Increasing Inequality in Transition Economies

believed to be more accurate. Indeed it is consumption inequality, based on the new data, which is reported in Figure 1 on Poland and Russia used to introduce this paper.5

The data in Table 2 are also helpful in illustrating the evolution of inequality over time and decomposing its sources by household groups in countries that are deemed broadly representative of four clusters in the region, viz., Poland, Hungary and Latvia for the EU-8 countries, Romania for South Eastern Europe, Kazakhstan and Russia for the middle income CIS countries and Georgia, Moldova and Tajikistan for the low income CIS countries. Table 3 presents key data on the Gini index of inequality for those countries.

Table 3. Representative countries: inequality indices (Gini) for comparable per capita consumption

Having consistent data is the first step towards understanding the drivers for its increase and attempting to predict future evolution. The next section starts with two stylized “explanations” for the inequality increase often used to contrast various countries experience.

IV. MAIN DRIVERS OF INEQUALITY IN TRANSITION

To what extent can one appeal to the literature on inequality in transition for guidance on explanations of these disparate trajectories of growth and inequality? That literature suggests that the principal determinants of inequality in transition were:

•Wage decompression and growth of the private sector

•Restructuring and unemployment, reverting to subsistence economy

•Fiscal adjustment affecting Government expenditure and taxation, corruption

•Price liberalization, inflation and arrears

•Asset transfer, growth of property income

•Technological change, increased mobility and globalization.

5 The data used in Figure 2 for China come from Ravallion and Chen (2004).

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Driver 1. Wage decompression and growth of the private sector

Transition involved the emergence of a private sector which was to grow over time. By 2004, over 60 percent of GDP was produced in the private sector [EBRD (2005)]. This changed the process of wage setting by introducing a tighter link between productivity and wages. It is usual to associate inequality outcomes more closely to labor market conditions, primarily to inequality in wages, which in turn depends on the level of returns to human capital and changes in endowments. Indeed, wage inequality is a major driver of overall inequality. At a first glance, data on wage inequality data appear to mirror those for inequality of consumption. Contrasting different data sources, based both on enterprise records and household surveys, Figure 4 reports available Gini indices for wages in Russia and Poland. While dispersion between different sources is indeed very large, the levels and patterns resemble the trends depicted in Figure 1 closely.

Figure 4. Gini index for monthly wages in Russia and Poland, various sources

Sources: ISSP: International Social Survey Program from Paternostro and Tiongson Russia: Russian Statistical office (GKS), and RLMS – Russian Longitudinal Monitoring Survey, Lukianova for contractual wages (cleaning out the effect of arrears). Poland: HBS – Household Budget survey reported net wages Keene and Prasad, 2002 estimate –own; LFS –Labor Force Survey from Newell and Socha (2003).

Why did wage inequality in Russia increase so rapidly? Returns to education alone seem to be insufficient to explain it. Cross country studies find that the returns to education increased from the “pre-transition” period to the “early transition” period. The meta-study by Fleisher and others (2004) suggests that the sharpest increases occurred during the early years of transition. Flabbi, Paternostro and Tiongson (2005) examine the evolution of the skills premium in transition economies through the late 1990s or the period thereafter through 2002 or 2003 using ISSP data, which is an internationally comparable survey. However, neither that study, nor the other sources reported in Table 4 below, produce any evidence that Russia stands out as having particularly large or distorted patterns of returns to education compared to Poland. Indeed it started with a much lower level of returns but already by mid 1990s had converged to Polish levels. This factor therefore cannot be used to explain excess inequality of Russian wages and other explanations are required.

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Driver 2: Restructuring and unemployment

Central to transition is the closure and restructuring of firms as resources are reallocated to more productive uses, together with the entry of new firms. Associated labor market developments have manifested themselves in a combination of (1) open unemployment, (2) lower labor force participation and (3) low productivity employment, such as subsistence agriculture or informal sector activities. A priori, there was little insight into what the incidence of job losses and its distribution across households would look like. Ex-post there are indeed important variations across countries and regions in the implied effects on inequality. The role of employment status as a contributor to inequality is examined in the decomposition of inequality among households partitioned by labor market status in Subsection V.2 below.

