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

W O R L D

B A N K

W O R K I N G

P A P E R

N O .

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Satisfaction with Life and Service Delivery in Eastern Europe and the Former Soviet Union Some Insights from the 2006 Life in Transition Survey Salman Zaidi Asad Alam Pradeep Mitra Ramya Sundaram

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 Service Delivery in Eastern Europe and the Former Soviet Union Some Insights from the 2006 Life in Transition Survey Salman Zaidi Asad Alam Pradeep Mitra Ramya Sundaram

THE WORLD BANK Washington, D.C.

Copyright © 2009 The International Bank for Reconstruction and Development / The World Bank 1818 H Street, N.W. Washington, D.C. 20433, U.S.A. All rights reserved Manufactured in the United States of America First Printing: 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 to the development community with the least possible delay. The manuscript of this paper therefore has not been prepared in accordance with the procedures appropriate to formally-edited texts.

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ISBN-13: 978-0-8213-7900-4 eISBN: 978-0-8213-7901-1 ISSN: 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 : some insights 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. Socioeconomic Factors—Europe, Eastern. W 85 S253 2009] RA407.5.E85S28 2009 362.1094—dc22 2008052797

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

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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 Concluding Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 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 Concluding Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 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

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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

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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

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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

Preface

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he past two decades in Eastern Europe and the former Soviet Union (ECA) 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. Little is known, however, about subjective welfare in the wake of this growth rebound, especially how people across ECA countries view their satisfaction with life as well as with the quality of services being providing by their governments. 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.1 The LiTS was carried out in 29 countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Former Yugoslav Republic 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 questionnaire was administered to a nationally representative sample of 1,000 households using faceto-face interviews.

The main objective of the LiTS was to assess the impact of transition on people, and so the survey questionnaire covered 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” through transition—from around 1989 to the present, especially key events and episodes that may have influenced their attitudes towards reforms, and collected information on individuals; family background, on their employment situation, and on coping strategies during transition. 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.

This volume presents the main findings of three studies by World Bank economists using data from the 2006 LiTS. Chapter 1 examines quantitative and qualitative dimensions welfare in countries of Eastern Europe and the former Soviet Union, with “satisfaction with life” being the key welfare measure used. Analysis suggests that more than 60 percent of the population reports satisfaction with life though this varies quite a bit across countries: Slovenia has the 1. For more details on the LiTS as well as the preliminary survey findings, see EBRD 2007.

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

Chapter 2 analyzes socioeconomic characteristics of different income groups across countries, and shows how the welfare measure derived from the LiTS provides a very useful and effective means to measure household welfare and/or rank households by relative economic status, both within as well as across countries. Moreover, this welfare measure also compares favorably with welfare measures using traditional and more extensive household budget surveys. The chapter provides a systematic examination of the sources of household incomes, patterns of asset ownership, as well as the sectoral occupation patterns of the working poor. Wages and pensions are the primary sources of income for the 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 countries. The data also that the poor have a relative disadvantage in that they are primarily employed in low productivity agriculture, and have limited educational attainment and non-professional skills.

Finally, chapter 3 focuses on three interlinked questions: (i) why are some people more likely than others to use publicly provided health services? (ii) what are some of the key influences 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 using publicly provided health services, and upon use, the level of satisfaction with services received? Analysis shows that the elderly, the relatively better-off, and those who have confidence in the government are more likely to use publicly provided health services, while those with compulsory/secondary education as well as those with some tertiary education are less likely to access these services. Satisfaction with publicly provided health services in the region is quite high, though there is considerable variation evident across countries. In general, while a large majority of respondents say that unofficial payments/gifts are never needed when using publicly provided health services, in cases where users have to pay for what should essentially be “free services,” this has a significant negative influence on satisfaction with service delivery.

Acknowledgments

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he 2006 EBRD-World Bank Life in Transition Survey (LiTS) was designed by the EBRD’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) and Pradeep Mitra (Chief Economist, Europe and Central Asia Region, World Bank) and Asad Alam (Sector Manager, ECSPE, World Bank). The core task team for the project was led by Peter Sanfey (EBRD) and Salman Zaidi (World Bank), and comprised James Anderson, Pauline Grosjean, Juan Munoz, Franklin Steves, and Utku Teksoz. Field work for the survey was carried out by the global market research firm Synovate, under the direction of Savvas Kyriakides.

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

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CHAPTE R 1

Key Factors Affecting Satisfaction with Life in Eastern Europe and the Former Soviet Union2

here has been a resurgence of interest recently among social scientists in studying subjective measures of individual well-being, and in analyzing how peoples’ sense of 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 Much research has been carried out to better understand why some people say they are satisfied with their lives, and others say they are not. While there is broad agreement in the literature about the diverse set of factors that affect individual well-being, much less consensus prevails about the relative importance of these factors, even among leading researchers.

For instance, Richard Layard has argued that non-income factors like health and family circumstances impact peoples’ sense of well-being more than income per se. Others have stressed 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 looks very similar indeed to the spatial distribution of average incomes across countries.

The past two decades in Eastern Europe and the former Soviet Union (ECA) 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.4 However, little is known about subjective welfare in the wake

T

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).

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2 World Bank Working Paper

of this growth rebound, especially how people across ECA countries view their satisfaction with life. Earlier work on ECA countries, using the World Values Survey, have had limited country 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 between market and socialist economies, and examining people’s preferences for redistributive spending by the state and for greater income equality.5 The chapter addresses three main questions: (1) what are prevailing levels of satisfaction 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 relative importance of income vs. non-income factors like health, family status?, and finally, (3) why are prevailing levels of SWL in ECA somewhat lower than what might be expected given relatively high income levels and good health status etc? The next section of this chapter provides an overview of satisfaction with life and its correlates in ECA countries. The results of the multivariate analysis are presented in the second section, which show that while per 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 in other people also play a crucial role. How do average satisfaction rates in ECA countries in 2006 compare to findings of similar surveys conducted earlier in time? How about in relation to countries in other parts of the world? These questions are taken up in the third section, where we compare the LiTS findings on satisfaction with life with similar results from other surveys conducted in other parts of the world as well as in the same set of countries earlier in time.

Satisfaction with Life as a Welfare Measure Respondents in the LiTS were asked to what extent they agreed with the statement: “All things considered, I am satisfied with my life now,” with responses coded as 1 = strongly disagree (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 themselves as satisfied with their lives outnumber those that are not 3 to 2. Yet, this varies considerably across 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 (see Figure 1.2). Most of the new member states of the European Union, which have perhaps seen the biggest political transformation in the Region, feature in the upper part of the distribution except for Hungary which is third from the bottom. Conversely, many of the countries of southeastern Europe and the south Caucuses show relatively low levels of satisfaction.

Despite the clear heterogeneity in satisfaction rates observed across countries and groups of countries, there are nonetheless some similarities evident across some groups. At the 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 preferences for inequality as well as for an excellent survey of the ECA-specific literature. The study uses data from early 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.

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

3

Figure 1.1. All Things Considered, I am Satisfied with my Life Right Now Slovenia Uzbekistan Tajikistan Belarus Estonia Kyrgyz Republic Slovakia Croatia Latvia Czech Republic Kazakhstan Lithuania Poland Russia Turkey Albania Ukraine Romania Bulgaria Montenegro Bosnia Moldova Macedonia, FYR Serbia Azerbaijan Hungary Armenia Georgia

0

10

20

30

Strongly Disagree

40 Disagree

50 Neither

60

70 Agree

80

90

100

Strongly Agree

country level, the LITS data show satisfaction with life to be positively correlated with absolute 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 clear divide across age groups: overall satisfaction rates among the youth are considerably higher than amongst the elderly (Figure 1.4). This is not surprising given that it has been the adult population which lived through the economic decline and dislocations of the 1990s and for whom the economic transition, with its attendant uncertainties and insecurities, has been the most acute.

What is striking is that even with higher unemployment rates, younger age cohorts are more positive about life satisfaction (see Figure 1.3). Similarly, overall satisfaction rates are quite low in Bosnia, Serbia, FYR of Macedonia, Georgia, and Hungary; yet those under 30 years appear to have a more positive outlook on life as compared to the rest of the population.

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.

4 World Bank Working Paper

Figure 1.2. SWL Rates by Region

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

50

Strongly Agree

40 30 20

South-Eastern Europe

Other

CIS-middle

0

EU member states

10 CIS-low income

% of respondents

60

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.

Other Subjective Measures of Well-being The data also suggest that the (self-reported) health status of respondents has a strong bearing on reported level of satisfaction with life (Figure 1.5).

In addition to these quantitative measures of well being, the LiTS questionnaire also includes a number of other questions on respondents’ perception of their relative economic standing, both at present and around 1989.7 The respondents were asked to what extent (on a five-point scale, ranging from 1: strongly disagree to 5: strongly agree) they agreed with statements like (a) I have done better in life than most of my high school classmates, 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 which respondents’ expressed level of satisfaction with life is related to their perceptions regarding both their current economic standing as well as how their economic standing has changed over time and in relation to their peers. These patterns of association are examined in the tables below, which illustrate the “average SWL score”—the average responses 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 is your household today?; (2) Now imagine the same ten-step ladder around 1989, on which step was your household at that time?

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

Figure 1.3. Correlation of Satisfaction with Life with Average Incomes and Level of Inequality (i) Level of Per capita GDP All things considered, I am satisfied with my life right now 70

Slovenia Uzbekistan Belarus

60

Estonia

Kyrgyz Croatia

Kazakhstan

Slovakia

Latvia

Czech

50

Lithuania Poland

Turkey

Russia

40

Albania

Ukraine

Mongolia

Romania

30

% above neutral on satisfaction with life

Tajikistan

Bosnia

Moldova

Bulgaria

Azerbaijan

Hungary

Macedonia

Armenia

20

Georgia

0

5000

10000

15000

20000

Per-capita GDP in 2005 (PPP$2000)

(ii) Level of Income Inequality All things considered, I am satisfied with my life right now 70

Slovenia Uzbekistan

60

Slovakia

Kyrgyz Croatia

Czech

Latvia

50

Kazakhstan Lithuania Poland

Turkey

Russia

40

Albania

Ukraine

Mongolia

30

Romania Montenegro

Bulgaria

Bosnia

Moldova

Serbia Hungary Macedonia

Azerbaijan

Armenia Georgia

20

% above neutral on satisfaction with life

Belarus

Estonia

Tajikistan

.3

.35 Gini coefficient of income inequality

.4

.45

5

6 World Bank Working Paper

of the satisfaction to life question, where 1 indicates strong disagreement and 5 indicates strong agreement All things considered, I am satisfied with my life right now with the statement that the respondent is satisfied with his/her life.

Table 1.1 reports average SWL scores by (i) selfassessed economic welfare and (ii) comparisons relative to peers, and reveals a number of interesting patterns.

For instance, looking across the table’s bottom row, 18-30 yrs 31-40 yrs 41-50 yrs 51-60 yrs 61-70 yrs 71+ yrs Age-Group of the Respondent we find average SWL score increases with self-assessed Agree Disagree level of welfare: respondents in the highest welfare quintile have an average SWL of 3.9 compared to Figure 1.5. Satisfaction with Life is Higher for the Healthy only 2.3 for those that place themselves in the lowest All things considered, I am satisfied with my life right now quintile. Moreover, within each self-assessed welfare group, we find average SWL scores to be positively correlated with extent to which the respondents feel they’ve done better than their high school classmates (looking down each of the columns).

Very good Good Medium Bad Very bad Satisfaction with life thus Self-Assessed Health Status depends, it seems, not just Agree Disagree on (self-perceived absolute welfare), but rather also on how individuals seem to think they have done relative to their peers. Thus, respondents that place themselves in the highest welfare quintile and who “strongly agree” that they’ve done better in life than their high school classmates have an average SWL score three times as high (4.8 vs. 1.6) as those that place themselves in the lowest quintile and who “strongly disagree” with having done better than their classmates.8 40 30 20

40 20 0

Percent of respondents

60

0

10

Percent of respondents

50

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

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

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

7

Table 1.1. Average SWL Score by Self-perceived Economic Status I Have Done Better in Life Than Most of My High School Classmates Strongly disagree Disagree Neither Agree Strongly agree Overall

Self-assessment of Own Economic Welfare (quintile) Lowest 1.6 2.3 2.6 2.7 2.8 2.3

2 1.9 2.7 3.0 3.5 3.6 3.0

3 2.3 3.1 3.4 3.8 4.0 3.5

4 2.2 3.0 3.7 3.9 4.6 3.8

Highest ... ...

3.7 4.0 4.8 3.9

Overall 1.8 2.7 3.2 3.6 4.0 3.1

. . . Cell has fewer than 30 observations

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 that respondents’ current satisfaction with life depend not just on current economic welfare, but also on how they think its changed over time (moving across the rows from left to right, average scores generally fall—i.e. we observe negative slopes across all rows except the last).

Table 1.2. Average SWL Score by Present and Past Self-assessed Economic Status Self-assessment of Own Economic Welfare (quintile) at Time of Survey Lowest 2 3 4 Highest Overall

Self-assessment of Welfare in 1989 (quintile): Lowest 2.5 3.4 3.6 3.8 ...

3.0

2 2.4 3.2 3.7 4.0 3.6 3.1

3 2.4 2.9 3.6 4.0 3.9 3.1

4 2.1 2.7 3.2 3.6 4.1 3.0

Highest 1.9 2.9 3.2 3.4 4.1 3.0

Overall 2.4 3.0 3.5 3.8 3.9 3.1

. . . Cell has fewer than 30 observations

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 dissatisfied with their lives outnumber those that are satisfied: by contrast, among those reporting “complete trust” in people today, those satisfied with their lives outnumber those that are dissatisfied by 3 to 1. Similarly, respondents reporting having greater trust in people today 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 higher than those above the shaded diagonal).

8 World Bank Working Paper

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

40 20 0

Percent of respondents

60

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

Complete distrust

2

3

4

Complete trust

Level of trust in people today Agree

Disagree

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 2.8 2.6 3.5 3.5 2.8

2 2.5 3.2 3.4 3.2 3.1 3.0

3 2.8 3.1 3.2 3.4 3.7 3.2

4 2.6 3.0 3.2 3.4 3.4 3.1

Highest 2.7 3.0 3.2 3.2 3.4 3.1

Overall 2.7 3.0 3.2 3.3 3.4 3.1

Key Factors Influencing SWL: Multivariate Analysis In order to better examine the correlate of satisfaction with life, we use an ordered probit model to analyze respondents’ expressed level of satisfaction with life according to the following model specification: yi* = β ′x i + ε i We do not observe y *i directly, but rather only observe whether yi = 1, 2, 3, 4, and 5, if αj−1 < y *i < αj ( j = 1, 2, 3, 4, and 5 respectively)—i.e. the expressed level of satisfaction with life on a 5 point scale. Various factors that we think influence the level of satisfaction expressed by the respondent are included in the vector xi (see below).

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

Variable swl health sc_now sc_change lnpcexp car secondhouse bankaccount creditcard mobilephone computer pew diff unemploy

Obs 27395 27996 27032 23769 27911 27993 28000 27978 27975 27993 27988 24067 28000 28000

Mean 3.119146 2.725782 2.64542 −1.009214 8.708414 40.81735 88.95 36.48938 31.13137 62.92645 27.55109 2.999668 −1.187571 .085

Std. Dev. 1.155076 1.002334 1.232121 1.473212 .8421385 49.15043 31.35176 48.1409 46.3039 48.30104 44.67794 1.008523 2.355999 .2788867

Min

Max

1 1 1 −4 2.204605 0 0 0 0 0 0 1 −9 0

5 5 5 4 12.06602 100 100 100 100 100 100 5 9 1

transfer hhsize age age2 country locality

28000 28000 28000 28000 28000 28000

.0237143 3.238929 46.50054 2479.074 14.60696 1.836929

.1521603 1.840137 17.79848 1760.618 8.247696 .7392072

0 1 17 289 1 1

1 12 97 9409 29 3

9

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 size age: Age in years age2: Age in years, squared country: 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. swl:

10 World Bank Working Paper

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 0.05 sample 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 0.11 to present Main income source is transfers 0.07 0.26 0.30 0.34 0.03 Person is currently unemployed 64 yr old, self-reported health status very bad, per-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, employed, 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.

