach
Poverty Headcount of 10 Percent 133
Preface
This is a book about income, inequality, and poverty during the remarkable
period of collapse of Communism and the "construction" of capitalism in
eighteen formerly socialist countries. It covers a period of almost ten years,
from the time of the early Gorbachevian reforms of 1987-88 to approximately
1996. The book was made possible by two almost simultaneous revolutions
that took place in the late 1980s. The first, of course, was the collapse of Com-
munism. The second was the opening up of information on income, inequal-
ity, and poverty, even in countries (such as the former Soviet Union) where
for the better part of the last seventy years such information had been treated
as a state secret.
This is indeed a period of great turmoil-comparable with the period
that followed both World Wars. It is the period of dramatic declines in in-
come, the reappearance of diseases long forgotten, growing poverty and un-
employment, and great uncertainty. But it is also a period when great for-
tunes are being made, consumer goods of incomparably better quality are
becoming available for many, and people have the opportunity to control
and radically alter their lives. Unlike during some previous episodes of tur-
moil, we now have relatively reliable and up-to-date information that allows
us to follow and analyze the developments. The goal of this book is precisely
this: to describe what happened during the transition in eighteen countries
-from the Czech Republic in the West to Kazakhstan and Russia in the East.
Specifically, the book will examine what happened to the real incomes of the
population, to the inequality with which incomes and expenditures are dis-
tributed, and to poverty. It will also attempt to find out why these changes
occurred.
A word about the data used in this book. All data on incomes and expen-
ditures come from household budget surveys. Most of these surveys are con-
ducted regularly (on an annual or quarterly basis) by national statistical of-
fices, while some are done by independent non-governmental organizations,
often in an ad hoc fashion. The surveys are of uneven quality. Some-for ex-
ample, the regular, official surveys conducted in most Central European coun-
tries-are of a quality comparable with surveys conducted in advanced mar-
ket economies. Because the quality of these surveys has remained unchanged
xi
xii Preface
or has improved during the transition, they allow us to measure the impact
of the transition relatively well. But in some other countries, the quality of
surveys has not improved, and their reliability may have deteriorated as cir-
cumstances changed. For example, while the surveys may have once offered
a more or less accurate picture of households employed in the state sector,
the decline in the size of that sector has significantly reduced the importance
of that information.
These are only some of the problems peculiar to transition economies.
Other data problems, common to surveys everywhere-such as inadequate
coverage of the very rich and very poor, differing concepts of income, and
differences in survey periods-further complicate the analysis undertaken
here. The interested reader can consult appendix 1, where issues related to
limitations of the data are discussed at length.
The book has its origin in the encouragement I received from Alan Gelb
of the World Bank. The book would not have been possible without the large
and varied data sources the World Bank has collected and developed during
the past seven years of work on the transition economies. In particular, I
benefited greatly from Jeni Klugman's and Jeanine Braithwaite's early work
on poverty in Russia, recently published by the World Bank as Poverty in
Russia: Public Policy and Private Responses.
I am grateful to the many colleagues, in the World Bank and elsewhere,
who helped me locate necessary data, or who gave me the benefit of their
advice. In particular, I am grateful to Jeanine Braithwaite, Mark Foley, and
Jeni Klugman, who generously allowed me to use their Russian data. I am
also grateful to Anna Ivanova, Nanak Kakwani, Alberto Martini, and Paolo
Roberti, who provided me with data for Belarus; Jifi Vecernik and Thesia
Gamer (Czech and Slovak Republics); Christiaan Grootaert, Gi-Taik Oh, and
Zsuzsa Ferge (Hungary); Helen Jensen and Edmunds Vaskis (Lithuania and
Latvia); Carlos Cavalcanti (Estonia); Mansoora Rashid (Romania); Jan
Rutkowski and Irena Topifnska (Poland); Tom Hopengartner (Ukraine); Milan
Vodopivec and Irena Krizman (Slovenia); Neeta Sirur (Bulgaria); Michael Mills
(Kyrgyz Republic); and Timothy Heleniak (countries of the former Soviet
Union).
The book also benefited from the comments of Martha de Melo, Clara
Else, Zsuzsa Ferge, Alan Gelb, Carol Graham, Christiaan Grootaert, Emmanuel
Jimenez, Jeni Klugman, Janos Kornai, Mark Kramer, Kathie Krumm, Robert
Liebenthal, Costas Michalopoulos, Aleksandra Posarac, Martin Schrenk, and
Maciej Zukowski. I am also thankful to the six anonymous reviewers who
read the draft manuscript and contributed valuable comments and sugges-
tions.
Preface xiii
Parts of this book were presented at seminars sponsored by various or-
ganizations: the Economic Development Institute of the World Bank in Wash-
ington, D.C., Harvard University, the Council of Europe in Strasbourg, the
Institute of Labor and Social Policy in Warsaw, the European Center in Vienna,
and the 1995 meetings of the American Economic Association in San Fran-
cisco. I am also grateful for the fine research assistance provided by Yvonne
Ying and Nadia Soboleva. Rachel Cantor did an excellent editing job. Virginia
Hitchcock ensured the consistency of the style and editing. Vicky Hilliard
and Kristi Stoner worked hard to put in shape the text, tables, and graphs: it
was not an easy book to produce.
To my mother, who has survived it all:
monarchy, capitalism, and Nazism;
Communism, socialism, and nationalism.
1
The Tectonic Changes
June 4,1989, was an eventful day. In Tehran, Imam Khomeini died. In Beijing,
tanks rolled in to suppress student demonstrations. In Poland, the first freely
contested election ever under a Communist regime took place.'
Political Developments: The New States
In June 1989, Communist regimes in Europe (including the U.S.S.R.) ruled
more than 24 million square kilometers of territory, or 17 percent of the world's
land area, and about 420 million people, or approximately 9 percent of the
world's population. These people lived in the Soviet Union, six smaller Com-
munist countries in Eastem Europe that were allied to the Soviet Union, and
two independent Communist states (Yugoslavia and Albania).2 In no coun-
try was organized opposition tolerated,' and in no country had genuine mul-
tiparty and free elections been held after 1947.4
Eight years after 1989-at the time of this writing-the landscape has en-
tirely changed. In all these countries, competitive elections have taken place,
even if these elections have not always been fair and transparent. In all these
countries, except a few (Armenia, Azerbaijan, Kazakhstan, Tajikistan,
Turkmenistan, and Uzbekistan), all kinds of political parties exist.5
1. Although the election was freely contested, the number of seats for the lower
house of Parliament was predetermined, giving Communists almost 50 percent of the
seats regardless of the outcome of balloting. The first entirely free elections in Eastern
Europe were held in March 1990 in Slovenia and Hungary.
2. Mongolia is not included in the analysis.
3. Technically, Bulgaria, the German Democratic Republic, and Poland were multi-
party states. Elections were held with predetermined seat allocations, however, and the
so-called opposition parties functioned as faithful allies of the ruling Communist party.
Yet, in a historic twist, it was the defection of the two "faithful" allied parties after the
June 1989 Polish elections that allowed non-Communists to form the government.
4. The last free election in any of these countries was the Hungarian election in
August 1947.
5. Turkmenistan had only a single-candidate election for Parliament (in December
1994). The president's term was simply extended by a referendum. In Uzbekistan, Birlik
and Elk opposition groups are banned and their leaders exiled. In Kazakhstan, parlia-
mentary elections in December 1995 featured almost no opposition parties, but a collec-
tion of government-sponsored parties, while the president's term was extended by refer-
endum. In Azerbaijan, the president was elected in September 1993 with 99 percent of the
vote and virtually no opponents. In February 1996, three major opposition parties were
banned. In Armenia, the major opposition party Dashnak was suspended in December
1994. In Tajikistan, a collection of opposition groups and the government are at war.
1
2 Income, Inequality, and Poverty during the Transition
The three Communist federations-Czechoslovakia, the Soviet Union, and
Yugoslavia-have disintegrated, spawning in their stead a total of twenty-
two countries. The number of countries in the world thus rose by approxi-
nmately 15 percent. One Communist country (the German Democratic Re-
public) disappeared. Only five of the former European Communist countries
(Albania, Bulgaria, Hungary, Poland, and Romania) have remained within
the same borders. Approximately 345 million are now citizens of countries
that did not exist prior to 1992. Forty-three million people in the forner
U.S.S.R. and 1.5 million in the former Yugoslavia now find themselves living
outside their ethnic republics.6
As figure 1.1 shows, the number of countries in Europe is now greater than
it has been in the last 140 years-even slightly greater than it was in 1860
before the Italian and German unifications. The difference now is that while
in 1860 the West was segmented (Italy in three states and Germany in twenty-
one), Eastern and Central Europe were largely unified under the Habsburg,
Ottoman, and Russian Empires.7
Figure 1.1. Number of Independent States in Europe, 1860-1995
50
Breakup of
Communist
40 federations
Aftermath of
World WarTI
30
20
German and
l0 Italian Nazi
unifications rule
0- I I I I 1.
1860 1880 1900 1920 1940 1960 1980 1995
Source: Calculated using Centennia software program.
6. The number for Yugoslavia does not include Bosnia and Herzegovina, the only
republic of twenty-three in the U.S.S.R., Czechoslovakia, and Yugoslavia not to have had
a "titular" nationality.
7. The Habsburg empire spun off six countries: Austria, Croatia, the Czech Repub-
lic, Hungary, Slovakia, and Slovenia. Also, parts of the Empire were, at different times,
taken over by Italy, Poland, Romania, and Yugoslavia. The Russian empire spun off
twelve European countries: Armenia, Azerbaijan, Belarus, Estonia, Finland, Georgia,
Moldova, Latvia, Lithuania, Poland, Russia, and Ukraine. The Ottoman empire spun off
three countries: Albania, Bosnia, and Bulgaria.
The Tectonic Changes 3
The current emergence of new countries implied huge needs for institu-
tional build-up, since many republics (particularly in the former Soviet Union)
had underdeveloped national institutions and little experience running their
own affairs. They are all practically "new" countries in the sense that they
either had almost no experience with self-government or were never inde-
pendent.8 Of the seventeen countries born out of the ruins of the three Com-
munist federations (not including Central Asian countries), five had not been
independent states during the last two centuries: Bosnia, the Czech Repub-
lic, Macedonia, Moldova, and Slovenia. Five (Armenia, Azerbaijan, Belarus,
Georgia, and Ukraine) were independent for three years or less during the
turmoil of the Russian civil war that followed the 1917 Revolution. Two coun-
tries (Croatia and Slovakia) were independent for a brief period of four to
five years during the Nazi rule over Europe. The three Baltic states had ap-
proximately twenty years of independent existence between the two World
Wars. Serbia was independent for about sixty years, from about 1860 until
1915, before it merged into Yugoslavia in 1918.9 And, finally, Russia, the big
exception, went through practically all the stages: an Empire, a Soviet Re-
public, and now, as the Russian Federation, an independent democratic coun-
try. Excluding Russia, however, the average duration of independence for
the other 'new" countries was less than nine years over a 200-year period
from the French Revolution until 1990.
In addition to the increase in the number of recognized countries, a num-
ber of states not recognized internationally have appeared-a new phenom-
enon in Europe. In virtually all countries with civil strife (see table 1.1), re-
gions have in their turn declared independence. Often such regions control
their own territory, a fact which in the past was sufficient to ensure them
international recognition. Such self-declared independent states include the
Transdnestrian Republic in Moldova, Chechnya in the Russian Federation,
South Ossetia and Abkhazia in Georgia, Nagorno-Karabakh in Azerbaijan,
and Kosovo in Yugoslavia. It is revealing that all regions, except the
Transdnestrian Republic, enjoyed the status of autonomous republic in the
U.S.S.R. or of province in the former Yugoslavia-a status one step below
that of a republic.'" The fissure along republican boundaries that rendered
asunder the three Communist federations is apparently continuing at the
next level of administrative organization.
8. Independence and self-governance are not necessarily the same thing: Hungary,
for example, was self-governing in the Austro-Hungarian Empire but was not indepen-
dent.
9. Montenegro, interestingly enough, had more experience of independence than
any other country (except, obviously, Russia). Both Montenegro and Serbia, as well as
Bulgaria and Romania, were officially recognized as independent states at the 1878 Berlin
congress.
10. Transdnestria was "added" to Moldova (then called Besarabia) when Besarabia
was annexed by the Soviet Union in 1940.
4 Income, Inequality, and Poverty during the Transition
Table 1.1. Countries at War or under Economic Blockade, 1991-96
Estimated Percentage
War on its internally decline in
territory displaced GDP
(pre-war Estimated persons (DP) between
population) dead and refugees 1987 and Economic
Country in millions) (in thousands) (in millions) 1996 sanctions
Armenia No (3.2) 0 DPs: 0.376 58 Azeri and
Ref.: n.a. Turkish
blockade
Azerbaijan Yes (7.2) 15 DPs: 0.9 66 No
Ref.: n.a.
Bosnia Yes (4.4) 250 DPs: 2.7 70a No
Refs.: 0.6
Croatia Yes (4.8) 20 DPs: 0.187 47 No
Refs.: 0.4
Georgia' Yes (5.5) 11 DPs: 0.28 67 Yes
Refs.: n.a.
Macedonia No (2.1) 0 n.a. 47 Greek
blockaded
Moldova Yes (4.3) 1 DPs: 0.015 61 No
Refs.: n.a.
Russia Yes (148.2) 100' DPs: 1.4 38 No
Refs.: 0.3
Tajikistan Yes (5.2) .50 DPs: n.a. 70 No
Refs.: 0.1
Yugoslavia No (10.4) 0.2 DPs: 0.65 41 UN
Refs.: 0.2 sanctionsb
Total 7 countries "450 =8f 40
with wars;
48 million peoples
n.a. = not available.
a. Estimate.
b. Imposed in May 1992; suspended in November 1995.
c. Includes two conflicts: in Abkhazia and Southern Ossetia.
d. Lifted in September 1995.
e. Casualties in Chechnya.
f. The numbers cannotbe added because of double-counting: out-refugees of one country are
in-refugees of another.
g. Excluding Russia.
Source: Data on refugees in Bosnia and Croatia are from UN High Commissioner for Refugees,
August 1995 (reported in Nasa Borba, August 20,1995). For Federal Republic of Yugoslavia, from
the census of refugees in April-June 1996. For countries of the former Soviet Union, from
International Migration Bulletin, No. 6, May 1995 (data at the end of 1994) and Heleniak (1997).
Sources for casualties: Tajikistan: Akchurin (1995); Azerbaijan: The Economist, November 13,1993,
p. 61; Croatia and Bosnia: Zimonjic-Peric (1995); Russia: RFE/RL Daily Service April 1994; Moldova,
Georgia and Azerbaijan: Gurr (1994).
The Tectonic Changes 5
War and civil strife have affected approximately 50 million people in the
war-tom countries profiled in table 1.1. Almost half a million people have
been killed and about 8 million (or approximately 15 percent of the countries'
population excluding Russia) have become refugees or displaced persons,
fleeing either persecution and war or leaving the newly independent states
where they are minorities for the relative safety of their "mother countries."
As shown in table 1.1, 4 million refugees and displaced people are from the
territory of the former Yugoslavia (or almost 20 percent of the former
Yugoslavia's population); 1.5 million are from the Transcaucasian countries;
and 1.5 million are from the Russian Federation. Those in war-torn countries
who have not been killed, maimed, or injured, or who have not become refu-
gees, have experienced plummeting standard of living. Best available esti-
mates suggest that nowhere has real per capita income decreased by less than
one-third. Major economic and social dislocations, of a size unseen since the
Second World War and its aftermath, are clearly underway.
By the end of 1997, only the bloodiest of these conflicts, the Bosnian had
been resolved. The intensity of all the conflicts, however, was less by the end
of 1997 than it had been one or two years before."'
Political instability has also been accompanied by coups, successful and
unsuccessful popular uprisings, and assassination attempts. A bloody coup
against Georgia's first democratically elected president brought Georgia's
former Communist party boss back to power. Later, Shevardnadze himself
was the target of a failed assassination attempt. Similarly, a creeping coup
against Azerbaijan's first elected president brought Azerbaijan's former Com-
munist leader, Haidar Aliyev, to power. Aliyev himself later faced an unsuc-
cessful coup. In Russia, the president dissolved Parliament, and the Parlia-
ment responded by attempting an armed rebellion. The president bombed
the deputies out of the Parliament building. Macedonia's first president nar-
rowly escaped an assassination attempt. The results of Armenia's presiden-
tial elections were so contested that the re-elected president had to call in
tanks and troops against his opponents. In Serbia, the ruling Socialists re-
fused to accept electoral defeat until three months of peaceful demonstra-
tions and international pressure obliged them to do so. In Albania, after the
fraud-marked parliamentary elections and collapse of a score of government-
sponsored pyramid schemes, the country plunged into anarchy. In Tajikistan,
an assassination attempt on the president failed but left several people dead.
The building of new democratic institutions is also hampered by the pau-
city of democratic traditions in virtually all transition countries."2 If democ-
11. The Abkhaz-Georgian, Nagomo-Karabakh, Chechen, and Transdnestrian conflicts
are in the cease-fire stage; war in Tajikistan is much less intense.
12. For a historical overview of political developments in Eastem Europe, see
Polonsky (1975) and Walters (1988). For Hungary, see Sugar, Hanak, and Frank, eds. (1990).
For Bulgaria, see McIntyre (1988). For the Baltic countries, see von Rauch (1974). For
Transcaucasia, see Goldenberg (1994). Very useful is also a beautiful historical atlas with
the text by Magocsi (1993).
6 Income, Inequality, and Poverty during the Transition
racy is defined as (a) universal and secret (male) suffragel3 with competitive
party elections, from which no important parties are banned; and (b) a gov-
ernment responsible to a parliament or to a democratically elected president,
then the longest pre-1990 democratic tradition is that of the Czech Republic
and Slovakia, which have twenty-three years of experience (1918-38 and 1945-
48). Romania and Serbia each have about twenty years of (checkered) experi-
ence with democracy,'4 followed by Latvia and Estonia each with about thir-
teen years (1920-33). Several countries had only a few years of experience
with democracy: Bulgaria during 1919-23 and 1926-35, Lithuania during
1920-26, Poland during 1922-26, and Armenia, Azerbaijan, and Georgia dur-
ing their brief periods of independence. All other countries under discussion
here have had no democratic experience at all-only a few democratic epi-
sodes, such as the period from March to November 1917 when Russia was
ruled by the provisional government, or Hungary between 1945 and 1947.
During the entire period following World War I, when universal franchise
became the norm in many Western countries, and until 1990, countries in
Eastern Europe and the former Soviet Union had an average of nine years of
democratic experience, and no country had lived in a democratic system for
more than twenty-five years.
Social Costs
The total value of goods and services produced by the vast area covered by the
transition economies, extending from the Baltic and the Adriatic in the West to
the Northern Pacific in the East, has declined since the transition started by at
least one-quarter in real terms. Expressed in dollars, the decline has been even
steeper, as many local currencies have depreciated. In 1989, the value of this
area's output, assessed at realistic (that is, not official) exchange rates, was about
US$1.2 trillion, or virtually the same as the gross domestic product (GDP) of the
Federal Republic of Germany, and three times that of China."5 The average an-
nual per capita income was about $3,000. In 1996, transition economies (exclud-
13. Democracy is defined in this restricted way in order to identify discrimination
based on social class and income, not gender.
14. Romania, from 1919 to 1938, and Serbia from 1903 to 1914, and then (as part of
Yugoslavia) from 1919 to 1929. (Obviously, other successor states of the former Yugoslavia
also experienced democracy between 1919 and 1929.) However, in both Romania and
Yugoslavia, there were problems. The respective prerogatives of the kings and parlia-
ments were not dearly spelled so that govemnments sometimes depended more on kings
than on parliaments (for example, Romania under Carol II during 1930-38, and Yugosla-
via under Alexander I during 1919-29). Not surprisingly, both kings (Alexander in 1929
and Carol in 1938) eventually suspended the constitution and assumed full power. Also,
fraud was massive during several Romanian elections and two large parties were banned
in Yugoslavia (the Communists after 1921 and the Croat Peasant Party briefly in 1925).
15. All data are from various years of the World Bank Atlas. All dollar amounts are
current U.S. dollars.
The Tectonic Changes 7
ing eastern Germany) produced goods and services valued at approximately
$880 billion, or only 40 percent of the united Germany's GDP and just 20 percent
more than China's GDP. The GDP of the Netherlands is equal to that of the
Russian Federation; the GDP of Finland is equal to that of Poland; and the GDP
of Hong Kong, China, is greater than the GDP of Ukraine. The region's average
GDP per capita has declined to about $2,000. After the Great Depression of 1929-
33, this decline represents the largest peacetime contraction of world output.
At the same time, poverty has increased substantially in the region. While
it was estimated that, in 1989, the number of people living on less than $4 per
day (at international prices) was 14 million (out of a population of approxi-
mately 360 million), it is now estimated that more than 140 million people
live below the same poverty line.16 (See chapter 5 for the calculations and the
discussion.) Social transfers, and free health care and education, once taken
for granted, are now rapidly shrinking. Mortality and morbidity, particu-
larly in the countries of the former Soviet Union, have increased substan-
tially in some cases, without peace-time precedent (Heleniak 1995). Unem-
ployment, almost nonexistent (except in the former Yugoslavia) before the
transition, affected more than 15 million people by 1996 and is still rising.
On a more positive note, the economic landscape is rapidly changing for
the better. In all the countries studied here, state ownership is being replaced
by private ownership (see table 1.2). While in 1989, only Hungary, Poland,
and Yugoslavia were members of the International Monetary Fund (IMF),
and none of them had a convertible currency, by 1996, thirteen of eighteen
countries shown in table 1.2 had convertible currencies. As an indicator of
monetary stabilization, by 1996, triple-digit inflation was present in only two
countries (Bulgaria and Turkmenistan), and in several countries (the Czech
Republic, Slovakia, and Slovenia), inflation was reduced to single-digit levels.
Such massive dislocations-the creation of new states, civil wars, and de-
clines in GDP-have had huge social costs. Conceptually, we can divide these
costs into three categories.
First, costs associated with decreases in output due to systemic changes
(that is, the transition to market economy) and to macroeconomic stabili-
zation. These costs are expressed in lower incomes, higher inequality, and
greater poverty.
Second, job-loss costs associated with the transition. Job-loss is sometimes
accompanied by poverty but not always. Unemployment is a distinct issue
from poverty.
Third, costs associated with civil strife. These are costs of lives lost, people
becoming refugees, and destruction of property.
In this book we shall deal only with the first type of costs. The second
(unemployment) will be discussed only to the extent that it affects poverty
16. This calculation does not include the countries at war or with civil strife listed in
table 1.1. (Russia is included though.)
Table 1.2. Speed of Reforms in Selected Countries, 1992-96
Current
account
Estimated privatized Non-state sector Share of retail trade (exchange Explicit producer
state assets share in total sales in private hands rate) subsidies CPI inflation
Country (percent) employment (percent) (percent) convertibility (percent of GDP) in 1996
Belarus Slow 28 (96) 39 (96) No 9 (92) 40
13 (95)
Bulgaria Slow No 310
Czech Republic Es
Estonia Fs
Hungary Medium 4 9)2
49 (end 94)
Kazakhstan Medium 37 (95) 5 (93) 29
8 (92)
Kyrgyz Medium T3
Republic 35 (93) ......
Latvia Eas
Lithuania Fast
Moldova Slow
Current
Non-state sector account
Estimated privatized share in total Share of retail trade (exchange Explicit producer
state assets employment sales in private rate) subsidies CPI inflation
Country (percent) (percent) hands (percent) convertibility (percent of GDP) in 1996
Poland Medium 64(4 ). ...
35 (93).
Romania Slow 9 No 6 (93) 57
8 (94)
Russia Fat63(96 7(6)Ys7 (93) 2
70 (end 94) _ - _
Slovakia Medium 21 (92) 949)Ys5 (926
Slovenia Slow 28 (94)
Turkmenistan Slw6 9)N 9)100
Ukraine Slow 33 (95) - 76 (96-),Ye 4(96) 40
1 (93) . . - -:B_.-.-.. ___
Uzbekistan Slow 63 (95) 74 (96) No 86
Note: Areas are shaded if a variable takes the value at least equal or 'better" than "fast" for privatized state assets, 40 percent for non-state share in total
employment, 50 percent for share of private retail trade, 'yes" for current account convertibility, 'less than 5 percent" for producer subsidies, and "less than
20 percent" for inflation. All data in parentheses refer to the year.
Source: See appendix 7.
OL
LZ6[ 13]YVAON
......... ... ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ -
~~~~~~ -4 ~ ~ ~ ~ ~ ~ ~ ~~~~~~~
a ~~~~~~~~~~~~~~~~~~~~~~~~~~
1' 1 ~~~~~~~~~~~~~~~~ ~~~~~ Z 0
... .. .. ..
.........."~
... .....s...1uJ unao~ 3f v~y ~~
The Tectonic Changes 11
and inequality. The third type is not, properly speaking, a cost of economic
transition, but rather the cost of nation-building and the creation of a "new
world order" Responsibility for the latter cost lies with politicians, not
economists.
In this book, the topics of income, inequality, and poverty shall be dis-
cussed only in reference to the countries listed in table 1.2, that is, the eigh-
teen countries unaffected by military conflict.17 They are shown in figure 1.2.
The organization of the rest of the book is as follows. Chapter 2 will
review the key features of income composition, income inequality, and
social policy in the former system. Chapter 3 will look at what happened
to the population's real income-its size and composition-during the
transition. Chapter 4 will analyze the inequality with which income and
expenditures are distributed and the change in the relative position of
different social groups during the transition. Chapter 5 will assess what
happened to poverty under the twin impact of declining income and grow-
ing inequality. Chapter 6 discusses social policy issues related to the tran-
sition. Chapter 7 presents a brief look at what might lie ahead.
17. The only exception is Russia, which had a limited military conflict on its terri-
tory, but is obviously too important not to include.
2
The Way It Was
Income Composition and Inequality
The defining characteristic of socialist countries was state ownership of the
means of production. Though this ownership took various forms-direct state
ownership, "social" ownership, or some form of collective ownership-and
though the management rights of the state varied-from direct allocative cen-
tral planning to bureaucratic interference in decentralized decision-making-
the role of the state was undeniably major. On average, 90 percent of the labor
force was employed by the state as compared to members of the Organisation
for Economic Co-operation and Development (OECD), where an average of
21 percent was so employed (see table 2.1). On the household level, the role of
the state was reflected in the fact that most income was received through the
mediation of the state: through wages paid to employees working in state-
owned enterprises (SOEs) (including farms) or in government; pensions paid
out of state-administered funds or directly out of the budget; and family al-
lowances, scholarships, etc. Only in countries with private agriculture (Po-
land, Yugoslavia, and, more recently, Bulgaria and Hungary) were private
sources of income of some importance. In other countries, private income was
Table 2.1. State Employment as a Proportion of the Labor Force, 1988
(percent)
Country Share
Socialist average 90.0
Czechoslovakia 98.8
U.S.S.R. 96.3
Romania 95.2
German Democratic Republic 94.7
Hungary 93.9
Bulgaria 91.5
Yugoslavia 78.9
Poland 70.4
OECD average 21.2
Note: All averages are unweighted. Czechoslovakia, Poland, and Romania, 1989; German
Democratic Republic, 1987. The state sector includes the government, social services run by the
state (health and education), and state-owned enterprises (SOEs), including agricultural
cooperatives (kolkhozes in the U.S.S.R.). "Social sector" in Yugoslavia is treated as a state sector.
OECD data from the late 1970s and mid-1980s.
Source: Milanovic (1994a, appendix 1).
12
The Way It Was 13
minimal and limited to income from small-scale agricultural plots, self-con-
sumption, gifts or remittances, moonlighting, and black market activities.
Table 2.2 shows the composition of household income in socialist countries
before the transition. Countries are arranged, from left to right, in the order of
the increasing importance of private sources in total gross income.' At one
end of the spectrum is Czechoslovakia, where an almost total "socialization"
of agriculture and severe restrictions on non-agricultural private business
meant that only a small fraction (5 percent) of total income was not received
through state mediation. In the Soviet Union, approximately 14.4 percent of
gross income was derived from private sources. In Bulgaria, Hungary, Yugo-
slavia, and Poland, the private income share was greater than 20 percent.
Labor income-virtually all of it derived from the state-ranged from 53
percent of total gross income (in Poland) to 72 percent (in the U.S.S.R.). Social
transfers ranged between 13 percent (Yugoslavia and the U.S.S.R.) and 25
percent (Czechoslovakia) of gross income.
Table 2.2. Composition of Income in Socialist Economies, 1988-89
(gross income=100)
Czecho-
Income source slovakia U.S.S.R. BulgariaHungary YugoslaviaPoland
Primary income 72.9 78.8 71.2 71.7 83.1 78.2
Labor income 69.5 72.0 56.5 55.0 62.2 53.0
Self-employment income 3.4 6.8 14.7 14.0 20.9 25.2
Property income n.a. n.a. n.a. 2.7 n.a. n.a.
Social transfers 25.4 13.6 21.2 22.4 13.3 20.7
Pensions 16.5 8.0 16.6 13.4 12.1 14.3
Child benefits 5.6 1.2 2.3 6.0 1.2 5.2
Other cash transfers 3.3 4.4 2.3 3.0 0.0 1.2
Other incomea 1.7 7.6 7.6 6.0 3.6 1.1
Gross income 100.0 100.0 100.0 100.0 100.0 100.0
Personal taxes 14.2 n.a. n.a. 16.5 1.2 1.6
Direct taxes 0.0 n.a. n.a. 10.7 1.2 1.6
Payroll tax (employee)b 14.2 0.0 0.0 5.8 0.0 0.0
Memo: Private incomec 5.1 14.4 22.3 22.7 24.5 26.3
n.a. = not available.
a. Includes private transfers (gifts, alimony, remittances), insurance and lottery receipts, and
rental income (if not included in property income).
b. Except in Hungary and Czechoslovakia, the entire payroll tax was paid by enterprises.
c. Equal to self-employment income, property income, and other income.
Source: Czechoslovakia: Microcensus 1988. Poland, Bulgaria and Yugoslavia: 1989 Household
Budget Surveys; Hungary: 1989 Income Survey; U.S.S.R.: 1988 Family Budget Survey.
1. The entire wage income is here allocated to the state sector. This is only a very
slight exaggeration because the private sector (outside of self-employment) was minimal.
14 Income, Inequality, and Poverty during the Transition
Table 2.3. Composition of Gross Income in Socialist, Market, and Developing
Countries, 1980s
(percent)
Socialist Market Developing
countries countries countries
Primary income 77 85 90
Labor income 63 64 35
Self-employment income 13 14 48
Property income 1 5 6 a
Occupational (private) pensions 0 2 0
Social transfers 19 14 3
Pensions 13 12 2
Child benefits 4 1 0
Other cash transfers 2 1 0
Other income b 6 1 7
Gross income 100 100 100
Total taxes 34 38 n.a.
Direct taxes 3 20 n.a.
Payroll tax (employee) 7 5 n.a.
Payroll tax (employer) c 24 13 n.a.
GDP in '000 $PPP (1988, per capita) 5.5 14.0 1.8 d
Note and Source: All averages are unweighted. Socialist is the average of the countries listed in
table 2.2. Market economies are the following eleven OECD countries: Australia, Canada, France,
West Germany, Israel, New Zealand, Norway, Spain, Sweden, the United Kingdom, and the
United States. All of the data are from 1979-81, with the exception of Australia and New Zealand
(1985-86) and Spain (1988). The source for the first seven countries is LIS data reported in
O'Higgins, Schmaus, and Stevenson (1989, p. 107); New Zealand and Australia from Saunders,
Stott, and Hobbes (1991); France from Sologoub (1988, p.5); Spain calculated from INE (1989,
pp. 366-67). Developing countries are represented by C6te d'Ivoire, Ghana, Jordan, Peru,
Madagascar, and Vietnam with sources respectively Kozel (1991, p.15), Boateng and others (1992,
p. 22), World Bank (1993, p. 33), World Bank (1994), World Bank (1996c, p.22), and Vietnam State
Planning Comrmittee General Statistical Office (1994, p. 218).
n.a. = not available.
a. Includes inputed rent.
b. Includes private transfers (gifts, alimony, remittances), insurance and lottery receipts.
c. Estimate based on 40 percent enterprise-paid payroll in socialist countries and 20 percent in
market economies.
d. Excludes Vietnam.
Features specific to socialism are more apparent when the average income
composition for former socialist countries is contrasted with income composi-
tion of developed market economies and of developing countries (see table 2.3).
There are three such features.
1. The share of primary income, that is, income that results from eco-
nomic activity (labor, capital, entrepreneurship), was smaller in so-
cialist than in market economies or developing countries. This is a re-
flection of three phenomena: (a) virtual absence of property incomes;
(b) absence of occupational pensions (which are considered part of
primary income); and (c) greater importance of income redistribution,
The Way It Was 15
whether via the state (social transfers in socialist countries represented
19 percent of gross income as compared with 14 percent in market
economies) or privately (6 percent in socialist countries as compared
with 1 percent in market economies).2
2. Much lower direct taxation under socialism (personal income taxes
plus employee-paid payroll tax): 10 percent of gross income as com-
pared with 25 percent for market economies. Total payroll taxes were
high, however (between 40 and 50 percent of net wages), and the over-
all tax burden was not much different from that found in market econo-
mies.
3. Child benefits were more important in socialist countries than in mar-
ket economies (4 percent of gross income as compared with 1 percent).
In addition, the following regularities ("stylized facts") regarding the dis-
tribution of income in socialist countries are generally accepted.
4. Overall income distribution was more egalitarian than in most market
economies even after allowing for (a) fringe benefits and various forms
of implicit income received by the nomenklatura and (b) direct subsi-
dies.3 The distribution of direct subsidies generally favored the poorer
segments of the population. 4 Element (a) pulled income inequality up
while element (b) pulled income inequality down. When both
nomenklatura benefits and subsidies were added to measured income,
overall inequality did not change much. The Gini coefficient was in
the range of 23 to 265 or slightly above that of very egalitarian Nordic
countries, but definitely below inequality in other OECD countries, or
other countries at similar level of development. 6
2. Most of other income is private transfers.
3. There are numerous studies comparing income inequality in socialism and
capitalism. I will cite only a few: Atkinson and Micklewright (1992), which present the
most detailed study of inequality and poverty in Central Europe (Czechoslovakia,
Hungary, and Poland) and Russia during the post-War period. Phelps-Brown (1988) and
Morrisson (1984) contrast East and West European income distribution. Muller and oth-
ers (1991) compare the German Democratic Republic and the Federal Republic of Ger-
many. Bergson's (1984) excellent review of inequality in the Soviet Union, with its exten-
sive bibliography, is a dassic in the field. Good comparative studies of several socialist
countries include Asselain (1987), Kende (1987), Debroy (1986), and Flakierski (1986,1989,
1993).
4. See Milanovic (1994, pp. 193-95), where results of several studies of East Eu-
ropean countries are discussed. For the German Democratic Republic, see Bird, Frick,
and Wagner (1995, p. 6), who show that the simultaneous inclusion of consumer subsi-
dies and benefits for the nomenklatura leaves the calculated Gini coefficient unchanged.
For discussion of Czechoslovakia, see Hirsl, Rusnok, and Fassman (1995, p. 2).
5. Except in Yugoslavia, where it was greater because of huge regional differences
in average income. For the exact values of the Gini before the transition, see table 4.1.
6. In the work on the Kuznets' curve, it was standard practice to introduce a dummy
variable for socialist countries. The dummy variable was always found significant and
negative. That is, socialism was found to reduce inequality below its expected level (see,
for example, Ahluwalia (1976) and Kaelble and Thomas (1991)).
16 Income, Inequality, and Poverty during the Transition
5. Cash social transfers were distributed alnost equally per head in so-
cialist countries in contrast to some market economies where such trans-
fers were more focused on the poor.7As shown in figure 2.1, the abso-
lute amounts of cash transfers in Czechoslovakia, Hungary, and Poland
were approximately equal for the poor and the rich (a value of 1 indi-
cates that the share of the decile in the total amount of transfers was 10
percent; a value of 2 indicates that the decile of recipients received 20
percent of transfers, and so on). The concentration coefficient of cash
transfers was thus very close to zero in socialist economies.8"9
The approximate equality of cash transfers per capita regardless of the re-
cipients' position in income distribution was the outcome of two separate
distributions: pensions, and other cash transfers, among which family allow-
ances were the most important. 10
Pensions tended to be slightly pro-rich in absolute terms. The positive cor-
relation between recipients' positions in income distribution and their level
of pensions was stronger in countries where pensions were relatively high
(that is, where the average-pension-to-average-wage ratio was greater than
7. Market economies differ greatly in terms of concentration of cash transfers. Dif-
ferences in results are driven by differences in (a) how pensions are administered (for
example, whether the pension system is mostly state-financed, in which case pensions
are a part of social transfers, or differently, occupational and other private pension schemes
are important), and (b) the relative position of pensioners (whether they are relatively
well off or not). Thus, while some countries (for example, Australia, Finland, and Swit-
zerland) target social transfers on the poor (so that the concentration coefficients of social
transfers is around -10), other countries (Belgium, Germany, and Sweden) have strongly
positive concentration coefficients for transfers showing that there is a positive correla-
tion between disposable income and the amount of received social transfers. For the defi-
nition of the concentration coefficient, see footnote 9 below. (Data on market economies
are derived from the LIS data base, and cover the late 1980s to the early 1990s; see Mlanovic
1995c, table 2.)
8. The discussion of the distributional incidence of transfers (stylized fact 5) and
taxes (stylized fact 8 below) is based on Milanovic (1994, p. 178) and Milanovic (1995).
Using an equivalent scale rather than a per capita measure (for example, assigning less
weight to children than to adults) would tend to place families with children higher, and
pensioners lower, in the income-distribution scale. Because pensions account for a bulk
of cash social transfers, the distributional impact of cash transfers might then become
more pro-poor.
9. The concentration coefficient shows the concentration (or cumulative percent-
age) of an income source (for example, social transfers or wages), when recipients are
ranked by amount of disposable income. The coefficient ranges from -1 (or -100 if ex-
pressed in percentages as is done here), when the entire income source is received by the
poorest (by income) recipient; through 0, when all recipients receive the same amount; to
+100 when the entire income source is received by the richest recipients. A concentration
coefficient's negative (or positive) value shows that a given source is negatively (or posi-
tively) correlated with overall income.
10. Family allowances indude all family-related transfers: birth grants, maternity
pay, regular monthly family allowances, and so on.
The Way It Was 17
Figure 2.1. Distribution of Social Cash Transfers by Income Decile
2.0 -
United Kingdom
, 1.5 Ireland!
1.0
0.5
1 2 3 4 5 6 7 8 9 10
Income decile
Note: Equal per capita distribution = 1. CSSR = Czechoslovak Socialist Republic.
Source: Milanovic (1995, p. 495).
50 percent) as in Eastern Europe. In the late 1980s, pension concentration
coefficients ranged between +5 and +10 in Eastern Europe (see the concen-
tration curves for Czechoslovakia and Hungary in figure 2.2, which just barely
depart-to the right-from the line of perfect equality). 11 By contrast, in the
Soviet Union, where the pension-wage ratio was smaller, pensioners belonged
to lower income groups, and the concentration coefficient was strongly nega-
tive (see data for Russia in figure 2.2). Transfers other than pensions were
mildly pro-poor with concentration coefficients ranging between -5 and -15.
Most progressivity of non-pension transfers was due to family allowances
and other family-related transfers (for example, child care and maternity al-
lowances). Thus, the mildly pro-rich or neutral pensions and the mildly pro-
poor family allowances combined to produce a basically flat distribution of
11. The concentration curve shows the cumulative percentage of income source (on
the y axis) against the cumulative percentage of recipients (on the x axis). An income
source is said to be pro-poor in absolute terms, if its absolute amount declines with in-
creases in income (for example, social assistance). Such a source is associated with a nega-
tive concentration coefficient and a concentration curve that lies above the 45-degree line
(for example, see pensions for the Russian Republic in figure 2.2). The opposite is true for
a source that is pro-rich in absolute terms.
18 Income, Inequality, and Poverty during the Transition
Figure 2.2. Concentration Curvefor Pensions in Hungary, Czechoslovakia,
and the Russian Republic, 1988-89
100 -
.~80
CSR1988
0 4
Russia 1989 /
60-
40-
CZ
40
0 20 40 60 80
Individuals ranked by household per capita income
Note: CSSR Czechoslovak Socialist Republic.
Source: Milanovic (1995, table 1).
social transfers-social transfers were paid more or less equally across the
entire income spectrum.
6. In countries where private sector incomes were low, the share of pri-
vate sector incomes in gross income was broadly flat across income
distribution. In countries with private agriculture, where private in-
comes were more important, the share of private income in gross in-
come exhibited an inverted U-shaped pattern-being high in relation
to overall income at the low and high end of income distribution. This
meant that most "private-oriented" households were either poor agri-
cultural households (often living barely above the subsistence level)
or very successful, even if not very numerous, private businesses (see
Milanovic 1991, and Marnie and Micklewright 1993, figure 5).
7. There is some debate regarding inequality of wage distribution. Ac-
cording to some (for example, Atkinson and Micklewright 1992, chap-
The Way It Was 19
ter 4; Phelps-Brown 1988, pp.39-51; and Lydall 1968, pp. 152-62), wage
distribution in socialist countries was more egalitarian than in market
economies. 12 According to others (for example, Redor 1992), the dif-
ferences between the two distributions were not systemic-that is, they
could be explained by elements other than the difference in the eco-
nomic system, such as the size of the country, enterprise concentra-
tion, and participation rates (although the latter two can be regarded
as system induced). Most authors, however, agree that the difference
between average pay of non-manual and manual workers was less in
socialist countries than it was in market economies and that returns to
education were therefore less (see Redor 1992, pp. 60ff.; Rutkowski
1996; and Jackman and Rutkowski 1994).'3
8. Direct taxation was proportional to wages because most direct taxes
were paid as proportional payroll taxes. Progressive direct taxes were
minimal and no country had a personal income tax system similar to
systems common in the West."4 Taxes had virtually no redistributive
effect: if, on average, direct taxes were proportional to wages, which
were not distributed much differently from overall income, it follows
that tax distribution was close to the distribution of income-taxes, in
other words, were proportional (or "flat," to use the current terminol-
ogy) rather than progressive (Milanovic 1994, pp. 184-86).
These eight regularities ("stylized facts") define the distribution of income
in socialist countries relatively well. But what was the logic of the system
that engendered such distribution?
Ideological Underpinnings
According to socialist ideology, most of the population was supposed to work
in the state sector. To have people employed by the state was both a state-
ment of the ideological objective and a means. The objective was to speed up
realization of a "developed socialism" characterized by a predominance of
the state sector. The means for achieving this objective was fast economic
growth, which in turn meant fast growth of the state sector, because the state
sector was (not unlike in Schumpeter's Capitalism, Socialism and Democracy)
regarded as more efficient than the private sector. It is worth remembering
that the time when Communists came to power was in many countries the
epoch of the "big is beautiful." The state sector was also considered to be the
12. For example, Phelps-Brown (1988) notes the absence of the Paretian right-end
tail (that is, highest earnings) in socialist wage distributions.
13. Redor (1992, p. 63) finds that the difference between average manual and non-
manual wage explained between 17 and 30 percent of the overall wage inequality in the
Federal Republic of Germany and France and practically none in Eastern Europe.
14. In 1988 Hungary became the first to introduce a real personal income tax system.
20 Income, Inequality, and Poverty during the Transition
best vehicle for transferring the labor force from agriculture to industry and
from rural to urban areas (particularly since socialism won mostly in agrar-
ian countries).
In consequence, the state sector was to be developed, while the private
sector was to be allowed to wither away, or was, at best, to be tolerated. 15
High participation rates for both men and women were to be encouraged, as
work in the state sector was also a way to be usefully integrated into society.
High participation of women was needed both to speed up growth (by uti-
lizing all labor resources) and as a proof of the equality of the sexes, an early
Communist objective. High participation rates combined with a generally
pro-children stance (as reflected in free health care and education) resulted
also in a heavy emphasis on family allowances, the size of which, in relation
to wages, could be several times greater than in market economies (see table
2.4). Family allowances also introduced an element of reward "according to
needs"-a harbinger of distribution as it should be, according to Marx, in a
developed Communist society.
Ideological views on wage distribution were ambivalent. On the one hand,
socialist ideology is against wide differences in income; on the other hand,
people should be paid according to their contribution, at least in theory, which
means that differences in abilities and effort should be recognized. Marx's
dictum that under socialism, workers are paid according to their work (as
opposed to "communism," where workers should be paid according to their
needs), as well as the influence of Taylorism on the early Bolshevik practice,
meant that wage differences were not only to be tolerated, but accepted. 16
Uravnilovka, or leveling, had, from the early days of Soviet Communism, ac-
quired a negative ideological connotation.
Manual labor was, in general, preferred to non-manual labor: small differ-
ences in wage between manual and non-manual labor reflected this ideo-
logical preference. The preference itself had roots in the crude (and actually
mistaken) interpretation of Marx's concept of "productive" and "unproduc-
tive" labor, and in the nomenklatura's belief that manual workers represented
the "salt of the earth" for the Communist party and were politically more
reliable than intellectuals."7
15. Incidentally, this view resulted in a desire to statistically demonstrate the progress
of socialization by contrasting the shares of the state and private sectors. This explains
why such statistics are more readily available in formerly socialist countries than they
are in market economies.
16. Taylorism remained quite alive well into the 1970s. Redor (1992, p. 159) points
out that in the late 1970s, piece-rate pay was much more common in Eastem Europe and
the Soviet Union than it was in OECD economies. About 50 percent of industrial workers
in socialist countries were paid according to the piece-rate system. Corresponding per-
centages in the West ranged from 5 percent in Belgium, to 12 percent in France, to 22
percent in West Germany.
17. A high percentage of Communist leaders were workers or came from a working-
class background. Often they were skilled (metal) workers rather than less-skilled (say,
textile) workers. Examples include Brezhnev in the U.S.S.R., Tito in Yugoslavia, Kadar in
Hungary, and Novotnr in Czechoslovakia.
The Way It Was 21
Table 2.4. Family Allowancefor Two Children as Percentage of Average Earnings,
1988
Country Percentage of average earnings
Hungary 24.9
Bulgaria 20.0
Czechoslovakia 19.6
Poland 17.0
Austria 16.9
Belgium 10.7
Netherlands 9.0
United Kingdom 8.2
France 6.5
Italy 5.4
United States, 0.0
Note: Does not include tax deductions available for families with children (important in the
United States and France) and thus underestimates the amount of family benefits in the market
economies.
a. No universal child allowance.
Source: Sipos (1994).
Finally, individual accumulation of wealth was frowned upon for ideo-
logical and pragmatic reasons. Ideologically, Communists were against big
differences in wealth. Large wealth inevitably "overflows" into the pro-
duction process: initially such wealth may be used for personal consump-
tion (for example, for purchasing a house and a car), but eventually it will
be used to obtain ownership of the "means of production" complete with
hired labor, an outcome which is obviously antithetical to socialism. On
pragmatic grounds, Communists were also against private wealth because
wealth provides "an island of liberty" for those who have it. The rich can
become independent from imposed political obedience. Thus, Communist
authorities preferred collective consumption and income-in-kind to cash
rewards. Collective consumption (for example, state-sponsored sanatoria
and kindergartens, or free vacations) and income-in-kind (for example,
plush houses for the nomenklatura) have the advantage that they appear
more "collectivistic," they cannot be accumulated, and they can easily be
withdrawn if one fails to toe the political line.
From the above tenets, the shapes of "ideal" socialist income distribu-
tion emerge. Most income is earned in the state sector (pensions received
after retirement are an extension of state-sector employment); high partici-
pation rates obtain; unemployment is non-existent; family allowances are
relatively high; wages are compressed with low pay in intellectual profes-
sions; collective consumption and income-in-kind are important; and wealth
accumulation is minimal. Actual income distributions in socialist coun-
22 Income, Inequality, and Poverty during the Transition
tries came reasonably close to this ideal. Actual income distributions, there-
fore, did not evolve by accident: they were logical extensions of the ideo-
logical premises of Communism, which, conveniently, often coincided with
the political interests of Communist rulers.
3
Income
Changes in income are the most decisive factor influencing poverty. This is
especially true when income declines are as great as those found in Eastern
Europe and the former Soviet Union. Changes in income distribution, as a
factor influencing poverty, then take a second place.
The Post-Communist Great Depression
Incomes
Table 3.1 shows the average (weighted) change in the officially recorded GDP
during 1987-96.l In Eastern Europe as a whole, growth fell from about 2
percent in 1987 and 1988 to slightly above zero percent in 1989. In 1990, the
average growth rate was strongly negative (-8.2 percent). The decline reached
its maximum in 1991 (-14.7 percent). Eventually, in the 1994-96 period, East-
em Europe grew at the rate of about 4 percent per year, and growth spread to
almost all countries in the region.2 In 1996, overall GDP in Eastern Europe
was approximately 80 percent of its 1987 level. The trend in the former Soviet
Union was similar to that in Eastern Europe with about a year, at first, and
then apparently longer time lag. The Soviet GDP continued to grow until
1989.3 It shrank by 2.5 percent in 1990 and by 6.5 percent in 1991, and in 1992
it fell precipitously by 16 percent. In the following two years, the region's
GDP experienced a double-digit decline. The combined GDP of countries
that composed the former U.S.S.R. was in 1996 approximately 60 percent of
its 1987 level.
1. The officially recorded decline in GDP almost certainly exaggerates the actual
decline (see the section, The effect on population incomes: how level and composition of income
changed, below). It is nonetheless necessary to begin with official measures-which in the
further text will be adjusted (to the extent possible). The year 1987 is used for comparison
purposes because it was a year before serious reforms were initiated and because eco-
nomic levels reached by socialist countries in 1987-while not likely to improve by much-
could have been sustained for a prolonged period.
2. In 1994 and 1995, only the Former Yugoslav Republic of Macedonia experienced
negative growth, as did Bulgaria in 1996.
3. According to the altemative calculations of Khanin (1992), however, the Soviet
GDP had begun to decline already by the second half of 1988 (yielding zero growth for
the year as a whole), and in 1989 it shrank by 2 percent. For a critique of Khanin's meth-
odology, see Kudrov (1995,1996). For a general discussion of Russian post-Soviet macro-
economic data, see Bloem, Cotterell, and Gigantes (1995) and Koen (1996).
23
24 Income, Inequality, and Poverty during the Transition
Figure 3.1. Distribution of Countries' Growth Rates in Eastern Europe, 1987-96
14
12 ...D.LI ....
10
1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
o Double-digit decline EL Single-digit decline Ol Growth
Figure 3.2. Distribpution of Countries' Growth Rates in the Former Soviet Union,
1987-96
12
0
1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
o Double-digit decline El Single-digit decline O Growth
Note: The Czech Republic, the Slovak Republic, and all republics of the former Yugoslavia and
Soviet Union are shown here as separate countries. German Democratic Republic is not included.
Bosnia and Herzegovina is not included after 1992.
Income 25
Table 3.1. GDP Growth Rates in Eastern Europe and the Former Soviet Union,
1987-96
(percent per annum)
Region 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Eastern Europe 1.9 1.6 0.5 -8.2 -14.7 -8.1 -1.9 4.1 5.5 3.6
Former Soviet Union 2.4 5.2 2.7 -2.5 -6.5 -16.1 -10.1 -14.0 -5.2 -4.7
Total 2.3 4.0 1.9 -3.7 -8.6 -14.3 -8.1 -9.2 -1.9 -2.0
Note: Eastem Europe indudes the German Democratic Republic until 1989.
