Satisfaction with Life and Service Delivery in Eastern … Zaidi Asad Alam Pradeep Mitra Ramya...

THE WORLD BANK

W O R L D B A N K W O R K I N G P A P E R N O . 1 6 2

Satisfaction with Life and ServiceDelivery in Eastern Europe and theFormer Soviet UnionSome Insights from the 2006 Life in TransitionSurvey

Salman ZaidiAsad AlamPradeep MitraRamya Sundaram

47612

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Salman ZaidiAsad AlamPradeep MitraRamya Sundaram

W O R L D B A N K W O R K I N G P A P E R N O . 1 6 2

Satisfaction with Life and ServiceDelivery in Eastern Europe andthe Former Soviet UnionSome Insights from the 2006 Life in Transition Survey

THE WORLD BANK

Washington, D.C.

11524-00_FM.qxd 1/19/09 11:41 AM Page i

Copyright © 2009The International Bank for Reconstruction and Development / The World Bank1818 H Street, N.W.Washington, D.C. 20433, U.S.A.All rights reservedManufactured in the United States of AmericaFirst Printing: February 2009

printed on recycled paper

1 2 3 4 5 12 11 10 09

World Bank Working Papers are published to communicate the results of the Bank’s work tothe development community with the least possible delay. The manuscript of this paper thereforehas not been prepared in accordance with the procedures appropriate to formally-edited texts.Some sources cited in this paper may be informal documents that are not readily available.

The findings, interpretations, and conclusions expressed herein are those of the author(s) anddo not necessarily reflect the views of the International Bank for Reconstruction and Development/The World Bank and its affiliated organizations, or those of the Executive Directors of TheWorld Bank or the governments they represent.

The World Bank does not guarantee the accuracy of the data included in this work. Theboundaries, colors, denominations, and other information shown on any map in this work donot imply any judgment on the part of The World Bank of the legal status of any territory orthe endorsement or acceptance of such boundaries.

The material in this publication is copyrighted. Copying and/or transmitting portions or allof this work without permission may be a violation of applicable law. The International Bankfor Reconstruction and Development/The World Bank encourages dissemination of its workand will normally grant permission promptly to reproduce portions of the work.

For permission to photocopy or reprint any part of this work, please send a request withcomplete information to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers,MA 01923, USA, Tel: 978-750-8400, Fax: 978-750-4470, www.copyright.com.

All other queries on rights and licenses, including subsidiary rights, should be addressed tothe Office of the Publisher, The World Bank, 1818 H Street NW, Washington, DC 20433, USA,Fax: 202-522-2422, email: [email protected].

ISBN-13: 978-0-8213-7900-4eISBN: 978-0-8213-7901-1ISSN: 1726-5878 DOI: 10.1596/978-0-8213-7900-4

Library of Congress Cataloging-in-Publication Data

Satisfaction with life and service delivery in Eastern Europe and the former Soviet Union : someinsights from the 2006 life in transition survey / Salman Zaidi . . . [et al.].

p. ; cm.—(World Bank working paper, ISSN 1726-5878 ; no. 162)Includes bibliographical references.ISBN 978-0-8213-7900-4

1. Health surveys—Europe, Eastern. 2. Health surveys—Asia, Central. I. Zaidi, Salman, 1967-II.World Bank. III. Series: World Bank working paper ; no. 162.

[DNLM: 1. Consumer Satisfaction—Europe, Eastern. 2. Health Services—Europe, Eastern.3. Data Collection—Europe, Eastern. 4. Quality of Life—Europe, Eastern. 5. SocioeconomicFactors—Europe, Eastern. W 85 S253 2009]

RA407.5.E85S28 2009362.1094—dc22

2008052797

11524-00_FM.qxd 1/19/09 11:41 AM Page ii

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

1. Key Factors Affecting Satisfaction with Life in Eastern Europe and the Former Soviet Union . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Satisfaction with Life as a Welfare Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Key Factors Influencing SWL: Multivariate Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Comparisons over Time and Across Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Concluding Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Annex: Tables and Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2. Employment, Sources of Income, and the Poor in Eastern Europe and the Former Soviet Union . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Choosing Between Alternate Survey-Based Welfare Measures . . . . . . . . . . . . . . . . . 29

How Good is the LiTS Welfare Metric? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Poverty Profile for ECA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Employment, Sources of Income, and Welfare. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45


Annex: Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3. Satisfaction with Publicly-provided Health Services in Eastern Europe and the Former Soviet Union . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Evolution of Publicly-provided Health Services in Eastern Europe and Central Asia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Utilization Rates, Satisfaction, and Prevalence of Informal Payments. . . . . . . . . . . 71

Estimation Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Key Findings and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Health Sector Reform in the Caucuses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87


Annex: Tables and Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

LIST OF TABLES

1.1. Average SWL Score by Self-perceived Economic Status . . . . . . . . . . . . . . . . . . . . 7

1.2. Average SWL Score by Present and Past Self-assessed Economic Status . . . . . . . 7

iii

11524-00_FM.qxd 1/19/09 11:41 AM Page iii

1.3. Average SWL Score by Present and Past Level of Social Capital . . . . . . . . . . . . . 8

1.4. Simulated Probabilities Derived from Ordered Probit Model . . . . . . . . . . . . . . 10

1.5. Ordered Probit Results: SWL by Country Groups . . . . . . . . . . . . . . . . . . . . . . . 11

1.6. Change over Time in Average SWL Rates by Country . . . . . . . . . . . . . . . . . . . . 14

1.7. Comparing GDP and SWL Changes in Recent Years . . . . . . . . . . . . . . . . . . . . . 15

A1.1. Satisfaction with Life Question by Country. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

A1.2. Perceptions Regarding Changes over Time in Economic Situation by Country. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

A1.3. Satisfaction with Changes over Time in Living Conditions by Country. . . . . . 21

A1.4. Average SWL Score: Colleagues in 1989 Rather than School Mates as Peers . . 22

A1.5. Tendency to Feel I’ve Done Worse During Transition Than Others,by Level of Income Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

A1.6. Fall in Social Capital? Decline in Reported Level of Trust in Other People . . . 24

2.1. Comparing Various Alternate Welfare Measures in the LiTS . . . . . . . . . . . . . . . 31

2.2. Subjective Assessment of Welfare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.3. Ownership of Assets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.4. Correlation Matrices: Decile Rankings Based on Various LiTS Welfare Measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.5. Asset Ownership Rates by Welfare Level using Alternate Ranking Criteria . . . 37

2.6. 2006 LiTS PCE Compared to Other Data Sources . . . . . . . . . . . . . . . . . . . . . . . 40

2.7. Overall Regional Poverty Rates from the 2006 LiTS . . . . . . . . . . . . . . . . . . . . . . 42

2.8. Overall Regional Poverty Rates from the 2006 LiTS . . . . . . . . . . . . . . . . . . . . . . 44

2.9. Distribution of the Poor by Employment Status of the Respondent. . . . . . . . . 45

2.10. Respondents Having Worked in Past 12 Months, by Age, Gender,and Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

2.11. Main Income Source by Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

2.12. Probit Model of Likelihood of Being Poor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

2.13. Public/Private Transfers Are More Important in the CIS and EU Member States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

A2.1. Overall Poverty Rates by Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

A2.2. Sensitivity of Poverty Rates with Respect to Choice of Poverty Line . . . . . . . . 56

A2.3. Distribution of the Poor by Geographic Region . . . . . . . . . . . . . . . . . . . . . . . . . 56

A2.4. Rural Urban Disparities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

A2.5. Mean Per Capita Expenditures ($PPP per year) . . . . . . . . . . . . . . . . . . . . . . . . . 59

A2.6. Decomposition of Inequality by Geographic Region . . . . . . . . . . . . . . . . . . . . . 60

A2.7. Ratios of Selected Expenditure Percentiles in Urban and Rural Areas . . . . . . . 60

A2.8. Poverty by Age Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

A2.9. Poverty by Whether Respondent Worked or Not During Past 12 Months . . . . 61

iv Contents

11524-00_FM.qxd 1/19/09 11:41 AM Page iv

A2.10. Poverty by Education Level of Household Head . . . . . . . . . . . . . . . . . . . . . . . . . 61

A2.11. Poverty by Household Head’s Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

A2.12. Poverty by Demographic Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

A2.13. Consumption Regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

3.1. Probit for Health Care Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

3.2. Ordered Probit: Satisfaction with Publicly-provided Health Services . . . . . . . . 80

3.3. Prevalence of Unofficial Payments for Selected Countries . . . . . . . . . . . . . . . . . 85

3.4. Change in Health Care Access Rates for Selected Countries . . . . . . . . . . . . . . . 86

A3.1. Priorities for Additional Government Spending, By Country . . . . . . . . . . . . . . 90

A3.2. Access Rates of PPHS, By Country . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

A3.3. Satisfaction with Medical Treatment in PPHS by Country . . . . . . . . . . . . . . . . 92

A3.4. Prevalence of Unofficial Payments in PPHS by Country . . . . . . . . . . . . . . . . . . 93

A3.5. Difference between General and Experienced Perception of Prevalence of Unofficial Payments in PPHS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

LIST OF FIGURES

1.1. All Things Considered, I am Satisfied with My Life Right Now. . . . . . . . . . . . . . . 3

1.2. SWL Rates by Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3. Correlation of Satisfaction with Life with Average Incomes and Level of Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4. Satisfaction with Life Among the Youth is Generally Higher than among the Elderly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.5. Satisfaction with Life is Higher for the Healthy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.6. Satisfaction with Life is Positively Correlated with Level of Trust in People. . . . . 8

1.7. Cross-Country Comparisons: SWL and Employment Rates . . . . . . . . . . . . . . . . 16

1.8. Cross-Country Comparisons: SWL and Level of Trust in Others . . . . . . . . . . . . 17

2.1. Distribution of Normalized Expenditures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.2. Distribution of the One-question Welfare Aggregate . . . . . . . . . . . . . . . . . . . . . . 32

2.3. Distribution of Subjective Welfare Rankings by Country . . . . . . . . . . . . . . . . . . . 34

2.4. Comparing the Various Alternate Welfare Measures . . . . . . . . . . . . . . . . . . . . . . . 39

2.5. Country Welfare Rankings: National Accounts vs. Survey-based Estimates . . . . 41

2.6. Regional Variation in Poverty Rates Across the ECA Region . . . . . . . . . . . . . . . . 43

2.7. Distribution of the Poor Across the ECA Region. . . . . . . . . . . . . . . . . . . . . . . . . . 43

2.8. Respondents that Report Having Worked During Past 12 Months . . . . . . . . . . . 46

2.9. Intercountry Differences in Main Income Sources of Households . . . . . . . . . . . 48

3.1. Rates of Satisfaction with the Publicly-provided Health System By Country. . . 66

3.2. Priorities for Additional Government Spending: 2006 LiTS. . . . . . . . . . . . . . . . . 69

3.3. Utilization of Publicly-provided Health System by Country . . . . . . . . . . . . . . . . 72

Contents v

11524-00_FM.qxd 1/19/09 11:41 AM Page v

3.4. Rates of Satisfaction By Type of Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

3.5. Percent of Respondents that Think that Unofficial Payments Are Needed . . . . . 74

3.6. Perceptions Regarding Unofficial Payments in Publicly-provided Health System By Country . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

3.7. Negative Correlation Between Satisfaction and Prevalence of Informal Payments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

3.8. Satisfaction with Publicly-provided Health Service and Self-assessed Health Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

3.9. General vs. Experienced Opinion of Need for Unofficial Payments . . . . . . . . . . 84

3.10. Changes in Access Rates and Prevalence of Unofficial Payments,2001 to 2006. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

vi Contents

11524-00_FM.qxd 1/19/09 11:41 AM Page vi

Preface

The past two decades in Eastern Europe and the former Soviet Union (ECA) havebeen times of tremendous change, with countries undergoing rapid transformation

from centrally-planned to market-oriented economies. While poverty increased duringthe initial years of transition, primarily on account of the sharp economic contraction,the resurgence of economic growth in the region since 1998 has resulted in a reboundin household incomes and living standards. Little is known, however, about subjectivewelfare in the wake of this growth rebound, especially how people across ECA countriesview their satisfaction with life as well as with the quality of services being providing bytheir governments. Data from the 2006 Life in Transition Survey (LiTS)—a joint initiativeof the European Bank for Reconstruction and Development and the World Bank—provides a unique opportunity to investigate the extent to which citizens of ECA countriesare satisfied with their lives and with the performances of their governments, and to studykey factors influencing their outlook in a systematic way across all countries of the region.1

The LiTS was carried out in 29 countries: Albania, Armenia, Azerbaijan, Belarus,Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Former YugoslavRepublic of Macedonia, Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania,Moldova, Mongolia (not in ECA, but included because its an EBRD client country),Montenegro, Poland, Romania, Russia, Serbia, Slovakia, Slovenia, Tajikistan, Turkey, Ukraine,and Uzbekistan between August and October 2006. In each country, the LiTS questionnairewas administered to a nationally representative sample of 1,000 households using face-to-face interviews.

The main objective of the LiTS was to assess the impact of transition on people, and sothe survey questionnaire covered four main themes. First, it collected personal informationon aspects of material well-being, including household expenditures, possession of con-sumer goods such as a car or mobile phone, and access to local public services and utilities.Second, the survey included measures of satisfaction and attitudes towards economic andpolitical reforms as well as public service delivery. Third, the LiTS captured individual“histories” through transition—from around 1989 to the present, especially key events andepisodes that may have influenced their attitudes towards reforms, and collected informa-tion on individuals; family background, on their employment situation, and on copingstrategies during transition. Finally, the survey also attempted to capture the extent towhich crime and corruption are affecting peoples’ lives, and the extent to which individuals’trust in other people and in state institutions has changed over time.

This volume presents the main findings of three studies by World Bank economistsusing data from the 2006 LiTS.

Chapter 1 examines quantitative and qualitative dimensions welfare in countries ofEastern Europe and the former Soviet Union, with “satisfaction with life” being the keywelfare measure used. Analysis suggests that more than 60 percent of the populationreports satisfaction with life though this varies quite a bit across countries: Slovenia has the

vii

1. For more details on the LiTS as well as the preliminary survey findings, see EBRD 2007.

11524-00_FM.qxd 1/19/09 11:41 AM Page vii

highest rates of satisfaction and Georgia the lowest. Econometric analysis suggests a positivecorrelation of the overall satisfaction-with-life variable with factors such as expenditureper capita, equality of incomes, youth, working status, non-agricultural employment,non-metropolitan living, and better education. However, the analysis also highlights theimportant role played by subjective factors like self-assessed health status, level of trust inpeople, relative economic status compared to peers and own-perception of improvementin economic status over time in determining overall satisfaction levels.

Chapter 2 analyzes socioeconomic characteristics of different income groups acrosscountries, and shows how the welfare measure derived from the LiTS provides a veryuseful and effective means to measure household welfare and/or rank households by relative economic status, both within as well as across countries. Moreover, this welfaremeasure also compares favorably with welfare measures using traditional and more exten-sive household budget surveys. The chapter provides a systematic examination of thesources of household incomes, patterns of asset ownership, as well as the sectoral occupationpatterns of the working poor. Wages and pensions are the primary sources of income forthe bulk of the population though the poor depend more on pensions. Asset ownership—in terms of cars, secondary residence, mobile phone, and computers—varies quite broadly,and asset inequality is quite significant across income groups both within and across coun-tries. The data also that the poor have a relative disadvantage in that they are primarilyemployed in low productivity agriculture, and have limited educational attainment andnon-professional skills.

Finally, chapter 3 focuses on three interlinked questions: (i) why are some people morelikely than others to use publicly provided health services? (ii) what are some of the keyinfluences on users’ satisfaction with quality and efficiency of medical treatment received?and (iii) how does the prevalence of informal payments impact people’s decision on usingpublicly provided health services, and upon use, the level of satisfaction with servicesreceived? Analysis shows that the elderly, the relatively better-off, and those who have con-fidence in the government are more likely to use publicly provided health services, whilethose with compulsory/secondary education as well as those with some tertiary educationare less likely to access these services. Satisfaction with publicly provided health services inthe region is quite high, though there is considerable variation evident across countries. Ingeneral, while a large majority of respondents say that unofficial payments/gifts are neverneeded when using publicly provided health services, in cases where users have to pay forwhat should essentially be “free services,” this has a significant negative influence on satis-faction with service delivery.

viii Preface

11524-00_FM.qxd 1/19/09 11:41 AM Page viii

Acknowledgments

The 2006 EBRD-World Bank Life in Transition Survey (LiTS) was designed by theEBRD’s Office of the Chief Economist and the World Bank’s Europe and Central Asia

(ECA) Region, under the general guidance of Erik Berglof (Chief Economist, EBRD) andPradeep Mitra (Chief Economist, Europe and Central Asia Region, World Bank) and AsadAlam (Sector Manager, ECSPE, World Bank). The core task team for the project was led byPeter Sanfey (EBRD) and Salman Zaidi (World Bank), and comprised James Anderson,Pauline Grosjean, Juan Munoz, Franklin Steves, and Utku Teksoz. Field work for thesurvey was carried out by the global market research firm Synovate, under the directionof Savvas Kyriakides.

Chapter 1 and 2 were written by Asad Alam, Pradeep Mitra, and Salman Zaidi, whileChapter 3 of this volume was written by Ramya Sundaram and Salman Zaidi. HelenaMakarenko processed the report. Funding for the survey was provided by Canada, TaiperChina, and the United Kingdom. In addition, the authors gratefully acknowledge supportprovided by the ECA Chief Economist Regional Studies Program and from the World BankResearch Support Budget. The assessment and views presented in this volume are those ofthe authors, and should not be attributed to the Executive Directors of the World Bank.

ix

11524-00_FM.qxd 1/19/09 11:41 AM Page ix

11524-00_FM.qxd 1/19/09 11:41 AM Page x

There has been a resurgence of interest recently among social scientists in studyingsubjective measures of individual well-being, and in analyzing how peoples’ senseof their personal welfare is impacted by not just their level of incomes, but also

other diverse factors like health, income inequality, and employment status.3 Muchresearch has been carried out to better understand why some people say they are satisfiedwith their lives, and others say they are not. While there is broad agreement in the literatureabout the diverse set of factors that affect individual well-being, much less consensusprevails about the relative importance of these factors, even among leading researchers.For instance, Richard Layard has argued that non-income factors like health and familycircumstances impact peoples’ sense of well-being more than income per se. Others havestressed that incomes play the main role in determining peoples’ satisfaction with life—Angus Deaton has noted that the map of average satisfaction levels across the world looksvery similar indeed to the spatial distribution of average incomes across countries.

The past two decades in Eastern Europe and the former Soviet Union (ECA) havebeen times of tremendous change, with countries undergoing rapid transformation fromcentrally-planned to market-oriented economies. While poverty increased during the initialyears of transition, primarily on account of the sharp economic contraction, the resurgenceof economic growth in the region since 1998 has resulted in a rebound in householdincomes and living standards.4 However, little is known about subjective welfare in the wake

CHAPTER 1

Key Factors Affecting Satisfactionwith Life in Eastern Europe and

the Former Soviet Union2

1

2. Asad Alam, Pradeep Mitra, and Salman Zaidi.3. See, for instance, Layard (2006), Kahneman and Krueger (2006), Helliwell (2007), Clark, Frijters,

and Shields (2007), Graham (2007), as well as many other papers referenced in these publications.4. For instance, see World Bank (2005).

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 1

of this growth rebound, especially how people across ECA countries view their satisfactionwith life. Earlier work on ECA countries, using the World Values Survey, have had limitedcountry coverage and used early data that mostly covered the first decade of transition.These earlier studies have also focused more on explaining differences in attitudes betweenmarket and socialist economies, and examining people’s preferences for redistributivespending by the state and for greater income equality.5

The chapter addresses three main questions: (1) what are prevailing levels of satis-faction with life (SWL) in ECA countries, and how have they been changing over time?(2) what are the main factors that help explain SWL, and in particular what is the relativeimportance of income vs. non-income factors like health, family status?, and finally, (3) whyare prevailing levels of SWL in ECA somewhat lower than what might be expected givenrelatively high income levels and good health status etc? The next section of this chapterprovides an overview of satisfaction with life and its correlates in ECA countries. The resultsof the multivariate analysis are presented in the second section, which show that whileper capita incomes and employment status are important drivers of satisfaction with life,other non-income factors such as health, relative economic status, and level of trust inother people also play a crucial role. How do average satisfaction rates in ECA countriesin 2006 compare to findings of similar surveys conducted earlier in time? How about inrelation to countries in other parts of the world? These questions are taken up in the thirdsection, where we compare the LiTS findings on satisfaction with life with similar resultsfrom other surveys conducted in other parts of the world as well as in the same set of coun-tries earlier in time.

Satisfaction with Life as a Welfare Measure

Respondents in the LiTS were asked to what extent they agreed with the statement: “Allthings considered, I am satisfied with my life now,” with responses coded as 1 = stronglydisagree (SD), 2 = disagree (D), 3 = neither disagree nor agree (N), 4 = agree (A), and 5 = strongly agree (SA). In the LiTS sample overall, respondents that reported themselvesas satisfied with their lives outnumber those that are not 3 to 2. Yet, this varies considerablyacross countries from a high of 8:1 in Slovenia to roughly 2:5 in Georgia (see Figure 1.1).

Satisfaction rates not only vary across countries but also across groups of countries (seeFigure 1.2). Most of the new member states of the European Union, which have perhapsseen the biggest political transformation in the Region, feature in the upper part of thedistribution except for Hungary which is third from the bottom. Conversely, many ofthe countries of southeastern Europe and the south Caucuses show relatively low levels ofsatisfaction.

Despite the clear heterogeneity in satisfaction rates observed across countries and groupsof countries, there are nonetheless some similarities evident across some groups. At the

2 World Bank Working Paper

5. See, e.g. Murthi, Mamta. and Erwin Tiongson, 2008, Attitudes to Equality: The “Socialist Legacy”Revisited, Policy Research Working Paper No. 4529, The World Bank for recent examination of preferencesfor inequality as well as for an excellent survey of the ECA-specific literature. The study uses data fromearly rounds of the World Values Survey: 1990, 1995–97, and 1999–01 and covers a smaller set of 4, 15,and 17 countries in these three rounds.

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 2

country level, the LITS data show satisfaction with life to be positively correlated withabsolute incomes (PPP-adjusted), and negatively correlated with level of income inequality(Figure 1.3).

One of the striking contrasts observed across most transition countries6 is the cleardivide across age groups: overall satisfaction rates among the youth are considerably higherthan amongst the elderly (Figure 1.4). This is not surprising given that it has been the adultpopulation which lived through the economic decline and dislocations of the 1990s andfor whom the economic transition, with its attendant uncertainties and insecurities, hasbeen the most acute.

What is striking is that even with higher unemployment rates, younger age cohortsare more positive about life satisfaction (see Figure 1.3). Similarly, overall satisfaction ratesare quite low in Bosnia, Serbia, FYR of Macedonia, Georgia, and Hungary; yet those under30 years appear to have a more positive outlook on life as compared to the rest of thepopulation.

Satisfaction with Life and Service Delivery in Eastern Europe and the Former Soviet Union 3

0 10 20 30 40 50 60 70 80 90 100

Georgia

Armenia

Hungary

Azerbaijan

Serbia

Macedonia, FYR

Moldova

Bosnia

Montenegro

Bulgaria

Romania

Ukraine

Albania

Turkey

Russia

Poland

Lithuania

Kazakhstan

Czech Republic

Latvia

Croatia

Slovakia

Kyrgyz Republic

Estonia

Belarus

Tajikistan

Uzbekistan

Slovenia

Strongly Disagree Disagree Neither Agree Strongly Agree

Figure 1.1. All Things Considered, I am Satisfied with my Life Right Now

6. Turkey and Mongolia are useful comparators in this regard, in the sense of being “non-transition”countries, and response patterns in these two countries do not show such marked differences by age group.

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 3

Other Subjective Measures of Well-being

The data also suggest that the (self-reported) health status of respondents has a strong bear-ing on reported level of satisfaction with life (Figure 1.5).

In addition to these quantitative measures of well being, the LiTS questionnaire alsoincludes a number of other questions on respondents’ perception of their relative eco-nomic standing, both at present and around 1989.7 The respondents were asked to whatextent (on a five-point scale, ranging from 1: strongly disagree to 5: strongly agree) theyagreed with statements like (a) I have done better in life than most of my high school class-mates, and (b) I have done better in life than most of the colleagues I had around 1989.Responses to these questions provide some interesting insights into the extent to whichrespondents’ expressed level of satisfaction with life is related to their perceptions regard-ing both their current economic standing as well as how their economic standing haschanged over time and in relation to their peers. These patterns of association are exam-ined in the tables below, which illustrate the “average SWL score”—the average responses


All things considered, I am satisfied withmy life right now

0

10

20

30

40

50

60

CIS

-low

inco

me

EU

mem

ber

stat

es

CIS

-mid

dle

Oth

er

Sou

th-E

aste

rn E

urop

e

% o

f res

pond

ents

Agree

Strongly Agree

Figure 1.2. SWL Rates by Region

Note: “CIS-low income” includes Moldova, Armenia, Azerbaijan, Georgia, Kyrgyz Republic, Tajikistan, and Uzbekistan; “EU member states” includes Slovenia, Estonia, Slovakia, Latvia,Czech Republic, Lithuania, Poland, and Hungary; “CIS-middle” includes Belarus, Ukraine, Kazakhstan, and Russia; SEE includes Albania, Bosnia, Bulgaria, FYR of Macedonia, Montenegro,Romania, and Serbia; “Other” includes Croatia and Turkey.