Driver 3. Changes in government expenditure and taxation

The system of social transfers was a sizeable factor initially thought to act to countervail increasing inequality. But in practice only in a few EU-8 countries, particularly Hungary, where social assistance programs expanded in real terms, did it have its intended effect. [Aghion and Commander (1998), Forster and Toth (1997), Garner and Terrell (1997), Keane and Prasad (2002a)]. In contrast, low-income CIS countries, faced with fiscal stringency, drastically reduced coverage of their safety nets to focus on the most needy. Other CIS countries aimed at retaining key benefits but compressed levels to a simple per capita distribution among the claimants. The role of transfers as a contributor to inequality is examined in the decomposition of inequality by source of income in Subsection V.1 below.

On the revenue side, the transition induced a dramatic shift in the composition and incidence of taxes, such as the introduction of value added tax, while witnessing declining tax compliance. Limited empirical evidence suggests that most changes were in favor of greater equality, but with significant variation across countries and time periods [Kattuman and Redmont for Hungary, and Garner and Terrell for the Czech and Slovak Republics, and Commander and Lee who provide some evidence for Russia).

Driver 4. Price liberalization, inflation and arrears

All socialist economies embarked on the process of transition with a substantial monetary overhang [Flemming and Micklewright (1999)]. Hence, when prices were liberalized, they jumped and inflation rates tended to persist with accommodating monetary policy stance. Experience from other high inflation episodes (e.g. in Latin America) points to strong redistributive effects. And aggregate data indeed indicate that the inflation tax in Russia appears to have had a powerful effect. In 1992, for example, it has been estimated that households were hardest hit by inflation, losing about 12 percent of GDP through this tax on financial assets (Commander and Lee). This amounted to roughly a quarter of household income and is likely to have been regressive. Similar if not more redistribution took place in Georgia, Ukraine, Bulgaria, Belarus and Georgia, but did not substantially affect the EU-8 countries.

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Arrears on pensions and social benefits payments appeared in the inflationary environment of several countries in the CIS and South Eastern Europe. Arrears were concentrated in the bottom part of the distribution and, in a highly inflationary environment, resulted in a cut in real wages in a highly unequalizing way (Lehmann and Wadsworth [2002]). Similar effects have been found by Klugman (2000) for Uzbekistan.

These factors were however largely transitory in nature and affected the shape of the distribution only in certain time periods.

Driver 5. Asset transfer and growth of property income

Perhaps the most visible sign of transition everywhere has been the large-scale transfer of previously publicly owned assets into the hands of private agents, a development that has produced a long-term shift in the distribution. The increase in the share of entrepreneurial income, and the share of families receiving financial incomes was an immediate result common to all transition economies. In Russia, for example, the share of property, interests and profits among households’ cash receipts increased from around 4 percent in 1989 to 20 percent in 2003 (Goskomstat). These sources of income are known to be unequalizing (Milanovic 1998). The role of entrepreneurial income as a contributor to inequality is examined in the decomposition by source of income in Subsection V.1 below.

Many privatization programs are believed therefore to have worsened the distribution of assets and income, at least in the short-run (Birdsall and Nellis [2003], Davies and Shorrocks 2005 for a review). As against this, it should be noted that a large part of national wealth was transferred in a rather equitable way through privatization of housing to tenants at belowmarket prices. In Russia, by early 1996 nearly 50 percent of the housing stock was in private hands, a proportion which had grown to 70 percent by 2005. Imputing an economic value to subsidized goods and assigning it to households in different parts of the distribution shows that this had mitigating effects on inequality (see Flemming and Mickewright 1999).