0.07 0.77

0.27 0.20

0.30 0.03

0.33 0.00

0.03 0.00

0.00

0.01

0.05

0.45

0.48

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

Table 1.5. Ordered Probit Results: SWL by Country Groups Overall coef (i) Health Status: Very good Good Medium Bad Very bad (ii) Level of trust in people Complete distrust Some distrust Neither trust nor distrust Some trust Complete trust Change in trust since 1989 (iii) Economic status: Log normalized expenditures ($) Household owns: A car Second home A bank account A credit/debit card A mobile phone A computer

EU sd

SEE

coef

sd

coef

0.146*** 0.030 0.095*** 0.019

0.317*** 0.096***

0.056 0.036

0.247*** 0.123***

−0.246*** 0.023 −0.469*** 0.039

−0.200*** −0.569***

−0.220*** 0.027 −0.020 0.023

CIS_L sd

coef

CIS_M

Other

sd

coef

sd

coef

sd

0.080 0.044

0.362** 0.101*

0.141 0.052

0.203* 0.134*

0.109 0.074

0.041 0.070

0.050 0.034 0.038 0.232*** Reference Category −0.237*** 0.049 −0.228*** −0.306*** 0.079 −0.497***

0.047 0.080

−0.347*** −0.405***

0.059 0.124

−0.313*** −0.330***

0.089 0.126

−0.216*** 0.000

0.053 0.039

−0.159*** 0.017

0.062 0.058

−0.221*** −0.076

0.079 0.064

−0.300*** −0.274***

0.099 0.092

0.146*** 0.023 0.280*** 0.039 0.021*** 0.007

0.175*** 0.386*** 0.030**

0.038 0.077 0.015

0.050 −0.149** 0.046 −0.027 Reference Category 0.106** 0.046 0.264*** 0.262*** 0.093 0.315*** 0.057*** 0.014 −0.033**

0.056 0.078 0.016

0.000 0.191** 0.035*

0.059 0.091 0.020

−0.022 −0.075 0.031

0.093 0.167 0.026

0.093*** 0.013

0.142***

0.026

0.082***

0.025

0.027

0.009

0.035

−0.005

0.048

0.112*** 0.034 0.171*** 0.053 0.048 0.052

0.034 0.039 0.041 0.036 0.041 0.036

0.044 0.136*** 0.059 0.039 0.084** 0.073*

0.034 0.052 0.038 0.039 0.043 0.039

0.045 0.079 0.097 0.088 0.042 0.077

0.103** 0.008 0.002 0.143** 0.056 −0.030

0.049 0.077 0.057 0.060 0.053 0.055

0.077*** 0.045* 0.178*** 0.071*** −0.031 0.052**

0.019 0.025 0.021 0.022 0.020 0.021

0.184*** 0.110** 0.068 0.046 0.013 −0.106** −0.032

0.246*** 0.124 0.096 0.048 0.099 0.078

0.077 0.085 0.085 0.085 0.088 0.078

(continued )

Table 1.5. Ordered Probit Results: SWL by Country Groups (Continued) Overall coef Done better than peers Strongly disagree Disagree Neither disagree or agree Agree Strongly agree Change in decile ranking: From 1989 to present: Transfers main income source Unemployed Residence Rural Urban Metropolitan areas Pseudo-R2

EU sd

SEE

coef

sd

coef

−1.008*** 0.033 −0.294*** 0.020

−0.962*** −0.366***

0.060 0.035

−0.965*** −0.197***

0.353*** 0.020 0.872*** 0.036

0.284*** 0.910***

0.037 0.068

0.121*** 0.003 −0.141*** 0.053 −0.238*** 0.029

0.118*** −0.145 −0.176***

0.007 0.092 0.065

CIS_L sd

coef

CIS_M

Other

sd

coef

sd

coef

sd

0.075 0.044

−1.453*** −0.439***

0.106 0.054

−0.729*** −0.149*

0.105 0.084

0.046 0.095

0.339*** 0.941***

0.052 0.088

0.281*** 0.628***

0.078 0.107

0.007 0.139 0.051

0.125*** 0.019 −0.262***

0.010 0.245 0.101

0.108*** 0.129 −0.153

0.013 0.153 0.130

−0.020 0.018 −0.124*** 0.032 0.052 0.034 −0.062 0.045 −0.088*** 0.022 −0.073* 0.038 −0.073* 0.042 −0.189*** 0.049 Other controls (age, age squared, household size, etc.) omitted.

0.130 0.131 0.125 0.140

0.156*** −0.036

0.047 0.073

0.060 −1.085*** 0.040 −0.345*** Reference Category 0.406*** 0.038 0.471*** 0.811*** 0.071 1.081***

0.129*** −0.298*** −0.184***

0.006 0.097 0.052

0.091*** −0.025 −0.280***

0.143

0.071 0.042 0.122

0.071 0.082

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

13

■ Social capital (using responses to the question on level of trust in other people as a proxy), appears to matter quite a bit—respondents that said they trusted other people were 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 are about 13 percent more likely to report being satisfied with their lives compared to those with half the average PCE. Correlation between satisfaction with life and income varies quite a bit across the various country groupings: for instance, the association between level of income and SWL is strongest in CIS low-income countries, but weakest in CIS middle-income countries.

■ Satisfaction with life tends to be positively correlated with ownership of miscellaneous durable 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/her life 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 very small differences in satisfaction levels across different age groups.

■ Relative status matters. A higher perceived relative position with respect to peers and an improvement in the self-perceived difference in decile ranking relative to 1989 increase 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 be satisfied with life than those who are unemployed; in other words, other things being equal the difference in satisfaction levels between the employed and the unemployed are greater in magnitude than those between people with half and twice the average per capita expenditures; the negative impact of unemployment status on overall satisfaction 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 in areas where, a priori, the relationship between satisfaction with life and other variables is not so obvious. For instance: ■ We find that where transfers are a significant source of income (for example, in the EU and SEE groups, a person is about five times as likely to report transfers as the main source of income as in the CIS middle-income group) those reporting transfers as the households’ main income source are significantly less likely to be satisfied with their lives compared to respondents whose households are not so heavily reliant on transfer 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 percent more likely to be satisfied with their lives as compared to those reporting transfers as their households’ main source of income.

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

14 World Bank Working Paper

Comparisons over Time and Across Countries How do average satisfaction rates from the 2006 LiTS compare to the findings from similar surveys—for example, the World Values Survey (WVS)10—conducted earlier in the same countries? Responses to the satisfaction with life question in the latter survey are recorded on a 1–10 point scale, so need to be adjusted accordingly before comparing directly with the LiTS findings. A comparison of the adjusted WVS scores (modified to a 1–5 scale) with the LiTS for the 21 countries for which such over-time comparisons are indeed possible are presented in Table 1.6. Comparing these two sets of findings thus reveals that over the past 6–8 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, and Montenegro (Table 1.6).

Table 1.6. Change over Time in Average SWL Rates by Country World Values Survey Albania Azerbaijan Belarus Bosnia Bulgaria Croatia Czech Republic Estonia Georgia Hungary Latvia Lithuania Moldova Montenegro Poland Romania Russia Serbia Slovakia Slovenia Turkey

Score 2.7 3.0 2.5 3.0 2.6 3.3 3.4 2.8 2.6 3.2 2.7 2.8 2.2 3.3 3.4 2.7 2.5 3.0 3.3 3.4 3.3

Year 1998 1997 1996 1998 1997 1996 1998 1996 1996 1998 1996 1997 1996 1996 1997 1998 1995 1996 1998 1995 1996

2006 LiTS 3.2 2.7 3.6 2.6 2.8 3.3 3.4 3.5 2.5 2.6 3.3 3.3 2.7 2.7 3.3 2.9 3.1 2.6 3.4 3.8 3.1

Change in Score 0.5 −0.3 1.1 −0.4 0.2 0.0 0.0 0.7 −0.1 −0.6 0.6 0.5 0.5 −0.6 −0.1 0.2 0.6 −0.4 0.1 0.4 −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, see http://www.worldvaluessurvey.org

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

15

On the whole, there appears to be a fairly close conformance across the region at the country level between changes in average SWL scores and performance of the economy.

For instance, countries like the Baltic states, Belarus, Moldova, Russia, and Albania, which have recorded relatively high growth rates in recent years also tend to be the ones where average SWL scores appear to have increased the most in recent years; by contrast, other countries with relatively poorly performing economies, like Bosnia, Hungary, Poland, and Turkey, also tend to be the ones with the least improvements (or in some cases actual declines) in average SWL scores (Table 1.7).

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, Poland, Turkey Slovenia, Czech Republic, Croatia —

Middle Montenegro, Serbia

Top Azerbaijan

Romania, Bulgaria, Slovakia Russia, Albania

Georgia Latvia, Lithuania, Estonia, Belarus, Moldova

How do average satisfaction rates in countries in the Europe and Central Asia region compare to countries with similar incomes in other regions in the world? Data from the recent 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 available over time, there is a high correlation between income growth and changes in happiness over time, (ii) Eastern European respondents appear to be less satisfied with their lives than Latin Americans.11 That individuals in transition countries tend to have lower self-reported SWL rates compared 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 adapt to 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 with the transitional recession. Despite the subsequent recovery of output, these indicators have 11. Stokes, Bruce: “Happiness is Increasing in Many Countries—But Why?—Rising Incomes a Big Reason, 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.

16 World Bank Working Paper

Figure 1.7. Cross-Country Comparisons: SWL and Employment Rates 70

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

60

Slovakia Kyrgyz

50

Croatia Kazakhstan

Latvia

Albania

Russia

40

Turkey

Czech

Lithuania

Poland

Ukraine Mongolia Romania

30

% above neutral on satisfaction with life

Belarus

Estonia

Tajikistan

Montenegro Azerbaijan

Bosnia

Armenia

Macedonia

Hungary

20

Georgia

Bulgaria Moldova Serbia

30

40

50

60

70

80

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

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

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

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

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

17

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

Figure 1.8. Cross-Country Comparisons: SWL and Level of Trust in Others All things considered, I am satisfied with my life right now 70

Slovenia Uzbekistan Estonia

Belarus

60

Slovakia

Kyrgyz Croatia

Latvia Kazakhstan

50

Czech Poland

Lithuania

Albania

Russia

40

Turkey

Ukraine

Mongolia Romania

30

% above neutral on satisfaction with life

Tajikistan

Georgia

20

Macedonia

Bulgaria Moldova Bosnia Montenegro Serbia Armenia Hungary Azerbaijan

10

20

30

40

50

% above neutral on trust in people today

Concluding Observations The Life in Transition Survey provides a useful new addition to welfare measures commonly applied to ECA countries. The survey provides information on this new â&#x20AC;&#x153;satisfaction with lifeâ&#x20AC;? measure across all ECA countries. The results show that most people in ECA report satisfaction with life. But there are large variations across ECA countries, with a highest rate of 90 percent in Slovenia and the lowest rate of 40 percent in Georgia. Our analysis confirms the importance of expected factors like income per capita, equality of incomes, youth, working status, non-agricultural employment, non-metropolitan living, and better education. However, our analysis also highlights the important role played by subjective factors like self-assessed health status, level of trust in people, relative economic status compared to peers and own perception of improvement in economic status over time are also important factors determining overall satisfaction levels. Satisfaction with life in ECA is also, not surprisingly, lower than the levels found in other countries. This likely reflects the transitional recessional and the social costs of the major economic transformation undertaken by most of these countries.

18 World Bank Working Paper

Annex: Tables and Figures Table A1.1. Satisfaction with Life Question by Country All things considered, I am satisfied with my life now

Strongly Strongly Agree Disagree

Disagree

% of respondents

60 Agree

50

Strongly Agree

40 30 20 10 Turkey

South-Eastern Europe

CIS-middle income

Neither

EU member states

0

CIS-low income

Agree

Percentage of Respondents Who . . .

Group/Country CIS-low income EU member states CIS-middle income South-Eastern Europe Turkey Overall sample: Slovenia Belarus Uzbekistan Tajikistan Estonia Czech Republic Slovakia Kazakhstan Kyrgyz Republic Poland Lithuania Croatia Latvia Albania Mongolia Russia

Strongly Disagree 9 9 9 18 15

Disagree 21 21 22 23 16

Neither 19 26 24 24 24

Agree 41 35 35 28 32

Strongly Agree 10 9 10 7 12

Overall 100 100 100 100 100

10 1 2 3 6 4 4 4 4 5 6 6 11 8 8 6 10

21 8 11 13 11 17 14 17 18 20 18 22 14 21 18 21 22

24 19 20 16 15 14 28 21 25 16 27 20 20 17 30 34 24

35 54 57 53 47 52 41 51 44 53 39 40 42 46 33 32 33

10 18 10 15 21 13 12 7 9 6 11 12 13 9 11 7 11

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 (continued )

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

19

Table A1.1. Satisfaction with Life Question by Country (Continued) Percentage of Respondents Who . . .

Strongly Disagree 15 12 13 13 15 20 15 20 21 21 23 20 19

Group/Country Turkey Ukraine Romania Bulgaria Moldova Montenegro Azerbaijan Bosnia Macedonia Hungary Serbia Armenia Georgia

Disagree 16 26 25 32 29 26 34 25 26 26 29 34 35

Neither 24 25 29 26 28 25 25 27 26 27 22 20 24

Agree 32 31 27 23 25 24 21 26 23 22 22 24 19

Strongly Agree 12 6 6 6 3 5 5 2 4 4 4 2 3

Overall 100 100 100 100 100 100 100 100 100 100 100 100 100

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 60 Agree

Strongly Disagree

Agree

% of respondents

50

Disagree

Strongly Agree

40 30 20 10

Neither

South-Eastern Europe

EU member states

CIS-low income

0 CIS-middle income

Strongly Agree

Percentage of Respondents Who . . .

Group/Country CIS-low income CIS-middle income EU member states South-Eastern Europe

Strongly Disagree 14 15 18 39

Disagree 31 32 28 29

Neither 15 16 20 14

Agree 32 28 26 14

Strongly Agree 8 9 8 4

Overall 100 100 100 100 (continued )

20 World Bank Working Paper

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

by Country (Continued) Percentage of Respondents Who . . .

Group / Country Overall sample: Belarus Estonia Albania Kazakhstan Mongolia Lithuania Czech Republic Uzbekistan Azerbaijan Latvia Poland Slovenia Russia Slovakia Tajikistan Armenia Turkey

Strongly Disagree 18 1 3 8 5 5 7 9 9 8 13 16 11 13 12 18 23 27

Disagree 30 12 13 9 20 23 24 23 26 28 28 23 29 29 33 30 28 27

Neither 16 18 17 12 13 19 16 21 15 20 15 20 21 18 17 11 11 12

Agree 26 56 45 55 47 45 41 32 36 41 36 32 28 29 29 30 30 21

Strongly Agree 9 12 22 16 15 10 12 15 13 3 9 10 10 11 9 11 7 12

Overall 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

Romania Georgia Moldova Kyrgyz Republic Bulgaria Ukraine Croatia Montenegro Hungary Serbia Macedonia Bosnia

19 24 20 21 23 25 42 41 40 41 44 53

33 36 41 48 40 45 24 35 35 34 38 31

24 13 16 6 19 13 18 11 14 15 11 11

19 23 19 22 14 15 13 11 9 7 5 4

5 5 4 2 3 3 3 2 2 2 1 1

100 100 100 100 100 100 100 100 100 100 100 100

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

21

Table A1.3. Satisfaction with Changes over Time in Living Conditions by Country I have done better in life than my parents 60 Agree

50 % of respondents

Strongly Agree

Strongly Disagree

Disagree

Strongly Agree

40 30 20

Agree 10 Neither CIS-low income

South-Eastern Europe

EU member states

CIS-middle income

0

Percentage of Respondents Who . . .

Group/Country CIS-middle income EU member states South-Eastern Europe CIS-low income Overall sample: Belarus Slovenia Estonia Albania Slovakia Romania Lithuania Latvia Czech Republic Russia Croatia Kazakhstan Poland Ukraine Bulgaria Tajikistan Uzbekistan Moldova

Strongly Disagree 5 6 11 8

Disagree 19 19 21 31

Neither 23 24 22 24

Agree 40 38 33 30

Strongly Agree 13 14 13 7

Overall 100 100 100 100

7 2 2 2 5 4 3 2 4 4 4 10 5 6 7 6 6 6 7

21 9 13 14 9 12 13 18 18 17 20 15 20 21 20 23 25 28 20

24 23 23 21 14 20 24 18 18 27 23 20 23 24 23 23 25 20 33

36 53 44 42 52 51 44 38 42 36 38 34 41 35 42 39 32 37 36

13 13 19 21 19 14 16 24 18 15 15 21 11 14 8 8 12 10 4

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 (continued )

22 World Bank Working Paper

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

by Country (Continued) Percentage of Respondents Who . . .

Strongly Disagree

Disagree

Neither

Agree

Strongly Agree

Overall

Serbia Hungary Macedonia Montenegro Turkey Bosnia Kyrgyz Republic

13 12 13 10 18 11 5

22 24 26 29 22 27 39

23 27 24 28 25 27 21

30 28 29 26 22 30 32

12 9 8 8 13 5 4

100 100 100 100 100 100 100

Georgia Mongolia Azerbaijan Armenia

11 11 12 14

36 36 39 42

23 30 30 24

24 20 16 17

6 3 3 3

100 100 100 100

Group/Country

Table A1.4. Average SWL Score: Colleagues in 1989 Rather than School Mates as Peers I Have Done Better in Life Than Most of the Colleagues I Had Around 1989 Strongly disagree Disagree Neither Agree Strongly agree Overall . . . Cell has fewer than 30 observations

Self-assessment of Own Economic Welfare (quintile): Lowest 1.6 2.3 2.6 2.8 3.5 2.3

2 1.9 2.6 3.0 3.4 3.6 2.9

3 2.3 2.9 3.4 3.7 4.2 3.4

4 2.2 2.9 3.6 4.0 4.2 3.8

Highest ... ...

3.3 4.0 4.9 3.9

Overall 1.8 2.6 3.1 3.5 4.0 3.0

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

23

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

Country Albania Belarus Slovenia Czech Republic Turkey Kazakhstan Poland Slovakia Estonia Lithuania Romania Kyrgyz Republic Moldova Hungary Russia Latvia Tajikistan Ukraine Uzbekistan Armenia Croatia Bulgaria Macedonia, FYR Azerbaijan Montenegro Serbia Bosnia Georgia Overall Correlation coefficient:

Average Reported Change in Decile Rank Between 1989 and Present 0.40 −0.08 −0.32 −0.37 −0.64 −0.76 −0.85 −0.85 −0.87 −1.01 −1.02 −1.05 −1.11 −1.18 −1.19 −1.19 −1.35 −1.35 −1.42 −1.44 −1.58 −1.60 −1.93 −2.09 −2.14 −2.20 −2.30 −2.49 ⴚ1.10

Prevailing Level of Income Inequality (Gini) in Country (based on PCEXP) 0.34 0.37 0.29 0.29 0.36 0.35 0.32 0.30 0.34 0.37 0.40 0.36 0.44 0.35 0.38 0.38 0.31 0.45 0.32 0.43 0.34 0.37 0.34 0.38 0.29 0.35 0.33 0.40 0.37 ⴚ0.16

24 World Bank Working Paper

Table A1.6. Fall in Social Capital? Decline in Reported Level of Trust in Other People (a) Trust in People Before 1989 60

Complete Distrust

Complete Trust % of respondents

Complete Trust

Some Trust

50

Some Distrust

Neither

40

30

20

10

Some Trust EU member states

South-Eastern Europe

CIS middle income

CIS low income

0

Percentage of Respondents Who Have . . .

