Has transition improved well-being?

Economic Systems 36 (2012) 11–30

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Economic Systems jo urnal home page : www.elsevier.com/loc ate/ecos ys

Has transition improved well-being? Carola Gruen, Stephan Klasen * Department of Economics and Courant Research Center ‘Poverty, equity, and growth in developing and transition countries’, University of Go¨ttingen, Germany

A R T I C L E I N F O

A B S T R A C T

Article history: Received 4 October 2010 Received in revised form 15 March 2011 Accepted 22 April 2011 Available online 16 September 2011

In this paper we examine trends in economic well-being in transition countries from 1988 to 2008 to determine whether the populations of transition countries are better off today than prior to the transition process. To do this, we examine economic performance, inequality-adjusted well-being measures, subjective wellbeing measures, and non-income dimensions of well-being. While for many of the transition countries some indicators of well-being show improvements compared to the pre-transition period, the sharp rise in inequality and low levels of social indicators and subjective well-being suggest that well-being in many countries is similar to, or even below, the levels experienced prior to transition. The only indicators which have shown consistent improvements are measures of political and civil liberties. ß 2011 Elsevier B.V. All rights reserved.

JEL classification: D6 O15 P27 Keywords: Transition economies Well-being Income inequality Subjective welfare measures

1. Introduction Some twenty years after the beginning of the transition in 1989, after the accession of altogether ten transition countries to the European Union between 2004 and 2007, and more than a decade after most transition countries had started to climb out of their post-transition recession, this paper examines whether transition has brought about a lasting improvement in the economic well-being of the populations in transition countries. While an earlier paper (Gruen and Klasen, 2001) suggested that economic well-being in the late 1990s was still lower than at the start of the transition process in virtually all countries, a decade of high economic growth since and broader approaches to the measurement of well-being suggest that revisiting this issue from a broader well-being perspective using the most recently available data is a worthwhile exercise.

* Corresponding author. E-mail addresses: [email protected] (C. Gruen), [email protected] (S. Klasen). 0939-3625/$ – see front matter ß 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.ecosys.2011.09.002

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For most former Soviet Bloc countries, transition first led to economic (and for some countries also political) instability characterised by severe contractions in income levels, rising unemployment, increasing income inequality, and a worsening of the overall economic situation (Atkinson and Micklewright, 1992; Klasen, 1994; Milanovic, 1998; Gruen and Klasen, 2001). After the transition shock, most countries managed to return to a positive per capita growth path by about the mid-1990s, and quite a few countries recorded high levels of growth in the first 7 years of the new millennium, just prior to the global financial crisis that has hit many transition countries more seriously than many other parts of the world (IMF, 2010). Fig. 1 illustrates the growth performance of 25 Central and Eastern European transition countries between 1988 and the lowest value reached after the transition shock between 1988 and 2008, and between 1988 and 2010, to take account of the impact of the global financial crisis.1 Five stylized facts are noteworthy. First, as is well known, the transition shock was massive. Per capita income levels at the nadir of the post-transition shock were between 10 and 75% lower than prior to transition.2 Second, from this low point, which varies by country but is concentrated around the mid-1990s, most transition countries have grown substantially. Third, the vast majority of transition countries, including all Eastern European countries, have surpassed their pre-transition per-capita income levels by a significant margin; top performer is Poland which more than doubled its pre-transition per-capita income, with all other EU accession countries also faring rather well. Fourth, several countries emerging from the former Soviet Union (Tajikistan, Ukraine, and Kyrgyz Republic) and Serbia still have lower income levels than prior to transition. Some of the countries with missing data, including several former Yugoslav Republics and Georgia, might also belong to that group.3 Fig. 1 also shows how the impact of the financial crisis has affected these trends.4 This leads to the fifth stylized fact, which is that the EU accession countries in particular were negatively affected by the global financial crisis. With the exception of Poland, all of them have per capita income levels in 2010 that are below 2008, thereby giving up some of the gains these countries had made in the past 10–15 years. Of particular note are the massive income losses in the Baltic States; further east, the income changes during the financial crisis are much more varied. But on the whole the impact of the financial crisis has been to reduce the gains made by many transition countries. The change of the economic system during the transition process had an impact not only on economic growth, but also on its distribution. Prior to transition, income inequality was very low. The first two columns of Table 1 show how income inequality measured by the Gini coefficient changed drastically since the onset of transition; for most countries the most drastic increase in inequality took place in the first part of the transition process. After 1995, trends are more heterogeneous. While for about half the countries inequality stabilized at this higher level, in about 1/4 of countries it continued to rise, while in the remaining 1/4 it fell somewhat (but remained above the starting levels). Note also that the magnitude of the worsening varies considerably across countries. Central European countries experienced relatively moderate increases in inequality, whereas in many former Soviet Republics the Gini coefficient rose by much more.

1 Note that this figure is not directly comparable to the income ratio for the period 1988–1999 presented in Gruen and Klasen (2001). Then, we used GNP per capita data adjusted for purchasing power (WDI, 1999; World Bank, 2000), a data series which is no longer provided by the World Bank. According to these data, the Czech Republic, Estonia, Hungary, Poland, Slovakia, and Slovenia had on average higher incomes in 1999 than in the pre-transition period. Re-calculating this income ratio using the PPP adjusted GNI per capita (WDI, 2004), only Hungary, Poland, and Slovenia would have surpassed their 1988 income levels in 1999. 2 There are some questions about the reliability and comparability of pre-transition income figures, esp. given the importance of rationing, poor quality products and services, distorted prices, etc. These are discussed in some detail in Gruen and Klasen (2001); the upshot of this discussion is that while there are clearly biases and comparability issues, it is not clear that pretransition income levels are systematically underestimated, especially also due to the influence of many subsidized goods and services. 3 The overall picture is very similar when using GDP per capita, measured in constant local currency (WDI, 2010) as the income concept; the one exception is Moldova, whose ratio of incomes 2008/1988 is only 0.65 when using GDP/capita while it was 1.15 using PPP adjusted GNI per capita. 4 We use data on actual (2009) and estimated (2010) growth rates in PPP-adjusted per capita incomes from the IMF (2010) to calculate these per capita income ratios.

