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13
Vulnerable Populations in Central Europe

Barbara Boyle Torrey, Timothy M. Smeeding, and Debra Bailey

INTRODUCTION

This chapter examines economic changes at the household level in Central Europe during the economic transition. Between 1988 and 1993, the gross domestic product (GDP) per capita in the Czech Republic, Hungary, and Poland fell 20, 15, and 12 percent, respectively. Real income change as measured by household budget surveys dropped 10, 24, and 18 percent, respectively (Milanovic, 1996b). These changes made some households much more economically vulnerable than they had been in the past, while others became less vulnerable.

The countries examined are the Czech Republic, Hungary, and Poland. National household surveys from each country have been standardized to facilitate comparisons over time and across countries. The chapter addresses the changes in relative poverty and income inequality in the five year period. It also focusses on the vulnerable populations (e.g., the young, pensioners, women, and rural populations) who were disproportionately affected during the earliest phase of the transition.

The authors would like to thank the U.S. Agency for International Development for their support of the Luxembourg Income Study and our research teams, also the U.S. National Institute on Aging for their research support. We would also like to thank, without implicating, our Central European teams led by Jiri Vecernik (Czech Republic), Endre Sik (Hungary), Branon Gorecki (Poland), and Ratislav Bednarik (Slovakia). Without their help this paper would not be possible. The authors accept responsibility for all errors of omission and commission.



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Transforming Post-Communist Political Economies 13 Vulnerable Populations in Central Europe Barbara Boyle Torrey, Timothy M. Smeeding, and Debra Bailey INTRODUCTION This chapter examines economic changes at the household level in Central Europe during the economic transition. Between 1988 and 1993, the gross domestic product (GDP) per capita in the Czech Republic, Hungary, and Poland fell 20, 15, and 12 percent, respectively. Real income change as measured by household budget surveys dropped 10, 24, and 18 percent, respectively (Milanovic, 1996b). These changes made some households much more economically vulnerable than they had been in the past, while others became less vulnerable. The countries examined are the Czech Republic, Hungary, and Poland. National household surveys from each country have been standardized to facilitate comparisons over time and across countries. The chapter addresses the changes in relative poverty and income inequality in the five year period. It also focusses on the vulnerable populations (e.g., the young, pensioners, women, and rural populations) who were disproportionately affected during the earliest phase of the transition. The authors would like to thank the U.S. Agency for International Development for their support of the Luxembourg Income Study and our research teams, also the U.S. National Institute on Aging for their research support. We would also like to thank, without implicating, our Central European teams led by Jiri Vecernik (Czech Republic), Endre Sik (Hungary), Branon Gorecki (Poland), and Ratislav Bednarik (Slovakia). Without their help this paper would not be possible. The authors accept responsibility for all errors of omission and commission.

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Transforming Post-Communist Political Economies HOUSEHOLD INCOME DATA The analysis in this chapter is based on household survey data collected in the Czech Republic, Hungary, and Poland between 1987 and 1992 and on the analyses of these data performed by Vecernik et al. (1995), Sik et al. (1995), and Gorecki et al. (1995). This section reviews these data sources and describes the Luxembourg Income Study database, which has standardized many of these data sets and made them comparable both among themselves and with those of other Organization for Economic Cooperation and Development (OECD) countries. Central European Household Surveys The Central European countries have a long tradition of using large, detailed household surveys to collect a broad range of demographic, income, and consumption data similar to those collected in Western European and U.S. surveys. The availability of survey data from 1987 and 1988 provides a baseline from which to measure subsequent change. Each survey is a nationally representative sample with a large sample size and detailed questions pertaining to household characteristics (Garner et al., 1991). The following surveys are used in this chapter: The Czechoslovak Microcensus (1988) and the Czech Republic Microcensus (1992) The Hungarian Household Panel Survey (1992, 1993, 1994) and the Hungarian Household Income Survey (1983, 1987) The Polish Household Budget Survey (1987, 1990, 1992) Selected characteristics of each survey are presented in Annex Table 13-1. Luxembourg Income Study The Luxembourg Income Study has standardized the income variables and demographic definitions used in the most recent of the Central European surveys so that comparisons among countries can be made. Disposable income is the income concept used for comparison. This concept includes government benefits net of taxes. The Central European surveys collected data on a number of different sources of income, such as earned income and public and private transfers. Households are characterized not only by the age and gender of the household head, but also by size. Household income is adjusted for the size of the household, using an equivalence scale similar to that used by the OECD in its most recent publications (e.g., Förster, 1993). Using this scale instead of employing straight per capita income adjustments significantly affects evalu-

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Transforming Post-Communist Political Economies ations of household welfare in countries where families have many children. These effects are discussed in more detail in the following section on poverty estimates. Despite the standardization of the Central European household surveys by the Luxembourg Income Study, biases remain in each survey. The sample population in each is the noninstitutionalized population. However, the Czech and Hungarian surveys exclude the military and police, while the Polish survey excludes the nonagricultural self-employed, who are a small but growing population. Another source of bias is the growing nonresponse rate of households that are surveyed. Under communist governments, the nonresponse rates were generally under 10 percent. Currently, the rate is increasing and is between 15 and 30 percent without sophisticated sampling of the nonresponding households; however, the effect of the bias is unknown. The most important bias in the surveys is likely to be the uneven inclusion of incomes from the informal economy. Estimates of the size and distribution of informal income in Hungary and the bias thereby introduced are discussed later in the section on income distribution. The strong statistical tradition in Central Europe provides some confidence that the current household survey data can be useful despite the above biases. But because the net effect of the various biases is unknown, the income given here should be interpreted with caution. The statistical offices collecting the data are aware of the increasing biases in their data and will be addressing the problem for future surveys. DID LEVELS OF RELATIVE POVERTY INCREASE DURING THE INITIAL PERIOD OF ECONOMIC TRANSITION? Before 1989, each Central European country had its own nationally defined minimum income level, which changed over time relative to the average income within each country. Also, the definition of ''minimum income" differed among the countries, so that their "national poverty rates" were incomparable. To make more accurate comparisons of relative poverty over time and across countries, this chapter uses a consistent definition of poverty to measure both trends within countries and relative levels of poverty among them. The definition we use is the common European Union and OECD definition: persons in households with incomes less than 50 percent of adjusted median income. A relative rather than an absolute definition of poverty is used because of the impracticality of translating different currencies into a single absolute standard over time. Purchasing power parities are used to convert currencies to a single standard. But the results are not consistent from one time period to another. Therefore, in examining income trends over time,