Group/Country CIS low-income CIS middle-income South-Eastern Europe EU member states Overall sample: Mongolia Uzbekistan Georgia Bulgaria Kazakhstan Kyrgyz Republic Tajikistan Lithuania Montenegro Hungary Russia Turkey Serbia Estonia Slovenia Macedonia Moldova Belarus Latvia

Complete Distrust 5 6 8 6

Some Distrust 8 11 10 13

Neither 14 14 20 29

Some Trust 34 39 45 41

Complete Trust 40 31 18 11

Overall 100 100 100 100

6 2 2 2 3 3 7 4 2 5 4 6 8 6 3 3 6 7 4 4

11 2 6 7 6 9 4 9 11 8 8 10 9 10 12 11 10 11 14 14

18 7 12 14 14 9 5 17 18 18 29 13 16 17 24 28 22 17 23 14

38 30 32 45 55 41 37 29 52 48 46 38 28 48 49 48 33 46 44 56

28 60 49 31 22 37 47 41 16 21 14 33 39 19 12 10 29 20 15 12

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 (continued )

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

25

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

Complete Distrust 6 10 6 7 7 6 7 15 10 5

Group/Country Ukraine Azerbaijan Armenia Bosnia Croatia Poland Slovakia Albania Romania Czech Republic

Some Distrust 14 9 14 12 9 12 12 8 16 20

Neither 17 17 20 22 23 30 31 16 30 38

Some Trust 38 28 36 41 49 40 40 47 36 33

Complete Trust 26 35 24 19 12 11 11 14 8 4

Overall 100 100 100 100 100 100 100 100 100 100

(b) Trust in People Today 60 50 % of respondents

Complete Trust Complete Distrust Some Trust

40

Some Trust Complete Trust

30 20 10

Neither

Some Distrust South-Eastern Europe

EU member states

CIS-low income

CIS-middle income

0

Percentage of Respondents Who Have . . .

Group/Country CIS-middle income CIS-low income EU member states South-Eastern Europe Overall sample: Georgia Belarus Estonia Kazakhstan

Complete Distrust 20 30 19 29

Some Distrust 26 21 27 22

Neither 17 16 27 23

Some Trust 29 24 24 23

Complete Trust 8 9 3 3

Overall 100 100 100 100

25 12 9 8 14

25 22 23 30 31

19 21 27 22 12

24 36 35 36 35

7 9 6 4 8

100 100 100 100 100 (continued )

26 World Bank Working Paper

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

Group/Country Slovakia Ukraine Slovenia Latvia Lithuania Russia Czech Republic Tajikistan Uzbekistan Moldova Poland Serbia Croatia Montenegro Romania Hungary Kyrgyz Republic Albania Azerbaijan Mongolia Armenia Bulgaria Bosnia Turkey Macedonia

Complete Distrust 13 17 11 13 13 23 10 29 30 24 17 26 21 21 24 23 36 38

Some Distrust 21 29 24 36 33 25 32 21 19 25 29 21 25 26 21 25 29 18

Neither 30 16 37 15 23 17 32 14 16 17 27 22 28 26 30 28 7 17

Some Trust 33 33 26 33 27 26 22 26 26 28 24 28 23 24 22 21 21 21

Complete Trust 4 6 2 3 4 8 4 10 10 6 4 3 3 3 3 3 8 6

Overall 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

40 31 35 33 32 47 47

17 27 26 31 24 20 17

19 19 18 15 24 14 20

16 19 16 19 19 11 14

8 5 5 3 2 8 3

100 100 100 100 100 100 100

CHAPTE R 2

Employment, Sources of Income, and the Poor in Eastern Europe and the Former Soviet Union14

he 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. The most recent comprehensive assessment 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 moderation of inequality that is taking place in the region. The key source for understanding these 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 assessment of income and poverty in the region, differences in structure of the various questionnaires used across different countries (for example, level of disaggregation, recall period, variable coverage of sources of income, and so forth) along with differences in definitions and concepts followed (for example, commodity classification schemes followed, treatment of imputed consumption, and so on) render the task of constructing a comparable measure of welfare across countries an extremely difficult one. The 2006 LiTS provides a hitherto unprecedented opportunity to systematically examine differences in socioeconomic characteristics of different income groups using the same survey instrument across all ECA countries. Using this new data source, this paper also provides a systematic examination of household welfare, the sources of household incomes and therefore of potential channels

T

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

27

28 World Bank Working Paper

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

Because our main concern here is with the survey data collected on household expenditures, 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) approximately how much their household spent on (i) food, beverages, and tobacco, (ii) clothing and footwear, (iii) transport and communications (including phone, mobile phone, and internet charges), (iv) recreation, entertainment, meals outside the home, and so forth, during the past 30 days, as well as (2) approximately how much their household spent on (v) education (including tuition, books, kindergarten expenses), (vi) health (including health insurance), (vii) furnishings (such as sheets, towels, blankets, linen), (viii) household durable goods (such as furniture, household appliances, TV, car), and (ix) other expenses (any additional expenses that the respondent would like to report) during the past 12 months.

These nine subcomponents of household expenditures were converted to annual amounts and aggregated to derive a per-capita (and, using the modified OECD scales,15 an normalized) measure of individual welfare.

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

The next section provides a more detailed discussion of the various pros and cons of each 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 profile for the region as a whole using $PPP2.15 and $PPP4.30 regional poverty lines using 2000 PPPs.16 However, before doing so, it is important to first assess how reliable is this welfare measure. The second section compares the estimates of private consumption per capita from LiTS with those obtained from other more traditional sources, such as the National Accounts as well as other more detailed nationally representative household surveys. The third section then presents the poverty profile for the region obtained using this welfare measure, and wherever possible also compares the extent to which the poverty estimates and profile based on the LiTS data is indeed consistent with those from traditional household surveys. Finally, the fourth section examines in more detail the differences between the poor and non-poor in employment status, sector of employment, as well as main sources 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 household 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).

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

29

Choosing Between Alternate Survey-Based Welfare Measures While there is general agreement on the merits of using household survey based estimates of per capita (or suitably normalized) consumption and expenditures as a summary measure of living standards, there is little consensus regarding how long the survey questionnaire should be to yield good estimates of household expenditures.17 Greater disaggregation in 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 out other information to be collected in the survey while short questionnaires can save time and money and still deliver reasonably accurate PCE estimates. Survey questionnaires currently in use vary in length from as much as several hundred items purchased/consumed over the past one year (for example, India’s National Sample Surveys) to as little as one question about the household’s total expenditures over the past one month.18 Exploiting the fact that the LiTS questionnaire enables the construction of consumption 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 consumption aggregates compared to those obtained from more traditional consumption modules in typical household budget/LSMS surveys in the same countries. While multiple assessment criteria can be used for this purpose, our primary interest is in assessing how well the short consumption module does in terms of enabling the analyst to rank households into broad welfare groups (such as quartiles/quintiles). We start first by first examining various possible individual welfare metrics that can be derived from the 2006 LiTS data. The LITS 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 consumption aggregate.”

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

■ A question where respondents are asked to place themselves on a ten-step ladder ranging from the poorest (1) to the richest (10). Respondents are asked this question 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 households 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 Margaret Grosh 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 tend to have shorter consumption modules (∼50–80 items) in comparison to typical household budget surveys (where, as noted above, the number of consumption items can be as high as 200–300 items).

30 World Bank Working Paper

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 per equivalent adult (ECE) rather than consumption per capita (PCE)? Rather than prejudge the issue in favor of either measure, particularly given the relatively widespread use of the latter measure in poverty analysis, we use both normalized welfare and per-capita welfare measures in our analysis.

Equivalence scales are the deflators used to convert household consumption aggregates into money metric utility measures of individual welfare.19 The modified OECD equivalence scale we use is the same as those used by Eurostat to make welfare comparisons across countries of the European Union. This scale assigns a weight of one to the first person in 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 would expect, applying this equivalence scale to the LiTS data results in normalized expenditures that are systematically higher than per-capita expenditures across all countries (Table 2.1). Not surprisingly, the difference between the two (normalized vs. per capita expenditures) 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, quite close 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 minimum amount of money that this household would need to make ends meet at the end of each month?” 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 percent higher than average ECE of $2,804 per-equivalent adult per annum (Table 2.1). In addition, OQE has, in general, both higher dispersion and is “more spiked” compared to the distribution 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, the first step, stand the poorest people and on the highest step, the tenth, stand the richest. On which step of the ten is your household today?” Thus, unlike the various welfare measures considered 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 had a 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 for Constructing Consumption Aggregates for Welfare Analysis, LSMS Working Paper 135, World Bank, Washington DC.

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

31

Table 2.1. Comparing Various Alternate Welfare Measures in the LiTS Expenditures Country Czech Republic Hungary Latvia Lithuania Bulgaria Romania Serbia Belarus Ukraine Estonia Croatia Poland Russia Slovenia Slovakia Bosnia Montenegro Moldova Georgia Kazakhstan Macedonia, FYR Armenia Turkey Kyrgyz Republic Albania Azerbaijan Uzbekistan Tajikistan Overall

Household Size 2.1 2.1 2.1 2.1 2.2 2.4 2.6 2.1 2.2 2.1 2.4 2.4 2.2 2.5 2.5 2.7 2.9 2.4 2.9 3 3.3 3.6 3.6 3.7 3.7 4 4.3 4.8 2.6

Per Equivalent Adult (ECE) 4,556 3,455 3,870 3,234 2,125 2,372 2,752 2,203 2,687 4,045 5,260 3,642 3,108 6,531 3,577 2,860 4,173 1,302 1,315 1,951 2,308 1,604 2,784 1,060 2,459 1,136 721 787 2,812

Per Capita (PCE) 3,673 2,774 3,099 2,575 1,672 1,865 2,163 1,728 2,094 3,145 4,052 2,803 2,372 4,980 2,708 2,143 3,095 958 947 1,386 1,633 1,092 1,891 704 1,577 708 440 466 2,120

Ratio of ECE/PCE 1.24 1.25 1.25 1.26 1.27 1.27 1.27 1.27 1.28 1.29 1.30 1.30 1.31 1.31 1.32 1.33 1.35 1.36 1.39 1.41 1.41 1.47 1.47 1.51 1.56 1.60 1.64 1.69 1.33

One-question Per Equivalent Adult Expenditures (OQE) 7,448 7,516 7,465 5,831 4,017 4,652 4,867 2,847 3,735 7,409 8,098 6,132 4,531 7,774 5,901 4,423 5,821 1,860 1,946 2,800 3,012 2,486 4,683 915 3,102 1,085 993 787 4,390

tails. Thus, one problem with this measure is that it does not result in self-rankings that are uniformly distributed across decile groups (in particular, respondents are more-likely-thanaverage to list themselves as belonging to the 3rd, 4th, or 5th decile groups) (see Table 2.2 and Figure 2.3).20 Three groups of “poor,” “middle,” and “rich” were formed by grouping 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 each country, one would have expected their responses to have been uniformly distributed across the ten response classes, assuming respondents could indeed make an accurate comparison of their own welfare in relation to that of other people in the same country.

Figure 2.1. Distribution of Normalized Expenditures 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

0 .2 .4 .6 .8 0 .2 .4 .6 .8

Density

0 .2 .4 .6 .8

0 .2 .4 .6 .8

Albania

6

8

10

4

6

8

10

0 .2 .4 .6 .8

4

4

6

8

10

4

6

8

10

4

6

8

10

4

6

8

10

Log of ECE Graphs by country

Source: 2006 LiTS. Graphs show distribution of (log) normalized expenditures by country, with the 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.

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

1 .5 0 0

.5

1

1.5

Density

1.5

0

.5

1

1.5

0

.5

1

1.5

Figure 2.2. Distribution of the One-question Welfare Aggregate

10

5

10

0

.5

1

1.5

5

5

10

5

10

5

10

5

10

Log of OQE Graphs by country

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

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

33

Table 2.2. Subjective Assessment of Welfare Percentage of Respondents Ranking Themselves in the Decile Group Country Albania Belarus Bosnia Bulgaria Croatia Czech Republic Macedonia, FYR Hungary Moldova Montenegro Poland Romania Serbia Slovakia Slovenia Turkey Ukraine Armenia Azerbaijan Estonia Georgia Kazakhstan Kyrgyz Republic Latvia Lithuania Russia Tajikistan Uzbekistan All countries:

Poorest 8.9 1.8 7.3 13.0 7.1 1.9 8.1 6.2 6.3 7.3 6.3 5.7 8.7 5.3 1.4 15.3 8.6 4.9 11.8 3.4 11.4 3.9 2.9 5.6 9.1 6.0 1.5

2 7.6 2.1 10.2 15.8 8.0 8.3 9.5 9.8 9.6 10.3 9.8 9.1 13.4 8.4 3.3 16.8 12.8 6.8 21.3 7.3 12.8 9.1 3.5 10.3 12.4 13.9 7.0

3 12.7 13.1 14.4 19.9 16.0 12.8 16.1 18.6 12.5 19.4 17.2 13.3 18.0 17.3 7.9 16.8 22.7 17.2 25.7 21.7 23.0 17.0 11.0 22.2 19.5 22.4 14.5

4 18.0 18.1 15.8 15.1 15.0 22.1 16.9 20.1 15.9 14.8 18.6 18.8 17.9 18.8 16.1 14.5 21.3 18.7 17.4 21.9 18.6 17.5 15.8 23.2 21.4 21.3 21.5

5 31.0 27.6 25.9 17.2 34.4 25.8 34.1 24.2 25.1 26.6 21.3 28.6 28.2 32.0 37.0 20.2 19.3 32.7 17.1 31.6 20.3 28.3 33.2 26.5 26.1 17.0 31.8

6 10.3 19.5 12.6 7.8 10.9 13.1 8.5 11.8 14.2 10.8 11.2 14.2 8.6 11.9 19.4 7.1 8.9 11.8 4.9 8.6 8.5 12.0 19.7 8.4 8.1 8.3 13.3

7 5.9 10.7 9.6 3.6 6.6 7.9 4.2 6.1 10.5 7.2 7.3 6.1 3.7 4.7 9.3 5.1 4.1 5.8 1.3 4.3 3.7 7.1 8.0 2.9 2.4 6.1 5.9

8 3.4 5.8 2.6 4.3 1.5 5.7 1.9 2.8 4.7 2.2 5.7 3.0 0.6 1.3 4.5 2.6 1.7 1.1 0.3 0.8 1.0 4.2 3.7 0.6 0.8 3.8 4.0

9 1.0 1.3 0.9 2.9 0.4 1.8 0.6 0.3 1.3 1.0 1.9 0.9 0.4 0.2 1.0 1.2 0.4 0.5 0.1 0.1 0.4 0.9 1.7 0.0 0.1 1.0 0.3

Richest 1.1 0.1 0.6 0.3 0.2 0.5 0.1 0.1 0.0 0.3 0.7 0.3 0.5 0.1 0.2 0.2 0.2 0.5 0.0 0.4 0.3 0.0 0.6 0.2 0.0 0.2 0.2

Overall 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

5.1 6.6

6.3 9.9

11.1 17.0

16.6 18.3

39.6 27.3

15.4 11.4

4.3 5.9

1.1 2.7

0.2 0.8

0.3 0.3

100 100

Source: 2006 LiTS (data are not weighted).

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

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

34 World Bank Working Paper

Figure 2.3. Distribution of Subjective Welfare Rankings by Country 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

0 .1 .2 .3 .4 0 .1 .2 .3 .4

Density

0 .1 .2 .3 .4

0 .1 .2 .3 .4

Albania

5

10

0

5

10

0 .1 .2 .3 .4

0

0

5

10

0

5

10

0

5

10

0

5

10

Subjective Welfare Ranking Graphs by country

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

of variation between countriesâ&#x20AC;&#x201D;for instance, ownership of mobile phones varies from 15 percent in Uzbekistan to 90 percent in Montenegro. Overall, 64 percent of households 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 respondents report owning a secondary residence. In general, asset ownership rates are quite high in wealthier countries (Croatia, Slovenia, Czech Republic), and lower in comparatively poorer Central Asia (Uzbekistan, Tajikistan, and the Kyrgyz Republic). About 30 percent of respondents said their household did not own any of the above-mentioned assets, with the ratio varying from a low of around 8 percent in the Czech Republic to over 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, correlation 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 different weights). 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 household in the middle of the income distribution ladder.

