The countries that we still, somewhat lazily, call the transition economies are becoming more diverse by the day. This increase in diversity is reflected in their incomes per capita: some countries have turned the corner on economic recession and are growing again, while others, most notably the two most populous transition countries, Russia and Ukraine, remain mired in depression.
Figure 1 illustrates the growing divergence in incomes by showing the coefficient of variation1 of GDP per capita in constant 1987 dollars for the countries of Central and Eastern Europe (CEE) and the countries of the former Soviet Union. Before the transition, this coefficient for the republics of the Soviet Union was about 0.3. Ten years later it exceeded 0.5. The increase started as soon as the Soviet Union disintegrated. Similar if less dramatic are the developments in CEE. If the same calculation were done using GDP per capita in current dollars, the divergence between the countries would be even greater.
Coefficients of Variation of GDP per Capita in Transition Economies
Source: World Bank DEC database.A similar phenomenon is observed if one looks at income inequality. Inequality has increased everywhere in the region, as shown in Figure 2 by the number of countries above the diagonal. But the dispersal of the Gini coefficients among countries is now also much greater than before the transition. In the past Gini coefficients ranged from 21 (in Czechoslovakia) to about 26 to 28 (in the Central Asian republics and Poland). Now, however, Ginis in the region span the range from the mid-20s to 40, and a few outliers (Kyrgyz Republic, Russia, and Ukraine) have Ginis about or even above 50, reflecting a level of inequality often associated with Latin American and African countries.
Gini Coefficients Before the Transition in 1994–95 in 18 Transition Economies
Gini in 1994–95
Source: Milanovic (1998, p. 41); Luxembourg Income Study data.Note: “No change” means no change compared with inequality before the transition. Gini coefficients are calculated on the basis of income per capita.1Gini coefficients for 18 market economies based on disposable income.Figure 3 shows for each transition country the loss in GDP between 1987 and 1997 and the increase in inequality, as measured by the Gini coefficient, over approximately the same period. Countries are arranged from left to right by the percentage of their income loss: the farther a country is to the right, the more it has lost relative to its pre-transition GDP. By 1997 only Poland’s GDP had actually risen, by 20 percent compared with 10 years before. Four other Central European countries (Czech Republic, Hungary, Slovak Republic, and Slovenia) and Uzbekistan had relatively small losses ranging from 2 to 9 percent of GDP compared with 1987.
Changes in Gini Coefficients and GDP Loss for 26 Transition Economies
source: World Bank Data.Figure 3 also shows that although the Gini coefficient has risen in all of the countries for which data are available, except the Slovak Republic, it has generally increased more in those that have suffered larger declines in incomes. This is not, of course, to assert a causal relationship. Many other factors might have driven inequality up, and GDP down. However, the fact of large GDP losses and large increases in inequality has very important implications for what happened to the percentage of people in poverty. This set of countries was therefore hit with a double whammy: large income losses and large increases in inequality. Both, of course, pushed poverty up. This again implies a rising disparity among the transition economies, this time in terms of poverty head counts.
The five Central European countries known as the Visegrad countries (Czech Republic, Hungary, Poland, Slovak Republic, and Slovenia) display features that are very different from the other transition economies. Their GDP losses, as we have seen, have been smaller. Their income inequality has increased less than elsewhere. Social spending as a share of GDP has risen more significantly than in the other transition economies. These differences make it increasingly inappropriate to generalize across all transition economies. But when we discuss the transition economies, the situation becomes much more varied, and it is not easy to identify groupings that share distinct common features.
Rapidly Rising Inequality: The Example of Russia
The general increase in inequality in the transition economies no longer comes as a surprise. What is surprising, however, is how large and fast the increase in inequality was in some of these countries.