Driver 6. Technological change and globalization

Technological change and modernization of the economy in a broad sense have been important in the evolution of inequality in many countries. Atkinson (2003) shows that transition economies were not alone in experiencing growing inequality: there has been an increase in inequality in many OECD countries on account of the change in technology associated with globalization, viz., a rise in the premium for skilled workers and a decline in the relative wage of unskilled workers. Figure 5 shows the extent of inequality increases in China, Russia, US, UK, Mexico, Sweden, Poland, Georgia and Hungary, using what is believed to be the most reliable indicator of dispersion in living standards for each country. The figure demonstrates that the increase in inequality in transition economies indeed occurred against the backdrop of a global increase in inequality with, however, important variations across countries. It is therefore inherently very difficult to separate transitionrelated determinants from the global factor of technological progress.

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Figure 5. Gini index changes between 1980s and 2000s

Source : ECAPOV2, Ravallion and Chen for China, LIS (www.lispoject.org) for other countries.

Note levels of Gini index are not comparable across countries as different concepts and definitions of welfare are used. In ECA for 2002s: current consumption per capita without housing rental values, correcting for regional price differences and without outliers. Data for early 1980s come from published sources and refer to total expenditures, not correcting for price differences. China – total incomes per capita, correcting for price differences. OECD – per equivalent adult total money incomes, correcting for regional price differences without outliers.

How different drivers interact with each other is very much a question of particular country circumstances, initial conditions, and most importantly, policy choices. The taxonomy of drivers offers some basic insights into the reasons behind the variation across countries with respect to increases in inequality. Models of transition provide further guidance regarding the role of policies.

Models of restructuring

Aghion and Blanchard (1994) proposed a theoretical model of transition dynamics describing the reallocation of productive resources in transition. The transition is formalized as a reallocation of labor and capital across state and private sectors, with unemployment as a transient step between the two7, and highlights ways in which endowments and policies in transition affect the distribution of income (Commander and Tolstopiatenko [1996]).

In the CIS, with an ungenerous benefits regime, and low initial values for closure and restructuring, the reallocation of labor to the private sector is protracted, and inequality rises gradually and steadily to high levels. More generous benefit regimes with higher probabilities of restructuring, as in Central Europe, lead to unemployment peaking at higher levels, but given a rapid movement of workers into the private sector and a generous floor in the form of unemployment benefits, the rise in inequality is less pronounced, reaching a hump at a lower level than that observed in the first scenario (Figure 6).

7 This state can also be re-interpreted as subsistence or informal sector employment (Radulovic 2003).

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Figure 6. Simulation results from the restructuring model with different configuration of parameters: two typical trajectories for inequality indices

Source: Commander, Simon and Andrei Tolstopiatenko (1997).

An attractive feature of the model is the conceptualization of restructuring: not as one–time shift in the behavior of agents, but as a whole array of outcomes with different degree of rent appropriation by insiders in partly restructured enterprises. The empirical study of the first ten years of transition revealed the coexistence of new, partly restructured and unrestructured firms as a defining feature of the move from a command to a market economy (World Bank 2002e). This introduces an additional source of variability and hence inequality, which is captured by the model.

However, a comparison of predictions from the model with empirical evidence shows a surprising reversal of patterns between Central and Eastern Europe and the CIS. The inverse U-shaped trajectory of inequality seems to emerge not in the CEE, but in some CIS countries. The failure of the model to predict the actual evolution of inequality may be a result of its limitations of the model8, or alternatively the effect of mitigation of inequality through offsetting policy measures.

Despite these limitations, the ability of the model to portray a large variation in the levels and shape of the development of inequality is instructive. It suggests that there is probably no single “transition” story as far as the evolution of inequality is concerned. Furthermore, the model results are broadly consistent with the story emerging from the earlier description of different drivers as being one which can yield a wide variety of outcomes across countries

8 This is essentially a model of labor reallocation which omits capital (or mixed) income, thereby by-passing one of the most important features of transition. Furthermore, parameters of the distribution within each sector are taken as exogenous and constant.