220% 200% 180% 160%

120% 100% 80% 60% 40% 20%

Lowest value/1988

2008/1988

Uzbekistan

Ukraine

Turkmenistan

Tajikistan

Slovenia

Slovak Republic

Serbia

Russia

Romania

Poland

Moldova

Macedonia

Lithuania

Latvia

Kyrgyz Republic

Kazakhstan

Hungary

Estonia

Czech Republic

Croatia

Bulgaria

Belarus

Azerbaijan

Armenia

Albania

0%

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140%

2010/1988

Fig. 1. Real per capita income ratios. Notes: Income concept for the years 1988–2008 is real GNI per capita, PPP adjusted, 2005 prices (WDI, 2010). Income data for 1988 were not available for all countries. Instead, we used the data of 1989 for Russia and Serbia; 1990 for Azerbaijan, Armenia, Belarus, Croatia, Czech Republic, Kazakhstan, Lithuania, Macedonia, Poland, and Slovenia; 1992 for Moldova. Income data for 2010 have been calculated using projected real GDP per capita growth rates (IMF, 2010).

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Table 1 Income inequality in transition countries. Country

Pre transition

Mid-1990s

Post transition

Armenia Azerbaijan Belarus Bulgaria Croatia Czech Republic Estonia Hungary Kazakhstan Kyrgyz Republic Latvia Lithuania Moldova Poland Romania Russia Slovak Republic Slovenia Tajikistan Ukraine Uzbekistan

32.2 35.9 22.7 21.8 36.7 21.7 29.1 24.3 30.0 25.8 26.7 26.6 24.2 28.5 24.6 23.9 22.5 25.4 36.0 23.3 28.0

43.9 45.9 30.3 31.9 35.2 23.9 34.5 25.9 40.7 52.2 32.8 34.2 43.9 35.2 32.4 45.1 29.0 25.2 38.6 39.7 33.3

41.9 41.3 29.8 32.9 34.3 31.6 38.5 29.2 37.5 41.6 44.5 40.5 40.4 39.6 37.7 36.3 34.6 29.5 38.9 33.5 49.6

Notes: Gini coeffcients have been retrieved from WIID (2008) and harmonized using a regression-based adjustment, except for Azerbaijan, 1995 (UNICEF, 2010). For details regarding the harmonization procedure, see main text and Table A.1.

This paper takes a closer look at welfare in transition countries before and after the transition. It updates and extends the analysis of Gruen and Klasen (2001), which investigated well-being in transition countries and comparable middle-income countries between 1988 and 1995. Similar to our previous analysis, we will apply pure income and distribution-sensitive well-being measures to measure the change in economic well-being, but will expand the analysis up to 2008. Prior to transition, socialist countries enjoyed a relatively high level of economic well-being thanks to their rather equal distribution of income. During the transition, mean incomes fell and inequality worsened, resulting in lower absolute and relative welfare levels. We assess the effect of these trends using distribution-sensitive well-being measures. In addition, and different from our previous analysis, we will also rely on subjective measures of well-being to assess the change in welfare related to the transition. In particular, we will link measures on subjective well-being (SWB) with data on income and income distribution to find out whether income inequality has a significant impact on the way people rate their lives. This allows us to get an alternative view on how welfare changed in a number of both transition countries and middle-income countries over the last two decades. Lastly, we will consider non-income dimensions of well-being to see whether they tell a different story from our income-based and subjective well-being measures. 2. Measuring welfare—some theoretical considerations There is a large axiomatic and applied literature on well-being measurement which we will only briefly summarize here.5 For international comparisons of economic welfare, real per capita income (now typically PPPadjusted) is still the most widely used indicator. It can be derived from utilitarian welfare economics, but requires strict assumptions regarding individual preferences and utility functions, particularly when comparisons across time and space are made (Gruen and Klasen, 2003b). Particularly problematic is its neglect of income distribution, which can only be justified with the highly unrealistic assumptions of linear utility functions or an equal or ‘optimal’ distribution (in the sense that the ethical worth of each marginal dollar is the same). As is well-known and was recently 5

For a more detailed discussion, refer to Sen (1979) or Gruen and Klasen (2003b, 2008).

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reviewed in detail by the Stiglitz–Sen–Fitoussi Commission (Stiglitz et al., 2010), there are many shortcomings with this measure and its neglect of income distribution was highlighted as a particular problem. To remedy this shortcoming, the literature also suggests a number of indicators that combine income and its distribution (e.g. Atkinson, 1970; Sen, 1982; Dagum, 1990). More recent empirical observation on risk aversion also supports the hypothesis that higher inequality does have a negative impact on individual welfare levels (e.g. Stodder, 1991; Amiel et al., 1999).6 The theoretical arguments for incorporating the distribution of income into a meaningful measure of economic welfare are multiple and well-known (e.g. Rawls, 1971; Sen, 1984, 1987).7 But only with the recent improvements regarding the availability of inequality data has it become possible to apply these measures and to conduct cross-country and cross-temporal studies (e.g. Kakwani, 1981; Jenkins, 1997; Gruen and Klasen, 2001, 2003a,b). Another approach in the literature to remedy some of the shortcomings of the income-based approach to well-being measurement is to consider multidimensional well-being indices, some of which can also be made to consider inequality in these multidimensional measures. See, for example, the 2010 Human Development Report for a discussion of these issues and some suggested inequalityadjusted multidimensional well-being measures.8 In the spirit of this approach and following Sen (1999), we will also consider a range of non-income dimensions of well-being to assess progress in these dimensions in transition countries. Related to this issue, a separate literature has developed that focuses on developing and analysing subjective measures of well-being (e.g. Easterlin, 1974, 1995; Morawetz et al., 1977; Frijters et al., 2004; Headey et al., 2008). Subjective well-being measures employ a different approach to capture welfare, as such indicators rely on the self-assessment of individuals. People are asked to evaluate their overall satisfaction with life or their happiness. Subjective well-being (SWB) therefore captures many different aspects people are concerned about and offers a self-rated view on welfare. The concept of SWB was developed in psychology (e.g. Diener, 1984) but has gained increasing significance in economics as well (Stiglitz et al., 2010). Empirical analyses conducted in both fields of study have convincingly demonstrated that maximising happiness or life satisfaction instead of income appears to be the most important objective for most people (e.g. Clark and Oswald, 1995, 2002; Oswald, 1997; Frey and Stutzer, 2002; Di Tella et al., 2001).9 To date, most of the empirical literature is on economic and non-economic determinants of SWB (see Conceicao and Bandura, 2008, for a recent review of the literature) and appropriate methods to analyse subjective data (e.g. Ferrer-iCarbonell and Frijters, 2004; Baetschmann et al., 2011). Little research has been done to develop a sound theoretical framework of SWB.10 Important questions such as the interpersonal comparability of subjective data and adaptation processes have also been addressed only recently (e.g. King et al., 2004; Clark et al., 2008; Wunder, 2009). There are two pieces of evidence that inequality reduces subjective well-being. First, there is strong evidence that relative (rather than absolute) incomes matter particularly strongly for subjective wellbeing (as preferences appear to be inter-dependent) and high inequality will increase income gaps between people, with negative implications for subjective well-being (Clark and Oswald, 1996; Easterlin, 1995). Secondly, there is some evidence of ‘inequality aversion’ in the sense that people appear to report lower levels of subjective well-being in places where there is high inequality, over and above the direct effect of relative incomes (Blanchflower and Oswald, 2003). Nevertheless, shifting the focus from economic indicators such as income to a self-rated assessment such as overall life satisfaction does not imply that other desirable objects become just a means. Diener and Scollon (2003) argue that maximising subjective well-being is not sufficient. Even if 6