7. The specific questions were: (1) Please imagine a ten-step ladder where on the bottom, the first step,stand the poorest people and on the highest step, the tenth, stand the richest. On which step of the ten isyour household today?; (2) Now imagine the same ten-step ladder around 1989, on which step was yourhousehold at that time?

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 4


Figure 1.3. Correlation of Satisfaction with Life with Average Incomes and Level of Inequality

Albania

Belarus

Bosnia Bulgaria

Croatia Czech

Macedonia

HungaryMoldova

Poland

Romania

Slovakia

Slovenia

Turkey

Ukraine

Armenia

Azerbaijan

Estonia

Georgia

Kazakhstan

Kyrgyz

Latvia

Lithuania

Mongolia

Russia

Tajikistan

Uzbekistan

2030

4050

6070

% a

bove

neu

tral

on

satis

fact

ion

with

life

0 5000 10000 15000 20000

Per-capita GDP in 2005 (PPP$2000)

All things considered, I am satisfied with my life right now

(ii) Level of Income Inequality

(i) Level of Per capita GDP

Albania

Belarus

BosniaBulgaria

CroatiaCzech

MacedoniaHungary

MoldovaMontenegro

Poland

Romania

Serbia

Slovakia

Slovenia

Turkey

Ukraine

ArmeniaAzerbaijan

Estonia

Georgia

Kazakhstan

Kyrgyz

LatviaLithuania

Mongolia

Russia

Tajikistan

Uzbekistan

2030

4050

6070

% a

bove

neu

tral

on

satis

fact

ion

with

life

.3 .35 .4 .45

Gini coefficient of income inequality


11524-01_Ch01.qxd 1/19/09 11:43 AM Page 5


of the satisfaction to lifequestion, where 1 indicatesstrong disagreement and 5 indicates strong agreementwith the statement that therespondent is satisfied withhis/her life.

Table 1.1 reports aver-age SWL scores by (i) self-assessed economic welfareand (ii) comparisons relativeto peers, and reveals a num-ber of interesting patterns.For instance, looking acrossthe table’s bottom row, we find average SWL scoreincreases with self-assessedlevel of welfare: respon-dents in the highest welfarequintile have an averageSWL of 3.9 compared toonly 2.3 for those that placethemselves in the lowestquintile. Moreover, withineach self-assessed welfaregroup, we find average SWLscores to be positively cor-related with extent to whichthe respondents feel they’vedone better than their highschool classmates (lookingdown each of the columns).Satisfaction with life thusdepends, it seems, not juston (self-perceived absolutewelfare), but rather alsoon how individuals seem to

think they have done relative to their peers. Thus, respondents that place themselves in thehighest welfare quintile and who “strongly agree” that they’ve done better in life than theirhigh school classmates have an average SWL score three times as high (4.8 vs. 1.6) as thosethat place themselves in the lowest quintile and who “strongly disagree” with having donebetter than their classmates.8

010

2030

4050

Per

cent

of r

espo

nden

ts

18-30 yrs 31-40 yrs 41-50 yrs 51-60 yrs 61-70 yrs 71+ yrs

Age-Group of the Respondent


Agree Disagree

Figure 1.4. Satisfaction with Life Among the Youth is Generally Higher than Among the Elderly

020

4060

Per

cent

of r

espo

nden

ts

Very good Good Medium Bad Very bad

Self-Assessed Health Status


Agree Disagree

Figure 1.5. Satisfaction with Life is Higher for the Healthy

8. A similar pattern is evident if instead one looks at responses to the “I have done better in life thanmost of the colleagues I had in 1989” statement. Please see Table A1 in Annex for these summary statistics.

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 6

Average SWL scores reported in Table 1.2, which shows average SWL scores by self-assessed economic welfare, both (i) today, and (ii) around 1989, suggest thatrespondents’ current satisfaction with life depend not just on current economic wel-fare, but also on how they think its changed over time (moving across the rows fromleft to right, average scores generally fall—i.e. we observe negative slopes across all rowsexcept the last).


. . . Cell has fewer than 30 observations

Table 1.1. Average SWL Score by Self-perceived Economic Status

I Have Done Better in Life Than Self-assessment of Own Economic Welfare (quintile)

Most of My High School Classmates Lowest 2 3 4 Highest Overall

Strongly disagree 1.6 1.9 2.3 2.2 . . . 1.8

Disagree 2.3 2.7 3.1 3.0 . . . 2.7

Neither 2.6 3.0 3.4 3.7 3.7 3.2

Agree 2.7 3.5 3.8 3.9 4.0 3.6

Strongly agree 2.8 3.6 4.0 4.6 4.8 4.0

Overall 2.3 3.0 3.5 3.8 3.9 3.1

Figure 1.6 illustrates how SWL varies by the level of respondents’ trust in people.Among those reporting “complete distrust” in people today, the proportion that are dis-satisfied with their lives outnumber those that are satisfied: by contrast, among thosereporting “complete trust” in people today, those satisfied with their lives outnumber thosethat are dissatisfied by 3 to 1. Similarly, respondents reporting having greater trust in peopletoday as compared to before 1989 tend, on average, to have higher SWL scores than others(as can be seen in Table 1.3, average scores in the cells below the diagonal tend to be higherthan those above the shaded diagonal).


Table 1.2. Average SWL Score by Present and Past Self-assessed Economic Status

Self-assessment of Own Economic Self-assessment of Welfare in 1989 (quintile):

Welfare (quintile) at Time of Survey Lowest 2 3 4 Highest Overall

Lowest 2.5 2.4 2.4 2.1 1.9 2.4

2 3.4 3.2 2.9 2.7 2.9 3.0

3 3.6 3.7 3.6 3.2 3.2 3.5

4 3.8 4.0 4.0 3.6 3.4 3.8

Highest . . . 3.6 3.9 4.1 4.1 3.9

Overall 3.0 3.1 3.1 3.0 3.0 3.1

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 7

Key Factors Influencing SWL: Multivariate Analysis

In order to better examine the correlate of satisfaction with life, we use an ordered probitmodel to analyze respondents’ expressed level of satisfaction with life according to thefollowing model specification:

We do not observe y i* directly, but rather only observe whether yi = 1, 2, 3, 4, and 5, ifαj−1 < y i* < αj ( j = 1, 2, 3, 4, and 5 respectively)—i.e. the expressed level of satisfaction withlife on a 5 point scale. Various factors that we think influence the level of satisfactionexpressed by the respondent are included in the vector xi (see below).

y xi i i* = ′ +β ε


020

4060

Per

cent

of r

espo

nden

ts

Complete distrust 432 Complete trust

Level of trust in people today


Agree Disagree

Figure 1.6. Satisfaction with Life is Positively Correlated with Level of Trust in People

Table 1.3. Average SWL Score by Present and Past Level of Social Capital

Level of Trust in People Before 1989

Level of Trust In People Today Lowest 2 3 4 Highest Overall

Lowest 2.7 2.5 2.8 2.6 2.7 2.7

2 2.8 3.2 3.1 3.0 3.0 3.0

3 2.6 3.4 3.2 3.2 3.2 3.2

4 3.5 3.2 3.4 3.4 3.2 3.3

Highest 3.5 3.1 3.7 3.4 3.4 3.4

Overall 2.8 3.0 3.2 3.1 3.1 3.1

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 8


Variable Obs Mean Std. Dev. Min Max

swl 27395 3.119146 1.155076 1 5

health 27996 2.725782 1.002334 1 5

sc_now 27032 2.64542 1.232121 1 5

sc_change 23769 −1.009214 1.473212 −4 4

lnpcexp 27911 8.708414 .8421385 2.204605 12.06602

car 27993 40.81735 49.15043 0 100

secondhouse 28000 88.95 31.35176 0 100

bankaccount 27978 36.48938 48.1409 0 100

creditcard 27975 31.13137 46.3039 0 100

mobilephone 27993 62.92645 48.30104 0 100

computer 27988 27.55109 44.67794 0 100

pew 24067 2.999668 1.008523 1 5

diff 28000 −1.187571 2.355999 −9 9

unemploy 28000 .085 .2788867 0 1

transfer 28000 .0237143 .1521603 0 1

hhsize 28000 3.238929 1.840137 1 12

age 28000 46.50054 17.79848 17 97

age2 28000 2479.074 1760.618 289 9409

country 28000 14.60696 8.247696 1 29

locality 28000 1.836929 .7392072 1 3

swl: Satisfied with life, coded as 1 = strongly disagree (SD), 2 = disagree (D), 3 = neither disagree nor agree (N), 4 = agree (A), and 5 = strongly agree (SA).

health: How would you assess your health, 1 = Very good, 2 = Good, 3 = Medium, 4 = Bad, 5 = Very bad.

sc_now: Level of trust in people today, 1 = Complete distrust, 2 = Some distrust, 3 = Neither, 4 = Some trust, 5= Complete trust.

sc_change: Difference between sc_now and sc_before (with both variables coded as above).lnpcexp: (log) Per equivalent adult (using OECD scales) annual expenditures (in PPP$).car, secondhouse, bankaccount, creditcard, mobilephone, computer: 100 = Respondent reports owning the item.pew: I have done better in life than most of my high school classmates/colleagues I had

around 1989. (responses coded the same as swl).diff: Change in self-perceived decile group ranking between 1989 and present.unemploy: 1 = Not working at present and actively looked for a job at this moment, 0 otherwise.transfer: 1 = Transfers (unemployment benefits, social benefits, and/or help from charities

and non-government organizations) are the most important source of livelihood for the household.

hhsize: Total household sizeage: Age in yearsage2: Age in years, squaredcountry: Country code (29 unique values for each of the 29 countries covered in the survey:

Mongolia excluded)locality: 1 = Urban, 2 = Rural, 3 = Metropolitan area.

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 9

The results of the ordered probit model using the above set of explanatory variables/controls(Table 1.5) confirm the following hypotheses:

■ Satisfaction with life is positively correlated with health status, with very goodhealth status increasing the probability of life satisfaction by more than 20 percent;the role played by overall health status is particularly important in the EU countrygroup.9


Table 1.4. Simulated Probabilities Derived from Ordered Probit Model

Predicted Probability of Response:

Strongly Strongly Ideal Type Disagree Disagree Neither Agree Agree

“Average” respondent: 0.05 0.23 0.30 0.38 0.04

Self-reported health status: Very good 0.03 0.18 0.28 0.44 0.06

Medium 0.05 0.23 0.30 0.38 0.04

Very bad 0.12 0.33 0.29 0.24 0.01

Trust in people today: Complete distrust 0.08 0.28 0.30 0.32 0.02

Neither 0.05 0.23 0.30 0.38 0.04

Complete trust 0.03 0.18 0.28 0.45 0.06

Per-capita expenditures half the sample mean 0.06 0.25 0.30 0.35 0.03

Per-capita expenditures twice the sample mean 0.05 0.22 0.29 0.40 0.04

Per-capita expenditures four times the 0.04 0.20 0.29 0.42 0.05sample mean

Done better than peers: Strongly disagree 0.26 0.40 0.22 0.11 0.00

Neither 0.05 0.23 0.30 0.38 0.04

Strongly agree 0.01 0.07 0.18 0.56 0.18

Moved from 3rd to 7th decile between 1989 0.01 0.11 0.23 0.52 0.11to present

Main income source is transfers 0.07 0.26 0.30 0.34 0.03

Person is currently unemployed 0.07 0.27 0.30 0.33 0.03

64 yr old, self-reported health status very bad, 0.77 0.20 0.03 0.00 0.00per-capita expenditures half sample mean, down 4 decile places, strongly disagrees she/he has done better than peers; transfers main income source, currently unemployed, complete distrust in people today; complete trust in people before 1989.

28 yr old, self-reported health status very good, 0.00 0.01 0.05 0.45 0.48employed, per-capita expenditures twice sample mean, up 4 decile places, agrees she/he has done better than peers; complete trust in people today; complete distrust in people before 1989.

9. As noted earlier, this country group excludes Romania and Bulgaria, which are included under“Other.”

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 10

Table 1.5. Ordered Probit Results: SWL by Country Groups

Overall EU SEE CIS_L CIS_M Other

coef sd coef sd coef sd coef sd coef sd coef sd

(i) Health Status:

Very good 0.146*** 0.030 0.317*** 0.056 0.247*** 0.050 0.034 0.080 0.362** 0.141 0.203* 0.109

Good 0.095*** 0.019 0.096*** 0.036 0.123*** 0.038 0.232*** 0.044 0.101* 0.052 0.134* 0.074

Medium Reference Category

Bad −0.246*** 0.023 −0.200*** 0.041 −0.237*** 0.049 −0.228*** 0.047 −0.347*** 0.059 −0.313*** 0.089

Very bad −0.469*** 0.039 −0.569*** 0.070 −0.306*** 0.079 −0.497*** 0.080 −0.405*** 0.124 −0.330*** 0.126

(ii) Level of trust in people

Complete distrust −0.220*** 0.027 −0.216*** 0.053 −0.159*** 0.050 −0.149** 0.062 −0.221*** 0.079 −0.300*** 0.099

Some distrust −0.020 0.023 0.000 0.039 0.017 0.046 −0.027 0.058 −0.076 0.064 −0.274*** 0.092

Neither trust nor distrust Reference Category

Some trust 0.146*** 0.023 0.175*** 0.038 0.106** 0.046 0.264*** 0.056 0.000 0.059 −0.022 0.093

Complete trust 0.280*** 0.039 0.386*** 0.077 0.262*** 0.093 0.315*** 0.078 0.191** 0.091 −0.075 0.167

Change in trust since 1989 0.021*** 0.007 0.030** 0.015 0.057*** 0.014 −0.033** 0.016 0.035* 0.020 0.031 0.026

(iii) Economic status:

Log normalized expenditures ($) 0.093*** 0.013 0.142*** 0.026 0.082*** 0.025 0.184*** 0.027 0.009 0.035 −0.005 0.048

Household owns:

A car 0.077*** 0.019 0.112*** 0.034 0.044 0.034 0.110** 0.045 0.103** 0.049 0.246*** 0.077

Second home 0.045* 0.025 0.034 0.039 0.136*** 0.052 0.068 0.079 0.008 0.077 0.124 0.085

A bank account 0.178*** 0.021 0.171*** 0.041 0.059 0.038 0.046 0.097 0.002 0.057 0.096 0.085

A credit/debit card 0.071*** 0.022 0.053 0.036 0.039 0.039 0.013 0.088 0.143** 0.060 0.048 0.085

A mobile phone −0.031 0.020 0.048 0.041 0.084** 0.043 −0.106** 0.042 0.056 0.053 0.099 0.088

A computer 0.052** 0.021 0.052 0.036 0.073* 0.039 −0.032 0.077 −0.030 0.055 0.078 0.078

(continued )

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 11

Done better than peers

Strongly disagree −1.008*** 0.033 −0.962*** 0.060 −0.965*** 0.060 −1.085*** 0.075 −1.453*** 0.106 −0.729*** 0.105

Disagree −0.294*** 0.020 −0.366*** 0.035 −0.197*** 0.040 −0.345*** 0.044 −0.439*** 0.054 −0.149* 0.084

Neither disagree or agree Reference Category

Agree 0.353*** 0.020 0.284*** 0.037 0.406*** 0.038 0.471*** 0.046 0.339*** 0.052 0.281*** 0.078

Strongly agree 0.872*** 0.036 0.910*** 0.068 0.811*** 0.071 1.081*** 0.095 0.941*** 0.088 0.628*** 0.107

Change in decile ranking:

From 1989 to present: 0.121*** 0.003 0.118*** 0.007 0.129*** 0.006 0.091*** 0.007 0.125*** 0.010 0.108*** 0.013

Transfers main income source −0.141*** 0.053 −0.145 0.092 −0.298*** 0.097 −0.025 0.139 0.019 0.245 0.129 0.153

Unemployed Residence −0.238*** 0.029 −0.176*** 0.065 −0.184*** 0.052 −0.280*** 0.051 −0.262*** 0.101 −0.153 0.130

Rural

Urban −0.020 0.018 −0.124*** 0.032 0.052 0.034 −0.062 0.045 0.156*** 0.047 0.071 0.071

Metropolitan areas −0.088*** 0.022 −0.073* 0.038 −0.073* 0.042 −0.189*** 0.049 −0.036 0.073 0.042 0.082

Other controls (age, age squared, household size, etc.) omitted.

Pseudo-R2 0.130 0.131 0.125 0.140 0.143 0.122

Table 1.5. Ordered Probit Results: SWL by Country Groups (Continued)



11524-01_Ch01.qxd 1/19/09 11:43 AM Page 12

■ Social capital (using responses to the question on level of trust in other people as aproxy), appears to matter quite a bit—respondents that said they trusted other peoplewere significantly more likely to be satisfied with their lives than those that did not.

■ Over the region as a whole, those with twice the average per capita expenditures areabout 13 percent more likely to report being satisfied with their lives compared tothose with half the average PCE. Correlation between satisfaction with life andincome varies quite a bit across the various country groupings: for instance, theassociation between level of income and SWL is strongest in CIS low-income coun-tries, but weakest in CIS middle-income countries.

■ Satisfaction with life tends to be positively correlated with ownership of miscellaneousdurable goods; other things being equal, a person owning a car, a mobile phone,and a computer is about 10 percent more likely to report being satisfied with his/herlife compared to a person not owning any of these durable goods.

■ As a group, the young are much more likely to be satisfied with life than the elderly;however, controlling for the influence of other factors, there are in fact only verysmall differences in satisfaction levels across different age groups.

■ Relative status matters. A higher perceived relative position with respect to peers andan improvement in the self-perceived difference in decile ranking relative to 1989increase the likely satisfaction with life; this is true across all the country groups.

■ Work status is very important; the employed are about 16 percent more likely to besatisfied with life than those who are unemployed; in other words, other things beingequal the difference in satisfaction levels between the employed and the unemployedare greater in magnitude than those between people with half and twice the averageper capita expenditures; the negative impact of unemployment status on overallsatisfaction with life is found to be quite strong across all country groups.

In addition, the findings of the regression analysis provide some additional insights too inareas where, a priori, the relationship between satisfaction with life and other variables isnot so obvious. For instance:

■ We find that where transfers are a significant source of income (for example, in theEU and SEE groups, a person is about five times as likely to report transfers as themain source of income as in the CIS middle-income group) those reporting transfersas the households’ main income source are significantly less likely to be satisfied withtheir lives compared to respondents whose households are not so heavily reliant ontransfer incomes. For instance, in the SEE (where this difference is the starkest)those not dependent on transfers as their main income source are about 48 percentmore likely to be satisfied with their lives as compared to those reporting transfersas their households’ main source of income.

■ Finally, in contrast to the findings of earlier studies which indicate that average livingstandards of people living in metropolitan areas are both better on average as wellas have been improving faster than those living elsewhere, the results of our analysisshow that, other things being equal, people living in metropolitan areas are in factless likely to be satisfied with their lives than those living either in other urban areasor else in rural areas. However, this might simply be an artifact of the failure of thewelfare measure used in the analysis (log per capita expenditures in PPP dollars) tocontrol for cost-of-living differences across these localities.


11524-01_Ch01.qxd 1/19/09 11:43 AM Page 13

Comparisons over Time and Across Countries

How do average satisfaction rates from the 2006 LiTS compare to the findings from similarsurveys—for example, the World Values Survey (WVS)10—conducted earlier in the samecountries? Responses to the satisfaction with life question in the latter survey are recordedon a 1–10 point scale, so need to be adjusted accordingly before comparing directly withthe LiTS findings. A comparison of the adjusted WVS scores (modified to a 1–5 scale) withthe LiTS for the 21 countries for which such over-time comparisons are indeed possible arepresented in Table 1.6. Comparing these two sets of findings thus reveals that over the past6–8 years, average SWL scores increased the most in Belarus, the Baltic states, and Russia,but in fact declined in some countries in the Balkans—for example, Bosnia, Serbia, andMontenegro (Table 1.6).


Table 1.6. Change over Time in Average SWL Rates by Country

World Values Survey 2006

Score Year LiTS Change in Score

Albania 2.7 1998 3.2 0.5

Azerbaijan 3.0 1997 2.7 −0.3

Belarus 2.5 1996 3.6 1.1

Bosnia 3.0 1998 2.6 −0.4

Bulgaria 2.6 1997 2.8 0.2

Croatia 3.3 1996 3.3 0.0

Czech Republic 3.4 1998 3.4 0.0

Estonia 2.8 1996 3.5 0.7

Georgia 2.6 1996 2.5 −0.1

Hungary 3.2 1998 2.6 −0.6

Latvia 2.7 1996 3.3 0.6

Lithuania 2.8 1997 3.3 0.5

Moldova 2.2 1996 2.7 0.5

Montenegro 3.3 1996 2.7 −0.6

Poland 3.4 1997 3.3 −0.1

Romania 2.7 1998 2.9 0.2

Russia 2.5 1995 3.1 0.6

Serbia 3.0 1996 2.6 −0.4

Slovakia 3.3 1998 3.4 0.1

Slovenia 3.4 1995 3.8 0.4

Turkey 3.3 1996 3.1 −0.2

10. The World Values Survey is a worldwide investigation of socio-cultural and political change,conducted by a network of social scientist at universities all around world. For more details, seehttp://www.worldvaluessurvey.org

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 14


On the whole, there appears to be a fairly close conformance across the region at thecountry level between changes in average SWL scores and performance of the economy.For instance, countries like the Baltic states, Belarus, Moldova, Russia, and Albania, whichhave recorded relatively high growth rates in recent years also tend to be the ones whereaverage SWL scores appear to have increased the most in recent years; by contrast, othercountries with relatively poorly performing economies, like Bosnia, Hungary, Poland, andTurkey, also tend to be the ones with the least improvements (or in some cases actual declines)in average SWL scores (Table 1.7).

11. Stokes, Bruce: “Happiness is Increasing in Many Countries—But Why?—Rising Incomes a BigReason, But Not the Only One” available at: http://pewglobal.org/commentary/display.php?AnalysisID=1020

12. E.g. see Peter Sanfey & Utku Teksoz “Does Transition Make You Happy?” EBRD working paper#91, April 2005.

Table 1.7 Comparing GDP and SWL Changes in Recent Years

Change in GDP (%) Between 2000 and 2005

Change in SWL Bottom Middle Top

Bottom Bosnia, Hungary, Montenegro, Serbia AzerbaijanPoland, Turkey

Middle Slovenia, Czech Romania, Bulgaria, GeorgiaRepublic, Croatia Slovakia

Top — Russia, Albania Latvia, Lithuania, Estonia, Belarus, Moldova

How do average satisfaction rates in countries in the Europe and Central Asia regioncompare to countries with similar incomes in other regions in the world? Data from therecent Pew Global Attitudes survey reveal two interesting findings of relevance to our work:(i) in countries in Latin America and Eastern Europe for which comparable data are availableover time, there is a high correlation between income growth and changes in happinessover time, (ii) Eastern European respondents appear to be less satisfied with their lives thanLatin Americans.11

That individuals in transition countries tend to have lower self-reported SWL ratescompared to those in non-transition countries has also been noted in other earlier studies,which have tended to attribute this to difficulties faced by people in these countries to adaptto the profound economic and social changes that have taken place over this period.12

In addition to the role played by falling incomes and rising inequality discussed above(and earlier), we offer three additional broad sets of factors that might help explain this,and examine some evidence in support of these conjectures: changes in (i) employment,(ii) trust, and (iii) rising inequality.

First, unemployment rates rose and activity rates fell in many transition countries withthe transitional recession. Despite the subsequent recovery of output, these indicators have

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 15

failed to return to their pre-transition levels with slower progress in job creation.13 As illus-trated in Figure 1.7, there is clearly a strong positive correlation evident between SWL andemployment rates, and the fact that employment rates in Eastern Europe continue to stagnateat fairly low overall rates may be part of the reason why, other things being equal, SWLscores are lower than in Latin America.

Second, the LiTS data reveal one very interesting finding: level of trust in other peopleappears to have been eroded considerably during the transition period (see appendixtable A8). This is not surprising given that social capital was seriously undermined duringthe economic crisis (World Bank 2005a). Given that SWL is clearly positively correlatedwith average level of trust in people (Figure 1.8), the secular decline in overall levels oftrust over time in ECA may be another reason why satisfaction rates are lower than inLatin America.

Third, income inequality has risen in most ECA countries during the transition. Therealso seems to be a fairly widespread feeling among respondents that somehow othershave done better during transition than they have. Given the importance of bench-marking themselves relative to peers in determining overall SWL as identified in ourearlier analysis, this in turn may be another reason why people in ECA are, on average,less satisfied than in other countries.


