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Eswar Prasad (*)
Poland experienced a sudden economic transformation in late 1989 and early 1990 that has become known as the "big bang." The noncommunist government that took power in 1989 ended food price controls in August 1989 and ended price controls on most other products in January 1990. This led to substantial inflation and changes in relative prices. Other aspects of the reforms, including reductions in state orders for manufactured goods and restraints on credit for state-owned enterprises, along with external shocks such as increased import competition and the collapse of the Council for Mutual Economic Assistance (CMEA) trade bloc, also contributed to large declines in real GDP (of 11.4 percent in 1990 and 7.0 percent in 1991 according to JMF estimates). (1)
The conventional wisdom is that the process of transition to a market economy has been accompanied by great increases in income inequality, both in Poland and in most of the other formerly centrally planned economies of Eastern Europe. For instance, in a cross-country study, Milanovic (1998) reports that, between 1987-88 and 1993-95, the Gini coefficient for household per capita income rose in 17 of 18 Eastern bloc countries. He notes that the average Gini increased from 0.24, a level similar to that in the Scandinavian and Benelux countries, to 0.33, a level similar to that in Canada and the United Kingdom. To put such an increase in historical perspective, it is roughly three times as great as the increase reported for the United States in the 1980s by Atkinson, Rainwater, and Smeeding (1995). For Poland, the Organization for Economic Cooperation and Development (OECD, 1997) reports that the Gini increased from 0.249 in 1989 to 0.290 in 1993, after which it stayed relatively flat through 1996.2
In this paper, we provide new evidence on changes in inequality in Poland during the transition. The main difference between our work and that of previous authors (reviewed in Section I) is that we have obtained for the first time direct access to the detailed microdata of the Polish Household Budget Survey (HBS) conducted by the Polish Central Statistical office (CSO) (3) for the years 1985-92. (4) Prior work on inequality in transition economies has been based primarily on aggregate data about income distributions that are published by the statistical bureaus of the various countries. But, as we discuss in Section II, the published aggregate income data for Poland and other transition economies do not correspond to conventional economic measures of household income. However, at least for Poland, meaningful income measures can be constructed using the household level microdata.
Using the HBS microdata, we find no evidence that income inequality increased in Poland in the first three years following the big bang. For instance, we find that Gini coefficients actually declined from 1989 to 1992. Interestingly, while our Ginis for 1992 are quite similar to those reported by the CSO and OECD, we obtain much higher Ginis for the pre-1990 period. We conclude that the published aggregate statistics seriously understate the degree of inequality that existed before the big bang. As a result, most of the post-big bang increase in inequality that is present in the aggregate statistics appears to be spurious.
In the HBS microdata we are able to distinguish between pre- and post-transfer income. We find that inequality in pre-transfer income did in fact increase substantially in the transition. Thus, it appears that transfer programs were quite successful in mitigating any increases in inequality. We find that these programs are well targeted in the sense that most transfers go to those at the low end of the income distribution. This is true even though transfer programs in Poland, as in other transition economies, tend to be based on class rather than income.
Another important difference between our work and that of previous authors is that we examine consumption inequality as well as income inequality. To the extent that households can smooth consumption over time, consumption inequality is certainly a more interesting measure. It is again our access to the detailed microdata that allows us to examine consumption inequality in a meaningful way. As we discuss in Section II, the aggregate consumption figures that were published by the Polish CSO, as well as by other former communist countries, did not correspond to conventional economic measures of consumption. After constructing reasonable consumption measures from the microdata of the HBS, we again find no evidence of increased inequality during the transition.
One reason for the interest in the changes in inequality that may be occurring in transition economies is that, to the extent that inequality has been increasing, it may create social unrest and political pressures that could stall the transition process. Our results suggest that, at least in Poland, such concerns may have been exaggerated. The existing social safety net appears to have done an adequate job of limiting the impact of transition on inequality.
Although we find no evidence of increases in overall inequality, our access to the HBS microdata enables us to examine whether certain socioeconomic groups have been relative winners or losers in the transition. We find that the transition did have significant distributional impacts across broadly defined socioeconomic groups. Some groups also experienced large increases in within-group inequality. For instance, among households for which labor income is the primary source of income, income differentials by education level of household head increased rapidly after the big bang. Gorecki (1994) previously noted such a pattern in the aggregate data released by the CSO. Before the transition, the wage structure in Poland was highly compacted, with wages of college-educated white collar workers little different from those of manual workers. Soon after the big bang, those with a college degree became much more concentrated in the upper quantiles of the income distribution, while those with only primary education be came much more concentrated in the lower quantiles. Such a widening of across-group income differentials is to an extent desirable, as it implies an enhanced incentive for human capital investment. But it also raises concerns that dissatisfaction and social unrest may be a problem among those groups that have fared poorly.
In the next section, we describe the prior research on income inequality in Poland during the transition in more detail. Then, in Section II, we describe the HBS data. As we explain there, the Polish HBS is of higher quality and was collected according to a more consistent methodology over the transition period than the microdata for any of the other former communist countries. Thus, while the Polish case is interesting for its own sake, an analysis of the HBS data also provides the best hope for arriving at conclusions about the effects of transition on consumption and income distributions that may be generalizable.