For a more detailed discussion on these issues, see for example Gruen and Klasen (2003b). For a recent study on the inefficiency of inequality, please refer to Schiff (2004) and Klasen (2008). 8 See Harttgen and Klasen (2011) for a critical review of the inequality-adjusted Human Development Index and an alternative approach to measure inequality in human development. 9 See Kahneman and Deaton (2010) for a recent analysis of the effects of income on subjective well-being and happiness in the US, where they find that higher income within a country has a larger and more persistent effect on life satisfaction than on happiness, where there appears to be an upper bound. 10 See Ng (2000) and Easterlin (2003) for some initial thoughts on what a better theory of well-being should entail. 7

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people report to be less happy, they still value particular components of their life like health, marriage, work, or leisure. Selezneva (2011) reviews recent subjective well-being studies in transition countries that focus on the link between life satisfaction (or happiness), income, work and family. In particular, it appears that fast changing circumstances and uncertainties impact on subjective indicators and the way people form expectations. Thus, SWB should be considered as an intriguing complementary route to measure welfare but does not render traditional welfare theory superfluous (see also Frey and Stutzer, 2002; Stiglitz et al., 2010). In the remainder of the section, we will give a brief overview regarding the measures we will apply in our analysis of the inequality-adjusted income-based measures of economic welfare. For a more extended theoretical discussion on those measures, see for example Gruen and Klasen (2001, 2003b). We apply two inequality-adjusted well-being measures which jointly consider mean income m and its distribution. Both measures assume that an unequal distribution of income will reduce the level of welfare W11 W ¼ mð1  IÞ;

0  I  1:

Both welfare measures are based on the Gini coefficient G. Sen (1982) proposed the following measure: S ¼ mð1  GÞ: Mean income and the Gini coefficient are combined to arrive at the welfare level. The Sen measure can be axiomatically derived from interdependent preferences for which there is considerable empirical support. The corresponding utility function not only considers individual incomes but the entire income distribution.12 Dagum (1990) developed a variant of this measure. Preferences are still assumed to be interdependent, but in addition, the Dagum measure takes up the fact that individuals care in particular about people above them in the income distribution. The lower their own rank in the income distribution, the higher the welfare reduction they experience D¼

mð1  GÞ 1þG

  2G ¼m 1 : 1þG

For a given distribution of income, the Dagum measure clearly imposes a larger welfare penalty than the Sen measure; the empirical and experimental literature on risk aversion and subjective wellbeing suggests that the implied penalties for inequality in these two measures are within the range of actually observed inequality aversion (Gruen and Klasen, 2008). When considering transfers in income and their effect on welfare, the measures behave similarly. Both are consistent with the Dalton principle of transfers. As the Gini coefficient is sensitive to changes around the mode of the distribution, Gini-based measures are sensitive to changes that happen among the middle income group. Whether or not this is a desirable property has been extensively debated (e.g. Atkinson, 1970; Dagum, 1990; Sen, 1997).13 3. Data We calculate the distribution-sensitive welfare measures for 21 transition countries and compare them to a pure income-based measure to see by how much economic welfare of each country has been reduced due to the unequal distribution of income. The income concept used is GNI per capita, 11 There are other measures that could be used, including welfare measures based on the Atkinson inequality indicator. While these measures penalize inequality differently, the results of this paper are not substantially affected by relying only on the Gini-based measures; see Gruen and Klasen (2001, 2008) for an application also using the Atkinson measures. The focus on the Gini-based measures is largely due to data availability issues: Ginis are more widely available than quintile shares which are needed for the Atkinson measures. 12 See also Dagum (1990). 13 Also note that the Gini-based measures are not sub-group consistent, which is one of the criteria inequality measures are frequently expected to fulfill.

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adjusted for purchasing power parity (WDI, 2010). Gini coefficients are taken from the World Income Inequality Database (WIID, 2008).14 The benchmark years for our analysis of economic welfare are 1988 and 2008. Gini coefficients and, to a lesser extent, data on per capita income were not always available for exactly these two points in time. Particularly for the second benchmark year we had to rely on inequality data that were mainly based on years prior to 2008. Regarding mean income in 1988, we sometimes had to use data for later years which may somewhat bias the achievements in terms of income growth in the post-transition period. The biggest compromise we had to make is with respect to Moldova, where income data is only available from 1992 (see also Fig. 1). The availability of Gini coefficients has improved dramatically over the last two decades. Not only are there different data sources to choose from. For one particular country, a single source frequently offers multiple Gini coefficients for one year. The Gini coefficients may be based on different income concepts (i.e. gross or net income, expenditure) and may use different reference units (i.e. household, person, family). We therefore try to address the heterogeneity of Gini coefficients by making regression-based adjustment of the inequality indices used for calculating the Sen and Dagum measures. A large sample of Gini coefficients has been regressed on income concepts and reference units to determine the effect of different underlying specifications (see also Table A.1 in Appendix A). Most estimated parameters corroborate what one would expect. Gini coefficients based on expenditure data or net income are significantly lower than those based on gross income. If the inequality index has been adjusted for household composition, it is also significantly lower. A somewhat surprising result that turns out to be robust across different samples and specifications is that indices based on household level as the reference unit are significantly higher compared to the baseline case (i.e. per person). Theoretically, one would have expected that pooling income will lead to lower inequality. In order to arrive at comparable inequality indices, we use the regression results to calculate adjusted Gini coefficients which are based on gross income per person. All calculations reported later on apply the adjusted Gini coefficients. The analysis of subjective welfare measures is based on the European and World Values Surveys.15 The surveys have been designed to investigate sociocultural and political change. In particular, we make use of the integrated data file 1981–2004 and add the fifth wave of the WVS covering the years 2005–2008 (World Values Survey Association, 2009).16 We do not just use the available waves from transition countries but waves 2–5 from all available countries for comparative purposes. Due to data limitations, the range of countries surveyed in the EVS and WVS differs somewhat from our sample on economic welfare measures. Table A.2 in Appendix A gives an overview of included transition and non-transition countries.17 On average, 1000 people have been interviewed in each country. They were asked to assess the political system of their country, whether they are satisfied with the government, about their family situation, their relationship with neighbors and friends, and religious matters, to mention just a few aspects of the surveys. As the indicator of subjective well-being, we use the following question: All things considered, how satisfied are you with your life as a whole these days? 1 = dissatisfied 2 3 ... 10 = satisfied