Figure 4 shows Gini coefficients from 1980 until the mid-1990s for Brazil, Russia, and the United States. The Gini coefficient is a sluggish measure. What was, in historical perspective, a large increase in inequality in the United States in the 1980s shows up as a gently upward sloping line. But that increase pales in comparison with Russia’s. In the 1980s, inequality increased in the United States by about ½a Gini point per year. This is about one-fourth or one-fifth the pace of the recent increase in the Gini coefficient in Russia. The speed with which inequality has increased in Russia is, indeed, probably unique in recorded world history. Of course, one needs to take into account that inequality was underestimated before the transition. But even if one revises the pretransition Ginis upward by a few points (as suggested by the difference in the Gini values calculated by the Goskomstat (State Committee of the Russian Federation of Statistics) and by the Russian Longitudinal Monitoring Survey in the years for which both are available), one still observes an extremely steep increase.
Gini Coefficients for Brazil, Russia, and the United States
Sources: Data for Russia: 1989–93 from Goskomstat Rossii; 1992–96 from Russian Longitudinal Monitoring Survey data; U.S. data from Current Population Survey; Brazilian data calculated from PNAD Survey (supplied by Francisco Ferreira).Note: Gini coefficients are calculated on the basis of disposable income per capita.The increase in Russia’s Gini is even more remarkable when set against the fact that, when Gini coefficients for many countries are combined in a pooled cross section, more than 90 percent of the variability is explained by the differences in cross-country Ginis (Li, Squire, and Zou, 1998). In other words, changes in inequality within countries over time are typically small. But this is not the case in Russia and a number of other transition economies.
It is also interesting, and puzzling, that this remarkable increase in inequality took place as Russia became more democratic. Empirical research and some theory have generally tended to argue that more authoritarian societies, characterized by a high concentration of both political and economic power, will tend to display high inequality (Hewitt, 1977; Muller, 1988). Thus, if anything, we would expect democratization to reduce inequality.
How To Account for Increasing Inequality: Looking at the Recipients
How can we account for the increase in inequality in the transition economies? Can we disaggregate the factors that pushed inequality up? If we can point to the factors that, at least in some accounting sense, raised inequality, this might provide us with some direction for further research and allow us to say something meaningful about the economic forces behind the increase.
I will start with a simple model that is discussed more extensively elsewhere (Milanovic, 1999). Let us assume that, before the transition, the economy was composed of a small private sector and a large state sector. I assume that the private sector consists mostly of self-dededdemployed labor and that its growth is blocked by legal restrictions on its size. (For example, in a number of socialist countries, nonagricultural private businesses were limited to 5 or 10 hired workers.) The state sector employs everybody else. There is no unemployment. Wages in the private sector are higher than in the state sector. Obviously, if labor could freely flow from the state to the private sector, the two wage rates would be equalized. But the restrictions on hiring in the private sector allow the two wage rates to remain different. In addition to the state sector workers and the self-employed, there are pensioners, who are paid out of tax revenues. The mean income of pensioners is the lowest of the three groups.
The social structure before the transition thus looks as follows. By far the largest percentage of household heads are working in the state sector, where wage differentiation is relatively small and wage levels are moderate (the state sector wage can be taken as a numeraire). Some household heads (say, 10 percent) are self-employed. Their average income is higher than that in the state sector, and the distribution of their incomes is more unequal. Finally, some household heads (say, 10 to 20 percent) are pensioners with relatively low income per head and very low income differentiation.
During the transition, the large group of state sector workers, with incomes intermediate between those of the other two sectors, bifurcates. Some workers remain in the state sector. But others transfer into the private sector, and still others lose their jobs. So whereas before the transition 60 or 70 percent of household heads were state sector employees, with a fairly moderate income differentiation, after the transition there is a hollowing out of that sector, with some people moving into highly paid private sector jobs and others joining the unemployment rolls.
Having thus briefly sketched the changes that have occurred during the transition, we can consider how this might translate in terms of the Gini coefficient. The overall Gini coefficient can be broken down into sectoral Gini coefficients as in equation (1):
where the Gi’s are the Gini coefficients of recipients only of private sector employees (Gy), state sector employees (Gw), and transfer recipients (Gt). The last of these groups includes pensioners and the unemployed (the latter, of course, only after transition has begun), because they are both paid out of tax revenues. The πs and ps are weights attached to these sectoral Gini coefficients: the πs are income shares and the ps population shares. The variable μ is average overall income. In addition, the expression includes terms showing the differences in mean incomes between these three categories. For example, y – ws shows the difference between average private sector income and the average state sector wage. Finally, because the Gini coefficient is not exactly decomposable by recipients, there is also an extra, “overlapping” term, L. This term accounts for the fact that someone in a sector with a higher average income (say, a private sector employee) may have a lower income than someone from a “poorer” sector (say, a pensioner). Of course, if all private sector workers were richer than all pensioners or state sector workers, there would be no overlapping, and L would equal zero.