Source. Official goverment statistics. Data for the Soviet Union are official statistics except for
Russia where the revised official GDP statistics published by the World Bank and Government
of the Russian Federation (1995) are used also for the period before the Soviet Union's breakup.
There are minimal differences between these statistics and PlanEcon statistics or the data
published by World Bank Socio-economic Data Division (1992).
How widespread, deep, and long-lasting was the post-Communist depres-
sion? Depression is defined as involving at least two years of continuous
GDP decline. According to this definition, all transition economies experi-
enced a depression after 1990; no country experienced fewer than three years
of consecutive GDP decline.
Both the GDP decline and its subsequent recovery affected all the countries in
the region (figures 3.1 and 3.2). In 1987, most socialist countries recorded growth
rates of between 2 and 3 percent per year, and only Albania, Armenia, Georgia,
Tajikistan, and the former Yugoslavia had negative growth.4 The situation did
not change much in 1988 and 1989. By 1990, however, all East European coun-
tries and all republics of the former Soviet Union (except two: Uzbekistan and
Turkmenistan) were in a decline. For three years, during 1990-92, in a remark-
able unison, the GDPs of almost all twenty-eight countries dropped, with the
GDP of more than half the countries shrinking more than 10 percent annually.
Only one country (Poland) posted positive, if minimal, growth (1.5 percent) in
1992. By 1993, half the East European countries (Albania, Poland, Romania, and
Slovenia) were growing again, and by 1994 and 1995, almost all were. In the
former Soviet Union, however, the decline was still universal in 1993. Three
countries turned the corner in 1994 (Armenia, Latvia, and Lithuania), and by
1996, nine of fifteen countries were growing again, although the largest coun-
tries (the Russian Federation and Ukraine) continued to post negative growth
rates.
The depth of the depression is best assessed by comparing it to the 1929-33
Great Depression. Figure 3.3 shows the GDPs of Russia and Poland, and the
United States and Germahy. Data on Poland and Russia are presented be-
cause of the size of these countries (combined, they produce almost one-half
the output of all transition economies) and because of the radicalism of their
reforms. For Poland and Russia, the base year is 1987; for Germany and the
4. The then-republics of Czechoslovakia, the U.S.S.R., and Yugoslavia are consid-
ered separate countries here for the sake of comparison.
26 Income, Inequality, and Poverty during the Transition
Figure 3.3. Real GDP in Poland and Russia (1987-95); and in the United States
and Germany (1927-35)
Germany,
100- .........
.4Poland
90
United StatesZ
r| 80 "
70-
Russia
60-
27-87 28-88 29-89 30-90 31-91 32-92 33-93 34-94 35-95
Years
Note: 1927 = 100 for the United States and Gernany; 1987 = 100 for Poland and Russia.
Source: The Great Depression data, unless noted otherwise, are from Fozouni, Gelb, and Schrenk
(1992).
United States, the most hard-hit countries during the Great Depression, the
base year is 1927. For all countries, peak output is reached in the third year
following the base year. If Russia is compared with Germany and the United
States, it emerges that initially, the decline was steeper in Germany and, particu-
larly, in the United States.5 By the third year of depression (1932 and 1992), GDPs
in the United States and Germany were approximately 20 percent below pre-
depression levels; in Russia, the GDP was 14 percent below. Then, the situation
reversed. In 1933, Germany grew quickly, at a rate of approximately 10 percent
per year, and the U.S. GDP declined, although by very little, while Russia's GDP
continued its plunge, in both 1993 and 1994 by 9 and 13 percent, respectively.
Thus, by 1935, Germany was above its base GDP level, the United States was
approximately 15 percent below, while Russia in 1995 was 34 percent below its
base level. In other words, the post-Communist depression in Russia is deeper
than the Great Depression was in the United States and Germany. The depres-
5. Throughout, revised GDP statistics for the Russian Federation published jointly
by the World Bank and Government of the Russian Federation (1995) are used. The
revised GDP is approximately 6 to 7 percent higher for the period 1991-93 than that
recorded by earlier official statistics. There is virtually no difference between the two
sets of numbers for 1990 and 1994 (see World Bank and Government of the Russian
Federation, 1995, p. 94).
Income 27
sion in Poland, although deeper during the first two years (1990-91), was not as
severe as the depressions in the other three countries. The Polish trough, reached
in 1991, was approximately 15 percent below the base level. Moreover, as al-
ready mentioned, Poland was the first transition economy to emerge from the
depression: in 1995, its GDP had returned to its 1987 level.
Russia had experienced another major GDP decline in 1917-21, during the
Revolution and Civil War, when its GDP declined by approximately one-half
(according to Block [1976] as quoted in Sokoloff [1993]). The decline of 1917-21
represented a fall of approximately 60 percent from the pre-War level (see table
3.2). The current post-Communist depression is about half as severe. During both
crises, agricultural output, not surprisingly, contracted less than industrial out-
put.
The duration of the depression is also important. A longer depression prob-
ably has a more deleterious effect on welfare than a shorter depression, even if
the overall decline is the same. The same conclusions can be drawn from the
permanent income hypothesis. A short, even if sharp, decline in income is treated
as transitory because it does not affect long-term income or consumption. A
drawn-out depression, on the other hand, reduces people's perception of their
long-term income and consumption, possibly by even more than the actual de-
cline, because they may expect decreases to continue. Thus, for example, the GDP
in the United States dropped in 1938 by about 5 percent to quickly recover the
following year. Understandably, no one talks of 1938 with nearly the same awe
with which people still refer to the Great Depression. In terms of duration, the
post-Communist depression appears as bleak as or worse than the 1929-33 De-
pression. Most transition countries have experienced between three and four years
of successive GDP declines, and some have experienced five (Romania, 1988-92;
Estonia, 1990-94) or six years (Hungary, 1988-93; Slovenia, 1987-92;6 and Belarus,
Table 3.2. Russia's Output Decline after the Dissolution of the Czarist Empire
and after the Dissolution of the U.S.S.R.
GDP Industrial output Agricultural output
Year 1913=100 1987=100 1913=100 1987=100 1913=100 1987=100
1917 or 1991 75 101 77 97 100 97
1919 or 1992 54 86 26 79 88 87
1920 or 1993 45 79 18 66 62 84
1921 or 1994 38 69 n.a. 52 n.a. 77
1922 or 1995 n.a. 66 n.a. 51 n.a. 70
n.a. = not available.
Source: 1913-22: GDP from Sokoloff (1993); industrial output (only large-scale) and agricultural
output (only cereal production in Central Russia) from Kritsman (1926), quoted in Pipes (1990,
p. 696). 1987-95: World Bank data.
6. Declines in Hungary and Slovenia were, however, small, at least by the stan-
dards of other transition economies. War-affected countries are not included here;
Azerbaijan, Croatia, and Georgia have each experienced six successive years of GDP de-
cline.
28 Income, Inequality, and Poverty during the Transition
1990-95) or even seven years (Moldova, Russia, and Ukraine, 1990-96) . During
the Great Depression, by contrast, GDP decreased for three successive years in
Germany, four years in the United States, and six years in France, while the GDPs
of the United Kingdom and Italy alternated between growth and decline.
While the effects of the two depressions were similar for the populations con-
cerned (and probably worse for Russians now than for Americans sixty years
ago), the impact of the two depressions on the rest of the world was different. In
the late 1980s, transition countries accounted for about 6 percent of the world's
GDP (at current exchange rates); about 6 percent of world trade, only half of
which was not among themselves; and approximately 9 percent of the world's
population. The shrinking of their GDPs by a quarter thus reduced the world's
GDP by approximately 1.5 percentage points. On the other hand, the major capi-
talist countries where the Great Depression began (the United States, the United
Kingdom, Germany, and France) accounted for more than half the world's out-
put, 2; of world trade, and 70 percent of industrial production (Gazier 1983; Romer
1993, p. 20n). Their combined GDP decline of about 20 percent between 1929 and
1933 meant that the world's GDP shrank as much as one-tenth; world industrial
output decreased by one-third (Bairoch 1993, p. 136), and the volume of world
trade dropped by 25 percent.7 Since they were the "core" countries, their links
with the rest of the world (via trade and capital flows), and the dependence of
the rest of the world on them, was much greater than the dependence of the rest
of the world on formerly socialist economies. The Great Depression led to export
declines and a sharp deterioration in the terms of trade for African, Asian, and
Latin American countries. Some authors hold that the Great Depression signaled
the beginning of underdevelopment in the Third World.
Wages and unemployment
One major difference between the Great Depression of 1929-33 and the cur-
rent post-Communist depression is the way in which wages and employment
have adjusted (see figures 3.4 and 3.5). During the Great Depression, wages in
all major countries (the United States, the United Kingdom, and Germany)
remained stable in real terms. At the same time, unemployment grew from an
initial rate of 5 to 10 percent of the labor force in the late 1920s to between 20
and 25 percent (and even more in Germany). The adjustment of the wage bill
was thus entirely borne by a quantity adjustment: that is, unemployment went
up. The wage bill was cut by between 15 and 20 percent in real terms, falling a
few percentage points short of the decline in output. The labor share in GDP
7. GDPs in major market economies declined, between the peak and the trough, as
follows: the United States by 30 percent (1929-33), Germany by 24 percent (1930-32),
France by 17 percent (1930-36), the United Kingdom by 5 percent (1929-32), and Italy by
5 percent (1929-34). Japan's GDP over approximately the same period grew by 20 per-
cent.
Income 29
Figure 3.4. Real Wages in Poland and Russia (1987-96); and in the United
States, the United Kingdom, and Germany (1927-36)
140-
130 United States
120 - ; _ - -.Germany
-' ~United Kingdom
90 -- - --
8 0
80 - -* -- Poland
70-
60-
50 -
40-
27-87 28-88 29-89 30-90 31-91 32-92 33-93 34-94 35-95 36-96
Years
Note: 1927=100 for the United States, Germany, and the United Kingdom. 1987=100 for Poland
and Russia.
Source: For the United Kingdom and the United States: Bain and Elsheikh (1976, appendix E).
For Germany, James (1986, p. 196) and Pierenkemper (1987). For Poland and Russia, World Bank
data.
thus expanded (Fozouni, Gelb, and Schrenk 1992; James 1986, p. 416).
Labor adjustment during the post-Communist depression occurred differ-
ently. In Russia and other countries of the former Soviet Union, registered, and
even actual unemployment, is very small, while real wages have declined be-
tween 40 and 60 percent.8 This type of wage-bill adjustment is thus exactly the
opposite of the adjustment that took place during the Great Depression.
The East European situation lies between these two extremes. Both real
wages and employment have decreased. On average, real wages dropped by
one-fourth between 1987-88 and 1994, while unemployment grew from zero
percent to between 12 and 15 percent of the labor force (except in the Czech
Republic, where unemployment is much lower). In no transition economy,
except those affected by war, has the severity of unemployment reached Great
Depression levels.
The real wage bill was cut by approximately one-third in Eastem Europe
8. In 1995, of all former Soviet republics, only Latvia had a registered unemploy-
ment rate of more than 5 percent of the labor force. Registered unemployment in Russia
was 3.2 percent, and actual unemployment was estimated at 9 percent.
30 Income, Inequality, and Poverty during the Transition
Figure 3.5. Unemployment Rates in Hungary, Poland, and Russia (1987-95); and in
the United States, the United Kingdom, and Germany (1927-35)
50 -
Germany
40 / -
X~~~~~~~~~~ // \
30 -
/ United \ . United
-~~~~~~~~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~States
20 /- Tg9I. ..
M 2 0 - / /^/~Poland =. -- '
10 - `-~Hungary
_ / <___~- -- Rssia
0*-
27-87 28-88 29-89 30-90 31-91 32-92 33-93 34-94 35-95
Years
Note: Percent of labor force (annual average).
Source: For the United Kingdom and the United States: Bain and Elsheikh (1976, appendix E).
For Germany, Pierenkemper (1987). For transitions economies, World Bank data.
and one-half in countries of the former Soviet Union; both cuts are larger
than those experienced by labor in major countries during the Great Depres-
sion. Further, in contrast with the Great Depression, the share of labor in-
come in the GDP was reduced in countries of the former Soviet Union, and
remained constant in countries of Eastern Europe.
The Effect on Population Incomes: How Level and Composition
of Income Changed
Decline in real population income
The decline in real population income provides a better indicator of hard-
ship than an aggregate measure such as GDP. This is so because GDP ac-
counting is faulty and because GDP movements do not always mirror changes
in population income and welfare.
Recorded GDP declines in former Communist countries overstate real de-
clines because official accounts err at both ends: by overstating the level of
GDP achieved prior to transition and by failing to record fully the growth of
the most dynamic sector, that is, the private sector after the transition. Previ-
ous GDPs were "padded up," principally for two reasons. First, all levels of
the Communist hierarchy had incentive to report better than actual fulfill-
Income 31
ment of planned targets because their promotions and income depended on
target overfulfillment.9 Second, published GDPs underestimated inflation
(and thus overestimated real output) and did not adequately account for the
low quality of goods.
On the other hand, the increase in private sector activities since the transi-
tion is not fully reflected in macroeconomic statistics. The informal or "shadow"
economy is estimated to account for between 10 and 15 percent of recorded
GDP in more "orderly" transition economies (for example, the Czech Republic
and Hungary) and up to 50 percent in countries affected by war and embar-
goes (for example, Armenia and the Federal Republic of Yugoslavia). '0 It seems
certain that in all transition countries, the share of the informal sector has in-
creased, whether because the absolute size of the sector has expanded or be-
cause it had shrunk less than the formal economy has. This increase in the
share of the informal sector implies that GDP declines are overstated.
The discrepancy between GDP and welfare in formerly socialist econo-
mies existed because some goods produced and recorded as part of the GDP
did not contribute much, or anything, to the welfare of the population (such
as production of armaments and shoddy goods recorded at higher than mar-
ket prices). llAlso, the transition-related declines in gross investments (which
have no immediate impact on welfare and were to a large extent wasteful
under socialism), in inventories (which tended to be excessively high), and
9. The most extreme example of this phenomenon was the hugely falsified cotton
harvest results under Uzbekistan's Communist ruler Rashidov in the 1970s and early 1980s.
10. Arvay and Wrtes 1994 study of Hungary represents the most detailed study of
the informal sector in a transition economy. They estimate that the informal sector not
covered by statistics has grown from between 11 and 12 percent of the GDP before the
transition to 17 percent in 1993. Kadera (1995) assesses the Czech shadow economy to be
8 to 10 percent of the recorded GDP. Slovak INFOSTAT agency estimates Slovak informal
economy to be about 12 percent of the recorded GDP in 1995 (Narodna Banka Slovenska,
1996, p. 28). Sadowski and Herer (1996) estimate the Polish informal economy to be 25
percent of the recorded GDP. In Russia, the informal or "shadow" economy was esti-
mated to be 20 percent of the GDP by the chairman of the Goskomstat (press conference on
December 6, 1994). Croatia's informal economy was estimated to have increased (under
very conservative assumptions) from 18 percent of recorded GDP in 1990 to 23 percent in
1995 (Madzarevic and Mikulic, 1995). The shadow economy in the Federal Republic of
Yugoslavia (Serbia and Montenegro) was estimated to have risen from 32 percent of the
official GDP in 1991 to 54 percent in 1993 because of hyperinflation and illegal activities
connected with United Nations sanction-busting and the war in Bosnia (see Yugoslav
Federal Government 1995).
11. Some argue that official GDPs in socialist countries were necessarily biased up-
ward by the use of arbitrary prices, including higher-than-market prices for inferior goods.
This view, however, fails to acknowledge that there were many other products (such as
energy, industrial inputs), as well as dwelling rents, and land value that were unrecorded
in GDP or assessed at less than market value. Also, the material concept of Net National
Product used by all Communist countries yielded estimates that were approximately 20
to 25 percent below the United Nations' System of National Accounts (SNA) concept of
gross national product (see Marer 1985, pp. 18-19).
32 Income, Inequality, and Poverty during the Transition
in arms production (in the Soviet Union) had little to do with population
welfare. 12
The problems with estimating population welfare either disappear or are
muted if real population income or expenditure is used. Some problems, such
as the decline in fixed investments or inventories, then disappear, by defini-
tion. Other problems become less acute, because population incomes, and
in particular (formal sector) wages and social transfers, are almost fully
captured by statistics. Private sector activities, both formal and informal,
are better captured by household-level statistics, for example, through
household surveys of incomes or expenditures, than they are by GDP
statistics. 13 Retail or consumer price indexes also reflect inflation-as it
affects consumers-better than GDP deflators do.
Estimates of real population income are derived from two sources: mac-
roeconomic sources (national accounts) and household budget surveys
(HBSs). Population income from macroeconomic sources is obtained by
adding all money funds received by the population, including cash wages
and, sometimes, in-kind fringe benefits; social transfers; entrepreneurial
income; dividends; and other sources of income, such as remittances, in-
come from the lease of assets, fees, and honoraria. Estimated home con-
sumption is then added to this figure. HBS income is defined in the same
way as it is in macroeconomic statistics, but it is obtained through regu-
lar household interviews and then extrapolated to the level of the popu-
lation. In some HBSs, an attempt is made to account for the differential
response of various population groups and to correct for income
underreporting. Almost all HBSs also include estimated consumption-
in-kind. 14
Another way to assess what happened to population welfare is to look
at real expenditures. Expenditures are also collected by most HBSs and
are often considered a more reliable measure than income. The reason for
this is that while people maybe reluctant to share information about their
12. According to Winiecki (1991), at least two components of the registered output
decline do not matter to population welfare. One part of the decrease was purely a statis-
tical artifact because of the earlier practice of padding output figures to show fulfillment
of plan targets. The second part of the decline was due to behavioral changes among
enterprises and, to some extent, individuals. For example, better availability of goods,
higher interest rates, and harder budget constraint led enterprises to reduce inventory
stocks, which in socialist economnies were inordinately high. The drawing-down of in-
ventories produced a short-term output decline, but in reality it represented an adjust-
ment to market conditions. Thus, the first component is fictitious, and the second, while
involving a real decline in output, had no effect on population welfare.
13. There are numerous problems associated with household surveys in transition
economies, however (see appendix 1).
14. Consumption-in-kind is an important source of income in transition economies.
It includes not only own consumption, but also a portion of small-scale production that
may be sold. The latter is not, strictly speaking, consumption-in-kind, but is often treated
as such because households that sell a portion of their output may not do so regularly or
may not be willing to report such sales as commercial activity.
Income 33
income with interviewers whom they assume to be agents of the govern-
ment, they may be less wary when asked to give information about ex-
penditures. Results from eleven countries do, on average, confirm that
HBS expenditures tend to be higher than HBS incomes: in six cases, ex-
penditures are more than 20 percent greater than income; 15 in three cases,
they are about the same; and in only two cases, income is higher than
expenditures (see table 3.3).
Table 3.4 and figure 3.6 show estimated changes during the transition in
real per capita GDP and in the two measures (macro and HBS) of population
real income. 16 Table 3.4 shows that (unweighted) income declines were the
least sharp in Eastern Europe. According to GDP and HBS data, real per capita
incomes were, on average, 21 percent and 25 percent smaller, respectively, in
1993-94 than they were in 1988; according to macroeconomic data, they were
perhaps 10 percent less. In the Baltics, real incomes seem to have fallen by
almost a half, according to the GDP data, and 40 percent according to HBS
data. In Moldova and the Slavic republics of the former Soviet Union, the
decrease in income was more than one-third, according to GDP data, one-
Table 3.3. Ratio between Expenditures and Income Reported in Household Budget
Surveys
Country (year of survey) Mean expenditures:mean income
Eastern Europe 1.03
Slovakia (1993) 0.83
Bulgaria (1993) 0.85
Hungary (1993) 1.00
Slovenia (1993) 1.02
Romania (1994) 1.19
Poland (1993) 1.30
Former Soviet Union 1.58
Estonia (1995) 1.02
Belarus (1995) 1.21
Russia (1993) 1.23
Ukraine (1995) 2.10
Kyrgyz Republic (1993) 2.32
Note: All regional averages are unweighted. Both income and expenditures are defined on a
net (that is, after tax) basis.
Source: For surveys, see appendix 4. See also information on surveys and caveats in appendix 1.
15. In two cases (the Kyrgyz Republic and Ukraine), expenditures are more than
twice HBS income, suggesting that even HBS income must be hugely underestimated.
16. Success in transition should not be assessed from these figures because they refer
to different dates.
34 Income, Inequality, and Poverty during the Transition
Table 3.4. Change in Real per Capita GDP and Real per Capita Population Income
between 1988 and 1993
(percent)
Real per Real per
Real per capita income capita income
Country capita GDP (macro data) (HBS data)
Eastern Europe -21 -7 a -25
Bulgaria (1989-93) -27 n.a. -45
Czech Republic -18 -7 -12
Hungary (1987-93) -15 +1 -26
Poland (1987-93) -12 -11 -26
Romania (1989-94) -26 -18 -43
Slovakia -29 -29 -29
Slovenia (1987-93) -21 +20 +8
Baltics -49 n.a. -41
Estonia (1988-94) -37 n.a. -37
Latvia (1988-95) -43 n.a. 45
Lithuania (1988-94) -66 -44 -42
Slavic republics -37 -49 -54
Belarus (1988-95) -34 -30 -44
Moldova -39 -67 -67
Russia -27 -33 42
Ukraine (1988-95) -49 -64 -62
Central Asia -28 -39 -54
Kazakhstan -26 -57 -61
Kyrgyz Republic -35 -58 -66
Turkmenistan -31 -40 -46
Uzbekistan -20 +1 43
n.a. = not available.
Note: The end years are 1988 and 1993 unless different years are given between brackets. All
regional means are unweighted. To insure comparability among the dates of different data sources,
the actual years of comparison for GDP and macroeconomic data are the same as the years for
which household surveys are available. Macroeconomic and HBS data for Lithuania do not
include consumption-in-kind in 1994; HBS data for Hungary do not include consumption-in-
kind in 1993.
a. Does not include Bulgaria.
Source: GDP and macro incomes: World Bank data. HBS incomes: calculated from surveys
presented in appendix 4. See also caveats in appendix 1.
half according to macroeconomic data, and more than one-half according to
HBSs. Similarly, in Central Asia, GDP declines were smaller than income
declines whether measured by macroeconomic data or by HBSs.
It is remarkable that of the sixty-two observations in table 3.4 and figure
3.6 17 only five show an increase: minimal 1 percent macroeconomic income
17. Sixty-two observations represent three observations for each income concept for
eighteen countries less three unavailable observations plus eleven expenditure observa-
tions (shown only in figure 3.6).
Income 35
Figure 3.6. Real per Capita GDP and Real per Capita Population Income in 1993-95
(pre-transition 100)
120
100 ..0
80 o 0
60
40
20
0
Po Hun Cz Uz Sn Rom Kaz Bul Rus SI Turk Bei Ky Est Mo La Uk Li
- GDP Macro -HBS a Expenditures
Note: Countries are arranged in ascending order of their GDP decline (Poland has the smallest
decline, Lithuania the greatest).
Source: Table 3.4.
increases in Hungary and Uzbekistan, and macroeconomic and HBS income
and expenditure increases in Slovenia.
Of interest is the relationship between the two measures of population real
income. For most countries where both sources of data are available, HBSs
show larger income declines than macroeconomic data do. In Hungary and
Romania, the difference amounts to more than one-quarter of initial (that is,
pre-transition) income; in Poland, it is 15 percent; and in Russia, it is 9 per-
cent."8 The correlation coefficient between changes in HBS and macroeconomic
income is a high 0.88. HBS income change and macroeconomic income change
are less strongly correlated with GDP change: the correlation coefficient is
0.68 for HBS income and GDP, and 0.66 for macroeconomic income and GDP.
A stronger correlation between macroeconomic and HBS data is expected
because they both measure the same thing: the real income of the population.
GDP, of course, includes components other than population income. Perhaps
more importantly, during the transition cost-of-living indices used to deflate
the HBS and macroeconomic income have moved differently from (often in-
creasing faster than) implicit GDP deflators.
18. Note, however, that the large difference between the two measures in Hungary
and Lithuania may be explained by non-inclusion of consumption-in-kind in 1993-94.
Table 3.5. Population Income by Sources in 1987-88 and 1993-94
(percent of GDP)
Non-wage Social transfers
Cash social private sector in kind (health
Wages transfers income and education) Total
Country 1987-88 1993-94 1987-88 1993-94 1987-88 1993-94 1987-88 1993-94 1987-88 1993-94
Eastern Europe 33 32 11 15 10 19 7 9 62 75
Bulgaria 27 25 11 14 9 21 7 11 55 71
Czech Republic 41 32 12 13 4 24 9 12 67 82
Hungary 32 37 13 19 10 14 7 12 63 83
Poland 27 32 9 20 22 25 7 9 65 85
Romaniaa 35 33 9 9 3 14 4 5 52 61
Slovakia 42 31 13 13 4 17 11 6 70 68
W Slovenia 28 34 11 16 10 20 7 7 57 77
Baltics 43 35 8 12 9 14 9 8 70 69
Estoniab 46 33 9 11 8 10 11 8 73 62
Latvia 38 33 8 14 8 12 8 9 62 69
Lithuaniab 46 40 8 10 12 18 9 8 76 76
Slavic republics 41 29 8 9 6 16 6 9 61 63
Belarus 40 37 6 8 7 17 7 12 59 74
Moldova 43 23 7 8 8 28 10 12 69 72
Russia 41 26 8 9 5 23 5 7 59 64
Ukraine 42 25 9 12 7 8 7 10 66 55
Note: All regional means are unweighted. Regional means as calculated avoid the rounding off errors.
Definitions: Wages equal income from employment. Social cash transfers equal pensions, family and child allowances, sick leave payments, unemployment
benefits, and social assistance. Non-wage private sector income equals income from sales of agricultural products, entrepreneurial income, interest and
dividends, income from abroad, gifts, and income (or consumption) in kind. Health and education equals total government expenditures on health and
education.
a. 1992-93 instead of 1993-94.
b. Does not include home consumption.
Source: Income of the population as a whole (from macroeconomic sources): World Bank data.
Income 37
Changing income composition
The composition of population disposable income changed during the tran-
sition. Population income can be divided into three categories: (a) wages; (b)
cash social transfers; and (c) self-employment, home consumption, property
income, private transfers, and other private sector income. The last category
can be termed "non-wage private sector" income. It underestimates the true
size of private sector income because it omits some informal sector income,
as well as wages earned in the private sector. The latter are classified together
with other wages. As the transition proceeds, this three-way classification
becomes less of an indicator of transformation toward a private property-
based economy than it was at the beginning of the transition process. This is
because at the beginning of the transition, practically all income from private
sector activities was included in the third category. It then served as a good
proxy for the size of non-state sector. As privatization expands, however,
more private sector income is eamed through private sector wages. The three-
way classification is thus somewhat biased against faster reformers. In any
case, it gives the lower-bound estimate for the size of the private sector.
Table 3.5 illustrates the changing composition of population disposable
income. The three types of income, as well as in-kind social transfers (health
and education), which are not part of population disposable income, are
shown as percentages of (current) market-price GDPs. Not surprisingly, the
share of total population income (inclusive of health and education) in GDP
increased in most countries between 1987-88 and 1993-94. The accounting
explanation for this is that other types of income (for example, gross operat-
ing surplus of enterprises, indirect taxes, and inventory build-up) contracted
more severely than the GDP did. '9 A more meaningful explanation is that
under conditions of rapidly declining GDPs, most governments tried to cush-
ion the population as much as possible from the effects of the depression.
Broad regularities revealed by data in table 3.5 are:
' The (unweighted) share of labor income in the GDP has remained con-
stant in Eastern Europe and has declined in the Slavic republics of the
former Soviet Union (by 12 points) and the Baltics (by 8 points).2'
- The share of social cash transfers in the GDP has risen in all three re-
gions (by between 1 and 4 percentage points of GDP).
* The share of non-wage private sector income in the GDP has increased
everywhere: by 9 percentage points of GDP in Eastern Europe, 10 points
in the Slavic republics, and 5 points in the Baltics. 21
19. Because GDPs in table 3.5 are at market prices (and not at factor cost), popula-
tion-income components cannot add up to 100 percent of GDP.
20. Data for the Central Asian republics are not presented because they are not reli-
able.
21. Private sector data for Estonia and Lithuania do not include consumption-in-
kind. Because consumption-in-kind has increased, in real terms probably, and certainly
as a share of GDP, the growth of private sector income is underestimated in the Baltics.
38 Income, Inequality, and Poverty during the Transition
* The share of health and education in the GDP has increased by about
2-3 GDP points in Eastern Europe and the Slavic republics of the former
Soviet Union.
In all East European countries (except Slovakia) the share of population in-
come in GDP increased. For all these countries combined, the share went from
62 to 75 percent, because of a large increase in private sector income and a smaller
increase in social transfers. In the Slavic and Baltic regions of the former Soviet
Union, population income share in the GDP remained virtually unchanged.
The situation is, of course, more differentiated at the country level. All tran-
sition economies can be divided into three categories, depending on the type
of change.
The first category ("the non-compensators") is characterized by a declin-
ing share of wages which is not "offset" in any meaningful way, by an in-
creased share of cash social transfers. This particular configuration can be
denoted as (- 0 +), where the negative sign, zero, and the positive sign denote a
decreased, unchanged, or increased share, respectively, of wages, cash social trans-
fers, and non-wage private sector income. "Non-compensators" include only
countries where each $5 in lost wages is accompanied by $1 or less in greater
social transfers: the Czech Republic, Estonia, Moldova, Romania, Russia, Slovakia,
and Ukraine belong to this group. For example, in Russia, the share of wages
decreased by 15 percentage points of GDP while social transfers increased by 1
GDP point.2
The second group ("the compensators") consists of countries with a (- + +)
configuration, where compensation, in the form of social transfers for lost wages,
is more generous. "Compensators" include Belarus, Bulgaria, Latvia, and
Lithuania.
The third group ("the populists") has a +++ +) configuration. In these coun-
tries, all sources of population income increased in terms of GDP. "Populist" coun-
tries clearly attempted to cushion the population, as much as possible, from the
effect of real GDP declines. Only Central European countries (Hungary, Poland,
and Slovenia) belong to this group.
Growth of non-wage private sector income
All configurations include a plus sign for non-wage private income, because non-
wage private income has risen as a share of GDP in all transition economies. In
Eastem Europe, its unweighted share increased from 10 percent of GDP before
the transition to 19 percent of GDP in 1993-94; in the Baltics, it went from 9 to 14
percent; in the Slavic republics of the former Soviet Union, it grew from 6 to 16
percent. These figures make clear that Eastem Europe began its transition with a
more sizable private sector than did the Soviet Union, particularly so the coun-
tries where agriculture was private or semi-private or which exhibited a more
22. Interestingly, but perhaps not surprisingly, only countries affected by war and
regional tensions (not shown in the table) display the configuration (- - +).
Income 39
liberal attitude toward the small private sector (Hungary, Poland, and Slovenia).
During the transition, however, the share of non-wage private sector income in
GDP grew as quickly in the Slavic republics of the former Soviet Union as it did
in Eastern Europe. In addition, it is important to recall that the informal sector
(which, by definition, is not included in national statistics) is larger in the coun-
tries of the former Soviet Union than it is in Eastern Europe. The share of private
sector income in the countries of the former Soviet Union is thus more underes-
timated than it is in Eastern Europe. The most important increase in the share of
non-wage private sector income occurred in the Czech Republic and Moldova
(an increase of 20 GDP points), Russia (18 points), Slovakia (13 points), and Bul-
garia (12 points).
The next chapter will investigate what happened to income distribution dur-
ing the transition and how it was affected by changes in income composition.
4
Inequality
Income Inequality
Table 4.1 illustrates the change in inequality that has occurred since the be-
ginning of the transition. 1 First, inequality increased in all countries except
the Slovak Republic. The average Gini coefficient of disposable (or gross) 2
income rose from 24 to 33. While the first value is equal to the average value
of low income-inequality OECD countries (such as the Benelux, the Federal
Republic of Germany, and Scandinavia), the second value places transition
economies at near the OECD mean, or at about the same level of inequality
as Commonwealth countries (for example, Australia, Canada, and the United
Kingdom) and Latin European countries (such as Italy and France).3
Second, the increase in the Gini coefficient was sharp: over a period of
about six years, the average Gini rose by 9 points. This is, on average, 11.
Gini points per year, a rise that is almost three times as fast as the rise
recorded in those Western countries where inequality rose most rapidly in
the 1980s: United Kingdom, the Netherlands, and United States (Atkinson,
Rainwater, and Smeeding 1995, p. 25).
Third, the dispersal of Ginis among transition economies increased. While
before the transition, their Gini coefficients (with the exception of Central Asian
republics, which were more unequal) lay within a very narrow range between
1. Results are based on HBSs presented in appendix 4. A discussion of problems
associated with these surveys and biases in estimating inequality is presented in appen-
dix 1.
2. Disposable and gross income in transition countries are not very different be-
cause direct personal income taxes are still very small.
3. Atkinson, Rainwater, and Smeeding (1995, p. 16) divide OECD countries into
four groups. Very low income-inequality countries, with Ginis between 20 and 22, in-
clude Nordic countries (Finland, Sweden, Norway) and Belgium. Low income-inequal-
ity countries, with Ginis between 24 and 26 include the Federal Republic of Germany
and the Netherlands. Latin Europe (France and Italy) and Commonwealth countries (Aus-
tralia, Canada, and the United Kingdom) have average levels of income-inequality, or
Ginis of between 29 and 31. Finally, high income-inequality countries include Ireland,
Switzerland, and the United States, with Ginis of between 33 and 35. Income concept is
disposable income; distribution is per equivalent adult using the OECD equivalence scale.
According to the empirical evidence presented in Coulter, Cowell, and Jenkins (1992),
the use of per capita income (as in table 4.1) will lead to slightly higher estimates of the
Gini coefficient than will the use of income per equivalent adult. For a discussion of
Coulter, Cowell and Jenkins results, see Banks and Johnson (1994).
40
Inequality 41
Table 4.1. Changes in Inequality during the Transition
Gini coefficient (annual)a
Income Expenditures
per capita per capita
Country 1987-88 1993-95 1993-95
Balkans and Poland 24 30
Bulgaria 23b 34
Poland 26 28e 31e
Romania 23b 29' 33C
Central Europe 21 24
Czech Republic 19 27'
Hungary 21 23 27
Slovakia 20 19
Slovenia 22 25
Baltics 23 34
Estonia 23 35d 31d
Latvia 23 31d
Lithuania 23 37
Slavic republics and Moldova 24 40
Belarus 23 28d 30d
Moldova 24 36
Russia 24 48d 50e
Ukraine 23 47c 44'
Central Asia 26 39
Kazakhstan 26 33
Kyrgyz Republic 26 55d 43d
Turkmenistan 26 36
Uzbekistan 28b 33
All transition 24 33
Note: For most countries income concept in 1993-95 is disposable income; in 1987-88, gross
income. Personal income taxes are small, and so is the difference between disposable and gross
income (see the exact definitions in appendix 4). Income indudes consumption-in-kind, except
for Hungary and Lithuania in transition years. Regional averages are unweighted.
a. Except when stated otherwise.
b. 1989.
c. Monthly.
d. Quarterly.
e. Semiannual.
Source: Calculated from the countries' household budget surveys given in appendix 4. See
also the discussion of HBSs and caveats in appendix 1. All expenditure data obtained from the
same surveys as income data.
19 and 24, the current range goes from around 20 (Slovakia) all the way to high
40's (Ukraine and Russia), and even mid-50's (the Kyrgyz Republic) (see also
figure 4.1).
These results illustrate not only sharply increasing income differentiation
among members of a single population, but also growing differences among
countries. Central European countries have registered only moderate increases
42 Income, Inequality, and Poverty during the Transition
Figure 4.1. Dispersal of Gini Coefficients in Transition Economies
58 -
Kyrgyz Republic
48 Ukraine 0 * Russia
: 38 - Lithuanias
Estonae * *Moldova *Turkmen.
:Bulgaria Kazakh.- Uzbek. i
Romania-
28 - Czech Republic LatviaBelarus Poland0
OSlovenia No........ I No change
Hungary. ...................
18 * S - -Siovak Republic
_ I I I l 1, 1 1 1
18 19 20 21 22 23 24 25 26 27 28
Pre-transition Gini
Source: Countries' household budget surveys (see appendix 4).
in inequality: their Gini rose, on average, from 21 to 24. The increase was
greater in the Balkans, even greater in the Baltics (where it rose from 23 to
34), and by far the greatest in Moldova and the Slavic republics of the former
Soviet Union (where it rose from 24 to 40).
Fourth, Ginis calculated on the basis of 1993-95 expenditures are not, on
average, lower than Ginis calculated on the basis of 1993-95 income. In five
cases, expenditure Ginis are higher (by 2 to 4 Gini points), and in three cases,
they are lower. Higher expenditure than income Ginis suggest that surveys
tend to underestimate both income levels (as discussed in chapter 3) and
income inequality because we would normally expect to find, particularly
during a depression, lower inequality among expenditures than among in-
comes.
While inequality rose everywhere, the exact shape of this change differed
among countries. Figures 4.2,4.3, and 4.4 show the change between 1987-88
and 1993-94 in income shares received by the five quintiles. 4
4. Income distribution data, as they appear in the original sources, are presented in
appendix 4. Decile data (from which the quintiles in figures 4.2-4.4 are calculated) are
presented in appendix 2.
Inequality 43
Figure 4.2. Changes in Quintile Shares in Hungary, Slovakia, and Slovenia
between 1987-88 and 1993-94: Little Change
1.5
1
Slovak Republic
0.
0. ._.__._..._._----__ ...................... ---.-----'--'-----'--'-------'-----''--------------------- .... . .................................................... --------
Slovenia
-0.5 J-
1 2 3 4 5
Quintile
Figure 4.3. Changes in Quintile Shares in Belarus, the Czech Republic, Latvia,
Poland, and Romania between 1987-88 and 1993-94: Moderate Regressive
Transfers
8-
6
2
-2 - - - teh Republic
1 2 3 4 5
Quintile
Source: Countries' household budget surveys (see appendices 2 and 4).
44 Income, Inequality, and Poverty during the Transition
Figure 4.4. Changes in Quintile Shares in Bulgaria, Estonia, Lithuania, Moldova,
Russia, and Ukraine between 1987-88 and 1993-94: Large Regressive Transfers
20
/
15- Rus,i, /
E 10- Ukraine/
0 Lithuania
U Estonia ,.F*i / ~~~~~~~~Bulgaria
Estonia~
0 Moldova ;Bli
-5- - - - -
-10 I
1 2 3 4 5
Quintile
Source: Countries' household budget surveys (see appendices 2 and 4).
We can divide countries into three groups. 5 The first group consists of
Hungary, Slovakia, and Slovenia (figure 4.2). Income shares in these coun-
tries barely changed at all. No quintile gained or lost more than 1 percentage
point of total income. 6
In the second group, maximum loss ranged between 1 and 2 percentage
points of total income, and was sustained by the bottom three quintiles (fig-
ure 4.3). The forth quintile either experienced a very small loss or retained its
pre-transition share. In all cases, the top quintile alone was the "winner."7
5. Central Asian republics are not considered here because of the unreliable nature
of the data.
6. A similar pattem of change is reported for eastern Gerrnany (see Speder 1995,
table 1). Between 1990 and 1993, the bottom quintile lost 1.2 percentage points of income;
the next three quintiles lost between 0.5 and 0.2 points, and the top quintile had gained
2.2 points. The Gini coefficient increased quite moderately from 19.5 in 1990 to 22.4 in
1993 (calculated from data provided by Speder 1995, table 2).
7. Note that this discussion relates to shares. Because overall income decreased in
all countries, even an increased share received by the top quintile did not necessarily
guarantee an increase in its real income. The real income decline of the bottom quintiles
was that much more severe because both its share of the pie and the size of the pie itself
decreased.
Inequality 45
However, depending on the amount lost by the bottom three or four quintiles,
the gain of the top quintile ranged from less than 2 percentage points in Po-
land to about 6 points in Latvia and the Czech Republic. On average, the
share of the bottom quintile was reduced from 10 to 11 percent of total in-
come to 9 to 10 percent; the share of the top quintile rose from 32 to 35 per-
cent of total income to 35 to 37 percent. By contrast, in 1992 the bottom quintile
in the United Kingdom, whose inequality was about average among OECD
countries, received about 7 percent of disposable income, while the top quintile
received just over 40 percent. Income distribution in this second group of
transition countries thus remained more equal than in the United Kingdom.
This was not the case for the countries in the third group (figure 4.4). Their
inequality is greater than the OECD average. The extent of income transfer
from the bottom 80 percent of the population to the top 20 percent was much
larger than among group 2 countries. Income loss by the bottom quintile var-
ied between 4 and 5 percentage points of total income. Only a slightly smaller
loss was sustained by the next two quintiles. In Russia and Ukraine, the sig-
nificant income losses extended to the fourth quintile as well. In these coun-
tries, sharp losses by 80 percent of the population translated into large gains
for the top quintile. Thus, in Russia, Ukraine, as well as in Lithuania, the top
quintile gained 20,14, and 11 percentage points of total income, respectively.
In Russia, the bottom quintile's share was halved, declining from 10 percent
of total income to less than 5 percent,8 while the share of the richest quintile
rose from 34 to 54 percent of total income.
These results show that:
* In all cases (except the Slovak Republic), redistribution was regressive.
e As regards poverty, the most unfavorable developments occurred in
the countries of the third group (Russia, Estonia, Ukraine, Moldova,
Bulgaria and Lithuania), where the poorest suffered greater absolute
losses than did the middle or top income classes. Because in all these
countries, real income decreased by between one-third and one-half,
this translated into real income losses of up to two-thirds for the bot-
tom quintile.
� Inequality in Russia, Ukraine, and the Baltics (in that order) seems to
be greater than the OECD average. In Eastern Europe, however, in-
equality remains distinctly less than the OECD average.
Is there a relationship between the type of adjustment identified in chapter
3 ("non-compensators, " "compensators, " and "populists") and increases in
the Gini coefficient? The only relationship that could be detected is that be-
8. In other words, an average Russian in the bottom quintile had an income equal
to half the mean before the transition; now his income is one-quarter of the mean.
46 Income, Inequality, and Poverty during the Transition
Figure 4.5. Relationship between Type of Adjustment and Increase in Gini Coefficient
30 -
25 - Russia*
Ukraine
20-
w 15 - Lithuania 0
Estonia *.
M ~~~Moldova --Bulgaria-
10 - Moldova--~~!~!aria a regression line
--*Latvia
Czech Republic
5 - Romama g Belarus* Slovenia
Poland 2
0- Slovakia v Hungary v
0 Non-compensators Compensators Populists
Type of adjustment
Source: Increases in the Gini coefficient are calculated from table 4.1.
tween the "populist" type of adjustment and low increases in inequality9
The average increase in Gini for the three "populist" adjustment coun-
tries (Poland, Hungary, Slovenia) was less than 2 Gini points, and except
for Slovakia they experienced smaller increases in inequality than any other
country. The average increase among "compensators" and "non-compen-
sators" was approximately 10 and 12 Gini points, respectively. Each move-
ment toward "tougher adjustment" (i.e., from "populists" to "non-com-
pensators") was associated, on average, with a Gini increase of about 4
points (see the regression line in figure 4.5). However, if Russia and Ukraine
are excluded, the average inequality increase among the "non-compensa-
tors" is less than among the "compensators:" the monotonic relationship
between "softer" adjustment and lower increase in inequality no longer
holds.
9. The partial correlation coefficient between the type of adjustment (where 1 =
non-compensators, 2 = compensators, 3 = populist) and the Gini increase is -0.42. On the
other hand, there is no statistically significant correlation (r = -0.04) between the type of
adjustment and the change in an economic liberalization index (defined for transition
economies by de Melo, Denizer, and Gelb 1996) that includes privatization, internal mar-
ket liberalization, and external market liberalization. Similarly, there is only a weak rela-
tionship between the change in the liberalization index and the change in the Gini (-0.11).
The Original Income Distribution Statistics 47
Distribution of Income Sources: Wages, Social Transfers,
and Private Sector Income
How can the increased income inequality during the transition be accounted
for?
Disposable income can be defined as the sum of wages (w), cash social
transfers (t), and non-wage private sector income (p). The Gini coefficient of
disposable income, (G), is equal to the weighted average of the concentration
coefficients of the three income sources (wages, transfers, and non-wage pri-
vate sector income) Ci where weights are sources' shares (S) in total income:",
4.1 G =Y3S.C. = S C ,+ S,C,+ SC.
i= zI t p p
The change in the Gini between two dates (before and after the transition)
can be written as:
4.2 AG =x AS,C, + ACQ,SW + AC,S + AC Sp +X3AS,ACi.
pp i_
The first term on the right hand side shows the change in Gini due to chang-
ing shares of different income sources; the next three terms show the change
due to changing concentration coefficients of income sources; and the last
term is an interaction term.
Table 4.2 shows the decomposed change in the Gini for selected countries
between a pre-transition year and 1993-96. The following conclusions can be
made.
First, the change in income composition has had little relation to increased
inequality. In the only country where income composition did have a signifi-
cant impact on inequality (Russia), it contributed to reduce inequality; that is,
income composition in 1994 was more favorable to equality than it was in
1989. This is chiefly because social transfers, which were the most equally
distributed income source in Russia before the transition, increased their share
in overall income after the transition. In other countries, only about 1 Gini
point was added to or subtracted from total inequality by changes in income
composition.
Second, higher concentration coefficients of wages drove the overall Gini
up in all countries. It was the most important factor behind increases in in-
10. The concentration coefficient captures both the inherent inequality with which a
given income source is distributed (source Gini coefficient) and the correlation of that
source with overall income. Thus, an inherently unequal source, such as social assis-
tance, with a high Gini coefficient will have a low or negative correlation with overall
income (because most social assistance recipients are poor), and its concentration coeffi-
cient will be low or negative. For a more detailed definition of concentration coefficient,
see footnote 9 in chapter 2.
11. The analysis here is based on HBS data. The three income sources add exactly to
disposable income.
Table 4.2. Decomposition of the Change in the Gini Coefficient between Pre-transition and 1993-96
Due to:
Change in concentration of:
Change in Out of which: Non-wage Overall
composition Social Non-pension private Interaction Gini
Country (years) of income Wages transfers Pensions transfers sector term change
Hungary (1989-93) -1.3 +5.9 -0.6 +1.4 -0.2 -0.6 -1.3 +2.2
Slovenia (1987-95) -0.2 +3.6 -0.6 -0.1 -0.4 +0.4 -3.8 +2.6
Poland (1987-95) -1.7 +3.4 +3.5 +3.2 -0.1 +0.8 +0.9 +7.0
Bulgaria (1989-95) +1.4 +7.8 +0.9 +0.4 +0.4 -0.4 +0.3 +10.0
Latvia (1989-96) -1.6 +15.0 -1.5 -2.0 +0.5 +1.4 -3.3 +10.0
Russia (1989-94) -3.4 +17.8 +5.1 +3.9 +0.4 +3.0 +1.2 +23.6
Note: All data in Gini points. The years in brackets show the dates between which the Gini change is calculated. The data sources and end-years for
Bulgaria, Latvia, Poland and Slovenia are not the same as in table 4.1. This explains differences in the overall Gini change.
Source: Calculated from the countries HBSs (see appendix 1).
Inequality 49
equality. Increased wage concentration was responsible for between 3.5 and
8 Gini points of increase in Eastern Europe, and for 15 to 18 Gini point in-
creases in Latvia and Russia. In the latter two countries, these huge increases
were due not only to a greatly increased concentration coefficient of wages,
but also to a high pre-transition share of wages in income. Thus, a very high
weight attaches to a more unequal concentration of wages that occurred dur-
ing the transition. An increase in the concentration of non-wage private sec-
tor income was responsible for 3 Gini points of increase in Russia and about
1.5 points in Latvia, while its impact was negligible in Eastern Europe.
Third, the effect of the changed concentration of transfers on inequality
was not uniform across countries. In Bulgaria, Hungary, and Slovenia, for
example, the concentration of transfers did not change. In Latvia, on the other
hand, better targeting of transfers reduced inequality by 1.5 Gini points. In
Poland and Russia, transfers contributed to an increase in inequality. This
was due to a greater concentration coefficient of pensions. Non-pension trans-
fers, because of their initially small size, did not anywhere have much of an
impact on inequality. 12
Decomposition of the increase in inequality in table 4.2 is based on the
change between the two data points (before the transition and 1993-1996).
The two end-data points can mask changes in the distribution or shares of
various income sources in the intervening years. For the six countries shown
in figures 4.6-4.17, annual HBSs are available. They allow us to chart annual
developments in the concentration and share of wages, cash social transfers
and non-wage private sector.
The developments in Bulgaria illustrated in figures 4.6 and 4.7 are straight-
forward, and to some extent typical of the transition. The rising concentra-
tion of wages (from around 20 to 35) contributed strongly to inequality. The
concentration coefficient of non-wage private sector income, which was al-
ready high before the transition, remained high while the share of non-wage
private sector income increased. This also pushed up overall inequality. Pen-
sions' concentration and share both remained unchanged, thus leaving in-
equality unchanged. Finally, non-pension transfers were too small (less than
5 percent of total income) to make any difference in the overall Gini.
Polish results illustrate a different story (see figures 4.8-4.9). Although wage
concentration increased markedly, the most important development was in
the area of pensions: their rising concentration and rising share in overall
income. Pensions thus contributed strongly to increase inequality. In 1995,
pensions had the same concentration coefficient as wages and non-wage pri-
vate sector income. The fact that concentration coefficients of the three in-
come sources converge means that income composition is almost equal across
12. To some extent, this conclusion differs from Cornia's observation that "the rela-
tive importance of redistribution [via transfers] has grown... Targeting of these [social]
transfers has generally improved or remained sufficiently progressive" (1994, p. 39).
50 Income, Inequality, and Poverty during the Transition
Figure 4.6. Composition of Disposable Income in Bulgaria, 1987-95
(percent)
60 -
50-
-40 -
Private .
� 30
a,20-,, .. '
Pensions
10 Other transfers
1987 1988 1989 1990 1991 1992 1993 1994 1995
Figure 4.7. Concentration Coefficients of Wages, Cash Social Transfers,
and Non-wage Private Sector Income in Bulgaria, 1989-95
60
50 --- ---- -*
Private -'
40- .. -'.^
30
.8 ~Wages/
2^ 20--
20
tJ 10 Pensions -
Other transfers ,
-10
1989 1990 1991 1992 1993 1994 1995
Note: The more unequally distributed the income source, the higher the coefficient of
concentration. The concentration coefficient shows how much a given source "pushes" up the
overall inequality.
Source: 1989-95 Bulgaria Household Budget Surveys.
Inequality 51
Figure 4.8. Composition of Disposable Income in Poland, 1987-95
(percent)
60 -
50
40
0 30 - Private ___
0
o , -' ".
20 --
Q 0 Pensions ".,."
10 - Other transfers
1987 1988 1989 1990 1991 1992 1993 1994 1995
Figure 4.9. Concentration Coefficients of Wages, Cash Social Transfers,
and Non-wage Private Sector Income in Poland, 1987-95
5!0 _
Private,.- ,,,
, 30 Wa
8 20 Pensions
0
10
-10 -Other transfers .