14

For a critical assessment of the data sources used, see Gruen and Klasen (2001). For alternative data on happiness, please refer to the Database of Happiness (Veenhoven, 2004). See Sanfey and Teksoz (2007) for a related analysis. We differ in the specified model and more waves used so that our results are not directly comparable to theirs. 17 We do not consider the first wave (1981–1984), as it only covers one future transition country (i.e. Hungary) and also misses some important control variables. 15 16

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We use the average responses for descriptive purposes and then specify a regression model of the drivers of subjective well-being. Apart from the common determinants that have been identified in the literature (especially age, gender, marital status, employment, and income levels (all of which are drawn from the same WVS, EVS source), we are particularly interested in assessing the impact of inequality on subjective well-being. We thus include income inequality as an additional regressor and run separate regressions for transition and non-transition countries. Also, we include civil liberties as a possible further driver of subjective well-being. Lastly, we investigate whether levels and determinants of subjective well-being are different in transition countries compared to elsewhere. Finally, we use data from the Transmonee Project and Freedom House to assess progress in nonincome dimensions of well-being where we focus on life expectancy, under 5 mortality rates, education, and indicators of political and civil liberties.

4. Results 4.1. Exploring distribution-adjusted economic welfare We study trends in well-being in transition countries by examining trends in our distributionadjusted well-being measures.18 Comparing the 21 transition countries included in Figs. 1 and 2, they show a much more negative assessment of changes in welfare in transition countries. As inequality rose in all 20 of the 21 transition countries listed (except Croatia, where the data only start in 1990), growth in distribution-adjusted well-being is correspondingly lower. The difference differs by country: in most countries, improvements in distribution-adjusted well-being are more than 20% lower than income growth, in some countries the difference reaches more than 40%, depending on whether the Sen or Dagum measure is used. While 18 of the 21 transition countries included experienced higher income levels in 2008 than in 1988, once inequality is considered, three of them (Moldova, Russia and Uzbekistan) are no longer better off than before, due to the negative welfare consequences of rising inequality. In quite a few more countries, the improvements in well-being are negligible once inequality is factored in (e.g. in Bulgaria, Latvia, Lithuania, and Romania); if the recent income declines during the financial crisis, which was particularly sizable in these countries, is considered (see Fig. 1), well-being in 2010 is actually below the levels in 1988 in these countries as well, depending on the inequality measures chosen. Thus in about half of the countries listed inequality-adjusted well-being is similar to or lower than at the start of the transition process. In addition, the declines in well-being in countries that already experienced a decline in per capita incomes (Kyrgyz Republic, Ukraine, and Tajikistan) are much larger when the rising inequality is considered.19 As already discussed by Gruen and Klasen (2001), these results are affected by some correlation between output decline and inequality increase in transition countries. Fig. 3 plots the change in inequality versus the change in income experienced by the transition countries between 1988 and 2008. The scatter diagram suggests a negative correlation between the two variables, which amplifies the welfare losses in those countries where income fell and inequality increased. But the correlation is rather weak, much weaker than found in Gruen and Klasen (2001), which examined the relationship between output decline and inequality increase between 1988 and 1995. It thus appears that in the early phase of transition, countries that suffered a particularly serious transition shock also suffered the largest increase in inequality, most likely related to the speed of economic reforms, the nature of the privatization process, and the ability to shield vulnerable groups (including pensioners, the unemployed, and low wage workers) from the effects of the transition shock.20 In contrast, after the 18 See Tables A.3 and A.4 in Appendix A for levels of well-being using our distribution-adjusted well-being measures for 1988 and 2008. Distribution-adjusted well-being levels using the Sen and Dagum measure are between 20 and 70% lower than per capita income levels, with the difference being much larger in 2008. 19 Compared to Gruen and Klasen (2001), the results on distribution-adjusted welfare are more positive, which is largely driven by the income growth that took place between 1995 and 2008. 20 See Gruen and Klasen (2001), Ivaschenko (2003) and Milanovic (1998) for a more detailed discussion of these issues.

100% 80% 60% 40% 20%

-20% -40% -60%

Income per capita

Sen

Uzbekistan

Ukraine

Tajikistan

Slovenia

Slovak Republic

Russia

Romania

Poland

Moldova

Lithuania

Latvia

Kyrgyz Republic

Kazakhstan

Hungary

Estonia

Czech Republic

Croatia

Bulgaria

Belarus

Azerbaijan

Armenia

-80%

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0%

Dagum

Fig. 2. Change in well-being, 1988–2008. Notes: Due to data limitations, we proxied the income distribution in 1988 using the following years: 1987 for Poland and Slovenia; 1989 for Croatia, Czech Republic, Hungary, Romania, and Uzbekistan. A similar approximation was necessary in 2008. Gini coefficients are from 2006, except for Croatia and Ukraine (2005), Tajikistan (2004), Kazakhstan (2003), Russia (2002), Azerbaijan and Uzbekistan (2001).

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20 100% 80%

Change in income

60% 40% 20% 0% -20% -40%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

-10%

-20%

-60%

Change in Gini coefficients (adjusted) Fig. 3. Change in income and inequality, 1988–2008. Notes: For exact year of Gini coefficients, see Notes to Fig. 2.

transition shock, the relation between income decline and inequality increase is no longer present, weakening the correlation over the longer time period. 4.2. Subjective well-being In Fig. 4, we plot the mean values for subjective well-being using the life satisfaction indicator across waves of the WVS and EVS. We show the transition countries that participate in all four waves and compare them to the aggregate of two groups of non-transition countries, also including only those countries that participated in four waves. One consists of OECD countries and one consists of

8 7.5 7 6.5 6 5.5 5 4.5 4

1989-1993

1994-1999

1999-2004

Bulgaria

Poland

Romania

Russian Federation

Non-TC, middle income

Non-TC, high income

2005-2007 Slovenia

Fig. 4. Trends in subjective well-being. Notes: Middle income non-transition countries include Argentina, Chile, China, India, Mexico, South Africa and Turkey. Rich non-transition countries covered in all 4 waves are Finland, Germany, Japan, South Korea, Spain, Sweden, Great Britain and the US.