How would the transition be reflected in equation (1)? There are three possible ways. The first is through movement of labor. We know that, by definition, the transition is about people leaving the state sector and moving into better-paid private sector jobs. But there are also state sector workers who have lost their jobs and have joined the unemployed. If, initially, Gy > Gw, the movement of workers into the private sector will increase overall inequality (the weight attached to a higher G will increase). Note that, according to this first scenario, the overall Gini is affected through movement of labor alone; the sectoral Ginis (Gw, Gy, and Gt) as well as the mean sector incomes stay the same. This is the mobility explanation.
Another scenario through which the overall Gini might change involves increases in individual (“inherent”) sector inequalities—that is, Gw, Gy, and Gt might go up. In this scenario, people do not move between the sectors, and mean incomes do not change, but inequality (income dispersion) within each sector becomes greater: wages, private sector income, and even pensions become more unequally distributed. This, too, is a possible definition of transition: widening income differences within sectors.
The third possibility is that labor stays where it was before the transition, and within-sector inequalities do not change (so that the sectoral Ginis stay the same), but mean sectoral incomes change in such a way that the rich (private) sector becomes richer, and the poor transfer recipients become even poorer. In other words, all pensioners remain pensioners, and inequality among them stays as before, but all pensions are, for example, simply cut in half. Then, obviously, the mean pension declines relative to the mean wage or mean private sector income, and because the differences between the mean sector incomes in equation (1) rise, the overall Gini rises, too.
We do not expect to find in real life a country in which one of these “pure” scenarios occurs to the exclusion of the others. The real-world change in the Gini will be the product of complex interactions in which each of these three scenarios occurs to some degree.
How To Account for Increasing Inequality: Looking at Income Sources
Next we look at how different income sources (as opposed to income recipients) have changed during the transition. When we look at sources of income, we no longer consider individuals to be either pensioners or state or private sector workers, but simply people who draw their income from different sources. In the Gini disaggregation by sector of recipient, people belonged either to one category or another (they were either pensioners, private sector workers, or state sector workers). In the Gini disaggregation by income source, in contrast, a person can, for example, be a pensioner who has an extra job, or a state sector worker with some property income.
Consider the income composition data from six countries: Bulgaria, Hungary, Latvia, Poland, Russia, and Slovenia. The data come from successive annual household budget surveys covering approximately the period 1987–96. Table 1 shows the results for each country for the two years (first and last) for which data are available. Total income is divided into four sources: labor income (wages), private nonwage income (including home consumption, self-employment income, property income, remittances, and gifts), pensions, and other cash social transfers.
Composition of Gross Income Before Transition and Several Years Later in Selected Transition Countries
(In percent of total household income)
Includes private sector wages.
Unweighted average of the countries listed.
Composition of Gross Income Before Transition and Several Years Later in Selected Transition Countries
(In percent of total household income)
| Wages | Nonwage Private Income |
Pensions | Other Social Transfers |
||||||
|---|---|---|---|---|---|---|---|---|---|
| Country and Years | Before | After | Before | After | Before | After | Before | After | |
| Central and Eastern Europe | |||||||||
| Bulgaria, 1989–95 | 57 | 47 | 22 | 31 | 17 | 18 | 5 | 4 | |
| Hungary, 1987–93 | 60 | 50 | 14 | 16 | 19 | 19 | 7 | 15 | |
| Poland, 1987–95 | 55 | 34 | 24 | 301 | 17 | 30 | 5 | 7 | |
| Slovenia, 1987–95 | 67 | 57 | 20 | 18 | 17 | 22 | 2 | 4 | |
| Regional average2 | 60 | 47 | 20 | 24 | 17 | 21 | 5 | 8 | |
| Former Soviet Union | |||||||||
| Russia,1989–96 | 74 | 49 | 5 | 27 | 8 | 18 | 7 | 6 | |
| Latvia, 1989–96 | 82 | 50 | 12 | 23 | 8 | 18 | 3 | 9 | |
| Regional average | 78 | 50 | 9 | 25 | 8 | 18 | 5 | 7 | |
Includes private sector wages.