Albania

Belarus

Bosnia

Bulgaria

CroatiaCzech

Macedonia Hungary

MoldovaMontenegro

Poland

Romania

Serbia

Slovakia

Slovenia

Turkey

Ukraine

Armenia

Azerbaijan

Estonia

Georgia

Kazakhstan

Kyrgyz

Latvia

Lithuania

Mongolia

Russia

Tajikistan

Uzbekistan

2030

4050

6070

% a

bove

neu

tral

on

satis

fact

ion

with

life

30 40 50 60 70 80

% respondents aged 65 years or under that worked during past 12 months


Figure 1.7. Cross-Country Comparisons: SWL and Employment Rates

13. See World Bank 2005, Enhancing Job Opportunities in Eastern Europe and the former Soviet Union,Washington, DC.

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 16

Concluding Observations

The Life in Transition Survey provides a useful new addition to welfare measures commonlyapplied to ECA countries. The survey provides information on this new “satisfaction withlife” measure across all ECA countries. The results show that most people in ECA reportsatisfaction with life. But there are large variations across ECA countries, with a highest rateof 90 percent in Slovenia and the lowest rate of 40 percent in Georgia. Our analysis confirmsthe importance of expected factors like income per capita, equality of incomes, youth,working status, non-agricultural employment, non-metropolitan living, and better edu-cation. However, our analysis also highlights the important role played by subjective factorslike self-assessed health status, level of trust in people, relative economic status compared topeers and own perception of improvement in economic status over time are also importantfactors determining overall satisfaction levels. Satisfaction with life in ECA is also, not sur-prisingly, lower than the levels found in other countries. This likely reflects the transitionalrecessional and the social costs of the major economic transformation undertaken by mostof these countries.


Albania

Belarus

Bosnia

Bulgaria

Croatia

Czech

MacedoniaHungary

MoldovaMontenegro

Poland

Romania

Serbia

Slovakia

Slovenia

Turkey

Ukraine

ArmeniaAzerbaijan

Estonia

Georgia

Kazakhstan

Kyrgyz

Latvia

Lithuania

Mongolia

Russia

Tajikistan

Uzbekistan

2030

4050

6070

% a

bove

neu

tral

on

satis

fact

ion

with

life

10 20 30 40 50

% above neutral on trust in people today


Figure 1.8. Cross-Country Comparisons: SWL and Level of Trust in Others

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 17

Annex: Tables and Figures


Table A1.1. Satisfaction with Life Question by Country

All things considered, I am satisfied with my life now

Disagree

Neither

Agree

StronglyAgree

StronglyDisagree

0

10

20

30

40

50

60

CIS

-low

inco

me

EU

mem

ber

stat

es

CIS

-mid

dle

inco

me

Sou

th-E

aste

rn E

urop

e

Tur

key

% o

f res

pond

ents

Agree

Strongly Agree

Percentage of Respondents Who . . .

Strongly Strongly Group/Country Disagree Disagree Neither Agree Agree Overall

CIS-low income 9 21 19 41 10 100

EU member states 9 21 26 35 9 100

CIS-middle income 9 22 24 35 10 100

South-Eastern Europe 18 23 24 28 7 100

Turkey 15 16 24 32 12 100

Overall sample: 10 21 24 35 10 100

Slovenia 1 8 19 54 18 100

Belarus 2 11 20 57 10 100

Uzbekistan 3 13 16 53 15 100

Tajikistan 6 11 15 47 21 100

Estonia 4 17 14 52 13 100

Czech Republic 4 14 28 41 12 100

Slovakia 4 17 21 51 7 100

Kazakhstan 4 18 25 44 9 100

Kyrgyz Republic 5 20 16 53 6 100

Poland 6 18 27 39 11 100

Lithuania 6 22 20 40 12 100

Croatia 11 14 20 42 13 100

Latvia 8 21 17 46 9 100

Albania 8 18 30 33 11 100

Mongolia 6 21 34 32 7 100

Russia 10 22 24 33 11 100

(continued )

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 18


Table A1.1. Satisfaction with Life Question by Country (Continued )


Strongly Strongly Group/Country Disagree Disagree Neither Agree Agree Overall

Turkey 15 16 24 32 12 100

Ukraine 12 26 25 31 6 100

Romania 13 25 29 27 6 100

Bulgaria 13 32 26 23 6 100

Moldova 15 29 28 25 3 100

Montenegro 20 26 25 24 5 100

Azerbaijan 15 34 25 21 5 100

Bosnia 20 25 27 26 2 100

Macedonia 21 26 26 23 4 100

Hungary 21 26 27 22 4 100

Serbia 23 29 22 22 4 100

Armenia 20 34 20 24 2 100

Georgia 19 35 24 19 3 100

(continued )

Table A1.2. Perceptions Regarding Changes over Time in Economic Situation by Country

The economic situation in this country is better today than in 1989


Strongly StronglyGroup/Country Disagree Disagree Neither Agree Agree Overall

CIS-low income 14 31 15 32 8 100




Disagree

Neither

Agree

StronglyAgree

StronglyDisagree

0

10

20

30

40

50

60

CIS

-low

inco

me

CIS

-mid

dle

inco

me

EU

mem

ber

stat

es

Sou

th-E

aste

rnE

urop

e

% o

f res

pond

ents

Agree

Strongly Agree

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 19


Table A1.2. Perceptions Regarding Changes over Time in Economic Situation by Country (Continued )


Strongly StronglyGroup / Country Disagree Disagree Neither Agree Agree Overall

Overall sample: 18 30 16 26 9 100

Belarus 1 12 18 56 12 100

Estonia 3 13 17 45 22 100

Albania 8 9 12 55 16 100

Kazakhstan 5 20 13 47 15 100

Mongolia 5 23 19 45 10 100

Lithuania 7 24 16 41 12 100


Uzbekistan 9 26 15 36 13 100

Azerbaijan 8 28 20 41 3 100

Latvia 13 28 15 36 9 100

Poland 16 23 20 32 10 100

Slovenia 11 29 21 28 10 100

Russia 13 29 18 29 11 100

Slovakia 12 33 17 29 9 100

Tajikistan 18 30 11 30 11 100

Armenia 23 28 11 30 7 100

Turkey 27 27 12 21 12 100

Romania 19 33 24 19 5 100

Georgia 24 36 13 23 5 100

Moldova 20 41 16 19 4 100


Bulgaria 23 40 19 14 3 100

Ukraine 25 45 13 15 3 100

Croatia 42 24 18 13 3 100

Montenegro 41 35 11 11 2 100

Hungary 40 35 14 9 2 100

Serbia 41 34 15 7 2 100

Macedonia 44 38 11 5 1 100

Bosnia 53 31 11 4 1 100

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 20


Table A1.3. Satisfaction with Changes over Time in Living Conditions by Country

I have done better in life than my parents

Disagree

Neither

Agree

StronglyAgree

StronglyDisagree

0

10

20

30

40

50

60

CIS

-low

inco

me

CIS

-mid

dle

inco

me

EU

mem

ber

stat

es

Sou

th-E

aste

rnE

urop

e

% o

f res

pond

ents

Agree

Strongly Agree






CIS-low income 8 31 24 30 7 100

Overall sample: 7 21 24 36 13 100

Belarus 2 9 23 53 13 100

Slovenia 2 13 23 44 19 100

Estonia 2 14 21 42 21 100

Albania 5 9 14 52 19 100

Slovakia 4 12 20 51 14 100

Romania 3 13 24 44 16 100

Lithuania 2 18 18 38 24 100

Latvia 4 18 18 42 18 100


Russia 4 20 23 38 15 100

Croatia 10 15 20 34 21 100

Kazakhstan 5 20 23 41 11 100

Poland 6 21 24 35 14 100

Ukraine 7 20 23 42 8 100

Bulgaria 6 23 23 39 8 100

Tajikistan 6 25 25 32 12 100

Uzbekistan 6 28 20 37 10 100

Moldova 7 20 33 36 4 100

(continued )

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 21


Table A1.3. Satisfaction with Changes over Time in Living Conditions by Country (Continued )



Serbia 13 22 23 30 12 100

Hungary 12 24 27 28 9 100

Macedonia 13 26 24 29 8 100

Montenegro 10 29 28 26 8 100

Turkey 18 22 25 22 13 100

Bosnia 11 27 27 30 5 100


Georgia 11 36 23 24 6 100

Mongolia 11 36 30 20 3 100

Azerbaijan 12 39 30 16 3 100

Armenia 14 42 24 17 3 100

Table A1.4. Average SWL Score: Colleagues in 1989 Rather than School Mates as Peers

I Have Done Better in Life Than Most Self-assessment of Own Economic Welfare (quintile):

of the Colleagues I Had Around 1989 Lowest 2 3 4 Highest Overall

Strongly disagree 1.6 1.9 2.3 2.2 . . . 1.8

Disagree 2.3 2.6 2.9 2.9 . . . 2.6

Neither 2.6 3.0 3.4 3.6 3.3 3.1

Agree 2.8 3.4 3.7 4.0 4.0 3.5

Strongly agree 3.5 3.6 4.2 4.2 4.9 4.0

Overall 2.3 2.9 3.4 3.8 3.9 3.0


11524-01_Ch01.qxd 1/19/09 11:43 AM Page 22


Table A1.5. Tendency to Feel I’ve Done Worse During Transition Than Others, by Level ofIncome Inequality

Average Reported Change in Prevailing Level of IncomeDecile Rank Between Inequality (Gini) in Country

Country 1989 and Present (based on PCEXP)

Albania 0.40 0.34

Belarus −0.08 0.37

Slovenia −0.32 0.29

Czech Republic −0.37 0.29

Turkey −0.64 0.36

Kazakhstan −0.76 0.35

Poland −0.85 0.32

Slovakia −0.85 0.30

Estonia −0.87 0.34

Lithuania −1.01 0.37

Romania −1.02 0.40

Kyrgyz Republic −1.05 0.36

Moldova −1.11 0.44

Hungary −1.18 0.35

Russia −1.19 0.38

Latvia −1.19 0.38

Tajikistan −1.35 0.31

Ukraine −1.35 0.45

Uzbekistan −1.42 0.32

Armenia −1.44 0.43

Croatia −1.58 0.34

Bulgaria −1.60 0.37

Macedonia, FYR −1.93 0.34

Azerbaijan −2.09 0.38

Montenegro −2.14 0.29

Serbia −2.20 0.35

Bosnia −2.30 0.33

Georgia −2.49 0.40

Overall �1.10 0.37

Correlation coefficient: �0.16

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 23


Table A1.6. Fall in Social Capital? Decline in Reported Level of Trust in Other People

(a) Trust in People Before 1989

(continued )

Some Trust

Complete Trust

Complete Trust

CompleteDistrust

Some Trust

Neither

Some Distrust

0

10

20

30

40

50

60

CIS

low

inco

me

CIS

mid

dle

inco

me

EU

mem

ber

stat

es

Sou

th-E

aste

rnE

urop

e

% o

f res

pond

ents

Percentage of Respondents Who Have . . .

Complete Some CompleteGroup/Country Distrust Distrust Neither Some Trust Trust Overall

CIS low-income 5 8 14 34 40 100

CIS middle-income 6 11 14 39 31 100



Overall sample: 6 11 18 38 28 100

Mongolia 2 2 7 30 60 100

Uzbekistan 2 6 12 32 49 100

Georgia 2 7 14 45 31 100

Bulgaria 3 6 14 55 22 100

Kazakhstan 3 9 9 41 37 100


Tajikistan 4 9 17 29 41 100

Lithuania 2 11 18 52 16 100

Montenegro 5 8 18 48 21 100

Hungary 4 8 29 46 14 100

Russia 6 10 13 38 33 100

Turkey 8 9 16 28 39 100

Serbia 6 10 17 48 19 100

Estonia 3 12 24 49 12 100

Slovenia 3 11 28 48 10 100

Macedonia 6 10 22 33 29 100

Moldova 7 11 17 46 20 100

Belarus 4 14 23 44 15 100

Latvia 4 14 14 56 12 100

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 24


Table A1.6. Fall in Social Capital? Decline in Reported Level of Trust in Other People (Continued )



Ukraine 6 14 17 38 26 100

Azerbaijan 10 9 17 28 35 100

Armenia 6 14 20 36 24 100

Bosnia 7 12 22 41 19 100

Croatia 7 9 23 49 12 100

Poland 6 12 30 40 11 100

Slovakia 7 12 31 40 11 100

Albania 15 8 16 47 14 100

Romania 10 16 30 36 8 100


(b) Trust in People Today

Some Trust

Complete Trust

CompleteTrust

CompleteDistrust

Some Trust

Neither Some Distrust

0

10

20

30

40

50

60C

IS-lo

w in

com

e

CIS

-mid

dle

inco

me

EU

mem

ber

stat

es

Sou

th-E

aste

rnE

urop

e

% o

f res

pond

ents




CIS-low income 30 21 16 24 9 100



Overall sample: 25 25 19 24 7 100

Georgia 12 22 21 36 9 100

Belarus 9 23 27 35 6 100

Estonia 8 30 22 36 4 100

Kazakhstan 14 31 12 35 8 100

(continued )

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 25


Table A1.6. Fall in Social Capital? Decline in Reported Level of Trust in Other People (Continued )



Slovakia 13 21 30 33 4 100

Ukraine 17 29 16 33 6 100

Slovenia 11 24 37 26 2 100

Latvia 13 36 15 33 3 100

Lithuania 13 33 23 27 4 100

Russia 23 25 17 26 8 100


Tajikistan 29 21 14 26 10 100

Uzbekistan 30 19 16 26 10 100

Moldova 24 25 17 28 6 100

Poland 17 29 27 24 4 100

Serbia 26 21 22 28 3 100

Croatia 21 25 28 23 3 100

Montenegro 21 26 26 24 3 100

Romania 24 21 30 22 3 100

Hungary 23 25 28 21 3 100


Albania 38 18 17 21 6 100

Azerbaijan 40 17 19 16 8 100

Mongolia 31 27 19 19 5 100

Armenia 35 26 18 16 5 100

Bulgaria 33 31 15 19 3 100

Bosnia 32 24 24 19 2 100

Turkey 47 20 14 11 8 100

Macedonia 47 17 20 14 3 100

11524-01_Ch01.qxd 1/19/09 11:43 AM Page 26

The past two decades in Eastern Europe and the former Soviet Union have beentimes of tremendous change, with countries undergoing rapid transformationfrom centrally-planned to market-oriented economies. While poverty increased

during the initial years of transition, primarily on account of the sharp economic con-traction, the resurgence of economic growth in the region since 1998 has resulted in arebound in household incomes and living standards. The most recent comprehensiveassessment of growth, poverty and inequality in ECA was done in 2005 (see World Bank 2005a). The study documented the sharp reduction in poverty and the modera-tion of inequality that is taking place in the region. The key source for understandingthese changes have been the household and labor force surveys conducted in most ECA countries.

While the above-mentioned study is a valiant attempt at providing a quantitative assess-ment of income and poverty in the region, differences in structure of the various question-naires used across different countries (for example, level of disaggregation, recall period,variable coverage of sources of income, and so forth) along with differences in definitionsand concepts followed (for example, commodity classification schemes followed, treatmentof imputed consumption, and so on) render the task of constructing a comparable measureof welfare across countries an extremely difficult one. The 2006 LiTS provides a hithertounprecedented opportunity to systematically examine differences in socioeconomic char-acteristics of different income groups using the same survey instrument across all ECA coun-tries. Using this new data source, this paper also provides a systematic examination ofhousehold welfare, the sources of household incomes and therefore of potential channels

CHAPTER 2

Employment, Sources of Income,and the Poor in Eastern Europe

and the Former Soviet Union14

27

14. Asad Alam, Pradeep Mitra, and Salman Zaidi.

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 27

for affecting them, the sectoral occupation patterns of the working poor which, in turn, is agood guide to the opportunities available to them for improving their income growth andliving standards, as well as of asset ownership and underlying inequalities which may con-strain future accumulation in human and physical wealth.

Because our main concern here is with the survey data collected on household expen-ditures, it is worth elaborating upon the specific questions used to gather this information.Respondents in the LiTS were asked two main questions in this regard: (1) approximatelyhow much their household spent on (i) food, beverages, and tobacco, (ii) clothing andfootwear, (iii) transport and communications (including phone, mobile phone, and inter-net charges), (iv) recreation, entertainment, meals outside the home, and so forth, duringthe past 30 days, as well as (2) approximately how much their household spent on (v) edu-cation (including tuition, books, kindergarten expenses), (vi) health (including healthinsurance), (vii) furnishings (such as sheets, towels, blankets, linen), (viii) household durablegoods (such as furniture, household appliances, TV, car), and (ix) other expenses (anyadditional expenses that the respondent would like to report) during the past 12 months.These nine subcomponents of household expenditures were converted to annual amountsand aggregated to derive a per-capita (and, using the modified OECD scales,15 an normal-ized) measure of individual welfare.

In addition to the questions on monthly and annual household expenditures, the LiTSalso included a number of other potentially useful questions from a welfare analysis stand-point, such as ownership of various types of assets (cars, computer, mobile phones, and soforth) as well as a question on the minimum amount of money that the household wouldneed in order to make ends meet at the end of each month. Furthermore, the LiTS alsoincludes a subjective welfare measure whereby respondents were asked to place themselveson a ten-step ladder (1=Poorest, 10=Richest), the response to which could potentially alsobe used as a welfare metric. Finally, LiTS provides a uniform module of sources of incomeasking respondents about the main sources of livelihood of their households.

The next section provides a more detailed discussion of the various pros and cons ofeach of the alternate welfare measures that could potentially be constructed from the LiTS.We then use the per capita expenditures welfare metric (PCE) to derive a poverty profilefor the region as a whole using $PPP2.15 and $PPP4.30 regional poverty lines using 2000PPPs.16 However, before doing so, it is important to first assess how reliable is this welfaremeasure. The second section compares the estimates of private consumption per capitafrom LiTS with those obtained from other more traditional sources, such as the NationalAccounts as well as other more detailed nationally representative household surveys. Thethird section then presents the poverty profile for the region obtained using this welfaremeasure, and wherever possible also compares the extent to which the poverty estimatesand profile based on the LiTS data is indeed consistent with those from traditional house-hold surveys. Finally, the fourth section examines in more detail the differences betweenthe poor and non-poor in employment status, sector of employment, as well as mainsources of income, while the fifth section provides some concluding observations.


15. These equivalence scales assign a weight of 1 to the first and 0.5 to each subsequent adult house-hold member, and a weight of 0.3 to each household member aged less than 14 years.

16. For a justification and more detailed description of these poverty lines, please see World Bank(2005).

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 28

Choosing Between Alternate Survey-Based Welfare Measures

While there is general agreement on the merits of using household survey based estimatesof per capita (or suitably normalized) consumption and expenditures as a summary mea-sure of living standards, there is little consensus regarding how long the survey question-naire should be to yield good estimates of household expenditures.17 Greater disaggregationin coverage of items is generally assumed to result in fuller reporting and greater accuracy.However, very detailed consumption modules are costly to administer and may crowd outother information to be collected in the survey while short questionnaires can save time andmoney and still deliver reasonably accurate PCE estimates. Survey questionnaires currentlyin use vary in length from as much as several hundred items purchased/consumed over thepast one year (for example, India’s National Sample Surveys) to as little as one questionabout the household’s total expenditures over the past one month.18

Exploiting the fact that the LiTS questionnaire enables the construction of consump-tion aggregates in 29 countries using data collected in the short consumption module(approximately 10 expenditure items in total), this paper assesses the adequacy of these con-sumption aggregates compared to those obtained from more traditional consumption mod-ules in typical household budget/LSMS surveys in the same countries. While multipleassessment criteria can be used for this purpose, our primary interest is in assessing how wellthe short consumption module does in terms of enabling the analyst to rank householdsinto broad welfare groups (such as quartiles/quintiles). We start first by first examining var-ious possible individual welfare metrics that can be derived from the 2006 LiTS data. TheLITS has several sources that could potentially be used for this purpose:

■ A battery of questions on total household spending on food, beverages, tobacco,transport and communications, recreation and entertainment, education, health,furnishings, and household durables—henceforth referred to as the “short con-sumption aggregate.”

■ A question on the minimum amount of money that the household would need inorder to make ends meet at the end of each month—the “one-question welfareaggregate.”

■ A question where respondents are asked to place themselves on a ten-step ladderranging from the poorest (1) to the richest (10). Respondents are asked this ques-tion both in relation to their standing today as well as around 1989.

■ A battery of binary-response questions (yes/no) on ownership of various assets,such as cars, computers, mobile phones, and so forth.

Normalized or Per Capita Expenditures?

In theory, just as a price index is used in order to make welfare comparisons across house-holds facing different cost-of-living, use of equivalence scales is a way of making comparable


17. Please see the related discussion in “Chapter 5: Consumption” by Angus Deaton and MargaretGrosh in Grosh and Glewe eds. 2000, Designing Household Survey Questionnaire for Developing Countries:Lessons from 15 years of the Living Standards Measurement Study.

18. Thus, for instance, the World Bank’s Living Standards Measurement Study (LSMS) surveys tendto have shorter consumption modules (∼50–80 items) in comparison to typical household budget sur-veys (where, as noted above, the number of consumption items can be as high as 200–300 items).

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 29

consumption aggregates of households with different demographic composition. In practice,however, does it really matter much if the welfare measure used is based on consumption perequivalent adult (ECE) rather than consumption per capita (PCE)? Rather than prejudge theissue in favor of either measure, particularly given the relatively widespread use of the lattermeasure in poverty analysis, we use both normalized welfare and per-capita welfare measuresin our analysis.

Equivalence scales are the deflators used to convert household consumption aggregatesinto money metric utility measures of individual welfare.19 The modified OECD equivalencescale we use is the same as those used by Eurostat to make welfare comparisons acrosscountries of the European Union. This scale assigns a weight of one to the first personin each household, and 0.5 to each subsequent adult household member. In addition,each household member aged less than 14 years is assigned a weight of 0.3. As one wouldexpect, applying this equivalence scale to the LiTS data results in normalized expendi-tures that are systematically higher than per-capita expenditures across all countries(Table 2.1). Not surprisingly, the difference between the two (normalized vs. per capitaexpenditures) is lesser in countries with relatively small households (Latvia, Lithuania,Estonia), compared to those countries where the average household size is higher(Tajikistan, Uzbekistan, Azerbaijan).

The distribution of (log) normalized expenditures in each country is, in general, quiteclose to a log-normal distribution (Figure 2.1).

One-question Welfare Aggregate (OQE)

When asked: “Living in this dwelling and doing what you do, what would be the minimumamount of money that this household would need to make ends meet at the end of eachmonth?” about three-fourth of respondents reported an amount greater than their total(normalized) expenditures. Average OQE across the LiTS sample is $4,376, about 56 per-cent higher than average ECE of $2,804 per-equivalent adult per annum (Table 2.1). In addi-tion, OQE has, in general, both higher dispersion and is “more spiked” compared to thedistribution of ECE (ref. Figure 2.1 and Figure 2.2).

Subjective Assessment of Welfare (SAW)

The LiTS included a question: “Please imagine a ten-step ladder where on the bottom, thefirst step, stand the poorest people and on the highest step, the tenth, stand the richest. Onwhich step of the ten is your household today?” Thus, unlike the various welfare measuresconsidered so far—PCE, ECE, and OQE, which are based on a continuous monetary scale—individuals’ subjective assessment of their welfare is based on a ten-point scale, with “1”denoting the poorest and “10” the richest. When replying to this question, respondents hada tendency to rank themselves in the middle of the income distribution rather than at the


19. For a more detailed discussion, see (for instance): Deaton, A. and S. Zaidi (2002) Guidelines forConstructing Consumption Aggregates for Welfare Analysis, LSMS Working Paper 135, World Bank, Wash-ington DC.

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 30


Table 2.1. Comparing Various Alternate Welfare Measures in the LiTS

Expenditures One-questionHousehold Per Equivalent Per Capita Ratio of Per Equivalent Adult

Country Size Adult (ECE) (PCE) ECE/PCE Expenditures (OQE)

Czech Republic 2.1 4,556 3,673 1.24 7,448

Hungary 2.1 3,455 2,774 1.25 7,516

Latvia 2.1 3,870 3,099 1.25 7,465

Lithuania 2.1 3,234 2,575 1.26 5,831

Bulgaria 2.2 2,125 1,672 1.27 4,017

Romania 2.4 2,372 1,865 1.27 4,652

Serbia 2.6 2,752 2,163 1.27 4,867

Belarus 2.1 2,203 1,728 1.27 2,847

Ukraine 2.2 2,687 2,094 1.28 3,735

Estonia 2.1 4,045 3,145 1.29 7,409

Croatia 2.4 5,260 4,052 1.30 8,098

Poland 2.4 3,642 2,803 1.30 6,132

Russia 2.2 3,108 2,372 1.31 4,531

Slovenia 2.5 6,531 4,980 1.31 7,774

Slovakia 2.5 3,577 2,708 1.32 5,901

Bosnia 2.7 2,860 2,143 1.33 4,423

Montenegro 2.9 4,173 3,095 1.35 5,821

Moldova 2.4 1,302 958 1.36 1,860

Georgia 2.9 1,315 947 1.39 1,946

Kazakhstan 3 1,951 1,386 1.41 2,800

Macedonia, FYR 3.3 2,308 1,633 1.41 3,012

Armenia 3.6 1,604 1,092 1.47 2,486

Turkey 3.6 2,784 1,891 1.47 4,683

Kyrgyz Republic 3.7 1,060 704 1.51 915

Albania 3.7 2,459 1,577 1.56 3,102

Azerbaijan 4 1,136 708 1.60 1,085

Uzbekistan 4.3 721 440 1.64 993

Tajikistan 4.8 787 466 1.69 787

Overall 2.6 2,812 2,120 1.33 4,390

tails. Thus, one problem with this measure is that it does not result in self-rankings that areuniformly distributed across decile groups (in particular, respondents are more-likely-than-average to list themselves as belonging to the 3rd, 4th, or 5th decile groups) (see Table 2.2and Figure 2.3).20 Three groups of “poor,” “middle,” and “rich” were formed by group-ing together those respondents that placed themselves in (a) the first three, (b) fourth and

20. Given that the LiTS interviewed a nationally representative sample of respondents from eachcountry, one would have expected their responses to have been uniformly distributed across the tenresponse classes, assuming respondents could indeed make an accurate comparison of their own welfarein relation to that of other people in the same country.