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

35

Table 2.3. Ownership of Assets Respondents (percent) reporting owning the consumer good Country Albania Belarus Bosnia Bulgaria Croatia Czech Republic Macedonia, FYR Hungary Moldova Montenegro Poland Romania Serbia Slovakia Slovenia Turkey Ukraine Armenia Azerbaijan Estonia Georgia Kazakhstan Kyrgyz Republic Latvia Lithuania Russia Tajikistan Uzbekistan All countries

Mobile phone 88 56 68 59 75 89 69 73 31 90 69 61 73 81 88 73 67 46 52 82 46 41 21 76 75 69 17 15 64

Car 24 30 51 38 60 56 49 46 24 58 48 33 46 58 78 22 26 23 17 46 20 29 24 38 49 31 18 20 32

Computer 10 34 29 19 42 51 28 37 12 32 45 31 37 47 65 15 27 10 5 47 8 13 5 34 38 33 4 2 28

Internet access 3 17 14 14 34 38 10 21 7 22 28 19 27 24 52 8 11 5 2 41 4 5 1 24 26 20 1 1 16

Secondary residence 7 10 18 9 30 19 8 6 3 19 7 11 24 11 16 8 8 6 4 15 15 3 9 14 11 12 4 6 10

Table 2.4. Correlation Matrices: Decile Rankings Based on Various LiTS Welfare Measures Variables ECE PCE OQE SAW

ECE 1.0000 0.8707 0.4530 0.2500

PCE — 1.0000 0.4560 0.2148

OQE — — 1.0000 0.1431

SAW — — — 1.0000

36 World Bank Working Paper

How good are these various welfare metrics in identifying the poor? One possible criterion to ascertain this is to first rank respondents into different groups—for instance, 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) the first five, (b) sixth and seventh and (c) higher decile groups (so as to form roughly three equal groups comprising respectively 33, 46, and 20 percent of the population). We then compare 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 percent of “poor households” (again, as classified by ECE) reporting owning one. In general, 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 assetownership rates across the various poor-middle-rich welfare classes (Table 2.5). This can also be seen by focusing on the 29–30 percent of respondents that report not having a mobile phone, car, computer, internet access, or a secondary residence—these respondents own none of the various asset variables on which questions were asked in the LiTS. While the so-classified “asset poor” are considerably more likely to be among the poor than the rich as classified by all other welfare metrics under consideration, the odds-ratio of probability of the asset poor being classified as poor to the probability of their being classified as rich is greater than five in the case of the ECE—higher than any other 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 also confirms that ECE and PCE are in fact quite highly correlated. In this section, we compare PCE from the LiTS with per capita consumption expenditures obtained from other more traditional sources, such as the National Accounts and other household surveys. Average PCE in the LiTS sample is US$ 2,049 per annum, but varies considerably across countries from $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 estimated per-capita 2006 GDP. Across the 15 countries for which survey data are available in the World Bank’s Europe and Central Asia Region household survey archive (henceforth ECAPOV), 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 the national 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—in Armenia 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 better to per-capita GDP estimates from the National Accounts, but not so in the remaining two—i.e. Estonia and Azerbaijan.

% of persons in households with Poor Internet access EU members 10 S.E. Europe 8 CIS-low income 0 CIS-middle 2 Turkey 2 Overall 5 Computers EU members 18 S.E. Europe 15 CIS-low income 1 CIS-middle 6 Turkey 5 Overall 11 Secondary residence EU members 6 S.E. Europe 12

(1) ECE

(2) PCE

(3) OQE

(4) SAW

Middle

Rich

Poor

Middle

Rich

Poor

Middle

Rich

Poor

Middle

Rich

All Groups

26 18 1 11 6 15

49 32 7 28 16 30

14 9 0 3 2 7

27 20 1 11 7 16

44 29 7 27 16 28

17 12 2 3 0 9

27 18 2 11 8 15

42 30 6 27 16 26

12 9 1 6 2 7

31 20 3 14 10 18

49 36 6 24 22 30

28 19 3 14 8 17

41 31 3 27 13 26

66 49 14 49 27 45

26 17 1 9 5 14

40 32 3 27 14 26

59 45 14 47 26 42

29 22 3 10 4 17

40 29 4 27 16 26

57 44 12 46 26 40

21 17 3 15 7 14

46 34 6 28 18 29

64 53 12 45 34 44

41 32 6 28 15 27

10 18

18 26

7 13

10 18

18 25

9 15

11 18

16 23

6 14

12 18

20 29

12 19 (continued )

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

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

37

(1) ECE

(2) PCE

(3) OQE

(4) SAW

% of persons in households with

Poor

Middle

Rich

Poor

Middle

Rich

Poor

Middle

Rich

Poor

Middle

Rich

All Groups

CIS-low income CIS-middle Turkey Overall

4 3 2 6

6 8 7 11

11 13 15 17

4 4 3 7

6 8 7 10

11 13 15 17

5 4 4 8

6 8 7 11

10 12 14 16

4 4 4 7

7 8 9 11

12 14 18 19

7 8 8 11

28 36 13 16 12 23

52 56 22 32 20 41

73 68 35 47 38 57

36 42 14 22 13 28

51 54 23 29 23 40

66 64 33 43 34 52

37 45 19 19 12 30

52 52 23 30 24 40

66 64 29 45 35 51

31 36 12 18 12 24

57 58 25 33 29 45

69 71 36 48 45 57

51 53 23 31 23 40

51 64 15 29 62 42

78 82 31 65 73 65

93 91 55 86 86 81

59 70 18 36 66 47

74 80 32 63 74 63

88 88 52 81 81 77

61 71 22 37 63 50

76 79 31 61 77 63

87 89 49 81 82 76

58 66 28 45 68 51

80 84 36 65 78 67

85 90 39 73 83 72

74 79 34 60 74 63

Cars EU members S.E. Europe CIS-low income CIS-middle Turkey Overall Mobile phone EU members S.E. Europe CIS-low income CIS-middle Turkey Overall

Note: Based on un-weighted observations.

38 World Bank Working Paper

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

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

39

Figure 2.4. Comparing the Various Alternate Welfare Measures Percent of Group that is "Asset-Poor" 60

"Poor"

50

"Rich"

40 30 20 10 0

ECE

PCE

OCE

SAW

than the average per capita 2005 GDP estimate. However, this discrepancy between data from the national accounts and household surveys is not unique to the LiTS per se—of the countries for which ECAPOV household survey data is available, the survey-based estimate exceeds mean per-capita GDP in 7 cases (Albania, Belarus, Moldova, Azerbaijan, Kyrgyz Republic, 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 these data are available. However, this share varies quite a bit across countries, from a low of 50 percent in the Czech Republic to 93 percent in Serbia and Montenegro. While the LiTS PCE definition does not fully coincide with the HFCE concept, a comparison of the two estimates is 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 budget surveys (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 suggests a poverty incidence in ECA in 2006 of about 10.5 percent for the lower poverty line and 33.6 percent for the upper poverty line (Table 2.7). This compares favorably with recent survey based estimates of around 10.8 percent and 37.8 percent poverty rates derived from the ECAPOV household survey database using the same poverty lines.22 To sum, the evidence presented in this section indicates that the LiTS consumption aggregate provides a credible welfare 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 Soviet Union—An Update,” The World Bank, Washington, D.C.

40 World Bank Working Paper

Table 2.6. 2006 LiTS PCE Compared to Other Data Sources Country Albania Belarus Bosnia Bulgaria Croatia Czech Republic Macedonia, FYR Hungary Moldova Montenegro Poland Romania Serbia Slovakia Slovenia Turkey Ukraine Armenia Azerbaijan Estonia Georgia Kazakhstan Kyrgyz Republic Latvia Lithuania Russia Tajikistan Uzbekistan Overall (15 country average)

Mean PCE 2006 LiTS 1,577 1,728 2,143 1,672 4,052 3,673 1,633 2,774 958 3,095 2,803 1,865 2,163 2,708 4,980 1,891 2,094 1,092 708 3,145 947 1,386 704 3,099 2,575 2,372 466

Per-capita GDP (2005) 1,535 1,868 1,486 2,071 5,138 6,515 1,889 5,691 429 1,369 5,194 2,259 1,369 4,761 11,382 3,390 959 1,128 1,182 5,866 971 1,972 319 5,023 4,838 2,444 237

ECA survey archive Mean PCE 1,647 2,565 — 1,987 4,995 — — — 900 — 2,974 — — — — — — 696 1,325 4,071 725 1,407 394 — — 2,459 554

440 2,049 (1,603)

673 2,842 (2,068)

516 — (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-specific growth rates based on annual changes in household final consumption expenditures (HFCE) from the National Accounts.

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

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

Panel A 6000

2006 LITS

4000

Croatia Czech Estonia Latvia Poland Slovakia Lithuania

Hungary

Russia Serbia Bosnia Ukraine Romania Turkey Belarus Bulgaria Macedonia Albania Kazakhstan

2000

LITS PCE estimate(2006)

Slovenia

Moldova Armenia Georgia Azerbaijan Tajikistan Uzbekistan

0

Kyrgyz

0

2000

4000

6000

8000

10000

8000

10000

National Accounts HFCE (2004)

Panel B

6000 4000

Croatia

Estonia

2000

Poland Belarus Russia Bulgaria Albania Kazakhstan Azerbaijan Moldova Georgia Uzbekistan Armenia Tajikistan Kyrgyz

0

ECAPOV PCE estimate

8000

ECAPOV estimate

0

2000

4000

6000

National Accounts HFCE (2004)

41

42 World Bank Working Paper

Table 2.7. Overall Regional Poverty Rates from the 2006 LiTS

Poverty Line ⴝ $PPP 2.15 Urban Rural Overall Poverty Line ⴝ $PPP4.30 Urban Rural Overall

Squared Poverty Gap (P2)

Headcount Rate (P0)

Poverty Gap (P1)

6.3 17.4 1.5

1.7 5.5 3.2

0.8 2.7 1.5

25.7 46.5 33.6

8.5 19.2 12.6

4.1 10.4 6.5

Headcount Rate Poverty Line ⴝ $PPP 2.15 ECAPOV LiTS Poverty Line ⴝ $PPP4.30 ECAPOV LiTS

10.8 10.5 37.8 33.6

Poverty Profile for ECA EU Member States and CIS Middle-income Countries Have the Lowest Poverty Rates in the Region Using the $2.15 and $4.30 PPP poverty lines with the LiTS PCE welfare measure,23 we find considerable spatial variation in poverty rates across the ECA region. Overall, the analysis shows 10.5 percent of the region’s population to be below the $PPP 2.15 poverty line, while 33.6 percent were below the $PPP 4.30 poverty line. Across different subregions, poverty rates were, as expected, highest among CIS low-income countries and lowest among EU member 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 are home to less than one-fifth the total number of poor in the region. Instead, mirroring the overall distribution of population across countries in the region, about two-thirds of the poor in the region in fact live in five middle-income countries—Turkey, Russia, Romania, Poland and Kazakhstan (Figure 2.7) (which also account for about two-thirds of the region’s total population).

23. These poverty estimates have been derived using PPP adjustments factors taken from the 2005 ICP 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 Europe includes Albania, Bosnia, FYR of Macedonia, Montenegro, Serbia; CIS low income includes Moldova, Armenia, Azerbaijan, Georgia, Kyrgyz Rep., Tajikistan, Uzbekistan (PPP data not available); “Other” includes Bulgaria, Croatia, Romania, Turkey.

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

Figure 2.6. Regional Variation in Poverty Rates Across the ECA Region 80 70

Poverty Rate (%)

60

$2.15 line

50

$4.30 line

40 30 20 10 0 EU member states

CIS-middle income

South-East Europe

Other

CIS-low income

ECA Region

Sources: Authorsâ&#x20AC;&#x2122; estimates based on the 2006 LiTS. $PPP data is from the 2005 International Comparisons Program Preliminary Results, December 2007.

Figure 2.7. Distribution of the Poor Across the ECA Region

Share of regon's total poor (percent)

40 35

34

34

30 25 20

16

15 10 6

5

5

Romania

Kazakhstan

Poland

5 0 Turkey

Russia

All other countries

Note: Poor = $PPP 4.30 line.

Sources: Authorsâ&#x20AC;&#x2122; estimates based on the 2006 LiTS. $PPP data is from the 2005 International Comparisons Program Preliminary Results, December 2007.

43

44 World Bank Working Paper

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 percent each) between other urban areas and rural areas. In Romania, Armenia, and the Baltic countries, more than half the urban population is in metropolitan areas, while in Russia and Kazakhstan, Serbia and Bosnia, and Poland, only about one-quarter or less reside there. Disparities in living conditions between urban and rural areas are generally the highest in CISmiddle-income countries, and lowest in EU member states (Table A2.4).25 Thus, depending on the particular poverty line used, about around 53–63 percent of the poor reside in rural regions, even though these areas account for less than 40 percent of the region’s total population (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 upon whether 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 among the unemployed. However, this is not necessarily true across all country groups; for instance, in CIS-middle-income countries, the non-working population face the highest poverty risk (34.7 percent) of all three subgroups, more than twice that among the employed (15.4 percent) (Table 2.8).

Table 2.8. Overall Regional Poverty Rates from the 2006 LiTS Group EU member states South-Eastern Europe CIS-low income countries CIS-middle-income countries Other Overall

Employed 13.2 19.9 64.0 15.4 44.8 24.8

Unemployed 29.3 40.1 75.5 26.6 50.2 43.8

Not working 23.4 32.7 72.9 34.7 58.6 44.9

Overall 18.2 27.3 69.3 21.3 51.8 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 households where the respondent was employed—i.e. among the “working-poor” (Table 2.9). Additional information pertaining to the profile of the poor in the ECA region is provided in Annex Tables A2.1–A2.13.

25. The considerable metropolitan/rural divide in living conditions in CIS-middle countries is corroborated by the average satisfaction with life (SWL) score of 3.5 in metropolitan vs. 2.9 in rural areas using 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).

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

45

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 27.0 100.0 states South-East 39.9 19.9 40.2 100.0 59.2 10.8 30.0 100.0 Europe CIS-low 45.4 18.6 36.1 100.0 55.3 13.2 31.5 100.0 income CIS-middle 59.7 7.8 32.6 100.0 78.4 5.2 16.5 100.0 income Other Total

40.2 46.7

8.3 11.1

51.5 42.3

100.0 100.0

53.2 69.1

8.9 7.0

38.0 23.9

100.0 100.0

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 recovered and are now higher than their pre-transition levels, labor market conditions remain difficult in many countries (see World Bank 2005a, 2005b). Employment ratesâ&#x20AC;&#x201D;the share of the working-age population this is employedâ&#x20AC;&#x201D;continue to be very low, in many cases well below the so-called Lisbon target of 70 percent by the year 2010 set by the European Commission (EC) its member states. The LiTS data confirm the relatively low level of labor force participation in most countries in the region: overall, only about 56 percent of respondents reported having worked during the 12 month period preceding the survey. These rates were generally 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 characteristics, and clearly illustrates the important role played by factors like age, gender, educational background, etc. in influencing the likelihood of respondents having worked during 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 important correlate of work status, with the data showing a clear inverted-J shaped ageprofile. 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 education level are about five times as likely to have worked as compared to those with no education.

46 World Bank Working Paper

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

Russia Belarus Latvia Czech Republic Estonia Slovenia Ukraine Kazakhstan Bulgaria Uzbekistan Mongolia Lithuania Serbia Hungary Slovakia Kyrgyz Republic Croatia Romania Moldova Tajikistan Poland Montenegro Albania Macedonia Bosnia Georgia Turkey Azerbaijan Armenia 0

20

40

60

80

Percent of respondents 15-64 years

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

By Age group 18–30 yrs 31–40 yrs 41–50 yrs 51–60 yrs 61–70 yrs 71+ yrs All ages Men Women

No degree / no education 14.5 34.9 26.3 28.3 7.1 2.4 14.5

Compulsory / secondary / vocational 59.0 74.8 71.1 51.4 10.5 3.0 53.1

Higher professional / post-graduate 80.0 85.9 90.7 69.5 29.6 9.4 75.3

Overall 63.2 75.7 74.6 54.6 12.9 3.4 55.6 66.9 45.5

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47

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 the region, and pensions for about one-fourth of all households (see Table 2.11).26 Wages are relatively more important for the CIS middle-income countries, with 62 percent of the respondents citing wages as the main income source. Pensions account for the second main income source and are relatively more important for the EU countries where more than one-third of the respondents report it as the main source of income. Not surprisingly, self-employment agricultural 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 are taken out, transfersâ&#x20AC;&#x201D;through various social assistance programsâ&#x20AC;&#x201D;are a relatively small part of household incomes. While this broad pattern prevails across the region, inter-country differences are distinct (see Figure 2.9).27 What is also interesting is that it is only in Turkey where self-employed income has a stronger importance, perhaps reflecting the historical legacy of market enterprise and entrepreneurship in Turkey. But the small size of respondents in other subregions in transition countries citing self-employment income, whether agriculture or non-agriculture, as a primary source of income suggests that efforts to promote entrepreneurship and the growth of 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 pensions. The odds-ratio provides a measure of the likelihood of each of the group factors contributing to poverty relative to the other group. The use of the bottom one-third, rather than an absolute measure of poverty common across countries, provides a sense of relative poverty in each country. The data clearly suggest that pensioners, agriculture workers, and those on transfers have greater likelihood of being poor. Interestingly, income from friends and family are no more important for the poor than for the overall population (contrast with top one-third or non-poor).

The sectoral pattern of employment of the bottom one-third who rely upon wages as the primary source of income suggest a clear divergence with those of the non-poor. The working poor in wage employment are more likely to be found in agriculture and other primary 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 as well as to report which of these was the most important income source for their household. (1) income from wages in cash, (2) wages in kind, (3) income from self-employment, (4) sales or bartering of farm products, (5) pensions, (6) unemployment benefits, (7) investments, savings, rental of property, (8) stateprovided social benefits, (9) community/privately provided social benefits, (10) help from relatives/friends in the country, (11) help from relatives/friends abroad, (12) stipend income, (13) help from charities and NGOs, and (14) other sources.