Unweighted average of the countries listed.
Composition of Gross Income Before Transition and Several Years Later in Selected Transition Countries
(In percent of total household income)
| Wages | Nonwage Private Income |
Pensions | Other Social Transfers |
||||||
|---|---|---|---|---|---|---|---|---|---|
| Country and Years | Before | After | Before | After | Before | After | Before | After | |
| Central and Eastern Europe | |||||||||
| Bulgaria, 1989–95 | 57 | 47 | 22 | 31 | 17 | 18 | 5 | 4 | |
| Hungary, 1987–93 | 60 | 50 | 14 | 16 | 19 | 19 | 7 | 15 | |
| Poland, 1987–95 | 55 | 34 | 24 | 301 | 17 | 30 | 5 | 7 | |
| Slovenia, 1987–95 | 67 | 57 | 20 | 18 | 17 | 22 | 2 | 4 | |
| Regional average2 | 60 | 47 | 20 | 24 | 17 | 21 | 5 | 8 | |
| Former Soviet Union | |||||||||
| Russia,1989–96 | 74 | 49 | 5 | 27 | 8 | 18 | 7 | 6 | |
| Latvia, 1989–96 | 82 | 50 | 12 | 23 | 8 | 18 | 3 | 9 | |
| Regional average | 78 | 50 | 9 | 25 | 8 | 18 | 5 | 7 | |
Includes private sector wages.
Unweighted average of the countries listed.
Consider first the share of labor income. Before the transition, labor income was almost entirely derived from state sector employment. Now, obviously, labor may be employed in either the private or the state sector. The share of labor income has decreased throughout. It fell from 60 percent to 47 percent in the four CEE countries in Table 1 and from 78 percent to 50 percent in Russia and Latvia.
Second, the importance of nonwage private sector income has increased in all six countries except Slovenia. This increase may be deemed either a positive or a negative development. It is positive if there has been a shift toward higher-value-added activities. These include, for instance, workers who decide to leave the state sector and start their own businesses (for example, architects or lawyers who open their own offices). But nonwage private sector growth might also reflect a negative development: people may return to the countryside and try to eke out a meager existence working on their own plot of land or engaging in subsistence agriculture. In one case, self-employment is a promodern, positive strategy; in the other, it is a demodernizing survival strategy. In addition, nonwage private income has traditionally been distributed rather unequally. This is inevitable because it includes people who are very diverse: some are at the top of the income pyramid, and others at the bottom. Thus the growth in the relative importance of this source of income (in the four CEE countries it rose from 20 percent of total income to 24 percent, and in Russia and Latvia much more sharply, from 9 percent to 25 percent) places upward pressure on the overall Gini coefficient.
Third, pensions have generally increased as a share of total income. The pretransition data for Latvia and Russia are not very reliable because of systematic underrepresentation of pensioner households in income and expenditure surveys (Milanovic, 1999). So the increase in the pension share from 8 percent to 18 percent of total income is probably an overestimate. However, the trends are unmistakable for CEE, where one can be much more confident in the comparability of the pre-and posttransition data, and where the proportion of pensioners in the total population has significantly increased.2
Finally, other cash social transfers were small before the transition and have remained so. However, they, too, have increased from 5 percent of total income to 7 or 8 percent now. Thus, the shares of each of the three income sources (nonwage private sector income, pensions, and other cash social transfers) have increased at the expense of the declining share of labor income.