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 31

Figure 2.1. Distribution of Normalized Expenditures

Source: 2006 LiTS. Graphs show distribution of (log) normalized expenditures by country, withthe appropriately-scaled normal distribution (same mean and standard deviation as the data)overlaid. For each country, the distribution has been censored at the 1% and 99% level.

Figure 2.2. Distribution of the One-question Welfare Aggregate

Source: 2006 LiTS. Graphs show distribution of (log) normalized one-question welfare aggregates bycountry, with the appropriately scaled normal distribution (i.e. same mean and standard deviationas the data) overlaid. For each country, the distribution has been censored at the 1% and 99% level.

0.2

.4.6

.80

.2.4

.6.8

0.2

.4.6

.80

.2.4

.6.8

0.2

.4.6

.8

4 6 8 10 4 6 8 10

4 6 8 10 4 6 8 10 4 6 8 10 4 6 8 10

Albania Belarus Bosnia Bulgaria Croatia Czech Republic

Macedonia Hungary Moldova Montenegro Poland Romania

Serbia Slovakia Slovenia Turkey Ukraine Armenia

Azerbaijan Estonia Georgia Kazakhstan Kyrgyz Republic Latvia

Lithuania Russia Tajikistan Uzbekistan

Den

sity

Log of ECEGraphs by country

0.5

11.

50

.51

1.5

0.5

11.

50

.51

1.5

0.5

11.

5

5 10 5 10

5 10 5 10 5 10 5 10






Den

sity

Log of OQEGraphs by country

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 32


fifth and (c) higher decile groups (comprising respectively 33, 45, and 21 percent of thepopulation).

Ownership of Assets

The LiTS also collected data on household ownership of various consumer goods, suchas cars, mobile phones, computers, internet access, etc (Table 2.3). In general, averageownership rates of these consumer durables are quite high, though there is a fair degree

Table 2.2. Subjective Assessment of Welfare

Percentage of Respondents Ranking Themselves in the Decile Group

Country Poorest 2 3 4 5 6 7 8 9 Richest Overall

Albania 8.9 7.6 12.7 18.0 31.0 10.3 5.9 3.4 1.0 1.1 100

Belarus 1.8 2.1 13.1 18.1 27.6 19.5 10.7 5.8 1.3 0.1 100

Bosnia 7.3 10.2 14.4 15.8 25.9 12.6 9.6 2.6 0.9 0.6 100

Bulgaria 13.0 15.8 19.9 15.1 17.2 7.8 3.6 4.3 2.9 0.3 100

Croatia 7.1 8.0 16.0 15.0 34.4 10.9 6.6 1.5 0.4 0.2 100

Czech Republic 1.9 8.3 12.8 22.1 25.8 13.1 7.9 5.7 1.8 0.5 100

Macedonia, FYR 8.1 9.5 16.1 16.9 34.1 8.5 4.2 1.9 0.6 0.1 100

Hungary 6.2 9.8 18.6 20.1 24.2 11.8 6.1 2.8 0.3 0.1 100

Moldova 6.3 9.6 12.5 15.9 25.1 14.2 10.5 4.7 1.3 0.0 100

Montenegro 7.3 10.3 19.4 14.8 26.6 10.8 7.2 2.2 1.0 0.3 100

Poland 6.3 9.8 17.2 18.6 21.3 11.2 7.3 5.7 1.9 0.7 100

Romania 5.7 9.1 13.3 18.8 28.6 14.2 6.1 3.0 0.9 0.3 100

Serbia 8.7 13.4 18.0 17.9 28.2 8.6 3.7 0.6 0.4 0.5 100

Slovakia 5.3 8.4 17.3 18.8 32.0 11.9 4.7 1.3 0.2 0.1 100

Slovenia 1.4 3.3 7.9 16.1 37.0 19.4 9.3 4.5 1.0 0.2 100

Turkey 15.3 16.8 16.8 14.5 20.2 7.1 5.1 2.6 1.2 0.2 100

Ukraine 8.6 12.8 22.7 21.3 19.3 8.9 4.1 1.7 0.4 0.2 100

Armenia 4.9 6.8 17.2 18.7 32.7 11.8 5.8 1.1 0.5 0.5 100

Azerbaijan 11.8 21.3 25.7 17.4 17.1 4.9 1.3 0.3 0.1 0.0 100

Estonia 3.4 7.3 21.7 21.9 31.6 8.6 4.3 0.8 0.1 0.4 100

Georgia 11.4 12.8 23.0 18.6 20.3 8.5 3.7 1.0 0.4 0.3 100

Kazakhstan 3.9 9.1 17.0 17.5 28.3 12.0 7.1 4.2 0.9 0.0 100

Kyrgyz Republic 2.9 3.5 11.0 15.8 33.2 19.7 8.0 3.7 1.7 0.6 100

Latvia 5.6 10.3 22.2 23.2 26.5 8.4 2.9 0.6 0.0 0.2 100

Lithuania 9.1 12.4 19.5 21.4 26.1 8.1 2.4 0.8 0.1 0.0 100

Russia 6.0 13.9 22.4 21.3 17.0 8.3 6.1 3.8 1.0 0.2 100

Tajikistan 1.5 7.0 14.5 21.5 31.8 13.3 5.9 4.0 0.3 0.2 100

Uzbekistan 5.1 6.3 11.1 16.6 39.6 15.4 4.3 1.1 0.2 0.3 100

All countries: 6.6 9.9 17.0 18.3 27.3 11.4 5.9 2.7 0.8 0.3 100

Source: 2006 LiTS (data are not weighted).

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 33

0.1

.2.3

.40

.1.2

.3.4

0.1

.2.3

.40

.1.2

.3.4

0.1

.2.3

.4

0 5 10 0 5 10

0 5 10 0 5 10 0 5 10 0 5 10






Den

sity

Subjective Welfare RankingGraphs by country

of variation between countries—for instance, ownership of mobile phones varies from15 percent in Uzbekistan to 90 percent in Montenegro. Overall, 64 percent of house-holds report owning mobile phones, 32 percent have cars, 28 percent own computers,while 16 percent have access to internet at home. Overall, one-in-ten respondentsreport owning a secondary residence. In general, asset ownership rates are quite high inwealthier countries (Croatia, Slovenia, Czech Republic), and lower in comparativelypoorer Central Asia (Uzbekistan, Tajikistan, and the Kyrgyz Republic). About 30 per-cent of respondents said their household did not own any of the above-mentionedassets, with the ratio varying from a low of around 8 percent in the Czech Republic toover 70 percent in Tajikistan.

Comparing the Various Welfare Measures

To what extent are these various LiTS welfare measures correlated with one another?Table 2.4 provides the correlation matrix for these variables. As one would expect, corre-lation between ECE and PCE is the highest among six possible pair-wise comparisons,given these two are based on essentially the same set of variables (albeit with differentweights). In general, ECE/PCE has the highest correlation with other welfare measures,while SAW has the lowest, due in part to the tendency of respondents to rank their house-hold in the middle of the income distribution ladder.


Figure 2.3. Distribution of Subjective Welfare Rankings by Country

Source: 2006 LiTS. X-axis denotes welfare ranking from 1 (poorest) to 10 (richest)

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 34


Table 2.3. Ownership of Assets

Respondents (percent) reporting owning the consumer good

Mobile Internet SecondaryCountry phone Car Computer access residence

Albania 88 24 10 3 7

Belarus 56 30 34 17 10

Bosnia 68 51 29 14 18

Bulgaria 59 38 19 14 9

Croatia 75 60 42 34 30

Czech Republic 89 56 51 38 19

Macedonia, FYR 69 49 28 10 8

Hungary 73 46 37 21 6

Moldova 31 24 12 7 3

Montenegro 90 58 32 22 19

Poland 69 48 45 28 7

Romania 61 33 31 19 11

Serbia 73 46 37 27 24

Slovakia 81 58 47 24 11

Slovenia 88 78 65 52 16

Turkey 73 22 15 8 8

Ukraine 67 26 27 11 8

Armenia 46 23 10 5 6

Azerbaijan 52 17 5 2 4

Estonia 82 46 47 41 15

Georgia 46 20 8 4 15

Kazakhstan 41 29 13 5 3

Kyrgyz Republic 21 24 5 1 9

Latvia 76 38 34 24 14

Lithuania 75 49 38 26 11

Russia 69 31 33 20 12

Tajikistan 17 18 4 1 4

Uzbekistan 15 20 2 1 6

All countries 64 32 28 16 10

Table 2.4. Correlation Matrices: Decile Rankings Based on Various LiTS Welfare Measures

Variables ECE PCE OQE SAW

ECE 1.0000 — — —

PCE 0.8707 1.0000 — —

OQE 0.4530 0.4560 1.0000 —

SAW 0.2500 0.2148 0.1431 1.0000

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 35


How good are these various welfare metrics in identifying the poor? One possiblecriterion to ascertain this is to first rank respondents into different groups—forinstance, three equal-sized welfare groups within each country (the poor, middle class,and rich)—based on the distribution of the respective measures. In the case of SAW,this is done by grouping together those respondents that placed themselves in (a) thefirst five, (b) sixth and seventh and (c) higher decile groups (so as to form roughly threeequal groups comprising respectively 33, 46, and 20 percent of the population). We thencompare asset-ownership rates by income group across different welfare metrics—to the extent that asset ownership is correlated with welfare status, we would expect a lower rate of ownership among the “poor” compared to the “rich.” While around 30 percent of “rich households” (as classified by ECE) own a mobile phone, only 5 per-cent of “poor households” (again, as classified by ECE) reporting owning one. In gen-eral, all four welfare metrics do quite well in discerning the poor from the rich; however,ECE consistently does the best job in the sense of giving the sharpest gradient in asset-ownership rates across the various poor-middle-rich welfare classes (Table 2.5). Thiscan also be seen by focusing on the 29–30 percent of respondents that report not hav-ing a mobile phone, car, computer, internet access, or a secondary residence—theserespondents own none of the various asset variables on which questions were asked inthe LiTS. While the so-classified “asset poor” are considerably more likely to be amongthe poor than the rich as classified by all other welfare metrics under consideration, theodds-ratio of probability of the asset poor being classified as poor to the probability oftheir being classified as rich is greater than five in the case of the ECE—higher than anyother measure (Figure 2.4).

How Good is the LiTS Welfare Metric?

The above analysis suggests that ECE is the preferred welfare measure from LiTS, but alsoconfirms that ECE and PCE are in fact quite highly correlated. In this section, we comparePCE from the LiTS with per capita consumption expenditures obtained from other moretraditional sources, such as the National Accounts and other household surveys. AveragePCE in the LiTS sample is US$ 2,049 per annum, but varies considerably across countriesfrom $440 in Uzbekistan to $4,980 in Slovenia (Table 2.6).

Across the entire LiTS sample, average PCE is about 72 percent of the region’s estimatedper-capita 2006 GDP. Across the 15 countries for which survey data are available in theWorld Bank’s Europe and Central Asia Region household survey archive (henceforthECAPOV), average PCE in the LiTS ($1,603) is also quite close to the ECAPOV average($1,814), though this close conformance overall hides a fair amount of variation at thenational level.21 As Table 2.6 shows, in 7 out of 28 countries presented (Montenegro,Moldova, Kyrgyz Republic, Ukraine, Tajikistan, Serbia, and Bosnia), the LiTS PCE is greater

21. In five cases, the difference in the LiTS and ECAPOV PCE measure is greater than 25 percent—inArmenia and Georgia, mean PCE from ECAPOV is lower than the LiTS, but higher in Belarus, Estonia,and Azerbaijan. In 3 cases—namely Armenia, Belarus, Georgia—the LiTS measure accords much betterto per-capita GDP estimates from the National Accounts, but not so in the remaining two—i.e. Estoniaand Azerbaijan.

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 36

Satisfaction with Life and Service D

elivery in Eastern Europe and the Former Soviet U

nion37

Table 2.5. Asset Ownership Rates by Welfare Level Using Alternate Ranking Criteria

% of persons in (1) ECE (2) PCE (3) OQE (4) SAW

households with Poor Middle Rich Poor Middle Rich Poor Middle Rich Poor Middle Rich All Groups

Internet access

EU members 10 26 49 14 27 44 17 27 42 12 31 49 28

S.E. Europe 8 18 32 9 20 29 12 18 30 9 20 36 19

CIS-low income 0 1 7 0 1 7 2 2 6 1 3 6 3

CIS-middle 2 11 28 3 11 27 3 11 27 6 14 24 14

Turkey 2 6 16 2 7 16 0 8 16 2 10 22 8

Overall 5 15 30 7 16 28 9 15 26 7 18 30 17

Computers

EU members 18 41 66 26 40 59 29 40 57 21 46 64 41

S.E. Europe 15 31 49 17 32 45 22 29 44 17 34 53 32

CIS-low income 1 3 14 1 3 14 3 4 12 3 6 12 6

CIS-middle 6 27 49 9 27 47 10 27 46 15 28 45 28

Turkey 5 13 27 5 14 26 4 16 26 7 18 34 15

Overall 11 26 45 14 26 42 17 26 40 14 29 44 27

Secondary residence

EU members 6 10 18 7 10 18 9 11 16 6 12 20 12

S.E. Europe 12 18 26 13 18 25 15 18 23 14 18 29 19

(continued )

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 37

38W

orld Bank Working Paper

Table 2.5. Asset Ownership Rates by Welfare Level Using Alternate Ranking Criteria (Continued )

% of persons in (1) ECE (2) PCE (3) OQE (4) SAW

households with Poor Middle Rich Poor Middle Rich Poor Middle Rich Poor Middle Rich All Groups

CIS-low income 4 6 11 4 6 11 5 6 10 4 7 12 7

CIS-middle 3 8 13 4 8 13 4 8 12 4 8 14 8

Turkey 2 7 15 3 7 15 4 7 14 4 9 18 8

Overall 6 11 17 7 10 17 8 11 16 7 11 19 11

Cars

EU members 28 52 73 36 51 66 37 52 66 31 57 69 51

S.E. Europe 36 56 68 42 54 64 45 52 64 36 58 71 53

CIS-low income 13 22 35 14 23 33 19 23 29 12 25 36 23

CIS-middle 16 32 47 22 29 43 19 30 45 18 33 48 31

Turkey 12 20 38 13 23 34 12 24 35 12 29 45 23

Overall 23 41 57 28 40 52 30 40 51 24 45 57 40

Mobile phone

EU members 51 78 93 59 74 88 61 76 87 58 80 85 74

S.E. Europe 64 82 91 70 80 88 71 79 89 66 84 90 79

CIS-low income 15 31 55 18 32 52 22 31 49 28 36 39 34

CIS-middle 29 65 86 36 63 81 37 61 81 45 65 73 60

Turkey 62 73 86 66 74 81 63 77 82 68 78 83 74

Overall 42 65 81 47 63 77 50 63 76 51 67 72 63

Note: Based on un-weighted observations.

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 38


Figure 2.4. Comparing the Various Alternate Welfare Measures

Percent of Group that is "Asset-Poor"

0

10

20

30

40

50

60

ECE PCE OCE SAW

"Poor" "Rich"

than the average per capita 2005 GDP estimate. However, this discrepancy between datafrom the national accounts and household surveys is not unique to the LiTS per se—of thecountries for which ECAPOV household survey data is available, the survey-based estimateexceeds mean per-capita GDP in 7 cases (Albania, Belarus, Moldova, Azerbaijan, KyrgyzRepublic, Russia, and Tajikistan) out of 15 countries for which this comparison is possible.

National Accounts data indicate that household final consumption expenditures(HFCE) were, on average, about 67 percent of GDP in 2004, the latest year for which thesedata are available. However, this share varies quite a bit across countries, from a low of 50percent in the Czech Republic to 93 percent in Serbia and Montenegro. While the LiTS PCEdefinition does not fully coincide with the HFCE concept, a comparison of the two estimatesis interesting in that it shows fairly close conformance between the two (Figure 2.5, Panel A),and in fact no worse than those derived from more detailed ECAPOV household budgetsurveys (Figure 2.5, Panel B).

What about poverty estimates based on the two respective household survey sources?Using the $2.15 and $4.30 PPP poverty lines with the LiTS PCE welfare measure suggestsa poverty incidence in ECA in 2006 of about 10.5 percent for the lower poverty line and33.6 percent for the upper poverty line (Table 2.7). This compares favorably with recent sur-vey based estimates of around 10.8 percent and 37.8 percent poverty rates derived from theECAPOV household survey database using the same poverty lines.22 To sum, the evidencepresented in this section indicates that the LiTS consumption aggregate provides a crediblewelfare metric with which to paint a profile of variation in living conditions across ECA,which is taken up in the next section.

22. See Alam, Sulla, and Yemtsov (2007), “Income Poverty in Eastern Europe and the former SovietUnion—An Update,” The World Bank, Washington, D.C.

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 39


Table 2.6. 2006 LiTS PCE Compared to Other Data Sources

ECA survey archiveCountry Mean PCE 2006 LiTS Per-capita GDP (2005) Mean PCE

Albania 1,577 1,535 1,647

Belarus 1,728 1,868 2,565

Bosnia 2,143 1,486 —

Bulgaria 1,672 2,071 1,987

Croatia 4,052 5,138 4,995

Czech Republic 3,673 6,515 —

Macedonia, FYR 1,633 1,889 —

Hungary 2,774 5,691 —

Moldova 958 429 900

Montenegro 3,095 1,369 —

Poland 2,803 5,194 2,974

Romania 1,865 2,259 —

Serbia 2,163 1,369 —

Slovakia 2,708 4,761 —

Slovenia 4,980 11,382 —

Turkey 1,891 3,390 —

Ukraine 2,094 959 —

Armenia 1,092 1,128 696

Azerbaijan 708 1,182 1,325

Estonia 3,145 5,866 4,071

Georgia 947 971 725

Kazakhstan 1,386 1,972 1,407

Kyrgyz Republic 704 319 394

Latvia 3,099 5,023 —

Lithuania 2,575 4,838 —

Russia 2,372 2,444 2,459

Tajikistan 466 237 554

Uzbekistan 440 673 516

Overall 2,049 2,842 —

(15 country average) (1,603) (2,068) (1,814)

Sources: 2006 LiTS, WDI, and various household survey data sets from the ECA survey archive.Mean PCE from the ECA survey archives have been projected to 2006 using country-specificgrowth rates based on annual changes in household final consumption expenditures (HFCE) fromthe National Accounts.

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 40


Figure 2.5. Country Welfare Rankings: National Accounts vs. Survey-based Estimates

Panel A

AlbaniaBelarus

Bosnia

Bulgaria

Croatia

Czech

Macedonia

Hungary

Moldova

Poland

RomaniaSerbia

Slovakia

Slovenia

TurkeyUkraine

Armenia

Azerbaijan

Estonia

Georgia

Kazakhstan

Kyrgyz

Latvia

LithuaniaRussia

TajikistanUzbekistan

020

0040

0060

00

LIT

S P

CE

est

imat

e(20

06)

0 2000 4000 6000 8000 10000

National Accounts HFCE (2004)

2006 LITS

Panel B

Albania

Belarus

Bulgaria

Croatia

Moldova

Poland

Armenia

Azerbaijan

Estonia

Georgia

Kazakhstan

Kyrgyz

Russia


020

0040

0060

0080

00

EC

AP

OV

PC

E e

stim

ate

0 2000 4000 6000 8000 10000

National Accounts HFCE (2004)

ECAPOV estimate

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 41


Poverty Profile for ECA

EU Member States and CIS Middle-income Countries Have the Lowest PovertyRates in the Region

Using the $2.15 and $4.30 PPP poverty lines with the LiTS PCE welfare measure,23 we findconsiderable spatial variation in poverty rates across the ECA region. Overall, the analysisshows 10.5 percent of the region’s population to be below the $PPP 2.15 poverty line, while33.6 percent were below the $PPP 4.30 poverty line. Across different subregions, povertyrates were, as expected, highest among CIS low-income countries and lowest among EUmember states and CIS-middle-income countries (Figure 2.6, and Appendix Table A2.1).24

But the Majority of the Poor Live in Middle-income Countries

Even though CIS low-income countries have the highest poverty rates, these countries arehome to less than one-fifth the total number of poor in the region. Instead, mirroring theoverall distribution of population across countries in the region, about two-thirds ofthe poor in the region in fact live in five middle-income countries—Turkey, Russia, Roma-nia, Poland and Kazakhstan (Figure 2.7) (which also account for about two-thirds of theregion’s total population).

Table 2.7. Overall Regional Poverty Rates from the 2006 LiTS

Squared PovertyHeadcount Rate (P0) Poverty Gap (P1) Gap (P2)

Poverty Line � $PPP 2.15

Urban 6.3 1.7 0.8

Rural 17.4 5.5 2.7

Overall 1.5 3.2 1.5

Poverty Line � $PPP4.30

Urban 25.7 8.5 4.1

Rural 46.5 19.2 10.4

Overall 33.6 12.6 6.5

Headcount Rate

Poverty Line � $PPP 2.15

ECAPOV 10.8

LiTS 10.5

Poverty Line � $PPP4.30

ECAPOV 37.8

LiTS 33.6

23. These poverty estimates have been derived using PPP adjustments factors taken from the 2005ICP Preliminary Results, December 2007 publication.

24. EU member states includes Czech Republic, Hungary, Poland, Slovakia, Slovenia, Estonia, Latvia,Lithuania; CIS middle-income countries include Belarus, Ukraine, Kazakhstan, Russia; South-East Europeincludes Albania, Bosnia, FYR of Macedonia, Montenegro, Serbia; CIS low income includes Moldova, Arme-nia, Azerbaijan, Georgia, Kyrgyz Rep., Tajikistan, Uzbekistan (PPP data not available); “Other” includes Bul-garia, Croatia, Romania, Turkey.

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 42

34

556

16

34

0

5

10

15

20

25

30

35

40

Turkey Russia Romania Kazakhstan Poland All othercountries

Shar

e of

reg

on's

tota

l poo

r (p

erce

nt)

0

10

20

30

40

50

60

70

80

EU memberstates

CIS-middleincome

South-EastEurope

Other CIS-low income ECA Region

Pove

rty

Rat

e (%

) $2.15 line

$4.30 line


Figure 2.6. Regional Variation in Poverty Rates Across the ECA Region

Figure 2.7. Distribution of the Poor Across the ECA Region

Sources: Authors’ estimates based on the 2006 LiTS. $PPP data is from the 2005 InternationalComparisons Program Preliminary Results, December 2007.

Note: Poor = $PPP 4.30 line.Sources: Authors’ estimates based on the 2006 LiTS. $PPP data is from the 2005 InternationalComparisons Program Preliminary Results, December 2007.

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 43


Considerable Rural/Urban Disparities, Especially in the CIS Middle-income Group

Roughly one-fifths of the ECA region’s population now resides in metropolitan regions,while the remainder of the region’s population is split roughly equally (about 40 percenteach) between other urban areas and rural areas. In Romania, Armenia, and the Baltic coun-tries, more than half the urban population is in metropolitan areas, while in Russia andKazakhstan, Serbia and Bosnia, and Poland, only about one-quarter or less reside there. Dis-parities in living conditions between urban and rural areas are generally the highest in CIS-middle-income countries, and lowest in EU member states (Table A2.4).25 Thus, dependingon the particular poverty line used, about around 53–63 percent of the poor reside in ruralregions, even though these areas account for less than 40 percent of the region’s total pop-ulation (Table A2.3).

In Most Countries, the Unemployed Have the Highest Risk of Poverty

Classifying the population covered in the LiTS into three main groups based uponwhether the primary respondent was (a) employed, (b) unemployed, or (c) not working(not in labor force), we find that the incidence of poverty is generally the highest amongthe unemployed. However, this is not necessarily true across all country groups; forinstance, in CIS-middle-income countries, the non-working population face the highestpoverty risk (34.7 percent) of all three subgroups, more than twice that among theemployed (15.4 percent) (Table 2.8).

Table 2.8. Overall Regional Poverty Rates from the 2006 LiTS

Group Employed Unemployed Not working Overall

EU member states 13.2 29.3 23.4 18.2

South-Eastern Europe 19.9 40.1 32.7 27.3

CIS-low income countries 64.0 75.5 72.9 69.3

CIS-middle-income countries 15.4 26.6 34.7 21.3

Other 44.8 50.2 58.6 51.8

Overall 24.8 43.8 44.9 33.6

But the Working Poor are the Largest Population Group Among the Poor

However, among the poor as a group, the largest share of the poor in-fact live in householdswhere the respondent was employed—i.e. among the “working-poor” (Table 2.9). Addi-tional information pertaining to the profile of the poor in the ECA region is provided inAnnex Tables A2.1–A2.13.