-20 -
1987 1988 1989 1990 1991 1992 1993 1994 1995
Note: The more unequally distributed the income source, the higher the coefficient of
concentration. The concentration coefficient shows how much a given source "pushes" up the
overall inequality.
Source: 1987-95 Poland Household Budget Surveys.
52 Income, Inequality, and Poverty during the Transition
Figure 4.10. Composition of Disposable Income in Slovenia, 1987-95
(percent)
80
70-
o 50-
� 40-
0
ii 30-
20 Private- .
10 - Pensions
Other transfers
O . .. . _,_,_.......___
1987 1988 1989 1990 1991 1992 1993 1994 1995
Figure 4.11. Concentration Coefficients of Wages, Cash Social Transfers,
and Non-wage Private Sector Income in Slovenia, 1987-95
40
30 - Private, ^'*
g1 10 - \Pjensions
200
o Other transfers
U -20 -2
1987 1988 1989 1990 1991 1992 1993 1994 1995
Note: The more unequally distributed the income source, the higher the coefficient of
concentration. The concentration coefficient shows how much a given source 'pushes" up the
overall inequality.
Source: 1987-95 Slovenia Household Budget Surveys.
Inequality 53
Figure 4.12. Composition of Disposable Income in Hungary, 1987-93
(percent)
60 W
g 50 -
40
40 -
o 30
Private
20 --
, 20 - ~~Pensions - - -- - . -_ - --'
10
Other transfers
1987 1988 1989 1990 1991 1992 1993
Figure 4.13. Concentration Coefficients of Wages, Cash Social Transfers,
and Non-wage Private Sector Income in Hungary, 1987-93
40 - Private , -' -
.g 3 Wages "
u 20-
A 10 - _Pensions
-10 .............................................................................-- ' ' - ' - - - .
. -10 - -
Other transfers
-20 - I I I I -
1987 1988 1989 1990 1991 1992 1993
Note: The more unequally distributed the income source, the higher the coefficient of
concentration. The concentration coefficient shows how much a given source "pushes" up the
overall inequality. "Private income" in 1993 does not include consumption-in-kind.
Source: 1987,1989, 1993 Hungary Household Budget Surveys.
54 Income, Inequality, and Poverty during the Transition
income brackets: in other words, a poor person will derive the same percent
of income from wages, pensions, or non-wage private sector income as a rich
person will. This is a rather unexpected outcome.
In Slovenia and Hungary (figures 4.10-4.13) none of the concentration co-
efficients showed a clear tendency to increase or decrease (the increased con-
centration of wages in Hungary is the only exception). This explains very
small increases in Ginis in both countries.
Russia represents a unique case: all income sources' concentration coeffi-
cients were higher in 1994 than before the transition and all have pushed
overall inequality up (see figures 4.14 and 4.15). The only factor that has mod-
erated the increase in inequality was a shift toward more equally distributed
income sources: that is, towards transfers and non-wage private sector in-
come, which prior to the transition had smaller concentration coefficients
than did wages.
Transfers and, in particular, pensions either left inequality unchanged (as
in Bulgaria, Slovenia, and Hungary) or they contributed to an increase in
inequality (as in Poland and Russia). The only exception noted here is Latvia,
where an improved (that is, more pro-poor) concentration of pensions resulted
from the introduction of almost flat pensions in 1992, a development which
is reflected in the steep, downward-sloping line for pensions in figure 4.17.13
Disparity among Social Groups
Changes in income distribution have also been accompanied by changes in the
relative position of social groups. Under previous regime, care was taken to
maintain some level of balance (or "parity") between the average income of
workers and of farmers."4 Communist concern with the relative position of vari-
ous social groups was also reflected in the design of HBSs, where survey repre-
sentativeness was ensured at the level of the social group, but not necessarily at
the level of the population (see appendix 1 for further discussion of HBSs).
Table 4.3 shows what happened to the average per capita income of work-
ers', farmers', and pensioners' households between 1987-88 and 1993-94. 15
As expected, average real incomes of all social groups have declined. How-
ever, it is the difference in the decline between the various groups that is of
interest here. When the difference in decline between two groups is less than
3 percentage points, it is assumed that no change in the groups' relative
13. The concentration coefficient of pensions decreased from 34 in 1989 to -4 in 1995.
14. Okrasa (1988, p. 637) argues that redistribution policies under Communism were
mostly designed to insure vertical equality among social groups. In some aspects, how-
ever (for example, in access to safe water and sanitation), rural populations, particularly
in the less developed republics of the former U.S.S.R., were at a disadvantage.
15. Workers are, in principle, both those employed in the state and private sector.
Many private sector workers are self-employed, however, and are thus included in the
category "self-employed" or "other" which is not shown here.
Inequality 55
Figure 4.14. Composition of Disposable Income in Russia, 1989-94
(percent)
80 -
70
c, 60 -
0
50
2 40
0
t 30 Private
20 - , -- '= - Pensions
10 ~ _ _ _ Other transfers
1989 1990 1991 1992 1993 1994
Figure 4.15. Concentration Coefficients of Wages, Cash Social Transfers,
and Non-wage Private Sector Income in Russia, 1989-94
70-
60 - Private
50- Wages
S 40 -.,~Other transfers
0)
U .-.. ~~~~~~~~~~~~Pensions
'IN
10
0 . ...........z /......... ....................................... ......................................... .. .. .........
-20_ * / | I I
1989 1990 1991 1992 1993 1994
Note: The more unequally distributed the income source, the higher the coefficient of
concentration. The concentration coefficient shows how much a given source "pushes" up the
overall inequality.
Source: 1989: Family Budget Survey; 1992 and 1994: Russian Longitudinal Monitoring Survey,
rounds 1 and 4 respectively.
56 Income, Inequality, and Poverty during the Transition
Figure 4.16. Composition of Disposable Income in Latvia, 1989-96
(percent)
80 -
70 -
60-
0
i 50-
U 40-
30
20 - Private , -
10 - ,, _,: _ _ ~ Pensions
- * Other transfers
10 -l
1989 1990 1991 1992 1993 1994 1995 1996
Figure 4.17. Concentration Coefficients of Wages, Cash Social Transfers,
and Non-wage Private Sector Income in Latvia, 1989-96
40 Private
' 30
Wage
0
h 20
10
u 10 ~~~~~~Pensions
Other transfers -- --- -
-10 - II
1989 1990 1991 1992 1993 1994 1995 1996
Note: The more unequally distributed the income source, the higher the coefficient of
concentration. The concentration coefficient shows how much a given source "pushes" up the
overall inequality.
Source: 1989 and 1992-93 Family Budget Survey; 1995-96: New Household Budget Survey.
Inequality 57
Table 4.3. Change in Real and Relative per Capita Income of Worker, Farmer, and
Pensioner Households
(workers' households real per capita income in 1987=100)
Country 1987-88 1993-94 Change in relative position
Belarus
Workers 102 86 W=F
Farmers 101 85
Bulgaria
Workers 102 53 W>F
Farmers 119 64
Czech Republic
Workers 102 73 P>W=F
Farmers 93 64
Pensioners 61 54
Hungary
Workers 100 73 P>W
Farmers 90 n.a.
Pensioners 92 68
Latvia
Workers 101 54 P>W>F
Farmers 100 41
Pensioners 59 34a
Lithuania
Workers 102 43 P>W>F
Farmers 100 33
Pensioners 58 46a
Poland
Workers 107 85 P>W>F
Farmers 121 77
Pensioners 95 89
Romania
Workers 100 76 F>P>W
Farmers 74 59
Pensioners 88 68
Russia
Workers 103 59 W>F
Farmers 88 30
Slovak Republic
Workers 102 71 P>W=F
Farmers 95 63
Pensioners 64 52
Slovenia
Workers 94 81 W>F
Farmers 84 66
a. 1992.
Source: Calculated from the countries' household budget surveys (see appendix 1).
58 Income, Inequality, and Poverty during the Transition
positions has taken place. For instance, if workers' real income lost 15 points
and farmers' real income lost 16 points (as in Belarus), their relative posi-
tions are considered unchanged. However, if workers lost 49 points and
farmers lost 55 points (as in Bulgaria), then workers are considered to have
done better than farmers. Remarkably, the following rules apply for all coun-
tries here:
* Pensioners' position in relation to workers has improved: P>W.
* Workers' position in relation to farmers has either improved (in six
countries) or remained the same (in three countries): W2F. 16
* Pensioners' position in relation to farmers has improved in all coun-
tries: P>F.
Farmers' households real incomes have declined the most almost every-
where. This was caused by a variety of factors: decreased agricultural pro-
duction in all countries; 17 removal of input subsidies (for example, for fertil-
izer and gasoline) combined with liberalization of food imports (in Poland,
Czechoslovakia, Russia, and the Baltic countries); 18 chaos associated with
land privatization and uncertainty of ownership (in Albania, Bulgaria, Esto-
nia, Romania, and Russia); and the end of an explicit government policy of
ensuring rural-urban income parity (Poland, Czechoslovakia). Workers have
done slightly better than farmers, and pensioners have done better than both.
How have the "new" private sector entrepreneurs, and the self-employed
done? Anecdotal evidence and common sense suggest that they would be among
those who benefited most from the transition. It is important, however, to keep
in mind that this group is more heterogeneous than workers or farmers are.
The private sector encompasses not only the self-employed professionals (for
example, doctors, accountants, engineers, and computer specialists) but also
coffee shop owners, shoemakers, hoteliers, and repairmen. It includes also small-
scale employers and "capitalists" (that is, owners of larger plants or factories).
The difference in income between a self-employed shoemaker and a large-scale
capitalist can be so large that it becomes all but meaningless to include them in
the same group. From the perspective of the transition, however, both are part
of the private sector, because their income comes from private activities.
Poland is the only country for which there is evidence about how the self-
employed have done since the transition. The Polish HBS introduced the cat-
egory of "self-employed" (outside agriculture) as a new social group in 1992.'9
The self-employed represent about 6 percent of the 1993 and 1994 sample. Their
16. Except in Romania, where farmers have done better than either pensioners or
workers.
17. In 1993-94, agricultural output in Eastern Europe, Russia, and Ukraine was about
20 percent below its 1987-88 level.
18. In addition, wage arrears in the Russian agricultural sector have been particu-
larly severe (see Braithwaite 1997, p. 65).
19. The first HBS results reflecting this new category were published in 1993.
Inequality 59
average income and expenditures are the highest of any group: approximately
20 percent above the overall mean in 1993 (World Bank 1995a, p. 10) and 30
percent in 1994 (Polish Central Statistical Office 1995 table 7 and 9). Being self-
employed reduces the probability of poverty by 11 percent, when other fac-
tors, such as education, type and size of household, location, and so on, are
controlled for.20
20. This result is obtained using the probit analysis. The coefficients on other ex-
planatory variables are virtually the same whether or not the probit regression is esti-
mated with the self-employment dummy variable.
5
Poverty
What Happens to Poverty When Income Goes Down?
Chapter 3 discussed the dramatic declines in output experienced in transi-
tion countries. As incomes decreased, poverty went up. In this chapter, two
simple measures of poverty will be used to study poverty: the headcount
index and the poverty deficit. The headcount index gives the percentage of
people who are poor because their income is below a certain threshold. The
poverty deficit is the sum of all income shortfalls (difference between the
threshold and one's income). In other words, it gives a weight to each poor
individual equal to the amount by which that individual's income falls short
of the poverty line. Poorer individuals thus "count" more, according to this
measure. The poverty deficit shows the total amount of money needed to
bring all those who are poor up to the poverty line. The poverty deficit is
often expressed as a percentage of GDP in order to show the effort a country
needs to make to "solve" its poverty problem.' When the poverty deficit is
divided by the total number of the poor, the average income shortfall of the
poor (sJ) is obtained. This shortfall can then be expressed as a percentage of
the poverty line, and will be here denoted by s.
For example, let the poverty line be 4, and let there be three individuals
with incomes of 1, 2, and 10. The first two are considered poor. The headcount
index is 2 out of 3 (that is, 66.6 percent). The total poverty deficit is equal to
(4-1) + (4-2)=5. Five units of income will be needed, then, to bring the poor
up to the poverty line. The average income shortfall of the poor in this ex-
ample is equal to 5 divided by 2, or 2.5. The average shortfall as a percentage
of the poverty line is 2.5 divided by 4, or 62.5 percent.
In chapter 4 it was concluded that income distribution has become more
unequal. Both the decline in overall income and the more unequal distribu-
tion of income will increase the incidence of poverty. The poverty deficit
should also then increase: if the poverty line is fixed and everyone's income
declines, then the overall amount needed to "solve" the poverty problem
must go up. The example provided in box 5.1 may help explain this. We may
1. The assumption behind the poverty deficit is that all money used for poverty alle-
viation is paid only to the poor (with no leakage to the non-poor) and is paid in exact
amount needed to bring the poor just up to, but not above, the poverty line (that is, there is
no "spillover"). This is the assumption of so-called perfect targeting. In reality, no more
than 50 to 60 percent of funds specifically destined for poverty relief are perfectly targeted.
60
Poverty 61
Box 5.1: How Poverty, According to Certain Measures, Changes as
Income Declines
Suppose there are 100 people with an average income of $10 (so that
their total income is $1,000). Let the poverty line be $4 and let there
be ten people with an income of less than $4. Let the average income
of these ten people be $2. Their average income shortfall is thus $2,
and the poverty deficit is $2 times 10 people, or $20. The shortfall
represents 2 percent of overall income or GDP. Suppose, then, that
income declines uniformly by 20 percent. Total income becomes $800.
The ten people who were poor remain poor, and their average in-
come drops to $1.60. The poverty deficit goes up to $2.40 times 10, or
$24. In addition, however, let an additional ten people "slide" below
the poverty line. Their incomes formerly ranged between $4 and $5
and are now between $3.20 and $4. Let their average income be $3.60.
Their poverty deficit will be $(4-3.60) times 10, or $4. The new total
poverty deficit will thus be equal to $28, or 3.5 percent of the new
GDP.
What does this example show? It shows, first, that a 20 percent
uniform decline in income results in a 10 percentage-point increase
in the headcount index. This gives an implicit elasticity of 0.5 (10
percentage points divided by 20 percent), which as will be seen later,
is realistic. Second, doubling the poverty headcount from 10 to 20
led to a 40 percent increase in the poverty deficit (from 20 to 28). A
smaller increase in the poverty deficit than in the headcount is due
to the decline in the average income shortfall, which went down from
$2 to $1.40. This is also very common as the "new" poor tend to fall
slightly below the poverty line. Third, the poverty deficit as a pro-
portion of GDP has risen by 75 percent, both because the poverty
deficit has gone up and because the GDP has shrunk. In terms of
percentage changes in the poverty measures, the following may be
expected to apply:
* The poverty headcount will increase the most.
* The poverty deficit as a share of GDP will increase less.
* The poverty deficit will increase even less.
* The average income shortfall might fall.
62 Income, Inequality, and Poverty during the Transition
also expect that the more income declines and the greater the increase in
inequality, the greater the increase in the headcount index and the poverty
deficit.
Other aspects of changes in poverty measures in the presence of large in-
come declines can be elucidated through simple algebra. For the sake of sim-
plicity, it is assumed here that income distribution does not change and all
incomes decrease by the same proportion. Such changes in income are con-
sidered "uniform."
The poverty deficit (PD) is obtained by multiplying the average income
shortfall of the poor, s (in percent) by the poverty line (z) and the number of
the poor (P). This amount can then be related to the country's GDP written as
the product of its GDP per capita (y) and its population (N). After some simple
manipulation, the poverty deficit/GDP ratio becomes:
PD szP z P z
5.1 GDP = =s HC
where HC is the headcount index.
As income shrinks across the board, the ratio between the (fixed) poverty
line and GDP per capita (z/y) increases by definition, as does the poverty
headcount. The average shortfall s may be reduced if the distance between
the new poor and the poverty line is small (as per the example in box 5.1).
There is a further change in the poverty deficit that is not directly revealed
by equation 5.1, however. To consider this, the poverty deficit is written as:
5.2 PD= f (z-y) f(y) dy.
The poverty deficit is equal to the sum of all distances between the poverty
line and actual income (z-y) multiplied by the number of people who have
such income,f(y), wheref(y) is the density function. An across-the-board slide
in income is formally equivalent to a corresponding increase in the poverty
line z. Consider what happens to the poverty deficit when the poverty line
increases infinitesimally. If equation 5.2 is differentiated with respect to z the
following is obtained:
5.3 dPD_ fz f(y) dy + (Z-Z) f(z)
z
Jo f(y) dy = F(z).
Equation 5.3 shows that the change in the poverty deficit following an
infinitesimal uniform decline in income is equal to the area between 0 and z
under the density functionf(y) as shown in figure 5.1.2 The clear implication
2. For a more intuitive explanation, see appendix 3.
Poverty 63
Figure 5.1. Income Density Function and the Poverty Line
f(z2)
f(z,)
ZI Z2 Mean
Income
is that the higher the poverty line, the greater the increase in the poverty
deficit for a given absolute decline in income. If, for example, the poverty line
is z2 then the poverty deficit will increase by more than it would if the pov-
erty line were z1 (see figure 5.1).
Because in the analysis in this chapter the same real poverty line is used
for all countries ($120 per capita per month at international prices; see the
section below, By how much has poverty increased?), the place of the poverty
line within the income distribution curve will differ between the richer and
poorer countries. For example, the purchasing power parity (PPP) $120 pov-
erty line amounts to only 38 percent of Hungary's average income (approxi-
mately equal to point z1 in figure 5.1), and 70 percent of Estonia's average
income (approximately equal to Z2 in figure 5.1). The areaJ f(y) dy will be
greater than the areaJ 2 f(y) dy. Thus it can be concluded that:
Jo
(A) A given absolute decrease in income will raise the poverty deficit
more in the case of a poor country (Estonia) than it will in the case of
a richer country (Hungary).
In addition, a uniform decline in income (equivalent to an increase in the
poverty line) will have a more than a proportional impact on the poverty
deficit: if incomes shrink by, say, 10 percent, the poverty deficit will rise by
more than 10 percent. This can be seen if equation 5.3 is multiplied by z/PD,
64 Income, Inequality, and Poverty during the Transition
thus yielding the elasticity of the poverty deficit with respect to the poverty
line (equation 5.4). The elasticity is equal to the reverse of the shortfall, and is
always greater than 1. The percentage shortfall would normally increase with
z and hence elasticity would decrease with z.3 This is equivalent to elasticity
5.4 increasing with income.
dPD z Z fz f(y) dy
dz PD
5.4 J ro-Z (z-y) f(y) dy
z 1
= - >1
z-yp s
where y- = average income of the poor.
In other words,
(B) If percentage declines in income are the same in poor and rich coun-
tries, the poverty deficit will increase, proportionately more, in rich
countries.
Finally, with a uniform infinitesimal slide in incomes, the poverty headcount
will increase by f(z), that is,
5 5 dHC dF(z) _
dz dz
(C) Because the density function is "thicker" (higher) around z2 than it is
around z, (see figure 5.1), the poverty headcount will, for a given
absolute decrease in income, increase more for a poorer than for a
richer country.
Another way to express this is to say that the relationship between HC
(with a given poverty line) and mean country income (or consumption) is
convex: the higher the income, the smaller the effect of a given change in
income on the headcount index (see Ravallion 1993, p. 6). The exception to
this would be if a country were so poor that the poverty line was situated
very much to the right of the mode where the density function is "thin."
By How Much Has Poverty Increased?
One of the central objectives, if not the central objective, of a comparative
study of poverty is to compare poverty rates and poverty deficits both among
3. For example, for an extremely high poverty line equal to the highest income in
the country (y), elasticity becomes y/(y- Y) and tends to 1.
Poverty 65
countries and within a given country at different points in time. For this sort
of comparison, however, a common poverty line is needed.
The same poverty line across countries implicitly treats each individual
equally, regardless of where he or she lives. To have the break-down of poor
people among countries is important because it helps the development of an
international anti-poverty policy. If the objective is to decide where, among a
given group of countries, available resources should best be directed in or-
der to help the poor, cross-country comparability is indispensable. It is rea-
sonable to assume that international effort should be directed toward coun-
tries in which, according to international standards, (1) many poor people
live, or (2) poverty rates are high even if the absolute number of poor is small
(because the country's population is small). For the first, it is necessary to use
the same poverty yardstick in order to determine the distribution of the poor
by country, and for the second, it is necessary to use the same yardstick to
compare headcount indexes in different countries. Once the poverty line is
fixed in time and among countries, a full comparison is possible. For ex-
ample, we can compare Hungary's poverty headcount in 1993 not only with
Hungary in 1989, but also with Russia in 1989.
The first step in calculating such a poverty line is to establish a single bench-
mark expressed in the same "currency" for all countries. Four international
dollars per capita per day will be used here.4 These dollars have the same
purchasing power over consumption goods in all countries. Then it is neces-
sary to find the purchasing power parity PPP exchange rate for each country:
how many units of local currency are needed to buy $1 worth of consump-
tion goods at international prices? These data are obtained from the multilat-
eral International Comparison Project (ICP), whose most recent round is for
1993.5 The PPP exchange rate for each country is then multiplied by $120 per
month to obtain the monthly poverty line expressed in domestic currency.
Of course, a poverty line that implies the same purchasing power over goods
and services across countries requires different amounts of actual dollars in
each country: the lower the country's price level compared with that of the
world, the fewer actual dollars are needed to buy the same amount of goods.
For example, in 1993, $51 per month was needed to reach the $PPP 120 pov-
erty line in Poland; $57 in Hungary; $30 in Bulgaria; and only $21 in Ukraine.
By contrast, it would require $165 per month to reach the same poverty line
in Switzerland, $125 in Finland, and $59 in Turkey.
Once the 1993 benchmark poverty line in domestic currency is established,
poverty lines for other years can be obtained, if needed, simply by inflating
4. This is calculated at 1990 international prices.
5. Data supplied to the author by European Comparison Project. Hungary, Poland,
and the former Yugoslavia have been included in the ICP since 1985. In 1990, Romania
and the U.S.S.R. were added. In the 1993 ICP exercise, all transition economies partici-
pated. Data for Central Asian countries were not published, however. For these countries
the base price level used is the 1990 ICP result for the Soviet Union as published in United
Nations Economic Commnission for Europe (1994).
66 Income, Inequality, and Poverty during the Transition
the 1993 poverty line by the cost-of-living index. Full comparability of differ-
ent points in time for a given country, as well as of different countries is thus
possible. It is, however, worth pointing out again that the actual dollar
amounts needed to reach the poverty line in each country, and in each year,
will differ. In Poland, the poverty line in 1987 amounted to $27 per capita per
month (at the official exchange rate); in 1993, because of the rapid real appre-
ciation of the zloty, it was, as mentioned before, $51. On the other hand, in
Moldova the poverty line in 1988 (as in the rest of the Soviet Union) was $25
per capita per month;6 but in 1994, one needed only $13 to reach the poverty
line.
More formally, the formula for calculating the poverty line can be written
as follows:
PLit = PL93 i, COLi,(t93)
where PLk, = the poverty line in domestic currency for country i and year t;
PL$93 = the common poverty line in international dollars at 1993 prices; ER*jg3
= the consumption purchasing power exchange rate of i country's currency
in 1993; and COLi(tS93) = the change in the cost-of-living index between year t
and 1993 for country i.
The amount of $PPP 4 per capita per day is a relatively high poverty line. It
is four times higher than the World Bank line of absolute poverty. But the
level of income of East European and former Soviet countries, and the com-
pression of their income distributions make $PPP 4 per day per capita a rea-
sonable poverty line.7
This line is below the "accounting" social minimum lines for most East
European countries, that is, the lines which are not used to define eligibility
for social assistance but represent same vague "desirable" minimum. How-
ever, they are widely published and often used to calculate the number of the
poor. The "accounting" social minimum lines in Eastern Europe range from
$PPP 170 to $PPP 300 per capita per month (see Milanovic 1996). For ex-
ample, the Polish social minimum in 1987 was zloty 14,222 per capita per
month, while the $PPP 120 poverty line for the same year is zloty 7,265. The
gap is even greater in 1993. The Polish social minimum for June 1993 was
zloty 2.1 million per capita per month; the poverty line as calculated here is
zloty 880,000. The situation is somewhat different in republics of the former
Soviet Union. At the beginning of the transition, like in Poland, social mini-
mum lines8 were higher than the poverty line used here. The Soviet social
6. The 1988 ruble exchange rate is calculated according to a two-to-one "blend"
between the official rate (Rs. 0.6) and the parallel market rate (Rs. 4.2).
7. In a recent World Bank study of income distribution and poverty in Latin America
and the Caribbean, the poverty line used was $PPP 2 per day per capita (see
Psacharopoulos and others 1992).
8. Actually, there was a single U.S.S.R.-wide line until 1991.
Poverty 67
minimum in 1987-88 was Rs. 78,9 while the poverty line used here is only Rs.
54. With time, however, the social minimum in all these republics was scaled
down in real terms, and by 1993-94, the new social minimums were very
close to the $PPP 120 poverty line. For example, in 1992 Russia officially
adopted a new subsistence minimum that was about two-thirds of the old
social minimum.'0 In July 1993 (the date of the Russian survey used here), the
official minimum was 21,206 rubles per month per capita, almost the same as
the poverty line proposed here of Rs. 21,496.11 Because both lines are indexed
by the cost-of-living index, their amounts continue to converge.
The results: universal increase in poverty
Table 5.1 shows the estimated poverty rates for the eighteen countries. For all
countries, poverty rates are calculated from "uncorrected" HBS data from
the two end-periods: 1987-88 and 1993-95. These "uncorrected" estimates
are here referred to as INCOMEI estimates. The average per capita $PPP in-
comes range from $PPP 75 per capita per month in the Kyrgyz Republic to
$PPP 480 in Slovenia (see also figure 5.2). (For further discussion of data
sources, biases, and main problems, see appendix 1.)
The total estimated number of the poor in the eighteen countries has risen
twelvefold from nearly 14 million before the transition or about 4 percent of
the population, to 168 million in 1993-95, or approximately 45 percent of the
population.
Poverty increased in all eighteen countries. The headcount increase, how-
ever, was very uneven. In the richer countries of Central Europe (the Czech
Republic, Hungary, the Slovak Republic, and Slovenia) the percentage of the
poor rose, on average, modestly from less than 1 percent to 2 percent. In
Poland, it rose from 6 percent to 20 percent. It is more difficult to interpret
results for the former Soviet republics because inadequate coverage of sur-
veys before the transition tended to underestimate poverty, while during the
transition, dramatic changes in the economy (such as, the expansion of the
informal sector) were not reflected in household surveys, thus biasing cur-
rent poverty estimates upwards. The two biases reinforce each other when
the overall change in poverty is being considered. Despite these and other
9. Or Rs. 84 if goods were valued at free market prices (see Braithwaite, 1994, p.4).
10. The Russian subsistence minimum defined in 1992 is based on the cost of a bundle
of goods, where the share of food is 68 percent. The bundle was devised by the Russian
Institute of Nutrition of the Academy of Sciences and by WHO. It became the official
standard for poverty measurement in Russia in March 1992. For details, see Gontmakher
and others (1995, pp. 43-52) and World Bank (1995e, p. 15).
11. The official poverty line was used in a detailed study of poverty in Russia (World
Bank 1995g) and in Klugman (1997). However, in both cases, this "central" per capita
poverty line was scaled to take into account economies of size and the lower expenditure
requirements of children. This explains why the headcount for Russia found in Foley
(1997) is about 40 percent, that is, about 10 percentage points lower than the one pre-
sented below in table 5.1.
Table 5.1. Estimated Poverty Headcount and Poverty Deficit in 1987-88 and 1993-95 Using HBS Income
1993-95 data
Total number Shortfall Total Average
Poverty of the poor as % of poverty income Type and
headcount (%) (millions) poverty deficit as % per capita year of
Country 1987-88 1993-95 1987-88 1993-95 line of GDP Elasticity, ($PPP pm) data
Balkans and Poland 5 32 3.6 22.4 28 2.2 0.5 193
Bulgaria 2b 15 0.1 1.3 26 1.1 0.3 282 A:93
Poland 6 20 2.1 7.6 27 1.4 0.4 213 SA:I/93
Romania 6b 59 1.3 13.5 32 5.4 0.7 123 M:3/94
Central Europe <1 2 0.1 0.4 25 0.1 0.1 348
& Czech Republic 0 <1 0 0.1 23 0.01 0.01 411 M:1/93
Hungary' 1 4 0.1 0.4 25 0.2 0.2 266 A:93
Slovakia 0 <1 0 0.0 20 0.01 0.01 332 A:93
Slovenia 0 <1 0 0.0 31 0.02 0.01 481 A:93
Baltics 1 29 0.1 2.3 33 3.1 0.6 204
Estonia 1 37 0.02 0.6 37 4.2 0.7 172 Q3:95
Latvia 1 22 0.03 0.6 28 2.3 0.5 213 Q4:95
Lithuaniac 1 30 0.04 1.1 34 2.9 0.5 212 A:94
Slavic republics 2 52 3.5 112.1 39 4.8 0.5 170
Belarus 1 22 0.1 2.3 26 1.2 0.5 197 Q1:95
Moldova 4 66 0.2 2.9 43 7.0 0.6 115 A:93
Russia 2 50 2.2 74.2 40 4.2 0.6 181 Q3:93
Ukraine 2 63 1.0 32.7 47 6.9 0.5 136 M:6/95
Total without
Central Asia 3 43 7.2 137.2 31 3.1 230
1993-95 data
Total number Shortfall Total Average
Poverty of the poor as % of poverty income Type and
headcount (%) (millions) poverty deficit as % per capita year of
Country 1987-88 1993-95 1987-88 1993-95 line of GDP Elasticityo ($PPP pm) data
Central Asia 15 66 6.5 30.7 47 9.8 0.5 113
Kazakhstan 5 65 0.8 11.0 39 9.2 0.7 115 A:93
Kyrgyz Republic 12 88 0.5 4.0 68 64.4 0.2 75 Q3:93
Turkmenistan 12 61 0.4 2.4 40 7.7 0.6 124 A:93
Uzbekistan 24 63 4.8 13.3 39 12.4 0.6 118 A:93
Total transition 4 45 13.6 168.0 35 3.5 215
Comparators
Brazil 33 48.3 44 4.4 0.3 466 A:89
Colombia 35 11.6 40 5.4 0.3 360 A:92
Ecuador 35 3.9 31 4.4 0.5 219 A:94
Paraguay 44 2.1 51 8.1 0.3 266 A:95
Malaysia 31 18 5.1 3.6 29 0.8 0.3 403 A:87,95
Turkey 31 16.7 33 3.8 0.5 255 A:87
United Kingdom 1 <1 0.6 0.5 good A:88,92
Note: "Slavic republics" includes Moldova. Poverty line = 120 international dollars per capita per month. A = annual data; SA = semiannual data;
M = monthly data; Q = quarterly data. 10/93 means that the data refer to October 1993. 1/93 means that the data refer to the first half of 1993.
Poverty rates were calculated using World Bank software POVCAL. For poverty headcount, the number of the poor, poverty deficit as percentage of GDP,
and income per capita the regional means are weighted averages; for elasticity and average shortfall, the regional means are unweighted averages.
a. Elasticity is percentage point change in poverty headcount divided by percentage change in income (around the poverty line).
b. 1989 data. c. Income does not include consumption-in-kind in 1993-95. d. Estimate based on the ratio between equivalent adult units and per capita measures.
Source: Transition economies: calculated from household budget surveys presented in appendix 4. For caveats and data biases, see appendix 1. For other
details regarding calculations, see appendix 5. For comparator countries, the sources are as follows: for the United Kingdom, Family Expenditure Survey data
as reported in United Kingdom Central Statistical Office (1991, appendix 1) and United Kingdom Central Statistical Office (1994, appendix 1); for Turkey,
Turkey Statistical Yearbook 1990, pp. 206-207; for Malaysia, Ahuja (1997, Annex 2); for Brazil, Psacharopoulos et al. (1992); for Colombia, World Bank (1994b, vol.
1, p. 1 and vol. 2, Annex 1); for Ecuador, World Bank (1995d, vol. 2, p. 4); for Paraguay, Encuesta de Hogares 1995.
EUROPE AND CENTRAL ASIA A.708O 'o
MONTHLY INCOME PER CAPITA ($PPP) AC CA
INCOME PER CAPITAA RC TIC C EA N
($PPP PER MONTHI)
SKaa
1,. . .. .. . .. .. .
o ________________________________________
IO~~~~~~~~~ 2O~~~~~~~~~~~~ 4O~~.. .............
TV 40 ar pI
o S~~~~~~~~~~~~oeat
p~~~~~~~~~~~~~~~~~~~.
AA~ ~ ~~~ ) RUSSIAN FEDERAIION~~~...................
~~~ C [ATYLA o.*. ESTOIB~............................
(.11 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ .........
............. ..XN
S \ .'~~~~ ~~...~~:.: ~~~ 'i-'- __________________________........ .... .. _ . .... ...
/ A. STIS~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. ........ ........
edi~~~~~~~~~~err...ane.....o...n P ... ... .TAa M . .A . . P . a ........
Sea A~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.. .e . A . A.pe .~ pp .......
.' p~~~~~~~~.... R S IN.. .....
Poverty 71
caveats (see appendix 1), it is safe to conclude that poverty increased greatly
in these countries. The Baltic countries started the transition with low pov-
erty rates, close to those of Central European countries. Their poverty rates
recorded very sharp increases reaching almost 30 percent of the population
in 1994-95. In Ukraine and Russia, poverty rates increased even more. Russia's
poverty headcount is estimated to have risen from 2 percent to 50 percent of
the population. Finally, the Central Asian countries began the transition with
relatively high poverty headcounts (in double digits for every country ex-
cept Kazakhstan). Data from 1993 show poverty rates over 60 percent for all
countries, including what is probably a too-high 88 percent for the Kyrgyz
Republic.
The composition of poverty has also changed. There are now many more
poor in the states of the former Soviet Union, as compared with Eastern Eu-
rope, than there were at the beginning of the transition. In 1987-88, about as
many poor people lived in Eastern Europe as in the European part of the
U.S.S.R. In 1993-95, however, for each poor person in Eastern Europe there
were five living in the European part of the former Soviet Union. If the Cen-
tral Asian countries are included, the preponderance of poverty in the former
Soviet Union compared with Eastern Europe becomes even greater (six-to-
one).
Elasticity of poverty with respect to income
Differences in how much poverty has increased do not depend only on how
much real income has declined and how much income inequality has risen,
but also on the absolute level of a country's income. This is because we use
the same absolute yardstick to measure poverty. Richer countries will have
lower poverty headcount than poor countries, even if their incomes declined
by the same percentage. The Czech Republic, Hungary, the Slovak Republic,
and Slovenia had relatively high average incomes both before the transition
and in 1993-95. With the poverty line of $PPP 120 per capita per month, there
were very small increases in poverty because almost no one's income was
below that level, either in 1987 or in 1993-95. The situation is different in
poorer countries, where the same percentage decline in income led to mas-
sive increases in poverty as many fell below the poverty line. In these coun-
tries, the poverty line lies in the region of "dense" income distribution, as can
be seen by comparing Moldovan income distributions to that in the Slovak
Republic (see figure 5.3 ).
The equation 5.6 shows that the elasticity of the poverty headcount with
respect to a given uniform percentage decline in income (that is, a decline
without a change in the income distribution curve) will vary among coun-
tries. Elasticity (as defined here: it is in effect semi-elasticity),
5.6 dF(z) - dF(z) z = f(z) z
dz/z dz
72 Income, Inequality, and Poverty during the Transition
Figure 5.3. Income Distributions in Moldova and Slovakia, 1992
12
10 A
75 8 Moldova
0 120 200 400 600 800 1000
$PPP per capita per month
Source: Countries' household budget surveys.
is equal to the product of the density function around the poverty line, and
the poverty line. The poverty line is the same for all countries. The value of
the density function at z, however, will be higher for poorer countries (com-
pare points A and B in figure 5.3 for z=$PPP120), and elasticity will tend to be
higher for them. (Note that the rule C above refers to the change in headcount
for a given absolute decrease in income in rich and poor countries.)
Indeed, elasticities in Balkans, Poland, and the former Soviet republics are
invariably around 0.5 to 0.6, meaning that an across-the-board decline in in-
come of 10 percent raises the poverty headcount by 5 to 6 percentage points.
For example, in Russia every 10 percent decline in income will make an addi-
tional 5 percent of the population, or about 7 million people, poor. In richer
countries, elasticities are much smaller, around 0.1.
How do these values compare with those in other countries? Are socialist
countries likely to have higher elasticities because of a more compressed in-
come distribution? Squire (cited in World Bank 1993, p. 41) estimates the elas-
ticity of poverty headcount with respect to expenditures to be 0.24 after con-
trolling for the initial headcount. His equation is AHC = -0.24 growth in
mean expenditure - 0.01 HCo (where HCo = initial headcount). Because initial
pre-transition headcounts were small in the countries studied here (0.04 on
average), the second term in Squire's equation would be close to zero. Squire's
equation would then yield elasticities of around 0.24, which is lower than
that observed here for all countries except Central Europe. In a study of ap-
proximately twenty countries, Ravallion (1993, p. 7) finds that the elasticity
of poverty rates to income (when income alone is included in the equation,
Poverty 73
that is, assuming that distribution does not change) is about 2.4. When his
equation is re-expressed in terms of headcount semi-elasticity as here, its
value is around 0.1, which is again much lower than what is found in transi-
tion economies.
Another important question is what will happen to elasticity as the economy
recovers. Is there likely to be a symmetrical movement that, accordion-like,
after having first pushed people below the poverty line, then raises them
above it? Whether this occurs depends on the shape of future growth. If in-
come growth occurs first at the top and the middle levels of income distribu-
tion, then inequality will continue to rise and poverty will remain stable.
This seems to have been the case in Poland, the only transition economy in
which real GDP has grown for four consecutive years (1992-95). While GDP
grew by 5 percent between 1992 and 1994 (and real personal incomes grew
by 4 percent), the poverty rate in 1994 was still slightly higher than in 1992,
as inequality seems to have risen. But when growth "trickles down," sharp
declines in poverty could result, as relatively large segments of the popula-
tion now hovering around the poverty line are pulled above it. Several ele-
ments suggest that such an optimistic scenario is likely. Most of the poor in
transition economies do not represent a distinct "underclass" as they do in
Latin America (see the section below, Who Are the Poor?): their educational
achievements are not much lower than those of the rest of the population;
their access to social services; their ownership of consumer durables and
apartments is also close to that of non-poor segments of the population."2
The declines in their incomes are recent and are not yet reflected in a marked
deterioration of their asset ownership. If it takes a long time for income growth
to "trickle down," however, these relatively favorable elements will be lost.
Alternative poverty calculations
Another implication of the poverty rate's high elasticity with respect to in-
come is that mistakes in income reporting are likely to have a substantial
impact on calculated poverty rates. Underreporting of income seems to be
widespread in transition economies. Thus, both inadequate income report-
ing and "bunching" of the population around the poverty line lead to a great
variability in calculated poverty rates. The rates "move around" a lot: they
12. Trying to "guess" who is poor in transition economies based on asset ownership
or access to social services fails (see Dupre 1994 on Russia). A Polish survey of recipients
of social assistance shows that almost 20 percent own cars and 60 percent own color
televisions (see Polish Central Statistical Office 1993, p. 21). Aside from automobiles and
videocassette recorders, a recent World Bank study found, "no strong correlation [in
Ukraine] between housing conditions or ownership of most consumer durables and the
frequency of poverty" (World Bank, 1996b).
74 Income, Inequality, and Poverty during the Transition
are not robust. For example, if the elasticity around the poverty line is 0.5 to
0.6, then a 20 percent underestimation of income, which is quite likely, will
lead to an overestimation of the poverty rate by between 10 and 12 percent-
age points. Because of uncertainty regarding the "true" poverty rates for the
years 1993-95, they are recalculated here in two additional ways. First, rates
were recalculated using macroeconomic incomes rather than data from HBSs
whenever macroeconomic data showed higher incomes (see table 3.4); then
they were recalculated using expenditures instead of income. Estimates based
on macro ("adjusted") income data are referred to as INCOME2; estimates
based on expenditures are referred to as EXPEN.
Table 5.2 shows that, using macroeconomic data instead of HBSs, the over-
all average poverty headcount drops from 45 (as in table 5.1) to 40 percent.
The total number of the poor goes down from 168 rnillion to 147 million. The
most important changes occur in Romania and Uzbekistan, where estimated
poverty rates decline by between 20 and 24 percentage points. Headcounts
in Poland and Russia decline by 6 points. Poland, Romania, Russia, and
Uzbekistan account for virtually the entire decline in the number of the poor.
This is because income adjustments in Central European countries had little
effect on poverty rates, because the poverty rates were low and the elastici-
ties small. In terms of regional poverty rates, the picture changes somewhat.
There is a sharper split between the Balkans, Poland, and the Baltic countries
(except Romania and Estonia), on the one hand, which have poverty rates of
about 20 percent, and the Slavic and Central Asian republics of the former
Soviet Union, on the other hand, where about half the population is poor.
The increase in the number of poor, compared with the situation before the
transition, is tenfold: from about 14 million to 147 million (see figure 5.4).
Table 5.3 shows a different set of poverty estimates, where expenditures
per capita are used instead of incomes per capita, for the eight countries for
which both HBS income and expenditure data are available for the same years.
In seven of these countries, the use of expenditures results in lower poverty
headcounts than did the use of HBS income. For Belarus, Poland, Romania,
and Russia, reported expenditures are significantly higher (between 19 and
30 percent higher) than reported INCOME1, and the headcounts are between
8 and 11 percentage points lower (see the two last columns in table 5.3). For
example, Russia's headcount goes from 50 to 39 percent; Romania's goes from
59 to 48 percent, and Poland's goes from 20 to 10 percent. The Kyrgyz Repub-
lic and Ukraine are in a class of their own, with reported expenditures in
these countries more than twice reported incomes. When expenditures are
considered rather than incomes, more than one-third of the population in
these countries, ceases to be poor! For the three Slavic republics of the former
Soviet Union, where the transition has brought about a huge increase in in-
formal sector incomes, the average poverty headcount goes down dramati-
cally from more than 50 percent, when unadjusted survey data are used, to
34 percent, when expenditure data are used. The latter figure probably offers
a more realistic estimate of poverty.
Poverty 75
Table 5.2. Estimated Poverty Headcount and Poverty Deficit in 1993-95 Using a
Higher (Macro) Income instead of HBS Income
Total Total
Poverty Shortfall number poverty
head- as % of of the deficit Income
count poverty poor as % of adjustment
Country (%) line (millions) GDP (%)
Balkans and Poland 22 27 15.6 1.2
Bulgaria 15 26 1.3 1.1 0
Poland 14 27 5.3 0.9 +15
Romania 39 28 8.9 2.4 +26
Central Europe 1 28 0.2 <0.1
Czech Republic <1 26 0.0 0.0 +5
Hungary 2 33 0.2 0.1 +27
Slovak Republic <1 20 0.0 0.0 0
Slovenia <1 33 0.0 0.0 +12
Baltics 29 33 2.3 3.1
Estonia 37 37 0.6 4.2 0
Latvia 22 28 0.6 2.3 0
Lithuania 30 34 1.1 2.9 0
Slavic republics
and Moldova 48 38 104.0 4.1
Belarus 22 26 2.3 1.2 0
Moldova 66 43 2.9 7.0 0
Russia 44 38 66.1 3.3 +10
Ukraine 63 47 32.7 6.9 0
Total without
Central Asia 38 31 122.1 2.5
Central Asia 53 44 25.0 7.5
Kazakhstan 62 38 10.6 8.2 +4
Kyrgyz Republic 86 67 3.9 57.7 +8
Turkmenistan 57 39 2.2 6.7 +6
Uzbekistan 39 32 8.3 4.4 +44
Total transition 40 35 147.1 2.8
Note: Poverty line = 120 international dollars per capita per month. HBS incomes are raised
across the board (thus leaving inequality unchanged) by the macroeconomic income minus HBS
percentage difference from table 3.4 (if macroeconomic incomes have declined more than HBS,
adjustment = 0).
Source: Calculated from the data presented in appendix 4. For caveats and data biases, see
appendix 1.
76 Income, Inequality, and Poverty during the Transition
Figure 5.4. Estimated Number of Poor Before the Transition and in 1993-95
(millions)
1993-95 1987-88
Russian Federation 66
Baltics 2
~~~~ ~~Central Europe 0.2
Balkans, Poland 16
Central Asia 25 6.5
Other Slavic 38
Total=147 million Total=14 million
Note: 1993-95 information is based on "adjusted" HBS data (INCOME2 set). "Other Slavic"
includes Moldova.
How Much Is Needed to Cover the Poverty Deficit?
A relatively shallow poverty
Tables 5.1-5.3 indicate that poverty in transition economies, while widespread,
is relatively "shallow." This means that the average income of the poor is not
substantially below the poverty line. Poverty shortfall (that is, the percent-
age by which the average income of the poor falls below the poverty line) in
the Balkans, Poland, the Baltic countries, and Central Europe is around 30
percent. This means that the average income of the poor person is about $PPP
2.8 per day. Poverty is deeper in the Slavic republics of the former Soviet
Union and in Central Asia, where the average shortfall is about 40 percent,
and the average income of the poor is thus $PPP 2.4. Broadly speaking, pov-
erty shortfalls increase with higher poverty headcounts. This means that as
poverty widens it also becomes deeper which, of course, puts a double pres-
sure on the poverty deficit."3
13. In general, there is no reason why this should be the case. The average shortfall
in any given country tends to decrease as the poverty headcount goes up (see box 5.1). If
the headcount is small, only the very bottom of income distribution (the "down and
Poverty 77
Table 5.3. Estimated Poverty Headcount and Poverty Deficit in 1993-95 Using
HBS Expenditures
EXPEN
Shortfall Total compared to
Poverty as % of poverty INCOMEI
head- poverty deficit as Head-
Country count (%) line % of GDP Elasticitya count Mean'
Poland 10 20 0.5 0.4 -10 +30
Romania 48 34 4.9 0.7 -11 +19
Hungary 7 20 0.3 0.3 +3 0
Estonia 34 28 2.9 0.7 -3 +2
Slavic republics 34 34 3.3 0.4 -17
Belarus 14 23 0.7 0.4 -8 +21
Russia 39 44 3.7 0.4 -11 +23
Ukraine 26 37 2.3 0.4 -37 +110
Kyrgyz Republic 55 46 27.2 0.5 -33 +132
Note: Poverty line = 120 international dollars per capita per month.
a. Elasticity is percentage point change in poverty headcount divided by percentage change in
income (across-the-board).
b. The difference between EXPEN and INCOMEI from table 3.3.
Source: Calculated from household budget surveys presented in appendix 4. For caveats and
data biases, see appendix 1.
The situation in transition economies may be contrasted with that in Latin
American countries. In Latin American countries, using a lower poverty line
of $PPP 60 per capita per month, average income shortfalls are approximately
40 percent.'4 The average income of a poor person in Latin America is there-
fore only $PPP 1.2 per day, or half the average income of a poor person in the
Commonwealth of Independent States15 and even less compared with a poor
person in Eastern Europe.
Widespread but shallow poverty has several policy implications. About
2.8 percent of GDP (using the INCOME2 data set)16 would be needed to elimi-
nate poverty, assuming perfect targeting, that is, assuming that transfers are
received only by the poor and in the exact amounts needed to bring them up
to the level of the poverty line.
Poverty deficits as a percentage of GDP vary widely among the countries.
The deficits are only 0.1 percent of GDP in Central European countries. In
out") are included, and they are likely to be very poor. As the headcount increases, the
poor are the "ordinary folk" whose income is often relatively close to the poverty line.
14. See Psacharopoulos and others (1992).
15. The Commonwealth of Independent States (CIS) comprises all but Baltic repub-
lics of the former Soviet Union.
16. Poverty deficits as a share of GDP are calculated in relation to countries' actual
GDPs in corresponding years.
78 Income, Inequality, and Poverty during the Transition
Figure 5.5. Estimated Annual Costs of Poverty Elimination Assuming Perfect
Targeting, 1993-95
Russian Federation 5.8 Balti
Central Europe 0.03
"'/" "' ~Balkans, Poland 1.5
Central Asia 2.1
Other Slavic 39
Total = $13.6 billion
Note: Current $ billion, INCOME2 data. "Other Slavic" includes Moldova.
Figure 5.6. Estimated Monthly Spending Required to Lift an Average Poor
Person out of Poverty, 1993-95
16
14-
12 -
En 10 - 0 0 g
0
Balkans Central Baltic Russia Other Central Total
and Europe Slavic Asia
Poland
Note: Current dollars using INCOME2 estimates. The poverty line is $PPP 120 per person per
month. "Other Slavic" includes Moldova.
Poverty 79
Belarus, Bulgaria, Lithuania, and Poland, the poverty deficit is approximately
1 percent of GDP. In Latvia and Romania, it is about 2 percent of GDP. In
Russia, the deficit is 3.3 percent of GDP."7 In the Central Asian republics,
Moldova, and Ukraine, the deficits are even higher.'8 These figures may be
contrasted with the poverty deficits of 3.8 percent of GDP in Turkey, 4.4 per-
cent in Brazil, and 5.4 in Colombia, figures which were obtained using the
same poverty line of $PPP 120 (see table 5.1). Eliminating poverty in Russia
would cost about what it would in Turkey, relative to GDP.
Relatively small poverty deficits (under 3 percent of GDP) in most transi-
tion economies appear to carry a favorable message for anti-poverty poli-
cies. It seems, at first glance, that elimination or substantial alleviation of
poverty is feasible. However, before estimates can be made of actual resources
needed to eliminate poverty, these numbers must be multiplied by a factor of
about 3 to account for realistic estimates of "leakage."'9 Then, even in coun-
tries where estimated deficits are about 3 percent of GDP, the welfare spend-
ing required becomes an "impossible" 9 percent of GDP.
How much would be needed in current dollars, assuming perfect target-
ing, to eliminate poverty in all eighteen transition economies? The annual
total for all countries is estimated at between $13.6 and $17.3 billion, depend-
ing on the income assumptions used (the former with INCOME2 data; the
latter with INCOMEI data). Figure 5.5 shows the regional breakdown as-
suming higher income and thus lower poverty deficit. As much as 70 percent
of the total $13.6 billion needed, would be spent in the Slavic republics of the
former Soviet Union and Moldova, with Russia alone accounting for $5.8
billion per year. The Balkans and Poland would need $1.5 billion per year,
and the Central Asian republics would need $2.1 billion per year. The re-
quirements of the other two regions, the Baltics and Central Europe, are small:
the Baltics would require an estimated $300 annually, while the Central Eu-
ropean countries would require only $30 million per year. In the case of the
Baltic countries, this is because the population is small, even if the headcount
ratios are high (the total population of the Baltic countries is less than that of
Bulgaria). Central Europe's needs are very modest because poverty rates there
are low.
The dollar costs of poverty elimination per one poor person (figure 5.6) are
relatively small in Central Asia (about $7 per month), slightly higher in the
Slavic republics of the former Soviet Union and the Balkans (about $8 per
17. If expenditures are used, almost the same poverty deficit is obtained for Russia
(3.7 percent of GDP).
18. If expenditures rather than income are used to calculate the poverty deficit in
Ukraine, however, the deficit drops from 6.9 percent of GDP to only 2.3 percent.