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emerging and developing countries (see Fig. 4 for a list of the countries included). One should note that the first wave that comprehensively covers transition countries took place around 1989–1993 (see Table A.2 for the exact years for each transition country), already after the start of the transition process, thus not reflecting pre-transition conditions, but the experiences of the early phase of the transition process. Several points are worth noting. First, the average levels of life satisfaction in OECD countries are remarkably stable over time, averaging at relatively high levels. Thus populations in OECD countries report high levels of life satisfaction that do not, however, grow further with additional income growth. This is not only true for the average of OECD countries but also for each individual country where satisfaction levels are very stable across time. Second, life satisfaction is much more volatile in transition countries, and in most transition countries the average levels are much lower. Life satisfaction in transition countries also appears to closely track income development in the transition process. In most countries, it falls further in the mid-1990s before recovering in the early 2000s. The trends differ slightly among transition countries, though. In Slovenia, things already improve in the mid-1990s, while in Poland they only recover after 2004. Third, of the transition countries, only Slovenia reaches the levels of OECD countries; this is not surprising, given that Slovenia is one of the most successful transition countries, entering the EU in 2004 and joining the Euro zone in 2007. The other transition countries remain far below the life satisfaction levels of OECD countries, but appear to reach the levels of other developing countries (that, individually, show a similar volatility over time to transition countries). They also seem to just reach the levels in 2007 that they already had in the early phases of transition. These descriptive results seem to suggest that life satisfaction in transition countries was deeply and negatively affected by transition, which is consistent with the findings on distribution-adjusted well-being measures. With higher growth and stable inequality since 2000, they have recovered, but remain rather low. In order to investigate this further, we now turn to the results of our regression models to see whether the drivers of subjective well-being differ between transition and non-transition countries, with particular emphasis on the role of income and inequality on subjective well-being. These are shown in Table 2. The first regression pools all the micro data on subjective well-being from transition and non-transition countries; now we use an unbalanced panel, i.e. also include countries where we only have data on one, two, or three waves (see Table A.2 for countries included). The overall results are in line with the literature on determinants of life satisfaction. In particular, richer groups within a country report higher life satisfaction, there is a u-shaped age pattern, unemployed and (to a lesser extent) retired people report lower satisfaction than employed people, divorced, separated and widowed persons are less satisfied, and women are more satisfied than men. While lower civil liberties (which equal a higher Freedom House score) reduce life satisfaction, overall inequality in a country has no significant effect on life satisfaction in the total sample, beyond the relative income effect. More interesting for our purposes are the dummies for country groups, where we compare transition countries to OECD and developing countries, and interact these effects with the dummy for waves of the survey (to proxy for time interactions). The results show clearly that life satisfaction, conditional on all the covariates, is significantly lower in transition countries than elsewhere; in addition, the interactions show that life satisfaction, conditional on covariates, was particularly low in transition countries in the mid- to late 1990s, when many transition countries were at the low point of the transition process. Thus, even conditional on the usual drivers of life satisfaction, populations in transition countries remain significantly less satisfied, and strongly so in the mid- to late 1990s.21 The further regressions consider transition and non-transition countries separately, once with and once without country fixed effects. While many determinants of life satisfaction are quite similar between the two groups of countries, there are some notable differences. In particular, income effects are rather similar, with richer households reporting much higher life satisfaction. The effect appears to be somewhat stronger in transition countries, suggesting that relative incomes matter even more there. The effects of higher civil liberties on life satisfaction are also similar. Marital status effects do 21 Results presented in Table 2 are fairly robust to alternative specifications. In particular, concerns regarding multicollinearity between age group and labour market status are unfounded: omitting either does not change the coefficients of the other variable.

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Table 2 Determinants of subjective well-being. Full sample Waves .021 1994–1999 1999–2004 .865** 2005–2007 .069 Income level Middle income .569** Higher income 1.009** Age group 25–34 .275** 35–44 .432** 45–54 .502** 55–64 .317** 65+ .103 Labour market status Part-time .087 Self-employed .032 Retired .199** Housewife .110 Student .069 Unemployed .771** Other .065 Marital status Living together .164 Divorced .465** Separated .533** Widowed .500** Single .187** Gini coeffcient .011 Male dummy .098** Civil liberties .263** Dummy TC .950* Dummy OECD .164 Interaction terms TC  wave 3 1.067* TC  wave 4 .525 TC  wave 5 .178 OECD  wave 3 .179 OECD  wave 4 1.092** OECD  wave 5 .136 Gini  wave 3 Gini  wave 4 Gini  wave 5 Country dummies No N R2

TC

Non-TC

TC

Non-TC

(.278) (.164) (.280)

.642* .059 .282

(.277) (.276) (.202)

.123 .399** .032

(.170) (.148) (.163)

1.073 1.456 .936

(.642) (.997) (.940)

(.065) (.087)

.595** 1.116**

(.111) (.148)

.550** .956**

(.079) (.104)

.513** 1.065**

(.099) (.135)

.543** .940**

(.069) (.103)

(.036) (.055) (.056) (.068) (.087)

.270** .635** .766** .663** .548**

(.055) (.079) (.084) (.101) (.125)

.254** .331** .362** .141 .121

(.048) (.066) (.064) (.077) (.111)

.269** .630** .760** .563** .394**

(.049) (.044) (.052) (.078) (.122)

.249** .368** .439** .253** .056

(.029) (.038) (.041) (.048) (.074)

(.055) (.070) (.065) (.102) (.116) (.082) (.166)

.064 .223 .181** .016 .433** .593** .060

(.093) (.128) (.048) (.163) (.081) (.107) (.096)

.129* .147 .047 .223 .239 .913** .462

(.062) (.081) (.095) (.126) (.161) (.111) (.228)

.101 .162 .199** .118 .371** .587** .038

(.064) (.088) (.048) (.092) (.092) (.070) (.080)

.133** .071 .018 .054 .052 .653** .230**

(.033) (.041) (.054) (.057) (.089) (.093) (.068)

(.158) (.062) (.115) (.045) (.064) (.012) (.037) (.077) (.398) (.364)