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

48 World Bank Working Paper

Figure 2.9. Inter-Country Differences in Main Income Sources of Households Main Source of Income of the Household Russia Belarus Kazakhstan Ukraine Slovakia Estonia Slovenia Latvia Bulgaria Czech Republic Hungary Romania Lithuania Serbia Croatia Bosnia Montenegro Poland Azerbaijan Moldova Mongolia Macedonia Armenia Tajikistan Uzbekistan Turkey Georgia Albania Kyrgyz Republic 0

20

40

60

80

Percent of respondents Wages

SE non-agri

Agriculture

Pensions

Friends/family

Transfers

Other

100

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49

Table 2.11. Main Income Source by Region Percent of respondents reporting category as main income source: Country Group (i) All households: EU member states South-Eastern Europe CIS-low income CIS-middle income Other

Wages

Overall (ii) Poorest one-third: EU member states South-Eastern Europe CIS-low income CIS-middle income Other Overall Odds-ratio (ii) / (i): EU member states South-Eastern Europe CIS-low income CIS-middle income Other Overall

Self-empl. non-agri. Agriculture Pensions

Friends/ family Transfers Other

Total

49.1

8.0

0.9

34.9

2.2

3.5

1.4

100.0

44.2 35.3 62.5 39.1

11.2 19.2 4.8 15.9

3.7 9.1 1.2 9.2

26.0 18.5 26.4 24.0

8.3 10.2 3.4 4.3

3.1 2.1 0.6 3.3

3.4 5.6 1.1 4.3

100.0 100.0 100.0 100.0

51.9

9.4

3.7

26.5

4.3

1.9

2.4

100.0

31.6

3.1

0.5

54.7

2.2

6.3

1.6

100.0

27.6 25.1 34.1 22.8

7.2 14.5 2.0 15.7

4.7 10.8 2.0 13.5

39.2 32.3 57.1 32.5

10.1 8.3 3.3 6.5

6.9 3.4 0.8 5.2

4.4 5.6 0.8 3.8

100.0 100.0 100.0 100.0

30.3

6.3

5.0

48.7

4.5

3.1

2.1

100.0

0.64

0.38

0.50

1.57

0.98

1.81

1.18

1.00

0.62 0.71 0.55 0.58 0.58

0.64 0.76 0.41 0.99 0.68

1.26 1.19 1.72 1.47 1.35

1.51 1.74 2.16 1.35 1.84

1.21 0.81 0.96 1.53 1.05

2.19 1.65 1.34 1.57 1.63

1.29 1.00 0.67 0.89 0.89

1.00 1.00 1.00 1.00 1.00

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

A more detailed analysis of the correlates of poverty using an ordered probit of the likelihood of being in the bottom one third of per capita expenditures (Table 2.12), suggests the following: â&#x2013; The likelihood of being relatively poor is highest for pensioners, farmers (especially in the CIS middle-income countries), and those dependent on transfers (except for Turkey). â&#x2013; The likelihood of relative poverty is lower for the working people, for those in selfemployed non-agriculture, the better educated (especially those with higher professional or postgraduate degrees), and those in urban or metropolitan areas.

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

(1 = poorest one-third income group ranked by PCE) Overall Household size Education level: No degree / no education Compulsory school education Secondary education Professional, vocational school Higher professional degree Post graduate degree Age category: 18–30 yrs 31–40 yrs

EU

SEE

CIS_L

CIS_M

coef 0.053***

sd 0.005

coef 0.025*

sd 0.013

coef 0.074***

sd 0.012

coef 0.043***

sd 0.008

0.126***

0.038

0.108

0.091

0.178**

0.075

0.026

0.098

coef 0.063***

Other sd 0.019

coef 0.057***

sd 0.015

−0.169

0.162

0.241***

0.071

Reference category

−0.180***

0.025

−0.272***

0.047

−0.365***

0.062

−0.043

0.051

−0.229**

0.091

−0.229***

0.066

−0.248***

0.024

−0.308***

0.043

−0.215***

0.054

−0.131**

0.058

−0.341***

0.088

−0.432***

0.064

−0.527***

0.030

−0.625***

0.057

−0.480***

0.072

−0.382***

0.062

−0.680***

0.097

−0.661***

0.088

−0.832***

0.124

−1.140***

0.197

−0.082

0.312

−0.455

0.327

−0.811**

0.382

−1.082***

0.355

0.061

0.040

0.048

0.042

0.076

0.017

0.027

0.097

Reference category 0.060 −0.105**

0.125

0.078

50 World Bank Working Paper

Table 2.12. Probit Model of Likelihood of Being Poor

0.111*** 0.177*** 0.264*** 0.534*** −0.154***

0.027 0.028 0.033 0.036 0.020

0.339*** 0.251*** 0.312*** 0.594*** −0.244***

0.059 0.058 0.066 0.069 0.045

0.064 0.127** 0.110 0.426*** −0.208***

−0.123*** 0.250*** 0.565*** 0.070* 0.727*** 0.144***

0.029 0.038 0.026 0.040 0.052 0.053

−0.371*** −0.176 0.493*** 0.306** 0.756*** 0.207

0.078 0.162 0.053 0.149 0.099 0.146

−0.197*** 0.396*** 0.422*** 0.052 0.723*** 0.154

−0.177***

0.019

−0.035

0.035

−0.064

−0.521*** −0.528***

0.025 0.038

−0.319*** −0.528***

0.045 0.078

−0.028 0.115** 0.231*** 0.426*** −0.093**

0.048 0.056 0.063 0.074 0.036

0.219*** 0.473*** 0.563*** 0.776*** −0.088

0.073 0.081 0.096 0.113 0.060

0.030 0.067 0.244*** 0.561*** −0.130**

0.078 0.081 0.090 0.095 0.057

Reference category 0.063 −0.080* 0.092 0.165*** 0.060 0.609*** 0.079 −0.048 0.103 0.660*** 0.109 0.065

0.048 0.057 0.053 0.062 0.114 0.082

−0.212** 0.597*** 0.700*** 0.186 0.549** 0.273

0.101 0.157 0.080 0.148 0.255 0.242

0.025 0.229** 0.558*** 0.452*** 0.713*** 0.223

0.084 0.103 0.071 0.137 0.123 0.144

0.042

−0.536***

0.050

−0.203***

0.054

0.049 0.075

−1.305*** −0.299**

0.103 0.127

−0.588*** −0.573***

0.062 0.103

0.059 0.063 0.076 0.089 0.046

0.044 −0.200*** Reference category −0.335*** 0.062 −0.508*** −0.576*** 0.086 −0.505***

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

41–50 yrs 51–60 yrs 61–70 yrs 71+ yrs Worked during past 12 months Main income source: Wages SE non-agriculture Agriculture Pensions Friends/family Transfers Other Place of residence: Urban areas Rural areas Metropolitan Constant

51

52 World Bank Working Paper

Some illustrative estimates from the above model: Simulated type Random respondent: Wage employees in capital:

Pensioners:

Rural Farmers:

EU members SEE CIS low CIS middle Other EU members SEE CIS low CIS middle Other EU members SEE CIS low CIS middle

Other Respondent 61â&#x20AC;&#x201C;70 years old, with no education, did not work in past 12 months, pensions main income source, lives in rural areas Respondent 26 years old, with post-graduate degree, worked in past 12 months; wages main income source, lives in metropolitan area

Predicted probability that person is poor: 0.33 0.17 0.19 0.16 0.03 0.12 0.42 0.43 0.50 0.49 0.42 0.22 0.45 0.39 0.62 0.39

0.73

0.04

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 most countries in ECA, transfers reach out to a significant part of the population in the CIS countries and in the EU member states (see Table 2.13). But they play only a small role in the incomes of the population, including the poor. This suggests that state-provided social benefits appear to be largely untargeted transfers, probably because of family allowances which tend generally to be untargeted transfers, and that their levels are inadequate. However, in the relatively richer ECA countries of the EU and in SEE, transfers are still reported as the primary income source by 3â&#x20AC;&#x201C;4 percent of the population.

Concluding Observations Our analysis above indicates that the welfare measure derived from the LiTS provides a very useful and effective means to measure household welfare and compare both within as well as across 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.

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

53

Table 2.13. Public/Private Transfers Are More Important in the CIS and EU Member States Percent of respondents receiving transfer payment Country Group CIS-middle CIS-low income EU member states Other South-East Europe Overall

State provided social benefits 12.7 12.2 9.4 9.6 4.1 11.1

Unemployment benefits 1.1 0.6 2.9 0.9 3.3 1.4

Community/privately provided benefits 0.1 0.6 3.4 0.7 0.4 0.8

Charities/NGOs 0.3 0.9 0.9 0.3 0.7 0.5

Percent of respondents reporting transfers as main income source Country Group South-East Europe EU member states Other CIS-low income CIS-middle income Overall

Poor 6.9 6.3 5.2 3.4 0.8

Middle 2.0 2.6 2.6 1.7 1.0

Rich 0.9 1.7 2.3 1.3 0.1

Overall 3.1 3.5 3.3 2.1 0.6

3.1

1.7

1.0

1.9

The profile suggests a diverse region, with significant differences among countries in the incidence of poverty but a preponderance of the poor in the more populous middle-income countries. Quantitative measures of poverty correspond well with the populationâ&#x20AC;&#x2122;s own subjective view of relative income status. Not surprisingly, poverty is correlated with low educational attainment, lack of skills as well as self-employment in agriculture. But, of some concern, is the finding that opportunities for the poor to upgrade their human capital and skills, or access finance and economic opportunities, may be limited by a distinct disadvantage in asset ownership. Even though income inequality is moderate in the region, this may be accentuated by the asset inequality.

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

First, are those who are working for wages, especially the poor, but whose wages may be low. In some cases, the low wages reflect the low educational and skill level of the population which may condemn such workers to low productivity employment. Public policy can help in strengthening the quality of public education, especially when it comes to the poor, providing incentives for firms to invest in lifelong learning so as to create opportunities for educational progression in life, and eliminating any barriers that may exist to the easy movement of 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 primarily on pensions for their income and who are at risk of poverty without these. For pensions, it is clear that public policyâ&#x20AC;&#x201D;though an appropriate combination of public and private

54 World Bank Working Paper

financingâ&#x20AC;&#x201D;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. Governments need to ensure that pensions cover all the population at an adequate, fiscally affordable rate.

Third, are those who are unemployed and rely upon public and private benefits to sustain themselves. To the extent that employment rates remain low in many countries and job creation 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 are unemployable, as may be the case because of skill obsolescence and mismatches, adequate unemployment benefits are needed to protect them from poverty and maintain minimum living standards. But public policy needs to be sufficiently discerning to prevent dependency on benefits and discourage labor market participation.

Finally, public policy should also address the issue of asset inequality which could lead to an inequality of opportunity.28 For instance, lack of ownership of a car in the absence of adequate and affordable public transport system could limit the poorâ&#x20AC;&#x2122;s ability to access better paying jobs. Similarly, lack of ownership of housing assets could limit access to finance and therefore the potential opportunities for entrepreneurship and self-employment income.

Given the important role that mobile telephones and internet connectivity is playing in todayâ&#x20AC;&#x2122;s economy and helping to bring the economic divide, any significant disadvantage for the 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 programs for promoting housing ownership for low-income families, and ensuring competition in product markets to ensure affordable telephone and internet connectivity.

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

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

55

Annex: Tables Table A2.1. Overall Poverty Rates by Region

Poverty Line = PPP$2.15 ECA Region Urban Rural EU member states Urban Rural South-Eastern Europe Urban Rural CIS-low income Urban Rural CIS-middle Urban Rural Other Urban Rural Poverty Line = PPP$4.30 ECA Region Urban Rural EU member states Urban Rural South-Eastern Europe Urban Rural CIS-low income Urban Rural CIS-middle Urban Rural Other Urban Rural

Squared Poverty Gap(P2)

Headcount Rate(P0)

Poverty Gap(P1)

10.5 6.3 17.4 2.3 2.0 2.6 8.1 6.0

3.2 1.7 5.5 0.6 0.5 0.7 3.3 2.9

1.5 0.8 2.7 0.3 0.2 0.3 2.2 2.1

10.6 30.1 21.3 36.8 3.9 2.1 7.4 20.2 12.8 32.5

3.9 10.1 7.6 11.9 1.1 0.5 2.3 5.9 3.2 10.4

2.2 5.0 4.0 5.7 0.5 0.2 1.1 2.7 1.2 5.0

33.6 25.7 46.5 18.2 15.8 22.2 27.1 20.5 34.7 69.2 56.0 79.1 21.3 15.6 32.2 52.2 42.7 68.0

12.6 8.5 19.2 5.0 4.4 6.1 10.2 7.8 12.9 31.1 23.8 36.6 6.4 4.0 11.0 21.4 15.4 31.5

6.5 4.1 10.4 2.0 1.7 2.5 5.6 4.5 6.9 17.7 13.3 20.9 2.9 1.6 5.3 11.5 7.5 18.1

56 World Bank Working Paper

Table A2.2. Sensitivity of Poverty Rates with Respect to Choice of Poverty Line Poverty Incidence(P0)

Change from Actual (%)

10.5 11.6 12.6 15.1 9.3 8.4 6.1

0.00 10.32 19.41 42.75 −11.41 −20.07 −42.04

33.6 36.0 38.3 42.8 31.6 29.2 24.2

0.00 7.16 13.90 27.33 −6.09 −13.05 −27.97

Poverty Line = PPP$2.15 Actual +5% +10% +20% −5% −10% −20% Poverty Line = PPP$4.30 Actual +5% +10% +20% −5% −10% −20%

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

Poverty Line = PPP$2.15 ECA Region Urban Rural EU member states Urban Rural South-Eastern Europe Urban Rural CIS-low income Urban Rural CIS-middle Urban Rural

Poverty Headcount Rate

Distribution of the Poor

Distribution of Population

10.5 6.3 17.4 2.3 2.0 2.6 8.1 6.0 10.6 30.1 21.3 36.8 3.9 2.1 7.4

100.0 37.1 62.9 100.0 56.3 43.7 100.0 39.2 60.8 100.0 30.4 69.6 100.0 35.8 64.2

100.0 61.8 38.2 100.0 62.0 38.0 100.0 53.5 46.5 100.0 43.0 57.0 100.0 65.9 34.1 (continued )

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

57

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

Other Urban Rural Poverty Line = PPP$4.30 ECA Region Urban Rural EU member states Urban Rural South-Eastern Europe Urban Rural CIS-low income Urban Rural CIS-middle Urban Rural Other Urban Rural

Poverty Headcount Rate 20.2 12.8 32.5

Distribution of the Poor 100.0 39.5 60.5

Distribution of Population 100.0 62.5 37.5

33.6 25.7 46.5 18.2 15.8

100.0 47.2 52.8 100.0 53.6

100.0 61.8 38.2 100.0 62.0

22.2 27.1 20.5 34.7 69.2 56.0 79.1 21.3 15.6 32.2 52.2 42.7 68.0

46.4 100.0 40.4 59.6 100.0 34.8 65.2 100.0 48.4 51.6 100.0 51.1 48.9

38.0 100.0 53.5 46.5 100.0 43.0 57.0 100.0 65.9 34.1 100.0 62.5 37.5

58 World Bank Working Paper

Table A2.4. Rural Urban Disparities Average PCE (US$) Country Metropolitan Urban Russia 6,093 2,787 Romania 3,255 1,856 Armenia 2,292 1,243 Belarus 2,869 2,568 Kazakhstan 2,947 2,130 Bulgaria 3,124 2,074 Ukraine 4,058 2,613 Lithuania 4,188 2,896 Kyrgyz Re 1,649 975 Moldova 1,918 1,353 Tajikistan 1,281 785 Georgia 1,828 1,165 Azerbaijan 1,549 1,155 Serbia 4,001 2,667 Mongolia 1,466 856 Albania 3,175 2,593 Bosnia 3,700 3,227 Croatia 6,423 5,602 Turkey 3,257 2,855 Poland 5,001 3,418 Hungary 4,087 3,549 Czech Rep 5,523 4,419 Montenegro 5,163 3,950 Uzbekistan 878 766 Latvia 4,335 3,820 Slovenia 7,990 6,192 Slovakia 4,269 3,636 Estonia 4,719 3,499 Macedonia, FYR 2,575 2,186 Overall 4,067 2,774

As Ratio of National Average

Rural Overall Metropolitan Urban Rural Overall 2,322 3,108 1.96 0.90 0.75 1.00 1,449 2,372 1.37 0.78 0.61 1.00 1,035 1,604 1.43 0.77 0.65 1.00 1,314 2,203 1.30 1.17 0.60 1.00 1,365 1,951 1.51 1.09 0.70 1.00 1,546 2,125 1.47 0.98 0.73 1.00 2,051 2,687 1.51 0.97 0.76 1.00 2,178 3,234 1.29 0.90 0.67 1.00 874 1,060 1.56 0.92 0.82 1.00 1,025 1,302 1.47 1.04 0.79 1.00 703 787 1.63 1.00 0.89 1.00 1,014 1,315 1.39 0.89 0.77 1.00 887 1,136 1.36 1.02 0.78 1.00 2,360 2,752 1.45 0.97 0.86 1.00 884 1,095 1.34 0.78 0.81 1.00 2,011 2,459 1.29 1.05 0.82 1.00 2,432 2,860 1.29 1.13 0.85 1.00 4,247 5,260 1.22 1.07 0.81 1.00 2,189 2,784 1.17 1.03 0.79 1.00 3,375 3,642 1.37 0.94 0.93 1.00 2,800 3,455 1.18 1.03 0.81 1.00 4,113 4,556 1.21 0.97 0.90 1.00 3,856 4,173 1.24 0.95 0.92 1.00 674 721 1.22 1.06 0.93 1.00 3,408 3,870 1.12 0.99 0.88 1.00 6,304 6,531 1.22 0.95 0.97 1.00 3,376 3,577 1.19 1.02 0.94 1.00 3,800 4,045 1.17 0.87 0.94 1.00 2,321 2,131