What has happened to the concentration coefficients, that is, to the distribution of each of these income sources? The concentration coefficient, denoted C in equation (2), measures both the inequality with which a source is distributed and its correlation with overall income—that is, whether it is a pro-poor or a pro-rich source. Social assistance is, of course, distributed very unequally, because very few people receive it. Capital income is distributed very unequally as well. However, we need to distinguish between the two: whereas one is a pro-poor source (since mostly poor people get it), the other is a pro-rich source. So instead of looking at the Gini coefficients (which may be the same for both), we use the concentration coefficient, which is equal to the Gini coefficient multiplied by the correlation of that source with total income. In the case of capital income, the correlation will be positive, and the concentration coefficient will be positive, too. The opposite is true for the concentration coefficient for social assistance. A high positive concentration coefficient therefore means that the source strongly contributes to overall inequality; a negative concentration coefficient means that the source reduces inequality. Of course, the importance of each source is weighted by its share in total income, where the subscripts w, y, p, and o stand, respectively, for wages, nonwage private sector income, pensions, and other transfers:
Table 2 shows the values of the concentration coefficients before the transition and in 1995–96 for the same six countries as in Table 1. We see that the concentration coefficient of labor has increased significantly in all the countries, from a range of 20 to 25 before the transition to an average of 32 in CEE, and much higher in Latvia and Russia. The cause of this increase has been the increase in the Gini coefficient of wages.
Concentration Coefficients Before Transition and Several Years Later in Selected Transition Countries
(In percent)
Unweighted average of the countries listed.
Country differences are so large that averaging is meaningless.
Concentration Coefficients Before Transition and Several Years Later in Selected Transition Countries
(In percent)
| Wages | Nonwage Private Income |
Pensions | Other Social Transfers |
||||||
|---|---|---|---|---|---|---|---|---|---|
| Country and Years | Before | After | Before | After | Before | After | Before | After | |
| Central and Eastern Europe | |||||||||
| Bulgaria, 1989–95 | 21 | 34 | 38 | 37 | 11 | 13 | -6 | 2 | |
| Hungary, 1987–93 | 25 | 35 | 30 | 26 | 14 | 21 | -13 | -16 | |
| Poland, 1987–95 | 25 | 37 | 40 | 17 | 37 | -10 | -10 | ||
| Slovenia, 1987–95 | 20 | 26 | 18 | 21 | 22 | 21 | -4 | -19 | |
| Regional average1 | 23 | 32 | 31 | 31 | 16 | 23 | -8 | -11 | |
| Former Soviet Union | |||||||||
| Russia, 1989–96 | 28 | 60 | 18 | 56 | -20 | 27 | 8 | 42 | |
| Latvia, 1989–96 | 23 | 41 | 16 | 43 | 34 | 9 | -7 | 7 | |
| Regional average | 25 | 50 | 17 | 50 | …2 | 18 | …2 | 25 | |
Unweighted average of the countries listed.
Country differences are so large that averaging is meaningless.
Concentration Coefficients Before Transition and Several Years Later in Selected Transition Countries
(In percent)
| Wages | Nonwage Private Income |
Pensions | Other Social Transfers |
||||||
|---|---|---|---|---|---|---|---|---|---|
| Country and Years | Before | After | Before | After | Before | After | Before | After | |
| Central and Eastern Europe | |||||||||
| Bulgaria, 1989–95 | 21 | 34 | 38 | 37 | 11 | 13 | -6 | 2 | |
| Hungary, 1987–93 | 25 | 35 | 30 | 26 | 14 | 21 | -13 | -16 | |
| Poland, 1987–95 | 25 | 37 | 40 | 17 | 37 | -10 | -10 | ||
| Slovenia, 1987–95 | 20 | 26 | 18 | 21 | 22 | 21 | -4 | -19 | |
| Regional average1 | 23 | 32 | 31 | 31 | 16 | 23 | -8 | -11 | |
| Former Soviet Union | |||||||||
| Russia, 1989–96 | 28 | 60 | 18 | 56 | -20 | 27 | 8 | 42 | |
| Latvia, 1989–96 | 23 | 41 | 16 | 43 | 34 | 9 | -7 | 7 | |
| Regional average | 25 | 50 | 17 | 50 | …2 | 18 | …2 | 25 | |
Unweighted average of the countries listed.
Country differences are so large that averaging is meaningless.