.244* .542** .713** .456** .125* .040** .010 .231**

(.103) (.084) (.143) (.071) (.052) (.013) (.039) (.078)

.146 .333** .483** .465** .171* .018 .183** .309**

(.164) (.079) (.141) (.056) (.084) (.015) (.049) (.080)

.297** .518** .705** .473** .237** .034 .044

(.084) (.084) (.089) (.062) (.040) (.032) (.025)

.189* .471** .700** .407** .288** .033 .096**

(.078) (.050) (.075) (.053) (.047) (.019) (.028)

.101 .147 .134

(.125) (.160) (.123)

(.421) (.327) (.391) (.305) (.214) (.313)

187,901 0.17

No

No

.057* (.024) .055 (.032) .022 (.030) Included

52,709 0.14

135,192 0.11

62,522 0.18

Included 135,192 0.18

Notes: Robust standard errors in parentheses. Reference categories: 1989–1993, lower income, age group 15–24, full-time employed, married. For the following transition countries, civil liberties scores were not available for any of the years covered in wave 2 (1989–1993): Belarus, Czech Republic, Estonia, Latvia, Lithuania, Russia, and Slovakia. * Significance level: 5%. ** Significance level: 1%.

not differ much either, particularly in the fixed effects specification. Significant differences are found in age and gender effects as well as the wave dummies. While the wave effects largely reflect the trends shown in the descriptive analysis, the age effects point to much lower life satisfaction among older groups in transition countries than elsewhere. Also, males are not significantly less satisfied, as they are in non-transition countries. These results suggest that there are some groups that appear to feel that they lost out in the transition process, particularly including older age groups and women, while younger people, especially students, appear to be much more satisfied.

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23

There is also a significant negative effect of inequality on subjective well-being in the transition country sample which is not found in the other samples. An increase in the Gini of 10 points (not uncommon during transition) reduces life satisfaction by 0.4. This suggests that high inequality in transition countries is seen as much more problematic there than elsewhere; this is not unexpected, as one would assume people conditioned in the socialist system to have stronger preferences for equality than people in market systems.22 In the fixed effects regression, the effect of the Gini coefficient is not significant on average (not shown here), but when interacted with the wave, it has a significant negative effect in the third wave (1994–1999) which is marginally significant and negative in the fourth (1999–2004) and ceases to have an effect in the fifth (2005–2008). Thus, it appears that the rising inequality in transition countries depressed life satisfaction in those periods, but by now populations in transition countries no longer view high inequality to be a problem, similar to elsewhere. Thus our descriptive and econometric analysis of subjective well-being confirms that life satisfaction in transition countries has been severely negatively affected by the transition process. There appears to be a strong generational effect, with older generations feeling less satisfied, and rising inequality levels further depress life satisfaction in transition countries. Also, relative incomes and income inequality affect life satisfaction more in transition countries than elsewhere, particularly in the mid- to late 1990s. 4.3. Other non-income dimensions of well-being In Table 3 we examine trends in non-income measures of well-being for transition countries. Here the record is also mixed, but generally somewhat more positive than with the inequality-adjusted income-based and subjective measures. Let us briefly discuss the indicators in turn. As far as life expectancy is concerned, one should first note that in socialist times, life expectancy, given the poor level of economic performance, was remarkably high, which was related to low inequality, good health access, full employment, and high economic stability for individuals (Klasen, 1993). During transition, the majority of countries (14 out of 25 listed for males and 13 for females) experienced a decline in life expectancy between 1988 and 1995. The decline was generally larger for males than for females. As a result, women have been able to slightly extend their life expectancy advantage from the already high advantages they enjoyed prior to transition (Klasen, 1993). The reasons for these gender-specific effects have been analysed in the literature (Paci, 2002). This literature suggests that the uncertainty and economic and social crisis brought about by the transition have worsened diseases as well as dangerous behavioral patterns among males (especially relating to alcohol abuse) with significant mortality consequences. By 2008, life expectancy has improved in most countries from this low level and now exceeds pre-transition values in most countries, particularly for women. But for males, life expectancy remains below the pre-transition levels in 5 countries, all successor states of the former Soviet Union (Russia, Ukraine, Belarus, Kazakhstan, and Lithuania). The picture is more favorable for under five mortality which, after some moderate declines in some countries during transition, has fallen in all transition countries by 2008. Thus, child health seems to have been much less affected by the negative effects of transition than adult health, and might have benefited from better access to health knowledge and technologies, as well as lower fertility. Turning to education, the record is more negative. In a large number of transition countries, particularly those affected by severe income declines, there have been large declines in educational enrolments. This reduces well-being directly as it denies educational opportunities to many children. It will also negatively affect economic development in the future. By 2008 (secondary) enrolments have generally recovered, but not to the levels previously enjoyed. Lastly, we consider some indicators of political and civil rights. Here the picture is quite positive (as already shown in Gruen and Klasen, 2001) compared to the pre-transition period. Virtually all 22 This is in line with previous research. Suhrcke (2001) analyses whether preferences for income inequality differ systematically between post-socialist countries and the Western world. His results show that people living in transition countries are less tolerant towards existing income differences; see also Sanfey and Teksoz (2007) for related results.

24

Table 3 Social and political indicators in pre- and post-transition period. Country