2,308 2,804

1.12 1.45

0.95 0.99

1.01 0.76

1.00 1.00

Metrop./ Rural 2.62 2.25 2.21 2.18 2.16 2.02 1.98 1.92 1.89 1.87 1.82 1.80 1.75 1.70 1.66 1.58 1.52 1.51 1.49 1.48 1.46 1.34 1.34 1.30 1.27 1.27 1.26 1.24 1.11 1.91

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

59

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

ECA Region Urban Rural Lowest quintile 2 3 4 Highest quintile EU member states Urban Rural Lowest quintile 2 3 4 Highest quintile South-Eastern Europe Urban Rural Lowest quintile 2 3 4 Highest quintile

Mean per capita expenditure 3,939 4,546 2,955 1,001 1,958 2,940 4,427 9,372 4,426 4,693 3,991 1,156 1,978 2,968 4,445

CIS-low income Urban Rural Lowest quintile 2 3 4 Highest quintile CIS-middle Urban Rural Lowest quintile 2 3 4

Mean per capita expenditure 1,990 2,534 1,581 944 1,913 2,892 4,304 8,359 4,892 5,555 3,610 1,080 1,988 2,947 4,467

8,664 3,899 4,379 3,346 952 1,977 2,936 4,423 8,597

Highest quintile Other Urban Rural Lowest quintile 2 3 4 Highest quintile

9,823 2,726 3,172 1,982 976 1,923 2,921 4,304 8,559

60 World Bank Working Paper

Table A2.6. Decomposition of Inequality by Geographic Region Overall inequality Overall ECA Region EU member states South-East Europe CIS-low income CIS-middle income Other Within group inequality Overall ECA Region Between group inequality Overall ECA Region Between group inequality as % of overall inequality Overall ECA Region

GE(0)

GE(1)

GE(2)

31.4 19.7 24.5 27.9 27.7 29.4

31.5 20.1 23.4 28.4 29.3 29.6

46.4 25.3 30.5 40.7 43.9 40.6

31.4

31.5

46.4

4.5

4.1

3.8

14.4

12.9

8.2

Table A2.7. Ratios of Selected Expenditure Percentiles in Urban and Rural Areas ECA Region EU member states South-Eastern Europe CIS-low income CIS-middle Other

p10 1.59 1.07 1.40 1.19 1.53 1.57

p25 1.60 1.15 1.39 1.34 1.44 1.63

p50 1.50 1.16 1.33 1.45 1.51 1.66

p75 1.52 1.17 1.35 1.60 1.51 1.53

p90 1.49 1.20 1.28 1.74 1.53 1.68

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

61

Table A2.8. Poverty by Age Groups 0–5 yrs 6–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64

Poverty Headcount Rate 50.8 45.1 34.0 27.8 29.1 31.6 30.0 26.6 26.2 27.3 27.8 31.6

Distribution of the Poor 10.4 16.1 8.1 6.1 7.0 7.0 6.8 5.9 5.7 5.3 5.1 3.9

Distribution of Population 6.9 12.0 8.0 7.4 8.1 7.5 7.6 7.5 7.3 6.6 6.2 4.2

65+ yrs Overall

38.9 33.6

12.3 100.0

10.6 100.0

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

Poverty Line = $PPP4.30 Yes No Overall

Poverty Headcount Rate

Distribution of the Poor

Distribution of Population

25.7 44.5 33.6

44.2 55.8 100.0

57.9 42.1 100.0

Table A2.10. Poverty by Education Level of Household Head Poverty Line = $PPP4.30 Highest Educational Attainment No degree/no education Compulsory school education Secondary Professional/vocational school Higher professional degree Post-graduate degree Overall

Poverty Headcount Rate

Distribution of the Poor

Distribution of Population

69.5 52.1 35.6 24.0 16.4 3.7 33.6

14.1 29.0 26.2 21.6 9.1 0.1 100.0

6.8 18.7 24.8 30.2 18.6 0.9 100.0

62 World Bank Working Paper

Table A2.11. Poverty by Household Headâ&#x20AC;&#x2122;s Gender

Poverty Line = $PPP4.30 Male Female Overall

Poverty Headcount Rate

Distribution of the Poor

Distribution of Population

35.5 28.4 33.6

77.2 22.8 100.0

73.1 26.9 100.0

Table A2.12. Poverty by Demographic Composition Poverty Line = $PPP4.30 Number of children 0â&#x20AC;&#x201C;6 years old no children 1 2 3 or more children Household size 1 2 3 4 5 6 7 or more Overall

Poverty Headcount Rate

Distribution of the Poor

Distribution of Population

27.3 43.2 57.2 90.8

59.2 23.6 10.8 6.5

72.9 18.4 6.3 2.4

18.0 22.0 24.2 31.6 55.0 71.7 82.9 33.6

5.9 14.8 16.6 21.1 16.7 10.5 14.4 100.0

11.0 22.7 23.0 22.4 10.2 4.9 5.9 100.0

63

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

Table A2.13. Consumption Regressions Urban Household characteristics Log of household size Log of household size squared Share of children 0–6 Share of children 7–16 Share of male adults Share of female adults Share of Elderly (≥=60) Country Group EU member states South-Eastern Europe CIS-low income CIS-middle Other Characteristics of household head Log of household head’s age Gender Male Female Education of the household head No degree/no education Compulsory school education Secondary Professional/vocational school Higher professional degree Post-graduate degree Employment status of the household head Yes No _cons Number of observations Adjusted R2 Note: 0.01—***; 0.05—**; 0.1—*;

Rural

coef

se

coef

se

−0.255*** −0.066*** (dropped) 0.388*** 0.698*** 0.658*** 0.416***

0.03 0.02

0.04 0.02

0.05 0.05 0.05 0.06

−0.321*** −0.026 (dropped) 0.334*** 0.575*** 0.564*** 0.320***

(dropped) 0.028* −0.626*** −0.110***

0.02 0.02 0.02

(dropped) −0.027 −0.787*** −0.456***

0.02 0.02 0.02

−0.115***

0.02

−0.342***

0.02

−0.314***

0.02

−0.239***

0.03

(dropped) −0.069***

0.01

(dropped) −0.070***

0.02

(dropped) 0.098*** 0.344*** 0.346*** 0.570***

0.03 0.03 0.03 0.03

(dropped) 0.076*** 0.263*** 0.282*** 0.488***

0.02 0.03 0.03 0.03

0.672***

0.05

0.707***

0.11

(dropped) −0.170*** 8.990*** 15,633 0.325

0.01 0.08

0.06 0.06 0.06 0.06

(dropped) −0.116*** 0.01 8.801*** 0.11 11,276 0.355

CHAPTE R 3

Satisfaction with Publicly provided Health Services in Eastern Europe and the Former Soviet Union29 n this paper we explore citizens’ satisfaction with publicly provided health services (PPHS) in the Eastern Europe and Central Asia region. In particular, we focus our analysis 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 with quality and efficiency of medical treatment received; and (c) how does the prevalence of informal payments impact people’s decision on using PPHS, and upon use, the level of satisfaction with services received?

The 2006 LiTS provides us with a unique opportunity to pose these questions. In addition to eliciting respondents’ perceptions of the quality and efficiency of PPHS, the survey explores priorities for public policies and investment, attitudes to a market economy and democracy, as well as living standards (including expenditure aggregates and sources of livelihood), 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 ‘subjective’ 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, called the “Semashko” model after the Soviet statesman who envisaged it, that was state owned and financed through general taxation. Although the system delivered quick improvements in population health when first implemented, the absence of market forces (or alternate mechanisms) to ensure provider and administrator accountability for health outcomes led to hospital-dominated networks that adapted very slowly, if at all, to changing disease patterns and medical innovations. The fall of the Soviet Union in 1991 and the

I

29. Ramya Sundaram and Salman Zaidi.

65

66 World Bank Working Paper

resulting macroeconomic contraction in this region led to severe shortfalls in health sector budgets. Countries began to undertake health sector reforms as they moved along the transition path, partly necessitated by the budget cuts, and partly in keeping with the other fundamental social and economic changes occurring in the region. Strong economic growth in recent years, the fruition of early reform efforts and the continuation of further reforms have resulted in a health sector that is extremely dynamic and diverse across the region, featuring a pluralism of health systems. Against this backdrop of transition and reforms, how satisfied 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 satisfied with the services they received from the publicly provided health system. Reported satisfaction rates vary considerably by country, with the “net satisfaction rate”—the difference between the share of satisfied and dissatisfied respondents—generally a little higher among EU 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 in this region have enjoyed relative political stability through the transition, and, with the exception 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 the exception of Romania, EU member states are at or near the top with regards to satisfaction with PPHS.

Figure 3.1. Rates of Satisfaction with the Publicly-provided Health System, By Country Slovenia Czech Republic Croatia Georgia Slovakia Armenia Lithuania Estonia Latvia Bulgaria Hungary Azerbaijan Bosnia Poland Belarus Turkey Mongolia Montenegro Macedonia Uzbekistan Russia Serbia Kyrgyz Republic Kazakhstan Romania Moldova Tajikistan Ukraine Albania 0

20

40

60

80

Percent of respondents Very dissatisfied

Dissatisfied

Neither

Satisfied

Very satisfied

100

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

67

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 broad based 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 payment mechanisms (Bonilla-Chacin, Murrugarra, and Temourov 2005) and have experienced a drop in informal payments (see the discussion on informal payments in a later section of this chapter). In contrast, the Kyrgyz republic, another low-income CIS country which has instituted far reaching health sector reform, but which has experienced tepid growth, ranks low in satisfaction. The scenario is reversed in Azerbaijan, which has experienced spectacular, but narrow based economic growth, and where the health sector reform has not been substantial. 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 rankings, 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 medical practices continuing to deviate considerably from internationally established evidence-based medicine.

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

One of the key findings of this analysis relates to the perception that unofficial payments are needed to obtain services. It is well documented that, with the decrease in public funding of the health care system, informal payments have emerged as a fundamental aspect 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 informal payments is complicated by the existence, particularly in the CIS countries, of strong traditions of innocent gift-giving as an expression of gratitude, usually after service is provided. The LiTS does not separate informal payments from gifts in its questionnaire; thus the results associated with informal payments that are presented in this paper should be interpreted with some caution in light of this omission.

We find that having to pay for essentially “free” services has a significant negative influence on satisfaction with PPHS. Our empirical model indicates that, other things being equal, respondents who say unofficial payments are often necessary are about 1.4 times more likely to report being dissatisfied with service delivery in the publicly provided health system compared to those who say that such payments are never needed. A comparison of the 2006 ECA LiTS survey to the 2001 survey referred to in Balabanova and others 2004 (for details, see a later section of this chapter) suggests that there has been a dramatic fall in the prevalence of informal payments in both Georgia and Armenia between 2001 and 2006, while there is an increase in Ukraine and Russia. This could go some distance in explaining why satisfaction rates are high in Georgia and Armenia, and low in Russia and Ukraine, although the total (public and private) PPP adjusted per capita health spending in 2004 is lower both in Georgia ($137) and Armenia ($211) when

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compared to Russia ($546) and Ukraine ($382). Similarly, a comparison of the LiTS to Lewis (2000) suggests that the incidence of informal payments have increased in Albania between 1996 and 2006; Albania ranks the lowest among all countries in terms of satisfaction with PPHS. The second key finding of this analysis relates to accessing PPHS. We find that those who 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 trusts the government and the police is about 7.5 percent more likely than a person who does not to have used PPHS in the past 12 months. Political scientists have theorized that a positive experience with public services leads to satisfaction and trust in the government (for instance, see Bratton 2007; Bouckaert and Van De Walle 2003). While we find a positive correlation 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 effect on accessing PPHS. The results have broad policy implications. Rapid and broad based economic growth accompanied by sensible health sector reform increases user satisfaction with PPHS. A two-pronged approach to health sector reform is suggested, particularly in middle and lowincome CIS countries. Firstly, in addition to improving the actual quality of service provided by improving primary care facilities and encouraging the practice of evidence-based medicine, the reform effort should address the complex set of circumstances that underlie the prevalence of informal payments. Providing citizens’ with means to hold providers directly accountable for quality of service, and for health outcomes, should remove some of the incentives for these payments. Secondly, a good communication strategy should be a key component of any effective health reform effort.

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

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

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

Well functioning PPHS are perceived as important by most ECA citizens. When asked about their first priority for extra government spending, 40 percent of LiTS respondents chose health care (Figure 3.2). This equals the sum of respondents who identify education and 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 of respondents in the EU member states (44 percent) and the lowest in South-East Europe and Turkey (33 and 29 percent).

The remainder of the paper is laid out as follows: the next section provides Figure 3.2. Priorities for Additional Government some background on the Spending: 2006 LiTS evolution of PPHS in ECA First Priority countries; section two describes the LiTS data used in the analysis along with sumOther mary tables and graphs of Housing key variables. In section three, the empirical stratHealth Pensions egy is described, while the results of the estimation are Environment presented in section four.

Section five probes into the Infrastructure Education strong performance of some low-income CIS countries in terms of satisfaction with PFHS, while section seven concludes.

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 the era of communism, and the system sought to provide universal care that was free at the point of access. When first implemented, the ‘Semashko’ model led to quick improvements in population health through introduction of practices that prevented the spread of communicable diseases, and through investment in infrastructure and in training physicians.

Until the 1960s, reported measures of life expectancy in this region were comparable to those of Western Europe (Balabanova 2007). In addition to the free, comprehensive health

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

The absence of market forces started becoming evident however, with health care budgets allocated predominantly to hospitals rather than to primary care facilities, and according to historic patterns and fixed norms of number of physicians and beds rather than on the changing health needs of the population. This led to a hospital dominated network of extensive infrastructure and poorly paid personnel that adapted very slowly to changes in disease patterns and innovations in medical technology. The greater failure of the Soviet era health care system was due to the lack of accountability of providers and administrators for quality of care provided and for health outcomes. There were no mechanisms by which sound clinical practices were rewarded or widely disseminated. With the intensification of the cold war, and the increasing isolation of the Soviet Union, there was very little 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 care systems in this region moved further away from evidence based medicine.

The lack of direct accountability also gave rise to alternate mechanisms through which patients could influence the quality and outcome of health services. Even during the communist era, those with connectionsâ&#x20AC;&#x201D;such as the party or military eliteâ&#x20AC;&#x201D;or those willing to provide private monetary incentives, could always ensure superior facilities. Jumping the queue, or expediting service through the use of under-the-table payments, was not uncommon.

The large upheavals that accompanied the break-up of the Soviet Union, and the severe economic downturn in the region during the 1990s, led to drastic cutbacks in government spending on the health sector, with further deterioration in the quality and distribution of health services. Many countries, particularly among the CIS, experienced decreases in reported measures of life expectancy and increases in infant and child mortality during the 1990s.30 There has been a recovery in economic growth in the region more recently, accompanied by varying degrees of health sector reforms in particular economies. Health financing reform has focused on moving from funding health services through general revenues to some form of social insurance system. While the EU member countries have achieved some success in raising money for national health insurance through payroll taxes (with shortfalls being met by transfers from general revenue), low-income CIS countries face challenges due to the narrow employment base given the significance of non-cash as well as informal activities in their economy. Thus health financing has become increasingly reliant on out-of-pocket payments in these countries (Bonilla-Chacin, Murrugarra, and Temourov 2005; Lewis 2000).

Rationalizing the excess human and physical capacity invested in the health system has been met with limited success, particularly among middle-income CIS countries. For instance, 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 be lower now than in 1990. In addition, in Kazakhstan, both infant and child mortality is currently higher than in 1990.

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

Stretching the ever shrinking set of resources across the large networks that exist has led 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 beginning to pose even greater challenges to the outdated hospital-dominated networks (World Bank 2005b). Life expectancy is currently falling in the former Soviet Union, with this region being only one of two regions in the world (the other being sub-Saharan Africa) where life expectancy is currently declining (Balabanova 2007). Against this backdrop of changing disease patterns and macroeconomic instability, the LiTS elicits citizen’s satisfaction with the 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 Eastern Europe and Central Asian countries, and Mongolia. A sample of 1,000 individuals was interviewed 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 LiTS questionnaire, particularly on responses to three sets of questions, which respondents were asked with reference to eight different public services.31 First, respondents were asked: “In your opinion, how often is it necessary for people like you to have to make informal payments/gifts in these situations.” One of the eight situations listed was “Receive medical treatment in the public health system.” Respondents could choose among 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 used these services?” A follow-up question was addressed to those who had used the service during the previous 12 months: “How satisfied were you with the quality and the efficiency of the service/interaction?” Responses to this third question were coded using a progressive 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 marriage certificate, etc), (iii) police (other than road police) (iv) civil courts (v) public health system (vi) public education (tertiary and vocational), (vii) unemployment benefits, and (viii) social security benefits.

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

Received treatment in the publicly provided health system during past 12 months Albania Kazakhstan Hungary Croatia Czech Republic Turkey Azerbaijan Latvia Tajikistan Ukraine Lithuania Macedonia Russia Estonia Romania Moldova Serbia Bulgaria Georgia Montenegro Uzbekistan Bosnia Poland Kyrgyz Republic Armenia Slovenia Mongolia Belarus Slovakia 0

20

40

60

80

Percent of respondents

Utilization of the Public Services in ECA Countries The number of respondents reporting having had an interaction with different services in the last 12 months varies quite considerably by type of service. Of the various services covered, the largest number of affirmative responses is for health, with slightly more than half of those surveyed having had an interaction with PPHS during the 12 month period preceding the date of the interview. The response rate for other services is much smaller32: the service with the second largest response rate is “interacting with the authorities granting official documents (e.g., passport, visa, birth or marriage certificate, etc.),” where a little more than one fifth of those surveyed responded “yes.”