Similarly, nonwage private sector income has become much more unequally distributed in Russia and Latvia, whereas in CEE the increase in inequality has been small or nil. However, before the transition, nonwage private sector income was the most unequal source in the four CEE countries (Table 2). What has happened now is that wage inequality has caught up with the inequality of nonwage income sources.
The concentration coefficient of pensions has not changed very much. In the case of Poland and Russia, pensions have, paradoxically, contributed to the increase in the overall Gini. As for the other cash transfers, their concentration coefficients have been, and remain, negative in most cases: they are clearly pro-poor. The problem is that they have not become more pro-poor—that is, their concentration coefficients have not decreased by much (in CEE the average value went down from –8 to –11). So a very slight improvement in their pro-poor targeting could hardly have made a dent in other forces that were pushing inequality up, namely, greater wage income inequality and the rising share of private sector incomes.
But we need to decompose the overall increase in inequality in order to identify the key factors more exactly. Table 3 does this for the two end years (1987–89 and 1995–96). Except in Russia, the change in the composition of income did not have much to do with the increase in the overall Gini. What was driving inequality up was, in the first place, the increasing concentration coefficient of labor. This measure has a positive sign for all countries and explains increases in the Gini of between 3.6 points in Slovenia and 23.6 points in Russia. For three countries (Hungary, Latvia, and Slovenia), the rising concentration coefficient of labor explains more than 100 percent of the overall increase ininequality (other sources reduced inequality). for the other three countries it explains between 75 and 80 percent of total Gini increases.
Decomposition of Changes in Gini Coefficients During Transition in Selected Transition Countries
Decomposition of Changes in Gini Coefficients During Transition in Selected Transition Countries
| Components of Change in Gini (in Gini points) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Change in concentration | ||||||||||
| Gini Coefficient | Social transfers | |||||||||
| Country and Years | In initial year | In final year | Change in Gini | Change in income composition | Wages | Total | Pensions | Non-pension transfers | Nonwage private sector | Interaction term (in Gini points) |
| Bulgaria, 1989–95 | 21.7 | 31.7 | +10.0 | +1.6 | +7.8 | +0.9 | +0.4 | +0.4 | -0.4 | +0.3 |
| Hungary, 1987–93 | 20.7 | 22.9 | +2.2 | -2.7 | +5.5 | +1.2 | +1.4 | -0.2 | -0.6 | -1.1 |
| Poland, 1987–95 | 25.0 | 35.6 | +10.6 | -1.3 | +7.9 | +3.3 | +3.4 | -0.1 | +0.6 | -0.1 |
| Slovenia, 1987–95 | 19.8 | 22.3 | +2.6 | -0.3 | +3.6 | -0.5 | -0.1 | -0.4 | +0.4 | -0.7 |
| Russia, 1989–96 | 21.9 | 51.8 | +29.9 | -6.2 | +23.6 | +6.0 | +3.7 | +2.3 | +4.3 | +2.2 |
| Latvia, 1989–96 | 22.6 | 32.6 | +10.0 | -1.8 | +15.0 | -1.5 | -2.0 | +0.5 | +1.4 | -3.1 |
Decomposition of Changes in Gini Coefficients During Transition in Selected Transition Countries
| Components of Change in Gini (in Gini points) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Change in concentration | ||||||||||
| Gini Coefficient | Social transfers | |||||||||
| Country and Years | In initial year | In final year | Change in Gini | Change in income composition | Wages | Total | Pensions | Non-pension transfers | Nonwage private sector | Interaction term (in Gini points) |
| Bulgaria, 1989–95 | 21.7 | 31.7 | +10.0 | +1.6 | +7.8 | +0.9 | +0.4 | +0.4 | -0.4 | +0.3 |
| Hungary, 1987–93 | 20.7 | 22.9 | +2.2 | -2.7 | +5.5 | +1.2 | +1.4 | -0.2 | -0.6 | -1.1 |
| Poland, 1987–95 | 25.0 | 35.6 | +10.6 | -1.3 | +7.9 | +3.3 | +3.4 | -0.1 | +0.6 | -0.1 |
| Slovenia, 1987–95 | 19.8 | 22.3 | +2.6 | -0.3 | +3.6 | -0.5 | -0.1 | -0.4 | +0.4 | -0.7 |
| Russia, 1989–96 | 21.9 | 51.8 | +29.9 | -6.2 | +23.6 | +6.0 | +3.7 | +2.3 | +4.3 | +2.2 |
| Latvia, 1989–96 | 22.6 | 32.6 | +10.0 | -1.8 | +15.0 | -1.5 | -2.0 | +0.5 | +1.4 | -3.1 |