1989

1995

2008

1990

Female

Male

Female

Male

Female

Male

76 75 74 76 75 75 75 75 74 73 72 75 76 74 72 76 72 74 – 75 77 72 68 75 72

70 69 67 67 69 67 68 66 65 64 64 65 67 70 66 67 67 64 – 67 69 67 62 66 66

75d 76 73 74 75 76 77 74 75 69 70 73 75 74 70 76 73 72 74d 76 77 69 67 73 72

69 69 65 63 67 67 70 62 65 58 61 61 63 70 62 68 66 58 69d 68 70 64 62 62 66

79 77 75 76 77 80 80 79 78 72 73 78 78 76d 73 80 77 74 76 79 82 75 73d 74 75d

72 70 70 65 70 72 74 69 70 62 64 67 66 72d 66 71 69 62 71 71 75 70 66d 62 70d

45 27 46 15 18 14 12 19 18 35 47 15 14 40 27 22 35 23 – 16 10 65 78 18 54

1995

37 20 43 17 19 10 9 20 12 38 41 23 16 25 27 15 26 23 13d 13 7 63d 66 20 42

Enrolmentb 2008

8 12 14 6 10 5 3 6 7 23 31 8 6 11 14 7 13 10 8 7 3 16 14d 12 20d

1989

89 – 89 98 99 – 93 103 87 102 103 96 95 – 94 88 102 95 – – 88 102d – 96 101

1995

71 95d 82 93 93 81 96 101 91 96d 87 89 84 77 81 95d 77 87d 93d 90 91 81 – 90d 92

Freedom House Indexc 2008

78d 88 106 95d 89 94d 95 99 97 92 85 98 99 84 88 100d 92 85 89 92 97 84 – 94 101

1988–89

1995–96

2008

PR

CL

PR

CL

PR

CL

7 – – 6 7 – 7 6 5 6 6 6 6 – 6 5 7 6 – 7 2 – 6 6 6

7 – – 5 7 – 6 5 4 5 5 5 5 – 5 5 7 5 – 6 3 – 5 5 5

3 4 6 5 2 4 1 2 1 6 4 2 1 4 4 1 4 3 – 2 1 7 7 3 7

4 4 6 5 2 4 2 2 2 5 4 2 2 3 4 2 3 4 – 3 2 7 7 4 7

3 5 6 7 1 2 1 1 1 6 5 2 1 3 3 1 2 6 3 1 1 6 7 3 7

3 4 5 6 2 2 1 1 1 5 4 1 1 3 4 1 2 5 2 1 1 5 7 2 7

Notes: Data on life expectancy and mortality rates are provided by UNICEF (2010). Enrolment rates are taken from WDI (2010). a Under five mortality rate, per thousand live births (UNICEF, 2010). b School enrolment, secondary gross rates (WDI, 2010). c Freedom House (2010) reports on political rights (PR), civil liberties (CL). Political rights and civil liberties are rated at a one to seven scale, with one representing the highest (best) score. d The data point was not available for a particular country and year and the closest available data point for the respective country and year is depicted.

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Albania Armenia Azerbaijan Belarus Bulgaria Croatia Czech Republic Estonia Hungary Kazakhstan Kyrgyz Republic Latvia Lithuania Macedonia Moldova Poland Romania Russia Serbia Slovak Republic Slovenia Tajikistan Turkmenistan Ukraine Uzbekistan

Mortality ratea

Life expectancy at birth (years)

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25

transition countries have moved from unfree to at least partially free societies in both the political and civil liberties dimensions. The big exceptions are some Central Asian former republics of the Soviet Union and Belarus, which remain largely unfree to this date. Since the mid-1990s, however, trends in political and civil liberties have diverged somewhat. In all of the transition countries that eventually joined the European Union there were further improvements in either political or civil liberties (or both) between 1995 and 2008, so that all EU accession countries rate highest or second highest on these measures. This is presumably also related to the explicit conditions the EU has set for admission in its acquis communitaire, which all new member countries had to adhere to prior to accession. In contrast, in the remaining transition countries, most have remained only partly free, progress towards improvements was slow or absent, and in some countries there were deteriorations. The most serious deterioration occurred in Russia, but notable deteriorations also occurred in Uzbekistan and Belarus. But the overall positive picture remains that in all transition countries political and civil liberties are now higher than before the transition, with dramatic improvements among EU accession countries. It is likely that at least for this group of countries the improvements in civil and political rights are to be lasting. Thus, measuring well-being in a non-income dimension suggests that transition countries have experienced some progress, notably in the child mortality and political and civil rights sphere. But the progress is not universal and there are still countries where education and life expectancy continue to be below pre-transition levels. Considering all indicators together, there continue to be several transition countries that fare neither better nor worse than prior to transition on most distribution-adjusted, subjective and nonincome indicators considered here, including Russia, Belarus, Uzbekistan, and Ukraine. Many more continue to fare worse in at least some of them. Thus, in terms of well-being, even 20 years after the fall of the Berlin wall, there has not been a consistent improvement in well-being in most transition countries; the sizable improvements in EU accession countries and the improvements in political and civil liberties in most transition countries remain to date the biggest achievements of the transition process. 5. Conclusion In this paper, we examined trends in well-being in transition countries using distribution-adjusted income, subjective well-being, and non-income indicators. After 20 years of transition, many transition countries have not regained the level of well-being they enjoyed at the start of the transition. This is true regardless of whether inequality-adjusted income measures, subjective measures of well-being, or some non-income measures (such as life expectancy or educational enrolments) are concerned. At the same time, there is a great deal of variation between transition countries. In particular, those countries that joined the EU have mostly witnessed considerable improvements in well-being; Slovenia is the one country where there appear to be hardly any differences to EU levels of well-being. In contrast, in quite a few of the successor states of the Soviet Union, the welfare impact of transition continues to be negative. Inequality is larger, inequality-adjusted well-being is lower or little improved, subjective well-being has not recovered, mortality is higher, enrolments are lower, and even political and civil liberties are not significantly better in quite a few states. The analysis of determinants of subjective well-being suggests that subjective well-being remains significantly lower in transition countries. Our regression results suggest that particularly older generations have lost out and that inequality depresses well-being in transition countries, particularly during the 1990s; students and young people are conversely much more satisfied with life in transition countries, pointing to a possibly more positive future. The results also show that the decade of growth in the 2000s has improved the much direr wellbeing situation in transition countries of the mid- to late 1990s. Despite this and sustained improvements in political and civil liberties, from a well-being perspective, the 20 years since the start of transition have brought little improvement for many transition countries and their populations; if we considered the impact of the recent financial crisis on well-being, the results would be even more disappointing.

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Clearly, these descriptive results call for more analysis on the determinants of this rather poor wellbeing performance, as does the heterogeneity in findings. In particular, the role of macro, sectoral, and social policies in transition countries, the role of EU accession, the linkages between political reforms, and economic well-being are all topics that deserve further analysis. Acknowledgements We thank the Thyssen Foundation for generously funding this project. We also thank participants at workshops in Munich, Goettingen, Cape Town and Assisi for helpful comments and discussion. We would also like to thank two anonymous referees and the editors of this symposium for helpful comments on an earlier version of the paper. Appendix A

Table A.1 Sensitivity of Gini coefficients to measurement concept. 5.30** 1.90** 0.68 4.24** 0.53 3.61** 0.91* 1.17 0.54 1.44** 1.98** 7.34** 8.34** 5.83** 1.57** 1.05 41.71**

Expenditure/consumption Disposable income Income concept unknown Family No equivalence scale Other equivalence scale Income SU and AE unknown 1950s 1960s 1970s 1980s TRANS  1960s TRANS  1970s TRANS  1980s TRANS  1990s OECD  disposable income Intercept

(0.38) (0.45) (0.37) (0.53) (0.34) (0.41) (0.45) (0.67) (0.44) (0.38) (0.31) (1.39) (1.20) (0.79) (0.52) (0.59) (0.34)

N R2

2646 0.25

Notes: Country fixed effects regression. Reference: Gross income per person. * Significance level: 5%. ** Significance level: 1%.