25. The considerable metropolitan/rural divide in living conditions in CIS-middle countries is cor-roborated by the average satisfaction with life (SWL) score of 3.5 in metropolitan vs. 2.9 in rural areasusing LiTS data; by contrast, no major differential in SWL scores is observed in other country groups(Hungary, Serbia, Slovakia, and Georgia show a similar trend as the CIS middle-income country group).

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 44


Table 2.9. Distribution of the Poor by Employment Status of the Respondent

Poor (<$PPP4.30) Non-Poor (>$PPP4.30)

Not Not Employed Unemployed working Overall Employed Unemployed working Overall

EU member 45.4 14.6 40.1 100.0 65.2 7.8 27.0 100.0states

South-East 39.9 19.9 40.2 100.0 59.2 10.8 30.0 100.0Europe

CIS-low 45.4 18.6 36.1 100.0 55.3 13.2 31.5 100.0income

CIS-middle 59.7 7.8 32.6 100.0 78.4 5.2 16.5 100.0income

Other 40.2 8.3 51.5 100.0 53.2 8.9 38.0 100.0

Total 46.7 11.1 42.3 100.0 69.1 7.0 23.9 100.0

Employment, Sources of Income, and Welfare

Labor Force Participation Remains Low

As the above analysis has shown, labor market status is an important correlate of welfare.Even though average living standards in virtually all countries in the region have recoveredand are now higher than their pre-transition levels, labor market conditions remain diffi-cult in many countries (see World Bank 2005a, 2005b). Employment rates—the share of theworking-age population this is employed—continue to be very low, in many cases wellbelow the so-called Lisbon target of 70 percent by the year 2010 set by the European Com-mission (EC) its member states. The LiTS data confirm the relatively low level of labor forceparticipation in most countries in the region: overall, only about 56 percent of respondentsreported having worked during the 12 month period preceding the survey. These rates weregenerally highest among CIS middle-income countries and lowest in South Eastern Europe(Figure 2.8).

Age, Gender, and Level of Education Are Key Correlates of Work Status

Table 2.10 reports the variation in employment rates by different respondent character-istics, and clearly illustrates the important role played by factors like age, gender, educa-tional background, etc. in influencing the likelihood of respondents having workedduring the 12 month period preceding the interview. For instance, men are considerably(about 1.5 times) more likely than women to have worked. Similarly, age is an impor-tant correlate of work status, with the data showing a clear inverted-J shaped age-profile. However, by far the most important determinant of work status appears to be the level of education of the respondent: those with higher professional / post-graduate edu-cation level are about five times as likely to have worked as compared to those with noeducation.

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 45

0 20 40 60 80

Percent of respondents 15-64 years

ArmeniaAzerbaijan

TurkeyGeorgiaBosnia

MacedoniaAlbania

MontenegroPoland

TajikistanMoldovaRomania

CroatiaKyrgyz Republic

SlovakiaHungary

SerbiaLithuaniaMongolia

UzbekistanBulgaria

KazakhstanUkraine

SloveniaEstonia

Czech RepublicLatvia

BelarusRussia


Table 2.10. Respondents Having Worked in Past 12 Months, By Age, Gender, and Education

Proportion of respondents that worked during 12 months preceding interview

By Highest educational attainment

Compulsory / Higher No degree / secondary / professional /

By Age group no education vocational post-graduate Overall

18–30 yrs 14.5 59.0 80.0 63.2

31–40 yrs 34.9 74.8 85.9 75.7

41–50 yrs 26.3 71.1 90.7 74.6

51–60 yrs 28.3 51.4 69.5 54.6

61–70 yrs 7.1 10.5 29.6 12.9

71+ yrs 2.4 3.0 9.4 3.4

All ages 14.5 53.1 75.3 55.6

Men 66.9

Women 45.5

Figure 2.8. Respondents that Report Having Worked During Past 12 Months(percent)

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 46


Wages and Salaries Are the Primary Source of Income

Wages and salaries are the main income source of about one-half of all households in theregion, and pensions for about one-fourth of all households (see Table 2.11).26 Wages are rel-atively more important for the CIS middle-income countries, with 62 percent of the respon-dents citing wages as the main income source. Pensions account for the second main incomesource and are relatively more important for the EU countries where more than one-third ofthe respondents report it as the main source of income. Not surprisingly, self-employmentagricultural and non-agricultural income as well as remittances from friends and relatives,both are relatively more important for the CIS low-income countries. Once pensions aretaken out, transfers—through various social assistance programs—are a relatively small partof household incomes. While this broad pattern prevails across the region, inter-country dif-ferences are distinct (see Figure 2.9).27

What is also interesting is that it is only in Turkey where self-employed income has astronger importance, perhaps reflecting the historical legacy of market enterprise and entre-preneurship in Turkey. But the small size of respondents in other subregions in transitioncountries citing self-employment income, whether agriculture or non-agriculture, as a pri-mary source of income suggests that efforts to promote entrepreneurship and the growthof new small businesses still has some ways to go.

But for the Poorest One-third, Pensions Are the Most Important Source of Income

The pattern of income for the poorest one-third is different, with a stronger reliance on pen-sions. The odds-ratio provides a measure of the likelihood of each of the group factors con-tributing to poverty relative to the other group. The use of the bottom one-third, rather thanan absolute measure of poverty common across countries, provides a sense of relativepoverty in each country. The data clearly suggest that pensioners, agriculture workers, andthose on transfers have greater likelihood of being poor. Interestingly, income from friendsand family are no more important for the poor than for the overall population (contrastwith top one-third or non-poor).

The sectoral pattern of employment of the bottom one-third who rely upon wages asthe primary source of income suggest a clear divergence with those of the non-poor. Theworking poor in wage employment are more likely to be found in agriculture and otherprimary activities, than in the service sector where productivity and wages are likely higher(see World Bank 2008 forthcoming). By contrast, the non-poor are disproportionately

26. Respondents in the LiTS were asked about the various sources of livelihood of their households aswell as to report which of these was the most important income source for their household. (1) incomefrom wages in cash, (2) wages in kind, (3) income from self-employment, (4) sales or bartering of farmproducts, (5) pensions, (6) unemployment benefits, (7) investments, savings, rental of property, (8) state-provided social benefits, (9) community/privately provided social benefits, (10) help from relatives/friendsin the country, (11) help from relatives/friends abroad, (12) stipend income, (13) help from charities andNGOs, and (14) other sources.

27. Annex Figure A2 illustrates the country rankings based on relative importance of different house-hold income sources.

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 47


Figure 2.9. Inter-Country Differences in Main Income Sources of Households

0 20 40 60 80 100

Percent of respondents

Kyrgyz Republic

Albania

Georgia

Turkey

Uzbekistan

Tajikistan

Armenia

Macedonia

Mongolia

Moldova

Azerbaijan

Poland

Montenegro

Bosnia

Croatia

Serbia

Lithuania

Romania

Hungary

Czech Republic

Bulgaria

Latvia

Slovenia

Estonia

Slovakia

Ukraine

Kazakhstan

Belarus

Russia

Main Source of Income of the Household

Wages SE non-agri

Agriculture Pensions

Friends/family Transfers

Other

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 48


Table 2.11. Main Income Source by Region

Percent of respondents reporting category as main income source:

Self-empl. Friends/Country Group Wages non-agri. Agriculture Pensions family Transfers Other Total

(i) All households:

EU member states 49.1 8.0 0.9 34.9 2.2 3.5 1.4 100.0

South-Eastern Europe 44.2 11.2 3.7 26.0 8.3 3.1 3.4 100.0

CIS-low income 35.3 19.2 9.1 18.5 10.2 2.1 5.6 100.0

CIS-middle income 62.5 4.8 1.2 26.4 3.4 0.6 1.1 100.0

Other 39.1 15.9 9.2 24.0 4.3 3.3 4.3 100.0

Overall 51.9 9.4 3.7 26.5 4.3 1.9 2.4 100.0

(ii) Poorest one-third:

EU member states 31.6 3.1 0.5 54.7 2.2 6.3 1.6 100.0


CIS-low income 25.1 14.5 10.8 32.3 8.3 3.4 5.6 100.0


Other 22.8 15.7 13.5 32.5 6.5 5.2 3.8 100.0

Overall 30.3 6.3 5.0 48.7 4.5 3.1 2.1 100.0

Odds-ratio (ii) / (i):

EU member states 0.64 0.38 0.50 1.57 0.98 1.81 1.18 1.00


CIS-low income 0.71 0.76 1.19 1.74 0.81 1.65 1.00 1.00


Other 0.58 0.99 1.47 1.35 1.53 1.57 0.89 1.00

Overall 0.58 0.68 1.35 1.84 1.05 1.63 0.89 1.00

employed in the higher productivity growing sectors of the economy such as transport andcommunications, financial intermediation, and other service sectors.

A more detailed analysis of the correlates of poverty using an ordered probit of the like-lihood of being in the bottom one third of per capita expenditures (Table 2.12), suggests thefollowing:

■ The likelihood of being relatively poor is highest for pensioners, farmers (especiallyin the CIS middle-income countries), and those dependent on transfers (except forTurkey).

■ The likelihood of relative poverty is lower for the working people, for those in self-employed non-agriculture, the better educated (especially those with higher profes-sional or postgraduate degrees), and those in urban or metropolitan areas.

Results of the ordered probit model of welfare status summarized in Table 2.12:

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 49

50W

orld Bank Working Paper

Table 2.12. Probit Model of Likelihood of Being Poor

(1 = poorest one-third income group ranked by PCE)



Household size 0.053*** 0.005 0.025* 0.013 0.074*** 0.012 0.043*** 0.008 0.063*** 0.019 0.057*** 0.015

Education level:

No degree / 0.126*** 0.038 0.108 0.091 0.178** 0.075 0.026 0.098 −0.169 0.162 0.241*** 0.071no education

Compulsory Reference categoryschool education

Secondary −0.180*** 0.025 −0.272*** 0.047 −0.365*** 0.062 −0.043 0.051 −0.229** 0.091 −0.229*** 0.066education

Professional, −0.248*** 0.024 −0.308*** 0.043 −0.215*** 0.054 −0.131** 0.058 −0.341*** 0.088 −0.432*** 0.064vocational school

Higher −0.527*** 0.030 −0.625*** 0.057 −0.480*** 0.072 −0.382*** 0.062 −0.680*** 0.097 −0.661*** 0.088professional degree

Post graduate −0.832*** 0.124 −1.140*** 0.197 −0.082 0.312 −0.455 0.327 −0.811** 0.382 −1.082*** 0.355degree

Age category:

18–30 yrs Reference category

31–40 yrs 0.017 0.027 0.097 0.061 0.040 0.060 −0.105** 0.048 0.042 0.076 0.125 0.078

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 50

Satisfaction with Life and Service D

elivery in Eastern Europe and the Former Soviet U

nion51

41–50 yrs 0.111*** 0.027 0.339*** 0.059 0.064 0.059 −0.028 0.048 0.219*** 0.073 0.030 0.078

51–60 yrs 0.177*** 0.028 0.251*** 0.058 0.127** 0.063 0.115** 0.056 0.473*** 0.081 0.067 0.081

61–70 yrs 0.264*** 0.033 0.312*** 0.066 0.110 0.076 0.231*** 0.063 0.563*** 0.096 0.244*** 0.090

71+ yrs 0.534*** 0.036 0.594*** 0.069 0.426*** 0.089 0.426*** 0.074 0.776*** 0.113 0.561*** 0.095

Worked during −0.154*** 0.020 −0.244*** 0.045 −0.208*** 0.046 −0.093** 0.036 −0.088 0.060 −0.130** 0.057past 12 months

Main income source:

Wages Reference category

SE non-agriculture −0.123*** 0.029 −0.371*** 0.078 −0.197*** 0.063 −0.080* 0.048 −0.212** 0.101 0.025 0.084

Agriculture 0.250*** 0.038 −0.176 0.162 0.396*** 0.092 0.165*** 0.057 0.597*** 0.157 0.229** 0.103

Pensions 0.565*** 0.026 0.493*** 0.053 0.422*** 0.060 0.609*** 0.053 0.700*** 0.080 0.558*** 0.071

Friends/family 0.070* 0.040 0.306** 0.149 0.052 0.079 −0.048 0.062 0.186 0.148 0.452*** 0.137

Transfers 0.727*** 0.052 0.756*** 0.099 0.723*** 0.103 0.660*** 0.114 0.549** 0.255 0.713*** 0.123

Other 0.144*** 0.053 0.207 0.146 0.154 0.109 0.065 0.082 0.273 0.242 0.223 0.144

Place of residence:

Urban areas −0.177*** 0.019 −0.035 0.035 −0.064 0.044 −0.200*** 0.042 −0.536*** 0.050 −0.203*** 0.054

Rural areas Reference category

Metropolitan −0.521*** 0.025 −0.319*** 0.045 −0.335*** 0.062 −0.508*** 0.049 −1.305*** 0.103 −0.588*** 0.062

Constant −0.528*** 0.038 −0.528*** 0.078 −0.576*** 0.086 −0.505*** 0.075 −0.299** 0.127 −0.573*** 0.103

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 51


Some illustrative estimates from the above model:

Public Transfers Have Reach But Are Not an Important Source of Income

Given the socialist legacy and the recent real increases in social assistance payments in mostcountries in ECA, transfers reach out to a significant part of the population in the CIS coun-tries and in the EU member states (see Table 2.13). But they play only a small role in theincomes of the population, including the poor. This suggests that state-provided social ben-efits appear to be largely untargeted transfers, probably because of family allowances whichtend generally to be untargeted transfers, and that their levels are inadequate. However, inthe relatively richer ECA countries of the EU and in SEE, transfers are still reported as theprimary income source by 3–4 percent of the population.


Our analysis above indicates that the welfare measure derived from the LiTS provides a veryuseful and effective means to measure household welfare and compare both within as well asacross countries. Using a per capita adult equivalent measure of household consumption,this paper develops a unique, contemporaneous profile of poverty in the ECA Region in 2006.

Simulated type Predicted probability that person is poor:

Random respondent: 0.33

Wage employees in capital: EU members 0.17

SEE 0.19

CIS low 0.16

CIS middle 0.03

Other 0.12

Pensioners: EU members 0.42

SEE 0.43

CIS low 0.50

CIS middle 0.49

Other 0.42

Rural Farmers: EU members 0.22

SEE 0.45

CIS low 0.39

CIS middle 0.62

Other 0.39

Respondent 61–70 years old, with no education, did not work in past 12 months, pensions main income source, lives in rural areas 0.73

Respondent 26 years old, with post-graduate degree, worked in past 12 months; wages main income source, lives in metropolitan area 0.04

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 52


The profile suggests a diverse region, with significant differences among countries in the inci-dence of poverty but a preponderance of the poor in the more populous middle-incomecountries. Quantitative measures of poverty correspond well with the population’s own sub-jective view of relative income status. Not surprisingly, poverty is correlated with low educa-tional attainment, lack of skills as well as self-employment in agriculture. But, of someconcern, is the finding that opportunities for the poor to upgrade their human capital andskills, or access finance and economic opportunities, may be limited by a distinct disadvan-tage in asset ownership. Even though income inequality is moderate in the region, this maybe accentuated by the asset inequality.

Given the analysis above, is there any role for public policy in improving the living stan-dards, especially for the poor? The diagnosis clearly reveals the importance of labor status forimproving outcomes with respect to satisfaction with life. The analysis reveals three labormarket groups towards which policy should be calibrated.

First, are those who are working for wages, especially the poor, but whose wages may below. In some cases, the low wages reflect the low educational and skill level of the populationwhich may condemn such workers to low productivity employment. Public policy can helpin strengthening the quality of public education, especially when it comes to the poor, pro-viding incentives for firms to invest in lifelong learning so as to create opportunities for edu-cational progression in life, and eliminating any barriers that may exist to the easy movementof factors of production towards more productive sectors or for the creation of new startups.

Second, are those who are non-participants on account of retirement, and who rely pri-marily on pensions for their income and who are at risk of poverty without these. For pen-sions, it is clear that public policy—though an appropriate combination of public and private

Table 2.13. Public/Private Transfers Are More Important in the CIS and EU Member States

Percent of respondents receiving transfer payment

State provided Unemployment Community/privatelyCountry Group social benefits benefits provided benefits Charities/NGOs

CIS-middle 12.7 1.1 0.1 0.3

CIS-low income 12.2 0.6 0.6 0.9

EU member states 9.4 2.9 3.4 0.9

Other 9.6 0.9 0.7 0.3

South-East Europe 4.1 3.3 0.4 0.7

Overall 11.1 1.4 0.8 0.5

Percent of respondents reporting transfers as main income source

Country Group Poor Middle Rich Overall

South-East Europe 6.9 2.0 0.9 3.1

EU member states 6.3 2.6 1.7 3.5

Other 5.2 2.6 2.3 3.3

CIS-low income 3.4 1.7 1.3 2.1

CIS-middle income 0.8 1.0 0.1 0.6

Overall 3.1 1.7 1.0 1.9

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 53


financing—needs to provide an adequate level of social insurance against working in old age.After the serious erosion and, in many cases, nonpayment of pensions during the crisis years,real increases in pensions are now targeting to provide a modicum of old age security. Gov-ernments need to ensure that pensions cover all the population at an adequate, fiscally afford-able rate.

Third, are those who are unemployed and rely upon public and private benefits to sus-tain themselves. To the extent that employment rates remain low in many countries and jobcreation has not progressed sufficiently rapidly to absorb the new entrants in the labor force,public policies that can spur job creation, especially in higher value added jobs, is essential.These would include measures to improve the investment climate, invest in human capital,and to promote labor market flexibility. To the extent that some of the unemployed areunemployable, as may be the case because of skill obsolescence and mismatches, adequateunemployment benefits are needed to protect them from poverty and maintain minimumliving standards. But public policy needs to be sufficiently discerning to prevent dependencyon benefits and discourage labor market participation.

Finally, public policy should also address the issue of asset inequality which could leadto an inequality of opportunity.28 For instance, lack of ownership of a car in the absence ofadequate and affordable public transport system could limit the poor’s ability to access bet-ter paying jobs. Similarly, lack of ownership of housing assets could limit access to financeand therefore the potential opportunities for entrepreneurship and self-employment income.Given the important role that mobile telephones and internet connectivity is playing intoday’s economy and helping to bring the economic divide, any significant disadvantage forthe poor would also limit income growth and perpetuate inter-generational inequalities. Yet,public policy can help by improving the quality of public transport, by supporting programsfor promoting housing ownership for low-income families, and ensuring competition inproduct markets to ensure affordable telephone and internet connectivity.

28. See, for instance, World Bank, World Development Report 2005.

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 54


Table A2.1. Overall Poverty Rates by Region

Squared PovertyHeadcount Rate(P0) Poverty Gap(P1) Gap(P2)

Poverty Line = PPP$2.15

ECA Region 10.5 3.2 1.5

Urban 6.3 1.7 0.8

Rural 17.4 5.5 2.7

EU member states 2.3 0.6 0.3

Urban 2.0 0.5 0.2

Rural 2.6 0.7 0.3

South-Eastern Europe 8.1 3.3 2.2

Urban 6.0 2.9 2.1

Rural 10.6 3.9 2.2

CIS-low income 30.1 10.1 5.0

Urban 21.3 7.6 4.0

Rural 36.8 11.9 5.7

CIS-middle 3.9 1.1 0.5

Urban 2.1 0.5 0.2

Rural 7.4 2.3 1.1

Other 20.2 5.9 2.7

Urban 12.8 3.2 1.2

Rural 32.5 10.4 5.0


ECA Region 33.6 12.6 6.5

Urban 25.7 8.5 4.1

Rural 46.5 19.2 10.4


Urban 15.8 4.4 1.7

Rural 22.2 6.1 2.5


Urban 20.5 7.8 4.5

Rural 34.7 12.9 6.9


Urban 56.0 23.8 13.3

Rural 79.1 36.6 20.9

CIS-middle 21.3 6.4 2.9

Urban 15.6 4.0 1.6

Rural 32.2 11.0 5.3

Other 52.2 21.4 11.5

Urban 42.7 15.4 7.5

Rural 68.0 31.5 18.1

Annex: Tables

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 55


Table A2.2. Sensitivity of Poverty Rates with Respect to Choice of Poverty Line

Poverty Incidence(P0) Change from Actual (%)


Actual 10.5 0.00

+5% 11.6 10.32

+10% 12.6 19.41

+20% 15.1 42.75

−5% 9.3 −11.41

−10% 8.4 −20.07

−20% 6.1 −42.04


Actual 33.6 0.00

+5% 36.0 7.16

+10% 38.3 13.90

+20% 42.8 27.33

−5% 31.6 −6.09

−10% 29.2 −13.05

−20% 24.2 −27.97

Table A2.3. Distribution of the Poor by Geographic Region

Poverty Distribution Distribution ofHeadcount Rate of the Poor Population


ECA Region 10.5 100.0 100.0

Urban 6.3 37.1 61.8

Rural 17.4 62.9 38.2


Urban 2.0 56.3 62.0

Rural 2.6 43.7 38.0


Urban 6.0 39.2 53.5

Rural 10.6 60.8 46.5

CIS-low income 30.1 100.0 100.0

Urban 21.3 30.4 43.0

Rural 36.8 69.6 57.0

CIS-middle 3.9 100.0 100.0

Urban 2.1 35.8 65.9

Rural 7.4 64.2 34.1

(continued )

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 56


Table A2.3. Distribution of the Poor by Geographic Region (Continued )

Poverty Distribution Distribution ofHeadcount Rate of the Poor Population

Other 20.2 100.0 100.0

Urban 12.8 39.5 62.5

Rural 32.5 60.5 37.5


ECA Region 33.6 100.0 100.0

Urban 25.7 47.2 61.8

Rural 46.5 52.8 38.2


Urban 15.8 53.6 62.0

Rural 22.2 46.4 38.0


Urban 20.5 40.4 53.5

Rural 34.7 59.6 46.5

CIS-low income 69.2 100.0 100.0

Urban 56.0 34.8 43.0

Rural 79.1 65.2 57.0

CIS-middle 21.3 100.0 100.0

Urban 15.6 48.4 65.9

Rural 32.2 51.6 34.1

Other 52.2 100.0 100.0

Urban 42.7 51.1 62.5

Rural 68.0 48.9 37.5

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 57


Table A2.4. Rural Urban Disparities

Average PCE (US$) As Ratio of National Average Metrop./Country Metropolitan Urban Rural Overall Metropolitan Urban Rural Overall Rural

Russia 6,093 2,787 2,322 3,108 1.96 0.90 0.75 1.00 2.62

Romania 3,255 1,856 1,449 2,372 1.37 0.78 0.61 1.00 2.25

Armenia 2,292 1,243 1,035 1,604 1.43 0.77 0.65 1.00 2.21

Belarus 2,869 2,568 1,314 2,203 1.30 1.17 0.60 1.00 2.18

Kazakhstan 2,947 2,130 1,365 1,951 1.51 1.09 0.70 1.00 2.16

Bulgaria 3,124 2,074 1,546 2,125 1.47 0.98 0.73 1.00 2.02

Ukraine 4,058 2,613 2,051 2,687 1.51 0.97 0.76 1.00 1.98

Lithuania 4,188 2,896 2,178 3,234 1.29 0.90 0.67 1.00 1.92

Kyrgyz Re 1,649 975 874 1,060 1.56 0.92 0.82 1.00 1.89

Moldova 1,918 1,353 1,025 1,302 1.47 1.04 0.79 1.00 1.87

Tajikistan 1,281 785 703 787 1.63 1.00 0.89 1.00 1.82

Georgia 1,828 1,165 1,014 1,315 1.39 0.89 0.77 1.00 1.80

Azerbaijan 1,549 1,155 887 1,136 1.36 1.02 0.78 1.00 1.75

Serbia 4,001 2,667 2,360 2,752 1.45 0.97 0.86 1.00 1.70

Mongolia 1,466 856 884 1,095 1.34 0.78 0.81 1.00 1.66

Albania 3,175 2,593 2,011 2,459 1.29 1.05 0.82 1.00 1.58

Bosnia 3,700 3,227 2,432 2,860 1.29 1.13 0.85 1.00 1.52

Croatia 6,423 5,602 4,247 5,260 1.22 1.07 0.81 1.00 1.51

Turkey 3,257 2,855 2,189 2,784 1.17 1.03 0.79 1.00 1.49

Poland 5,001 3,418 3,375 3,642 1.37 0.94 0.93 1.00 1.48

Hungary 4,087 3,549 2,800 3,455 1.18 1.03 0.81 1.00 1.46

Czech Rep 5,523 4,419 4,113 4,556 1.21 0.97 0.90 1.00 1.34

Montenegro 5,163 3,950 3,856 4,173 1.24 0.95 0.92 1.00 1.34

Uzbekistan 878 766 674 721 1.22 1.06 0.93 1.00 1.30

Latvia 4,335 3,820 3,408 3,870 1.12 0.99 0.88 1.00 1.27

Slovenia 7,990 6,192 6,304 6,531 1.22 0.95 0.97 1.00 1.27

Slovakia 4,269 3,636 3,376 3,577 1.19 1.02 0.94 1.00 1.26

Estonia 4,719 3,499 3,800 4,045 1.17 0.87 0.94 1.00 1.24

Macedonia,FYR 2,575 2,186 2,321 2,308 1.12 0.95 1.01 1.00 1.11

Overall 4,067 2,774 2,131 2,804 1.45 0.99 0.76 1.00 1.91

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 58


Table A2.5. Mean Per Capita Expenditures ($PPP per year)