Utilization rates of PPHS varied quite considerably by country, from a high of around two-thirds of all respondents in Albania to one-third only in Slovakia33 (Figure 3.3). While Slovenia 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 vocational education—only 12% of those surveyed reported an interaction with the vocational and tertiary public education system in ECA. 33. It is important to note that the LiTS survey did not enquire in detail about health seeking behavior. Therefore we do not have information on the health status of those we accessed publicly provided health services, or details about those who needed to use the system, but did not do so for various reasons.

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

73

countries such as Belarus, Armenia and the Kyrgyz Republic probably reflect the barriers to 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 systems received the most favorable ratings (Figure 3.4), with 49 percent and 45 percent of all respondents reporting themselves as being satisfied with the education and medical treatment they received (24 and 20 percent respectively reported being dissatisfied with services received).

Figure 3.4. Rates of Satisfaction, By Type of Service Public education Publiclly provided health system Request for documents Social security benef Road police Courts Unemployment benefits Other police 0

10

20

Very unsatisfied

30

40

Unsatisfied

50 Indifferent

60

70 Satisfied

80

90

100

Very satisfied

Reported satisfaction rates with publicly provided health services vary quite considerably by country. The EU member countries typically have higher “net satisfaction rates”— the difference between the share of satisfied and dissatisfied respondents—when compared to the middle-income CIS countries (Ukraine, Kazakhstan, Russia). The low-income CIS countries (Georgia, Armenia, Azerbaijan) also perform a little better than the middleincome 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 often it is necessary for people to have to make unofficial payments/gifts when using public services, a large majority said that such payments are never needed—however for publicly provided health services, the share reporting such payments to be usually/always needed was notably higher than for other services (Figure 3.5).

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

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Figure 3.5. Percent of Respondents that Think that Unofficial Payments Are Needed Publiclly provided health system Road police Public education Request for documents Police Courts Social security benef Unemployment benefits 0

10

20

Never needed

30 Seldom

40

50

60

Sometimes

70

Usually

80

90

Always needed

Figure 3.6. Perceptions Regarding Unofficial Payments in Publicly-provided Health System, By Country Estonia Slovenia Georgia Czech Republic Kazakhstan Turkey Belarus Latvia Croatia Poland Mongolia Serbia Montenegro Armenia Bosnia Slovakia Macedonia Lithuania Bulgaria Russia Kyrgyz Republic Azerbaijan Romania Moldova Hungary Uzbekistan Tajikistan Ukraine Albania 0

10

20

30

40

Percent of respondents Always needed

Often needed

50

100

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

75

Finally, on comparing country rankings of satisfaction with publicly provided health services with prevalence of unofficial payments, the data indicate that prevalence of unofficial payments adversely impacts the level of satisfaction with the service (Figure 3.7)â&#x20AC;&#x201D;this apparent link is subjected to more rigorous examination later on in this paper.

Figure 3.7. Negative Correlation Between Satisfaction and Prevalence of Informal Payments 70

Satisfaction with Publicly Provided Health System

Slovenia

Croatia Armenia

Georgia

Bulgaria

Czech Slovakia

Lithuania

Latvia

Azerbaijan

50

Turkey

Macedonia

Montenegro Mongolia

Belarus

Hungary Kyrgyz

Bosnia

Uzbekistan Romania

Serbia Poland

Moldova

Russia

Albania

40

% above neutral on satisfaction

60

Estonia

Kazakhstan Tajikistan

30

Ukraine

0

20

40

60

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

Estimation Strategy In our estimation, we take into account the fact that responses to satisfaction with PPHS is obtained only from that subsample of individuals who choose to access the service, that is, we correct for the selection bias. While people donâ&#x20AC;&#x2122;t completely control whether they fall ill or not, once ill, they do make some choice about whether to use PPHS. This is confirmed by many studies that research health utilization behavior, which find that not all individuals who should seek treatment do so (Balabanova and others 2004; World Bank 2005a). Next, with regard to the level of satisfaction with PPHS, the ordered nature of the dependent variable suggests the use of an ordered probit or logit model. However, models with

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

We first use a binary probit model to study factors that influence whether or not an individual accesses PPHS during the past 12 months. We compute a correction factor, the inverse mills ratio, from this estimation. At the second stage, we use an ordered probit model to analyze the level of satisfaction with the PPHS, incorporating the correction factor calculated in the first step. In other words, the first equation determines sample selection, which we write as follows: z *i = γ ′w i + u i 0, and zi = 0 otherwise; that is, we observe whether We do not observe z *, i just zi = 1 if z *> i the 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 written as: y *i = β ′x i + ε i 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 provided health service, we observe the level of satisfaction with the service received. Factors that influence the level of satisfaction are included in the vector xi. These include individual-level characteristics such as per-capita expenditure, gender, age, general satisfaction with life, etc, in addition to country-level characteristics such as per capita GDP, growth rate, etc. We can reformulate the model as: Selection equation:

Prob ( z i = 1) = Φ ( γ ′w i )

Prob ( z i = 0 ) = 1 − Φ ( γ ′w i ) Main equation:

Prob ( yij = 1) = Φ ( α j − β ′x i ) − Φ ( α j −1 − β ′x i )

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 hypothesis that the selection and the main equations are independent at the 1 percent level. The variables used in the analysis are summarized below; the results of the selection equation are reported in Table 3.1, and the main equation in Table 3.2.

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

Variable access_health trust logpcexp female over_65 health educ locality country access_health: lpgpcexp: trust female: over_65: health: educ: locality: country:

Obs 28999 28999 28909 28999 28999 28995 28991 28999 28999

Mean .516 .200 7.62 .529 .155 2.79 2.16 1.76 18.8

Std. Dev. .500 .400 .833 .499 .362 .937 .494 .765 7.89

Min 0 0 .98 0 0 1 1 1 1

77

Max 1 1 10.25 1 1 5 3 3 29

Used PPHS during past 12 month: 1 = Yes, 0 = No (log) Per equivalent adult (using OECD scales) annual expenditures Respondent has some or complete trust in the police and in the government/ cabinet of ministers 1 = Female respondent, 0 otherwise Respondent aged over 65 years Self-assessed health status is 1 = “Very good”, 2 = “Good”, 3 = “Medium”, 4 = “Bad”, 5 = “Very bad” 1 = “No education/no degree”; 2 = “Compulsory/secondary education”; 3 = “Higher/ post-graduate” 1 = Rural; 2 = Urban; 3 = Metropolitan 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 per equivalent adult) are more likely to access PPHS than those who are poor. This confirms that monetary costs have some bearing on access to services, and belies the entitlement to universal access expressly stated in many constitutions across this region. Females are also much more likely to use the service than males. This effect likely captures the routine use of 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 the rest of the surveyed population, a finding that is consistent with Balabanova and others (2004). The individual attributes that decreases health care access include self assessed good 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 compared 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 professionals less often than people with higher education. However, a crucial difference between their findings and ours is that they are able to control for differences in morbidity levels. If average morbidity rates among those with higher education are significantly lower than among those with less education, there is no contradiction necessarily between this and our finding that, other things being equal, the likelihood of having accessed the health service during the past 12 months is negatively associated with the level of education of the respondent.

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Table 3.1. Probit for Health Care Access Overall sample Individual-level variables: Log normalized expenditures Trust in government Female Over 65 years age Female and over 65 years of age Health status Very good Good Medium Bad Very bad Locality Urban Rural Metropolitan Education level No education/no degree Compulsory/secondary education Higher professional/post-graduate Other controls: Country-level dummies Pseudo-R2

Coefficient

s.d.

0.112*** 0.092** 0.164*** 0.239*** −0.276***

0.028 0.045 0.039 0.079 0.093

−0.279*** −0.158*** Reference category 0.158*** 0.287***

0.062 0.042

−0.073*

0.041

Reference category −0.105**

0.049

0.054 0.087

Reference category −0.092 0.018

0.073 0.087

. . . not shown here . . .

0.0268

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

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

Trust in Government and Police We find that people who trust the government/police are significantly more likely to access PPHS, compared to those who do not trust the government or police. Given that the LiTS is a cross-sectional survey, one cannot determine whether greater trust in government leads to greater willingness to access public services, or whether this correlation is driven by other unobserved factors. Nevertheless, this finding seems to lend credence to the idea that there exists a social contract between the government and citizens; a functioning government,

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

Thus policies targeting reform of specific public sectors may be handicapped in that they do not try to reform ‘the general culture’ of government. Concurrently reforming public service delivery, along with the improvement in the performance of the police, and other institutions, could lead to an improvement in citizens’ overall perception of the functioning of the government. If this has the effect of encouraging more people to use publicly provided health services, then they would directly experience the improved quality of these 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 how satisfied they were with the quality and efficiency of the medical treatment received. Their responses, coded as follows: 1=Strongly Disagree (SD), 2=Disagree (D), 3= Neither disagree or agree (N), 4=Agree (A), and 5=Strongly Agree (SA), are summarized below, while the results of the second-stage estimation, after including the correction factor calculated from step one of the estimation procedure, are reported in Table 3.2.

The first point to note is that the coefficient of the inverse mills ratio correction factor is significant, indiLevel of Percent Cum. cating that we can safety reject the Satisfaction Freq. (weighted) (weighted) hypothesis that the selection and SD 1,536 11.3 11.3 main equations are independent.

D 2,615 18.7 30.0 The negative coefficient implies that N 3,049 24.5 54.5 unobserved characteristics of responA 6,174 38.3 92.8 dents that affect the likelihood of SA 1,333 7.2 100.0 their having used PPHS during the Total 14,707 100.0 100.0 past 12 months are inversely related to the level of satisfaction with the quality and efficiency of service.

As shown above, the variables at the individual level that enhance satisfaction with PPHS include (i) living in urban areas, (ii) self assessed good health, and (iii) satisfaction with life in 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 system in the post-transition period, the quality of service available in urban centers were vastly superior to service in rural areas. This could well account for the increased satisfaction of urban residents. Self-assessed health status is seen as a fairly good indicator of general health and morbidity. This is corroborated by the finding, above, that those with good self-assessed health status access PPHS less frequently. Once ill, they probably also recover faster, leading to greater satisfaction with treatment received.

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

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Table 3.2. Ordered Probit: Satisfaction with Publicly-provided Health Services Overall sample Inverse Mills Ratio (from selection equation) Individual-level variables: Log normalized expenditures Female Age group 19–30 years 31–40 years 41–50 years 51–60 years 61–70 years 71+ years Health status Very good Good Medium Bad Very bad Satisfied with life Strongly disagree Disagree Neither disagree nor agree Agree Strongly agree How often are unofficial payments needed Never Seldom Sometimes Usually Often Locality Urban Rural Metropolitan Education level No education/no degree Compulsory/secondary education Higher professional/post-graduate

Coefficient −3.074***

s.d.

0.231

−0.190*** −0.167***

0.021 0.025

0.021 Reference category 0.097*** 0.086*** −0.028 0.000

0.027

0.605*** 0.482*** Reference category −0.248*** −0.632***

0.059 0.032

−0.285*** −0.072*** Reference category 0.215*** 0.386***

0.033 0.026

0.127*** 0.027 Reference category −0.080*** −0.134***

0.026 0.029

0.028 0.031 0.035 0.041

0.033 0.060

0.024 0.036

0.029 0.030

0.060** 0.023 Reference category 0.039 0.031 Reference category 0.075* 0.042 −0.169*** 0.046 (continued )

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

81

Table 3.2. Ordered Probit: Satisfaction with Publicly-provided Health Services (Continued) Overall sample Country-level variables: Per capita GDP 2005 (US$ ‘000) Growth rate of per capita GDP (2004 to 2005) Other controls: Country-level dummies Pseudo-R2

Coefficient

s.d.

0.245*** 0.056***

0.023 0.007

. . . not shown here . . .

0.0297

Note: .01 − ***; .05 − **; .1 − *.

Source: Staff calculations based on 2006 LiTS data.

Figure 3.8. Satisfaction with Publicly-provided Health Service and Self-assessed Health Status Satisfaction with publicly provided health system by self-assessed health status Very good

Good

Medium

Bad

Very bad

35

40

45

50

55

Percent of satisfied respondents

more satisfied with life is also likely to be more satisfied with a given quality of health service. Kahneman and Krueger additionally cite a couple of experiments that suggest that those who are satisfied with life in general may be less susceptible to morbidities35. It may simply be that those who are more satisfied with life fall ill less frequently and recover more easily. The health outcome for any illness episode, given a fixed quality of treatment, may 35. In one study, subjects were exposed to a cold virus, and their symptoms were monitored. Those who reported a higher level of life satisfaction at baseline were less likely to come down with a cold, and quicker to recover if they became sick. Another study subjected individuals to a controlled wound, which was then monitored. The study found that subjects who were more satisfied with their lives healed quicker than others.

82 World Bank Working Paper

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

Similarly, other things being equal, respondents aged 41–60 years also tend to report higher 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 professional/post-graduate educational attainment tend to be negatively associated with satisfaction with PPHS—i.e. the richer the respondent and the higher their education level, the less likely they are to be satisfied with PPHS. This likely indicates that such individuals tend to judge the quality and efficiency of PPHS using more exacting standards as compared to the 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 child delivery care they receive. Balabanova 2007 reports that, particularly in maternal and child health in Russia, there is a “culture of over-medicalization tolerating ineffective and wasteful clinical practices dependent on mothers’ ability to pay informally rather than need, administrative incentives to retain underused capacity, and user disempowerment.” 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 agreed the price with the doctor. They also told us that as long as we arrange for their private services (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 the growth rate of per capita GDP to have significant positive effects on the satisfaction with PPHS. There are many ways through which macroeconomic well-being affects satisfaction with PPHS—it enables increased spending on health services, either by the government or by private households, it fosters optimism about the future, and so on. Once again, due to the cross-sectional nature of the LiTS survey, it is not possible for us to disentangle the means by which economic well-being affects satisfaction with PPHS, we merely note the strong and significant correlation.

The Impact of Unofficial Payments Unofficial payments, as the name implies, are payments that do not go through official channels, and that are often made for what are meant to be free services. The reasons why they exist are myriad, and have been well documented (Lewis 2000; Belli, Gotsadze, and Shahriari 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” payments, 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 supplies (such as gauze, bed clothes, medicines) that are not budgeted or paid for by the government, or (c) payments that are required by medical personnel or administrators to

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83

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

Given these different motivations, varied strategies are required to decrease the incidence of unofficial payments. Gift-giving is a strong and innocent cultural tradition to express gratitude to medical healers that has existed in the ECA region, particularly in CIS countries, for many generations. Gifts are often in-kind, and are more prevalent in rural areas within close-knit communities than in more impersonal urban areas. While these characteristics could be used in surveys to try and distinguish between voluntarily given gifts and unofficial payments that are demanded, there is no reason to wish to decrease or eliminate the practice of giving gifts (such a campaign would be as meaningless and ineffective as 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 because of declining revenues but little rationalization of existing infrastructure resulting in inadequate medical equipment, drugs and supplies. Health care reforms that address these issues, including identifying and eliminating funding gaps that ensure that all materials needed for a given health service are indeed available should suffice to decrease such types of unofficial payments.

The third type of payment, which are requested by medical personnel or administrators, for access to (or to expedite) what should essentially be free service, is indeed a form of corruption. Some of the factors that contribute to the existence of such payments include underpaid or unpaid doctors, as well as financing and administration of hospitals and facilities that make them unresponsive to the evolving medical needs of the population. 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 health outcomes. Simply running anti-corruption campaigns without instituting mechanisms that provide effective accountability for health outcomes to users of the PPHS are likely to be ineffective. As Vian and Burak (2006) find in Albania, there is no difference in the moral beliefs of people who intend to make unofficial payments the next time they access the PPHS and those who do not intend to do so. On the other hand, people who do not intend to make unofficial payments report other means by which they can influence health outcomes, such as connections with medical personnel, etc.

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

One advantage of the LiTS has been the general nature of the question: â&#x20AC;&#x153;In your opinion, how often is it necessary for people like you to have to make informal payments/gifts when receiving medical treatment in the public health system.â&#x20AC;? Responses were obtained both from those who used the system in the recent past (last 12 months) and those who did not. A comparison of general perception (the responses from those who have not used PPHS in the last 12 months) with the opinion of experienced users is revealing. It appears

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Figure 3.9. General vs. Experienced Opinion of Need for Unofficial Payments Respondents that think unofficial payments are needed in publicly provided health system Estonia Slovenia Kazakhstan Georgia Turkey Czech Republic Mongolia Croatia Latvia Montenegro Serbia Belarus Slovakia Poland Bosnia Bulgaria Armenia ECA REGION Lithuania Macedonia Russia Azerbaijan Hungary Tajikistan Romania Ukraine Uzbekistan Kyrgyz Republic Moldova Albania 0

20

40

60

Percent of respondents Did not use

Used in past 12 months

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 access is an important factor causing dissatisfaction with PPHS. For instance, dissatisfaction with health care is highest in Albania, with 48 percent of respondents being dissatisfied or very dissatisfied with the service interaction. Albania also has the largest percentage of people (48 percent), who believe that unofficial payments are usually or always needed to access PPHS. Our empirical model indicates that, other things being equal, respondents who say unofficial payments are often necessary are about 1.4 times more likely to report being dissatisfied with PPHS compared to those who say that such payments are never needed. As reasoned above, one of the motivations for unofficial payments has been to ensure higher quality of service than otherwise available. Thus, one might expect to find that, in some cases at least, unofficial payments go hand-in-hand with higher satisfaction with the service interaction. If this effect exists at all, it is likely very small, and is overwhelmed by the amount of dissatisfaction generated by having to pay for free services. Bratton, 2007, finds that the perception that officials are corrupt decreases citizen satisfaction with services in African 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 a limited set of ECA countries in different time periods (Lewis 2000; Balabanova and others 2004). 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,

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

85

and reported prevalence of unofficial payments derived from the LiTS with those from earlier studies provides Prevalence of unofficial another means of analyzing the payments in PPHS complex interaction between these 2001: three sets of variables.