19. See the subsection 'Leakage" and actial cost of poverty alleviation below.
80 Income, Inequality, and Poverty during the Transition
month), and high in the Baltics and Central Europe ($14 per month). This is
chiefly because most currencies in the Commonwealth of Independent States
are more undervalued with respect to purchasing-power levels, than are those
in Eastern Europe and the Baltics. (This is rapidly changing, however, as wit-
nessed by the brisk real appreciation of the ruble in 1992-95.) While the total
number of the poor in Russia alone is almost four times as high as in Eastern
Europe and the Baltics combined, the estimated dollar costs of poverty elimi-
nation are only three times as high. This is not because the poor are less poor
in Russia-on the contrary, Russian poor's average shortfall below the pov-
erty line is greater-but rather because the Russian poverty line in current
dollar terms is relatively low.20
How should a given amount of internationalfunds be distributed?
If the objective is to minimize the number of poor in the combined transition
economies with a given amount of international funds, in US dollars, then
the best approach would be to help those who are nearest to the poverty line.
And yet, the situation is not so simple. Those nearest to the poverty line are
not necessarily those with the lowest $PPP shortfall, but those with the low-
est actual dollar shortfall. For example, the average poverty shortfall from the
$PPP 120 poverty line is 33 percent in Hungary and 67 percent in the Kyrgyz
Republic (see table 5.2). Is it less expensive, then, for the international com-
munity to help a poor person in Hungary? No-because the Hungarian 33
percent $PPP shortfall translates into $19 per month, while to fill up the 67
percent shortfall in the Kyrgyz Republic requires only $10 per month. The
reason for this is simple: although in PPP dollars the gap is greater in the
Kyrgyz Republic, in current dollar terms it is much smaller. This is because a
dollar is worth much more in the Kyrgyz Republic than it is in Hungary.
If countries are ranked by how inexpensive it is to help the average poor
person to reach the poverty line, then Uzbekistan, Belarus, and Moldova top
the list with less than $6 per person per month.2' They are followed by
Slovakia, Bulgaria, Romania, and Lithuania, which require less than $8 per
20. In 1993, the same poverty line ($PPP 120) amounted to $21 in Russia compared
with $51 in Poland, $57 in Hungary, and $60 in Estonia.
21. These rankings are only approximate because they disregard differences in in-
come distribution among countries. Although the average dollar shortfall from the pov-
erty line in one country may be less than in another, it does not follow that it would be
cheaper to help all the poor from the first country. There may be some poor from the
second country who are very close to the poverty line and who would be very inexpen-
sive to help. Ideally, all the poor from all the countries would be ranked by the size of the
shortfall expressed in current dollars.
Poverty 81
person per month. Russia comes next with exactly $8. The most expensive
countries are Estonia ($22) and Slovenia ($24). Assuming that the interna-
tional community has a given amount of funds F and that it is concerned
with a representative poor in each country, whose distance from the poverty
line is equal to the average shortfall, the optimal strategy would be to help
first the poor in "less expensive" countries and then, gradually, to move to
more "expensive" countries until funds are exhausted. Under this scenario,
it is unlikely that "expensive" countries would be helped at all.
A different objective could be envisaged. If what matters is to reduce the
level of deprivation among the poor, and deprivation is assumed to be an
increasing function of the shortfall, then reducing the poverty of a very poor
person by x dollars matters more (that is, it yields greater utility) than reduc-
ing the poverty of a less poor person by the same amount. Put another way,
the marginal utility of money is a decreasing function of income. If depriva-
tion is a quadratic function of the distance from the poverty line, then the
objective function, guiding the allocation of funds, becomes:
5-7~ ~ ~ ~i kt �- E, ( yij -> d,)'
5.7 di L 1'(
where z = the poverty line in $PPP (same across the countries), k = number of
countries, Pi = number of poor people in country i, yij = income of j-th indi-
vidual in country i in PPP terms, d.. international transfer to that individual,
E, = purchasing power of a dollar in i-th country (that is, the reverse of the
country's price level relative to the world).2 The objective is to minimize
overall deprivation. Equation 5.7 must be minimized under conditions of a
given international amount of dollar funds (F), and only for poor people.
This means that those whose income is above z are not included in the mini-
mization of 5.7. These two conditions are written:
I 'Idj = F (fund exhaustion)
(z - yj - dIJE,) 2 0 (irrelevance of the non-poor).
The second condition ensures perfect targeting because the income of a
poor person after the transfer cannot exceed the level of the poverty line.
Assuming, for the sake of simplicity, that only the representative poor in
each country is addressed (that is, disregarding differences in countries' in-
22. The minimization of equation 5.7 is equivalent to the minimization of Foster-
Green-Thombeck's function P2 for all transition economies combined.
82 Income, Inequality, and Poverty during the Transition
come distributions among the poor), the objective function simplifies, and
we can write the Lagrangean:23
5.8 mdin L = (z - Yi - 2X d - F)
where y, = average poor person's income in PPP terms. z, E1's and F are
given.
The first-order condition is:24
i -2Ei (z - y- diE,) + = 0
and the solution is:
5.9 d. E (z - yi - E
In order that all di's be positive, X must be less than or equal to the mini-
mum value of 2(z-y,)E, (conditionA). Let the country with the minimum value
of 2(z-y,)E, be called "the binding country" (because it binds the value of X
from the above). The "binding country" may be denoted by b. X, the mar-
ginal change in deprivation if the amount of available funds, F, changes in-
finitesimally, reflects how binding the funds constraint is. A high value of X
means that the constraint is fairly binding (that is, the marginal utility of an
additional dollar is high).
From equation 5.9, it can be observed that di's are not necessarily posi-
tively related to Ei: countries with more depreciated currencies (high E) will
not necessarily receive more funds per person. This is because the depth of
poverty also influences di. If poverty of a representative poor (z- y,) is deep,
then gains from reduced hardship may be significant (since the objective func-
tion is squared) even if the "bang for the buck" (E,) is small. The condition for
di and E, to move in the same direction is that k be greater than the country's
(z-y,)E, (condition B).25 This condition may or may not be satisfied.
23. Note that in the case where Ei=1, the problem reduces to:
mdin L k=X' (z - y, - di)2 + k ( d - F)
which is minimized when
(z - y- d,) = 2
that is, when the poverty deficit after transfers for each country is the same. This is equiva-
lent to the condition derived by Kanbur (1987) namely that to minimize P2 across several
categories (countries, social groups, and so on) one needs to equalize P1's.
24. The second order condition is satisfied: 2Ei2>0.
Poverty 83
Figure 5.7. Optimal Allocation of Funds by Countries if the Objective Is
to Minimize Deprivation of the Poor
Russian Federation 4
Balkans, Poland 6
Central Asia 17
Other Slavic 30
Total funds = 100
Note: "Other Slavic" includes Moldova. Based on INCOME2 data.
Now, using the actual values for eighteen transition economies, the opti-
mal dI's are calculated. Optimal di's, or transfers per representative poor per-
son, are then multiplied by the number of poor in each country to obtain the
optimal allocation of international funds. The optimal distribution of funds,
assuming the objective function 5.8, will be such that 46 percent of the funds
would be allocated to Russia, 28 percent to Ukraine, 8 percent to Kazakhstan,
and 4 percent each to Romania (the highest in Eastern Europe) and the Kyrgyz
Republic (figure 5.7). The remaining thirteen countries would share 10 per-
cent of the funds. The optimal allocation of money would thus be very heavily
concentrated on five countries. Appendix 6 shows the exact distribution by
countries.26
The lesson here is threefold:
* In general, it is less expensive for the international community to help
the poor in countries where the exchange rate is low in comparison
with its purchasing parity.
* It is probably less expensive to help the poor rapidly because the real
exchange rates in the CIS countries still seem to be below their long-
25. For d, and E. to be positively related we must have:
8d - = E2 (z _ Yi _ x ) > �
From the last relation, it directly follows that 1 must be greater than (z-y1)E,.
26. The binding country is Slovenia. The countries for which condition B is satisfied
are: Poland, Bulgaria, Hungary, Czech Republic, Slovakia, Slovenia, Latvia, and Estonia.
84 Income, Inequality, and Poverty during the Transition
term equilibrium-that is, they are still rising. It is easy to imagine a
situation where real incomes in domestic currency and nominal dol-
lars rise, the poverty rates decline, but the overall cost of poverty alle-
viation from an international perspective does not fall or even rises, as
the effect of currency appreciation offsets the effect of the decreasing
number of the poor.
* Since funds are limited, it is useful to try to explicitly model what the
objective function is because different objectives lead to different re-
sults. If the objective is to reduce the number of the poor, then the
distribution of the poor (that is, how many are near the poverty line)
will matter a great deal. Countries with much shallow poverty will be
helped first. If a representative poor is targeted (for example, because
data on the distribution of the poor are not available) and our objec-
tive is to minimize the number of the poor or the poverty deficit, then
countries with low domestic price levels will benefit, because the cost
of "vaulting" an average poor above the poverty line is relatively low.
If the objective is minimization of hardship, then the depth of poverty
plays a more important role, and the amount of transfers is determined
through the interaction between the (squared) depth of poverty and
the "bang for the buck" that can be obtained.
"Leakage" and actual cost of poverty alleviation
All these calculations assume perfect targeting. Relaxing the assumption of
perfect targeting increases estimated outlays needed to "solve" poverty by a
factor greater than 2-2.5, which are the numbers derived from the Western
experience.27 This can be shown as follows. Data on the distribution of social
assistance reported here (see table 5.9 below) show that the bottom quintile
receives 28 percent of total social assistance in transition economies, and 42
percent in market economies. Assuming that the bottom quintile is targeted
in both cases, it follows that to arrive at an estimate of the actual cost of pov-
erty elimination, the poverty deficit needs to be multiplied by a factor of 2.4
in market economies (100 divided by 42) and by 3.6 in transition economies
(100 divided by 28).
An implication of the shallowness of poverty in transition economies is
that the poor are not a distinct underclass. A lack of distinct poverty features
implies that the costs of identifying the poor and the amount of "leakage"
can be high.28 If people who are poor cannot be identified easily through
27. See, for example, data quoted in Atkinson (1995, pp. 29-30) or Sawhill (1988, p.
1101).
28. This, in addition to relative inexperience of social assistance offices in transition
countries in administering such programs, probably explains their lower targeting effi-
ciency as compared with market economies. This is indirectly confirmed by the fact that
there is no difference between the two sets of countries in the targeting efficiency of
unemployment benefits, where eligibility criteria are dearer (see table 5.9).
Poverty 85
certain categorical criteria (for example, place of residence, age, ethnic group,
occupation, family size), and if their income cannot be gauged accurately
(because it is barely below or above the poverty line and because it includes
much in-kind income and income from the informal sector), then the costs of
anti-poverty policies rise. The ratio between overall spending and the amount
of cash actually "delivered" to the poor rises. The choice between (i) relying
on growth as the primary anti-poverty instrument, or (ii) relying on welfare,
then, moves in favor of the former. In conclusion, shallowness of poverty by
itself represents an argument in favor of growth-based (as against redistributive)
policies as the toolsfor poverty reduction.
Formally, the expected cost of poverty alleviation can be represented as
the product of the "real" poverty deficit (PD) and a factor (greater than 1)
that represents "leakage" (or payments to the non-poor), "spillover" (or pay-
ments to the poor in excess of what they need to reach the poverty line), and
administrative costs. Empirical results in Poland show that both "leakage"
and "spillover" increase when (a) the percentage of the poor increases, and
(b) the poor are less distinct from the rest of the population. An increase in
the percentage of potential claimants-if the distinctness of the poor from
the rest of the population is assumed to be fixed-means that the probability
of errors of targeting goes up.29 The proportion of mistakes will be greater if,
for example, half the population is poor than if only 5 percent is poor. The
probability of such errors further increases if income differences between the
poor and the non-poor are small, that is, if the poor are not distinct. As shown
in equation 5.10, the "effective" poverty deficit as a percent of GDP (PD*/GDP)
is equal to the "calculated" PD/GDP "grossed up" by a factor L, which is a
positive function of the headcount index and the "shallowness" of poverty
(1 /s). Both a high headcount index and shallow poverty are characteristics
of transition economies and increase the cost of poverty alleviation.
5.10 PDP GDP (1 + L(HC,s))
GDP GDP
How to Explain Increases in Poverty?
Letting both income and distribution change
The descent into poverty is the product of two forces: lower income and greater
income inequality. This is illustrated in figures 5.8 and 5.9 with the example
of the Czech Republic. Individuals are ranked according to their per capita
monthly disposable income, and the height of the ordinate indicates the in-
come level for each decile at constant prices. In 1993, real income for all but
the top decile was less than it was in 1988. The percentage decline in income
was greater for the poor: the lowest income decile lost 24 percent in real terms,
while the ninth decile lost only 12 percent. Meanwhile, the top decile gained
20 percent. The Gini coefficient rose from about 19.4 to 26.6.
29. See Milanovic (1995a, pp. 43-45).
86 Income, Inequality, and Poverty during the Transition
Figure 5.8. Distribution of Income in the Czech Republic, 1988 and 1993
0 -
- Uniform percentagedecline
1988 ------
1993
1 2 3 4 5 6 7 8 9 10
Income decile
Note: In 1988 crowns. Vertical axis in logarithms.
Source: For 1988, Microcensus; for 1993, Survey of Economic Expectations and Attitudes. Both
reported in Veeemik et al. (1994).
If 900 crowns per capita per month is taken to be the poverty line for 1988,
an amount equal to the poverty line actually used in Czechoslovakia during
that year30 then approximately 6 percent of the population was poor in 1988
(see point A in figure 5.9 which "zooms" on the bottom quintile from fig-
ure 5.8). If income declines across the board by 12 percent, which indeed
was the actual average decrease in real income between 1988 and 1993,
then the percentage of the poor rises to about 10 percent (point B). In addi-
tion, however, the income distribution curve itself changed. The actual 1993
distribution is given by the line Actual 93. The headcount ratio now jumps
to 17 percent (point C). Thus, both the overall decline in income and the
change in distribution combine during the transition to raise the number
of poor.
When income distribution becomes more unequal, as it did during the
transition, the real income of the poor has to rise dramatically-in this case
it would have to more than double; see the ratio between AF and FG in
figure 5.9) just to maintain the poverty rate constant. Such an increase in
30. Czechoslovakia differed from other East European countries in that its poverty
line was not a mere "accounting" poverty line but was used in social policy (see also
section Should OECD-like Social Assistance Be Introduced in Transition Economies? in chap-
ter 6). In 1988, the Czechoslovak per capita poverty line (for a family of four) was 875
crowns per month, or the equivalent of 210 intemational dollars.
Poverty 87
Figure 5.9. Decomposing the Change in the Poverty Rate: Parts Due to
Change in Income and Change in Distribution
C.#
1200- Actual 1988
c ~~~~~~~~~~~~~Unfform
S ~~~~~~~~~~~~~~percentage
X / _ _ < ~~~~~~~~~~~~ decline
- A , / ctual 1993
600
6 10 17 20
Cumulative percentage of recipients
Note: AB=due to decline in income, BC=due to distributional change.
Source: For 1988, Microcensus; for 1993, Survey of Economic Expectations and Attitudes. Both
reported in Vecernik et al. (1994).
the income of the poor cannot, of course, take place while overall income
declines (that is, it cannot occur at the same time as income distribution
"worsens"). A notable feature of all transition economies however is a si-
multaneous decline in income and an increase in inequality; both effects
contributed to rising poverty in these countries. In contrast, in the 1980s in
Latin America, when poverty also increased, these two negative changes
occurred simultaneously in only two countries: Brazil and Peru (Morley
1994).
Decomposition into growth and distribution effects
The increase in poverty can be broken down into a portion due to declining
income and a portion due to more unequal distribution.3' The typical pattern
31. The decomposition procedure used is the one described by Kakwani and Subbarao
(1990), and Kakwani (1995, pp. 38-39). The formula for the change in poverty between
period 0 and period 1 due to income is
2 ( lo Poo + Pil Pol)
(Continued on the next page)
88 Income, Inequality, and Poverty during the Transition
in transition economies seems to include first a period of quick decline in
output with relatively small changes in the distribution of income, and then
a second period in which inequality increases, the GDP bottoms out and,
eventually, rises again. Poverty should be driven up by declining incomes
during the first period and by increased inequality in the second (at least for
as long as growth is not sufficient to offset the impact of more unequal distri-
bution).32 This pattern is illustrated for Poland and Russia during the period
1990-94 (figures 5.10 and 5.11). In Poland during 1990-93 almost all the in-
crease in the poverty headcount (right-hand side axis) was due to the decline
in real income (solid area in figure 5.10). However, beginning in 1992, the
increase in the Gini coefficient and the associated change in the distribution
also begin to add to poverty (the shaded area). By 1994, rising incomes began
to pull poverty down while poverty due to a widening income distribution
increased.
In Russia, the income decline was, of course, more severe: in 1994, real
income was less than half what it was in 1988. The increase in the poverty
headcount was also much sharper. Here again, however, widening income
inequality becomes gradually more important in driving poverty up (see the
shaded area in figure 5.11). In 1993 and 1994, the growth and distribution
effects each explain about the same proportion of the overall increase in pov-
erty. Interestingly, even a slight decrease in the Gini coefficient in 1994 (com-
pared with 1993) did not stop the growing role of income distribution in
explaining poverty. This suggests that while Gini might have decreased be-
cause of some reduction in the share of the rich, the distributional change
continued to be unfavorable to those close to the poverty line and pushed
some down into the ranks of the poor.
(Continuedfrom the previous page)
where P,. is the poverty rate with the income level from period i and the distribution from
period j. The change in poverty due to change in distribution is:
D = 2 (Pol- PmO + PIl - P10).
It can easily be seen that P,1 - Poo = Y + D.
Downscaling all incomes is equivalent to raising the poverty line. To calculate Plo the
poverty line is shifted upward (because income between 0 and 1 has declined) using the
formula: PL
PLo = IL
01+r
where PLO* is the new poverty line, PLO the original poverty line and r = rate of income
decline (negative). Similarly, to calculate P0, we redefine PL, as:
PL;= PL, (1 + r).
32. The same pattem is observed by Comia (1994, p. 39) and UNICEF (1995, Table
1.2, p. 11).
Poverty 89
Figure 5.10. Breakdown of the Increase in Poverty Headcount between Growth
and Distribution Effects in Poland, 1990-94
100 - 35
hIncome.-'
80 - Gini 3
8g 60- .. 2
Q An~I.......................... . . . ......... . ......2
0~~~~~~~~~~~~~~~~~~1
S 20
X, , I , O5
1990 1991 1992 1993 1994
Note: 1987 real per capita income = 100. Real per capita income = solid line (left axis). Gini
coefficient = broken line (right axis). Headcount increase compared to 1987 (right axis). Growth
effect: solid area; distribution effect: shaded area.
Figure 5.11. Breakdown of the Increase in Poverty Headcount between Growth
and Distribution Effects in Russia, 1990-94
120 - 70
110 60.
Income
100 - ........- ...... ... . 50
. 80 - .. .......40
62 0 . ... . -..-- ----------- ----\- -----,- ll--44? ..... .. ... ... ....... .... .. .... .... ... ... ... ... ........ ,:30
L^ 60 - 30 g
Gmin
~40 . .. ...........20
20 10
1990 1991 1992 1993 1994
Years
Note: 1988 real per capita income = 100. Real per capita income = solid line (left axis). Gini
coefficient = broken line (right axis). Headcount increase compared to 1988 (right axis). Growth
effect: solid area; distribution effect: shaded area.
Table 5.4. Explaining the Increase in Poverty Headcount between 1987-88 and 1993-94
Constant AiNCOME AGINI PPP INCOME
Equation (t-value) (t-value) (t-value) (1988) ALIB k2 (F) SE
A 24.0 (1.5) -0.63 (3.1)a 0.50 (0.8) -0.0041 (1.3) 0.70 (11.8) 14.2
B 1.7 (0.4) -0.28 (1.6) 1.33 (2.6)b 0.44 (6.5) 12.9
C 22.6 (2.1) -0.75 (4.5)a 0.28 (0.5) -30 (2.0) 0.74 (14.4) 13.2
D 25.2 (3.l)b -0.47 (3.2)a 0.94 (2.4)b -41 (3.2)- 0.68 (11.0) 9.7
Note: AHC (change in poverty headcount) is the dependent variable. It is the difference between the 1987-88 poverty headcount from table 5.1 (column 2)
and the 1993-94 poverty headcount calculated using the macroeconomic INCOME2 data (table 5.2).
AINCOME is the change in population real (macroeconomic) income from table 3.4, column 3.
AGINI is the difference between the 1987-88 and 1993-94 Ginis from table 4.1.
PPP INCOME is annual per capita household income in 1988 obtained from HBSs and expressed in intemational 1990 dollars.
ALIB is the change in the liberalization index between 1989 and 1994 (maximum value of the index = 1, minimum value = 0).
t-values are in parentheses.
a. Significant at 1 percent.
b. Significant at 5 percent.
Poverty 91
Cross-country analysis: economic policy and increase in poverty
The previous analysis of poverty shows that the increase in poverty was driven
by two strong forces: a decline in population incomes and an increase in in-
equality. This can be studied more formally by estimating the first-difference
equation 5.11 across all transition economies:
5.11 AHC = fct (AINCOME, AGINI, PPP INCOME)
where AHC = change in poverty headcount between 1987-88 and 1993-94,
AINCOME = percentage change in real income over the same period, AGINI =
= change in Gini points over the same period, and PPP INCOME = $PPP
income per person (from household surveys) in 1988. PPP INCOME is used
to control for the fact that poverty headcounts for different countries are cal-
culated using the same real poverty line of $PPP 4 per day per person. Given
the same percentage decrease in income and the same increase in the Gini
coefficient, the richer country's headcount will change less because fewer
people will fall below the given (real) poverty threshold. In other words, a 10
percent decline in income will push fewer people below the $PPP 4 line in a
relatively rich country such as the Czech Republic, and many more people in
the poorer Kazakhstan (see also equation 5.6).
Equation A in table 5.4 shows the results of running equation 5.11 over
fifteen transition economies.33 All coefficients have the predicted signs: lower
income and higher Gini increase poverty; higher initial (1988) income level
reduces the increase in the headcount. However, only the coefficient of
AINCOME is statistically significant; AGINI and PPP INCOME are not.34 Each
percent decrease in real population income is associated with an increase of
the poverty headcount by 0.63 points. This is virtually the same (semi-) elas-
ticity as found in the section, Elasticity of poverty with respect to income.
Equation B takes into account the fact that estimates of poverty increase
are not equally accurate. Here, observations are weighted using an explicit
indicator of survey quality. 35 Now the AGINI coefficient becomes statistically
significant while the importance of AlNCOME diminishes.
Is there a relationship between the increase in poverty and the speed of
reform, that is, independent from, and in addition to, the effect that the speed
33. Macroeconomic income data are not available for Bulgaria, Estonia, and Latvia
(see table 3.4).
34. Multicollinearity reduces the significance of the coefficients: the partial correla-
tion between AINCOME and AGINI is 0.6 and between AINCOME and PPP INCOME it is
-0.5. This means that income loss has been accompanied by increased inequality and has
been greater in poorer countries.
35. The indicator ranges from 4 = perfect survey to 1 = unreliable survey. Values are
obtained as 4 minus the estimated bias for 1993-94 surveys given in table A1.4. Thus, the
value for Poland is 3.5, for Bulgaria 2, and so on.
92 Income, Inequality, and Poverty during the Transition
of reforms may have on real income and the Gini coefficient (which are al-
ready included in the regression).36 In equation C, an estimate is added for
the speed of reforms (LIB), expressed as the change in the liberalization in-
dex, which ranges from 0 (no reform) to 1 (full-scope reform).37 In this equa-
tion, which is identical in formulation to equation A, the direct effect of re-
forms on the change in poverty is not significant. However, if a weighted
OLS formulation is used to account for the quality of observations (equation
D), ALIB shows that faster reforms reduce poverty. Moreover, with this equa-
tion, all coefficients become statistically significant: 1 percent of decrease in
real income is associated with an increase in the headcount of 0.47 percent-
age points; 1 Gini point increase in inequality is associated with a little less
than I percentage point increase in headcount, and each 0.1 point "increase"
in reform reduces the poverty headcount by 4 points.32 Adjusted R2 is almost 0.7.
Who Are the Poor?
Poverty by social class
The section in chapter 4, Disparity among social groups, demonstrated that
the approximate order of gain by socioeconomic group during the transition
was as follows: pensioners > workers39 2 farmers. Given this, it would be
reasonable to expect the reverse ordering in terms of poverty rates. This is
illustrated in figure 5.12, which shows relative poverty rates for the main so-
cioeconomic groups. Relative poverty rates are obtained by taking the actual
poverty rate for one group-for example, workers-and dividing it by the
average rate for all social groups (that is, by the country average). Values
smaller than 1 indicate that a given socioeconomic group has a smaller pro-
portion of the poor than the country average. This approach is used here
because the data in figures 5.12 and 5.18-5.25 come from a variety of sources
that use different poverty lines. Expressing poverty rates in relative terms
makes comparisons possible."
Workers' households have, in all countries, lower poverty rates than the
average. Poverty incidence among pensioners is also lower than the average,
except in Bulgaria and Estonia. In Belarus and the Slovak Republic, the inci-
36. That is, speedy reforms might lead to an increase in inequality which, of course,
would already be reflected in the Gini coefficient. However, that effect seems negligible
as indicated by low correlation coefficients between ALIB and AGINI (-0.15), and ALIB
and AINCOME (-0.13). This also suggests orthogonality of the right-hand side variables.
37. The index is calculated by de Melo, Denizer, and Gelb (1996).
38. The effect is independent of countries' income level. If countries' PPP income
level in 1988 is induded in regression D as a control, the same results hold.
39. Unless otherwise specified, workers include both white-collar and blue-collar
workers who are employed in the public, private, or mixed sectors (but not the self-
employed and not those employed in the small-scale private sector).
Poverty 93
Figure 5.12. Social Group and Relative Poverty Rates, 1992-95
Workers . .
Farmers -_-_-_-_-_-
Pensioners
Self-employed ...._*____
Unemployed
0 1 2 3 4
Relative poverty rate
*Poland Romania IM Hungary ]Romania
E Bulgaria FII Belarus 0 Russia | Estonia
Note: Poverty rate equal to country average = 1.
Source: Calculated from: Poland 1993: World Bank (1995, Table 2.5). Romania 1994: World
Bank (1995a, Table 3.1, p. 17). Hungary 1992-93: T6th and Forster (1994, Table 5, p. 34). Slovakia
1992: calculated from Microcensus 1992, p. 30. Bulgaria 1992: calculated from HBS 1992, p. 109.
Russia 1993: World Bank (1994a), Klugman (1995). Belarus 1995: World Bank (1995c, p. 36). Estonia
1995: World Bank (1996, p. 13).
dence of poverty among pensioners is even less than among workers' house-
holds. In all countries, farmers' poverty rates are between 1�2 and 2 times the
average (three times the average in Estonia). The self-employed, often
thought to be among the "winners" in the transition, have lower poverty
rates than the average in Hungary and Poland, about average in Belarus
and the Slovak Republic, but, interestingly, higher than the average in Esto-
40. The resulting comparability, while satisfactory, is not full. This is because the
level of the poverty line used will also influence the relative incidence of poverty. To take
an extreme example, suppose that the poverty line used is so high that virtually all house-
holds in a country are poor. Then all socioeconomic groups will have the same poverty
incidence and the relative poverty rates will be unity for all. Fortunately, the sources used
here all employ broadly comparable poverty lines ranging between $PPP 100 and $PPP
200 per capita per month.
94 Income, Inequality, and Poverty during the Transition
Figure 5.13. Poverty Headcount by Socioeconomic Group in Poland, 1987-94
60 -
Unemployed.
50 ...
40-
0
o 30-
= ,, - ~~~~~~~Farmers --_
> 20 - ixed
0
Pensioners -
lo
- - Workers Self-employed
1987 1988 1989 1990 1991 1992 1993 1994
Note: Poverty line = 1993 minimnum pension (= official poverty line) indexed by the cost-of-
living.
Source: Calculated from Polish HBSs (various years).
nia and Romania. This probably reflects issues of classification (what house-
hold are classified as "self-employed") as well as the lack of homogeneity noted
above among the self-employed, a group that includes both small-scale sub-
sistence farmers and rich private entrepreneurs. Finally, households with at
least one unemployed person have poverty rates of between 13; (Belarus, Ro-
mania, and Russia) and four times (Poland) greater than the average.
From this information the following conclusions can be drawn: the unem-
ployed and farmers are, in all countries, more likely to be poor than the aver-
age person; pensioners' likelihood to be poor is about average; and workers'
households are slightly less likely. No regularity emerges regarding the posi-
tion of the self-employed.
In addition to a comparison between countries, historical single country
data can be used. Historical data are preferable because they allow for the
tracking of changes in poverty, but they are also more difficult to find. For
Poland, however, detailed and relatively consistent data are available on the
incidence of poverty by social group going back to the late 1970s. In figure
5.13 they are shown dating back to 1987. The incidence of poverty among
farmers' and mixed (worker-farmer) households has increased by more than
it has among workers and pensioners. Farmers, who traditionally have had
high poverty rates, still have the highest incidence of poverty (except for the
unemployed), but they are now closely followed by mixed households.4"
41. During the stagnation of the 1980s, however, mndxed households fared better than
others (Milanovic, 1992).
Poverty 95
Table 5.5. Relative Poverty Ratesfor Different Types of Unemployed Households
With more
With one un- With long- than one Household
employed term un- unem- head un-
Country at least employed ployed employed
Estonia 1995 2.2 3.3
Hungary 1993 1.0 3.3 4.9 3.7a
Poland 1993 1.7 2.6 3.2 4.0
Russia 1992 1.5 2.5
Note: Long-term unemployed is unemployed for more than a year.
a. Becomes 9 if head of household is long-term unemployed.
Source: Calculations for Estonia from World Bank (1996); Hungary from World Bank (1995b);
Poland from World Bank (1995); and Russia from World Bank (1995a, p. 17).
The self-employed and the unemployed are two socioeconomic groups
that have become more important during the transition, and are, for the first
time, included in Polish HBSs. These two groups have the lowest and high-
est incidence of poverty, respectively. The absence of data for the years prior
to 1993 does not allow a comparison of the position of these two groups be-
fore and after the transition. Anecdotal evidence suggests, however, that the
incidence of poverty among the self-employed has always been low. On the
other hand, the current unemployed once belonged to either workers' or
mixed households4l and their poverty incidence was probably not too dis-
similar from the average of these groups. Thus, during the transition their
position must have deteriorated markedly.
Unemployment and poverty
The link between unemployment and poverty is clear. The higher the rates of
unemployment and, particularly, the higher the share of the long-term un-
employed (that is, those who are unemployed for more than a year), the greater
the poverty. The long-term unemployed either lose their entitlement to un-
employment benefits or receive only a fraction of earlier benefits. A low rate
of unemployment and a virtual absence of long-term unemployed make the
correlation between unemployment and poverty weak in the Czech Repub-
lic.43 But the situation is different in countries where unemployment rates are
greater and the share of the long-term unemployed is high. As shown in
table 5.5, in Poland and Hungary in 1993, households with a long-term un-
employed member had poverty incidences that were 2.6 and 3.3 times higher
42. Only very few current unemployed would have been farmers. Polish agricul-
ture was private, and it is unlikely that a farmer would abandon agriculture to register as
an unemployed without right to benefits.
43. A point made by Professor Jifn Vecernik at the Third Central European Forum
held at the Institute for Human Sciences, Vienna, 21-23 January 1994.
96 Income, Inequality, and Poverty during the Transition
than the average, respectively; households with two or more unemployed
members were 3.2 and 4.9 times more likely, respectively, to be poor than the
average; and households headed by an unemployed person were about 4
times more likely to be poor than the average.
In Poland and Bulgaria, 30 percent of poverty could be attributed to unem-
ployment. In Hungary, the share is even 60 percent (see table 6.1 below). In
Russia, where the unemployment rate is much lower, this is true for only 11
percent of the poor.44 Even if unemployment does not continue to rise in transi-
tion economies, the share of the long-term unemployed will probably still in-
crease, and the link between unemployment and poverty will become stronger.
How the working population became poor
The working poor represent a sizable proportion of those who were pulled
below the poverty line during the transition. A study of poverty in Poland
finds that in 1993 60 percent of the poor were working poor (World Bank
1994a, vol. 1, p. iv); similar results are obtained for Russia, where 66 percent
of the poor are working poor (World Bank 1994a, p. 15). Those whose rela-
tive wage position appears to have deteriorated during the transition were
primarily manual workers (in declining industries) and low-skilled clerical
staff who often had only a vocational education or less. The latter were prob-
ably the most overstaffed professional category in socialist countries. The
streamlining of production, together with a reduced need for various report-
ing functions related to the bureaucratic management of the economy, re-
duced demand for this category of worker. In Poland, for example, the posi-
tion of people with only a vocational education has deteriorated more than
that of any other educational group (World Bank 1995, p. 81).
Two examples illustrate how working people have slid below the poverty
line. The first is that of Ukraine, and to some extent Russia, where even the
minimum conditions necessary for working people to stay ahead of poverty
are no longer satisfied. The second is that of a richer country, Poland, where
the minimum conditions are satisfied even if many working people are still
poor.
The situation in Ukraine and Russia is depicted in figures 5.14 and 5.15.
The average wage and average pension are expressed in terms of a constant
real per capita poverty line, which is taken to be the 1993 official Russian
poverty line (also virtually equal to the poverty line discussed above of $PPP
120). At minimum, the average wage should be twice the (per capita) pov-
erty line. This means that the "typical" dual-income working couple that is, a
couple that has two children and is earning the average wage, should not be
poor. Not all such dual-income, four-member families would remain above
the poverty line, however, because approximately 60 percent of workers nor-
44. The same figure for Canada in 1990 was 28 percent (calculated from Statistics
Canada, 1991, pp. 154-64).
Poverty 97
Figure 5.14. Average Wage, Pension, and per Capita Income as a Ratio
of the Poverty Line in Russia, 1987-96
6
4
3 ~~~~~Income\
�
2 _ ~~~.-_P~ err sion ~ ~~~~~ ~
1~~~~~ . - .,rr_*;
1-----------------------------------'-- ,.-.'----.---.-------
O I I , I I .
1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Note: The poverty line is the official Russian Ministry of Labor minimum subsistence line for
1993. In 1993, the average subsistence minimum per person was Rs. 20,562, or $22 per month, or
$PPP 120. This line is indexed using consumer price index. Average income comes from
nacroeconomic income data. All amounts are monthly averages.
Figure 5.15. Average Wage, Pension, and per Capita Income as a Ratio
of the Poverty Line in Ukraine, 1987-95
6
5
4
.3-- Income _
0 . . . . . I,.
Pension,._-' '-_.."
1987 1988 1989 1990 1991 1992 1993 1994 1995
Note: The poverty line for Ukraine is the official Russian Ministry of Labor minimum subsistence
line for 1993 (average value). This line is indexed using Soviet and Ukrainian (as appropriate) CPI
index. Average income comes from macroeconomic income data. All amounts are monthly averages.
98 Income, Inequality, and Poverty during the Transition
Figure 5.16. Cumulative Wage Distribution in Ukraine (1992) and Poland (1993)
100*
8; A
0
~60
40
- - - -I - - -
20 ~~~Ukraine
U Poland .
10 40 50 70 100 200
Wage (mean wage = 100)
Note: Horizontal axis in logarithms.
mally earn less than the average wage. But because there are offsetting ele-
ments such as women who are systematically low earners marrying men
who are high earners, family allowances, income-in-kind,45 and so on, the
average wage-to-poverty-line ratio of 2 to 1 seems a satisfactory approxima-
tion. In Russia and Ukraine, the ratio declined from about 4 (and even 5 in
Russia, due mostly to high wages paid to workers in Siberia) before the tran-
sition, to about 2 in 1995 and 1996. (In Ukraine, the ratio has been slightly
below 2 since 1993.) Thus, a dual-income couple earning the average wage
cannot be sure of staying above the poverty line in Ukraine, and barely man-
ages to do so in Russia. Because the average wage-to-poverty-line ratio in
Ukraine in 1995 is only 1.8, only those earning approximately 10 percent above
the average wage should be able to stay out of poverty. As figure 5.16 shows,
fewer than 20 percent of the employed fulfill this condition (point A).
Similar conclusions emerge if the relationship between the average pen-
sion and the poverty line is considered. The average pension should, at a
minimum, be equal to the poverty line: one-person or two-person pension-
ers' households with no dependents and no other sources of income should
be able to avoid poverty. With that objective in mind, governments often link
the minimum pension to the official poverty line, as in the Czech Republic,
Hungary, Poland, Russia, and Slovakia. The "average" pensioner should, then,
45. Workers' families receive about 20 percent of total income from non-wage pay-
ments.
Poverty 99
be able to avoid poverty. This, however, is not always the case. The mini-
mum pension that is linked to the poverty line may be the minimum full-
entitlement pension. Disability, social, family, or early-retirement pensions
may be below the minimum pension. The vagaries of slow indexation, or
delays in pension payments, may further push some pensioners (at least tem-
porarily) below the poverty threshold, and the overall average pension may
barely stay at the subsistence level. In Russia, this has been the case since
1992 (see figure 5.14). In 1995 and 1996, because of huge pension arrears, the
paid-out average pension was 10 to 20 percent below the official poverty line.46
In Ukraine, the average pension has been less than the poverty line since
1993, and in 1994-95 it was approximately 40 percent below the poverty line.
Thus even the minimum pension requirement identified above is no longer
satisfied in Russia and Ukraine.
The average per capita income in Ukraine, as estimated using macroeco-
nomic sources (see figure 5.15), is equal to the poverty line since 1993. Put-
ting aside for the moment possible underestimations of macroeconomic in-
come, this fact alone (combined with a log-normal income distribution)
ensures that approximately 60 percent of the population lives below the pov-
erty line.47
In Poland, the wage requirement is satisfied, as the average wage in 1996
was about 234 times greater than the Polish official poverty line (see figure
5.17).
Who are the working poor in Poland? The Polish per capita poverty line
was 35 percent of the average wage in 1995 and 1996. This means that if only
one member of a "typical" family of four is employed earning the average
wage, the family will fall short of the poverty threshold (because the family
income needed to stay above the poverty line is 35 percent times 4, or 140
percent of the average wage). Even if economies of scale in consumption,
lower needs of children, and additional sources of income are accounted for,
such a family is clearly hovering near the poverty line. If the spouse is out-
side the labor force, or his/her unemployment benefit has expired, or even if
she is receiving an unemployment benefit'48 but the earning of the working
spouse is less than the average wage, the family is in a precarious situation.
Translated into practical terms, this means that to stay ahead of poverty, a
typical Polish family has to have two wage earners. While this was typical
under Communism, it no longer is, as participation rates have declined and
many have become unemployed. Consequently, many families in Poland with
only one employed member will probably be poor or near poverty.
But this is not all. If both husband and wife are employed and each earns
approximately 70 percent of the average wage or less, they too will be poor.
46. The average pension includes all supplementary pension payments.
47. The mean of the log-normal distribution is at approximately the 60-65th per-
centile.
48. The unemployment benefit was a flat 35 percent of the average wage.
100 Income, Inequality, and Poverty during the Transition
Figure 5.17. Average Wage, Pension, and per Capita Income as a Ratio
of the Poverty Line in Poland, 1987-96
4
4 < X~~age
3 Incomex
Pension .
0 1 I 1 1 1 1 I
1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Note: The poverty line is the 1993 minimum pension equal to $68 per month or almost $PPP
170. It is indexed using the cost-of-living index. Average income comes from macroeconomic
income data. All amounts are monthly averages.
Figure 5.16 shows that about 30 percent of the employed in Poland fall into
this category (point B).
The working person's situation in Poland can be contrasted with that of
his counterpart in the United Kingdom or the United States. The minimum
wage in the latter two countries is a little more than $30 per day.49 This is
three (in the United States), and four times (United Kingdom), greater than
the per capita poverty line for a four-member household.50 This means that,
for a typical family, only one member of the household need work at the
minimum wage for the whole family to be near the poverty line.51 Virtually
all "typical" families with one or more employed members earning more
49. This assumes an eight-hour work day and twenty-two work-days per month.
50. The U.S. federal poverty line for a four-member household is about $10 per per-
son per day. The U.K. Income Support is about $5 per person per day plus about $3 for
the housing allowance.
51. The U.S. federal poverty line is among the highest (in PPP terms) in the world. It
differs from European poverty lines, however, because it does not represent a "guaran-
teed minimum income." It defines which individuals and households are eligible for
federal assistance (for example, housing benefits, Medicaid, food stamps). The states de-
fine their own-lower or higher-effective poverty lines for the allocation of state-fi-
nanced benefits.
Poverty 101
Figure 5.18. Household Size and Relative Poverty Rates, 1993-95
1
:............
,~3
...:::-: .........
0
s , ............
5Q . . ..
............... . .. ------.. ,
O~~~ .... . .................. ...............
6+._ _ _ _ _ _ _ _ _ _ _ _
0 0.5 1 1.5 2 2.5 3
Relative poverty rate
*Poland Hungary L' Czech Republic S Slovakia
FIBelarus t Russia E'Ukraine
Note: Poverty rate equal to country average = 1.
Source: Calculated from: Poland 1993: World Bank (1995). Hungary 1992-93: T6th and Forster
(1994, Table 4). Czech Republic and Slovakia 1993: Veeemik et al. (1994). Belarus 1993: Roberti
(1994, Table 3). Russia 1993: Center for Economic Conjuncture (1994). Ukraine 1995: Family budget
surveys 1995 reported in Ukrainian Committee for Statistics (1996).
than the minimum wage will be above the poverty threshold. The position of
minimum-wage workers in the United States and the United Kingdom vis-i-vis
their countries' poverty lines therefore, is similar to that of an average-wage worker
in Poland vis-a-vis the Polish poverty line.52 In both cases, if only one member of
the household is employed at that wage (the minimum in the United States
and United Kingdom, and the average wage in Poland), the household is in
a precarious position.
Poverty by size and type of household
Poverty also varies as a function of size and type of household (see figure
5.18). In general, larger households tend to be poorer both in per capita terms
52. Note that, in addition, the poverty line is approximately $PPP 10 per person per
day in the United States, $PPP 8 in the United Kingdom, and $PPP 6 in Poland.
102 Income, Inequality, and Poverty during the Transition
Figure 5.19. Relative Poverty Rates for Single-parent Households, 1993-95
2.5 -
2
2- ~ ~ ~ ...... . ...... ... ... ..
0.5
0
Belarus 93 Poland 93 Hungary 93 Estonia 95
Note: Poverty rate equal to the country average = 1.
Source: Calculated from: Belarus: Roberti (1994, Table 3). Poland: World Bank (1995, Table
2.17). Hungary: World Bank (1995b, p. 22). Estonia: World Bank (1996).
(meaning that total household income rises more slowly than the number of
household members) and in equivalent terms (when economies of scale and
lower consumption needs of children are taken into account). The position of
larger households is the same in transition economies. Poverty rates for five-
member households are everywhere between 1.5 and 2.5 times higher than
the average. On the other hand, for one- and two-person households, pov-
erty rates are less than one-half the average almost everywhere. Exceptions
include declines in poverty rates in the Czech Republic and Hungary from
three-person to four-person households.
Not surprisingly, single-parent households also have a higher-than-aver-
age incidence of poverty (see figure 5.19).
Poverty by age
The high correlation between household size and poverty implies that chil-
dren will be one of the most poverty-stricken groups. This is shown in figure
5.20. Children under the age of fourteen are between 20 and 70 percent more
likely to be poor than the average person. In all eight countries shown here,
there is a remarkable similarity in the way in which poverty rates decline
with age. Poverty rates for people of retirement age (55 for women, 60 for
men) are only about one-half the country average in Hungary and Poland,
Poverty 103
Figure 5.20. Age and Relative Poverty Rates, 1992-95
Children AA A = A .
Adults < 30
.... - . . . . -'.. . . . . . .
Middle age
Retirement age
0 0.5 1 1.5 2
Relative poverty rate
Russia * Poland E Hungary 9 Bulgaria
0 Latvia II Belarus El Czech Republic |]Estonia
Note: Poverty rate equal to the country average = 1.
Source: Calculated from: Russia Q2/1993: McAuley (1994, p. 37). Poland 1993: World Bank
(1995, figure B, p. iii). Hungary 1992-93: Andorka and Speder (1994, table 2). Bulgaria 1992: HBS
1992, calculated from table 4, p. 111. Latvia 1992: HBS 1992, calculated from p. 23 and p. 26.
l3elarus 1995: World Bank (1995c, p. 36). Czech Republic 1993: Vecemik and others (1994, tables
IV/3a and IV/3b, pp. 21-22). Estonia 1995: World Bank (1996, p. 17).
and even less in the Czech Republic and Slovakia,s3 a finding consistent with
findings above regarding the relatively favorable position of pensioners in
Eastern Europe. Among the countries shown here, only in Estonia do the
elderly have higher-than-average poverty rates. This is due to low and flat
(i.e. almost equal for all) pensions in existence in Estonia since 1992.
The most dramatic reversal in relative poverty rates between the old and
the young probably occurred in eastern Germany in the wake of the 1990
Unification. In the German Democratic Republic, the aged had relatively high
poverty rates because the pension-wage ratio, as in the Soviet Union, was
53. Slovakia is not shown in figure 5.20 due to lack of space.
104 Income, Inequality, and Poverty during the Transition
Figure 5.21. Age and Relative Poverty Rates in Eastern Germany, 1990 and 1992
2 -
1 0.5 ~ 1990
~0.5-
Under 16 16-30 3145 45-60 61-75 over 75
Age
Note: Poverty rate equal to eastern German average = 1.
Source: Calculated from Krause (1992, reported in Heimerl [1993]).
low (about 35 percent). Pensioners in the former German Democratic Repub-
lic "gained" from the Unification, however, because their pensions were first
converted from Ostmarks into DMs at a favorable one-to-one rate, and then
the pensioners were absorbed into the more generous West German pension
system.T4 As shown in figure 5.21, almost no eastern German pensioners in
1992 were among the poor. By contrast, the relative poverty rate among chil-
dren in 1992 was about the same as prior to Unification. The figure 5.21 also
shows that in 1992, poverty rates were steadily decreasing with age, as they
were in other transition economies. Before Unification, however, poverty rates
peaked for the very young and the very old.
54. For example, at the time of the Unification the average income of a pensioner
household in West Germany was 90 percent of the country-wide average while in East
Germany, it was 76 percent (Heimerl, 1993, p. 77).
55. Vedernik (1994a) finds that in the Czech Republic the rate of return to one addi-
tional year of education has increased from 3.5 percent before the transition to 6.2 percent
after the transition. Similarly, in comparing Czech and Slovak earnings distributions for
1984 and 1993, Chase (1995) finds the rate of return to education to have risen by between
2 and 3 percent. World Bank (1995) and Rutkowski (1996) find that in Poland this rate
went up from 6.4 percent in 1989 to 7.5 percent in 1992. Rutkowski (1995, p.29) also finds
increasing returns to education in Bulgaria.
Poverty 105
Figure 5.22. Education of Household Head and Relative Poverty Rates, 1993-95
Primar ~~. . . . . . . . . . .... ...........v
Y. . . . . . . . . . . . . . . . .
Vocational . . . . . . . . . . .. . . . . . .
Secondary
University _:.,..
0 0.5 1 1.5 2
Relative poverty rate
L Poland 11 Hungary L] Czech Republic I Slovak
1 Estonia [ Belarus E Romania
Note: Poverty rate equal to the country average = 1.
Source: Calculated from: Poland 1993: World Bank (1995, Table 2.8). Hungary 1993: World
Bank (1995b, p.24). Czech Republic and Slovak Republic 1993: Vecernik and others (1994, Tables
IV/3a and IV/3b, pp. 21-22). Estonia 1995: World Bank (1996, p. 14). Belarus 1995: World Bank
(1996a, pp. 6-7). Romania 1994: World Bank (1995a, Table 3.1, p. 17).
Education and poverty
The educated, not surprisingly, are less likely to be poor. In addition, increas-
ing returns to education have probably further strengthened the link between
high levels of education and low levels of poverty.55 As can be seen from
figure 5.22, university education virtually guarantees in every country but
the Slovak Republic and Belarus that a household will not be poor.56 House-
holds whose head has only an elementary education are between 20 and 60
percent more likely than the average household to be poor. Vocational edu-
cation yields an average probability of being poor. Secondary education re-
duces the probability to about half the average.
56. In these two countries, the probability is half the country average.
106 Income, Inequality, and Poverty during the Transition
Figure 5.23. Size of Locality and Relative Poverty Rates, 1993-95
Village
. . . . . . . .. . . . . . . . . .,
Small city
Medium city . 1,
. .....-:: ...... .......-: -- ---:::::::::1..
Large city . ..:;.
.....~ . ... ...
0 0.5 1 1.5 2
Relative poverty rate
* Poland 0 Hungary m: Czech Republic
n Slovakia [ Ukraine
Note: Poverty rate equal to the country average = 1.
Source: Calculated from: Poland 1993: World Bank (1995, Table 2.16). Hungary 1992-93: T6th
and others (1994, Table A 1.2). Czech Republic and Slovak Republic 1993: Vecemik and others
(1994, Tables IV/3a and IV/3b, pp. 21-22). Ukraine 1995: World Bank (1996b, p. 22).
Hungary displays the sharpest decline of poverty with education level. In
Belarus and Slovakia there is an increase in the incidence of poverty as one
moves from household heads with vocational education to household heads
who are secondary school graduates. This might suggest a slower restructur-
ing as typically demand for workers with vocational education declines dur-
ing the transition.
Regional aspect of poverty
Poverty rates decline with increase in the size of the locality. Low rates of
unemployment and the concentration of highly skilled people in capital cit-
ies, increasing returns to education, and the finding above that farmers expe-
rience higher levels of poverty than other groups, suggest that larger cities
would be richer. Figure 5.23 confirms it for four East European countries.
Poverty 107
Unfortunately, reliable data do not exist for the rest of Eastern Europe and
countries of the former Soviet Union, except for Ukraine, where, on the con-
trary, urban poverty is more prevalent. The importance of home-consump-
tion in total population income in Ukraine, and particularly in rural areas, is
the key reason for lower poverty in Ukrainian villages. Whether this feature
may be shared by other countries of the former Soviet Union is likely but
impossible to confirm because of lack of data.
Poverty and gender
Figure 5.24 exhibits poverty rates by age and sex. Data from Bulgaria and
Russia (the only two countries for which data is available) show that poverty
rates for men decrease as they retire, while for women they remain unchanged.
Figure 5.25 contrasts age profiles of poverty in female-headed and male-
headed households in Poland. Female-headed households have a higher in-
cidence of poverty than male-headed households, regardless of age. The dif-
ferential rises with age, however. As in Bulgaria and Russia, the poverty rate
for people over 70 years old continues to decrease for male-headed house-
holds, and rises for female-headed households. This finding may be due to
the fact that women receive lower pensions. It explains, in part, why overall
poverty rates tend to be higher for women than for men. It would also seem
that most of the overall reduction of poverty with age (such as noted in fig-
ure 5.20) may be due to lower poverty among males.
Figure 5.24. Sex, Age, and Relative Poverty Rates in Bulgaria and Russia, 1992-93
0.9
_,8 - -_
.e 0.7 -
0.6
0.5
Active Retired
Note: Poverty rate equal to the country average = 1.
Source: Calculated from: Russia Q2/1993: McAuley (1994, p. 37). Bulgaria 1992: HBS 92, table 4,
p. 111.