Mean per capita expenditure

ECA Region 3,939

Urban 4,546

Rural 2,955

Lowest quintile 1,001

2 1,958

3 2,940

4 4,427

Highest quintile 9,372

EU member states 4,426

Urban 4,693

Rural 3,991


2 1,978

3 2,968

4 4,445


South-Eastern Europe 3,899

Urban 4,379

Rural 3,346

Lowest quintile 952

2 1,977

3 2,936

4 4,423


Mean per capita expenditure

CIS-low income 1,990

Urban 2,534

Rural 1,581

Lowest quintile 944

2 1,913

3 2,892

4 4,304


CIS-middle 4,892

Urban 5,555

Rural 3,610


2 1,988

3 2,947

4 4,467


Other 2,726

Urban 3,172

Rural 1,982

Lowest quintile 976

2 1,923

3 2,921

4 4,304


11524-02_Ch02.qxd 1/19/09 11:44 AM Page 59


Table A2.6. Decomposition of Inequality by Geographic Region

GE(0) GE(1) GE(2)

Overall inequality

Overall ECA Region 31.4 31.5 46.4


South-East Europe 24.5 23.4 30.5


CIS-middle income 27.7 29.3 43.9

Other 29.4 29.6 40.6

Within group inequality


Between group inequality


Between group inequality as % of overall inequality


Table A2.7. Ratios of Selected Expenditure Percentiles in Urban and Rural Areas

p10 p25 p50 p75 p90

ECA Region 1.59 1.60 1.50 1.52 1.49

EU member states 1.07 1.15 1.16 1.17 1.20

South-Eastern Europe 1.40 1.39 1.33 1.35 1.28

CIS-low income 1.19 1.34 1.45 1.60 1.74

CIS-middle 1.53 1.44 1.51 1.51 1.53

Other 1.57 1.63 1.66 1.53 1.68

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 60


Table A2.8. Poverty by Age Groups

Poverty Headcount Rate Distribution of the Poor Distribution of Population

0–5 yrs 50.8 10.4 6.9

6–14 45.1 16.1 12.0

15–19 34.0 8.1 8.0

20–24 27.8 6.1 7.4

25–29 29.1 7.0 8.1

30–34 31.6 7.0 7.5

35–39 30.0 6.8 7.6

40–44 26.6 5.9 7.5

45–49 26.2 5.7 7.3

50–54 27.3 5.3 6.6

55–59 27.8 5.1 6.2

60–64 31.6 3.9 4.2

65+ yrs 38.9 12.3 10.6

Overall 33.6 100.0 100.0

Table A2.9. Poverty by Whether Respondent Worked or Not During Past 12 Months

Poverty Distribution DistributionHeadcount Rate of the Poor of Population

Poverty Line = $PPP4.30

Yes 25.7 44.2 57.9

No 44.5 55.8 42.1

Overall 33.6 100.0 100.0

Table A2.10. Poverty by Education Level of Household Head

Poverty Distribution DistributionPoverty Line = $PPP4.30 Headcount Rate of the Poor of Population

Highest Educational Attainment

No degree/no education 69.5 14.1 6.8

Compulsory school education 52.1 29.0 18.7

Secondary 35.6 26.2 24.8

Professional/vocational school 24.0 21.6 30.2

Higher professional degree 16.4 9.1 18.6

Post-graduate degree 3.7 0.1 0.9

Overall 33.6 100.0 100.0

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 61


Table A2.11. Poverty by Household Head’s Gender

Poverty Distribution DistributionHeadcount Rate of the Poor of Population

Poverty Line = $PPP4.30

Male 35.5 77.2 73.1

Female 28.4 22.8 26.9

Overall 33.6 100.0 100.0

Table A2.12. Poverty by Demographic Composition

Poverty Distribution DistributionPoverty Line = $PPP4.30 Headcount Rate of the Poor of Population

Number of children 0–6 years old

no children 27.3 59.2 72.9

1 43.2 23.6 18.4

2 57.2 10.8 6.3

3 or more children 90.8 6.5 2.4

Household size

1 18.0 5.9 11.0

2 22.0 14.8 22.7

3 24.2 16.6 23.0

4 31.6 21.1 22.4

5 55.0 16.7 10.2

6 71.7 10.5 4.9

7 or more 82.9 14.4 5.9

Overall 33.6 100.0 100.0

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 62


Table A2.13. Consumption Regressions

Urban Rural

coef se coef se

Household characteristics

Log of household size −0.255*** 0.03 −0.321*** 0.04

Log of household size squared −0.066*** 0.02 −0.026 0.02

Share of children 0–6 (dropped) (dropped)

Share of children 7–16 0.388*** 0.05 0.334*** 0.06

Share of male adults 0.698*** 0.05 0.575*** 0.06

Share of female adults 0.658*** 0.05 0.564*** 0.06

Share of Elderly (≥=60) 0.416*** 0.06 0.320*** 0.06

Country Group

EU member states (dropped) (dropped)

South-Eastern Europe 0.028* 0.02 −0.027 0.02

CIS-low income −0.626*** 0.02 −0.787*** 0.02

CIS-middle −0.110*** 0.02 −0.456*** 0.02

Other −0.115*** 0.02 −0.342*** 0.02

Characteristics of household head

Log of household head’s age −0.314*** 0.02 −0.239*** 0.03

Gender

Male (dropped) (dropped)

Female −0.069*** 0.01 −0.070*** 0.02

Education of the household head

No degree/no education (dropped) (dropped)

Compulsory school education 0.098*** 0.03 0.076*** 0.02

Secondary 0.344*** 0.03 0.263*** 0.03

Professional/vocational school 0.346*** 0.03 0.282*** 0.03

Higher professional degree 0.570*** 0.03 0.488*** 0.03

Post-graduate degree 0.672*** 0.05 0.707*** 0.11

Employment status of the household head

Yes (dropped) (dropped)

No −0.170*** 0.01 −0.116*** 0.01

_cons 8.990*** 0.08 8.801*** 0.11

Number of observations 15,633 11,276

Adjusted R2 0.325 0.355

Note: 0.01—***; 0.05—**; 0.1—*;

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 63

11524-02_Ch02.qxd 1/19/09 11:44 AM Page 64

In this paper we explore citizens’ satisfaction with publicly provided health services(PPHS) in the Eastern Europe and Central Asia region. In particular, we focus ouranalysis on three inter-linked questions: (a) why are some people more likely than

others to use PPHS; (b) what are some of the key influences on users’ satisfaction withquality and efficiency of medical treatment received; and (c) how does the prevalence ofinformal payments impact people’s decision on using PPHS, and upon use, the level ofsatisfaction with services received?

The 2006 LiTS provides us with a unique opportunity to pose these questions. In addi-tion to eliciting respondents’ perceptions of the quality and efficiency of PPHS, the surveyexplores priorities for public policies and investment, attitudes to a market economy anddemocracy, as well as living standards (including expenditure aggregates and sources oflivelihood), and demographic characteristics. Thus we are able to assess the impact of‘objective’ variables such as gender, expenditure, age and education level, as well as ‘sub-jective’ variables such as the level of trust in government and police, satisfaction with life,and perception of corruption, on satisfaction with PPHS.

Countries in the Europe and Central Asia region inherited a health care system, calledthe “Semashko” model after the Soviet statesman who envisaged it, that was state ownedand financed through general taxation. Although the system delivered quick improve-ments in population health when first implemented, the absence of market forces (oralternate mechanisms) to ensure provider and administrator accountability for health out-comes led to hospital-dominated networks that adapted very slowly, if at all, to changingdisease patterns and medical innovations. The fall of the Soviet Union in 1991 and the

CHAPTER 3

Satisfaction with Publiclyprovided Health Services

in Eastern Europe and the Former Soviet Union29

65

29. Ramya Sundaram and Salman Zaidi.

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 65

resulting macroeconomic contraction in this region led to severe shortfalls in health sectorbudgets. Countries began to undertake health sector reforms as they moved along the tran-sition path, partly necessitated by the budget cuts, and partly in keeping with the other fun-damental social and economic changes occurring in the region. Strong economic growth inrecent years, the fruition of early reform efforts and the continuation of further reforms haveresulted in a health sector that is extremely dynamic and diverse across the region, featur-ing a pluralism of health systems. Against this backdrop of transition and reforms, how sat-isfied are the citizens of ECA with publicly provided health services in 2006?

Overall, we find that 45 percent of all respondents report themselves as being satisfiedwith the services they received from the publicly provided health system. Reported satis-faction rates vary considerably by country, with the “net satisfaction rate”—the differencebetween the share of satisfied and dissatisfied respondents—generally a little higher amongEU member states than among middle-income CIS countries (Figure 3.1).

A first glance at country ranking suggests that a combination of (a) economic growth,and (b) health sector reform, have an impact on satisfaction. Most EU member states inthis region have enjoyed relative political stability through the transition, and, with theexception of Romania, began recovering from recessions in the second half of the 1990s.They have also successfully instituted radical reform of their health sectors. With theexception of Romania, EU member states are at or near the top with regards to satisfactionwith PPHS.


0 20 40 60 80 100


AlbaniaUkraine

TajikistanMoldovaRomania

KazakhstanKyrgyz Republic

SerbiaRussia

UzbekistanMacedonia

MontenegroMongolia

TurkeyBelarusPolandBosnia

AzerbaijanHungaryBulgaria

LatviaEstonia

LithuaniaArmeniaSlovakiaGeorgiaCroatia

Czech RepublicSlovenia

Very dissatisfied Dissatisfied

Neither Satisfied

Very satisfied

Figure 3.1. Rates of Satisfaction with the Publicly-provided Health System, By Country

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 66

Georgia and Armenia, two low-income CIS countries, also do very well, with over 60 percent of respondents being satisfied with the care received. Armenia has seen broadbased economic growth, with per capita GDP increasing in double digits in the recent past.In addition, both Georgia and Armenia have instituted effective reforms in provider paymentmechanisms (Bonilla-Chacin, Murrugarra, and Temourov 2005) and have experienced adrop in informal payments (see the discussion on informal payments in a later section of thischapter). In contrast, the Kyrgyz republic, another low-income CIS country which has insti-tuted far reaching health sector reform, but which has experienced tepid growth, ranks lowin satisfaction. The scenario is reversed in Azerbaijan, which has experienced spectacular, butnarrow based economic growth, and where the health sector reform has not been substan-tial. Azerbaijan stands at the middle of the rankings according to the “net satisfaction rate.”

The middle-income CIS countries are clustered at the bottom of the satisfaction rank-ings, despite spending more per capita on health services than the low-income CIS countries.Health sector reform in these countries has been incremental and piecemeal with medicalpractices continuing to deviate considerably from internationally established evidence-basedmedicine.

What are the individual factors that influence satisfaction with publicly providedhealth services? To determine this, we employ an estimation strategy that accounts forselection bias—it takes into account the fact that responses to satisfaction with PPHS areonly obtained from the subsample of respondents that chose to access the service duringthe 12 months preceding the survey. Therefore, we jointly estimate factors that influencewhether a respondent chooses to use PPHS, and upon use, his/her satisfaction with servicesreceived. Finally, we compare our results with a health utilization survey that was conductedin 2001 in 8 countries (Balabanova and others 2004).

One of the key findings of this analysis relates to the perception that unofficial pay-ments are needed to obtain services. It is well documented that, with the decrease in pub-lic funding of the health care system, informal payments have emerged as a fundamentalaspect of health care financing in many ECA countries (Lewis 2000; Balabanova 2007;Allin, Davaki, and Mossialos 2006; and several others). The study of the effects of informalpayments is complicated by the existence, particularly in the CIS countries, of strong tra-ditions of innocent gift-giving as an expression of gratitude, usually after service is pro-vided. The LiTS does not separate informal payments from gifts in its questionnaire; thusthe results associated with informal payments that are presented in this paper should beinterpreted with some caution in light of this omission.

We find that having to pay for essentially “free” services has a significant negativeinfluence on satisfaction with PPHS. Our empirical model indicates that, other thingsbeing equal, respondents who say unofficial payments are often necessary are about 1.4 timesmore likely to report being dissatisfied with service delivery in the publicly providedhealth system compared to those who say that such payments are never needed. A com-parison of the 2006 ECA LiTS survey to the 2001 survey referred to in Balabanova andothers 2004 (for details, see a later section of this chapter) suggests that there has been adramatic fall in the prevalence of informal payments in both Georgia and Armeniabetween 2001 and 2006, while there is an increase in Ukraine and Russia. This could gosome distance in explaining why satisfaction rates are high in Georgia and Armenia, andlow in Russia and Ukraine, although the total (public and private) PPP adjusted percapita health spending in 2004 is lower both in Georgia ($137) and Armenia ($211) when


11524-03_Ch03.qxd 1/19/09 11:45 AM Page 67

compared to Russia ($546) and Ukraine ($382). Similarly, a comparison of the LiTS toLewis (2000) suggests that the incidence of informal payments have increased in Albaniabetween 1996 and 2006; Albania ranks the lowest among all countries in terms of satis-faction with PPHS.

The second key finding of this analysis relates to accessing PPHS. We find that thosewho have confidence in the government (as proxied by trust in government and the police)are more likely to use PPHS—other things being equal, a respondent that says he/she truststhe government and the police is about 7.5 percent more likely than a person who does notto have used PPHS in the past 12 months. Political scientists have theorized that a positiveexperience with public services leads to satisfaction and trust in the government (forinstance, see Bratton 2007; Bouckaert and Van De Walle 2003). While we find a positivecorrelation of 0.097 between satisfaction with PPHS and trust in government and police,we additionally find that trust in government and police has a positive and significant effecton accessing PPHS.

The results have broad policy implications. Rapid and broad based economic growthaccompanied by sensible health sector reform increases user satisfaction with PPHS. Atwo-pronged approach to health sector reform is suggested, particularly in middle and low-income CIS countries. Firstly, in addition to improving the actual quality of service pro-vided by improving primary care facilities and encouraging the practice of evidence-basedmedicine, the reform effort should address the complex set of circumstances that underliethe prevalence of informal payments. Providing citizens’ with means to hold providersdirectly accountable for quality of service, and for health outcomes, should remove someof the incentives for these payments. Secondly, a good communication strategy should bea key component of any effective health reform effort.

Take the case of a reform effort to improve the quality of a historically poorly providedpublic service. Simply taking steps to improve the quality of the service is likely to be insuf-ficient: the government must also actively seek to change the poor perception of the servicein citizens’ minds. For instance, if people have typically experienced poor service when theygo to a public hospital, they are less likely to want to go back. They might delay or avoidseeking service until they are very sick. Even a significantly improved public hospital wouldfind it challenging to provide good treatment in the later stages of a disease. Instead, by con-currently trying to improve both the actual quality, as well as the perception of the qualityof publicly provided health services, the government might persuade people to seek serviceearly, ensuring better treatment outcomes than otherwise.

Effective communications should also be aimed at boosting awareness that the gov-ernment is committed to delivering free, and good quality, basic health services, and thatcitizens should hold PPHS accountable for such results. A striking example of the need toclearly communicate the government’s health policy comes from Georgia. By law, all chil-dren 0–3 are entitled to free publicly provided health services in Georgia. A large numberof parents seem apparently unaware of this policy as disclosed by this focus group partic-ipant in Georgia in 2001: “My friend told me that in their polyclinic children receive ser-vices for free . . . I do not know if there is any age limitation. I think this is an initiative oftheir polyclinic.” (Belli, Gotsadze, and Shahriari 2004). By encouraging and providing citi-zens with the means to seek redress if substandard treatment is provided, or if they areasked for unofficial payments, the government can co-opt the patient to be an active playerin the reform process.


11524-03_Ch03.qxd 1/19/09 11:45 AM Page 68

Communication strategies that simply focus on an anti-corruption message, but thatdo not provide users with alternate means of holding providers accountable are not likelyto succeed. Vian and Burak (2006) find that there is no difference in moral beliefs betweenpeople in Albania who intend to make informal payments and those who do not—themain difference is that people who do not intend to make informal payments are morelikely to report that they have connections with medical personnel, which may be substi-tuting for informal payments.

Well functioning PPHS are perceived as important by most ECA citizens. When askedabout their first priority for extra government spending, 40 percent of LiTS respondentschose health care (Figure 3.2). This equals the sum of respondents who identify educationand housing (the second and third categories) as the top priority, 27 and 13 percent,respectively. In terms of country groupings, healthcare was picked by the highest share ofrespondents in the EU member states (44 percent) and the lowest in South-East Europe andTurkey (33 and 29 percent).

The remainder of thepaper is laid out as follows:the next section providessome background on theevolution of PPHS in ECAcountries; section two de-scribes the LiTS data used inthe analysis along with sum-mary tables and graphs ofkey variables. In sectionthree, the empirical strat-egy is described, while theresults of the estimation arepresented in section four.Section five probes into thestrong performance of somelow-income CIS countriesin terms of satisfaction withPFHS, while section sevenconcludes.

Evolution of Publicly Provided Health Services in Eastern Europe and Central Asia

Health care delivery in the ECA countries was centrally managed and financed during theera of communism, and the system sought to provide universal care that was free at thepoint of access. When first implemented, the ‘Semashko’ model led to quick improvementsin population health through introduction of practices that prevented the spread of com-municable diseases, and through investment in infrastructure and in training physicians.Until the 1960s, reported measures of life expectancy in this region were comparable tothose of Western Europe (Balabanova 2007). In addition to the free, comprehensive health


Other

Housing

Pensions

Environment

EducationInfrastructure

Health

First Priority

Figure 3.2. Priorities for Additional GovernmentSpending: 2006 LiTS

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 69

care system, factors such as high levels of education and adequate access to water and san-itation enabled countries to achieve better reported health outcomes than other countrieswith similar income levels (Bonilla-Chacin, Murrugarra, and Temourov 2005).

The absence of market forces started becoming evident however, with health care bud-gets allocated predominantly to hospitals rather than to primary care facilities, and accord-ing to historic patterns and fixed norms of number of physicians and beds rather than onthe changing health needs of the population. This led to a hospital dominated network ofextensive infrastructure and poorly paid personnel that adapted very slowly to changes indisease patterns and innovations in medical technology. The greater failure of the Sovietera health care system was due to the lack of accountability of providers and administra-tors for quality of care provided and for health outcomes. There were no mechanisms bywhich sound clinical practices were rewarded or widely disseminated. With the intensifi-cation of the cold war, and the increasing isolation of the Soviet Union, there was very lit-tle interaction between specialists behind the iron curtain and those in the western world.Unable to benefit from the new medical breakthroughs in the rest of the world, health caresystems in this region moved further away from evidence based medicine.

The lack of direct accountability also gave rise to alternate mechanisms through whichpatients could influence the quality and outcome of health services. Even during the com-munist era, those with connections—such as the party or military elite—or those willingto provide private monetary incentives, could always ensure superior facilities. Jumpingthe queue, or expediting service through the use of under-the-table payments, was notuncommon.

The large upheavals that accompanied the break-up of the Soviet Union, and thesevere economic downturn in the region during the 1990s, led to drastic cutbacks in gov-ernment spending on the health sector, with further deterioration in the quality and dis-tribution of health services. Many countries, particularly among the CIS, experienceddecreases in reported measures of life expectancy and increases in infant and child mortalityduring the 1990s.30

There has been a recovery in economic growth in the region more recently, accompa-nied by varying degrees of health sector reforms in particular economies. Health financingreform has focused on moving from funding health services through general revenues tosome form of social insurance system. While the EU member countries have achieved somesuccess in raising money for national health insurance through payroll taxes (with short-falls being met by transfers from general revenue), low-income CIS countries face chal-lenges due to the narrow employment base given the significance of non-cash as well asinformal activities in their economy. Thus health financing has become increasingly relianton out-of-pocket payments in these countries (Bonilla-Chacin, Murrugarra, and Temourov2005; Lewis 2000).

Rationalizing the excess human and physical capacity invested in the health systemhas been met with limited success, particularly among middle-income CIS countries. Forinstance, maternal in-patient capacity in Russia has not declined in response to the large


30. Reported life expectancy in Belarus, Kazakhstan, Russia, Ukraine, and Uzbekistan continues to belower now than in 1990. In addition, in Kazakhstan, both infant and child mortality is currently higherthan in 1990.

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 70

decline in birth rates, and increasing numbers of pregnant women are hospitalized to pre-serve excess capacity (Danishevski and others 2006). Some low-income CIS countriessuch as Georgia, Armenia, the Kyrgyz republic and Moldova are further along the reformtrajectory than others, likely necessitated by the almost total collapse in health sectorfunding in these countries. Despite this, Bonilla-Chacin, Murrugarra, and Temourov(2005) confirm that there still are more beds and health facilities in these countries thanin the EU. In Georgia an interviewed hospital provider had this to say: “We are too manycompared to the workload, and our remuneration depends on how many patients wetreat. Thus, doctors have an internal agreement, whereby we ‘rotate’: 1 week one doctorserves all patients, and the following week another one comes in” (Belli, Gotsadze, andShahriari 2004).

Stretching the ever shrinking set of resources across the large networks that exist hasled to a reduction in the effectiveness of services (World Bank 2005a). At the same time,the spread of communicable diseases, such as HIV/AIDS and tuberculosis, are beginningto pose even greater challenges to the outdated hospital-dominated networks (World Bank2005b). Life expectancy is currently falling in the former Soviet Union, with this regionbeing only one of two regions in the world (the other being sub-Saharan Africa) where lifeexpectancy is currently declining (Balabanova 2007). Against this backdrop of changingdisease patterns and macroeconomic instability, the LiTS elicits citizen’s satisfaction withthe publicly provided health services in their country.

Utilization Rates, Satisfaction, and Prevalence of Informal Payments

The EBRD-World Bank Life in Transition Survey (LiTS), conducted in September 2006,probes the relationship between living standards and satisfaction with life in 28 EasternEurope and Central Asian countries, and Mongolia. A sample of 1,000 individuals wasinterviewed in each country, making a total of 29,000 respondents across ECA.

The analysis in this paper focuses on the attitudes and values section of the LiTSquestionnaire, particularly on responses to three sets of questions, which respondentswere asked with reference to eight different public services.31 First, respondents wereasked: “In your opinion, how often is it necessary for people like you to have to makeinformal payments/gifts in these situations.” One of the eight situations listed was“Receive medical treatment in the public health system.” Respondents could chooseamong five options: 1: Never, 2: Seldom, 3: Sometimes, 4: Usually, and 5: Always.Respondents were next asked: “During the past 12 months, have you personally usedthese services?” A follow-up question was addressed to those who had used the serviceduring the previous 12 months: “How satisfied were you with the quality and the effi-ciency of the service/interaction?” Responses to this third question were coded using aprogressive five-point scale; 1: Very dissatisfied, 2: Dissatisfied, 3: Indifferent, 4: Satisfied,and 5: Very satisfied.


31. These included (i) the road police, (ii) official documents (e.g., passport, visa, birth or marriagecertificate, etc), (iii) police (other than road police) (iv) civil courts (v) public health system (vi) publiceducation (tertiary and vocational), (vii) unemployment benefits, and (viii) social security benefits.

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 71

Utilization of the Public Services in ECA Countries

The number of respondents reporting having had an interaction with different services inthe last 12 months varies quite considerably by type of service. Of the various services cov-ered, the largest number of affirmative responses is for health, with slightly more than halfof those surveyed having had an interaction with PPHS during the 12 month period pre-ceding the date of the interview. The response rate for other services is much smaller32: theservice with the second largest response rate is “interacting with the authorities grantingofficial documents (e.g., passport, visa, birth or marriage certificate, etc.),” where a littlemore than one fifth of those surveyed responded “yes.”

Utilization rates of PPHS varied quite considerably by country, from a high of aroundtwo-thirds of all respondents in Albania to one-third only in Slovakia33 (Figure 3.3). WhileSlovenia and Slovakia have some private provision of health, the lower access rates for


32. The scope of enquiry into the public education system was restricted to tertiary and vocationaleducation—only 12% of those surveyed reported an interaction with the vocational and tertiary publiceducation system in ECA.

33. It is important to note that the LiTS survey did not enquire in detail about health seeking behav-ior. Therefore we do not have information on the health status of those we accessed publicly providedhealth services, or details about those who needed to use the system, but did not do so for various reasons.

0 20 40 60 80


SlovakiaBelarus

MongoliaSloveniaArmenia

Kyrgyz RepublicPolandBosnia

UzbekistanMontenegro

GeorgiaBulgaria

SerbiaMoldovaRomania

EstoniaRussia

MacedoniaLithuania

UkraineTajikistan

LatviaAzerbaijan

TurkeyCzech Republic

CroatiaHungary

KazakhstanAlbania

Received treatment in the publicly provided health system during past 12 months

Figure 3.3. Utilization of Publicly-provided Health System, By Country

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 72

countries such as Belarus, Armenia and the Kyrgyz Republic probably reflect the barriersto access that out of pocket payments represent.