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and over time. A number of these factors are directly or indirectly influenced by policies which have a role to play in the development of inequality.

V.DECOMPOSING INEQUALITY CHANGE IN TRANSITION

The most direct approach to capturing the relative importance of thee drivers empirically is to decompose inequality into its components and associate each component with a particular channel of redistribution. The structure of inequality by income source can be looked at in two ways: as inequality coming from between and within-economic group differences.

Following A. Shorrocks (1982), the contribution of each component of income to total inequality can be obtained from the product of (i) the concentration coefficient for each component and (ii) the respective weights of those components in total income. Concentration coefficients in turn depend on how unequally an income source is distributed (“own Gini”) and how closely it is correlated with total incomes.9 The product of the share of a particular type of income, and its concentration coefficient equals the contribution of that income component to the Gini index. The sum of these contributions equals the Gini index. Following Milanovic (1998), the main income sources taken to represent key drivers of the level and changes in inequality in this analysis are: wage income, pensions, social transfers and non-wage income (a combination of all other income sources, ranging from in-kind subsistence income, farm incomes, remittances to property income and incomes from selfemployment and entrepreneurial activities). This stylized framework, by focusing on the relative importance of structural shifts versus own (or within-) inequality effects, is helpful in understanding how inequality levels changed during transition.

Given its potential for an analytical description of changes in the sources of inequality, it is somewhat surprising that there are only a few studies which use this framework (see Commander at al., Shutz, Kattuman and Redmont, Kyslitsyna). This section of the paper performs such decomposition exercises going back fifteen years, as opposed to the hypothetical exercises presented by Milanovic (1998), to understand the implications for inequality of employment reallocation between shrinking state and growing private sectors. Subsection V.1, which follows, looks at the evolution of the structure of income over time and the contribution of each component to inequality in Russia and Poland over the period 1987 through 2002, the choice of these countries being dictated by data limitations. Next, subsection V.2 presents group-based decompositions using data on consumption inequality for a larger group of countries. These are the key contributions of this paper.

V.1. Decomposition of inequality by income sources

Table 5 and Table 6 report levels and changes over time in the structure of incomes, the concentration coefficient of each component of income and its contribution to the Gini index of income inequality in Poland and Russia respectively.

9 Note that whenever the concentration coefficient of income source k is greater than the overall Gini coefficient, an increase in the income source k (holding everything else constant), will increase inequality.

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The following points may be noted.

First, by the end of the period, the Gini index was seven points larger in Russia (0.41) than in Poland (0.34).

Second, the directions of change in the income structure in both countries were generally similar, reflecting transition-related drivers, viz., falling share of wages and a rising share of both entrepreneurial incomes (profits and income from self-employment) and pensions. The changes are consistent across periods of economic decline and growth, with wages and transfers moving in opposite direction. But the distributional outcomes were very different.

Only to a limited extent were the differences due to changes in the composition of the source of income, such as the much steeper rise in social transfers (including pensions) in Poland compared to Russia. To examine to what extent these differences matter, it is not sufficient to simply compare actual changes across countries because the observed change is a complex result of interactions between drivers pulling inequality in different directions. What is needed is a counterfactual. However, producing a fully satisfactory counterfactual distribution is difficult and requires building a model of household income (Bourguignon et al), which goes beyond the scope of this paper. But it is feasible to conduct simulations using either base period concentration coefficients or income shares. It is recognized that such a counterfactual is purely hypothetical, because share and concentration often change for the same reason.

With this caveat in place, simple simulations show that the differences in the pace of structural change in income sources do not fully explain the inequality differential between Russia and Poland. In fact, an application of Poland’s income structure to Russia’s concentration coefficients yields a Gini index for Russia which is 0.5—1 point lower than it actually was. And application of Russia’s income structure to Poland would have led to a Gini index 2—4 points higher than actual in the latter country.

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