Turning now to the efficiency of social transfers, the key question is, To what extent do these transfers reach the poor and thus offset the increasing inequality driven by the strong forces of greater differentiation of wages and private sector income? Unfortunately, on that score the changes in the transition economies do not present a very bright picture. On the one hand, pensions have not really had much of an impact in reducing inequality. Indeed, in some countries (Poland and Russia) pensions actually contributed to increasing inequality. This has happened as the ratio between the average pension and the average wage went up, and pensioners moved up the income ladder. Figure 5 shows this change very clearly for Poland. Before the transition (left panel), the distribution of pensions followed almost exactly the same pattern as the distribution of family cash benefits: the modes of the distributions for both pensioners and children (in respect of whom the family benefits are paid) were in the fourth income decile. But by 1995 the situation had changed (right panel). The mode for family benefits has shifted downward, to the second decile. (Note that this distribution of family benefits follows very closely that of unemployment benefits.) But the distribution of pensions has turned strongly pro-rich. The people in the top decile receive 2.4 times as much in the form of pensions as the average Pole, whereas before the transition people in the top decile received only 70 percent of the average. Clearly, the reason is that many more pensioners in Poland are now among the “rich,” whereas in the past only a few pensioners made it to the top income decile. Only in Latvia, because of the Latvian pension reform of 1992–93, which introduced pensions that depended almost solely on the length of service, and not on previous earnings, do we notice a strong pension compression. There the concentration coefficient fell from 34 before the transition to 9 in 1996 (Table 2).
Poland: Distribution of Pensions, Family Benefits, and Unemployment
Source: Author’s calculations from Polish Household Budget Surveys.Note: Deciles are formed according to gross household income per capita.However, in general the lack of a substantial impact of pensions on inequality is not surprising. The primary role of pensions is not to reduce inequality or to fight poverty but to smooth income fluctuations. Pensions are only marginally different from labor income—and then only to the extent that most publicly funded pension schemes (as exist in transition economies) include an element of redistribution in favor of lower-earning households.
The primary onus of the fight against poverty (and against widening income disparities) falls on nonpension transfers. These include family benefits, unemployment allowances, and social assistance. However, as Table 3 shows, their role in that respect has not really expanded to any significant extent. These transfers have become only slightly more pro-poor than they were 10 years ago. This improvement in targeting is principally due to unemployment benefits, which, of course, did not exist in the previous system. However, the amounts of these benefits are very modest (Boeri and Edwards, 1998), and they reach only a part of the unemployed. This is because only the registered unemployed are entitled to benefits, and there is a large difference, particularly in the former Soviet Union, between registered and actual unemployed. For example, registered unemployment rates in Russia and Latvia in 1998 were 3 and 8 percent, respectively, whereas unemployment rates estimated from labor force surveys, using the standard definition of unemployment adopted by the International Labor Organization, were 12 and 15 percent, respectively.
What Has Really Happened: Some Preliminary Conclusions
We can conclude that, of the three possible “pure” mechanisms that might have brought about the increase in inequality (labor mobility, increases in individual sector inequalities, rising differences in mean sector incomes), the first, that is, mobility out of the state sector, must have been very important. The share of labor earnings in total income fell in all countries (by 13 percentage points on average in CEE, and more than 25 percentage points in Russia and Latvia; Table 1), whereas the ratio of the average wage to average income changed little, or even increased (Milanovic, 1999). These two facts imply that the number of employees decreased (something we also know from labor surveys). As we assumed initially, some of these employees went into self-employment, others became unemployed, and still others (mothers of small children, for example) withdrew from the labor force.