Satisfaction with Publicly-provided Health Services Compared to Other Services and Across Countries

Of all services covered in the LiTS, the public education and publicly provided health sys-tems received the most favorable ratings (Figure 3.4), with 49 percent and 45 percent of allrespondents reporting themselves as being satisfied with the education and medical treat-ment they received (24 and 20 percent respectively reported being dissatisfied with servicesreceived).


0 10 20 30 40 50 60 70 80 90 100

Other police

Unemployment benefits

Courts

Road police

Social security benef

Request for documents

Publiclly provided health system

Public education

Very unsatisfied Unsatisfied Indifferent Satisfied Very satisfied

Figure 3.4. Rates of Satisfaction, By Type of Service

Reported satisfaction rates with publicly provided health services vary quite consider-ably by country. The EU member countries typically have higher “net satisfaction rates”—the difference between the share of satisfied and dissatisfied respondents—when comparedto the middle-income CIS countries (Ukraine, Kazakhstan, Russia). The low-income CIScountries (Georgia, Armenia, Azerbaijan) also perform a little better than the middle-income CIS countries. Albania has the lowest net satisfaction rate.

Perceptions Regarding Prevalence of Unofficial Payments/Gifts

Turning to the third key variable of interest: when LiTS respondents were asked how oftenit is necessary for people to have to make unofficial payments/gifts when using public ser-vices, a large majority said that such payments are never needed—however for publiclyprovided health services, the share reporting such payments to be usually/always needed wasnotably higher than for other services (Figure 3.5).

There is considerable variation at the country level, from a low of less than 10 percentin Estonia, Slovenia and Georgia to around 48 percent in Albania (Figure 3.6).

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 73


Never needed Seldom Sometimes Usually Always needed

0 10 20 30 40 50 60 70 80 90 100

Police

Unemployment benefits

Courts

Road police

Social security benef

Request for documents

Publiclly provided health system

Public education

Figure 3.5. Percent of Respondents that Think that Unofficial Payments Are Needed

0 10 20 30 40 50


AlbaniaUkraine


HungaryMoldovaRomania

AzerbaijanKyrgyz Republic

RussiaBulgaria

LithuaniaMacedonia

SlovakiaBosnia

ArmeniaMontenegro

SerbiaMongolia

PolandCroatiaLatvia

BelarusTurkey

KazakhstanCzech Republic

GeorgiaSloveniaEstonia

Always needed Often needed

Figure 3.6. Perceptions Regarding Unofficial Payments in Publicly-provided Health System, By Country

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 74

Finally, on comparing country rankings of satisfaction with publicly providedhealth services with prevalence of unofficial payments, the data indicate that prevalenceof unofficial payments adversely impacts the level of satisfaction with the service (Figure 3.7)—this apparent link is subjected to more rigorous examination later on inthis paper.


Albania

BelarusBosnia

Bulgaria

Croatia

Czech

Macedonia Hungary

Moldova

Montenegro

PolandRomaniaSerbia

Slovakia

Slovenia

Turkey

Ukraine

Armenia

Azerbaijan

Estonia

Georgia

Kazakhstan

Kyrgyz

Latvia Lithuania

Mongolia

Russia

Tajikistan

Uzbekistan

3040

5060

70

% a

bove

neu

tral

on

satis

fact

ion

0 20 40 60

% saying informal payments usually/always required when using publicly provided

Satisfaction with Publicly Provided Health System

Figure 3.7. Negative Correlation Between Satisfaction and Prevalence of Informal Payments

Estimation Strategy

In our estimation, we take into account the fact that responses to satisfaction with PPHSis obtained only from that subsample of individuals who choose to access the service, thatis, we correct for the selection bias. While people don’t completely control whether theyfall ill or not, once ill, they do make some choice about whether to use PPHS. This is con-firmed by many studies that research health utilization behavior, which find that not allindividuals who should seek treatment do so (Balabanova and others 2004; World Bank2005a). Next, with regard to the level of satisfaction with PPHS, the ordered nature of thedependent variable suggests the use of an ordered probit or logit model. However, models with

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 75

ordered dependent variables that correct for sample selection are practically non-existent, andstandard tools are not readily available to estimate such models. We proceed following a two-step “Heckman ordered probit” procedure.

We first use a binary probit model to study factors that influence whether or not anindividual accesses PPHS during the past 12 months. We compute a correction factor, theinverse mills ratio, from this estimation. At the second stage, we use an ordered probitmodel to analyze the level of satisfaction with the PPHS, incorporating the correctionfactor calculated in the first step. In other words, the first equation determines sampleselection, which we write as follows:

We do not observe z i*, just zi = 1 if z i*> 0, and zi = 0 otherwise; that is, we observe whetherthe respondent interacts with publicly provided health services (zi = 1) or doesn’t (z1 = 0). Factors that we believe influence this decision, including variables such as per-capita expenditure, gender, self-assessed health status, etc, are included in the vector wi. Next, the equation that determines satisfaction with the public service is writ-ten as:

We do not observe y i*. When zi = 1, we only observe whether yi = 1, 2, 3, 4, and 5 if αj −1 < y i*< αj, ( j = 1, 2, 3, 4, and 5)—i.e. when a survey participant interacts with the publicly pro-vided health service, we observe the level of satisfaction with the service received. Factorsthat influence the level of satisfaction are included in the vector xi. These include individ-ual-level characteristics such as per-capita expenditure, gender, age, general satisfactionwith life, etc, in addition to country-level characteristics such as per capita GDP, growthrate, etc. We can reformulate the model as:

Selection equation:

Main equation:

yij is observed only if zi = 1,

(ui, εi) bivariate normal with correlation ρ.

Key Findings and Results

We find that people do choose whether or not to use PPHS—we can reject the hypothesisthat the selection and the main equations are independent at the 1 percent level. The vari-ables used in the analysis are summarized below; the results of the selection equation arereported in Table 3.1, and the main equation in Table 3.2.

Prob y x xij j i j i=( ) = − ′( ) − − ′( )−1 1Φ Φα β α β

Prob z wi i=( ) = − ′( )0 1 Φ γ

Prob z wi i=( ) = ′( )1 Φ γ

y xi i i* = ′ +β ε

z w ui i i* = ′ +γ


11524-03_Ch03.qxd 1/19/09 11:45 AM Page 76


Variable Obs Mean Std. Dev. Min Max

access_health 28999 .516 .500 0 1

trust 28999 .200 .400 0 1

logpcexp 28909 7.62 .833 .98 10.25

female 28999 .529 .499 0 1

over_65 28999 .155 .362 0 1

health 28995 2.79 .937 1 5

educ 28991 2.16 .494 1 3

locality 28999 1.76 .765 1 3

country 28999 18.8 7.89 1 29

access_health: Used PPHS during past 12 month: 1 = Yes, 0 = Nolpgpcexp: (log) Per equivalent adult (using OECD scales) annual expenditurestrust Respondent has some or complete trust in the police and in the government/

cabinet of ministersfemale: 1 = Female respondent, 0 otherwiseover_65: Respondent aged over 65 yearshealth: Self-assessed health status is 1 = “Very good”, 2 = “Good”, 3 = “Medium”, 4 = “Bad”,

5 = “Very bad”educ: 1 = “No education/no degree”; 2 = “Compulsory/secondary education”; 3 = “Higher/

post-graduate”locality: 1 = Rural; 2 = Urban; 3 = Metropolitancountry: Country code (29 unique codes for each of the 29 countries covered in the survey)

Factors Influencing Health Care Access

We find that relatively better-off persons (as measured by log annual expenditures perequivalent adult) are more likely to access PPHS than those who are poor. This confirmsthat monetary costs have some bearing on access to services, and belies the entitlement touniversal access expressly stated in many constitutions across this region. Females are alsomuch more likely to use the service than males. This effect likely captures the routine useof PPHS by women for child birth and during pregnancy. Similarly, elderly respondents(those aged over 65 years) are also much more likely to have used PPHS compared to therest of the surveyed population, a finding that is consistent with Balabanova and others(2004). The individual attributes that decreases health care access include self assessedgood health and level of education—we find that people with compulsory/secondary education as well as people with some tertiary education are less likely to access PPHS com-pared to those with no education (note that the effect of compulsory/secondary education—is not statistically significant at the 10 percent level).34 This in turn may be because those

34. Balabanova and others (2004) find that, when ill, people with lower education consult profession-als less often than people with higher education. However, a crucial difference between their findings andours is that they are able to control for differences in morbidity levels. If average morbidity rates amongthose with higher education are significantly lower than among those with less education, there is no con-tradiction necessarily between this and our finding that, other things being equal, the likelihood of havingaccessed the health service during the past 12 months is negatively associated with the level of education ofthe respondent.

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 77

with more education tend to adopt lifestyles that are healthier than those with less/no edu-cation, and are therefore likely to suffer lower morbidities. Finally, we find that respon-dents living in rural and in metropolitan areas are somewhat more likely to use PPHS thanthose living in urban areas, though the effect is not statistically significant at the 1 percentlevel.

Trust in Government and Police

We find that people who trust the government/police are significantly more likely to accessPPHS, compared to those who do not trust the government or police. Given that the LiTSis a cross-sectional survey, one cannot determine whether greater trust in government leadsto greater willingness to access public services, or whether this correlation is driven by otherunobserved factors. Nevertheless, this finding seems to lend credence to the idea that thereexists a social contract between the government and citizens; a functioning government,


Table 3.1. Probit for Health Care Access

Overall sample

Coefficient s.d.

Individual-level variables:

Log normalized expenditures 0.112*** 0.028

Trust in government 0.092** 0.045

Female 0.164*** 0.039

Over 65 years age 0.239*** 0.079

Female and over 65 years of age −0.276*** 0.093

Health status

Very good −0.279*** 0.062

Good −0.158*** 0.042

Medium Reference category

Bad 0.158*** 0.054

Very bad 0.287*** 0.087

Locality

Urban −0.073* 0.041

Rural Reference category

Metropolitan −0.105** 0.049

Education level

No education/no degree Reference category

Compulsory/secondary education −0.092 0.073

Higher professional/post-graduate 0.018 0.087

Other controls:

Country-level dummies . . . not shown here . . .

Pseudo-R2 0.0268

Source: Staff calculations based on 2006 LiTS data. [Note: .01 − ***; .05 − **; .1 − *;]

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 78

and the experience of the rule of law in everyday lives goes hand-in-hand with a greaterwillingness to use public services.

Thus policies targeting reform of specific public sectors may be handicapped in thatthey do not try to reform ‘the general culture’ of government. Concurrently reformingpublic service delivery, along with the improvement in the performance of the police, andother institutions, could lead to an improvement in citizens’ overall perception of the func-tioning of the government. If this has the effect of encouraging more people to use pub-licly provided health services, then they would directly experience the improved quality ofthese services, leading to a positive feedback loop to fall in place.

Factors Influencing Satisfaction with Publicly-provided Health Services

Respondents who reported having used PPHS during the past 12 months were asked howsatisfied they were with the quality and efficiency of the medical treatment received. Theirresponses, coded as follows: 1=Strongly Disagree (SD), 2=Disagree (D), 3= Neither disagreeor agree (N), 4=Agree (A), and 5=Strongly Agree (SA), are summarized below, while theresults of the second-stage estimation, after including the correction factor calculated fromstep one of the estimation procedure, are reported in Table 3.2.

The first point to note is that thecoefficient of the inverse mills ratiocorrection factor is significant, indi-cating that we can safety reject thehypothesis that the selection andmain equations are independent.The negative coefficient implies thatunobserved characteristics of respon-dents that affect the likelihood oftheir having used PPHS during thepast 12 months are inversely relatedto the level of satisfaction with thequality and efficiency of service.

As shown above, the variables at the individual level that enhance satisfaction with PPHSinclude (i) living in urban areas, (ii) self assessed good health, and (iii) satisfaction with lifein general (individuals who are more satisfied with life are also more satisfied with PPHS).

Balabanova 2007 reports that, as inequalities increased in the health care delivery sys-tem in the post-transition period, the quality of service available in urban centers werevastly superior to service in rural areas. This could well account for the increased satisfac-tion of urban residents. Self-assessed health status is seen as a fairly good indicator of gen-eral health and morbidity. This is corroborated by the finding, above, that those with goodself-assessed health status access PPHS less frequently. Once ill, they probably also recoverfaster, leading to greater satisfaction with treatment received.

The reason for the positive association between satisfaction with life in general and sat-isfaction with publicly provided health services is likely due to the personality of the respon-dent (Figure 3.8). Psychologists report that “measures of temperament and personalitytypically account for much more of the variance of reported life satisfaction than do life cir-cumstances” (Kahneman and Krueger 2006). Thus a respondent who is temperamentally


Level of Percent Cum.Satisfaction Freq. (weighted) (weighted)

SD 1,536 11.3 11.3

D 2,615 18.7 30.0

N 3,049 24.5 54.5

A 6,174 38.3 92.8

SA 1,333 7.2 100.0

Total 14,707 100.0 100.0

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 79


Table 3.2. Ordered Probit: Satisfaction with Publicly-provided Health Services

Overall sample

Coefficient s.d.

Inverse Mills Ratio (from selection equation) −3.074*** 0.231

Individual-level variables:

Log normalized expenditures −0.190*** 0.021

Female −0.167*** 0.025

Age group

19–30 years 0.021 0.027

31–40 years Reference category

41–50 years 0.097*** 0.028

51–60 years 0.086*** 0.031

61–70 years −0.028 0.035

71+ years 0.000 0.041

Health status

Very good 0.605*** 0.059

Good 0.482*** 0.032

Medium Reference category

Bad −0.248*** 0.033

Very bad −0.632*** 0.060

Satisfied with life

Strongly disagree −0.285*** 0.033

Disagree −0.072*** 0.026

Neither disagree nor agree Reference category

Agree 0.215*** 0.024

Strongly agree 0.386*** 0.036

How often are unofficial payments needed

Never 0.127*** 0.026

Seldom 0.027 0.029

Sometimes Reference category

Usually −0.080*** 0.029

Often −0.134*** 0.030

Locality

Urban 0.060** 0.023

Rural Reference category

Metropolitan 0.039 0.031

Education level

No education/no degree Reference category

Compulsory/secondary education 0.075* 0.042

Higher professional/post-graduate −0.169*** 0.046

(continued )

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 80

more satisfied with life is also likely to be more satisfied with a given quality of health ser-vice. Kahneman and Krueger additionally cite a couple of experiments that suggest thatthose who are satisfied with life in general may be less susceptible to morbidities35. It maysimply be that those who are more satisfied with life fall ill less frequently and recover moreeasily. The health outcome for any illness episode, given a fixed quality of treatment, may


Table 3.2. Ordered Probit: Satisfaction with Publicly-provided Health Services (Continued )

Overall sample

Coefficient s.d.

Note: .01 − ***; .05 − **; .1 − *.Source: Staff calculations based on 2006 LiTS data.

Country-level variables:

Per capita GDP 2005 (US$ ‘000) 0.245*** 0.023

Growth rate of per capita GDP (2004 to 2005) 0.056*** 0.007

Other controls:

Country-level dummies . . . not shown here . . .

Pseudo-R2 0.0297

35 40 45 50 55

Percent of satisfied respondents

Very bad

Bad

Medium

Good

Very good

Satisfaction with publicly provided health system by self-assessed health status

Figure 3.8. Satisfaction with Publicly-provided Health Service and Self-assessed Health Status

35. In one study, subjects were exposed to a cold virus, and their symptoms were monitored. Thosewho reported a higher level of life satisfaction at baseline were less likely to come down with a cold, andquicker to recover if they became sick. Another study subjected individuals to a controlled wound, whichwas then monitored. The study found that subjects who were more satisfied with their lives healed quickerthan others.

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 81

be better for those with a higher baseline satisfaction with life, leading to increased levels ofsatisfaction with the health service received.

Similarly, other things being equal, respondents aged 41–60 years also tend to reporthigher levels of satisfaction with PPHS compared to the 31–40 year-old reference category(differences for other age groups are not statistically significant). By contrast, however,higher living standards, as proxied by per capita expenditures, as well as higher profes-sional/post-graduate educational attainment tend to be negatively associated with satisfac-tion with PPHS—i.e. the richer the respondent and the higher their education level, theless likely they are to be satisfied with PPHS. This likely indicates that such individuals tendto judge the quality and efficiency of PPHS using more exacting standards as compared tothe poor/less well-educated.

In addition, women also tend to express lower satisfaction than men. Women routinely use health services during pregnancy and child-birth. A likely explanation for women’s lower satisfaction could be associated with the type of prenatal and childdelivery care they receive. Balabanova 2007 reports that, particularly in maternal andchild health in Russia, there is a “culture of over-medicalization tolerating ineffective and wasteful clinical practices dependent on mothers’ ability to pay informally ratherthan need, administrative incentives to retain underused capacity, and user disempow-erment.” Similarly, although services for pregnant women are ostensibly free in Georgia,informal payments are routinely demanded. One patient who was interviewed in Tbilisi in 2001 reports “For the delivery, we had to pay the doctor 200 Gel . . . We agreedthe price with the doctor. They also told us that as long as we arrange for their privateservices (their guaranteed assistance during delivery) several month prior to delivery,the public coverage does not work, and we are not eligible for free services.” (Belli et al,2004).

Finally, turning to variables at the country-level, we find both per capita GDP and thegrowth rate of per capita GDP to have significant positive effects on the satisfaction withPPHS. There are many ways through which macroeconomic well-being affects satisfactionwith PPHS—it enables increased spending on health services, either by the government orby private households, it fosters optimism about the future, and so on. Once again, due tothe cross-sectional nature of the LiTS survey, it is not possible for us to disentangle themeans by which economic well-being affects satisfaction with PPHS, we merely note thestrong and significant correlation.

The Impact of Unofficial Payments

Unofficial payments, as the name implies, are payments that do not go through officialchannels, and that are often made for what are meant to be free services. The reasons whythey exist are myriad, and have been well documented (Lewis 2000; Belli, Gotsadze, andShahriari 2004; Balabanova 2007; Bonilla-Chacin 2005; Allin, Davaki, and Mossialos 2006).At the same time, it serves us well to differentiate between three types of “unofficial” pay-ments, which have all typically been lumped together in most data, including in the LiTS.Unofficial payments could be (a) gifts, which are voluntarily given, (b) payments for sup-plies (such as gauze, bed clothes, medicines) that are not budgeted or paid for by the gov-ernment, or (c) payments that are required by medical personnel or administrators to


11524-03_Ch03.qxd 1/19/09 11:45 AM Page 82

provide or expedite what should be free services. Only those unofficial payments that fallunder category (c) can truly be called corruption.

Given these different motivations, varied strategies are required to decrease the inci-dence of unofficial payments. Gift-giving is a strong and innocent cultural tradition toexpress gratitude to medical healers that has existed in the ECA region, particularly in CIScountries, for many generations. Gifts are often in-kind, and are more prevalent in ruralareas within close-knit communities than in more impersonal urban areas. While thesecharacteristics could be used in surveys to try and distinguish between voluntarily given giftsand unofficial payments that are demanded, there is no reason to wish to decrease or elim-inate the practice of giving gifts (such a campaign would be as meaningless and ineffectiveas trying to eliminate the practice of tipping for restaurant service in the United States).

Unofficial payments that are used to purchase essential materials (as categorized under(b) above) do not go either to medical personnel or to administrators. They exist becauseof declining revenues but little rationalization of existing infrastructure resulting in inad-equate medical equipment, drugs and supplies. Health care reforms that address theseissues, including identifying and eliminating funding gaps that ensure that all materialsneeded for a given health service are indeed available should suffice to decrease such typesof unofficial payments.

The third type of payment, which are requested by medical personnel or administra-tors, for access to (or to expedite) what should essentially be free service, is indeed a formof corruption. Some of the factors that contribute to the existence of such paymentsinclude underpaid or unpaid doctors, as well as financing and administration of hospitalsand facilities that make them unresponsive to the evolving medical needs of the popula-tion. Reforms that address these deficiencies, while complex and difficult to implement,would likely lead to some decline in unofficial payments that are used to influence healthoutcomes. Simply running anti-corruption campaigns without instituting mechanismsthat provide effective accountability for health outcomes to users of the PPHS are likely tobe ineffective. As Vian and Burak (2006) find in Albania, there is no difference in the moralbeliefs of people who intend to make unofficial payments the next time they access thePPHS and those who do not intend to do so. On the other hand, people who do not intendto make unofficial payments report other means by which they can influence health out-comes, such as connections with medical personnel, etc.

Measuring unofficial payments, particularly those that indicate corruption, are diffi-cult, due to the very nature of the transaction. It is not always straightforward to distinguishbetween formal and informal payments, or between payments that are demanded and thatare given voluntarily out of gratitude. Another confounding factor has been the prevalenceof unofficial payments for some types of services (inpatient services in hospitals) as opposedto other services (outpatient physicians visits); and in some geographic areas (urban) asopposed to others (rural). Thus, depending on the sample of respondents, and on the actualquestion fielded, one could get widely varying estimates of unofficial payments.

One advantage of the LiTS has been the general nature of the question: “In your opin-ion, how often is it necessary for people like you to have to make informal payments/giftswhen receiving medical treatment in the public health system.” Responses were obtainedboth from those who used the system in the recent past (last 12 months) and those whodid not. A comparison of general perception (the responses from those who have not usedPPHS in the last 12 months) with the opinion of experienced users is revealing. It appears


11524-03_Ch03.qxd 1/19/09 11:45 AM Page 83

that general perception consistently underestimates the prevalence of unofficial payments(Figure 3.9).

Our analysis indicates that the perception that unofficial payments are necessary for accessis an important factor causing dissatisfaction with PPHS. For instance, dissatisfaction withhealth care is highest in Albania, with 48 percent of respondents being dissatisfied or very dis-satisfied with the service interaction. Albania also has the largest percentage of people (48 per-cent), who believe that unofficial payments are usually or always needed to access PPHS.

Our empirical model indicates that, other things being equal, respondents who sayunofficial payments are often necessary are about 1.4 times more likely to report being dis-satisfied with PPHS compared to those who say that such payments are never needed. Asreasoned above, one of the motivations for unofficial payments has been to ensure higherquality of service than otherwise available. Thus, one might expect to find that, in somecases at least, unofficial payments go hand-in-hand with higher satisfaction with the ser-vice interaction. If this effect exists at all, it is likely very small, and is overwhelmed by theamount of dissatisfaction generated by having to pay for free services. Bratton, 2007, findsthat the perception that officials are corrupt decreases citizen satisfaction with services inAfrican countries, and this is in agreement with the findings in this analysis.

We found two other sources that report the incidence of unofficial payments in alimited set of ECA countries in different time periods (Lewis 2000; Balabanova and others2004). Table 3.3 summarizes the prevalence of unofficial payments from these two sources,and from the experienced users in LiTS. Comparing changes in access rates, satisfaction level,


0 20 40 60Percent of respondents

AlbaniaMoldova

Kyrgyz RepublicUzbekistan

UkraineRomaniaTajikistanHungary

AzerbaijanRussia

MacedoniaLithuania

ECA REGIONArmeniaBulgaria

BosniaPoland

SlovakiaBelarusSerbia

MontenegroLatvia

CroatiaMongolia

Czech RepublicTurkey

GeorgiaKazakhstan

SloveniaEstonia

Respondents that think unofficial payments are needed in publicly provided health system

Did not use Used in past 12 months

Figure 3.9. General vs. Experienced Opinion of Need for Unofficial Payments

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 84

and reported prevalence of unofficialpayments derived from the LiTS withthose from earlier studies providesanother means of analyzing thecomplex interaction between thesethree sets of variables.

Although the usual caveatsapply regarding the need for cautionwhen comparing the findings of dif-ferent surveys, these three sourcesprovide a rough indication at leastof changes in prevalence of unoffi-cial payments in these 15 coun-tries between the late 1990s and2006. Comparing the Lewis findingswith the LiTS, we see that the inci-dence of unofficial payments (or inthe case of the LiTS of the opinionthat unofficial payments are neces-sary to access PPHS) declined inall countries except Albania andBulgaria between the late 1990s and2006. Comparing the findings ofBalabanova and others (2004) withthe LiTS, we see that the percent-age of people who had to pay

informally/make a gift declined in Georgia (dramatically so), Armenia, and Kazakhstan,between 2001 and 2006. This percentage has remained fairly constant in Moldova andKyrgyzstan, while it has, by contrast, increased in Belarus, Russia, and Ukraine.

Evidence that unofficial payments act as barriers to access emerges when one comparesaccess rates in the LiTS to that in Balabanova et al study. Firstly, Balabanova and others (2004)find that, among all respondents who report an illness that justified seeking attention, about21 percent did not do so. The reason for not seeking care cited by 78 percent of respondentswas the lack of money to pay for treatment. Comparing access rates from the two data sourcesby country, we find that access rates have not changed by much in Kyrgyzstan, Moldova andUkraine, but have increased in Armenia, Kazakhstan, and in Georgia (quite dramatically inthe case of the latter); declined in Russia, and almost halved in Belarus (Table 3.4).