Balabanova 2006: Although the usual caveats Country Lewis36 et al37 ECA-LiTS38 apply regarding the need for caution Georgia 65 12 when comparing the findings of difKazakhstan 38 12 ferent surveys, these three sources Armenia 91 (1999) 56 30 provide a rough indication at least Moldova 70 (1999) 46 48 of changes in prevalence of unoffiKyrgyzstan 75 (1996) 42 46 cial payments in these 15 countries between the late 1990s and Belarus 8 24 2006. Comparing the Lewis findings Ukraine 27 44 with the LiTS, we see that the inciRussia 74 (1997) 19 37 dence of unofficial payments (or in Azerbaijan 78 (1995) — 40 the case of the LiTS of the opinion Poland 78 (1998) — 25 that unofficial payments are necesTajikistan 66 (1999) — 44 sary to access PPHS) declined in Slovak all countries except Albania and Republic 60 (1999) — 25 Bulgaria between the late 1990s and Latvia 31 (2000) — 17 2006. Comparing the findings of Albania 22 (1996) — 55 Balabanova and others (2004) with Bulgaria 21 (1997) — 30 the LiTS, we see that the percentage 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 and Kyrgyzstan, while it has, by contrast, increased in Belarus, Russia, and Ukraine.

Evidence that unofficial payments act as barriers to access emerges when one compares access 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, about 21 percent did not do so. The reason for not seeking care cited by 78 percent of respondents was the lack of money to pay for treatment. Comparing access rates from the two data sources by country, we find that access rates have not changed by much in Kyrgyzstan, Moldova and Ukraine, but have increased in Armenia, Kazakhstan, and in Georgia (quite dramatically in the 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 of unofficial payments between the Balabanova et al (2004) study and the LiTS is highlighted in Figure 3.10, which contrasts the extent to which the two data sources indicate that the Table 3.3. Prevalence of Unofficial Payments for Selected Countries

36. Per cent of patients that reported making informal payments (the year for each observation is included in brackets) 37. Respondents that reported paying informally/making a gift during their most recent consultations; 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 say that unofficial payments/gifts are usually / always necessary.

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prevalence of unofficial payments went down over this period, with changes in the share of the populaAccess Rates Among the tion that reported using publicly Population Aged 18 and Over provided health care facilities dur2001: ing the past 12 months.

Balabanova 2006: As the figure clearly shows, the Country et al39 ECA-LiTS40 Difference*** eight countries for which this comGeorgia 24 46 +22 parison is possible fall into three Kazakhstan 50 64 +14 main groups. In the first group, comArmenia 30 39 +9 prising Georgia, Kazakhstan, and Moldova 53 50 −3 Armenia, declines in the reported Ukraine 58 54 −4 prevalence of unofficial payments Kyrgyzstan 44 40 −4 during this period was accompanied by significant increases in the share Russia 66 52 −14 of the population utilizing public Belarus 66 37 −29 health care facilities. In the second group, which includes Moldova, Ukraine, and the Kyrgyz Republic, access rates did not change by very much during this period, and neither did the prevalence of unofficial payments (with the exception of Ukraine). Finally, in the third group, with Russia and Belarus, the increase in prevalence of unofficial payments between 2001 and 2006 was accompanied by significant declines in the share of the population accessing PPHS. These findings underline the barrier that unofTable 3.4. Change in Health Care Access Rates for Selected Countries

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

Moldova Ukraine Kyrgyzstan

Russia Belarus -40

-30

-20

-10

Increase: Access rates

0

10

20

30

40

50

60

Decline: prevalence of unofficial payments

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

39. Survey conducted in autumn 2001. Access rate refers to consulting a health care professional (public 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.

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

87

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 the perception of the necessity of unofficial payments negatively impacts satisfaction levels.

Finally, given the strong negative influence of unofficial payments on satisfaction with PPHS, one may plausibly be able to argue that the LiTS question indeed captures unofficial payments 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, and to a smaller extent Azerbaijan, on satisfaction with PPHS. All three countries suffered catastrophic economic decline with the fall of the Soviet Union—real GDP per capita shrank by 19.2 percent on average per year in Georgia, by 16.8 percent per year in Azerbaijan, and by 8.4 percent per year in Armenia, from 1991 to 1995.41 This economic collapse led to sharply shrinking budgets, particularly for the health sector. The expenditure on PPHS as a percent of GDP decreased dramatically in Georgia, from about 3 percent in 1991 to a little above 0 percent in 1994. Available data for the latter half of the decade show that health outcomes stagnated (and in some instances actually worsened) during this period.42 Spending recovered to about 1 percent in 1995, and has remained at that level ever since. While the decline 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 in the 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 spending (both formal and informal) in Georgia has been estimated to range between 66 and 87 percent of total health spending, while household expenditure on health plus donor contributions account for between 63 and 69 percent of total health spending in Armenia. They also document the drastic decline in health care utilization, particularly among the poor, as a result of these increasing costs of access.

Most countries in the low-income CIS embarked on health care reforms—with the focus being to realign facilities and medical personnel to give more emphasis to the primary health care system. In addition, the reforms have tried to raise additional revenues for the health sector. All low-income CIS countries have implemented some cost-sharing arrangements by charging fees for some health services. In 1995, the Government of Georgia formally removed entitlement to free health care from its constitution. Public insurance coverage was limited to services included in a basic benefit package (BBP). A social insurance contribution was imposed on formal employment, with shortfalls being met by transfers from general taxation (Belli, Gotsadze, and Shahriari 2004).

Many low-income CIS countries have also tried to rationalize costs. Substantial progress has been made in decreasing the number of hospital beds, although the numbers still 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.

88 World Bank Working Paper

health care staff, with Georgia and Moldova achieving significant reductions in the number of health sector workers. Georgia and Armenia have shown some progress in privatizing hospitals. The most dramatic evidence of changes in health care practice have been visible in 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 international agencies have supported a particular innovation in the Caucasus (Armenia, Georgia and Azerbaijan), aimed at mitigating the situations of the rural poorâ&#x20AC;&#x201D;community health insurance schemes. Ensuring equity of access to primary health care has been an explicit objective of the scheme. Balabanova 2007 reports that Oxfamâ&#x20AC;&#x2122;s schemes have contributed to improved access to and quality of care, through rehabilitation of local health posts, training of nurses, and subsidy of running costs. She further reports that these schemes have grown to be major providers of health care to rural communities, to the point where they have assumed significant responsibility for public sector provision.

Real GDP per capita in Georgia, Armenia and Azerbaijan began to grow, more modestly in the late 1990s but quite dramatically since 2000. Real GDP per capita growth averaged 12.6 percent per year from 2001 to 2005 in both Armenia and Azerbaijan, and by 8.4 percent per year in Georgia over the same period. We posit that the reforms in the health sector undertaken in Georgia and Armenia, and the innovations in community health insurance schemes, combined with the transformational and broad based growth occurring in both countries, have led to recovery in the quality of PPHS from an extremely low base in the early 1990s. These factors help explain, to some extent, the significant rise in access rates and decline in prevalence of unofficial payments in countries like Georgia and Armenia. While Azerbaijan has equally strong economic performance, it has not progressed 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 variables that have a significant positive effect on the satisfaction with PPHS.

Concluding Observations In this paper we investigate satisfaction with publicly provided health services (PPHS) in 28 ECA countries and Mongolia using data from the Life in Transition survey conducted in these countries in the fall of 2006. The countries in this region inherited a health system that was state owned and financed through general taxation, and that was largely characterized by over capacity, both in terms of human as well as physical factors, at the time of the fall of the Soviet Union. The system was largely unsustainable, particularly given the economic decline in the region in the 1990s.

Most countries undertook reform of the health sector, with mixed results. The EU member 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 stable financing for the health sector. Thus, among ECA countries, many EU member are at or near the top with regards to satisfaction with PPHS. A surprising finding from the LiTS has been the levels of satisfaction in a few low-income CIS countries, particularly Georgia and Armenia. Some discussions of these findings are

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

89

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 derived from the service provided. We find that factors that have a significant positive effect on accessing 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 negative effect on accessing health care are (a) self-assessed good health, and (b) level of education (those with higher levels access the health care system less than those with lower levels of education.)

Factors that have a significant positive effect on satisfaction with service received include (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 more satisfied with life are also more satisfied with PPHS), and (f) being between 41–60 years of age. Factors that have a negative, and significant, effect on satisfaction include (a) the perception 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 payments for health service is the most urgent reform needed in this region—particularly among the CIS countries. The primary reason for the existence of these informal payments seems to be to influence availability and quality of services received through the PPHS as there are no other means available to hold providers and administrators accountable. Thus, the establishment of mechanisms that enable provider accountability is a first step towards the elimination of informal payments. Sustained and long-term engagement in the reform process is 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 increased access of PPHS. Thus, changing the culture of government is as important as undertaking reforms in the health care system. A well coordinated communications strategy that is aimed at informing health sector users of their entitlements, and of emphasizing government commitment to reform, could influence citizens’ perception of what they should expect from health care services, and could co-opt them into partners in enabling change.

90 World Bank Working Paper

Annex: Tables and Figures Table A3.1. Priorities for Additional Government Spending, By Country 1. First priority: Percentage of Respondents Choosing: Group/Country EU member states South-Eastern Europe CIS-low income CIS-middle Total 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 Mongolia Russia Tajikistan Uzbekistan

Education Healthcare Housing Pensions Environment Infrastructure Overall 22

44

10

17

2

4

100

32

33

8

16

3

8

100

27 21 27 24 28 39 23 35

41 42 40 23 32 27 53 32

9 19 13 9 21 8 3 8

16 13 13 21 13 18 17 19

3 4 3 5 2 1 2 2

4 2 3 18 4 6 4 4

100 100 100 100 100 100 100 100

29 39 16 24 27 21 18 28 31 32 58 21 20 29 30 24 22

41 33 53 45 35 41 42 41 50 37 29 49 48 41 41 35 44

11 7 8 8 11 11 15 7 5 13 4 13 6 12 4 11 12

13 14 13 14 13 19 21 12 9 12 5 11 21 15 15 23 13

5 2 4 2 3 1 2 4 2 4 1 3 2 1 4 2 7

2 4 6 6 10 6 2 8 3 2 3 3 2 3 5 5 3

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

32 32 24 40 21 40 24

37 36 49 28 40 29 44

13 6 10 7 21 9 8

9 18 14 9 13 14 16

2 2 0 5 4 6 4

7 5 2 11 1 2 4

100 100 100 100 100 100 100

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

Table A3.2. Access Rates of PPHS, By Country Percent of population who ______ the public health care system in the last 12 months Country Albania Belarus Bosnia Bulgaria Croatia Czech Republic Macedonia, FYR Hungary Moldova Montenegro Poland Romania Serbia Slovak Republic Slovenia Turkey Ukraine Armenia Azerbaijan Estonia Georgia Kazakhstan Kyrgyzstan Latvia Lithuania Mongolia Russia Tajikistan Uzbekistan

Accessed 65.7 37.3 42.1 48.4 61.1 59.5 51.9 61.3 49.4 45.3 41.3 51.4 49.1 33.4 38.3 58.0 54.3 38.5 56.9 51.6 45.7 64.0 39.9 56.5 54.4 37.8 51.7 55.4 44.7

Did not access 34.3 62.7 57.9 51.6 38.9 40.5 48.1 38.7 50.6 54.7 58.7 48.6 50.9 66.6 61.6 42.0 45.7 61.5 43.1 48.4 54.3 36.0 60.1 43.5 45.6 62.2 48.3 44.6 55.3

91

92 World Bank Working Paper

Table A3.3. Satisfaction with Medical Treatment in PPHS by Country 50

Satisfied Very satisfied

Very unsatisfied

40

% of respondents

Very satisfied

Unsatisfied Satisfied

30

20

10

Indifferent CIS-middle income

CIS-low income

SouthEastern Europe

EU member states

0

Percentage of respondents that are: Very Unsatisfied

Unsatisfied

Indifferent

Satisfied

Very Satisfied

Overall

EU member states South-Eastern Europe CIS-low income CIS-middle income

10 14 13 10

15 17 20 23

24 19 17 28

43 40 43 36

8 11 7 5

100 100 100 100

Total Slovenia Czech Republic Croatia Georgia Slovakia Lithuania Armenia Estonia Latvia Hungary Bulgaria Belarus Poland Bosnia Azerbaijan Turkey Montenegro Mongolia

11 3 3 7 4 8 4 9 5 6 7 9 3 9 13 13 17 12 12

19 6 7 8 11 6 16 17 23 20 16 21 24 14 14 19 11 20 17

24 26 30 20 22 29 23 11 12 19 27 11 23 31 24 13 21 19 22

38 46 48 45 54 39 45 53 50 48 42 48 43 42 39 47 37 36 42

7 20 11 20 10 18 11 11 10 9 8 12 6 4 11 8 14 13 6

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

Group/Country

(continued )

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

93

Table A3.3. Satisfaction with Medical Treatment in PPHS by Country (Continued) Percentage of Respondents that are: Group/Country Russia Uzbekistan Kazakhstan Macedonia Serbia Kyrgyz Republic Moldova Romania Tajikistan Ukraine Albania

Very Unsatisfied

Unsatisfied

Indifferent

Satisfied

Very Satisfied

Overal

10 13 9 17 16 16 13 19 17 10 21

21 21 22 16 20 23 24 19 21 28 27

28 18 31 16 19 11 21 16 25 28 12

37 42 34 45 37 44 37 39 33 31 38

5 6 5 5 9 6 6 6 4 3 2

100 100 100 100 100 100 100 100 100 100 100

Table A3.4. Prevalence of Unofficial Payments in PPHS by Country 15

Usually Always

% of respondents

Always

Usually Never

10

5

Sometimes CIS-low income

CIS-middle income

SouthEastern Europe

EU member states

0 Seldom

Percentage of respondents that say unofficial payments are needed: Group/Country

Never

Seldom

Sometimes

Usually

Always

Total

EU member states South-Eastern Europe CIS-middle income CIS-low income

51 55 39 37

11 9 15 16

18 15 20 18

12 11 15 14

8 10 12 15

100 100 100 100

Overall sample: Estonia Slovenia

42 75 73

14 11 9

19 11 12

14 2 5

11 1 1

100 100 100 (continued )

94 World Bank Working Paper

Table A3.4. Prevalence of Unofficial Payments in PPHS by Country (Continued) Percentage of respondents that say unofficial payments are needed: Group/Country Georgia Kazakhstan Belarus Turkey Czech Republic Croatia Latvia Poland Serbia Mongolia Bosnia Armenia Montenegro Slovakia Macedonia Russia Lithuania Bulgaria Kyrgyz Republic Romania Azerbaijan Moldova Hungary Tajikistan Uzbekistan Ukraine Albania

Never

Seldom

Sometimes

Usually

Always

Total

59 63 59 66 53 65 60 57 61 47 57 45 51 46 44 40 35 41 41 47 39 35 39 24 34 25 26

21 14 17 9 18 9 12 11 8 16 10 20 11 14 13 15 14 10 13 9 19 16 10 21 12 13 8

12 13 12 13 19 13 15 17 14 21 14 16 20 19 20 20 27 25 17 14 12 17 18 21 21 22 18

6 5 8 7 8 11 8 10 9 11 9 11 9 13 11 14 16 13 18 16 12 19 19 18 14 20 18

2 5 4 5 2 3 5 5 8 6 10 8 9 8 13 10 8 11 11 14 18 13 13 15 19 20 30

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

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

95

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 Country Albania Belarus Bosnia Bulgaria Croatia Czech Republic Macedonia, FYR Hungary Moldova Montenegro Poland Romania Serbia Slovakia Slovenia Turkey Ukraine Armenia Azerbaijan Estonia Georgia Kazakhstan Kyrgyzstan Latvia Lithuania Mongolia Russia Tajikistan Uzbekistan

Usually/Always

Accessed

Not Accessed

Accessed

Not Accessed

27.9 59.6 52.0 40.7 68.1 62.9 42.4 37.4 26.5 56.7 48.5 38.3 59.4 54.0 72.8 73.5 31.7 42.5 45.3 85.2 74.6 74.6 26.0 64.3 33.7 53.2 35.4 30.2 31.7

46.8 85.5 77.9 59.9 82.0 82.7 73.0 68.3 75.7 65.7 81.6 74.2 78.0 63.7 87.4 77.6 46.8 79.4 73.9 87.3 84.9 80.7 72.8 81.7 67.9 68.4 76.7 64.1 57.6

54.9 23.7 29.5 29.6 16.5 13.5 31.9 43.6 48.5 19.8 25.3 44.4 22.6 24.9 9.6 12.4 44.5 30.2 40.2 4.7 11.9 11.6 46.1 16.5 32.0 16.0 36.9 44.0 45.3

33.5 5.0 10.7 19.4 9.0 4.7 13.6 15.5 15.0 16.7 7.3 16.0 11.9 18.9 4.8 10.2 33.7 11.2 17.1 1.8 4.3 9.1 16.8 8.4 13.2 17.0 11.0 20.0 23.1

Bibliography

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Kahneman D, and A. Krueger, 2006 “Developments in the Measurement of Subjective Well-Being.” Journal of Economic Perspectives 20(1):3–24.

Lewis, M. 2000. “Who is paying for health care in Eastern Europe and Central Asia?” The World Bank, Washington, D.C. Vian, T, and L. Burak. 2006. “Beliefs about informal payments in Albania.” Health Policy and Planning 21(5):392–401.

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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.

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