108 Income, Inequality, and Poverty during the Transition
Figure 5.25. Poverty Ratesfor Male- and Female-headed Households in Poland,
1993
1.4-
1.4 __ ~Female
1.2 Mal
> 0.8
0
A 0.6
0.4
0.2
0 . l l
<30 30-39 40-49 50-59 60-69 70+
Age
Note: Poverty rate equal to the country average = 1.
Source: Poland 1993: World Bank (1995, Annex Table 5.11).
The Incidence of Cash Social Transfers
In this section, the extent to which cash social transfers are focused on the
lowest income groups is analyzed. The analysis will not address pensions
except for income-tested state pensions because, strictly speaking, pensions
are not an anti-poverty transfer. They are a deferred labor income. Although
in all pension schemes there is sorne redistribution in favor of poorer people
(in the sense that pensions do not exactly mirror contributions made during
a person's active life), pensions are nonetheless "earned" incomes in the same
sense as wages. Their main function is income-smoothing over a person's
lifetime.
The second reason not to discuss pensions here is somewhat more compli-
cated. Pensions account for between 70 and 80 percent of total cash social
transfers. They also represent most of the income received by pensioners: on
average, more than 80 percent. The result is that, if the average level of pen-
sions in a country is relatively low, pensioners will tend to be poor. The poorer
the pensioners, the better targeted pension spending will appear simply be-
cause most pensions will be received by the poor. Thus, the "good" poverty-
focus of pensions in countries where pensions are low, as in Russia, will be
misleading. Other more extreme and erroneous conclusions regarding tar-
geting may be drawn if a comparison is made between the number of pen-
Poverty 109
Table 5.6. Concentration Coefficients of Family Benefits
before the Transition (1988-89) and in 1993-95
Change in
Country 1988-89 1993-95 targeting
Eastern Europe -20 -17
Bulgaria -17 +5 Worse
Hungary -20 -18 Same
Poland -28 -22 Worse
Romania -9 -19 Better
Slovakia -28 -30 Same
Baltics -12
Estonia -12
Latvia 4 -12 Better
Slavic republics -2
Russia +15 -2 Better
Ukraine -1
Note: Family-related benefits include monthly and regular family allowances, one-time child
allowances (given at birth), matemity allowances and other family-related transfers. Individuals
are ranked by their household's disposable per capita income. The regional averages are
unweighted.
Source: Bulgaria is calculated from 1989 HBS and 1995 Gallup survey; Estonia, Hungary, Latvia,
Poland, and Slovakia from countries' HBSs presented in appendix 4; Romania from data given
in World Bank (1995a, Table 2.3, p. 8); and Russia from Russian Longitudinal Monitoring Survey,
Round 4 October 1993-February 1994; Ukraine from World Bank (1996b, Table A9).
sion recipients who are poor before and after receiving the pension. For an
important transfer like pension, most recipients would obviously be poor
without it. Pensions would then appear to be extremely well-targeted as they
would lift a large proportion of their recipients out of poverty. The same
could be, however, said about wages, because without wages almost all work-
ers would be poor. It therefore makes sense only to look at the targeting of
transfers (1) whose implicit or explicit function is poverty alleviation (for
example, only income-tested pensions should be included). If it is not en-
tirely clear that (1) is satisfied-as, for example, in the case of a universal
family allowance-then the following condition must hold: (2) the size of
transfer must be such that it cannot decisively influence the position of the
recipient household. This condition is needed to avoid including transfers
such as pensions which will always lift many recipients out of (pre-pension)
poverty. Family benefits satisfy condition (2). For almost no family are they
likely to be a major source of income so that they determine the family's
position in the income distribution curve. Then if it is established that family
benefits are paid mostly to the poor, one can infer that they are well targeted.
Table 5.6 compares the targeting of family benefits before the transition
and in 1993-95. Targeting is approximated by the concentration coefficient:57
57. For definition see footnote 9, chapter 2.
110 Income, Inequality, and Poverty during the Transition
the lower its value, the better the targeting (with negative values indicating a
negative correlation between the absolute amount of a transfer and the rank
of a household according to disposable income).
Family-related benefits have negative concentration coefficients every-
where, except for Bulgaria in 1995 and Russia before the transition. These
benefits appear not to have become more pro-poor during the transition. This
is rather surprising because children and larger families have become poorer
(relative to the rest of the population). Benefits that are, by definition, tied to
children should show more focus on the poor simply because their recipients
have slid down the income distribution ladder. The fact that this has not
happened, and that the reverse seems to have happened in Bulgaria and Po-
land, is probably due to the changes in the eligibility rules, for example, in-
troduction of income testing that has led to some poor families being refused
the benefit or to the declines in take-up rates among the poor.
Table 5.7 shows the targeting of a new transfer (the unemployment ben-
efit) that did not exist before the transition, and an "old" transfer (social as-
sistance) that has gained in importance since the onset of the transition. Un-
employment benefits are well targeted-with concentration coefficients, in
Eastern Europe and the Baltics, all exceeding -15 in absolute amounts (Rus-
sia and Ukraine, where unemployment benefits, even if low, are received by
better-off households rather than the poor, represent exceptions.) This is a
good, although not a surprising, result because the unemployed are often
among the poor, and payment of unemployment benefits is governed by clear
rules, so there are relatively few classification errors (for example, a poor
Table 5.7. Concentration Coefficients of Unemployment Benefits
and Social Assistance, 1993-95
Country Unemployment benefits Social assistance
Easter Europe -26 -17
Bulgaria -36 -3
Hungary -28 -25
Poland -18 -13
Romania -17 +9
Slovakia -29 -53
Baltics -31' -5
Estonia -37 -16
Latvia -25 +10
Lithuania -10
Slavic republics +27 +13
Russia +33 +25
Ukraine +21 +2
Note: Data for Slovakia are from 1992. Individuals are ranked by household's per capita
disposable income. The regional averages are unweighted.
a. Does not include Lithuania.
Source: As given in table 5.6, plus Lithuania as reported in Cornelius and Weder (1996).
Poverty 111
unemployed person who is denied a benefit). These results show that unem-
ployment benefits do have an important anti-poverty role to play, and that
their progressivity is similar in most transition countries reflecting in turn
similarities among countries in the position of the unemployed and the size
of the benefit.
How well the system of poverty alleviation performs is best assessed by
looking at social assistance. Social assistance (or welfare) is a discretionary
benefit the targeting of which reflects both the rules that govern it and the
ability of social assistance offices to implement these rules, that is, to income-
or means-test potential claimants. Social assistance benefits should be more
focused on the poor than any other kind of transfer because they are the only
transfer whose function is specifically poverty alleviation. Good targeting
may be difficult to achieve, however, because social workers lack the rela-
tively clear rules that govern the payment of categorical benefits, such as
family or unemployment allowances. Also, social assistance as defined in
statistics and household surveys is often a "mixed bag" that includes not
only discretionary cash or in-kind payments, but also many heterogeneous
benefits such as expenditures on nursery homes for the aged, mental institu-
tions, benefits paid by enterprises to their poor workers, and enterprise-pro-
vided free meals. Both the extent of these "other" social assistance benefits
and their statistical coverage vary from country to country, which makes re-
sults regarding social assistance transfers particularly difficult to compare.
These elements explain, at least in part, why social assistance in virtually all
countries seems less well targeted than unemployment benefits. The Slovak
Republic, however, stands out for its good performance, while Latvia, Ro-
mania, and Russia stand out for their poor performance.
Did transfers become better targeted during the transition as GDPs de-
clined and both poverty and inequality increased? Unfortunately, it does not
seem that there was any systematic improvement in targeting since the tran-
sition. Pre-transition data are not available for most countries of the former
Soviet Union, but in Eastern Europe, all non-pension transfers combined ap-
pear to have become only slightly more pro-poor: the overall concentration
coefficient moved from an average of -13 before the transition to -18 in 1993-
95 (see table 5.8). This improvement is due primarily to the introduction of
relatively well-targeted unemployment benefits. Distribution of non-pension
benefits in the three Baltic countries is close to a flat per capita amount given
across income distribution. In Russia and Ukraine, the incidence of non-pen-
sion transfers is, paradoxically, pro-rich: only family benefits are "neutral"
(almost equal per capita); other cash transfers are pro-rich.
The absence of significant improvements in targeting shows that social
transfers have not contributed much toward mitigating increases in poverty
since the onset of the transition. This is in contrast to Chile-a country that
also experienced a sharp decline in income-where since the mid-1970s very
focused transfers have played an important role in checking the spread of
poverty (see Graham 1994, chapter 2). Similarly, better targeting of transfers
112 Income, Inequality, and Poverty during the Transition
Table 5.8. Concentration Coefficients of Non-pension Cash Social Transfers
before the Transition (1988-89) and in 1993-95
Change in
Country 1988-89 1993-95 targeting
Eastern Europe -13 -18
Bulgaria -6 +2 Worse
Hungary -13 -16 Better
Poland -17 -22 Better
Romania -10 -36 Better
Slovakia -14 -21 Better
Slovenia -16 -17 Same
Baltics -2
Estonia -6
Latvia -7 +3 Worse
Lithuania -4
Slavic republics +16
Russia +13 +14 Same
Ukraine +23
Note: Non-pension transfers indude family-related benefits, unemployment benefits, social
assistance, sickness benefits, scholarships, and miscellaneous cash and in-kind transfers.
Source: Bulgaria is calculated from 1989 HBS and 1995 Gallup survey; Estonia, Hungary. Latvia,
Poland, Slovenia, and Slovakia from countries' HBSs presented in appendix 4; Lithuania from
data in Lithuanian Statistical Yearbook 1994-95; Romania from data given in World Bank (1995a,
Table 2.3, p. 8); Russia from Russian Longitudinal Monitoring Survey, Round 4, October 1993-
February 1994; Ukraine from World Bank (1996b, Table A9).
played a role in the United Kingdom by offsetting the impact of declining
overall cash social transfers and increased inequality. Between 1979 and 1989,
the share of social transfers in the income of the British population went down
from 17 to 13 percent; meanwhile, the concentration coefficient of transfers
improved from -20 to -30 (Milanovic 1994, table 2, p. 181 and figure 5, p. 190).
Table 5.9 contrasts results for transition economies with those for advanced
market economies (Targeting is approximated by the percentage of transfer
that reaches the bottom quintile of income distribution.) It must be noted
that market economies are not a homogeneous category. There is much vari-
ance among them in the focus of non-pension transfer payments. Some, such
as Australia and Chile, have extremely narrowly targeted social assistance,
unemployment benefits. Others, such as the United Kingdom and the United
States, have very focused social assistance but not unemployment benefits.
Finally, in the Northern European countries (the Netherlands, Norway, and
Sweden), all three types of transfers are unfocused. Table 5.9 shows, first,
that social assistance is much better targeted in market economies: in six of
twelve market economies, the bottom quintile receives at least 40 percent of
social assistance. Only one transition economy (out of eight) meets this level.
Second, there is virtually no difference between market and transition coun-
tries in the targeting of unemployment benefits. Third, all non-pension trans-
Poverty 113
Table 5.9. Percentage of Social Assistance, Unemployment Benefits, and
Non-pension Cash Social Transfers Received by the Bottom Quintile of Population
All non-
Social Unemployment pension cash
Country (year) assistance benefits social transfers
Transition economies 28 (16) 29 (19) 22 (8)
Slovakia (1992) 52 37 31
Estonia (1995) 36 56 26
Bulgaria (1995) 36 46 19
Hungary (1993) 35 33 29
Poland (1993) 29 26 25
Romania (1992) 23a 25 25
Russia (1994) 6 8 12
Ukraine (1995) 6 0 8
Market economies 42 (17) 27 (15) 23 (6)
Australia (1989) 78 50 30
United States (1991) 70 15 19
United Kingdom (1991) 55 29 33
Chile (1990) 51 57 31
Finland (1991) 43 20 26
West Germany (1984) 40 26 14
Belgium (1992) 35 38 23
Ireland (1987) 35 34 28
Netherlands (1987) 31 11 18
Norway (1986) 27 13 21
Switzerland (1982) 25 20 16
Sweden (1987) 21 10 9
n.a = not available.
Note: Shares for transition and market economies are unweighted averages. Standard deviations
are shown between brackets. Countries are ranked according to the share of social assistance
accruing to the lowest quintile. Individuals are ranked by their household's disposable per capita
income.
a. Also includes other unspecified allowances.
Source: Market economies: calculated from individual household data from LIS (Luxembourg
Income Study) using unemployment compensation (variable V21), means-tested social transfers
(variables V25 and V26), and all government cash transfers except pensions (sum of variables
V16 through V26 minus V19). Chile was calculated from Schkolnik and Aguero (1993, p. 245).
All non-pension cash social transfers for Chile include income-tested state pensions. Transition
economies: sources are as given in table 5.6, except for Romania: HBS 1992.
fers are about equally focused in the two types of economies. In both cases,
the poorest quintile receives only slightly more than its population share.
Even the within-group variability in targeting seems to be about the same as
shown by the very similar standard deviations.
The only area where transition economies clearly seem to lag, in terms of
targeting, is therefore social assistance. If, for the sake of simplicity, the poor-
that is, those whom policymakers want to reach-are assumed to occupy
exactly the bottom quintile, and only the bottom quintile, then little over
114 Income, Inequality, and Poverty during the Transition
one-fourth of expended social assistance in transition economies can be said
to reach them. The poverty deficit-calculated on the assumption of perfect
targeting-would then have to be multiplied by a "gross-up" factor of 3.6
(that is, 100 divided by 28) to get an estimate of the actual amount of money
needed to eliminate the poverty deficit (see equation 5.10 above). By con-
trast, the gross-up factor in market economies is less than 2.4.
Unlike social assistance, targeting of unemployment benefits cannot be
much improved. Unemployment benefits are relatively "passive" instruments:
they are paid after certain requirements are satisfied-for example, if some-
one has been dismissed, or is without a job and looking for one, or has contri-
bution record. These requirements do not generally include a low income. In
addition, the gap between transition and market economies in progressivity
of unemployment benefits is almost non-existent. Of course, unemployment
benefits could be income-tested, as they are in Chile,58 but this does not seem
a politically acceptable option in Eastern Europe and in countries of the former
Soviet Union.
How much could better targeting of non-pension transfers contribute to
reducing poverty? There the illustrative analysis must proceed by several
steps. First, non-pension cash and in-kind transfers normally account for only
5 percent of household disposable income in transition economies. From equa-
tion 4.2 it is clear that with a concentration coefficient of -18 (equal to the
average value for Eastern Europe in 1993-95, as per table 5.8) and a share of 5
percent, non-pension transfers reduce the overall Gini coefficient by slightly
less than 1 point (-18 times 0.05 = 0.9). If targeting were improved so that it
reached the OECD level of social assistance targeting (that is, a concentration
coefficient of -30),59 the overall Gini would be reduced by 1.5 points. The gain
from better targeting would be 0.6 Gini points. Second, how would a Gini
reduction of 0.6 translate in terms of poverty reduction? According to table
5.4 (equation D), each one-point decline in the Gini is associated with about a
1 percentage point reduction in poverty. Achieving the OECD level of target-
ing could therefore be expected to reduce the poverty headcount by approxi-
mately 0.6 points. Third, because the estimated poverty headcount in transi-
tion economies is about 40 percent (see table 5.2), approximately one in
sixty-six poor people (0.6 divided by 40), would cease to be poor thanks to
better targeting of social transfers. In terms of actual people, it would mean
that about 2.2 million people in transition countries would escape poverty.
58. Which explains a very high share received by the bottom quintile.
59. The average concentration coefficient of means-tested social assistance for mar-
ket economies included in table 5.9 is -31.
6
Selected Issues in Social Policy
Should OECD-like Social Assistance Be Introduced in Transition
Economies?
Probably the key issue that faces policymakers designing safety net programs
in transition economies is choosing between the following two concepts of
social assistance.
The first is the OECD or, more narrowly, the northwest European concept.
Its key features are (1) there is an official poverty line;' (2) income below the
poverty line and assets below a certain minimum are a sufficient condition
for welfare eligibility; and (3) social assistance offices try, in principle, to cover
the entire gap between the poverty line and income, and that gap is filled
with cash. Such a concept can be termed the minimum income guarantee
(MIG) system.2 The overall cost of means-tested benefits in OECD countries
is typically between 0.75 and 1.5 percent of GDP and benefits account for
about 2 percent of household disposable income.3 No transition economy
has such a system.4
The second concept of social assistance is what exists in transition econo-
mies-shorn of the enterprise-based welfare where still present. The system
differs from MIG in the following respects. First, although income testing is
1. Examples include the French revenu minimum d'insertion, the minimum pension
in Finland and Norway, and the survey-based (that is, calculated fiom actual expendi-
tures of the bottom decile or quintile of the population) poverty lines in Belgium, the
Federal Republic of Germany, and Sweden (see Veit-Wilson 1996, pp. 45-47). Differently,
there may be a de facto, even if not de jure, official poverty line. For example, the British
government is, for political reasons, adamant that the Income Support Line is not an
official poverty line, even if it functions as such.
2. In reality, not all the gap will be identified (that is, the take-up rate is less than
100 percent) or filled (because of mistakes in assessment). The MIG system can be imple-
mented in different ways: through direct payments that bring recipients up to the pov-
erty line, a universal negative income tax (that is, the same amount is paid to everybody
and then is subject to taxation), or a specific negative income tax to the poor (like the
earned-income tax credit in the United States).
3. OECD countries included here are the "old" OECD countries: Western Europe,
Northern America, and Japan. More recently, Hungary, the Czech Republic, Poland, South
Korea, and Mexico have joined OECD, but their welfare systems differ from those of
other OECD countries.
4. Social assistance in the former Czechoslovakia and in today's Czech Republic
and Slovakia comes closest to it (see Vecernik 1991, p. 2 quoted by Sipos 1992, p. 38).
115
116 Income, Inequality, and Poverty during the Transition
an integral component of the system, having an income less than the poverty
line is a necessary but not sufficient condition for receiving social aid. Addi-
tional criteria must also be fulfilled. These criteria are related to households'
low earning capacity (almost zero elasticity of labor supply): single-parent
status, presence in the household of handicapped or elderly members; or
"dysfunctionality": alcoholism in the family, drug abuse, mental incapacity.
For example, Poland's 1990 law on social assistance lists eleven such addi-
tional criteria, at least one of which must be present, in addition to low in-
come, before a household can be eligible for social assistance (Fijalkowski
1992, pp. 63-85); Latvian 1994 law lists four additional criteria (Latvia, 1994,
articles 53 and 81). The Czech Republic's 1991 social assistance law also stipu-
lates that beneficiaries must be unable to "increase their income due to their
age or health situation" (Vernik 1994, p. 7) the wording which is identical
to the one used in the Ukrainian law on social help to the "underprovisioned"
families (Ukraine Council of Ministers, 1995, pp. 44-45).
Second, social assistance in transition economies is viewed as a temporary
relief and is often provided in-kind-for example, through hot meals or food
vouchers, drugs, child care assistance, payment for kindergartens, payment
of utilities and rent, provision of wood and coal, and so on.
Third, categorical social assistance in transition economies plays a more
important role than under MIG-for example, through state pensions for the
aged, family allowances, additional allowances for large families, milk and
food for school children, and special assistance for regions with a high con-
centration of poor. Although most OECD countries have categorical programs
in addition to MIG, the scope and financial importance of these programs are
smaller, particularly when compared to countries of the former Soviet Union,
where practically all social assistance is (still) based on indicator (categori-
cal) targeting.5
Fourth, social assistance in transition economies does not aim to cover the
entire difference between the poverty line and actual income. The amount
covered depends on the judgment of local social assistance workers. Such
systems may be called income testing as screening (ITS) systems to indicate
that income is tested in order to screen applicants, but that there is no mini-
mum income guarantee. MIG and ITS systems are similar respectively to the
"Type A" system, which seeks to eliminate poverty, and the "Type B" sys-
tem, which only seeks to alleviate it, as defined by Sipos (1994).
ITS systems exist in virtually all countries in Eastern Europe and the former
Soviet Union. Officially, the Czech Republic, Estonia, Hungary, Poland, and
5. For example, in Lithuania in 1994 there were no less than ten types of family
allowance (childbirth grant, child care benefit, adoption and foster parents' allowance,
single-mother benefit, preschool child benefit, military-family child benefit, alimony ben-
efit, and so on; see Cornelius 1995). A World Bank study of Ukraine (1996b, p. 49) lists
twelve types of family benefits and ten types of social assistance (for example, social
pensions, housing subsidies, cash allowances for Chemobyl victims, food subsidies for
Chemobyl victims, funeral aid, and transportation subsidies). In Hungary, in 1995, there
were 35 kinds of family and social assistance benefits (Sipos, 1995, Annex 1).
Selected Issues in Social Policy 117
Table 6.1. The Composition of the Poor, 1993-95
Hungary Bulgaria Estonia Poland Russia Belarus
1993 1994 1995 1993 1993-4 1995
Total "categorical" poor 77 76 73 62 56 44
Unemployeda 60 30 24 30 11 5
Pensioners, 3 35 9 6 26 21
People in single-
parent fanilies 3 2 32 1 3 3
Childrenb 12 9 8 25 16 15
Other poor 23 24 27 38 44 56
Note: All poor = 100. Poverty line is $PPP120 per capita per month except in Belarus (about
$PPP100).
a. All individuals living in poor households whose head is a pensioner or where there are
unemployed are included in these two groups, respectively.
b. All children living in poor families where the head is not a pensioner, unemployed, or a
single parent.
Source: Hungary: HBS 1993; Bulgaria: HBS 1994; Estonia: HBS July-September 1995; Poland:
HBS 1993; Russia: RLMS (Russian Longitudinal Monitoring Survey) Round 4 (October 1993 to
February 1994); Belarus: New Household Budget Survey 1995.
Slovakia have ITS systems, where low income plus family dysfunctionality
or zero elasticity of labor supply are the requirements for social assistance. In
Russia and Ukraine, a "weaker" form of ITS system is applied. Although
social workers are supposed to observe official poverty lines (which in a coun-
try as vast as Russia vary by region), much is left to the workers' discretion;
moreover, implementation depends on local availability of funds (see Foley
and Klugman 1997).6
To guide a policymaker choice between these two social assistance con-
cepts (MIG and ITS), four criteria are proposed.
First, it is necessary to ascertain the correlation between poverty and char-
acteristics that are both observable and difficult to hide or alter. The stronger
the correlation, the stronger the case for ITS, which relies more heavily on
categorical benefits. Thus, in countries where family size and age are good
predictors of poverty, categorically based benefits (such as family allowance
and minimum pension) can be efficient instruments for combating poverty.
As shown in table 6.1, in Bulgaria, Hungary, and Estonia unemployment ben-
efits, the minimum pension, and family allowances may be expected to reach
more than three quarters of all poor. In Belarus and Russia, where the work-
ing poor are more numerous, categorical benefits are not likely to be as effi-
cient.
The same, of course, applies to the correlation between poverty and other
characteristics, such as age and region. For example, in countries such as
6. A regionally diverse system does not exist only in large countries such as Russia
and Ukraine. Latvia, too, has a regionally based social assistance system, with the result
that the poor in different parts of the country are treated differently.
118 Income, Inequality, and Poverty during the Transition
Bolivia and Chile (see Grosh 1994; Graham 1994; World Bank 1995f, chapter
1), where poverty is strongly regionalized and the distances between the poor
and rich areas are relatively great, governments subsidize staple foods that
are sold only in poor areas. A regional approach may be relevant for large
countries with low population densities and a heavy regional concentration
of inefficient industries. Russia would be an obvious candidate.
Second, income-testing must be feasible for MIG to be chosen. If the corre-
lation between poverty and easily observable household characteristics is
weak, thus favoring adoption of MIG, one needs to check if it is possible to
determine incomes with sufficient precision. During the transition, personal
incomes have become difficult to monitor. Many people are engaging in un-
reported economic activities; the "gray economy" is blossoming. The rela-
tionship between reported and actual income becomes weak. If many in the
private sector operate on the margins of legality, then certifications produced
by their employers (for example, vouching that workers' wages are suffi-
ciently low so that they can qualify for various benefits) may be worthless
for determining actual incomes.7 The lower the correlation between reported
and actual income, the weaker the case for MIG because both effectiveness
(the number of poor reached by programs) and efficiency (the proportion of
money disbursed to the poor) will be low. In the section, Decline in real popu-
lation income, in chapter 3, it was noted that income underreporting seems to
be particularly great in the Slavic, Central Asian, and Caucasian republics of
the former Soviet Union. The case against using MIG will be strong in these
countries.
The third criterion relates to financial feasibility. In all transition econo-
mies, a new transfer has already emerged: unemployment benefits. Is there
room for yet another transfer: MIG-based social assistance? This is an em-
pirical question. If other types of transfers (pensions, family allowances, and
so on) are scaled down, then universal welfare may be introduced. But how
costly will universal welfare be? The answer to this question hinges on how
high the guaranteed minimum income is pitched, how many people are likely
to fall below that line and by how much, and what are the expected take-up
rates, and the "leakage." As discussed in the section, How Much Is Needed to
Cover the Poverty Deficit?, in chapter 5 the "true" poverty deficit in transition
countries amounts, on average, to 9 to 10 percent of GDP. Clearly these costs
cannot be financed. Only in some Central European countries where the pov-
7. For example, private firms in some transition economies pretend to employ people
who then "work" for six months until they qualify for unemployment benefits and are
then promptly "fired." The employer's only cost would be, say, 45 percent payroll tax for
six months. If the wage replacement rate is 60 percent and the duration of unemploy-
ment benefits is a year, then both the employer and employee can make a profit.
8. Note that this discussion does not address the issue of whether "leakage" is more
or less under MuG than it is under ITS. The point is simpler: if "leakage" is assumed to be
the same under both systems, then MIG programs will be more expensive because they
aim to eliminate the entire poverty deficit, whereas ITS attempts only to alleviate poverty.
Selected Issues in Social Policy 119
erty deficit-to-GDP ratio is relatively low can the guaranteed income be a
financially feasible option. It is almost certainly not financially feasible in the
Soviet successor states.8
The fourth criterion is the ability of local administrations to implement
centrally mandated schemes. Even if MIG is feasible on the basis of the three
previous criteria, it may be impossible to implement because of administra-
tive weakness. Or it may be impossible to implement in some less developed
parts of a country. Then a case could be made for simpler local schemes.
Based on the above discussion, two types of conclusions can be made: con-
clusions regarding elements that favor the selection of either system and con-
clusions regarding information needed to make an informed choice.
When is a MIG-like system to be preferred? (1) If the correlation between
household characteristics and poverty is weak, (2) if determination of house-
hold incomes is reliable, (3) if the poverty gap is not large, and (4) if local
administration is reasonably capable. In market economies, for example, the
last three criteria are satisfied, and the correlation between household char-
acteristics and poverty is probably the same as in Eastern Europe.9 In Central
Asian countries, on the other hand, none of the last three criteria is likely to
be satisfied.
Because the choice between MIG and ITS is an empirical question, the in-
formation needed to make an informed choice becomes relatively easy to
define. To address the first criterion-the correlation between household char-
acteristics and poverty-household surveys and poverty profiles are needed.
Using this information, it is possible to learn whether poverty is strongly
related to some household characteristics or not (as has been done for ex-
ample in the section, Who Are the Poor?, in chapter 5).
To address the second criterion-can household incomes be ascertained?-
information regarding the functioning of personal income taxation, the esti-
mated size of the unrecorded economy, the relationship between incomes
and expenditures (based on household surveys), is needed.
To address the third criterion-is universal welfare financially affordable?-
a calculation of poverty deficits for a set of "reasonable" $PPP poverty lines
is required.10
To address the fourth criterion-is local administrative capability satisfac-
tory?-information is needed from those with first-hand experience in deliv-
9. The unemployed and pensioners accounted for 47 percent of all poor in Canada
in 1990 (calculated from Statistics Canada 1991, pp. 154-64), a percentage almost the
same as in Poland (see table 6.1).
10. It is not necessary to spend too much time calculating sophisticated social minima.
These are long and costly exercises, subject to much controversy, and may be irrelevant.
If the poverty deficit is too big (more than 2 percent of GDP) for almost any "reasonable"
range of poverty lines (say, between $PPP 60 and $PPP 120 per capita per month) then it
does not make sense to investigate what the "best" poverty line might be. Complicated
poverty line exercises should be postponed until a case can be made that the poverty line
lies within the financially feasible range.
120 Income, Inequality, and Poverty during the Transition
ering social assistance. This is an important task. Even if it is established that
MIG does not make sense in a given country, there are still at least two rea-
sons to look at the local administration: to learn whether local administra-
tions would be able to administer even simple local schemes and to help the
central administration define such local schemes.
Transfer Payments to Different Groups of Recipients
A key problem encountered when designing social transfers is to balance the
needs of individuals with disincentive effects (mostly on the supply of labor)
that can arise due to "over-generous" social payments.
The disincentive effects of transfers on different types of recipients can be
considered using the following example. Let there be three categories of
people. A are people with a zero elasticity of labor supply (Ls) such as inva-
lids, the handicapped, and social pensioners. Group B are poor (unemployed)
people with a positive Ls who are net recipients of government transfers and
who, at the margin, have to decide between receiving government transfers
and working. Group C are working people, net tax payers, whose labor sup-
ply is a function of net wage (equal to gross wage minus tax to finance social
transfers to A and B). Let there also be the restriction that the disposable
(post-transfer and post-tax) income of A and B must be lower than the dis-
posable income of Cs.
Labor decisions of As are unaffected by transfers. Their supply of labor is
inelastic."' The supply of labor by Cs will be reduced, however (the labor
supply curve will shift from Lso to Lsl, figure 6.1) because they have to pay
taxes to finance transfers to As. The output loss is X(L*) - X(L), where X =
output, L* = the equilibrium labor supply of As in the absence of taxation,
and L = equilibrium labor supply with taxes, where, implicitly, the price of
output = 1 and output depends on L alone. The deadweight loss represented
by the cross-hatched area in figure 6.1 is the cost of the scheme. The gain is
the utility gain of As. If the utility gain is greater than the deadweight loss,
then the move (that is, the progressive transfer from the richer Cs to the poorer
As) is acceptable.
Cs have to pay additional taxes to finance transfers to Bs, however. The
analysis for Cs is the same as before. Bs' work effort will decrease because
they receive transfers and the elasticity of their labor supply is positive (fig-
ure 6.2). Their labor supply schedule shifts to the left because Bs receive some
income-unemployment benefits-without working (that is, their consump-
tion of leisure increases with higher income). The amount of output loss will
depend on the elasticity of labor demand (Ld) for Bs. If Ld is fixed, then B's
work effort and, thus, output will stay the same. If Ld is perfectly elastic and
11. If the elasticity of the labor supply is zero, the welfarist perspective (where utility
of leisure is taken into account) and the non-welfarist perspective (where leisure is ig-
nored or does not matter) coincide (see Kanbur, Keen, and Tuomala 1995).
Selected Issues in Social Policy 121
Figure 6.1. The Effect of Taxes on Supply of Labor of the Working Population
Ls
Ld/
Wage LsO
B
A
0 L L* Labor
Note: OB = gross wage = OA (net wage) + AB (taxes).
the wage rate for Bs is given, then there will be maximum output loss (see
movement from Q to P in figure 6.2).
Consequently, when the demand for Bs' labor is fixed, there is no differ-
ence in the analysis of C-financed transfers to As and Bs because in both
cases, output produced by the latter two groups is not affected. For all in-
tents and purposes, then, As and Bs can be treated as the same group. If the
demand for Bs' labor is elastic, however, transfers will lead to a decrease in
Bs' work effort and output. The argument could then be made that to mini-
mize output loss, transfers to Bs should be reduced to below the level paid to
As, or should even be eliminated altogether.
In practical terms, much will depend on who the poor (the Bs) are. The
greater the labor demand and supply elasticities of Bs, the greater the output
loss and the stronger the argument for giving low transfers to Bs. If, for ex-
ample, labor demand for skilled and unskilled unemployed is the same, and
labor supply of the skilled less elastic, then the argument can be made that
skilled Bs should receive higher transfers than unskilled Bs, for example,
through past earning-related unemployment allowances.
This point illustrates the importance of assessing the elasticities of labor
demand and supply when designing social transfers for various types of re-
cipients. A strong case can be made for transfers to As, or to those Bs who
have obsolete skills and for whom labor demand is inelastic. The case is not
so strong for Bs whose labor supply and demand are elastic.
122 Income, Inequality, and Poverty during the Transition
Figure 6.2. Labor Supply of the Poor (with and without Transfers)
Wage L
L,(y0)
L,
p Q Labor
Note: y, = income without transfers. y, = income with transfers.
Guaranteed Minimum Income and the Supply of Labor
When people are paid the exact difference between their income and the
social minimum (i.e., guaranteed minimum income is in place), their dis-
posable income does not change if they work because the additional earn-
ings are "taxed away" through lower social transfers. A typical situation is
illustrated in figures 6.3 and 6.4. Up to point A, which is equal to the guar-
anteed minimum income, all earned income is taxed at the rate of 100 per-
cent; between points A and B (where B is the income level at which social
assistance is entirely phased out), the marginal tax rate is less than 100 per-
cent but still in excess of the statutory tax rate. Only if gross income is greater
than B does the effective tax rate become equal to the statutory rate. A typi-
cal marginal tax schedule then looks as in figure 6.4: lowest incomes are the
most heavily taxed. Labor supply of those with lowest income is likely to be
reduced, particularly up to point B because the marginal tax rates are high.
The reduction in labor supply represents an additional "cost" of guaranteed
minimum income.
All things being equal, the cost increases if the guaranteed social mini-
mum and the minimum wage are very close (and if the country is poor, the
two cannot be very different). Thus, for example, if a person's maximum
Selected Issues in Social Policy 123
Figure 6.3. Gross and Net Income if There Is a Minimum Income Guarantee
Gross income
/o Net income
Statutory tax
MIG
//I
0 A B Gross income
Note: The net income schedule is shown by a bold line.
OA = minimum income guarantee (=MIG). B = social assistance phased out.
attainable earned gross income is barely in excess of MIG, he will be subject
to an almost 100 percent marginal taxation throughout. Gains to working are
miunimal. Browning (1993, pp. 9-10) argues that the costs, i.e., foregone out-
put due to lower labor supply, are substantial and that they increase when
income distribution is compressed. In a poor country, the costs in terms of
GDP increase for two reasons. First, direct costs increase because more people
fall below the poverty line than would in a richer country with the same
poverty line. The expenditures needed to close the poverty gap are greater.
Second, because the guaranteed social minimum is not much less than the
minimum wage, people have weak incentives to work, and foregone output
is greater.
The relationship between the poverty line and minimum wage is illus-
trated for six transition economies (arranged from left to right in ascending
order according to their average dollar manufacturing wage) in table 6.2. If
the poverty lines that are currently used to screen social assistance applicants
124 Income, Inequality, and Poverty during the Transition
Figure 6.4. Marginal Tax Rate in the Presence of a Minimum Income Guarantee
Tax rate
100%
Statutory tax rate
0 A B Gross income
Note: OA = guaranteed minimum income. B = social assistance phased out.
(see line 3) were used as the guaranteed minimum income (gapfill lines), then
returns to working at the minimum wage, compared to receiving welfare,
would be substantially negative in Russia, only 5 percent in Estonia, 35 per-
cent in Poland, and from 50 to 60 percent in Bulgaria and Hungary. For a
"typical" family (of two adults and two children) the returns to both adults
working would be even less. Only in Hungary, where wages are relatively
high (almost 4.5 times higher in dollar terms than in the former Soviet Union),
and thus the wedge between the minimum wage for both adults and the pov-
erty line for the entire family is relatively wide (50 percent), would it be worth-
while to work. In other countries, even if MIG system were financially fea-
sible, disincentive effects would be strong. These results may be compared
with results for market economies, where retums to working are between 120
and 190 percent in Portugal, France, and the United Kingdom. The last two
have a MIG-type system. The data also show why, using the current federal
poverty line, a MIG-type system would be difficult to implement in the United
States: the retums to work would be about the same as in Hungary.
Table 6.2. Relationship between Poverty Lines and Wages, 1992-94
(in current dollars at current exchange rates, per month)
Estonia Russia Bulgaria Poland Hungary Portugal France U.K U.S.
early 1994 April 1994 June 1993 end-1993 Q2/93 1993 1992 mid-1993 1993
Poverty line for 1 adult (PLI) 22 45 29 68 72 90 383 285 614
Poverty line for 2-adult,
2-children household (PL4)a 59 121 78 184 194 243 825 750 1,230
Minimum wage 23 15b 45 92 115 255 1,000 825c 930
Universal family allowance
(FA) per childdR 9 7 9 9 32 17 175f 60 0
Income-to-poverty line ratio
Minimum wage to PLi 1.1 0.3 1.6 1.3 1.6 2.8 2.6 2.9 1.5
Both adults at the minimum
wage plus 2 FAs to PL4 1.1 0.4 1.4 1.1 1.5 2.2 2.8h 2.49 1.5h
Average manufacturing wage 92 110 122 240 325 560 1,600 1,750 1,950
Note: Poverty line for one adult. Russia: Ministry of Labor subsistence minimum; Bulgaria, Poland, and Portugal: the minimum pension; Estonia: official
subsistence benefit; France, revenue minimum d'insertion; United Kingdom: income support and housing benefit; United States: the federal poverty threshold
(the effective poverty line is less and varies by state).
a. The poverty line for the standard four-member household is calculated using the OECD equivalence scale, where requirements of a four-member
household are equal to 2.7 adult units except for the United States, United Kingdom, and France where the actual four-member family poverty lines are used.
b. The official minimum wage in Russia was less than $10 but few were paid that little (0.2-0.3 percent of workers). The official minimum wage is used
mostly as a scalar to calculate various social payments. An estimated effective minimum wage is used here instead.
c. There is no official minimum wage in the United Kingdom. An effective minimum wage is used here.
d. Family allowances vary with the number of children and their ages. An approximate average value was chosen in each case.
e. To compare working and not working, only the universal part of the family allowance is relevant because the income-tested part is lost if working.
f. Spending on universal family allowance is about a half of total family allowance expenditures (see d'Agostino and Trombert, 1992, p. 180). Only a half
of total in inputed here.
g. Becomes 2.7 if family credit (allowance received by low income working families) is included.
h. Does not include family tax credit received if working (earned-income tax credit in the United States and France).
Source: Transition economies: World Bank data; France from d'Agostino and Trombert (1992); United Kingdom: United Kingdom Central Statistical Office
(1994, pp. 71, 184); United States: US Bureau of the Census, CD-Rom, Income and Poverty 1993, Table 1; Portugal, personal communication by Luisa Ferreira.
Data on average manufacturing wage from Institut der deutschen Wirtschaft reported in German Brief, June 5, 1993.
126 Income, Inequality, and Poverty during the Transition
Informal Sector and Pension Reform
Transition economies presently function as two-sector economies. On the one
hand, there is a shrinking formal sector on which are assessed all pension
contributions and other taxes. On the other hand, there is the informal sector,
which, by definition, pays no taxes and whose workers do not accrue any
social security (including pension) rights.
The situation in transition economies is not unique. In many developing
countries there is also a sharp division between the formal and informal sec-
tors. There are, however, two important differences between transition econo-
mies and developing countries. First, transition economies face a much greater
current liability in the form of pensions that must be paid. In developing
countries, few people-mostly public sector employees-have accrued pen-
sion rights: current public sector workers, who may account for less than 10
percent of the labor force, are taxed to pay for pensions of past public sector
workers, who are similarly few in numbers. In transition economies, on the
other hand, a shrinking labor force in the formal sector must pay for pension
rights accrued by almost 90 percent of the former labor force. Second, transi-
tion economies have a more unfavorable demographic structure with a higher
percentage of older people and a correspondingly smaller percentage of
people of working age.
The model
In this section, a model is presented that shows the link between growth of
the informal sector and the need for pension reform. The economy consists
of two sectors: the formal and informal sectors. Both sectors produce the same
good, which they price the same (it is the numeraire); both use the same
technology; and both have the same demand for labor and face the same
labor supply. They are identical in all respects, except that in the formal sec-
tor there is a tax on wages that is used to finance current pensions.
In the formal sector, workers are paid Mf which is take-home cash wage
plus aT where T is the payroll tax and a is the parameter that indicates how
much of these taxes current workers expect to get back in the future in the
form of pensions. In other words, the actual wage is composed of two com-
ponents: the current cash wage and the expected value of the future pension.
For example, if current taxation is perceived simply as yielding an equiva-
lent net present value in the form of (future) pensions, the tax is in effect a
deferred wage and a takes the value of 1. More generally, a shows the ex-
pected net present value of future pensions to be obtained from paying a
dollar in current taxes. If workers perceive current taxation as likely to yield
nothing in terms of future income, then a = 0 and Tf is a pure tax.'2
12. Gramlich (1996, pp. 64-65) shows a ratios for U.S. workers of different age co-
horts and household sizes. Many "baby boomers" are likely to receive less than what
they paid. This is true for all single workers (with a=0.8 for those with low earnings to
a=0.4 for those with steady high earnings) as well as for most married workers (although
their a is higher).
Selected Issues in Social Policy 127
In the informal sector, workers are paid only in cash, a money wage Mj,
which we assume equal to the sum of M and T in the formal sector.
Suppose now that for workers in the forma{sector a=l. They are, at the
margin, indifferent between current and deferred wage. The labor supply
curves in the formal and informal sector will be the same, as would employ-
ment and wages. In order to have the pension system in balance, it is neces-
sary simply to solve for such a T7 which is sufficient to pay for all current
pensions. The level of equilibrium T7, whether high or low, would have no
effect on the formal sector labor supply because workers view their current
taxes simply as equivalent deferred wage.
Now let a be less than 1. This could happen for a variety of reasons. Cur-
rent workers could believe that demographic trends are so unfavorable that
the state would be unable to impose a tax rate on future workers that is high
enough to pay for their pensions (this is the situation in the United States). A
variant of this is the belief that because of a future disequilibrium in the so-
cial security system, the state would renege on its current promises and would,
for example, income test future pensions. Workers can also perceive the state
to be fundamentally unstable and untrustworthy. This is the case in many
transition economies. Political and social changes in the last several years
have been dramatic: not only have the states repudiated their implicit and
explicit promises, they have also conducted confiscatory policies-for ex-
ample, wiping out nominal savings and imposing high inflation taxes on the
population. The credibility of these states is low. Additional uncertainty has
been created by the political turmoil that gave birth to many new states. A
given state may not even exist when it is time for a worker to claim his or her
pension rights, an unpleasant experience that many pensioners in the former
Soviet Union and Yugoslavia have already had to face.
In general, a may be thought of as a function of the expected future tax
rate. If workers expect that the future tax rate, needed to finance their future
pensions, will be very high, they are more likely to believe that such a tax
would be unsustainable and that it would ultimately lead to the government's
reneging on its obligations. The relationship between the current a and the
future tax rate t* (future variables are denoted by asterisk) is depicted in
figure 6.5. There is a threshold tax rate t* below which a = 1. As the ex-
pected future tax rate increases, workers expect to lose some of their contri-
butions, and a declines. At some t*=t* the whole current contribution is
thought of as a pure tax.
In addition, independent factors (for example, lack of confidence in the
state) may also shift the relationship so that a declines more sharply. The
curve may shift inward (see the southwesterly movement shown by the ar-
row in figure 6.5).
When a becomes less than 1, a part of t is perceived as a pure tax. There is
a wedge between gross and net wages. The labor supply curve in the formal
sector shifts leftward from AlAl to A2A2 (figure 6.6). The equilibrium formal
sector employment declines, and the equilibrium gross wage rises (the new
equilibrium moves from 0 to E in figure 6.6). The relative size of the formal
sector (compared to the informal sector) shrinks.
128 Income, Inequality, and Poverty during the Transition
Figure 6.5. Relationship between the Expected Future Tax Rate on the Formal Sector
and Current Value of Contributions
Current value of contributions (a)
0 Future tax rate (tf*)
tf,min tjffla
Note: Current value of contributions shows the share of current contributions that workers
expect to receive through future pensions.
The new formal sector equilibrium will depend on how important the de-
crease in a is, how elastic labor supply is with respect to both wage and ax,
and how elastic labor demand is. At any event, it is clear that the equilibrium
gross wage in the formal sector, Gf will now be greater than the equilibrium
wage in the informal sector, but that the money wage in the formal sector M
will be less than money wage in the informal sector." 3
In addition, T must be such that current pensions are paid out. This is
shown in equation 6.1, where P is the current average pension, N is the num-
ber of current pensioners, and N is the number of current formal sector work-
ers:
6.1 TpN1=P Np.
Since N1 and G depend on cc, and Tf = tfG where t is the tax rate on current
gross wages in the formal sector, and p = ratio between current pension and
gross wage, the following is obtained:
6.2 tf G/oa) Nf(a)=-pG/ua) Np.
Both p and Np are fixed in the short run as denoted by the bars in equation
6.2. The first is fixed by law; the second is fixed by demographics and past
pension practices and rules.
13. Note that the gross (and net) wages in the two sectors were initially the same
(when a was 1).
Selected Issues in Social Policy 129
Figure 6.6. Shift of Formal Sector Labor Supply Curve as a Function of Current
Discount of Contributions
Gross wage
A2 (a<l)
< z~~~~E A, (ac=l)
Formal employment
Note: Current discount of contributions is equal to 1-a.
If the ratio between those currently employed in the formal sector and the
stock of current pensioners is written as R = Nf/Np, equation 6.2 becomes:
6.3 tfR(a)=7T
Relation 6.3 simply states that if the current pension-to-current wage ratio
(pT) is, say, 50 percent and the ratio of current contributors to current pension-
ers is 2 (a situation common in many transition economies), then the payroll
tax needed to pay pensions alone must be 25 percent.
The same identify must hold in the future. If the expected R* is less than
the current R, the future tax rate will have to be higher than the current rate
(to maintain p at the same level). If this is indeed the case and this future tax
rate is t *>tf, then it follows from equation 6.3 that the future equilibrium will
depend on the future a. Current workers clearly cannot guess what future
workers' expectations will be. So it can be assumed that current workers as-
sume that future workers' a* will be the same as their current a. To obtain
the current equilibrium it is then necessary to solve equations in 6.4 for tfB
tjand a:
tf R (a) = p (present equilibrium)
6.4 If R*(a) = PT (future equilibrium)
a =f(t') (determination of a).
Let the solution be t , t * (tj'>t because R*<R) and al. The formal sector
equilibrium will obtain at point 1l (see figure 6.6). With still lower employ-
130 Income, Inequality, and Poverty during the Transition
ment and higher wages than before (i.e., than at point E), the share of the
formal sector will be further reduced. This illustrates the dynamic nature of
the process. A simple exogenous shock, such as decreased confidence in the
state, is sufficient to shift the labor supply curve leftward, leading to a reduc-
tion in formal sector employment. Suppose that the new tax rate is sufficient
to ensure current payment of pensions (so that the current equilibrium con-
dition is satisfied). However, workers begin to notice that the current size of
the formal sector compared to the future stock of pensioners has decreased.
They realize that to keep the present replacement (pension-to-wage) rules in
the future, future taxes need to be even higher. As they realize this and begin
to doubt the ability of future governments to impose such high taxes, their
discount of future pensions rises, and their labor supply shifts further to the
left-upsetting the existing equilibrium. There is thus a vicious circle which
leads to the shrinking of the formal sector. Solving the current pension prob-
lem is not sufficient for the long-run equilibrium because the way the current
problem is solved (by raising taxes) may influence workers' perceptions about
the future taxes and thus their current expectation of how much of their
present taxes they are likely to recoup.
Heterogeneous workers
Current workers are not all the same: they belong to different age cohorts,
and they have different expectations regarding the likelihood that their gov-
ernment will stick to its obligations. Older workers who are closer to retire-
ment age will make the same type of calculation as younger workers, but
their expected R* will be larger simply because the demographic picture is
expected to deteriorate less within a few years than over a longer period.
Consequently, older workers' expected future tax rate, that is, the tax rate
that will finance their pensions, is less and their ca is greater than younger
workers'. Their formal sector labor supply curve will therefore shift to the
left by less than that of younger workers. This is in fact what can be observed
in transition economies. The formal sector is "aging." Young people are loath
to join the formal sector, among other reasons, because they do not expect
that the state will make good its promise to pay future pensions. They prefer,
quite rationally, to move to the informal sector where, even if the gross wage
is less, the net wage is greater. There is thus an interesting reversal in dis-
counting: discount is high for the young, low for the old. An additional rea-
son why the older workers' discount may be lower is that because of demo-
graphics, older workers and pensioners may feel sufficiently numerous to
use their political power to ensure payment of pensions.
The problem to which the "aging" of the formal sector leads is twofold:
1. If predominantly older cohorts remain in the formal sector, there may
be abrupt declines in formal sector employment as these cohorts re-
tire. The tax rate that then would have to be imposed on remaining
formal sector workers would become sharply higher, which in turn
Selected Issues in Social Policy 131
would lead to a withdrawal of their labor supply, and then to an even
further increase in the tax rate. The vicious circle would be in full swing.
2. At some point in the future, a large percentage of the population would
be without any formal pension rights. However, governments may be
politically unable to ignore them, and may be obliged to provide for
their old-age support.
The solution
There are several possible solutions to this vicious circle. It is clear that if the
trend is to be reversed, the net present value of future payments must be
brought closer to the level of current contributions paid. This can be done in
two ways.
One possible solution is to introduce private, fully funded pension schemes
where current contributions and future payments are automatically linked.
In principle, a would increase, but in practice, however, a higher a cannot be
taken for granted. Workers must be sure that private pensions are not part of
a scam to despoil them of their contributions. Thus rigorous regulation of
private pension schemes would be needed to inspire confidence in their fu-
ture solvency.
If a private pension plan is voluntary, the equilibrium between contribu-
tions and future payments is established, by definition. No worker who be-
lieves that the net present value of future payments is less than his or her
contribution would join. It is doubtful, however, that entirely voluntary pri-
vate pension plans would be large enough to allow for risk-sharing. This is
why avoiding adverse selection requires that contributions be mandatory.
The second possible solution is a sharp reduction in current pensions. The
effect of such a reduction would be to reduce the current tax rates paid in the
formal sector, to reduce future expected pensions (by less than current taxes),
and thus to reduce the wedge between current contributions and expected
future returns (pensions). Workers could again begin to expect that the state
would honor its obligations because the current and future tax burden (needed
to finance pensions) would appear sustainable.
7
A Look Ahead
Under the twin impact of declining incomes and rising inequality, poverty in
transition economies has risen dramatically. The increase was driven first by
abrupt declines in income and then, as the income decline stabilized and, in
most countries, turned to positive growth, by widening income disparities.
Of the eighteen countries studied here, only one (the Slovak Republic) shows
unchanged inequality, as measured by the Gini coefficient. In terms of in-
equality, Central European countries are still slightly below average OECD
levels, while the Baltic republics, Central Asian republics, and Russia are over-
taking high-inequality OECD countries such as Switzerland and the United
States and are approaching levels of the more unequal developing countries.
These findings are not surprising. Based on a sample of approximately 100
countries and covering almost the entire post-World War II era, Deininger
and Squire (1995, p. 25) find that in 70 percent of cases, when the GDP de-
clines, the poor lose relatively more in terms of income-that is, inequality
increases.
What will the future bring? Will, for example, the Central Asian republics
and Russia exhibit the inequality and poverty characteristics of today's de-
veloping countries? Will the turn-around in growth that has already taken
place in Eastern Europe and is expected in 1998 in Central Asian countries,
Russia, and Ukraine eventually pull most of the current poor out of poverty?