An important finding emerging from comparison of access rates and prevalence ofunofficial payments between the Balabanova et al (2004) study and the LiTS is highlightedin Figure 3.10, which contrasts the extent to which the two data sources indicate that the


Table 3.3. Prevalence of Unofficial Paymentsfor Selected Countries

Prevalence of unofficialpayments in PPHS

2001:Balabanova 2006:

Country Lewis36 et al37 ECA-LiTS38

Georgia 65 12

Kazakhstan 38 12

Armenia 91 (1999) 56 30

Moldova 70 (1999) 46 48

Kyrgyzstan 75 (1996) 42 46

Belarus 8 24

Ukraine 27 44

Russia 74 (1997) 19 37

Azerbaijan 78 (1995) — 40

Poland 78 (1998) — 25

Tajikistan 66 (1999) — 44

SlovakRepublic 60 (1999) — 25

Latvia 31 (2000) — 17

Albania 22 (1996) — 55

Bulgaria 21 (1997) — 30

36. Per cent of patients that reported making informal payments (the year for each observation isincluded in brackets)

37. Respondents that reported paying informally/making a gift during their most recent consulta-tions; data is from surveys of adults aged 18 years and older conducted in Autumn 2001.

38. Of the respondents who accessed the public health system in the last 12 months, percent that saythat unofficial payments/gifts are usually / always necessary.

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 85

-40 -30 -20 -10 0 10 20 30 40 50 60

Belarus

Russia

Kyrgyzstan

Ukraine

Moldova

Armenia

Kazakhstan

Georgia

Increase: Access rates Decline: prevalence of unofficial payments

Figure 3.10. Changes in Access Rates and Prevalence of Unofficial Payments, 2001 to 2006

Source: Balabanova and others (2004) for 2001, LiTS data for 2006.

prevalence of unofficial paymentswent down over this period, withchanges in the share of the popula-tion that reported using publiclyprovided health care facilities dur-ing the past 12 months.

As the figure clearly shows, theeight countries for which this com-parison is possible fall into threemain groups. In the first group, com-prising Georgia, Kazakhstan, andArmenia, declines in the reportedprevalence of unofficial paymentsduring this period was accompaniedby significant increases in the shareof the population utilizing publichealth care facilities. In the secondgroup, which includes Moldova,

Ukraine, and the Kyrgyz Republic, access rates did not change by very much during thisperiod, and neither did the prevalence of unofficial payments (with the exception ofUkraine). Finally, in the third group, with Russia and Belarus, the increase in prevalenceof unofficial payments between 2001 and 2006 was accompanied by significant declines inthe share of the population accessing PPHS. These findings underline the barrier that unof-


Table 3.4. Change in Health Care Access Ratesfor Selected Countries

Access Rates Among thePopulation Aged 18 and Over

2001:Balabanova 2006:

Country et al39 ECA-LiTS40 Difference***

Georgia 24 46 +22

Kazakhstan 50 64 +14

Armenia 30 39 +9

Moldova 53 50 −3

Ukraine 58 54 −4

Kyrgyzstan 44 40 −4

Russia 66 52 −14

Belarus 66 37 −29

39. Survey conducted in autumn 2001. Access rate refers to consulting a health care professional (pub-lic or private); (access rates have been translated from a graph, so the numbers are approximate).

40. Survey conducted in autumn 2006. Access rate refers to accessing the public health system.

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 86

ficial fees impose upon access to health service, with access rates falling as unofficial fees rise.They also provide strong corroborative evidence to our earlier reported findings that theperception of the necessity of unofficial payments negatively impacts satisfaction levels.Finally, given the strong negative influence of unofficial payments on satisfaction withPPHS, one may plausibly be able to argue that the LiTS question indeed captures unofficialpayments that act as barriers to access rather than unofficial payments that are given as gifts.

Health Sector Reform in the Caucuses

In this section we probe a little into the strong performance of Georgia and Armenia, andto a smaller extent Azerbaijan, on satisfaction with PPHS. All three countries sufferedcatastrophic economic decline with the fall of the Soviet Union—real GDP per capitashrank by 19.2 percent on average per year in Georgia, by 16.8 percent per year in Azerbai-jan, and by 8.4 percent per year in Armenia, from 1991 to 1995.41 This economic collapse ledto sharply shrinking budgets, particularly for the health sector. The expenditure on PPHS asa percent of GDP decreased dramatically in Georgia, from about 3 percent in 1991 to a littleabove 0 percent in 1994. Available data for the latter half of the decade show that health out-comes stagnated (and in some instances actually worsened) during this period.42 Spendingrecovered to about 1 percent in 1995, and has remained at that level ever since. While thedecline in spending was more gradual in Armenia, spending has hovered between 1 and 2 percent of GDP in the last decade. Health sector expenditure in Azerbaijan fluctuated inthe early 1990s, before settling down to between 2 and 3 percent of GDP (Bonilla-Chacin,Murrugarra, and Temourov 2005; UNICEF TransMONEE Database 2006).

This collapse in public expenditure led to dramatic increases in out of pocket payments—Bonilla-Chacin, Murrugarra, and Temourov (2005) report that out-of-pocket private spend-ing (both formal and informal) in Georgia has been estimated to range between 66 and87 percent of total health spending, while household expenditure on health plus donor con-tributions account for between 63 and 69 percent of total health spending in Armenia. Theyalso document the drastic decline in health care utilization, particularly among the poor, as aresult of these increasing costs of access.

Most countries in the low-income CIS embarked on health care reforms—with thefocus being to realign facilities and medical personnel to give more emphasis to the pri-mary health care system. In addition, the reforms have tried to raise additional revenuesfor the health sector. All low-income CIS countries have implemented some cost-sharingarrangements by charging fees for some health services. In 1995, the Government ofGeorgia formally removed entitlement to free health care from its constitution. Publicinsurance coverage was limited to services included in a basic benefit package (BBP). Asocial insurance contribution was imposed on formal employment, with shortfalls beingmet by transfers from general taxation (Belli, Gotsadze, and Shahriari 2004).

Many low-income CIS countries have also tried to rationalize costs. Substantialprogress has been made in decreasing the number of hospital beds, although the numbersstill exceed those available in the EU. There is some mixed progress in rationalization of


41. World Development Indicators 2007.42. Public Information Document: Georgia-Primary Health Care Development Project, World Bank,

Washington DC, January 2002.

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 87

health care staff, with Georgia and Moldova achieving significant reductions in the num-ber of health sector workers. Georgia and Armenia have shown some progress in privatizinghospitals. The most dramatic evidence of changes in health care practice have been visiblein Georgia and Armenia, with much less reform in Azerbaijan (Bonilla-Chacin, Murrugarra,and Temourov 2005).

Combined with these general reforms in the health sector, Oxfam and other inter-national agencies have supported a particular innovation in the Caucasus (Armenia, Georgiaand Azerbaijan), aimed at mitigating the situations of the rural poor—community healthinsurance schemes. Ensuring equity of access to primary health care has been an explicitobjective of the scheme. Balabanova 2007 reports that Oxfam’s schemes have contributedto improved access to and quality of care, through rehabilitation of local health posts, train-ing of nurses, and subsidy of running costs. She further reports that these schemes havegrown to be major providers of health care to rural communities, to the point where theyhave assumed significant responsibility for public sector provision.

Real GDP per capita in Georgia, Armenia and Azerbaijan began to grow, more mod-estly in the late 1990s but quite dramatically since 2000. Real GDP per capita growth aver-aged 12.6 percent per year from 2001 to 2005 in both Armenia and Azerbaijan, and by8.4 percent per year in Georgia over the same period. We posit that the reforms in thehealth sector undertaken in Georgia and Armenia, and the innovations in communityhealth insurance schemes, combined with the transformational and broad based growthoccurring in both countries, have led to recovery in the quality of PPHS from an extremelylow base in the early 1990s. These factors help explain, to some extent, the significant risein access rates and decline in prevalence of unofficial payments in countries like Georgiaand Armenia. While Azerbaijan has equally strong economic performance, it has not pro-gressed as far in health sector reform. Our empirical model provides some support for this;we find that the growth rate of per capita GDP is one of the few macroeconomic variablesthat have a significant positive effect on the satisfaction with PPHS.


In this paper we investigate satisfaction with publicly provided health services (PPHS) in28 ECA countries and Mongolia using data from the Life in Transition survey conductedin these countries in the fall of 2006. The countries in this region inherited a health systemthat was state owned and financed through general taxation, and that was largely charac-terized by over capacity, both in terms of human as well as physical factors, at the time ofthe fall of the Soviet Union. The system was largely unsustainable, particularly given theeconomic decline in the region in the 1990s.

Most countries undertook reform of the health sector, with mixed results. The EUmember countries have the most positive outcome, with (i) mixed financing of health,through insurance and general taxation, (ii) rationalization of health sector capacity,(3) and more recently, solid economic growth, that has enabled them to provide stablefinancing for the health sector. Thus, among ECA countries, many EU member are at ornear the top with regards to satisfaction with PPHS.

A surprising finding from the LiTS has been the levels of satisfaction in a few low-incomeCIS countries, particularly Georgia and Armenia. Some discussions of these findings are


11524-03_Ch03.qxd 1/19/09 11:45 AM Page 88


presented in section six above. Finally, many middle-income CIS countries (Ukraine, Kazakhstan, Russia) are among the lower ranked in terms of citizens’ satisfaction with PPHS.

We use a two-step Heckman probit to estimate the effects of various factors on (i) why some people select to use PPHS (ii) upon use, the level of satisfaction derivedfrom the service provided. We find that factors that have a significant positive effect onaccessing health care include (a) being relatively better off, (b) female, (c) elderly, and(d) having trust in government and police. The factors that have a significant negativeeffect on accessing health care are (a) self-assessed good health, and (b) level of educa-tion (those with higher levels access the health care system less than those with lowerlevels of education.)

Factors that have a significant positive effect on satisfaction with service receivedinclude (a) per capita GDP (b) growth rate of per capita GDP (c) living in urban areas, (d) self assessed good health, (e) satisfaction with life in general (individuals who are moresatisfied with life are also more satisfied with PPHS), and (f) being between 41–60 years ofage. Factors that have a negative, and significant, effect on satisfaction include (a) the per-ception that unofficial payments are needed to access service, (b) higher living standards,(c) higher professional/post graduate education, and (d) being female.

In terms of broad policy implications, the reduction/elimination in informal paymentsfor health service is the most urgent reform needed in this region—particularly among theCIS countries. The primary reason for the existence of these informal payments seems to beto influence availability and quality of services received through the PPHS as there are noother means available to hold providers and administrators accountable. Thus, the estab-lishment of mechanisms that enable provider accountability is a first step towards the elim-ination of informal payments. Sustained and long-term engagement in the reform processis needed to address this and several other complex factors that give rise to these fees.

This paper finds that trust in government and police go hand-in-hand with increasedaccess of PPHS. Thus, changing the culture of government is as important as undertakingreforms in the health care system. A well coordinated communications strategy that is aimedat informing health sector users of their entitlements, and of emphasizing government com-mitment to reform, could influence citizens’ perception of what they should expect fromhealth care services, and could co-opt them into partners in enabling change.

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 89


Table A3.1. Priorities for Additional Government Spending, By Country

1. First priority:Percentage of Respondents Choosing:

Group/Country Education Healthcare Housing Pensions Environment Infrastructure Overall

EU member states 22 44 10 17 2 4 100

South-EasternEurope 32 33 8 16 3 8 100

CIS-low income 27 41 9 16 3 4 100

CIS-middle 21 42 19 13 4 2 100

Total 27 40 13 13 3 3 100

Albania 24 23 9 21 5 18 100

Belarus 28 32 21 13 2 4 100

Bosnia 39 27 8 18 1 6 100

Bulgaria 23 53 3 17 2 4 100

Croatia 35 32 8 19 2 4 100

Czech Republic 29 41 11 13 5 2 100

Macedonia 39 33 7 14 2 4 100

Hungary 16 53 8 13 4 6 100

Moldova 24 45 8 14 2 6 100

Montenegro 27 35 11 13 3 10 100

Poland 21 41 11 19 1 6 100

Romania 18 42 15 21 2 2 100

Serbia 28 41 7 12 4 8 100

Slovakia 31 50 5 9 2 3 100

Slovenia 32 37 13 12 4 2 100

Turkey 58 29 4 5 1 3 100

Ukraine 21 49 13 11 3 3 100

Armenia 20 48 6 21 2 2 100

Azerbaijan 29 41 12 15 1 3 100

Estonia 30 41 4 15 4 5 100

Georgia 24 35 11 23 2 5 100

Kazakhstan 22 44 12 13 7 3 100

Kyrgyz Republic 32 37 13 9 2 7 100

Latvia 32 36 6 18 2 5 100

Lithuania 24 49 10 14 0 2 100

Mongolia 40 28 7 9 5 11 100

Russia 21 40 21 13 4 1 100

Tajikistan 40 29 9 14 6 2 100

Uzbekistan 24 44 8 16 4 4 100

Annex: Tables and Figures

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 90


Table A3.2. Access Rates of PPHS, By Country

Percent of populationwho ______ the public

health care systemin the last 12 months

Country Accessed Did not access

Albania 65.7 34.3

Belarus 37.3 62.7

Bosnia 42.1 57.9

Bulgaria 48.4 51.6

Croatia 61.1 38.9

Czech Republic 59.5 40.5

Macedonia, FYR 51.9 48.1

Hungary 61.3 38.7

Moldova 49.4 50.6

Montenegro 45.3 54.7

Poland 41.3 58.7

Romania 51.4 48.6

Serbia 49.1 50.9

Slovak Republic 33.4 66.6

Slovenia 38.3 61.6

Turkey 58.0 42.0

Ukraine 54.3 45.7

Armenia 38.5 61.5

Azerbaijan 56.9 43.1

Estonia 51.6 48.4

Georgia 45.7 54.3

Kazakhstan 64.0 36.0

Kyrgyzstan 39.9 60.1

Latvia 56.5 43.5

Lithuania 54.4 45.6

Mongolia 37.8 62.2

Russia 51.7 48.3

Tajikistan 55.4 44.6

Uzbekistan 44.7 55.3

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 91


Table A3.3. Satisfaction with Medical Treatment in PPHS by Country

Percentage of respondents that are:

Very VeryGroup/Country Unsatisfied Unsatisfied Indifferent Satisfied Satisfied Overall



CIS-low income 13 20 17 43 7 100


Total 11 19 24 38 7 100

Slovenia 3 6 26 46 20 100


Croatia 7 8 20 45 20 100

Georgia 4 11 22 54 10 100

Slovakia 8 6 29 39 18 100

Lithuania 4 16 23 45 11 100

Armenia 9 17 11 53 11 100

Estonia 5 23 12 50 10 100

Latvia 6 20 19 48 9 100

Hungary 7 16 27 42 8 100

Bulgaria 9 21 11 48 12 100

Belarus 3 24 23 43 6 100

Poland 9 14 31 42 4 100

Bosnia 13 14 24 39 11 100

Azerbaijan 13 19 13 47 8 100

Turkey 17 11 21 37 14 100

Montenegro 12 20 19 36 13 100

Mongolia 12 17 22 42 6 100

Satisfied

Very satisfiedVerysatisfied Very

unsatisfied

Satisfied

Indifferent

Unsatisfied

0

10

20

30

40

50

CIS

-low

inco

me

CIS

-mid

dle

inco

me

EU

mem

ber

stat

es

Sou

th-

Eas

tern

Eur

ope

% o

f res

pond

ents

(continued )

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 92


Table A3.3. Satisfaction with Medical Treatment in PPHS by Country (Continued )

Percentage of Respondents that are:

Very VeryGroup/Country Unsatisfied Unsatisfied Indifferent Satisfied Satisfied Overal

Russia 10 21 28 37 5 100

Uzbekistan 13 21 18 42 6 100

Kazakhstan 9 22 31 34 5 100

Macedonia 17 16 16 45 5 100

Serbia 16 20 19 37 9 100


Moldova 13 24 21 37 6 100

Romania 19 19 16 39 6 100

Tajikistan 17 21 25 33 4 100

Ukraine 10 28 28 31 3 100

Albania 21 27 12 38 2 100

Table A3.4. Prevalence of Unofficial Payments in PPHS by Country

Percentage of respondents that say unofficial payments are needed:

Group/Country Never Seldom Sometimes Usually Always Total




CIS-low income 37 16 18 14 15 100

Overall sample: 42 14 19 14 11 100

Estonia 75 11 11 2 1 100

Slovenia 73 9 12 5 1 100

Seldom

Sometimes

Usually

Never

Always

0

5

10

15

% o

f res

pond

ents

Usually

Always

CIS

-low

inco

me

CIS

-mid

dle

inco

me

EU

mem

ber

stat

es

Sou

th-

Eas

tern

Eur

ope

(continued )

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 93


Table A3.4. Prevalence of Unofficial Payments in PPHS by Country (Continued )

Percentage of respondents that say unofficial payments are needed:

Group/Country Never Seldom Sometimes Usually Always Total

Georgia 59 21 12 6 2 100

Kazakhstan 63 14 13 5 5 100

Belarus 59 17 12 8 4 100

Turkey 66 9 13 7 5 100


Croatia 65 9 13 11 3 100

Latvia 60 12 15 8 5 100

Poland 57 11 17 10 5 100

Serbia 61 8 14 9 8 100

Mongolia 47 16 21 11 6 100

Bosnia 57 10 14 9 10 100

Armenia 45 20 16 11 8 100

Montenegro 51 11 20 9 9 100

Slovakia 46 14 19 13 8 100

Macedonia 44 13 20 11 13 100

Russia 40 15 20 14 10 100

Lithuania 35 14 27 16 8 100

Bulgaria 41 10 25 13 11 100


Romania 47 9 14 16 14 100

Azerbaijan 39 19 12 12 18 100

Moldova 35 16 17 19 13 100

Hungary 39 10 18 19 13 100

Tajikistan 24 21 21 18 15 100

Uzbekistan 34 12 21 14 19 100

Ukraine 25 13 22 20 20 100

Albania 26 8 18 18 30 100

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 94


Table A3.5. Difference between General and Experienced Perception of Prevalence of Unofficial Payments in PPHS

Percentage of respondents that say unofficial payments are needed(Accessed: used PPHS in last 12 months; Not accessed: Did not use)

Never/Seldom Usually/Always

Country Accessed Not Accessed Accessed Not Accessed

Albania 27.9 46.8 54.9 33.5

Belarus 59.6 85.5 23.7 5.0

Bosnia 52.0 77.9 29.5 10.7

Bulgaria 40.7 59.9 29.6 19.4

Croatia 68.1 82.0 16.5 9.0

Czech Republic 62.9 82.7 13.5 4.7

Macedonia, FYR 42.4 73.0 31.9 13.6

Hungary 37.4 68.3 43.6 15.5

Moldova 26.5 75.7 48.5 15.0

Montenegro 56.7 65.7 19.8 16.7

Poland 48.5 81.6 25.3 7.3

Romania 38.3 74.2 44.4 16.0

Serbia 59.4 78.0 22.6 11.9

Slovakia 54.0 63.7 24.9 18.9

Slovenia 72.8 87.4 9.6 4.8

Turkey 73.5 77.6 12.4 10.2

Ukraine 31.7 46.8 44.5 33.7

Armenia 42.5 79.4 30.2 11.2

Azerbaijan 45.3 73.9 40.2 17.1

Estonia 85.2 87.3 4.7 1.8

Georgia 74.6 84.9 11.9 4.3

Kazakhstan 74.6 80.7 11.6 9.1

Kyrgyzstan 26.0 72.8 46.1 16.8

Latvia 64.3 81.7 16.5 8.4

Lithuania 33.7 67.9 32.0 13.2

Mongolia 53.2 68.4 16.0 17.0

Russia 35.4 76.7 36.9 11.0

Tajikistan 30.2 64.1 44.0 20.0

Uzbekistan 31.7 57.6 45.3 23.1

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 95

11524-03_Ch03.qxd 1/19/09 11:45 AM Page 96

Allin, S., K. Davaki, and E. Mossialos. 2006. “Paying for ‘free’ health care: the conundrumof informal payments in post-communist Europe.” Global corruption report 2006.Berlin: Transparency International.

Balabanova, D. 2007. “Health Sector Reform and Equity in Transition.” Processed. http://www.wits.ac.za/chp/kn/Balabanova.pdf

Balabanova, D., M. McKee, J. Pomerleau, R. Rose, and C. Haerpfer. 2004. “Health ServiceUtilization in the Former Soviet Union: Evidence from Eight Countries.” Health ServicesResearch Supplement Part 2, 39(6):1927–49.

Belli, P., G. Gotsadze, and H. Shahriari, 2004. “Out-of-pocket and informal payments inhealth sector: evidence from Georgia.” Health Policy 70:109–23.

Bonilla-Chacin M. E., E. Murrugarra, and M. Temourov. 2005. “Health Care during Tran-sition and Health Systems Reform: Evidence from the Poorest CIS countries.” SocialPolicy and Administration 39(4):381–408.

Bouckaert G., and S. Van de Walle, 2003. “Quality of Public Service Delivery and Trust inGovernment.” In A. Salimen, ed., Governing Networks: EGPA Yearbook. Amsterdam:IOS Press.

Bratton, M. 2007. “Are you being served? Popular Satisfaction with Health and EducationServices in Africa.” http://www.afrobarometer.org/papers/AfropaperNo65.pdf

Danishevski, K., D. Balabanova, M. McKee, and J. Parkhurst, 2006. “Delivering babiesin a time of transition in Tula, Russia.” Health Policy Plan 21(3):195–205.

EBRD (European Bank for Reconstruction and Development). 2007. Life in Transition, a Survey of People’s Experience and Attitudes. London.

Falkingham, J. 2004 “Poverty, Out-of-Pocket Payments and Access to Health Care: Evidencefrom Tajikistan.” Social Science Medicine 58:247–58.

Bibliography

97

11524-04_Bibliography.qxd 1/19/09 11:46 AM Page 97

Kahneman D, and A. Krueger, 2006 “Developments in the Measurement of SubjectiveWell-Being.” Journal of Economic Perspectives 20(1):3–24.

Lewis, M. 2000. “Who is paying for health care in Eastern Europe and Central Asia?” TheWorld Bank, Washington, D.C.

Vian, T, and L. Burak. 2006. “Beliefs about informal payments in Albania.” Health Policyand Planning 21(5):392–401.

World Bank. 2005a. Growth, Poverty, and Inequality: Eastern Europe and the Former SovietUnion. Washington, D.C.

———. 2005b. MDGs in Europe and Central Asia: Performance and Prospects. Washington,D.C.



Eco-AuditEnvironmental Benefits Statement

The World Bank is committed to preserving Endangered Forests and natural resources. Weprint World Bank Working Papers and Country Studies on 100 percent postconsumer recy-cled paper, processed chlorine free. The World Bank has formally agreed to follow the rec-ommended standards for paper usage set by Green Press Initiative—a nonprofit programsupporting publishers in using fiber that is not sourced from Endangered Forests. For moreinformation, visit www.greenpressinitiative.org.

In 2008, the printing of these books on recycled paper saved the following:

Trees*

355*40′ in height and 6-

8” in diameter

Total Energy

247 mil.BTUs

Net Greenhouse Gases

31,256Pounds CO2 Equivalent

Water

129,550Gallons

Solid Waste

16,663Pounds


THE WORLD BANK1818 H Street, NW

Washington, DC 20433 USA

Telephone: 202 473-1000

Internet: www.worldbank.org

E-mail: [email protected]

ISBN 978-0-8213-7900-4

SKU 17900

Satisfaction with Life and Service Delivery in Eastern Europe and the

Former Soviet Union is part of the World Bank Working Paper series.

These papers are published to communicate the results of the Bank’s

ongoing research and to stimulate public discussion.

The past two decades in Eastern Europe and the former Soviet Union

have been times of tremendous change, with countries undergoing

rapid transformation from centrally-planned to market-oriented

economies. While poverty increased during the initial years of

transition, primarily on account of the sharp economic contraction, the

resurgence of economic growth in the region since 1998 has resulted in

a rebound in household incomes and living standards. Data from the

2006 Life in Transition Survey (LiTS)—a joint initiative of the European

Bank for Reconstruction and Development and the World Bank—

provides a unique opportunity to investigate the extent to which

citizens of ECA countries are satisfied with their lives and with the

performances of their governments, and to study key factors

influencing their outlook in a systematic way across all countries of the

region.

The main objective of the LiTS was to assess the impact of transition

on people, covering four main themes. First, it collected personal

information on aspects of material well-being, including household

expenditures, possession of consumer goods such as a car or mobile

phone, and access to local public services and utilities. Second, the

survey included measures of satisfaction and attitudes towards

economic and political reforms as well as public service delivery. Third,

the LiTS captured individual “histories”—key events and episodes that

may have influenced their attitudes towards reforms, and information

on family background, employment, and coping strategies. Finally, the

survey also attempted to capture the extent to which crime and

corruption are affecting peoples’ lives, and the extent to which

individuals’ trust in other people and in state institutions has changed

over time.

World Bank Working Papers are available individually or on standing

order. Also available online through the World Bank e-Library

(www.worldbank.org/elibrary).

Satisfaction with Life and Service Delivery in Eastern … Zaidi Asad Alam Pradeep Mitra Ramya...

Documents

Transcript of Satisfaction with Life and Service Delivery in Eastern … Zaidi Asad Alam Pradeep Mitra Ramya...