Table A.2 Mean life satisfaction of countries used in regression analysis. Country

Wave 2 1989–1993

Wave 3 1994–1999

Wave 4 1999–2004

Wave 5 2005–2007

Transition countries Albania Armenia (ARM) Belarus (BLR) Bosnia and Herzegovina Bulgaria (BGR) Croatia (HRV) Czech Republic (CZE) Estonia (EST) Georgia Hungary (HUN) Kyrgyz Republic (KGZ) Latvia (LVA) Lithuania (LTU) Macedonia

– – 5.55 – 5.05 – 6.68 6.00 – 6.02 – 5.70 6.02 –

4.73 4.28 4.34 5.52 4.59 6.18 6.37 5.02 – – – 4.88 4.95 5.56

5.17 – 4.81 5.75 5.45 6.64 7.05 5.89 – 5.81 6.49 5.23 5.11 –

– – – – 5.21 – – – 4.95 – – – – –

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27

Table A.2 (Continued ) Country

Wave 2 1989–1993

Wave 3 1994–1999

Wave 4 1999–2004

Wave 5 2005–2007

Moldova (MDA) Poland (POL) Romania (ROM) Russia (RUS) Serbia Slovak Republic (SVK) Slovenia (SVN) Ukraine (UKR) Non-transition countries Austria Bangladesh Belgium Brazil Burkina Faso Canada Chile China Colombia Cyprus Denmark Egypt El Salvador Finland France Germany Great Britain Greece India Indonesia Iran Iraq Ireland Italy Japan Jordan Luxembourg Malaysia Malta Mexico Morocco Netherlands New Zealand Nigeria Norway Pakistan Philippines Portugal South Africa South Korea Spain Sweden Switzerland Tanzania Thailand Turkey Uganda Vietnam Zambia

– 6.59 5.87 5.39 – 6.61 6.29 –

3.75 – 4.86 4.42 – 6.05 – 4.02

4.54 6.18 5.18 4.62 – 6.00 7.25 4.55

5.45 7.00 5.73 – 6.03 – 7.27 –

– – – 7.38 – 7.89 – 7.29 – – – – – 7.68 – 7.01 7.52 – 6.69 – – – – 7.27 6.52 – – – – 7.39 – 7.79 – 6.58 7.72 – – 7.04 6.74 – – – – – – 6.41 – – –

– 6.46 – 7.14 – – 6.86 6.83 8.42 – – – 7.46 7.77 – 6.95 7.57 – 6.54 – – – – – 6.59 – – – – 7.55 – – 7.66 6.58 7.68 – – – 6.03 – – 7.78 8.02 – – 6.17 – – –

8.02 5.79 7.42 – – – 7.12 6.53 – – 8.28 5.35 – 7.87 6.99 7.39 7.31 6.63 5.18 6.94 6.29 5.22 8.22 7.17 6.51 5.60 7.92 – 8.24 8.11 5.91 7.83 – 6.88 – 4.86 6.65 6.92 6.22 – 6.97 7.62 – 3.81 – 5.61 5.78 6.51 –

– – – 7.62 5.53 – 7.26 6.77 8.31 7.35 – 5.77 – 7.82 6.85 6.89 7.58 – – 6.89 6.44 4.46 – 6.92 – – – 6.84 – 8.23 – 7.70 – – 7.99 – – – – 6.39 7.31 7.75 7.95 – 7.21 7.45 – 7.09 6.09

Notes: In wave 2, the exact survey years for the included transition countries were as follows: 1989/1990: Poland; 1990: Belarus, Bulgaria, Estonia, Latvia, Lithuania, and Russia; 1990/1991: Czech Republic and Slovak Republic; 1991: Hungary; 1992: Slovenia; 1993: Romania.

C. Gruen, S. Klasen / Economic Systems 36 (2012) 11–30

28 Table A.3 Welfare measures 1988. Rank 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

GNI/cap UZB KGZ MDA ARM TJK AZE BLR KAZ BGR POL ROM UKR LVA EST LTU HRV RUS SVK HUN CZE SVN

Sen 2004 2395 2504 2936 3480 4754 6434 7089 7815 8171 8344 8348 9737 10,641 11,879 12,516 12,630 12,647 12,665 16,320 16,401

UZB KGZ MDA ARM TJK AZE KAZ BLR POL BGR ROM UKR LVA EST HRV LTU HUN RUS SVK SVN CZE

Dagum 1443 1777 1898 1990 2226 3046 4960 4973 5838 6111 6287 6403 7134 7542 7925 8715 9594 9611 9795 12,237 12,777

UZB KGZ ARM MDA TJK AZE KAZ BLR POL BGR ROM UKR LVA HRV EST LTU HUN RUS SVK SVN CZE

1127 1413 1505 1528 1636 2241 3814 4053 4541 5017 5044 5193 5629 5798 5841 6882 7721 7757 7993 9759 10,498

Notes: Income concept is real GNI per capita, PPP adjusted 2005 prices (WDI, 2010). AZE: Azerbaijan; KAZ: Kazakhstan; TJK: Tajikistan; UZB: Uzbekistan. For other country codes, see Table A.2.

Table A.4 Welfare measures 2008. Rank 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

GNI/cap TJK KGZ UZB MDA ARM UKR AZE KAZ BGR ROM BLR LVA RUS HRV LTU POL HUN EST SVK CZE SVN

Sen 1761 2025 2455 2752 5611 6721 8101 10,458 10,571 11,062 11,340 14,638 14,706 16,166 16,400 16,418 18,093 18,885 20,515 23,223 27,182

TJK KGZ UZB MDA ARM UKR AZE KAZ ROM BGR BLR LVA RUS LTU POL HRV EST HUN SVK CZE SVN

Dagum 1076 1183 1237 1641 3261 4469 4755 6536 6886 7094 7962 8125 9367 9759 9923 10,621 11,615 12,819 13,425 15,894 19,164

TJK UZB KGZ MDA ARM UKR AZE KAZ ROM BGR LVA BLR RUS LTU POL HRV EST HUN SVK CZE SVN

775 827 835 1169 2298 3348 3365 4753 4999 5339 5623 6135 6872 6946 7110 7908 8386 9925 9977 12,081 14,799

Notes: See Table A.3.

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