Or will those who have become poor during the transition remain poor?
These are difficult questions-almost impossible to answer with any de-
gree of confidence. For the sake of simplicity, they are divided into two ques-
tions: (1) assuming that growth picks up, will today's poor be pulled quickly
out of poverty, and (2) will income inequality stabilize? With regard to the
first question, it is clear that the current poor, according to many household
indicators, such as education levels, housing conditions, and ownership of
essential consumer durables, are not much worse off than the rest of the popu-
lation. Furthermore, the average income or expenditure shortfall of the poor
(20-30 percent of the poverty line) is relatively small, which explains the high
flows in and out of poverty observed in transition economies (for Russia, see
Commander, Tolstopiatenko and Yemtsov, 1997). Both facts are grounds for
optimism. The economy could, after pushing some people below the pov-
erty line during its decline, bring them back up during a phase of growth. In
addition, given the existing poverty shortfall, several years of broadly shared
moderate growth (4 to 5 percent per year) should be sufficient to help the
poor cross the poverty threshold. The experience of Poland, however-the
132
A Look Ahead 133
Figure 7.1. The Year When the Countries are Expected to Reach Poverty
Headcount of 10 Percent
Poland
Bulgaria
Lithuania
Latvia
Belarus
Romania
Uzbekistan
Estonia
Russia
Kazakhstan
Turkmenistan
Moldova
Ukraine
Kyrgyz Republic I l l
1995 2000 2005 2010 2015 2020
Note: The Czech Republic, Hungary, Slovakia, and Slovenia are not included because their
headcounts are less than 10 percent. All other headcounts are calculated on the assumption of
unchanged Gini and a constant annual growth rate of 5 percent per capita.
only transition economy that has witnessed five years of consecutive growth-
or of Estonia-one of three countries of the former Soviet Union to have grown
for two years in a row-is not very encouraging. Economic growth in these
countries does not seem to have helped the poor much. The poverty headcount
in both countries has stabilized or slightly decreased since 1993-94, but aver-
age shortfalls of the poor appear to be increasing as inequality continues to
rise. In Latin America, similarly, resumption of growth in the 1990s after a
period of GDP decline in the 1980s reduced inequality in only one country-
Colombia (see Schwartz and Ter-Minassian 1995, p. 5). Predictions about the
ability of growth to pull people out of poverty must therefore be made cau-
tiously. Furthermore, the danger is that if economic growth fails to help the
poor quicldy, they can become an underclass whose characteristics may gradu-
ally diverge more and more from those of the non-poor.
Thus, if growth is to make a substantial dent in poverty relatively quickly,
it will be necessary to stabilize income inequality at current-often high-
levels. Assuming that inequality remains at current levels, and using the av-
erage growth rate of 5 percent per capita per year,' the time when current
1. Because "current" levels in this context refers to survey results for 1993-95, al-
lowance is made in the calculations presented here for actual growth rates between the
time of the survey and 1996. The calculations also assume that the average income short-
fall is as it was in 1993-95, and that the income shortfall of the poor is uniformly distrib-
uted (that is, the shortfall ranges from 0 percent to twice the average shortfall). For ex-
ample, if the average poverty shortfall is 25 percent and the poverty headcount is 20
percent, then each 5 percent of annual growth will "vault" 2 percent of the people over
the poverty line.
134 Income, Inequality, and Poverty during the Transition
headcounts should drop to 10 percent of the population-probably an ac-
ceptable share of the poor, both from political economy and fiscal aspects-
can be easily calculated. Leaving aside the countries where poverty rates are
already less than 10 percent, only Poland, among the other countries, can
expect to reduce poverty to 10 percent before the year 2000 (see figure 7.1).
Lithuania, Bulgaria, Latvia, Belarus, and Romania could reach this level be-
tween the years 2000 and 2005. All other countries will not reduce poverty
headcount to 10 percent of the population until well into the next century-
even under the very favorable circumstances assumed here. Poverty appears
to be here to stay in transition economies.
Appendix 1
Description of the Surveys Used
and Data Problems
The Surveys Used
Because there are considerable differences in the quality and type of data
used for East European countries and countries of the former Soviet Union,
the sources for these two groups will be discussed separately. Conclusions
regarding biases, however, will be made with respect to both groups, although
there too, as will become clear, certain differences exist.
Sources for the transition years: Eastern Europe
Type of surveys. For all countries in Table All, except the Czech Republic,
data come from official surveys conducted by the countries' statistical offices
(CSOs). The Czech source is the "socioeconomic" survey of Economic Expec-
tations and Attitudes (EEA) conducted twice per year on a representative (al-
though small) sample.' The EEA survey was selected in this instance because
official survey data, based on large, representative samples, were not avail-
able for the transition period studied here (1993-96). Such surveys (called
microcensuses) are conducted in the Czech and Slovak Republics every five
years; the most recent microcensus was conducted in Czechoslovakia in 1992,
and the next was planned for the two republics in 1997.
Representativeness. For all countries, except Slovakia, the surveys are na-
tionally representative. Family budget surveys (FBSs) for 1993 were used for
the Slovakia. FBSs include five social groups (blue-collar workers, white-col-
lar workers, the self-employed, farmers, and pensioners) but they do not in-
clude pensioner-headed households with economically active members and
households headed by the unemployed.2
1. For example, Hungary's CSO survey covers more than 8,000 households; the
EEA survey, for a country of about the same population size, includes fewer than 2,000
households.
2. The same FBS survey exists for the Czech Republic, but income-distribution sta-
tistics are published only for workers (blue-and white-collar), farmers, and pensioners,
and not for the self-employed who represent about 13 percent of the sample (see Czech
Statistical Office 1994). It is unclear why full data from this survey were not published.
135
Table Al.l. Characteristics of the Transition-year Surveys Used for East European Countries
Expenditures
Income exclude Other problems
Source of data; Repre- Access to includes personal with income or
survey Period sentative individual Income consumption- (directand expenditure
Country conducted by: Data reported in: covered survey data concept in-kind wage) taxes definitions
Poland CSO, 1993; Individual data First half Yes Yes Dispos- Yes Yes No
HBS. available; some of 1993 able
results in Polish
Central Statistical
Office (1994).
Hungary CSO, 1993; Individual data 1993 Yes Yes Dispos- No Yes No
HBS. available. able
Czech Czech Personal com- January Yes No Dispos- Yes n.a. No information
Republic Academy munication by 1993 able on expenditures.
of Sciences, Jifi Vecernik,
EEA survey. Academy of
Sciences; some
results in Institute
of Sociology, Eco-
nomic Expectations
and Attitudes, I-VII,
Prague.
Slovakia CSO, 1993; Individual data 1993 Noa Yes Dispos- Yes Yes No distribution
FBS. available. Grouped able of expenditures.c
data reported in
Slovak Statistics (1994).
Expenditures
Income exclude Other problems
Source of data; Repre- Access to includes personal with income or
survey Period sentative individual Income consumption- (direct and expenditure
Country conducted by: Data reported in: covered survey data concept in-kind wage) taxes definitions
Slovenia CSO, 1993; Personal com- 1993 Yes No Dispos- Yes Yes Income defini-
HBS. munication by able tion incorrect.-
Irena Krizman No distribution
of the Statistical of expenditures.c
office of Slovenia.
Some results
published in
Slovenian Statistics
(1994).
s Romania CSO, Individual data March Yes Yes Gross Yes n.a.
Integrated available. Survey 1994
household discussed in World
survey. Bank (1997), vol. 2.
Bulgaria CSO, 1993; Biudzeti na 1993 Yes No Gross Yes No (taxes Income defini-
HBS. donakinstvata v included in tion incorrect.b
Republika B'lgariya, other No distribution
National Statistical expenditures) of expenditures.c
Institute, Sofia 1994,
p. 68.
Note: CSO=country's statistical office. HBS=Household budget survey. FBS=Familybudget survey. EEA=Survey of economicexpectationsand attitudes. n.a.=not
available.
a. Excludes pensioner-headed households with economically active members and households headed by the unemployed.
b. Disposable income calculated by deducting some revenue items from the income concept used by the CSO (and according to which the individuals and
households are ranked).
c. There is only distribution of expenditures by income groups or deciles formed according to income.
138 Income, Inequality, and Poverty during the Transition
Time period. For four countries (Hungary, Slovakia, Slovenia, and Bulgaria),
surveys include income or expenditures for all of 1993. For Poland, the sur-
vey covers only the first six months of 1993, and for the Czech Republic and
Romania the survey covers only one month (January 1993, and March 1994
respectively). The Czech and Romanian data are the least satisfactory as there
is reason to believe that a shorter survey time period will lead to overestima-
tion of both inequality and poverty. This is because incomes (and, to a lesser
extent, expenditures) do not follow a uniform pattern. Individuals may be
sick or unemployed or out of the labor force for a month, and their family's
income may be temporarily low, so that the household is classified as poor.
In reality, over the period of a year, the household may be relatively well off.
The same is true for households with sudden income gains (for example,
payment of overdue wages) with the result that both ends of income distri-
bution and the overall inequality are overestimated.
Type of data. Individual income data were available for four countries (Po-
land, Hungary, Slovakia, and Romania). For the other three (Bulgaria, the
Czech Republic, and Slovenia), decile or group data formed according to
CSOs' definitions of per capita income were available. However, the defini-
tion of income is not satisfactory for Bulgaria or Slovenia, as it includes some
revenue items (for example, withdrawal from saving accounts, and money
received from sale of assets). These items had to be deducted from CSO-
defined income in order to obtain a correct disposable income. When this
operation is performed on grouped data (as opposed to individual data) in-
come inequality is underestimated because, strictly speaking, it is not the
Gini coefficient of disposable income that is being measured but rather the
concentration coefficient of disposable income. The problem is negligible in
the case of Bulgaria because the "wrong" items account for less than 1 per-
cent of CSO-defined income, and the rankings of individuals according to
one or another definition of income cannot change much. In Slovenia, how-
ever, they account for about 8 percent of CSO-defined income.
Income concept. For all countries except Bulgaria, the income concept used
here is disposable income. Disposable income equals gross income minus
payroll and direct personal income taxes (PIT). For Bulgaria, gross income is
used. The difference between gross and disposable income, however, is small
because in Bulgaria, gross income already excludes payroll taxes withdrawn
at the source, and payroll taxes represent by far the largest share of personal
taxes. In all cases except Hungary, consumption-in-kind is included in in-
come.
Data on expenditures. For the Czech Republic, no data on expenditures were
available. For Bulgaria, the Slovak Republic, and Slovenia, expenditure data
exist, but only for households ranked by their per capita income. Because
3. The concentration coefficient of expenditures can be calculated when recipients
are ranked by income. But this statistic is likely to be significantly lower than the Gini
coefficient of expenditures because of reranking of recipients.
Description of the Surveys Used and Data Problems 139
individual data were not available for these countries, individuals could not
be reranked by expenditure, so neither expenditure distribution nor Gini
coefficients of expenditures could be obtained.3 Both income and expendi-
ture individual data were available for Poland, Hungary, and Romania.
Definition of income and expenditures. The components of disposable income
and expenditures used here are standard. Disposable income is equal to all
wage earnings (from primary and secondary jobs, and so on) plus cash social
transfers plus income from property and entrepreneurship plus received gifts
plus the value of consumption-in-kind. It excludes payroll and PIT taxes. Ex-
penditures include expenditures on food, housing, education and culture,
health, transportation and communication, clothing, private transfers given,
and the value of consumption-in-kind. To make expenditures consistent with
disposable income, personal taxes (both those withdrawn at the source and
direct PIT) are excluded. The same definitions apply to all countries except
Bulgaria where, as mentioned above, PIT could not be separated from other
expenditures for either income and expenditure calculations.
Sourcesfor the transition years: countries of theformer Soviet Union
The sources for these countries are much more diverse (see table A1.2). Be-
cause the quality of pre-transition Soviet official Family Budget Surveys (FBS)
was not satisfactory, there was a pressing need to revise the surveys and,
often, to undertake entirely new ones.
Basically, countries adopted three approaches to the revision of Soviet sur-
veys. In the Baltic countries and Belarus, new, representative surveys were
introduced between 1994 and 1995 to replace the old Soviet-style surveys; it
is the results of these new surveys that are used here.4
In the Kyrgyz Republic, Russia, and Ukraine, new survey instruments were
introduced through the cooperation of international agencies (the World Bank,
in particular) and CSOs. Alongside these new and improved surveys, how-
ever, the old FBSs continued to exist. The design of the FBSs was somewhat
improved (as in Russia), but they were increasingly plagued by high refusal
rates and remained unrepresentative. Eventually, it is hoped, the current FBS
will be replaced by a better official survey-a process that has begun in Rus-
sia. This gives in total seven countries with new surveys.
For Moldova and the Central Asia countries (except Kyrgyz Republic), the
old FBSs are being used here, either because the countries had not yet intro-
duced new surveys (by the time of this writing) or because the existing ad hoc
surveys are not fully representative. Some of these countries, however, are in
4. For a detailed description of the new surveys for Belarus, see Martini, Ivanova,
and Novosyolova (1996); for Latvia, see Lapins and Vaskis (1996); for Estonia, see Statis-
tical Office of Estonia (1995), pp. 30-36; and for Lithuania, see Zaborskas (1996) and
Kazlauskas and Jensen (1993).
Table A1.2. Characteristics of the Transition-year Surveys Usedfor Countries of the Former Soviet Union
Expenditures
Income exclude Other problems
Source of data; Repre- Access to includes personal with income or
survey Period sentative individual Income consumption- (direct and expenditure
Country conducted by: Data reported in: covered survey data concept in-kind wage) taxes definitions
Estonia CSO, 1995; Individual data Third Yes Yes Dispos- Yes Yes No
HBS. available. quarter able
of 1995
Latvia CSO, 1995; Data provided by Fourth Yes No Dispos- Yes n.a. No information
HBS. the Latvian Com- quarter able on expenditures.
mittee on Statistics. of 1995
Lithuania CSO, 1994; Lithuanian HBS re- 1994 Yes No Gross No n.a. No information
HBS. ported in Statistical on expenditures.
Yearbook of Lithuania
1994-95, Vilnius:
Lithuanian Dept.
of Statistics, p. 188
and Cornelius
(1995, table 3).
Russia Russian Individual data Third Yes Yes Dispos- Yes Yes No
Longitudinal available. quarter able
Monitoring 1993
Survey Round 3.
Ukraine Ukraina Individual data June and Yes Yes Gross Yes Noa Large under-
1995 survey. available; some results first week estimation of
reported in World of July incomes.
Bank (1996b,table A9). 1995
Expenditures
Income exclude Othier problems
Source of data; Repre- Access to includes personal with income or
survey Period sentative individual Income consumption- (direct and expenditure
Country conducted by: Data reported in: covered survey data concept in-kind wage) taxes definitions
Belarus CSO, 1995; New Household budget First Yes No Dispos- Yes Yes No
HBS. survey (data provided quarter able
by Anna Ivanova). 1995
g Kyrgyz Kyrgyzstan Individual data October- Yes Yes Dispos- Yes Yes Large under-
Republic Multipurpose available. November able estimation of
Poverty 1993 incomes.
Survey.
Moldova CSO, 1993; Statistical Bulletin 1993 No No Gross Yes n.a. No information
Turkmen. FBS. CIS, August 1994. on expenditures.
Kazakh. Income definition
Uzbek. incorrect.b
Note: CSO=country's statistical office. HBS=Household budget survey. FBS=Family budget survey.
a. Personal income taxes minimal (less than 1 percent of expenditures).
b. Income indudes some revenue items (for example, insurance compensations; sale of assets) that could not be deducted.
142 Income, Inequality, and Poverty during the Transition
the process of either overhauling its old official survey, as is the case in
Moldova, or conducting a new parallel survey, as is the case in Kazakhstan.
The diversity of the surveys in the former republics of the Soviet Union,
mentioned above, is. illustrated in table A1.2. All but two of the new surveys
are quarterly surveys: the Lithuanian survey is an annual survey, and
Ukraine's is virtually a monthly survey. As was the case with the Czech sur-
vey, discussed above, the Ukrainian survey is likely to produce an overesti-
mation of inequality and poverty. The Ukrainian and Kyrgyz surveys also
yield a major difference between income and expenditure amounts. In other
surveys where both expenditure and income data are available, overall ex-
penditures range from being equal to overall income to being approximately
20 percent higher. In Ukraine and the Kyrgyz Republic, however, expendi-
tures are twice as great as income. Income underreporting is clearly a large
problem with both surveys.
All seven new surveys listed here are representative. For four (Estonia,
Russia, Ukraine, and Kyrgyz Republic) individual income and expenditure
data were available. For the other three (Latvia, Lithuania, and Belarus), decile
data were available.
The income concept used is either disposable or gross income. In Lithuania
and Ukraine, where gross income is used, PIT is minimal, so the difference
between the two income concepts is negligible.
All surveys except that of Lithuania include consumption-in-kind. Because
the importance of consumption-in-kind has increased in transition econo-
mies (see chapter 3), and particularly in the countries of the former Soviet
Union, Lithuanian survey is likely to underestimate disposable income, thus
yielding an overestirnation of poverty. In addition, as consumption-in-kind
plays an income-equalizing role, calculated inequality in Lithuania will be
higher than actual inequality.
Expenditure data were available for five countries: Russia, Ukraine, Belarus,
the Kyrgyz Republic, and Estonia.
Comparing pre-transition and transition years
Table A1.3 lists the characteristics of surveys used for the pre-transition pe-
riod.
The type of survey used for Bulgaria, Hungary, Poland, and Slovenia is the
same for pre-transition and transition years. With the exception of Poland's
survey, whose representativeness has improved,5 there was no substantive
change in these surveys. For the Central Asian countries and Moldova, pre-
transition and transition surveys are also the same: the Soviet-style FBS. Given
5. Households whose principal eamer was employed in the private sector outside
agriculture were not included in pre-transition Polish surveys; army and police person-
nel were also excluded.
Description of the Surveys Used and Data Problems 143
significant shortcomings with FBSs, however, this is more of a problem than
an advantage (see the following section). For all other countries, the survey
instruments before the transition and the transition differ.
The systemic change-transition from a planned to a market economy-
affected the survey's results in three different ways. First, refusal rates have
increased, particularly among the rich. Second, coverage of wage and social
transfer income has deteriorated from nearly 100 percent before the transi-
tion, as reported household earnings are no longer double-checked against
enterprise or pension authority records. Third, the omission or inadequate
coverage of informal (and illegal) sector income has become an even greater
problem as such incomes have increased in both absolute and relative terms.
Users of the surveys cannot correct these problems; they must be corrected
by the agencies that conduct the surveys. The bottom-line effect of these sys-
temic changes-assuming an unchanged survey design-is that incomes are
more underestimated than they were in the past (and that increases in pov-
erty will therefore be biased upwards); the effect of these changes on inequality
estimates is less clear. In the past, surveys underestimated inequality by not
taking into account the many fringe benefits received by the elite and, in the
Soviet case, by systematically excluding the poor.6 Today, they might under-
estimate inequality by not covering those with high incomes who refuse to
participate.
It is up to individual researchers to determine how strong an emphasis
they wish to place on such systemic changes-that is, to decide on whether
they believe that these changes vitiate comparisons between pre-transition
and transition surveys. Here it is assumed that systemic changes in Eastern
Europe were not great enough to render comparisons of poverty and inequal-
ity before and after the transition unreliable. An argument could be made,
however, that comparisons are much less reliable in some republics of the
former Soviet Union. Not only was the systemic change there much more
profound than it was in Eastem Europe (witness the explosion of the infor-
mal sector and rising refusal rates), but pre-transition surveys were funda-
mentally flawed in that they were basically surveys of employed families to
which a quota of pensioners was added.
Comparisons between 1987-89 and 1993 survey results for Bulgaria, Hun-
gary, Poland, and Slovenia are both straightforward and warranted: the sur-
vey instruments are the same, and the systemic changes that affected survey
results are minimal. The situation is somewhat different in the Czech and
Slovak Republics. For these countries, pre-transition data are derived from
the 1988 microcensus, whose quality is fairly high. The quality of data for
Czechoslovakia in 1988 is better than the quality of 1993 data for the Czech
and Slovak Republics for reasons of population coverage (in Slovakia) and
shortness of survey time period (in the Czech Republic).
6. Subsidies were also not included; yet the effect of subsidies (with the possible
exception of housing subsidies) was to reduce inequality.
Table A1.3. Characteristics of Pre-transition Surveys Usedfor East European Countries and the Soviet Union
Income
Source of data; Repre- Access to includes Other problems
survey Period sentative individual Income consumption- with income
Country conducted by: Data reported in: covered survey data concept in-kind definitions
Poland CSO, 1987; Budzetygospodarstw 1987 No, No Grossc Yes No
HBS. domowych w 1987. roku,
Central Statistical
Office, Warsaw, 1988.
Hungary CSO, 1987; Csalddi Kdlts6gvetgs 1987 Yes No Disposable Yes No
HBS. 1987, Central Statistical
Office,Budapest, 1989,
pp.78-9, 102-3, 126-27.
Czech CSO, 1988; Microcensus, Czechoslovak 1988 Yes No Disposable Yes Recipients
Republic, Microcensus. Statistical Office, Prague; ranked by
Slovakia based on the decile money income
calculations reported in (i.e. exclusive
Vecernik (1994). of consumption-
in-kind).
Slovenia Yugoslav SO; Anketa o potro�nji 1987 Yes No Disposable Yes Income
1987 HBS. domacinstava u 1987, definition
Federal Statistical Office, incorrect.b
Belgrade, 1988 (all data
presented by republics).
Bulgaria CSO, 1989; Biudzeti na domakinstvata 1989 Yes No Grossc Yes Income
HBS. v Republika B'lgariya, definition
National Statistical incorrect.b
Institute, Sofia.
Income
Source of data; Repre- Access to includes Other problems
survey Period sentative individual Income consumption- with income
Country conducted by: Data reported in: covered survey data concept in-kind definitions
Romania CSO, 1989; Provided by the 1989 Noe No Grossc Yes No
FBS. Statistical office.
Soviet CSO, 1988 Narodnoe khozyaystvo 1988 No' No GrossC Yes Income
c Uniond FBS. SSSR 1988, definition
Moscow:Goskornstat incorrect.b
SSSR, p. 94.
Note: CSO=country's statistical office. HBS=Household budget survey. FBS=Family budget survey.
a. Non-agricultural private sector, army and police not included.
b. Disposable or gross income calculated by deducting some revenue items from the CSO income concept and according to which the individuals and
households are ranked.
c. Taxes are less than 1 percent of gross income.
d. The only exception is Uzbekistan where the same (FBS) source is used for 1989.
e. Overrepresentation of wage-earners; underrepresentation of pensioners. Households headed by the unemployed are excluded (see Rashid, 1994, pp. 8-9).
f. Based on the "branch of production" sampling. Overrepresentation of workers, and "average" earners and "typical" families; underrepresentation of
pensioners. See the discussion in the text below.
146 Income, Inequality, and Poverty during the Transition
What Biases Are Inherent in the Data?
Having reviewed the data sources and their differences, data problems and
deficiencies, the next obvious question is: what can be said a priori about the
bias inherent in each of the surveys?
The fact that these biases are listed here is an indication that not much can
be done to remedy them. Yet they are worth mentioning here for two rea-
sons: to encourage caution in interpreting the results, and to delineate areas
that most clearly need to be improved in the future.
Survey design
First there is a problem with sampling. The household surveys used here
have been justly criticized for containing several biases. The East European
surveys were sample surveys. In several countries, however (for example,
Poland), they were not designed to be representative of the entire population
but rather to be representative of individual socioeconomic groups. This prob-
ably reflected a Marxist view of society as composed of social classes and an
overriding concern with intergroup equity. The data were thus representa-
tive of households where the primary earner worked in the state sector or of
pensioners' households, but they could not be combined easily to obtain an
accurate picture of the whole population. This was the case for two reasons.
First, the sample shares of the groups that were included were not always
proportional to their shares in the population (for example, there were too
many workers represented and not enough pensioners), and the results were
not corrected for systematic differences in refusal rates. Second, some groups
were left out of surveys altogether. These groups included both those with
high incomes (self-employed entrepreneurs, and army and police person-
nel) and those with low incomes (the institutionalized population and the
unemployed). Income distribution was thus truncated at both ends of the
income spectrum.
Soviet data were even more problematic. Surveys were based on quota
sampling rather than on random sampling. Households were selected through
the so-called branch (of production) approach. Workers and farmers were
chosen by their managers and asked to cooperate with statistical authorities.
The results, then, were biased: the employed were systematically overrepre-
sented in relation to the non-employed, though to correct some of the bias a
quota of pensioners and students was added;7workers in large enterprises
7. Pensioners' households were simply "added on"-that is, statistical offices were
asked to add a quota of pensioners which was often below their true share in the popula-
tion.
8. If results are to be unbiased, then the probability of selecting a larger household
should be proportionately greater than the probability of selecting a smaller household.
If the selection criterion is employment, however, and participation rates are high, then
households (e.g., one with two adults and three children, and another with two adults
only) will have approximately the same probability of being selected, regardless of their
size.
Description of the Surveys Used and Data Problems 147
and with longer work records were selected more often than those working
in small firms and with shorter work records. Because the selection criterion
was employment, larger households were undersampled.8 The survey es-
sentially functioned as a panel-the same households stayed in the sample
year after year-but to further complicate matters, the survey was not ex-
plicitly designed to be a panel, and household identification numbers were
not systematically maintained. The panel nature of the survey further biased
the results: because households remained in the sample indefinitely, the share
of older working households, which presumably had higher-than-average
earnings, was too high.
In conclusion, there were two kinds of biases in many pre-transition sur-
veys. First, there was a bias toward sampling representative of various socio-
economic groups but not of the population as a whole. People who did not
"fit" into one of these social groups were likely to be left out, and often these
people were at greater risk of poverty than the average citizen. Second, there
was a bias in Soviet and Romanian household surveys that followed the so-
called branch principle toward the "average" or "typical" household.9
Of concern here is the bias against including the poor in surveys (or, more
broadly, misrepresenting the distribution curve so that its left-end tail is shown
as being "thinner" or "shorter" than it is in reality). Some bias against the
coverage of the poor happens in virtually all household surveys. The very
poor are difficult to survey: institutionalized population, homeless people,
poor ethnic minorities, and illegal foreigners are almost always left out. For
example, it has been argued that Hungarian surveys leave out almost all of
the poor Gypsy population.'0 The same is the case with other East European
countries, such as the Czech Republic, Romania, and the Slovak Republic,
where Gypsies represent a sizable minority.
Table Al.4 assesses the reliability of HBS estimation of poverty, both be-
fore and during the transition. An ordinal value of -3 means that the survey,
in the judgment of the author, strongly underestimates poverty; a value of -0.5
indicates the "usual" bias caused by inadequate coverage of minorities or
institutionalized populations; a value of 0 indicates an unbiased HBS, at least
from the perspective of poverty estimation." A positive value indicates a
likely overestimation of poverty. In all countries of the former Soviet Union,
the bias against including the poor is estimated to have been strong before
the transition (a value of -3). This seems to have changed in about half of the
countries (Baltic and Slavic republics) where underestimation of poverty is
now smaller. East European data were already more reliable than the Soviet
data before the transition, and since the transition, data from some countries
(Poland and Romania) have improved.
9. In addition, Romanian results prior to 1989 were doctored to such an extent that
they are worthless. Bulgaria also followed "the branch principle" in the 1970s, but aban-
doned it later.
10. This point was made by Zsuzsa Ferge in a personal communication to the author.
11. The survey could still be biased toward, say, underreporting of top incomes.
148 Income, Inequality, and Poverty during the Transition
Table A1.4. Estimated Bias against Including Poor People in Household Budget
Surveys
Country 1987-88 1993-94
Eastern Europe -1 -1
Poland -1 -0.5
Bulgaria -2 -2
Romania -3 -2
Hungary -0.5 -0.5
Czech -0.5 -2
Slovak -0.5 -0.5
Slovenia -0.5 -0.5
Baltics -3 -1
Lithuania -3 -1
Latvia -3 -1
Estonia -3 -1
Slavic -3 -2
Russia -3 -1
Ukraine -3 -2
Belarus -3 -1
Moldova -3 -3
Central Asia -3 -3
Kazakhstan -3 -3
Kyrgyzstan -3 -3
Turkmenistan -3 -3
Uzbekistan -3 -3
Note: The surveys assessed here are the same ones listed in Tables Al.l, A1.2 and A1.3.
-3: very strong bias against including the poor. -0.5: bias similar to that in developed market
economies.
Source: Author's estimates.
One implication of table Al.4 is that the bias against including the poor
has decreased most significantly in Baltic countries, where surveys have all
improved significantly during the transition. Bias has also decreased in
Belarus, Russia, and Ukraine. This means that poverty increases between the
pre-transition period and 1993-94 are particularly likely to be overestima-
tions for these countries. Bias in Eastern Europe and Central Asia has not
changed much. In Easter Europe, surveys were already quite good before the
transition, and improvements in Romania and Poland are matched by an
apparent deterioration in the Czech Republic. In Central Asia, either new
surveys were not undertaken or, if they were (as in the Kyrgyz Republic),
their quality was not significantly better than that of the old surveys.
Underreporting of income
The second problem has to do with income. The use of income data rather
than expenditure data tends yield underestimations of "true" welfare be-
Description of the Surveys Used and Data Problems 149
cause people tend to underreport their sources of income."2 People are less
careful, however, when asked to remember their expenditures. An example
of this tendency is shown in figure A1.1, which gives income and expendi-
ture data by ventiles (5 percent of recipients) for Poland in 1993. Individuals
are ranked on the horizontal axis according to their household per capita
income. The lowest ventile reports expenditures equal to twice reported in-
come and equivalent to expenditures reported by the third ventile. This sug-
gests that those in the lowest income ventile may not in fact be very different
from those who report significantly higher incomes. Either they have severely
underreported their income or permanent income substantially diverges from
current income. Whatever the case, in the statistics used here these people
would be considered poor.
Poverty rates are thus biased upwards. In general, countries with larger
informal sectors ("gray economy") and small-scale private sectors will be
more affected. Data in such countries will systematically show lower incomes
and higher poverty rates than data from countries in which most income is
earned either in the state sector or in the wage-reporting (and thus tax-pay-
ing) private sector, or is received in the form of social transfers. Comparisons
over time will also be affected. As the share of the informal sector increases
during the transition, the problem of income underreporting becomes more
serious. On the other hand, surveys have been improved and greater effort is
being made to include "gray" sources of income. For example, all countries
except those that still use the Soviet-type surveys, now include the self-em-
ployed in their HBSs. Hungarian statistical authorities have been imputing
tips, "fees" (a polite term for bribes), and "gray" income. Finally, while "gray"
income often remains illegal (because people do not pay taxes on it), it is no
longer politically necessary to ignore it. In the past, both households and
enumerators were aware that such sources of income were not only illegal
but also "politically incorrect." Both preferred to look the other way and ig-
nore all non-official sources of income. This source of bias is now gone.
To account for the problem of income underreporting, poverty calcula-
tions here use HBS data for both income and expenditures. Moreover, to ad-
just for frequent underreporting in HBS data, as compared with macroeco-
nomic income data, HBS income data here are increased by the percentage
difference between the two measures. When HBS data show higher income
than macroeconomic data, no adjustment is done, however. These are re-
12. Underreporting is also a problem in market economies, particularly with regard
to self-employment income and capital income. Atkinson, Rainwater, and Smeeding (1995,
table A3) find, using Luxembourg Income Study data, that self-employment income is
underestimated in market economies (compared with national accounts data) by between
10 percent (Canada) and 60 percent (West Germany). They also estimate that property
income in almost all industrialized countries (United States, United Kingdom, Italy, Ger-
many, Finland, Canada, and Australia) is underestimated by one-half. According to Michel
(1991, p. 185), the U.S. Current Population Survey understates so-called non-earned in-
come by about 40 percent.
150 Income, Inequality, and Poverty during the Transition
Figure Al.l. Income and Expenditures per Capita, Poland, 1993
4000
I
3500
3000
o I"
, 2500
i> ~~~~~~~~~~Income ,
o 2000
N
X , I xpenditure
i 1500
1000-
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Ventile
Note: Ventile is 5 percent of the population.
Source: Polish 1993 Household budget survey.
ferred to as "adjusted" or "corrected" income data (INCOME2 data). The
adjustment, however, is a crude one, in the sense that it raises everyone's
income by the same percentage. In the absence of data on the pattern of
underreporting, however, this is the only available solution.
Per capita versus equivalent units
International comparisons of poverty are complicated, to say the least. The
choice of units of analysis (per capita or per equivalent adult) may also lead
to differences in results. First, the use of a per capita poverty line, rather than
an equivalent-scale derived poverty line, exaggerates poverty. This is because
with an equivalence scale the needs of additional family members (often chil-
dren) do not rise in proportion to number of people as they do when a per
capita measure is used. The use of a per capita poverty line exaggerates pov-
erty even more, then, in countries with a larger average family size.13 For
13. Coulter, Cowell, and Jenkins (1992) show that the poverty headcount charts a U-
shaped pattern, first decreasing and then rising, as the equivalence scale moves from 0
(full economies of scale, where only total household income matters) to 1 (per capita
calculations). The same results are obtained by Michael Forster (1993, p. 21) in an empiri-
cal study of thirteen OECD countries.
Description of the Surveys Used and Data Problems 151
example, Mamie and Micklewright (1993) compare Ukraine and Uzbekistan
using the same Soviet FBSs for 1989. They find that of the 38 percentage point
difference in the headcount index between the two republics obtained using
a per capita measure, the larger household size in Uzbekistan accounts for 14
percentage points. There are several reasons why the per capita line is used
here. First, data from all countries are published in that form. Second, econo-
mies of scale in consumption under socialism were typically less than in
market economies because the main source of such economies of scale (hous-
ing, utilities, and so on) were heavily subsidized; and this is still true, al-
though not to the same extent."4 Third, the use of per capita poverty compari-
son allows for an easy transition from per capita comparisons to GDP per
capita comparisons.
Quarterly versus yearly data
A final problem concerns the period over which data are collected. Normally,
surveys are designed so that households report (that is, keep track of or re-
call) income and expenditures for one quarter or one month."5 These data are
then "blown up" for the entire year. The same thing is done by another (quar-
terly or monthly) set of households, until four or twelve such sets are added
up to obtain the final annual survey results. Under conditions of high infla-
tion, however, data collected during different months, and even weeks and
days, represent wholly different real quantities of goods and services and
cannot be summed up without being adjusted for inflation. Statistical agen-
cies, however, sometimes do not make such adjustments, or if they do, they
sometimes make them inadequately. Under such conditions, quarterly data
on income or expenditure distribution-from which poverty figures are cal-
culated here for several countries of the former Soviet Union-are preferred
because they refer to a shorter time period and imply about the same real
command over goods and services. The usual drawback of the short-period
data-namely, that they overestimate income inequality and poverty (because
the shorter the time period, the more people who will report extraordinarily
low and high incomes)-is then more than offset by the advantage of using
data where the same reported money amounts represent approximately the
same real quantities.
14. If the price of all goods that provide economies of scale in consumption is zero,
then economies of scale no longer matter.
15. To increase the response rate (which was a main source of bias), Polish house-
hold surveys began requiring households to track their income and expenditures for one
month rather than three months. The response rate increased from 65 to 80 percent (see
Kordos 1994).
152 Income, Inequality, and Poverty during the Transition
How Biases Affect Comparability between Pre-transition and
Transition Years
Ideally, a household survey would: (a) be representative of the country as a
whole; (b) gather information regarding annual income and expenditures;
(c) use disposable income or net expenditures as welfare indicators; (d) in-
clude consumption-in-kind; and (e) define both income and expenditures
"correctly," that is, the way they are defined in economic theory. Note, how-
ever, that even the "best" achievable survey could still contain biases. The
"best" survey would still probably understate the two tail-ends of income
distribution, because the poorest and the richest segments of society are typi-
cally undersurveyed, and it would also underestimate some sources of in-
come, such as property (which is routinely underestimated by up to 50 per-
cent in industrial countries) and entrepreneurship.
No transition-year survey used here meets all five criteria (see table A1.5)
but several come close. Bulgarian and Slovenian surveys have only a slightly
incorrect definition of income; adjustments could not be fully made to ac-
count for this as individual data were not available. Hungary's 1993 survey
does not include consumption-in-kind in income,16 and semi-annual rather
than annual data were available for Poland.
Among the East European data, the Czech data are the least satisfactory.
Both the poverty headcount and inequality are overestimated, because the
time period of the survey is so short (see table A1.5).
In Hungary, the absence of consumption-in-kind leads to a slight increase
in inequality and poverty, because consumption-in-kind is generally greater
in poorer households.
Poland's poverty headcounts may also be slightly overestimated, because
the pre-harvest period only-that is, the first six months of the year-was
included-and incomes are generally higher among the agricultural popula-
tion during the second half of the year.
In Bulgaria, the use of gross income reduces the poverty headcount and
inequality to the extent that PITs are progressive. PITs are small, however, as
most taxes are deducted at the source, so the downward bias is negligible.
Finally, Slovak data exclude pensioner-headed households with economi-
cally active members. It is not clear how this exclusion might affect poverty
and inequality, because it is not clear if excluded households are richer or
poorer than the average, or how they are distributed along the income spec-
trum.
In conclusion, with the exception of Czech and Slovak data, the quality
problems and the conceptual differences between the various East European
data sets are not significant.
16. It includes consumption-in-kind in expenditures.
Description of the Suirveys Used and Data Problems 153
Table AI.5. Survey Defects: Pre-transition and Transition Years
Country Pre-transition Transition
Poland Incomplete coverage Semi-annual data
of recipients
Hungary No consumption-in-kind
Czech None Monthly data
Slovakia Not fully representative
Slovenia Income definition problem
Bulgaria Income definition problem;
gross income instead of disposable
Romania Not fully representative Monthly data
income definition problem
Estonia Quarterly data
Latvia Quarterly data
Lithuania Gross income;
no consumption-in-kind
Russia Quota sample, not fully Quarterly data
Belarus representative; Quarterly data
Ukraine income definition problem Monthly data;
large underestimate of
income
Kyrgyz Quarterly data;
large underestimate of
income
MoldovaF
Kazakhstan | Quota sample, not fully
Turkmenistan representative;
Uzbekistan income definition problem
Note: The surveys assessed here are the same ones listed in Tables AM.l, A1.2 and A1.3.
Source: Author's estimates.
For data from countries of the former Soviet Union, the problems are more
serious. At most, Soviet surveys satisfied only three of the five criteria listed
above: use of annual data, inclusion of consumption-in-kind and use of dis-
posable income.17 The new, improved surveys suffer from too short an ob-
servation period (between one month and one quarter), and in Kyrgyz Re-
public and Ukraine, there is large-scale income underreporting (see table A1.5).
What can be said, then, regarding non-systemic biases in estimating changes
in the poverty headcount and in inequality between 1987-89 and 1993-95
17. Even if, strictly speaking, income was defined as gross, the difference between
gross and net income was negligible.
154 Income, Inequality, and Poverty during the Transition
Table A1.6. Bias in Estimating Change in Poverty and Inequality
During the Transition
Country Poverty headcount bias Inequality bias
Poland Unclear Overestimates increase
Hungary Overestimates increase
Czech republic
Slovakia Unclear Unclear
Slovenia [ None
Bulgaria
Romania
Estonia
Latvia
Lithuania Overestimates increase
Russia
Belarus
Ukraine
Kyrgyz republic
Moldova
Kazakhstan
Turkmnenistan Unclear
Uzbekistan
Note: The surveys assessed here are the same ones listed in Tables Al.1, A1.2 and A1.3.
Source: Author's estimates.
(see table A1.6)? For Poland, the exclusion (in 1987) of the non-agricultural
private sector and of army and police personnel yielded too-low inequality
estimates; the measured increase in inequality during the transition will there-
fore be overestimated. For Hungary, absence of data on consumption-in-kind
in transition years will lead to an overestimation of both income-poverty
and income inequality. For the Czech Republic, increases in both poverty
and inequality will be overestimated because monthly rather than annual
data were used in 1993. For Bulgaria and Slovenia, no biases canbe discerned
a priori. In Romania and the former Soviet Union, pre-transition coverage
was biased toward "average" or "typical" households, thus underestimat-
ing both inequality and poverty. In the countries where the quality of sur-
veys has improved, then comparisons over time will show increases in pov-
erty and inequality that are greater than actual. Where the quality of surveys
has remained unchanged (Moldova and Central Asia, except the Kyrgyz Re-
public), it is unclear which way the bias will go.
What do these biases imply, in a nutshell, for the estimation of poverty
rates? In the past, income data were reliable for those covered in a survey,
but the surveys' coverage was biased. They excluded the poorest and over-
Description of the Surveys Used and Data Problems 155
represented average, "standard" households. As a result, poverty rates were
shown to be lower than they were in reality. Improvements in survey design
mean that current data are more representative of the population as a whole.
On the other hand, however, households now underreport income more than
they did in the past,18 so that while survey design is now adequate, income
reporting is now biased. Unfortunately, rather than offsetting each other, both
elements (more representative surveys and income underreporting) tend to
show the increase in poverty rates to be greater than actual. The gap between
current (high levels of) poverty and pre-transition (low levels of) poverty,
while certainly large, then appears even larger.
18. In Poland, in the past, wages and pensions reported by survey participants al-
most perfectly matched the macroeconomic values. Currently, they underestimate mac-
roeconomic values by 10 to 20 percent (see Kordos 1994).
Appendix 2
Decile Shares of Total Income
In tables A2.1-A2.18, decile shares are calculated from the original HBS data
given in appendix 4. All the detailed sources are given there.
Note: A = annual data; SA = semi-annual data; M = monthly data; Q =
quarterly data. For example, M:10/1993 means that the data refer to Octo-
ber 1993, Q:1/1995 that the data refer to the first quarter of 1995, and SA:I/
1993 that the data refer to the first half of 1993.
Table A2.1. Belarus
Decile A:1988 Q:1/1995
First 4.47 3.38
Second 6.01 5.32
Third 6.99 6.38
Fourth 7.89 7.31
Fifth 8.80 8.30
Sixth 9.76 9.34
Seventh 10.83 10.58
Eighth 12.12 12.03
Ninth 13.92 14.46
Tenth 19.20 22.88
Table A2.2. Bulgaria
Decile A:1989 A:1993
First 4.49 2.80
Second 6.14 4.31
Third 7.05 5.54
Fourth 7.86 6.65
Fifth 8.67 7.74
Sixth 9.53 8.90
Seventh 10.51 10.25
Eighth 11.75 12.03
Ninth 13.59 14.95
Tenth 20.42 26.84
156
Decile Shares of Total Income 157
Table 2.3. Czech Republic
Decile A:1988 M:1/1993
First 5.41 4.60
Second 6.51 5.90
Third 7.41 6.60
Fourth 8.11 7.30
Fifth 8.91 8.00
Sixth 9.91 8.90
Seventh 11.01 9.90
Eighth 11.11 11.40
Ninth 14.11 13.90
Tenth 17.52 23.50
Table 2.4. Estonia
Decile A:1988 Q:3/1995
First 4.20 2.06
Second 5.79 4.07
Third 6.88 5.41
Fourth 7.91 6.68
Fifth 8.93 7.83
Sixth 9.99 9.03
Seventh 11.15 10.48
Eighth 12.49 12.50
Ninth 14.24 16.03
Tenth 18.41 25.90
Table 2.5. Hungary
Decile A:1987 A:1993
First 4.96 4.40
Second 6.46 6.22
Third 7.31 7.22
Fourth 8.08 8.03
Fifth 8.85 8.79
Sixth 9.66 9.60
Seventh 10.58 10.51
Eighth 11.72 11.79
Ninth 13.37 13.65
Tenth 19.00 19.79
158 Income, Inequality, and Poverty during the Transition
Table 2.6. Kazakhstan
Decile A:1988 A:1993
First 4.05 3.07
Second 5.42 4.42
Third 6.46 5.60
Fourth 7.47 6.72
Fifth 8.51 7.84
Sixth 9.63 9.06
Seventh 10.90 10.50
Eighth 12.43 12.39
Ninth 14.56 15.45
Tenth 20.57 24.94
Table A2.7. Kyrgyz Republic
Decile A:1988 M:10-11/1993
First 5.14 0.80
Second 5.43 1.89
Third 6.14 3.20
Fourth 7.02 4.23
Fifth 8.05 5.58
Sixth 9.23 7.16
Seventh 10.62 9.17
Eighth 12.35 12.01
Ninth 14.76 16.96
Tenth 21.26 39.18
Table A2.8. Latvia
Decile A:1988 Q:4/1995
First 4.46 3.18
Second 5.98 4.86
Third 6.99 6.09
Fourth 7.92 7.14
Fifth 8.86 8.12
Sixth 9.85 9.14
Seventh 10.95 10.30
Eighth 12.24 11.81
Ninth 14.00 14.26
Tenth 18.73 25.09
Decile Shares of Total Income 159
Table A2.9. Lithuania
Decile A:1988 A:1994
First 4.59 2.38
Second 6.05 3.83
Third 7.01 5.08
Fourth 7.91 6.25
Fifth 8.81 7.43
Sixth 9.77 8.70
Seventh 10.85 10.21
Eighth 12.13 12.20
Ninth 13.91 15.51
Tenth 18.96 28.41
Table A2.10. Moldova
Decile A:1988 A:1993
First 4.27 2.58
Second 5.69 3.80
Third 6.80 4.97
Fourth 7.77 6.15
Fifth 8.70 7.40
Sixth 9.67 8.80
Seventh 10.77 10.49
Eighth 12.17 12.75
Ninth 14.32 16.42
Tenth 19.86 26.65
Table A2.11. Poland
Decile A:1987 SA.1/1993
First 4.08 3.20
Second 5.63 5.17
Third 6.63 6.33
Fourth 7.56 7.37
Fifth 8.50 8.40
Sixth 9.52 9.49
Seventh 10.69 10.78
Eighth 12.12 12.38
Ninth 14.21 14.87
Tenth 21.06 22.00
160 Income, Inequality, and Poverty during the Transition
Table A2.11. Romania
Decile A:1989 M:3/1994
First 4.08 3.52
Second 5.89 5.22
Third 6.96 6.25
Fourth 7.99 7.21
Fifth 8.91 8.17
Sixth 9.84 9.27
Seventh 10.82 10.59
Eighth 12.13 12.32
Ninth 14.22 14.95
Tenth 19.16 22.51
Table A2.13. Russia
Decile A:1988 Q:3/1993
First 4.14 1.64
Second 5.77 3.22
Third 6.82 4.19
Fourth 7.79 5.05
Fifth 8.77 5.99
Sixth 9.80 7.09
Seventh 10.95 8.49
Eighth 12.31 10.66
Ninth 14.19 14.14
Tenth 19.45 39.52
Table A2.14. Slovak Republic
Decile A:1988 A:1993
First 5.34 5.70
Second 6.55 6.85
Third 7.37 7.58
Fourth 8.14 8.27
Fifth 8.92 8.96
Sixth 9.76 9.70
Seventh 10.69 10.54
Eighth 11.82 11.57
Ninth 13.40 13.06
Tenth 17.99 17.78
Decile Shares of Total Income 161
Table A2.15. Slovenia
Decile A:1987 A:1993
First 4.55 4.34
Second 5.94 5.76
Third 6.87 6.69
Fourth 7.74 7.57
Fifth 8.63 8.47
Sixth 9.59 9.44
Seventh 10.68 10.57
Eighth 12.02 11.97
Ninth 13.94 14.04
Tenth 20.05 21.16
Table A2.16. Turkmenistan
Decile A:1988 A:1993
First 5.12 2.72
Second 5.38 3.98
Third 6.08 5.15
Fourth 6.96 6.29
Fifth 7.99 7.48
Sixth 9.19 8.79
Seventh 10.60 10.36
Eighth 12.36 12.47
Ninth 14.82 15.91
Tenth 21.51 26.85
Table A2.17. Ukraine
Decile A:1988 M:6-7/1995
First 4.38 1.51
Second 5.93 2.76
Third 6.92 3.92
Fourth 7.83 5.07
Fifth 8.75 6.27
Sixth 9.73 7.62
Seventh 10.82 9.24
Eighth 12.14 11.45
Ninth 13.99 15.20
Tenth 19.51 32.74
162 Income, Inequality, and Poverty during the Transition
Table A2.18. Uzbekis tan
Decile A:1989 A:1993
First 3.83 2.99
Second 5.14 4.29
Third 6.24 5.47
Fourth 7.26 6.61
Fifth 8.26 7.77
Sixth 9.33 9.04
Seventh 10.58 10.55
Eighth 12.21 12.54
Ninth 14.80 15.74
Tenth 22.35 25.00
Appendix 3
Change in the Poverty Deficit Due
to a Uniform Slide in Income
The poverty deficit is, by definition, equal to
PD =|z(z-y) f(y) dy
which is shown in Figure A3.1 as the sum of rectangles such as ABEz and
CDFz. Obviously, the poverty deficit will be equal to the summation of all
such rectangles for all values of y<z. Now, if the poverty line z increases in-
finitesimally, each rectangle expands slightly to the right by the amount f(y).
These "expansions" are shown by cross-hatched areas in Figure A3.1. For all
the existing rectangles, the increase will be equal to
dPD = fly) dy
which is exactly the same result as given by equation 5.3.
Figure A3.1. The Effect of a Higher Poverty Line on the Poverty Deficit
14 X
Income, y
163
Appendix 4
The Original Income
Distribution Statistics
Definitions: Gross income = earnings from labor + cash social transfers + self-
employment income + other income (from property, entrepreneurial income,
gifts) + consumption-in-kind.
Disposable income = gross income - direct personal taxes.
Money income does not include consumption-in-kind.
Note: All average income values refer to average income per capita.
Cost of living (COL) deflator is always between the two survey dates.
164
The Original Income Distribution Statistics 165
Table A4.1. Bulgaria
1989 1993
Average per capita Average per capita
gross income gross income
Number of people (leva per capita Population (leva per capita
(in 000) per year) percentages per year)
334 914 7.1 5,326
367 1,329 12.7 10,127
726 1,547 16.1 14,009
885 1,791 16.2 17,872
952 2,023 12.5 21,893
924 2,265 9.3 25,880
710 2,506 6.8 29,746
605 2,745 4.4 33,763
526 2,990 3.5 37,732
1,971 4,157 11.4 60,279
Gini 23.3 Gini 34.3
Mean income 2,591 Mean income 23,659
Real mean income 100 Real mean income 54.4
Mean income in $ 1,178 Mean income in $ 857
Source: 1989: Bulgarian HBS.
1993: Bulgarian HBS reported in Biudzeti na domakinstvata v Republika B'Igariya, National
Statistical Institute, Sofia, 1994, page 68. [Non-income items deducted in both surveys.]
Exchange rate: 1989: $1 = 2.2 leva.
1993: $1 = 27.6 leva.
COL deflator: 16.8
166 Income, Inequality, and Poverty during the Transition
Table A4.2. Czech Republic
1988 January 1993
Average per capita Average per capita
disposable income disposable income
Population (crowns per capita Population (crowns per capita
deciles per month) deciles per month)
10 1,004 10 1,611
10 1,208 10 2,066
10 1,376 10 2,311
10 1,506 10 2,556
10 1,655 10 2,802
10 1,840 10 3,117
10 2,045 10 3,467
10 2,063 10 3,992
10 2,621 10 4,868
10 3,253 10 8,230
Girii 19.4 Gini 26.6
Mean income 1,857 Mean income 3,502
Real mean income 100 Real mean income 87.7
Mean income in $ 130 Mean income in $ 122
Source: 1988: Microcensus.