In comparing the relative welfare of households with different levels of income or consumption, an important consideration is that an adjustment needs to be made for household size and, more generally, for the demographic composition of households. Most previous studies on inequality in transition economies have used per capita measures or equivalence scales constructed using industrial country data. An additional contribution of this paper is the construction of a full set of equivalence scales for Poland, which differ in some important respects from those based on industrial country data.
Section III describes our procedure for constructing equivalence scales. Section IV presents our main empirical results on the evolution of inequality. Section V analyzes income and consumption mobility. Section VI concludes.
I. Review of Prior Research
Several other studies have examined income inequality in Poland during the transition. But they report rather contradictory results, even though they all use income data from the HBS. For instance, OECD (1997, Figure 22, p. 86) reports that the Gini based on household per capita income for Poland is 0.25 for 1989, drops to 0.23 in 1990, and then rises substantially to 0.26, 0.27, and 0.29 over the period 1991-93. In contrast, Gorecki (1994) also finds a drop in inequality from 1989 to 1990, but finds no evidence of a subsequent increase in 1991. Similarly, Milanovic (1993) reports Gini values of 0.260, 0.255, and 0.247 for 1989-91. Thus, the OECD figures imply a very large increase in income inequality in 1991, while the Milanovic and Gorecki figures do not show this. The OECD (1997) and Milanovic (1998) figures are consistent, however, in implying that large increases in inequality had occurred by 1993.
The prior studies were based on aggregate statistics published by the CSO, with the exception of Milanovic (1998), who had access to the microdata for just the first six months of l993. (5) The Gini values in the studies cited above were thus approximated using aggregate data on the income distribution published by the CSO in the annual publication Budzety Gospodarstw Domowych, which we henceforth refer to as the Surveys. (6) The accuracy of these approximations is certainly subject to question.
A more important point is that the aggregate income statistics reported by the CSO, as well as those reported in household budget surveys done in other former communist countries, differ in a number of important ways from measures of income that would be considered economically meaningful in the West. For example, for farmers, income includes gross farm revenues, rather than net revenues. This is an important problem, because approximately one-fourth of Polish households are either farm households or mixed farmer/worker households. In light of this, one must question any results on income inequality based on the aggregate data. Because we have access to the detailed microdata, we are able to make important adjustments to income in order to obtain a meaningful measure (in this example, by calculating net farm income). (7)
Furthermore, the aggregate consumption figures published by the Polish CSO, as well as by other former communist countries, do not correspond to Westernstyle measures of consumption. Rather, they correspond to something like total money outflows. For instance, for farm households, consumption includes farm investment and purchases of supplies. An indication of the strange nature of the aggregate consumption data is provided by Milanovic (1998, p. 41), who reports that for 1993 the Gini for consumption is 0.31, which substantially exceeds the Gini of 0.28 that he calculates for income. Also, on page 33 he reports that for 1993 the ratio of consumption to income is 1.30, an unreasonably high figure.
It is again our access to the detailed microdata that allows us to examine consumption inequality in a meaningful way. Once we make necessary adjustments to the categories that are included in consumption, we find the more plausible results that consumption Ginis are generally smaller than income Ginis and that the aggregate consumption to income ratio falls in the 0.894 to 0.955 range during 1985-92.
Note that previous research on inequality in Poland and other transition economies has relied almost exclusively on Gini coefficients to measure inequality. In this paper, we provide a more detailed characterization of changes in the income and consumption distributions. We examine alternative entropy measures besides the Gini, we examine quantile ratios, and we examine kernel density estimates of the income and consumption distributions. In addition, prior studies have generally used household per capita income rather than accommodating household economies of scale by using equivalence scales. We examine the sensitivity of our results to choice among a number of alternative equivalence scales.
II. The Household Budget Surveys
The Polish Central Statistical Office has been collecting detailed microdata on household income and consumption at least since 1978, using fairly sophisticated sampling techniques. In the Polish HBS, the primary sampling unit is the household. A two-stage geographically stratified sampling scheme is used, where the first-stage sampling units are the area survey units and the second-stage units are individual households. Households are surveyed every month for a full quarter in order to monitor their income and spending patterns, and supplementary information is collected from these households once every year. A certain fraction of the households interviewed in a quarter are interviewed in the same quarter of the following year, thereby adding a limited panel aspect to the data. The typical sample size is about 25,000 households per year (6,250 per quarter). The CSO uses the data obtained from these household surveys to create aggregate tabulations that are then presented in its monthly and annual Statistical Bulletins, or Surveys.
The HBS contains very detailed information on consumption. We have aggregated across many of the very detailed consumption categories provided in the surveys to classify total household expenditure into these 16 categories: (1) food, (2) alcohol and tobacco, (3) clothing and footwear, (4) house purchases, (5) house construction, (6) household nondurables (including energy), (7) household durables (including furnishings, appliances), (8) rent, (9) health, (10) hygiene, (11) education, (12) "cultural" durables (radio, TV, sporting goods, etc.), (13) recreation and tourism, (14) vehicles, (15) transportation, and (16) other expenditures. In this paper, we use a coarser breakdown in which the nondurable components of categories 4 through 16 are aggregated into two categories: nonfood commodities and services.
Information on sources and amounts of income is available for both households and individuals within each household. Total income is broken down into four main categories: (1) labor income (including wages, salaries, and nonwage compensation), (2) pensions, (3) social security and other transfers, and (4) other income. For farm households, farm income and expenditures, as well as consumption of the farm's produce, are also reported. Finally, the HBS also contains information on characteristics of the dwelling, stocks of …