The second possible cause, an increase in individual sector inequalities, is an even better candidate, because we find that in all countries the concentration coefficient of wages went up. This factor is important because wages represent a large share of total income (between 60 and 80 percent before the transition). Even if we were to use today’s wage share in the Gini decomposition analysis, this component would still be the largest. In contrast, increasing inequality in the nonwage private sector was significant only in Russia.
The third possible “pure” scenario is widening differences in mean incomes. The evidence for this scenario is rather weak. Of course, we lack information on average income in the private sector (and even its meaning is unclear, given the very heterogeneous composition of the sector), but we know that the ratio of the average pension to the average wage was stable in most countries or even increased (Table 4). This contradicts the third scenario, which assumes widening disparities—that is, that the poorest sector (transfer recipients) has fallen behind the middle sector (wage earners).
Average Pension as a Fraction of the Average Wage in Selected Transition Countries
Data are for 1996.
Unweighted average of the countries listed.
Average Pension as a Fraction of the Average Wage in Selected Transition Countries
| Country | 1987 | 1997 | |
|---|---|---|---|
| Central and Eastern Europe | |||
| Bulgaria | 44 | 321 | |
| Hungary | 55 | 56 | |
| Poland | 51 | 64 | |
| Slovenia | 55 | 66 | |
| Regional average2 | 51 | 55 | |
| Former Soviet Union | |||
| Russia | 37 | 34 | |
| Latvia | 40 | 35 | |
| Regional average | 39 | 35 | |
Data are for 1996.
Unweighted average of the countries listed.
Average Pension as a Fraction of the Average Wage in Selected Transition Countries
| Country | 1987 | 1997 | |
|---|---|---|---|
| Central and Eastern Europe | |||
| Bulgaria | 44 | 321 | |
| Hungary | 55 | 56 | |
| Poland | 51 | 64 | |
| Slovenia | 55 | 66 | |
| Regional average2 | 51 | 55 | |
| Former Soviet Union | |||
| Russia | 37 | 34 | |
| Latvia | 40 | 35 | |
| Regional average | 39 | 35 | |
Data are for 1996.
Unweighted average of the countries listed.
Having concluded that most of the huge increase in inequality is due to increased inequality among wage earners, the next question is whether this is good or bad. Unfortunately, there is no clear-cut answer to this question. We know from a number of studies of CEE (see Rutkowski, 1996, for Poland; Vecernik, 1994, and Chase, 1977, for the Czech and Slovak Republics; Orazem and Vodopivec, 1995, for Slovenia; and Rutkowski, 1995, for Bulgaria) that the returns to education have uniformly increased, from about 4 percent per additional year of schooling to 7 or 8 percent. This is, in principle, a good development because we know that one of the socialist system’s inefficiencies was a lack of sufficient reward to education and a “leveling off” of wages. However, the situation in Russia, and perhaps in most of the former Soviet Union, is not so clear: the variance of earnings for a given level of education has exploded: an institute mathematician can end up a dollar billionaire (as Boris Berezovsky did) or as an unpaid state employee. Similarly ambiguous is the fact that a larger share of overall earnings than in the past is left unexplained by the usual regressors (education and experience). Again, to the extent that there are individual elements that the regressions cannot capture, such as drive, ambition, or entrepreneurship, this may be a positive development. But it may be that what is behind these residuals is the ability of some people, thanks to their political connections or corruption, to capture huge economic rents.3
This opens a promising avenue for further research into what really underlies the increasing wage inequality. What are the relative contributions of education, entrepreneurship, and political connections? And in a deeper sense, it opens the question of what kind of capitalism these large increases in inequality point to. Is increasing inequality the result of better returns to education and of “primitive accumulation of capital,” where eventually the new capital owners will, to legitimize their ownership, generate demand for the rule of law? Or is it the manifestation of what Max Weber called “political capitalism,” where political connections and political power are the necessary requirements for the accumulation of wealth?
The coefficient of variation is the ratio between standard deviation and the mean.
Between 1987 and 1997, the proportion of pensioners in the population increased from 25 percent to 30 percent in Bulgaria, from 22 percent to 31 percent in Hungary, from 17 percent to 24 percent in Poland, and from 22 percent to 26 percent in Russia and Estonia. It stayed at 23 percent in Slovenia (World Bank DEC database).
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