January 1993: Economic Expectations and Attitudes.
Both reported in Vedernik et al., (1994).
Exchange rate: 1988: $1 = 14.26 crowns.
January 1993: $1 = 28.8 crowns.
COL deflator = 2.15.
The Original Income Distribution Statistics 167
Table A4.3. Hungary
1987 1993
Average per capita Average per capita
disposable income disposable income
Number of (forints per capita Percentage of (forints per capita
people per year) population per year)
2,685 25,556 5 50,871
6,487 36,517 5 72,592
7,863 45,583 5 83,253
5,647 54,904 5 91,253
3,717 64,444 5 98,185
2,217 74,117 5 104,321
3,247 100,885 5 109,882
5 115,485
5 120,649
5 126,021
5 131,499
5 137,660
5 143,554
5 151,284
5 160,493
5 170,285
5 182,769
5 199,991
5 231,400
5 323,619
Gini 21.0 Gini 22.6
Mean income 53,523 Mean income 140,253
Real mean income 100 Real mean income 74.1
Mean income in $ 1,139 Mean income in $ 1,524
Source: 1987: Household Budget Survey reported in Csalddi K6lts6gvet6s 1987, Budapest: Central
Statistical Office, 1989, pp. 126-7
1993: Household Budget Survey (individual data)
Exchange rate: 1987: $1 = 47 forints.
1993: $1 = 92 forints.
COL deflator: 3.53
168 Income, Inequality, and Poverty during the Transition
Table A4.4. Slovakia
1988 1993
Average per capita Average per capita
disposable income disposable income
Number of (crowns per capita Number of (crowns per capita
people per year) people per year)
176,693 8,776 539 21,472
237,593 10,854 900 26,594
472,822 11,985 836 31,398
640,711 13,080 831 36,429
800,156 14,290 640 40,671
1,003,174 15,508 401 45,126
1,161,046 16,730 263 49,714
1,257,160 17,971 501 65,838
1,277,633 19,178
1,104,486 20,397
974,158 21,617
871,625 22,848
738,219 24,069
665,506 25,274
579,495 26,553
490,502 27,720
434,651 28,968
367,593 30,226
15,519 31,401
280,371 32,625
45,630 33,811
206,727 34,954
163,851 36,348
257,475 38,024
605,074 47,184
Gini 19.5 Gini 18.3
Mean income 22,269 Mean income 37,103
Real mean income 100 Real mean income 71.2
Mean income in $ 1,562 Mean income in $ 1,201
Source: 1988: Microcensus. Data for Czechoslovakia used as approximation for Slovakia.
1993: Family Budget Surveys reported in Prfjmy, vydavky a spotreba domacnostiza rok 1993, vol.1-3,
Bratislava: Slovak Statistics, 1994.
Exchange rate: 1988: $1 = 14.26 crowns.
1993: $1 = 30.9 crowns.
COL deflator: 2.34.
The Original Income Distribution Statistics 169
Table A4.5. Slovenia
1987 1993
Average per capita Average per capita
gross income (000 disposable income
Number of dinars per capita Number of (tolars per capita
people per year) people per year)
6,329 427.9 15 52,421
51,084 704.4 141 115,138
132,829 926.7 664 176,929
269,561 1,140.3 1,566 238,303
316,854 1,394.5 1,896 296,622
260,982 1,627.4 1,531 358,115
283,689 1,887.7 1,137 414,344
160,472 2,119.7 786 472,635
439,256 2,808.7 526 521,310
1,603 740,262
Gini 21.5 Gini 25.1
Mean income 1,793.4 Mean income 397,547
Real mean income 100 Real mean income 108.4
Mean income in $ 4,808 Mean income in $ 3,512
Source: 1987 Yugoslav HBS, published in Ankete o potrosnji dona6instava u 1987, SZS, Belgrade
1988.
1993 Slovenian HBS. Computer spreadsheets supplied by Irena Krizman (Slovenian Office of
Statistics). [Non-income items deducted in both surveys.]
Exchange rate: 1987: $1 = 373 YUD.
1993: $1 = 113.2 tolars.
COL deflator = 0.2045 (excluding a factor of 1000 due to the 1990 Dinar denomination).
170 Income, Inequality, and Poverty during the Transition
Table A4.6. Ronania
1989 March 1994
Average per capita Average per capita
gross income income
Population (lei per capita Population (lei per capita
deciles per month) deciles per month)
10 737 10 16,676
10 1,065 10 24,683
10 1,259 10 29,750
10 1,444 10 34,069
10 1,611 10 38,626
10 1,780 10 43,335
10 1,957 10 49,122
10 2,194 10 56,797
10 2,571 10 68,679
10 3,464 10 105,505
Gini 23.3 Gini 28.6
Mean income 1,808 Mean income 46,563
Real mean income 100 Real mean income 56.4
Mean income in $ 25 Mean income in $ 29
Source: 1989: Romanian HBS. Computer spreadsheets supplied by Mansoora Rashid (World
Bank).
March 1994: Integrated household survey (individual data available).
Exchange rate: 1989: $1 = 73 lei (blend between the official and parallel rate).
March 1994: $1 = 1601 lei.
COL deflator = 45.7.
The Original Income Distribution Statistics 171
Table A4.7. Poland
1987 1st half of 1993 (June prices)
Average per capita Average per capita
gross income disposable (000
Number of (zloty per capita Percentage of zloty per capita
people per month) population per month)
8,074 6,462 5 367.32
9,785 9,038 5 622.84
24,235 12,056 5 748.86
21,419 15,880 5 850.51
12,798 19,792 5 937.38
7,499 23,841 5 1,018.40
4,133 27,815 5 1,099.93
5,305 39,768 5 1,178.03
5 1,255.24
5 1,340.70
5 1,423.07
5 1,511.75
5 1,611.45
5 1,720.73
5 1,841.38
5 1,986.75
5 2,170.09
5 2,425.09
5 2,823.10
5 3,978.22
Gini 25.6 Gini 28.4
Mean income 16,418 Mean income 1,545.54
Real mean income 100 Real mean income 73.5
Mean income in $ 62 Mean income in $ 89
Source: 1987: HBS published in Bud2eti Gospodarstw Domowych 1987, GUS, Warsaw, 1988.
I half of 1993: HBS (individual data available). [Non-income items deducted in both surveys.]
Exchange rate: 1987: $1 = 265 zloty.
June 1993: $1 = 17,300 zloty.
COL deflator = 128.
172 Income, Inequality, and Poverty during the Transition
Table A4.8. Estonia
1988 3rd quarter of 1995 (July prices)
Upper bound of Average per capita
gross income disposable income
(Rs. per capita Percentage of Population (EEK per capita
per month) population deciles per month)
75 3.9 10 196.39
100 9.0 10 387.37
150 28.0 10 515.64
200 25.5 10 636.65
Open 33.6 10 746.32
10 860.79
10 998.96
10 1,191.17
10 1,527.56
10 2,468.20
Gini 23.0 Gini 35.4
Mean income 177.2 Mean income 949.84
Real mean income 100 Real mean income 63.1
Mean income in $ 81 Mean income in $ 86
Source: 1988: Soviet HBS published in Narodnoe khozyaystvo SSSR 1988, p. 94.
July-September 1993: HBS (individual data available).
Exchange rate: 1988: $1 = 2.2 roubles (blend between the official and parallel rate).
July 1995: $1 = 11.1 Estonian kroons.
COL deflator = 8.495 (excluding a factor of 10 due to the conversion from roubles to kroons).
The Original Income Distribution Statistics 173
Table A4.9. Lithuania
1988 1994
Upper bound of Average per capita
gross income money incone
(Rs. per capita Percentage of (in litai per capita
per month) population Sharesa per year)
75 3.6 36 561
100 10.7 36 1,012
150 34.6 24 1,292
200 27.1 24 1,494
Open 24.0 27 1,741
28 2,043
27 2,419
25 2,920
26 3,715
22 6,847
Gini 22.5 Gini 37.3
Mean income 164.6 Mean income 2,230
Real mean income 100 Real mean income 58.2
Mean income in $ 75 Mean income in $ 558
Source: 1988: Soviet HBS published in Narodnoe khozyaystvo SSSR 1988, p. 94.
1994: Lithuanian HBS reported in Statistical Yearbook of Lithuania 1994-95, Vilnius: Lithuanian
Department of Statistics, p. 188, and in Comelius (1995, Table 3).
Exchange rate: 1988: $1 = 2.2 roubles (blend between the official and parallel rate).
1994: $1 = 4 litai.
COL deflator = 1.94 (excluding a factor of 100 due to the conversion from roubles to lita).
a. The data were given in terms of household deciles and average number of individuals per
household (per decile). They are converted here in terms of individuals.
174 Income, Inequality, and Poverty during the Transition
Table A4.10. Latvia
1988 4th quarter of 1995
Upper bound of Average per capita
gross income disposable income
(Rs. per capita Percentage of Percentage of (lats per capita
per month) population population per month)
75 3.2 10 15.634
100 9.5 10 27.620
150 31.8 10 34.254
200 27.2 10 38.564
Open 28.3 10 43.436
10 48.705
10 54.776
10 65.068
10 80.033
10 113.420
Gini 22.5 Gini 31.0
Mean income 171.16 Mean income 54.15
Real mean income 100 Real mean income 55.3
Mean income in $ 78 Mean income in $ 101
Source: 1988: Soviet HBS published in Narodnoe khozyaystvo SSSR 1988, p. 94.
4th quarter of 1995: New Latvian,HBS. Data provided by the Latvian Committee on Statistics.
Exchange rate: 1988: $1 = 2.2 roubles (blend between the official and parallel rate).
4th quarter of 1995: $1 = 0.536 lats.
COL deflator = 0.572 (exduding a factor of 200 due to the conversion from roubles to lats).
The Original Income Distribution Statistics 175
Table A4.11. Russia
1988 June-September 1993 (July prices)
Upper bound of Average per capita
gross income gross income (000
(Rs. per capita Percentage of Rs. per capita Percentage of
per month) population per month) population
75 6.3 5.272 10.0
100 13.1 10.441 10.0
150 34.0 13.654 10.0
200 24.6 16.503 10.0
Open 22.0 19.523 10.0
23.042 10.0
27.689 10.0
34.795 10.0
46.125 10.0
126.323 10.0
Giri 23.8 Gini 48.0
Mean income 158.0 Mean income 32.337
Real mean income 100 Real mean income 58.0
Mean income in $ 72 Mean income in $ 32
Source: 1988: Soviet HBS published in Narodnoe khozyaystvo SSSR 1988, p. 94.
1993: Russian Longitudinal Monitoring Survey Round 3 (individual data available).
Exchange rate: 1988: $1 = 2.2 roubles (blend between the official and parallel rate).
July 1993: $1 = 1025 roubles.
COL deflator = 353.5.
176 Income, Inequality, and Poverty during the Transition
Table A4.12. Belarus
1988 1st quarter 1995 (March prices)
Upper bound of Average per capita
gross income net income (Bel.
(Rs. per capita Percentage of rubles per capita Percentage of
per month) population per month) population
75 5.0 122,752 10
100 12.9 193,148 10
150 36.8 231,371 10
200 25.8 265,346 10
Open 19.5 301,228 10
339,073 10
384,033 10
486,706 10
524,828 10
830,398 10
Gini 22.8 Gini 28.4
Mean income 156.1 Mean income 362,888
Real mean income 100 Real mean income 56.2
Mean income in $ 71 Mean income in $ 32
Source: 1988: Soviet HBS published in Narodnoe khozyaystvo SSSR 1988, p. 94.
1 quarter 1995: New Household Budget Survey (data provided by Anna Ivanova).
Exchange rate: 1988: $1 = 2.2 roubles (blend between the official and parallel rate).
March 1995: $1 = 11,525 Belarussian roubles.
GDP deflator = 4131 (exduding a factor of 10 due to denomination change 10 Russian roubles =
1 Belorussian rouble). GDP deflator used because more reliable than COL.
The Original Income Distribution Statistics 177
Table A4.13. Ukraine
1988 June and July 1995 (June prices)
Upper bound of Average per capita
gross income gross income (000
(Rs. per capita Percentage of karbovanets per capita Percentage of
per month) population per month) population
75 8.1 458.8 10.0
100 16.8 979.7 10.0
150 38.5 1,338.5 10.0
200 22.4 1,713.4 10.0
Open 14.2 2,143.2 10.0
2,555.9 10.0
3,066.2 10.0
3,855.8 10.0
5,291.6 10.0
12,404.1 10.0
Gini 23.3 Gini 47.4
Mean income 143.8 Mean income 3,380.7
Real mean income 100 Real mean income 37.6
Mean income in $ 65 Mean income in $ 24
Source: 1988: Soviet HBS published in Narodnoe khozyaystvo SSSR 1988, p. 94.
June-July 1995: Ukraina 1995 survey.
Exchange rate: 1988: $1 = 2.2 roubles (blend between the official and parallel rate).
June 1995: $1 = 142,000 karbovanets.
COL deflator = 62,464.
178 Income, Inequality, and Poverty during the Transition
Table A4.14. Moldova
1988 1993
Upper bound of Upper bound of
gross income gross income
(Rs. per capita Percentage of (lei per capita Estimated 000
per month) population per month) of people
75 13.0 3 28.4
100 19.8 4 65.5
150 37.3 6 247.0
200 18.9 8 355.5
Open 11.0 10 399.8
12 398.8
14 372.7
16 335.6
18 295.4
20 256.4
22 220.8
24 189.2
26 161.7
28 138.1
30 117.9
32 100.8
34 86.2
36 73.9
38 63.4
40 54.6
42 47.1
44 40.7
46 35.2
Open 260.9
Gini 24.1 Gini 36.5
Mean income 133.4 Mean income 19.76
Real mean income 100 Real mean income 32.7
Mean income in $ 61 Mean income in $ 12
Source: 1988: Soviet HBS published in Narodnoe khozyaystvo SSSR 1988, p. 94.
1993: Annuarul Statistic al Republica Moldova 1993, Chisineu: Moldovan Department of Statistics,
p.139.
Exchange rate: 1988: $1 = 2.2 roubles (blend between the official and parallel rate).
1993: $1 = 1.6 lei.
COL deflator = 0.453 (excluding a factor of 1000 due to conversion).
The Original Income Distribution Statistics 179
Table A4.15. Turkmenistan
1988 1993
Upper bound of Upper bound of
gross income gross income (000
(Rs. per capita Percentage of Rs. per capita Percentage of
per month) population per month) population
75 36.6 7.5 3.0
100 23.0 10.0 5.3
150 25.8 15.0 14.5
200 9.4 20.0 15.4
Open 5.2 25.0 13.5
30.0 10.9
35.0 8.5
40.0 6.5
45.0 5.0
50.0 3.8
60.0 5.1
70.0 3.1
80.0 1.9
90.0 1.2
100.0 0.7
110.0 0.5
Open 1.1
Gini 26.4 Gini 35.8
Mean income 104.3 Mean income 30.34
Real mean income 100 Real mean income 53.6
Mean income in $ 47 Mean income in $ 34
Source: 1988: Soviet HBS published in Narodnoe khozyaystvo SSSR 1988, p. 94.
1993: Statistical Bulletin CIS, August 1994, pp. 73ff.
Exchange rate: 1988: $1 = 2.2 roubles (blend between the official and parallel rate).
1993: $1 = 892 roubles.
COL deflator = 543.
180 Income, Inequality, and Poverty during the Transition
Table A4.16. Kyrgyz Republic
1988 October-November 1993 (October prices)
Upper bound of Average disposable
gross income income (som
(Rs. per capita Percentage of per capita Decile of
per month) population per month) population
75 37.1 3.2793 10
100 23.1 10.1726 10
150 26.0 15.8309 10
200 9.2 21.9331 10
Open 4.6 29.2737 10
38.5805 10
50.6507 10
68.6812 10
97.3894 10
239.2659 10
Gini 26.0 Gini 55.3
Mean income 103 Mean income 69.3
Real mean income 100 Real mean income 33.8
Mean income in $ 47 Mean income in $ 9
Source: 1988: Soviet HBS published in Narodnoe khozyaystvo SSSR 1988, p. 94.
October-November 1993: Kyrgyz Multipurpose Poverty Survey (individual data available).
Exchange rate: 1988: $1 = 2.2 roubles (blend between the official and parallel rate).
October 1993: $1 = 7 soms.
COL deflator = 2.05 (excluding a factor of 230 due to conversion).
The Original Income Distribution Statistics 181
Table A4.17. Kazakhstan
1988 1993
Upper bound of Upper bound of
gross income gross income (000
(Rs. per capita Percentage of Rs. per capita Percentage of
per month) population per month) population
75 15.9 7.5 3.4
100 19.3 10.0 6.5
150 33.7 15.0 18.2
200 18.1 20.0 18.3
Open 13.0 25.0 14.9
30.0 11.1
35.0 8.0
40.0 5.6
45.0 3.9
50.0 2.8
60.0 3.4
70.0 1.7
80.0 0.9
90.0 0.5
100.0 0.3
110.0 0.2
Open 0.3
Gini 25.7 Gini 32.7
Mean income 134 Mean income 25.34
Real mean income 100 Real mean income 38.5
Mean income in $ 61 Mean income in $ 22
Source: 1988: Soviet HBS published in Narodnoe khozyaystvo SSSR 1988, p. 94.
1993: Statistical Bulletin CIS, August 1994, pp. 73 ff.
Exchange rate: 1988: $1 = 2.2 roubles (blend between the official and parallel rate).
1993: $1 = 1168 roubles.
COL deflator = 491.
182 Income, Inequality, and Poverty during the Transition
Table A4.18. Uzbekistan
1989 1993
Upper bound of Upper bound of
gross income gross income (000
(Rs. per capita Percentage of Rs. per capita Percentage of
per month) population per month) population
50 16.0 1.5 0.02
75 27.7 2.0 Ox.07
100 22.7 3.0 3.7
125 14.1 4.0 6.8
150 8.3 5.0 8.7
175 4.7 6.0 9.4
200 2.7 7.0 9.3
225 1.6 8.0 8.6
250 0.9 9.0 7.7
Open 1.4 10.0 6.7
11.0 5.8
12.0 5.0
13.0 4.2
14.0 3.5
15.0 3.0
16.0 2.5
17.0 2.1
18.0 1.8
19.0 1.5
20.0 1.3
22.0 2.0
24.0 1.4
Open 4.1
Gini 28.2 Gini 33.3
Mean income 93.5 Mean income 10.0
Real mean income 100 Real mean income 56.9
Mean income in $ 28 Mean income in $ 11
Source: 1989: Soviet HBS reported in Marnie and Micklewright (1993, Table 1).
1993: Statistical Bulletin CIS, August 1994, pp. 73 ff.
Exchange rate: 1989: $1 = 3.4 roubles (blend between the official and parallel rate).
1993: $1 = 951 roubles.
COL deflator = 188.
Appendix 5
Poverty Headcount Calculations Based
on the Original Income Distribution
Statistics Given in Appendix 4
Note: Poverty line is shown in square brackets next to the country name, in
domestic currency, in nominal amounts at the time of survey (e.g., Poland
[72651). It is always equal to $PPP120 (at 1990 international prices). A indi-
cates annual survey; Q, quarterly; M, monthly. Average shortfall shows by
how much (in percent), the average income of the poor is less than the pov-
erty line. Elasticity shows by how many percentage points the poverty
headcount will increase (fall) if real income declines (rises) by 1 percent. Ex-
change rate is the average dollar exchange rate at the time of survey.
183
>S C l > o m N 0 C
00 (ON ~~Cl en ~ CA
. N -4 CO N LIj C
tt 1 CD 0
<~~~~~
R^ ~ ~~~~ L^' c oe
01 t CA41 Cl4
Cl~~~~~~~~~~~~~~~L
*D q 2 ~ ~~ ~~ ~~ ~~ ~~~~~N It 1- a? C
V)~~~~~I .O 4 Cl O O
S Cl Cl Cl Cl C4 Cl
;0
It~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I
a~~~~~~~~~~~~~~~~~O .o L N� )
LI) LI) CO 0 0
4.- ~ ~ ~~~~.
z L C
b ~ ~~~ C> Cl o4 ClNo
tCl
LI) CO0 0 to
o -
U j!.,>_ N o 00 o 0o (
R: ;tE t m O O
0 t]t � e | �.......
Uo Q o o � o zy o .....
- =aa - P .. ..... BB. ............B
0 8 : :g :g, EE E ! g g gg ., .! g ,g, . ,,,! ,,| , lE,! . , E ,, . g,B ,' - , , . .BB......................B
O z J _ Bai. ili i- 8 ig. ~~~~~~~~~~~~~~~~................... ........... . E i Eg-ggg...........EErgB 'i B
X ; ,::! tt -S,Z,~~~~~~~~~~~~~~~~~~~. .................................................. .- ..",....'-.."_. .""" '.... 9 'BEiEE Ei
# # ffi $ Cl _ gga B ~~~~~~~~~~~~~~~~~~.>.g....<............................ ........... ........ - .......BBB..
> gL, k E ....................................... s = = ; B B B '' < 8 a g f = ' M ' B ; f 8 ' B B a0................ ............
,O !i O p m ~~~~~~~~~~~................................ . ...........=lmBaB'"BB.-'g
184
Average per
Poverty capita
Country Survey headcount Average Exchange Poverty line income
[Poverty line] period (in %) shortfall Elasticity Gini rate ($ pm) ($ pm)
CSSR 15011 1988 A 0.05 26.3 0.0 19.5 14.3 35 129
Slovakia 13,428J 1993 A 0.12 19.6 0.01 18.3 30.9 36 100
c I~u1~Bulgar 191 1989 A 1.4 31.7 0.05 23.3 2.2 34 98
Ul
BU 1993 A 14.6 25.8 0.32 34.3 27.6 30 71
1989A 5.8 15.0 0.22 23.3 73 11 25
March 1994 59 32 0.7 28.6 1601 28 29
(Continued on thefollowing page)
Table A5. (Continued)
Average per
Poverty capita
Country Survey headcount Average Exchange Poverty line income
[Poverty line] period (in %) shortfall Elasticity Gini rate ($ pm) ($ pm)
1987A 0.07 46.3 0.00 21.5 373 78 401
1993 A 0.3 30.8 0.01 25.1 113.2 73 293
< :lRus00gilX4::: -:gg:::l00 1988 A 1.5 22.3 0.07 23.8 2.2 25 72
Rus 3Q/1993 49.7 39.6 0.57 48.0 1,025 21 32
(july prices)
*Ukgltkrfi&|ggXt ilBBllllllll 1988 A 1.9 21.7 0.09 23.3 2.2 25 65
June and 1 62.9 46.7 0.51 47.4 142,000 21 24
mi11iox~1 week of
July 1995;
(June
prices)
Average per
Poverty capita
Country Survey headcount Average Exchange Poverty line income
[Poverty line] period (in %) shortfall Elasticity Gini rate ($ pm) ($ pm)
Bel.arus 1541 1988 A 1.1 23.7 0.05 22.8 2.2 25 71
Belarus 1l9g $$ ---l1Q/1995 22.3 25.6 0.53 28.4 11,525 19 32
(March
prices)
N Mo 1988 A 3.5 8.2 0.22 24.1 2.2 25 61
;o - N1993 A 65.9 43.4 0.55 36.5 1.6 13 12
Xs ( 1988 A 0.9 23.5 0.04 23.0 2.2 25 81
.st ( . .3Q/1995 37.2 37.1 0.7 35.4 11.1 60 86
(July prices)
(Continued on the following page)
Table A5. (Continued)
Average per
Poverty capita
Country Survey headcount Average Exchange Poverty line income
[Poverty line] period (in %) shortfall Elasticity Gini rate ($ pm) ($ pm)
1988 A 0.7 22.5 0.03 22.5 2.2 25 78
4Q/1995 21.7 27.6 0.49 31.0 0.536 58 101
1988 A 0.8 24.0 0.04 22.5 2.2 25 75
1994 A 29.8 33.6 0.48 37.3 4 26 46
1988 A 4.6 8.9 0.27 25.7 2.2 25 61
1993 A 64.9 38.7 0.66 32.7 1,168 23 22
Average per
Poverty capita
Country Survey headcount Average Exchange Poverty line income
[Poverty line] period (in %) shortfall Elasticity Gini rate ($ pm) ($ pm)
Uzbeldslan [5& 1989 A 23.8 19.8 0.60 28.2 3.4 16 28
1993 A 62.6 38.9 0.64 33.3 951 11 11
1988 A 12.0 10.4 0.59 26.0 2.2 25 47
lz [11111 Oct-Nov. 88.0 68.0 0.20 55.3 7 16 9
1993
(October
prices)
lenistan t541 1988 A 11.8 10.1 0.60 26.4 2.2 25 47
Turktnenistin 1993 A 60.9 40.0 0.61 35.8 892 33 34
(Continued on thefollowing page)
Table A5. (Continued)
Average per
Poverty capita
Country Survey headcount Average Exchange Poverty line income
[Poverty line] period (in %) shortfall Elasticity Gini rate ($ pm) ($ pm)
Expenditure-based Measures
1993 9.8 19.9 0.36 30.6 17,300 51 116
6 months
March 1994 47.6 33.8 0.72 32.8 1601 28 35
M
Jul-Sept. 95 33.7 28.4 30.7 11.1 60 87
(July prices)
3Q/1993 39.4 43.8 0.44 49.6 1025 21 39
(July prices)
UkrE1 g;llilll#0-SgJune and 1 25.7 37.2 0.35 43.8 142,000 21 50
nil y-0SNgS -<X week of
July 1995
(June
prices)
Average per
Poverty capita
Country Survey headcount Average Exchange Poverty line income
[Poverty line] period (in %) shortfall Elasticity Gini rate ($ pm) ($ pm)
-~{ungary 16$.3$41 1993 6.7 20.1 0.29 27.0 92.0 57 127
Belarus 1219,4851 1Q/1995 14.5 22.8 0.37 29.6 11,525 19 39
Kygyz X 1 Oct-Nov. 54.9 46.0 0.50 43.3 7 16 19
1993
(October
prices)
Appendix 6
Distribution of International Funds
Based on Minimization of
Deprivation Function
Country Percentage offunds
Balkans and Poland 6.1
Poland 1.2
Bulgaria 0.6
Romania 4.3
Central Europe 0.1
Hungary 0.1
Czech Republic 0.0
Slovak Republic 0.0
Slovenia 0
Baltics 0.8
Lithuania 0.3
Latvia 0.1
Estonia 0.4
Slavic and Moldova 76.2
Russian Federation 46.0
Ukraine 27.7
Belarus 0.9
Moldova 1.6
Central Asia 16.8
Kazakhstan 7.7
Kyrgyz Republic 4.3
Turkmenistan 2.1
Uzbekistan 2.8
Note: Total funds = 100. Based on 'adjusted" HBS data (INCOME2).
192
Appendix 7
Sources for Table 1.2
Privatization: Czech Republic, Hungary, Estonia, and Lithuania (by value of
assets): World Development Report 1996, first draft, Chapter 3, Table 3.2. Hun-
gary: Prime Minister Gyula Horn's press conference in February 1995 (by
value of assets: out of Ft. 2 billion worth of state property, Ft. 0.43 billion
privatized). Belarus: G. Lych, "Belrus' na outi k rynochney ekonomike,"
Voprosy ekonomiki, No. 10, 1995, p. 88 (by number of enterprises). Poland:
percent of all employed working in newly privatized firms (which used to be
SOEs) at the end of 1993 as reported in World Bank Country Assistance Strat-
egy Paper, July 1, 1994. Kazakhstan: Kazakhstan, The Transition to a Market
Economy, World Bank report 1993, p.80 (in August 1992; by book value of
capital). Russia: Minister of Finance Anatoly Chubais' press conference June
30,1994 as reported by Interfax (percentage of industrial enterprises that have
been privatized). Ukraine: Journal of Commerce, 14 December 1994. Kyrgyz
Republic: presentation by Cevdet Denizer at the World Bank, Policy Research
Department, June 1995.
Non-state sector employment: Hungary: Statistical Yearbook 1995, p. 239. Latvia:
Monthly Bulletin of Latvian Statistics, July 1997, p. 45. Bulgaria: Statistical Year-
book 1996, p. 77. Poland: Statistical Yearbook 1996, p. 351. Czech Republic: Sta-
tistical Yearbook 1996, p. 262. Slovakia: Statistical Yearbook of Slovakia 1993, p.144
(includes both private and mixed sectors). Russia: Russia in Figures 1997, Mos-
cow: Goskomstat, 1997, p. 34. Belarus: Short Statistical Yearbook 1997, p. 42
(excludes employment in kolkhozes). Ukraine, Moldova, Turkmenistan,
Uzbekistan, and Kyrgyz Republic: CIS 1995 Statistical Yearbook, p. 18 (includes
both private and mixed sectors: Ukraine 26 and 7, Moldova 60 and 6,
Uzbekistan 57 and 6, Turkmenistan 52, Kyrgyzstan 64 and 3). Kazakhstan:
Statistical Yearbook 1995, p.25. Romania: Statistical Yearbook 1996, p.142.
Slovenia: IMF, "Slovenia: Recent Economic Developments", August 21,1995.
Retail trade privatization: Czech Republic: Roundtable: Privatization in East-
ern Europe, edited by Ben Slay, RFE/RL Research Report, 13 August 1993.
Slovakia: Statistic7ka Revue Slovenskej Republiky, Bratislava, No.2, 1996, p.19.
Romania: Monthly Bulletin of Statistics, May 1994, p. 7. Ukraine, Uzbekistan,
Kyrgyz republic, Russia, Moldova, Kazakhstan and Belarus: CIS Statistical
Bulletin, September 1997, No. 18 (178), pp. 88-89 (includes only private sector
and consumer cooperatives). Turkmenistan: OMRI Daily News, February 6,
1997. Lithuania: Statistical Yearbook 1996, p. 416. Latvia: Monthly Bulletin of
Latvian Statistics, July 1997, p. 124. Estonia: RFE/RL Daily report, December
30, 1993. Romania: Statistical Yearbook 1996, p. 659. Bulgaria: Statistical Refer-
193
194 Income, Inequality, and Poverty during the Transition
ence Book of the Republic of Bulgaria 1995, National Statistical Institute, Sofia,
1995, p.16.
Current account convertibility: IMF Annual Report 1996, Washington:IMF,
1996, p.12.
Subsidies: Hungary: Monthly Bulletin of Statistics, No.5,1996, p.73. Lithuania:
Statistical Yearbook 1996, p. 94. Latvia: personal communication by Mansour
Farsad, World Bank country economist. Ukraine, Final 1996 budget numbers
reported by IntelNews Daily Dispatch, Kiev and Baltimore, March 17, 1997.
Estonia: IMF Economic Review, No.4, p.58. Bulgaria: An Economic Assessment,
OECD, 1992, p.15. Belarus: personal communication by Chandrashekar Pant,
World Bank country economist for Belarus. Poland: Biuletin Statystyczny,
Warsaw:GUS, July 1996, p. 55. Romania, Russia and Kazakhstan: IMF, Ex-
penditure Policy Division Staff, "Social Safety Nets for Economic Transition:
Options and Recent Experience", February 1995, Table 2, p.16, mimeo.
Slovenia: personal communication by Milan Vodopivec, Research department,
World Bank. Bulgaria and Slovakia: Ke-young Chu and Gerd Schwartz, "Out-
put Decline and Government Expenditures in European Transition Econo-
mies," IMF Working paper, June 1994, p. 13. Czech Republic: IMF, "Czech re-
public: Recent Economic Developments," IMF, November 13, 1996, p. 91.
Turkmenistan: J. Braithwaite: "Social Welfare and Income Distribution in Four
FSU countries", Table 5, processed. Moldova: personal communication by K.
Gilbertson, World Bank country economist for Moldova.
Inflation: within-the-year (December-on-December) increase.
Country Data Sheets
The following Country Data Sheets present economic and social data from
1987 to 1996 for four countries: Russia, Ukraine, Poland, and Latvia. The same
data have been collected for the other fourteen transition economies covered
in this book. They are not included here to save space, but can be obtained
from author on request.
Branko Milanovic, World Bank
1818 H Street N.W., Washington, D.C. 20433, USA.
E-mail: bmilanovic@worldbank.org
Abbreviations Used
CPI = Consumer price index
COL = Cost of living index
GDP = Gross domestic product
HH = Household(s)
HBS = Household budget survey
p.m. or pm = per month
p.a. = per annum
p.c. or pc = per capita
PL = Poverty line
UEB = Unemployment benefit(s)
195
196 Income, Inequality, and Poverty during the Transition
Russia
Years 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Currency ruble ruble ruble ruble ruble ruble ruble ruble ruble ruble
STANDARD OF UVING (yearly average)
llousehold survey data
Average per copita income
by social grup (per mouth) I
Workers (from 94 - urbon) 152 164 176 198 400 3,950 38,600 145,600 322,700 454,000
Furmers (from 94 - rurol) 127 143 140 175 344 1,900 19,000 78,100 174,200 244,000
Pensioners 117 229 1,862 23,188 80,774 217,345
Mixed
Overoll 146 164 178 197 397 3,300 35,400 119,800 284,200 397,300
Mecro doto
Average per aopito income
by social group (per month)
Workers
Farmers
Pensioners
Mixed
Overall 146 184 198 215 466 3,979 43,906 207,785 533,252 779,000
Average wage (pm; inctl. soc. benef) 214 233 263 303 557 6,011 59,204 217,886 484,542 802,742
Average pension (pm) 80 83 87 102 266 1,613 20,536 79,162 183,533 302,275
Pension:wage (percent) 37.4 35.6 33.1 33.7 47.8 26.8 34.7 36.3 37.9 37.7
Poverty threshold (per month)
Miuimum wage 70 70 70 70 130 714 5,962 17,560 41,071 72,700
'lnimum pension 60 60 60 70 161 1,102 11,328 40,669 89,632
(A) Per capita PL (oll populafion) 2/ 75 75 83 100 220 1,893 20,562 86,431 264,150 369,417
Per capita PL (for o family of 4) 54 61 154 1,895 16,462
PL fr I adult (at free market price) 82 04 87 93 190 1,093
Poverty threshold (A) os % of
Average wage 35.0 32.2 31.6 33.0 39.5 31.5 34.7 39.7 54.5 46.0
Average pension 93.8 90.4 95.4 98.0 82.7 117.4 100.1 109.2 143.9 122.2
Average income per capita 51.4 45.7 46.6 50.9 55.4 57.4 58.1 72.1 92.9 93.0
for all HHs (survey data)
STRUCTURE OF INCOME (absolute nominell values; yeary average)
Hlousehold survey date (pc per month)
Labor income (wages and salaries) 128 149 271 2,737 20,532 55,587 111,691
Social transfers 30 23 63 486 6,089 20,845 47,461
Pensions 14
Fomily and child allowances 3
Other social transfers 3
Private sector income (ind. ogro) 3/ 7 9 29 375 2,623 4,672 7,673
Other income (including ia4dnd) 13 16 34 352 6,156 38,695 49,477
TOTAL INCOME 178 197 397 3,950 35,400 119,800 284,200
Country Data Sheets 197
Russia (continued)
Merro dete: ell HHils (bn pa, Meer)
Labor income (wages and salaries) 203 221 252 293 519 5,121 48,274 169,152 344,201 513,000
Wages paid by cooperarinno 2 12 19 21 71 4,583
Soearl benefis 39 42 44 50 129 994 11845 63,506 116,299 115,830
Pensions 48 125 960 11,595 41,859 85,078 125,346
Fomily and child allawancen
Other soc. tran. (int. stipends) 2 2 2 2 5 34 250 810
Priwt sector income (cin. agro) 7 8 10 14 34 280 4,151 11,34
FinLaial income (interest, insurance) 10 11 11 17 119 185 4,050 30,854
Onher income 5 6 8 9 29 520 11,629 89,283 450,246 597,991
Ttal mnney incoms 264 287 325 383 830 7,100 19,99 364,137 910,746 1,346,821
Incomoe in in bnd
TOTAL INCOME 264 287 325 383 830 7,100 79,949 364,137 910,746 1,346,821
TOTAL INCOME P.C. (per month) 151 163 183 215 465 3,980 44,875 204,688 533,267 756,301
UNEMPLOYMENT (thousand persons; yeorly average)
Number of registered unemployed 44 272 728 1,286 2,041 2,832
Structure of the unemployed
Number of women 196 1,400
Number of young (15-24 years) 85 221 357
Number with higher education 29 75 135
Number of those receiVing UEB 7 155 473 1,068 1,737
Those with UEB in total unemployed (%) 15.7 57.2 64.9 83.1 85.1
GOVERNMENT SOCIAL EXPENDITURES (obsolute nominal values; bn per year)
Pensions 30.9 32.5 34.6 41.4 67.9 757 10,312 36,848 85,078 125,346
Family ollowances 8.4 9.3 11.4 12.3 11.6 54 1,029 4,411 1,199 19,458
Child allowances (paid by SIF) 3.1 2
Sick leave (poid by SIF) / 1,624 6,630 17,458 18,151
Unemployment benefits v 0.2 8 141 908 3,519 3,708
Other scholarships, bread subsidies, 1.5 211 1,887 9,326
sodal ossistance
Education 16.8 18.4 19.7 22.3 50 679 6,918 27,453 56,400
Heahh 10.1 11.2 12.6 14.7 34 468 5,415 19,707 40,400
Estimate of enterprise-finanred benefits 7.9 68 472
GENERAL MACRO DATA
Cost of ling index (1987-100) 6/ 100.0 101.0 107.0 114.0 233 4,050 40,384 171,385 497,493 738,078
GDP or GNP nominal (bn) 496 531 573 644 1,399 19,201 171,510 630,111 1,658,900 2256,000
GDP or GNP real in 1987 (bn) 496 523 537 526 500 427 390 341 326 307
Real growth rote (%) 5.6 2.5 -2.0 -5.0 .14.5 -8.7 -12.6 4.3 -6.0
Exchonge nate (per US$1)
Official rate 0.6 0.6 0.6 0.6 30.0 190.0 928 2,204 4,562 5,152
Market rate 4.2 5.5 8.9 18.8 58.7 222 928 2,131 4,562 5,152
'Actuoal rate 1.8 2.2 3.4 6.7 30.0 190 928 2,131 4,562 5,152
Number of persons receiving
pensions (thousands) 32,224 32,637 33,155 33,813 35,043 36,395 37,138 36,300 37,000 37,500
Total population (thousands) 145,908 146,857 147,621 148,255 148,704 148,673 148,465 148,249 148,306 148,400
Pensioners as % of total population 22.1 22.2 22.5 22.8 23.6 24.5 25.0 24.5 25.0 25.3
198 Income, Inequality, and Poverty during the Transition
IRussia (continued)
Years 1987 1988 1989 1990 1991 1992 7993 1994 1995 1996
Currency fuble ru61 ruble ruble rubb ruble ruble ruble tubbe ru6lo
STANDARO OF IUVING (1987 rblies; yerly average)
Household survey doto
Averoge per capito income
by sociol group (per month) '/
Workers 152 162 164 174 172 98 96 85 65 62
Farmers 127 142 138 154 148 41 47 46 35 33
Pensioners 103 98 46 57 52 44
Mixed
Overall 146 162 166 172 170 81 88 70 57 54
MAacr data
Average per copito income
by soaisl group (per month)
Workers
Farmers
Pensioners
Mixed
Overoll 146 182 185 189 200 98 109 121 107 106
Average wage (per month) 214 231 246 266 239 148 147 127 97 109
Average pension (per month) 80 82 81 89 114 40 51 46 37 41
Poverty threshold (per monath)
Minimum wage 70 69 65 61 56 18 15 10 8
Minimum pension 60 59 56 61 69 27 28 24 18
(A) Per capita PL (all populotion) 2/ 75 74 78 88 94 47 51 50 53 50
Per copita PL (for a family of 4) 50 54 66 47 41
PL for I adult (at free market prices) 82 83 81 82 82 47
STRUICTIRE OF INCOME (1987 rubies; yearly average)
Household srvety dato (pc per moant)
Labor income (wages and solaries) 120 131 116 68 51 32 22
Sociol transhrs 28 20 27 12 15 12 10
Pensions 13
Family and child allowances 2
Other sodol tronsfers 3
Private sector income (incl. agro) 3/ 6 8 12 9 6 3 2
Other income (including in-kind) 12 14 14 9 15 23 10
Total private 19 21 27 18 22 25 11
TOTAL INCOME 166 172 170 98 88 70 43
Macro data: all HIHs (bn per ywer)
Labor income (wages and salaries) 203 218 235 257 223 126 120 99 69 78
Wages paid by cooperatives 2 11 17 9 2 11
Social benefits 39 41 41 44 55 25 29 37 23 24
Pensions 42 54 24 29 28 17 17
Family ond child allowances
Other soc. tran. (ind. stipends) 2 2 1 1 2 1 1 0
Private sector income (ind. agro) 7 8 9 13 15 7 10 7
Finoncial income (interest, insurance) 10 n 11 15 51 5 10 18
Other income 5 6 7 8 12 13 29 52 91 81
Total money income 264 284 303 336 356 175 198 212 183 182
Income in kind
TOTAL INCOME 264 284 303 336 356 175 198 212 183 182
TOTAL INCOME P.C. (per month) 151 161 171 189 200 98 111 119 107 102
Country Data Sheets 199
I Russia (continued)
Years 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Currency ruble ruble ruble ruble ruble uble ruble ruble ruble ruble
GOVERNMENT SOCAL EPENDITURES (1987 rubles; bw per year)
Pensions 30.9 32.2 32.3 36.3 29.1 18.7 25.5 21.5 17.1 17.0
Family allowances 8.4 9.2 10.7 10.8 5.0 1.3 2.5 2.6 0.2 2.6
Child allowances (paid by SIF) 1.3 0.1
Sick leave (paid by SIF) I/ 4.0 3.9 3.5 2.5
Unemployment benefits I/ 0.1 0.2 0.3 0.5 0.7 0.5
Other scholorships, breoad subsidies, 0.6 5.2 4.7 5.4
social assistance
TOTAL CASH TRANSFERS 39.3 41.4 43.0 47.1 36.2 25.5 37.1 33.9 21.6 22.6
Education 16.8 18.2 18.4 19.6 21.5 16.8 17.1 16.0 11.3
Health 10.1 11.1 11.8 12.9 14.5 11.6 13.4 11.5 8.1
TOTAL IN-KIND TRANSFERS 26.9 29.3 30.2 32.5 36.0 28.3 30.5 27.5 19.5
Estimote of enterprise-finonced benefits 7.4 29.0 11.6
TOTAL TRANSFERS 66.2 70.7 80.6 79.6 101.2 65.4 67.7 61.4 41.0
200 Income, Inequality, and Poverty during the Transition
Russia (continued) I
Years 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Currency ruble ruble ruble ruble ruble ruble ruble ruble ruble ruble
GOVERNMENT SOCIAL EXPENDITURES
Pensions 6.2 6.1 6.0 6.4 4.8 3.9 6.0 5.8 5.1 5.6
Fomily allowonces 1.7 1.8 2.0 1.9 0.8 0.3 0.6 0.7 0.1 0.9
Child allowonces 0.2 0.01
Sick leave 0.9 1.1 1.1 0.8
Unemployment benefits 0.01 0.04 0.1 0.1 0.2 0.2
Other: scholarships, bread subsidies, 0.1 1.1 1.1 1.5
sodol ossistance
TOTAL CASH TRANSFERS 7.9 7.9 8.0 8.3 6.0 5.4 0.7 9.2 6.5 7.4
Education 3.4 3.5 3.4 3.5 3.6 3.5 4.0 4.4 3.4
Heahh 2.0 2.1 2.2 2.3 2.4 2.4 3.2 3.1 2.4
TOTAL IN-KIND TRANSFERS 5.4 5.6 5.6 5.7 6.0 6.0 7.2 7.5 5.8
Estimate of enterprise-finaiced benefits 1.4 4.8 2.5
TOTAL TRANSFERS 13.4 13.4 15.0 14.1 16.8 13.8 15.9 16.7 12.3
STRVCTURE OF INCOME (macro)
Labor income 41.0 41.5 44.0 45.5 37.1 26.7 20.1 26.8 20.7 25.4
Sodal benefits 7.9 7.9 8.0 8.3 6.0 5.4 8.7 9.2 6.5 7.4
Total privote income 4.4 4.7 5.0 6.3 13.0 5.1 8.6 20.9 27.1 26.5
TOTAL PERSONAL INCOME 53.3 54.1 57.0 60.1 56.1 37.2 48.5 56.9 54.4 59.3
Health and educartion 5.4 5.6 5.6 5.7 6.0 6.0 7.2 7.5 5.8
TOTAL (inc. health and eduaotion) 58.7 59.6 62.7 65.8 62.1 43.1 55.6 64.4 60.2
Country Data Sheets 201
Russia (continued)
Yeoa 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Currency $ $ $ $ $ $ $ $ $ $
_rrmr,s ME
STANDARD OF LIVING (nominal values; yearly average)
Household survey data
Average per capita income
by soCial group (per month)
Workers 84 73 52 30 13 21 42 68 71 00
Formers 71 64 44 26 11 10 20 37 38 47
Pensioners 18 8 10 25 42 40
Mixed
Overall 81 73 52 30 13 17 38 56 62 77
Macro data
Averoge per copito income
by social group (per month)
Workers
Farmers
Pensioners
Mixed
Overoll 01 02 50 32 16 21 4U 90 117 151
Averoge wage (per month) 119 104 78 46 19 32 64 102 106 156
Average pension (per month) 44 37 26 15 9 8 22 37 40 59
Poverty thfeshold (per month)
Minimum woge 39 31 21 11 4 4 6 8 9
Minimum pension 33 27 18 11 5 6 12 19 20
(A) Per capito PL (nll population) v1 42 33 24 15 7 10 22 41 58 72
Per capita PL (for a family of 4) 16 9 5 10 18
PL for 1 adult (at free market price) 46 37 26 14 6 10
GDP (bn per yeor) 275 237 169 97 47 101 185 2% 364 438
GOP per capito per year 1,887 1,614 1,144 653 314 680 1,245 1,995 2,452 2,950
GDP per copita per month 157 135 95 54 26 57 104 166 204 246
NOTE:
sIF = Sociol insuronce fund.
1/ HBS data for 1985 used insteod of dato for 1907.
2/ New lower (Ministry of Lobor) minimum odopted in November 1992.
3/ 1994 and ff. includes entrepreneurial income.
4/ Years 1993-95 include child 0llowonnes too.
5/ UEB + early retirement + financial aid.
6/ Cost of living index retail price index for consumer goods.
202 Income, Inequality, and Poverty during the Transition
Ukraine
Years 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Gurrency ruble ruble ruble ruble ruble ruble karbov. korbov. karbov. hryvno
STANDARD OF LIVING (yearly average)
Household survey data
Average per copita income
by sodal group (per month)
Workers 154 162 174 196 377
Farmers 131 136 151 171 323
Pensioners 99 105 119 136 280
Mixed
Overall 148 149 162 182 349 3,722 5,966,000
Macao data
Average per copita income
by social group (per month)
Workers
Farmers
Pensioners
Mixed
Overall 127 138 153 176 329 3,788 100,860 861,753 3,864,375 63.9
Average wage (pm, intl. soc. benef) 185 200 218 248 476 6,458 163,100 1,437,700 8,184,000 154
Average pension (pm) 73 75 85 104 210 2,119 75,181 500,000 2,350,000 50
Pension:wage (percent) 40 38 39 42 44 33 46 35 29 32
Poverty threshold (per month)
Minimum wage 108 119 967 13,642
Minimum pension
(A) Per capita PL (all population) I/ 75 75 83 100 220 2,846 698,892 3,443,250
Per copito PL (for a family of 4) 2,874
Minimum consumption basket 9 100 4,038 194,842
Poverty threshold (A) as % of
Average wage 40.5 37.5 38.1 40.3 46.2 44.1 48.6 42.1
Average pension 102.6 99.7 97.4 %.5 104.7 134.3 139.8 146.5
Average income per copito 58.9 54.3 54.3 56.7 66.8 75.1 81.1 89.1
from mocro dato
STRUCTURE OF INCOME (absolute nominel values; yearly average)
Nousehold survey data (pr per month)
labor income (wages and salaries) 101 104 109 123 205 2,230 2,547,482
Social transfers 13 12 21 24 67 387 530,974
Pensions
Family and child allowances
Other sodal tronsfers
Private sector income (ind. agro) 17 16 16 19 40 730 1,903,154
Other income (including in-kind) 11 11 16 16 37 380 996,322
TOTAL INCOME 141 143 162 182 349 3,726 5,977,932
Country Data Sheets 203
Ukraine (continued)
years 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Currency ruble ruble ruble ruble ruble ruble korbov. karbov. korbov. bryvna
Macro dotoh: oll HHs (bn per yeor)
Labor income (wages and salaries) 56 61 68 78 135 1,648 35,956 337,677 1,549,129 23.8
Woges paid by cooperatives 0 0 2 3 3 69 1,566
Social benefits 13 14 1S 16 41 418 13,981 106,772 486,907 8.7
Pensions 11 11 12 13 29 361 13,684 104,180 477,161
Family and child allowances 2 2 2 3 11 5
Other soc. tarn. (ind. stipends) 0 1 1 1 1 1S 297 2,592 9,746
Private sector income (incl. agro) 4 4 5 6 12 72 2,523 13,651 66,845 1.4
Financial income (interest, insurance) 3 4 4 5 7 32 1,452 10,355 4.8
Other income 3 3 4 3 14 196 9,320 70,458 266,754 0.2
Total money income 19 86 95 108 208 2,366 63,232 538,913 2,369,635 38.8
Income in kind
TOTAL INCOME 79 86 95 108 208 2,366 63,232 538,913 2,369,635 38.8
TOTAL INCOME P.C. (per month) 129 139 154 174 334 3,788 100,860 861,753 3,864,375 63.9
UNEMPLOYMENT (I esaond persons, yearly average)
Number of registered unemployed 7.0 70.5 77.9 93.8 114.6 280.2
Structure of the unemployed
Number of women
Number of young (15-24 years)
Number with higher education 16.8 26.0
Number of those receiving UEB 5.2 52.5 40.0 47.4 57.1 140.8
Thse with UEB in total unemployed (%) 74.3 74.5 51.4 50.5 49.8 50.3
GOVERNMENT SOC14I EXPENDITURES (absolute aeminel values; ba per year)
Pensions 10.6 11.3 12.1 12.6 28.5 361 11,447 81,781 419,727 7.0
Moternity, birth all. 0.1 0.1 0.1 0.4 0.5 5 130 1,180
Child allowances 0.5 0.5 0.5 0.6 3.7 26 352 2,259
Sick leove 1.1 1.2 1.3 1.5 2.2 26 1,002 11,003
Unemployment benefits 0.1 11 241 3,300
SA, rehab. vouchers, Chernobyl, 0.5 0.5 0.5 0.5 4.4 55 1,217 13,842 33,300 1.5
stipends
Education 6.0 6.5 6.8 7.5 18.3 281 7,236 70,199 316,985
Health 3.1 3.4 3.8 4.4 17.4 199 6,014 58,979 262,498
Estimate of enterprise.financed benefits
GENERAL MACRO DATA
Cost of living index (1987-100) 100 100 103 109 246 3,493 182,225 1,769,659 8,415,373 152
GDP or GNP nominal (bn) 136 142 154 167 299 5,033 158,273 1,203,769 5,451,000 80
6DP or GNP weol in 1987 (bn) 136 140 146 141 129 116 100 81 72 65
Real growth rote (%) 2.7 4.5 -3.8 -8.4 -9.7 -14.2 -19.0 -11.4 -9.0
Exchange rote (per US$1)
Officiol rate 0.6 0.6 0.6 0.6 30.0 163 4,796 31,663
Market rate 4.2 5.5 8.9 18.8 58.7 319 9,508 62,306 155,319 1.8
'Actuol' rate 1.8 2.2 3.4 6.7 30.0 241 7,152 46,984 155,319 1.8
Number of persons receiving
pensions (thousands) 12,432 12,853 12,748 13,084 13,568 14,191 14,477 14,515 14,500 14,488
Total population (thousands) 51,293 51,521 51,750 51,860 51,944 52,057 52,244 52,114 51,100 50,600
Pensioners as % of total population 24.2 24.9 24.6 25.2 26.1 27.3 27.7 27.9 28.4 28.6
204 Income, Inequality, and Poverty during the Transition
Ukraine (continued)
Years 1987 1988 1989 1990 199? 1992 1993 1994 1995 1996
Currency ruble ruble ruble ruble ruble ruble korbou. korbov. korbov. hryrno
STANDARD OF LIVING (1987 rubIes; yearly average)
Household survey dato
Average per capita incnome
by sosial group (per month)
Workers 154 162 168 179 153
Farmers 131 136 146 156 132
Pensioners 124 114
Mixed
Overall 148 149 157 167 142 107 71
Macro data
Average per topita income
by social group (per month)
Workers
Formers
Pensioners
Mixed
Overall 127 138 148 161 134 108 55 49 46 42
Average wage (pm; ind. soc, bnef.) 185 199 211 227 194 185 90 81 97 102
Average pension (per month) 73 75 82 95 86 61 41 28 28 33
Paverty threshold (per month)
Minimum wage 91 49 28 7
Minimum pension
(A) Per copito PL (all populotion) ' 75 75 80 91 90 81 39 41
Per capita PL (for a family of 4) 82
Minimum consumption bosket 93 92 116 107
STRUCTURE OF INCOME (1987 rubles; yearly average)
Household survey dato (pc per month)
Labor income (wages and solodes) 101 104 105 112 83 64 30
Social transfers 13 12 20 22 27 11 6
Pensions
Family and child allowances
Other sociol transfers
Private sector income (ind. ogro) 17 16 16 17 16 21 23
Other income (including in-kind) 11 11 16 15 15 11 12
Total private 28 27 31 32 31 32 34
TOTAl INCOME 141 143 157 167 142 107 71
Mocro dato: oll HHs (bn per yeor)
Labor income (wages and salaries) 56.4 61.2 66.2 71.2 55.1 47.2 19.7 19.1 18.4 15.7
Wages pcid by cooperatives 0.4 2.4 3.1 1.4 2.0 0.9
Social benefits 13.4 14.2 14.5 14.3 16.6 12.0 7.7 6.0 5.8 5.7
Pensions 2v 10.6 11.3 11.7 11.5 11.6 10.3 7.5 5.9 5.7
Family and child allowances 2.3 2.4 2.3 2.3 4.4 0.2
Other soc. tran. (ind. stipends) 8.5 0.5 0.5 0.5 0.6 0.4 0.2 0.1 0.1
Private sector income (ind. agro) 3.7 3.7 4.4 5.4 4.8 2.1 1.4 0.8 0.8 0.9
hnancial income (interest, insurance) 3.3 3.6 3.6 5.0 2.7 0.9 0.8 0.6 3.2
Other income 2.7 3.1 3.5 3.2 5.5 5.6 5.1 4.0 3.2 0.1
Total money income 79.4 85.7 92.3 99.0 84.7 67.8 34.7 30.5 28.2 25.6
Income in kind
1OTAL INCOME 79.4 85.7 92.3 99.0 84.7 67.8 34.7 30.5 28.2 25.6
TOTAL INCOME P.C. (per month) 129 139 149 159 136 -108 55 49 46 42
Country Data Sheets 205
Ukraine (continued)
Yeors 1987 1988 1989 1990 1991 1992 1993 1994 1995 196
Currency ruble ruble ruble ruble ruble ruble korbov. korbov. korbov. hryvno
GOVERNMENT SOCIAL EXPENDITURES (1987 rubles; In per yeer)
Pensions 10.6 11.3 11.7 11.5 11.6 10.3 6.3 4.6 5.0 4.6
Maternity, birth all. 0.1 0.1 0.1 0.4 0.2 0.2 0.1 0.1
Child allowances 0.5 0.5 0.5 0.5 1.5 0.7 0.2 0.1
Sick leave 1.1 1.2 1.3 1.4 0.9 0.8 0.5 0.6
Unemployment benefits 0.04 0.3 0.1 0.2
SA rehab. vauchers, Chernobyl, 0.5 0.5 0.5 0.5 1.8 1.6 0.7 0.8 0.4 1.0
stipends
TOTAL CASH TRANSFERS 12.8 13.6 14.0 14.3 16.1 13.8 7.9 6.4 5.4 5.6
Education 6.0 6.5 6.6 6.9 7.5 8.0 4.0 4.0 3.8
Health 3.1 3.4 3.7 4.0 7.1 5.7 3.3 3.3 3.1
TOTAL IN-KIND TRANSFERS 9.1 9.9 10.3 10.9 14.5 13.7 7.3 7.3 6.9
Estimate of enterprise-financed benefits
TOTAL TRANSFERS 21.9 23.4 24.3 25.2 30.6 27.6 15.2 13.7 12.3
206 Income, Inequality, and Poverty during the Transition
Ukraine (continued) j
Years 19B7 1988 1989 1990 1991 1992 1993 1994 1995 1996
Currency ruble rubl ruble ruble ruble ruble karbov karbov. karbov. hryvna
GOVERNMENT SOCIAL EXPENDITURES
Pensions 7.8 7.9 7.9 7.5 9.5 7.2 7.2 6.8 7.7 8.7
Maternity, birth all. 0.1 0.1 0.1 0.2 0.2 0.1 0.1 0.1
Child allowances 0.4 0.4 0.3 0.4 1.2 0.5 0.2 0.2
Sick lease 0.8 0.8 0.8 0.9 0.7 0.5 0.6 0.9
Unemployment benefits 0.03 0.2 0.2 0.3
SA, rehab. vouchers, Chernobyl, 0.4 0.4 0.3 0.3 1.5 1.1 0.8 1.1 0.6 1.9
stipends
TOTAL CASH TRANSFERS 9.4 9.6 9.4 9.3 13.2 9.6 9.1 9.4 8.3 10.6
Education 4.4 4.6 4.4 4.5 6.1 5.6 4.6 5.8 5.8
Health 2.3 2.4 2.5 2.6 5.8 4.0 3.8 4.9 4.8
TOTAL IN-KINO TRANSFERS 6.7 7.0 6.9 7.1 11.9 9.5 8.4 10.7 10.6
Estimate of entesprise-financed benefits
TOTAL TRANSFERS 16.1 16.5 16.3 16.5 25.1 19.1 17.5 20.1 18.9
STRUCIURE Of INCOME (macro)
Labor incume 41.4 43.1 44.4 46.6 45.3 32.7 22.7 28.1 28.4 29.5
Social benefits 9.4 9.6 9.4 9.3 13.2 9.6 9.1 9.4 8.3 10.6
Total private icome 7.1 7.3 7.8 8.8 10.7 6.0 8.4 7.8 6.1 7.9
TOTAL PERSONAL INCOME 57.9 60.0 61.6 64.8 69.1 48.3 40.2 45.3 42.9 48.1
health and education 6.7 7.0 6.9 7.1 11.9 9.5 8.4 10.7 10.6
TOTAL (inc. health ond education) 64.5 67.0 68.5 71.9 81.1 57.9 48.6 56.0 53.5
Country Data Sheets 207
Ukraine (confinued)
Yeors 1987 1988 1989 1990 1991 1992 1993 1994 1995 199
Currency S $ $ $ $ $ $ $ $
STANDARD OF UVING (nominal values; yearly average)
Household su rvey doto
Averoge per capita income
by social group (per month)
Workers 86 72 51 29 13
Formers 73 61 45 26 11
Pensioners 55 47 35 20 9
Mixed
Overall 82 66 48 27 12 15 38
Macro data
Average per capita income
by social group (per month)
Workers
Formers
Pensioners
Mixed
Overall 71 62 45 27 11 16 14 18 25 35
Avernge wage (pm; incl. soc. benef) 103 89 64 37 16 27 23 31 53 84
Average pension (pm) 41 34 25 16 7 9 11 It 15 27
Poverty threshold (per montrh)
Minimum wage 1S 4 4 2
Minimum pension
(A) Per capito P1 (ol popuiotion) v/ 42 33 24 15 7 12 15 22
Per capita PL (for a family of 4) 12
Minimum consumption basket 28 15 17 27
GDP (bn per year) 76 63 45 25 10 21 22 26 35 44
GOP per capita per year 1,476 1,232 878 484 192 402 424 492 687 870
GDP per copita per month 123 103 73 40 16 33 35 41 57 73
NOTE:
Korbovanets from November 1992; 1 korb. - 1 Rs.
Hryvna from June 19%; 1 hr - 100,000 karb.
1/ Russian data for used for 1987-91. From 1993 on, value of 32 essential products.
2/ 1993-95 pension dato indude fomily benefits.
208 Income, Inequality, and Poverty during the Transition
Poland
rears 1987 1988 1989 1990 1991 1992 1993 1994 1995 199
Currency zloty zloty z/oty zloty zloty zloty zloty zloty noew zl. now zl.
STANDARD OF LIVING (yearly overage)
Household survey doto
Average per rapito income
by sodail group (per month)
Workers 16,517 29,756 115,955 563,044 919,023 1,428,300 1,762,679 2,346,900 301 395
Farmers 18,040 34,795 134,398 577,278 823,880 1,173,300 1,547,510 2,051,800 282 343
Pensioners 15,425 25,160 84,472 493,384 945,899 1,265,800 1,844,934 2,458,600 320 402
Mixed 16,942 31,974 124,517 596,258 930,171 1,293,600 1,409,101 1,945,600 261 321
Self-employed 2,188,500 2,97,000 386 488
SOciol tronsfer recipients 953,300 1,258,700 156 200
Overoll 16,491 29,435 124,560 558,215 923,040 1,340,310 1,724,041 2,309,300 301 303
Afocro doto
Averoge per capita income
by social group (per month)
Workers
Farmers
Pensioners
Mixed
Self-employed
Social tronsfer recipients
Overall 21,458 39,300 149,650 803,942 1,461,883 2,104,217 2,430,833 3,813,910 524
Average wage (net; per month) 31,180 57,543 217,999 1,029,600 1,756,300 2,438,600 3,201,300 4,250,408 570 710
Averogse pension (net, per mwnth) 15,807 25,667 93,806 584,800 1,147,600 1,548,400 2,039,700 2,792,200 369 447
Pension:wage (percent) 51 45 43 57 65 63 64 66 65 63
Poverty threshold (per montib)
Per capita (for family of 4) v 11,800 18,200 60,100 395,900 722,000 1,129,000 1,641,000 1,837,000 245 298
Poverty threshold as % of
Average wage 37.8 31.6 27.6 38.5 41.1 46.3 51.3 43.2 42.9 41.9
Average pension 74.7 70.9 64.1 67.7 62.9 72.9 80.5 65.8 66.3 66.6
Average income per copito 55.0 46.3 40.2 49.2 49.4 53.7 67.5 48.2 46.7
from macro dato
STRUCTIURE OF INCOME labsolute nonrinal values; yearly average)
Household survey data (pc per month)
labor income/ wages and salaries 8,943 15,975 64,611 287,571 467,975 684,899 759,110 1,027,639 133 174
Social transfers 3,555 6,534 24,755 135,424 304,566 467,743 559,944 743,595 98 123
Pensions 17,164 103,678 434,759 591,181 80
Family and child allowances 6,094 23,184 65,800
Other sociol transfers 1,497 8,562 59,385
Private income (agriculture) 4,004 9,570 22,131 120,936 159,524 173,427 183,214 247,095 34 39
Other income (ind. selfemployed) 156 281 1,262 6,265 10,816 14,271 221,771 290,972 35 47
TOTAL INCOME 16,658 32,360 112,759 550,196 942,881 1,340,341 1,724,039 2,309,300 301 383
Country Data Sheets 209
I Poland (continued)
Yeors 198; 1988 1989 1990 1991 1992 1993 1994 1995 1996
Cutrency zloty zloty zloty zloty zloty zloty zloty zloty noew zl. few zl.
_ ' ' ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'--
Moraro doto: oil NHs (hn per year)
Labor income (gross) V 4,551 8,169 31,181 140,467 249,159 389,650 492,576 669,797 92.7 119.6
Social benefits 1,557 2,766 10,820 63,573 145,034 232,352 308,003 433,420 57.4
Pensions (gross) 1,220 2,038 7,669 47,879 101,825 167,838 231,941 332,293 44.7 55.0
Family ond child allowances 199 492 2,401 9,012 17,028 22,665 23,707 24,376 3.2
Other sodol htonsfers /scholorships 12 30 107 784 876 897 1,235 138 3.6
Priwte income (agriculure) 1,096 2,319 9,174 24,760 28,516 54,300 79,658 100,951 14.5
Private income besides agriculture 506 1,047 4,694 47,466 78,592 200,929 296,380 377,348 51.5
Other income (as residual) 1,989 3,553 12,305 91,478 40,118 62,564 107,802 183,531 26.9
Total money income 9,700 17,854 68,175 367,744 541,419 939,795 1,284,419 1,765,047 242.8
Income in kind
TOTAL INCOME 'I 9,700 17,854 68,175 367,744 541,419 939,795 1,284,419 1,765,047 243
TOTAL INCOME P.C. (per month) 21,404 39,273 149,356 802,591 1,177,745 2,038,583 2,779,767 3,813,910 524
UNEMPLOYMENT (thousand persons; yearly average)
Number of registered unemployed 9.6 721.8 1,669.5 2,354.9 2,733.7 2,909.6 2,694.3 2,507.2
Structure of the unemployed
Number of women 357.1 870.7 1,251.5 1,435.6 1,443.7 1,454.2
Number of young (15.24 years) 386.3 398.3 733.1 958.7
Number with higher educotion 45.1 64.1 55.6 52.3 7.5
Number of those receiving UEB 1,311.6 1,394.3 1,422.8 1,549.0 1,300.0
Those with UEB in total unemployed (%) 55.7 51.0 48.9 57.5 51.9
GOVERNMENT SOCIAL EXPENDITURES (abselute nominal values; bn per year)
Pensions (gross) 1,229 2,055 7,685 47,879 101,825 167,838 231,941 332,293 44.37 54.97
Family allowances 187 468 2,370 8,405 15,167 24,145 23,552 28,461 2.81 3.73
Child allowunces 13 24 32 607 1,860 2,358 2,828 3,164 0.37 0.43
Sick leave 14 21 54 4,272 9,226 4,392 8,093 14,044 1.72 2.82
Unemployment benefits 1,891 11,145 14,975 18,078 25,337 3.63 4.41
Other scholarships 12 30 107 801 876 897 1,235 3,430
Social assistance 1,204 3,384 6,149 8,706 11,900 1.19 1.41
Education 607 1,011 4,342 28,250 34,778 49,805 64,444 93,178 12.48 8.33
Health 541 898 3,773 24,612 38,854 56,734 71,321 95,173 13.13 16.74
Estimate of enterprise-financed benefits 225 356 903
GENERAL MACRO DATA
Csst of living index (1987-100) 100 159 572 3,766 6,569 9,394 12,710 16,802 2.1 2.6
GDP or GNP nominal (bn) 16,940 29,629 118,319 591,518 824,330 1,142,430 1,557,800 2,104,073 286.0 363.8
GDP or GNP reol in 1987 prices 16,940 17,634 17,670 15,620 14,433 14,577 15,131 15,963 16,841 17,768
Real growth rote 4.1 0.2 -11.6 -7.6 2.6 3.8 5.5 7.0 6.0
Exchange rote (per US1)
Official rate 265 431 1,439 9,500 10,576 13,626 18,145 22,727 2.4 2.7
Morket rote 1,030 1,979 5,565 9,570 10,731 13,647 17,983 22,727 2.4 2.7
Number of persons receiving
pensions (thousands) 6,477 6,669 6,827 7,104 7,944 8,495 8,730 8,919 9,085 9,200
Totol population (thousands) 37,764 37,885 38,038 38,183 38,309 38,417 38,505 38,566 38,609 38,618
Pensioners os % of total population 17.2 17.6 17.9 18.6 20.7 22.1 22.7 23.1 23.5 23.8
210 Income, Inequality, and Poverty during the Transition
Poland (continued)
Years 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Currency zloty zloty zloty zloty zloty zloty zloty zloty new zI. new zi.
STANDARD OF LIVING (1987 zlety; yearly average)
Household survey dato
Average per capita income
by sucial group (per month)
Workers 16,517 18,714 20,286 14,949 14,903 15,205 13,869 13,968 14,029 15,364
Farmers 18,040 21,884 23,513 15,327 12,542 12,490 12,176 12,211 13,149 13,339
Pensioners 15,425 15,824 14,778 13,100 14,399 13,475 14,516 14,632 14,887 15,641
Mixed 16,942 20,109 21,784 15,831 14,160 13,771 11,087 11,s79 12,145 12,494
Self.employed 17,219 17,717 17,982 18,962
Social tronsfer recipients 7,501 7,491 7,260 7,789
Overall 16,491 18,513 21,791 14,821 14,051 14,268 13,565 13,144 13,997 14,90S
M1acro data
Average per copito income
by social group (per month)
Workers
Farmers
Pensioners
Mixed
Overall 21,458 24,717 26,181 21,345 22,254 22,400 19,126 22,699 24,404
Average wage (per month) 31,180 36,190 38,138 27,336 26,736 25,960 25,188 25,296 26,531 27,608
Average pension (per month) 15,807 16,143 16,411 15,527 17,470 16,483 16,048 16,618 17,169 17,376
Poverty threshold (per month)
Per copita (for family of 4) 2v 11,800 11,447 10,514 10,511 10,991 12,019 12,911 10,933 11,391 8,568
STRUCTURE OF INCOME (1957 zloty; yeadly average)
Household survey doto (pc per month)
L[bor incomel wages and salaris 8,943 10,047 11,304 7,635 7,124 7,291 5,973 6,116 6,201 6,751
Social transfers 3,555 4,109 4,331 3,596 4,636 4,979 4,406 4,426 4,549 4,788
Pensions 3,003 2,753 3,421 3,518 3,723
Family ond child ollowunces 1,066 616 518
Other sodaol tronsfers 262 227 467
Private income (agriculture) 4,004 6,019 3,872 3,211 2,428 1,846 1,442 1,471 1,596 1,517
Other income (mid. Self-employed) 156 177 221 166 165 152 1,745 1,732 1,652 1,827
TOTAL INCOME 16,658 28,352 19,727 14,608 14,353 14,268 13,565 13,744 13,997 14,882
Macro data: all fHs (bn per yeor)
Lubos income (gross) " 4,551 5,138 5,455 3,729 3,793 4,148 3,876 3,986 4,315 4,651
Sodal benefits 1,557 1,740 1,893 1,688 2,208 2,473 2,423 2,580 2,671
Pensions (gross) 1,220 1,282 1,342 1,271 1,550 1,787 1,825 1,978 2,084 2,137
Family ond child allowances 199 309 420 239 259 241 187 145 148
Other sodol tronsfers/scholarships 12 19 19 21 13 10 10 1 169
Private income (agriculture) 1,096 1,459 1,605 657 434 578 627 601 673
Private income besides agrculture 506 658 821 1,260 1,196 2,139 2,332 2,246 2,396
Other income (as residual) 1,989 2,234 2,153 2,429 611 666 848 1,092 1,251
Total money income 9,700 11,229 11,927 9,764 8,242 10,004 10,106 10,505 11,306
Income in hind
TOTAL INCOME ' 9,700 11,229 11,927 9,764 8,242 10,004 10,106 10,505 11,306
TOTAL INCOME P.C. (per month) 21,404 24,700 26,129 21,309 17,929 21,701 21,871 22,699 24,404
Country Data Sheets 211
Poland (continued) 1
oneas 1987 1986 1989 1990 1991 1992 1993 1994 1995 19%
Currency zloty zloty zloty zloty zloty zloty zloty zloty new zL. new z.L
GOVERNMENT SOCIAL EXPENDITURES (1987 zloty; ha per year)
Pensions (gross) 1,228.6 1,292.5 1,344.4 1,271.2 1,550.1 1,786.7 1,824.9 1,977.7 2,066.1 2,136.9
Family allowances 186.7 294.4 414.6 223.1 230.9 257.0 185.3 169.4 130.7 144.9
Child allowances 12.6 14.8 5.5 16.1 28.3 25.1 22.3 18.8 17.4 16.8
Sick leave 13.7 13.1 9.4 113.4 140.4 46.8 63.7 83.6 80.2 109.5
Unem,ployment benefits 50.2 169.7 159.4 142.2 150.8 169.0 171.3
Other: scholnrships 11.8 18.7 18.7 21.3 13.3 9.5 9.7 20.4
Socinl ossistonce 32.0 51.5 65.5 68.5 70.8 55.4 54.7
TOTAL CASH TRANSFERS 1,453 1,634 1,793 1,727 2,184 2,350 2,317 2,491 2,519 2,634
Educotion 607 636 760 750 529 530 507 SSS 581 324
Health 541 564 660 653 591 604 561 566 612 651
TOTAL IN-KIND TRANSFERS 1,149 1,200 1,420 1,404 1,121 1,134 1,068 1,121 1,193 974
Estimote of enterprise-financed benefits 225 224 158
TOTAL TRANSFERS 2,828 3,057 3,370 3,131 3,305 3,484 3,385 3,612 3,712 3,609
212 Income, Inequality, and Poverty during the Transition
Poland (continued)
Yeors 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Currency zloty zloty zloty zloty zloty zloty zloty zloty new zi. new zl.
GOVERNMENT SOCIAL EXPENDITURES
Pensions (gross) 7.3 6.9 6.5 8.1 12.4 14.7 14.9 15.8 15.5 15.1
Family allowances 1.1 1.6 2.0 1.4 1.8 2.1 1.5 1.4 1.0 1.0
Child allowances 0.1 0.1 0.03 0.1 0.2 0.2 0.2 0.2 0.1 0.1
Sick leave 0.1 0.1 0.05 0.7 1.1 0.4 0.5 0.7 0.6 0.0
Unemployment benefits 0.3 1.4 1.3 1.2 1.2 1.3 1.2
Other scholorships 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2
Sodal assistance 0.2 0.4 0.5 0.6 0.6 0.4 0.4
TOTAL CASH TRANSFERS 8.6 8.8 8.7 11.0 17.4 19.3 18.9 19.9 18.9 18.6
Education 3.6 3.4 3.7 4.8 4.2 4.4 4.1 4.4 4.4 2.3
llealth 3.2 3.0 3.2 4.2 4.7 5.0 4.6 4.5 4.6 4.6
TOTAL IN-KIND TRANSFERS 6.8 6.4 6.9 8.9 8.9 9.3 8.7 9.0 9.0 6.9
Estimote of enterprise-financed benefits 1.3 1.2 0.8
TOTAL TRANSFERS 16.7 16.4 16.3 19.9 26.3 28.6 27.6 28.8 27.9 25.5
STRUCTURE OF INCOME (macro)
L[bor income 26.9 27.6 26.4 23.7 30.2 34.1 31.6 31.8 32.4 32.9
social benefits 9.2 9.3 9.1 10.7 17.6 20.3 19.8 20.6 18.9 18.6
Total private income 21.2 23.4 22.1 27.7 17.9 27.8 31.1 31.5 32.4
TOTAL PERSONAL INCOME 57.3 60.3 57.6 62.2 65.7 82.3 82.5 83.9 83.7
Heolth ond education 6.8 6.4 6.9 8.9 8.9 9.3 8.7 9.0 9.0 6.9
TOTAL (inc. heolth and education) 64.0 66.7 64.5 71.1 74.6 91.6 91.2 92.8 92.7
Country Data Sheets 213
Poland (continued)
Yeors 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Currency $ $ S $ $ $ $ $ $ $
STANDARD OF LIVING (nominal values; yearly average)
Household survey dots
Average per capita income
by sodiol group (per month)
Workers 62 69 81 59 93 105 97 103 124 147
Farmers 68 81 93 61 78 86 85 90 116 127
Pensioners 58 58 59 52 89 93 102 108 132 149
Mixed 64 74 87 63 88 95 78 86 107 119
Self-employed 121 131 159 181
Sotol transfer recipients 53 SS 64 74
Overall 62 68 87 59 87 98 95 102 124 142
htacro data
Average per capito income
by soial group (per month)
Workers
Farmers
Pensioners
Mixed
Overoll 81 91 104 85 138 154 134 168 216
Average woae (net; per month) 118 134 151 108 166 179 176 187 235 263
Average pension (net; per month) 60 60 65 62 109 114 112 123 152 166
Poverty threshold (per month)
Per capita (for family of 4) I/ 45 42 42 42 68 83 90 81 101 110
GDP (bn per yeor) 64 69 82 62 78 84 86 93 118 135
6DP per capita per year 1,692 1,816 2,162 1,631 2,035 2,182 2,230 2,401 3,050 3,495
GDP per capita per month 141 151 180 136 170 182 186 200 254 291
NOTE:
roam January 1, 1995 new zloty -10,000 old zloty.
1/ From 1992 onward, gross values.
2/ Social minimum. Revised social minimum from 1994 onwards.
214 Income, Inequality, and Poverty during the Transition
Latyia
Years 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Currency ruble ruble ruble ruble ruble ruble lot lot lot lot
STANDARD OF LIVING (yeerly average)
Household survey duto
Average per capita income
by sodal group (per montfr) /
Workers 168 177 193 223 430 2,944 30.0 55.9
Farmers 176 168 182 205 373 2,457 22.4 47.5
Pensioners 102 105 112 282 1,811 45.8
APxed
Overall 163 170 186 215 414 2,775 27.4 40.3 49.3 51.5
Macro duto
Averoge per ropito income
by social group (per month)
Workers 196 198
Farmers 214 230
Pensioners
Mixed
Overall 169 184 213 237 482 3,445 27.9 29.1 33.0
Average wage (gross per month) 2/ 209 227 250 322 600 4,300 47.2 74.3 89.4 98,7
Average pension (per month) 83,1 85.4 92.3 94.7 162 1,582 14.8 27.9 30.2 44.0
Pension:wage (percent) 39.8 37.6 36.9 29.4 27.0 36.8 31.3 37.6 33.8 44.6
Poverty threshold (per month)
Minimum wage 70 70 70 100 140 876 15.0 22.0 28.0
Minimum level of living 100 153 1,436
Per capita PL (for a family of 4) 83 764
(A) Per capito PL (all population) 75 75 83 108 348 3,714 37.6 51.5 63.8 73.4
Minimum consumption basket 3/ 30.3 38.4 47.2 52.2
Poverty threshold (A) as % of
Average wage 35.9 33.0 33.2 31.1 58.0 86.4 79.6 69.3 71.4 74.3
Average pension 90.3 87.8 89.9 105.6 214.8 234.8 254.7 184.5 211.1 166.8
Average inoame per capita 44.4 40.7 39.0 42.2 72.1 107.8 134.5 176.7 193.1
ftom macro data
STRUCTURE OF INCOME (absolute nomuinel values; yearly average)
Hlousehold survey data (pc per month)
Labor income/ wages and solories 119 127 138 156 258 1,782 17.2 27.0 25.7 25.8
Social tronsfers 21 22 23 24 87 496 5.6 7.0 11.6 14.0
Pensions 13 13 14 15 32 271 2.8 7.8 9.4
family and child allowances 0.3 0.3 0.3 0.3 1 127 2.3 1.0 1.1
Other social tronsfers 8 8 9 9 54 98 0.5 2.8 3.5
Private sector income (agriculture) 10 12 9 14 25 219 1.5 2.2
Other income (including ivkind) 13 11 17 22 45 278 3.1 4.1 12.0 11.8
TOTAL INCOME 163 170 186 215 414 2,775 27.4 40.3 49.3 51.6
Country Data Sheets 215
Latvia (continued)
Years 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Currency ruble ruble ruble ruble ruble ruble lot let lot lot
Mcro dota: all Hfls (bn per yeor)
Labor income/wages and salaries 3.3 3.6 4.2 5.8 9.6 54.8 0.5 0.7 0.8
Sodal benefits 0.7 0.8 0.8 0.9 3.2 22.6 0.2 0.2 0.2
Pensions 0.6 0.6 0.7 0.7 0.1 0.2 0.2
Family and child allawonoes 0.1 0.1 0.2 0.2
Other social tronsfers/scholarships 0.02 0.03 0.03 0.03
Private sector income (self-employed) 0.5 0.6 0.8 0.7 2.1 31.0 0.1
Other income 0.8 0.9 1.1 0.2 0.5 0.4 0.004
Total money income 5.4 5.9 6.8 7.6 15.4 108.8 0.9 0.9 1.0
Income in kind
TOTAL INCOME 5.4 5.9 6.8 7.6 15.4 108.8 0.9 0.9 1.0
TOTAL INCOME P.C. (per month) 169 184 213 237 482 3,445 27.9 29.1 33.0
UNEMPLOYMENT ltmsoosnd persons; yearly average)
Number of registered unemployed 14.9 62.5 83.8 86.6 95.4
Structure of the unemployed
Number of women 7.2 33.9 46.0
Number of young (15-24 yeors) 4.4 10.8
Number with higher educotion 2.3 4.8
Number of those receiving UEB 12.2 44.3 37.5 37.1 38.7
Those with UEB in total unemployed (Y) 81.9 70.9 44.8 42.8 40.6
GOVERNMENr SOCIAL EXPENDITURES (absolute nominal volues; bm per year)
Pensions 0.6 0.6 0.6 0.7 2.2 12.4 0.1 0.2 0.2 0.3
family allowances 0.001 0.001 0.001 0.002 0.5 3.0 0.03 0.02 0.02
Child allowances 0.04 0.04 0.04 0.004 0.2 1.3 0.01
Sick leave 0.04 0.04 0.03 0.04 0.03 0.2 0.01
Unemployment benefits 0.2 0.01 0.01 0.01
Other social assistonce 0.1 0.1 0.1 0.1 0.2 2.3 0.02
Educotion 0.5 0.4 0.5 0.6 1.2 9.1 0.1 0.1 0.2 0.2
Health 0.3 0.3 0.3 0.4 0.7 5.7 0.04 0.1 0.1 0.1
Estimate of enterprise-financed benefits 0.7 0.7 0.7 0.7 0.001
GENERAL MACRO DATA
Cst of living index (1987-100) 100 104 109 119 299 3,148 33 45 56 66
GOP or GNP nominol (bn) 8.9 9.3 10.3 12.5 28.7 200.9 1.5 2.0 2.0 2.8
GOP or GNP real in 1987 (bn) 9.5 10.0 10.6 10.9 9.8 6.4 5.4 5.5 5.5 5.7
Real growth rate (%) 5.2 6.8 2.9 -10.4 .34.9 -14.9 2.0 0.0 2.8
Exthange rate (per US$1)
Official rate 0.6 0.6 0.6 0.6 0.6 134
Mlorket rate 1.6 1.5 1.6 46.2 142 0.7 0.6 0.5 0.6
'Actual' rate 1.8 2.2 3.4 6.7 30.0 142 0.7 0.6 0.5 0.6
Number of persons receiving
pensions (thousands) 575 585 592 605 628 654 662 663 660 661
Total population (thousonds) 2,641 2,666 2,674 2,671 2,662 2,632 2,586 2,550 2,529 2,501
Pensioners as % of total population 21.8 21.9 22.1 22.6 23.6 24.9 25.6 26.0 26.1 26.4
216 Income, Inequality, and Poverty during the Transition
Latvia (continued)
Years 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Currency ruble ruble ruble ruble ruble ruble lot lot lot lot
STANDARD OF LIVING (1987 rubles, yearly overage)
Hfousehold survey doto
Average per copita income
by social group (per month) 'I
Workers 168 171 178 187 143 94 91 85
Formers 176 162 168 172 124 78 68 72
Pensioners 94 94 58 70
Mixed
Overoll 163 164 171 181 138 88 83 90 88 78
Amoao dSote
Averoge per capita income
by saciol group (per month)
Workers 196 191
Formers 214 222
Pensioners
Mixed
Overall 169 178 196 199 161 109 85 65 59
Averoge wege (per month; gross) 2/ 209 219 230 271 200 137 144 166 160 150
Average pension (per month) 83 82 85 80 54 50 45 62 54 67
Poverty threshold (per month)
Minimum wage 70 68 65 84 47 28 46 49 50
Minimum level of living 84 51 46
Per copita PL (for o fomily of 4) 70 24
(A) Per copito PL (all population) 75 72 76 84 116 118 114 115 114 112
M9nimum consumption basket 3/ 92 86 85 80
STRUCTURE OF INCOME (1987 rubles, yeary overage)
Household survey dota (pc per month)
Lobor income/ wages and solaries 119 122 127 131 86 57 52 60 46 39
Social tronsfers 21 21 21 20 29 16 17 16 21 21
Pensions 13 13 13 13 11 9 8 14 14
Family and child allowances 0.3 0.3 0.3 0.3 0.5 4 7 2 2
Other sodal transfers 8 8 8 7 18 3 2 5 5
Private sector income (agriculture) 10 11 8 11 8 7 5 5
Other income (including inkind) 13 10 15 18 15 9 9 9 22 18
Total private 23 22 24 30 23 16 14 14 22 18
TOTAL INCOME 163 164 171 181 138 88 83 90 88 79
Macro dato: all Hls (bn per year)
Lobor income/ wages and solaries 3.3 3.5 3.8 4.9 3.2 1.7 1.5 1.5 1.4
Social benefits 0.7 0.7 0.7 0.8 1.1 0.7 0.7 0.5 0.4
Pensons 0.6 0.6 0.6 0.6 0.4 0.5 0.4
Family and child allowances 0.1 0.1 0.1 0.2
Other sociol htonsfers /scholorships 0.02 0.03 0.02 0.03
Prvate sector income (selfemployed) 0.5 0.6 0.7 0.6 0.7 1.0 0.4
Other income 0.8 0.8 1.0 0.2 0.2 0.01 0.01
Total money income 5.4 5.7 6.3 6.4 5.1 3.5 2.6 2.0 1.8
Income in kind
TOTAL INCOME 5.4 5.7 6.3 6.4 5.1 3.5 2.6 2.0 1.8
TOTAL INCOME P.C. (per month) 169 178 196 199 161 109 85 65 59
Country Data Sheets 217
Latvia (continued)
Years 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
CUtrency ruble ruble ruble ruble ruble ruble lot lot lt lot
j M-,. ................~',.
GOVERNMENT SOCIAL EXPENDITURES (1987 rubles; bin per year)
Pensions 0.6 0.6 0.6 0.6 0.7 0.4 0.4 0.5 0.4 0.5
Family allowonces 0.001 0.001 0.001 0.001 0.2 0.1 0.1 0.03 0.03
thild allowances 0.04 0.03 0.03 0.004 0.1 0.04 0.03
Sick leave 0.04 0.03 0.03 0.04 0.01 0.01 0.02
Unemployment benefits 0.01 0.02 0.02 0.02
Other: social ossistance 0.1 0.1 0.1 0.1 0.1 0.1 0.05
TOTAL CASH TRANSFERS 0.7 0.7 0.7 0.7 1.1 0.6 0.5 0.6 0.5 0.5
Education 0.5 0.4 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.2
Health 0.3 0.3 0.3 0.3 0.2 0.2 0.1 0.2 0.2 0.2
TOTAL IN-KIND TRANSFERS 0.7 0.7 0.8 0.8 0.6 0.5 0.4 0.5 0.5 0.4
Estimote of enterprise-financed benefits 0.7 0.7 0.6 0.6 0.001
TOTAL TRANSFERS 2.2 2.2 2.1 2.1 1.7 1.1 0.9 1.1 0.9 0.9
218 Income, Inequality, and Poverty during the Transition
Latvia (continued)
Years 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Curreney ruble ruble ruble ruble ruble ruble lot bat I,,t lot
GOVERNMENT SOCIAL EXPENDITURES
Pensions 6.3 6.4 5.9 5.5 7.8 6.2 9.6 11.7 10.3 10.8
Fomily allowances 0.01 0.01 0.01 0.01 1.8 1.5 1.9 0.8 0.7
Child allowances 0.4 0.4 0.4 0.03 0.7 0.6 0.8
Sick leave 0.4 0.4 0.3 0.4 0.1 0.1 0.5
Unemployment benefits 0.1 0.4 0.4 0.4
Other socal assistonce 0.8 0.8 0.7 0.7 0.7 1.1 1.1
TOTAL CASH TRANSFERS 7.9 7.9 7.3 6.6 11.2 9.7 12.3 13.2 11.6 12.3
Education 5.3 4.8 4.9 4.5 4.1 4.5 6.5 6.1 6.7 5.9
Health 3.2 3.5 3.4 3.3 2.6 2.8 2.4 3.9 4.2 4.1
TOTAL IN-KIND TRANSFERS 8.4 8.3 8.2 7.8 6.7 7.4 8.9 10.0 10.8 10.0
Estimate of enterprise-financed benefits 8.2 7.8 6.6 5.8 0.04
TOTAL TRANSFERS 24.5 24.0 22.2 20.2 17.9 17.1 21.2 23.3 22.4 22.3
STRUCTURE OF INCOME (marse)
Labor income 37.1 38.7 40.5 46.3 33.5 27.3 33.7 32.8 32.3
Social benefits 7.9 7.9 7.3 6.6 11.2 9.7 12.3 13.2 fl.6 12.3
Total private income 8.2 7.7 8.0 9.0 7.8 7.8 9.7 9.4 15.5 12.8
TOTAL PERSONAL INCOME 53.2 54.3 SS.9 61.9 52.5 44.8 55.7 55.4 59.4
Health and educotion 8.4 8.3 8.2 7.8 6.7 7.4 8.9 10.0 10.8 10.0
TOTAL (inc. health and education) 61.6 62.6 64.1 69.7 59.2 52.1 64.6 65.5 70.2
Country Data Sheets 219
Latvia (continued)
Yeors 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Curtency $ $ $ $ $ $ $ $ 1 $
STANDARD OF LIVING (nominal values; yearly average)
Household survey doto
Average per capita income
by sdcial group (per month) '
Workers 93 79 57 34 14 21 45 102
Farmers 98 75 54 31 12 11 33 86
Pensioners 45 31 11 9 13 83
Mixed
Overall 91 76 55 32 14 20 41 72 94 94
Macro doto
Average per capita income
by social group (per month)
Workers 109 1 8
Farmers 119 103
Pensioners
Mixed
Overall 94 82 63 36 16 24 41 52 63
Average wage (per month; gross) " 116 101 74 48 20 30 70 133 169 179
Averoge pension (per month) 46 38 27 14 5 11 22 50 57 80
Poverty threshold (per month)
Minimum wage 39 31 21 15 5 6 22 39 53
Minimum level of living 15 5 10
Per capita PL (for o family of 4) 12 5
(A) Per capita PL (all population) 42 33 24 15 12 26 56 92 121 133
Minimum consumption basket 3/ 45 69 89 95
GOP (bs per year) 4.9 4.2 3.0 1.9 1.0 1.4 2.2 3.7 4.5 5.0
GDP per capita per year 1,862 1,560 1,135 702 359 537 841 1,432 1,769 2,010
GDP per capita per month 155 130 95 59 30 45 70 119 147 168
NOT7E
Lot from 1993; 1 let - 200 rubles.
1/1993 and 1994, money income olone.
2/ From 1993, gross wages.
3/ Officially called 'crisis minimum basket.'
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Subject Index
Albania, 2, 5, 25, 58 Czechoslovakia, 2, 13, 16, 17,58; poverty
Armenia, ln, 3, 5, 25, 27n, 31 line in, 86n
Azerbaijan, in, 3, 5, 27n
Eastern Europe 49; income decline, 33;
Balkans, 72; inequality in, 42; poverty in, income shares, 37; non-pension trans-
74, 76 (see also individual countries) fers, 111; private sector in, 39; unem-
Baltic republics, 3,58, 71; income decline, ployment in, 29; unemployment ben-
33; income shares, 37, 38; inequality efits, 110; wage decline, 29;
in, 42,132; non-pension transfers, 111; Eastern Germany: poor in, 104
poverty in, 74, 76, 79, 80; unemploy- Estonia, 27, 37n, 38, 58, 81, 124; inequal-
ment benefits, 110 (see also individual ity in, 45; poor in, 117; poverty in, 92,
countries) 93; social assistance system, 116
Belarus, 3, 27, 58, 74, 80, 134; income
shares, 38; poor in, 105, 106, 117; pov- Georgia, 3, 5, 25
erty in, 79, 92, 93, 94 German Democratic Republic, 2; poor in,
Besarabia, 3n 103
Bosnia and Herzegovina, 2n, 3 Great Depression, 26ff
Bulgaria, 2, 3n, 7, 12, 13, 23n, 58, 80, 124,
134; family benefits, 110; income Habsburg Empire, 2n
shares, 38; inequality in, 44ff, 49, 54; Health and education expenditures: dur-
poor in, 92, 96, 107, 117; poverty in, ing transition, 38
79; private sector in, 39; returns to Hungary, ln, 2, 3n, 7, 12, 13,16,17,27,31,
education, 104n 35, 65n, 71, 124; income shares, 38,39;
inequality in, 44ff, 54; poor in, 95, 102,
Central Asian republics, 34, 65n, 71, 118; 106, 117; poverty in, 93; poverty line
inequality in, 40, 132; poverty in, 74, in, 98; social assistance system, 116
76, 79 (see also individual countries)
Central Europe, 71,118; inequality in, 41, Income during transition: changing in-
132; poverty in, 67, 79, 80 (see also in- come shares, 37ff; compared to expen-
dividual countries) ditures 32, 33; consumption-in-kind
Communism: and attitude toward private 32n; decline, 30ff; measurement, 32
sector, 20; and attitude toward wealth, (see also Output during transition)
21; and composition of income, 13ff; Inequality during transition, 40ff; and dif-
consumer subsidies, 15n; family ben- ferences among social groups, 54; and
efits in, 15, 20; inequality in, 15; distribution of income sources, 47ff;
nomenklatura, 15, 21; pensions in, 17; income vs. expenditure distribution,
and private sector income, 18; social 42; regressive redistribution, 45; and
transfers in, 16; state employment in, type of adjustment, 46
12; taxation in, 15, 19; and wage dis- Intemational Comparison Project (ICP),
tribution, 18, 20; 65
Croatia, 3, 27n, 31n
Czech republic, 3, 7, 29, 31, 71; income Kazakhstan, ln, 71, 83
shares, 38; inequality in, 45; poverty Kuznets curve, 15n
in, 85, 86; poverty line in, 98; poor in, Kyrgyz Republic, 33n, 41, 74, 83
102, 103; private sector in, 39; returns
to education, 104n; social assistance Latvia, 25,134; income shares, 38; inequal-
system, 116 ity in, 45, 49
233
234 Index
Lithuania, 25, 35n, 37n, 80, 134; income Romania, 2,3n, 35,58, 65n, 74,80,83,134;
shares, 38; inequality in, 45; poverty income decline, 25,27; income shares,
in, 79 38; poverty in, 93
Russia, 2n, 3, 5, 17, 31n, 35, 38, 58, 72, 74,
Macedonia, 3,5, 23n 83, 118, 124, 132; family benefits, 110;
Minimum income guarantee (MTG), 115ff, iinequality in, 41, 45ff, 54, 132; non-
122, 124 pension transfers, 111; output decline
Moldova, 3,28, 71,80; income decline, 33; in, 25ff; poor in, 73n, 96, 98, 99, 107,
income shares, 38; inequality in, 42, 117; poverty in, 71,79,80,88,94; pov-
45; poverty in, 79; private sector in, 39 erty line, 67, 98; private sector in, 39;
Montenegro, 3n social assistance system, 117; unem-
ployment 29; unemployment benefits,
OECD (Organisation for Economic Co- 110; wage decline 29
operation and Development) coun-
tries, 12; inequality in, 15, 40, 45; so- Serbia, 3, 5
cial assistance in, 115ff; social transfers Shadow economy, 31
in, 16n, 112 Slavic republics (Russia, Ukraine,
Ottoman Empire, 2n Belarus), 118; income shares, 37, 38;
Output (GDP) during transition, 6, 23; income decline, 33; inequality in, 42;
measurement problems, 23n, 30, 31n; poverty in, 74, 76, 79; private sector
32n (see also Income during transition) in, 39 (see also individual countries)
Slovakia, 3, 71, 80; inequality in, 40, 41,
Pensions, 108; and informal sector, 126ff 44ff; poor in, 103,105,106; poverty in,
Pensioners during transition, 54,58,92,93, 92,93; poverty line, 98; private sector
102ff, 107 in, 39; returns to education, 104n
Poland, 1, 2, 7, 12,13,16,25,26,35,58,59, Slovenia, ln, 3, 7, 25, 71, 81; income de-
65n, 72, 74, 124, 132, 134; family ben- cline, 27; income shares, 38, 39; in-
efits, 110; income shares, 38, 39; in- equality in, 44ff, 54
equality in, 45ff, 49,54; output decline Social assistance: and incentives, 120ff,
in, 26ff; poverty in, 67, 79, 88, 93, 94; 122ff; and income testing, 118ff; and
poor in, 73n, 95, 96, 99, 100, 102, 107; "leakage," 84; during transition, 111
returns to education, 104n; social as- Social transfers during transition, 108; tar-
sistance in, 85, 116; social minimum geting of family benefits, 109; target-
line, 66; poverty line in, 98 ing of unemployment benefits, 110
Poverty: elasticity with respect to income,
71ff ; in Latin America, 77, 133; mea- Tajikistan, ln, 5,27
surement, 60ff Taylorism, 20
Poverty during transition, 67ff, 73ff; and Transition countries: civil wars in 5;
age, 102; and economic policy, 91; and democratic tradition 5ff; output de-
education, 105; and gender, 107; and cline, 6, 23ff; poverty in, 6
household size, 101; and how to dis- Turkmenistan, ln, 7, 25
tribute money, 80ff; and improvement
in targeting, 114; income and distri- Ukraine, 3,83, 132; income shares, 38; in-
bution effect, 85ff, 88; regional aspect, equality in, 41, 45ff; non-pension
106; "shallow" poverty, 76; and social transfers, 111; output decline, 25, 28;
class, 92ff; and unemployment, 95; poverty in, 71, 79; poor in, 73n, 96, 98,
using expenditure measure, 74 99, 107; social assistance system, 117;
Poverty line, 65; World Bank poverty line, unemployment benefits, 110
66 Uravnilovka, 20
Private sector income: during transition, U.S.S.R.,2,13,17,32,65n,127; socialmini-
38,39 mum in, 66
Index 235
Uzbekistan, ln, 25, 31n, 74, 80 Yugoslavia, 2,3,7,12,13,15n, 27, 65n, 127;
Federal Republic of, 31
Name Index
Ahluwalia, Montek, 15n Gramlich, Edward M., 126n
Alexander I, King of Yugoslavia (1921- Grosh, Margaret, 118
1934), 6n
Aliyev, Haidar (President of Azerbaijan), Hanak, Peter, 5n
5 Heimerl, Daniela, 104n
Arvay, Janos, 31n Herer, W., 31n
Asselain, Jean-Charles, 15n Hirsl, Miroslav, 15n
Atkinson, Anthony, 15n, 18, 40n, 84n
Jackman, Richard, 19
Bairoch, Paul, 28 James, Harold, 29
Banks, James, 40n Jenkins, Stephen P., 40n
Bergson, Abram, 15n Johnson, Paul, 40n
Bird, Edward J., 15n
Block, H. 27 Kadar, Janos (head of Hungarian Com-
Bloem, Adriaan M., 23n munist party, 1956-88), 20n
Braithwaite, Jeanine, 58n, 67n Kadera, Vladimir, 31n
Brezhnev, Leonid (Soviet leader, 1964- Kaelble, Hartmut, 15n
1982), 20n Kakwani, Nanak, 87n
Browning, Edgar K., 123 Kanbur, Ravi, 82n, 120n
Keen, Michael, 120n
Carol II, King of Romania (1930-40), 6n Kende, Pierre, 15n
Chase, Robert, 104n Khanin, Gregory, 23n
Commander, Simon, 132 Khomeini, Ruhollah (Imam), 1
Cornelius, Peter, 116n Klugman, Jeni, 67n
Cornia, Gionanni Andrea, 49n, 88n Koen, Vmcent, 23n
Cotterell, Paul, 23n Kudrov, Valentin, 23n
Coulter, Fionna 40n
Cowell, Frank, A., 40n Lydall, Harold, 19
Debroy, B., 15n Madzarevic, Sanja, 31n
de Melo, Martha, 46n, 92n Magocsi, Paul R., 5n
Denizer, Cevdet, 46n, 92n Marer, Paul, 31n
Dupre, 73n Marnie, Shiela, 18
Marx, Karl, 20
Fassman, Martin, 15n Mclntyre, Robert, 5n
Fijalkowski, Tadeusz, 116 Micklewright, John, 15n, 18
Flakierski, Henryk, 15n Mikulik, Davor, 31n
Fourouni, Shirin, 29 Milanovic, Branko, 15n, 16n, 18, 19, 66,
Frank, Tibor, 5n 112, 85n, 94n
Frick, Joachim R., 15n Morley, Samuel, 87
Morrisson, Christian, 15n
Gazier, Bernard, 28 Muller, Klaus, 15n
Gelb, Alan, 29, 46n, 92n
Gigantes, Terry, 23n Novotny, Antonin (head of Czechoslovak
Goldenberg, Suzanne, 5n Communist Party, 1953-1968), 20n
Gontmakher, Evgeny, 67n
Graham, Carol, 118 Okrasa, Wlodimierz, 54n
236
Index 237
Phelps-Brown, Henry, 15n, 19 Speder, Zsolt, 44n
Polonsky, Anthony, 5n Squire, Lyn, 72
Psacharopoulos, George, 66n, 77n Subbarao, Kalanidhi, 87n
Rainwater, Lee, 40n Sugar, Peter F., 5n
Ravallion, Martin, 64, 72 Ter-Minassian, Theresa, 133
Redor, Dominique, 19 Thomas, Mark, 15n
Romer, Christina D., 28 Tito, Josip Broz (president of Yugoslavia,
Rusnok, Jifi, 15n 1945-1980), 20n
Rutkowski, Jan, 104n Tolstopiatenko, Andrei, 132
Rutkowski, Michal, 19 Tuomala, Matti, 120n
Sadowski, Wieslaw, 31n Vecernik, Jifi, 95n, 115n, 116
Sawhill, Isabel, 84n Veit-Wilson, John, 115n
Schrenk, Martin, 29 Vertes, Andras, 31n
Schumpeter, Joseph, 19 von Rauch, Georg, 5n
Schwartz, Gerd, 133
Shevardnadze, Eduard (president of Wagner, Gert G., 15n
Georgia), 5 Walters, Garrison E., 5n
Sipos, Sandor, 115n, 116, 116n Winiecki, Jan, 32n
Smeeding, Timothy, 40n
Sokoloff, Georges, 27 Yemtsov, Ruslan, 132
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