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Transforming Post-Communist Political Economies a relative measure of poverty provides the best consistent definition and a readily understandable concept. The equivalence scale used is 1.0 for the first adult, .66 for additional adults, and .33 for each child. Adjusted (or equivalent) income (EI) is defined as disposable income (DPI) divided by family size (S), raised to an exponent (E) to capture economies of scale (EI = DPI/S E). An E-value of .60 is similar to the values used in recent OECD studies of .55 (Förster, 1993) and .50 (Atkinson et al., 1995). It should be noted that estimates of appropriate economies of scale vary by country and lifestyle. In Central Europe, where housing and energy costs are still relatively low, the economies of scale of several people living together are closer to 1 (or per capita) than in countries where housing and energy are more expensive. While there is no single "right" estimate for economies of scale, it is an important value judgment in estimating relative poverty. For comparison, we also use household incomes unadjusted for size. Prior to 1989, only a small percentage of the population in these three Central European countries fell below 50 percent of median income or the poverty line. While the percentage of persons classified as poor on the basis of our measure of equivalent income increased from the mid-1980s to the early 1990s in all three countries, this percentage remained quite small by international standards in 1992 (Table 13-1, column 1, figures for all persons, equivalent income) and closely approximated relative income poverty rates in the European Community (e.g., Gottschalk and Smeeding, 1995). Far more people have between 50 and 75 percent of equivalent median income than fall below 50 percent (Table 13-1, column 2). While the percentage of people falling below 75 percent of adjusted median income (column 3) was much higher than the percentage falling below 50 percent, the change in the percentage of people falling below the 75 percent level in all three countries was very small between the 1980s and the 1990s. There were only modest increases in Hungary and Czechoslovakia, there was no change in the territory that became the Czech Republic, and there was only a slight increase in Poland between 1987 and 1992. Relative measures were quite stable given the declines in the macro economy. The percentage of households with household income falling below 50 percent of unadjusted median income decreased in the Czech Republic and Poland and remained constant in Hungary (Table 13-1, column 1). If we use 75 percent of unadjusted median income level as our poverty line, we find that the percentage of households classified as poor remained constant in Hungary and fell in the Czech Republic and Poland (albeit by a small amount). In all three countries, the rate of relative poverty was consistently higher at the household than at the individual level, partly because the poor households are disproportionately small and therefore have fewer income earners. Household units appear to have absorbed the economic shocks of the transition

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Transforming Post-Communist Political Economies better than individuals as measured by the change in relative poverty rates. Changes in household composition were very small, and therefore are unlikely to explain the differences between the changes in individual and household poverty rates. In summary, the relative poverty rates in all three countries increased for individuals whose income fell below 50 percent of the median equivalent income and increased by a smaller percentage if poverty is defined as those receiving less than 75 percent of median income. On an unadjusted basis, relative household poverty rates in the three countries decreased or remained stable over the period. DID INCOME INEQUALITY INCREASE SIGNIFICANTLY DURING THE TRANSITION? Before 1989, there was generally less per capita income inequality in Central Europe than in Western countries (Atkinson and Mickelwright, 1992). And as in Western countries, the Gini coefficients for person-weighted equivalent income distributions were consistently lower than those based on unadjusted household income. Prior to 1989, income inequality among individuals was the smallest in Czechoslovakia among the three countries. Poland ranked next in the late 1980s (Table 13-2). Per capita income inequality increased during the early phase of the economic transition by 10 percent in the Czech Republic (1988 to 1992) and by 12 percent in Poland (1987 to 1992) as measured by the Gini coefficient (Table 13-2). Income inequality measured in terms of households increased by 11 percent in the Czech Republic, but it remained remarkably stable in Poland over this period. In Poland, the household Gini coefficient jumped in 1990, the year in which real GDP declined 12 percent, but 2 years later it had returned nearly to the pretransition level. The Atkinson measure of income inequality allows comparison of distributional preferences. In this measure, the parameter e represents the weight attached by society to inequality in the income distribution. This parameter ranges from zero, or indifference, to infinity, where society is concerned only with the relative position of the lowest-income group (Atkinson, 1975). The value of the Atkinson measure can be interpreted as the proportion of total income that would be required to achieve the current level of social welfare if all incomes were equally distributed. The lower the Atkinson value, the greater the welfare of those in the lowest-income groups. We have selected Atkinson measures with e = 1 and e = 2, the latter giving greater weight to changes in the income distribution among those with the lowest incomes. Because the Atkinson measure is more responsive to changes in the lower tail of the distribution than is the Gini coefficient, disproportionate increases in the former measure would mean that the poor have suffered

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Transforming Post-Communist Political Economies TABLE 13-1 Distribution of All Households and Persons into Brackets Defined by Percentage of Median Income Median Income 0-50% (1) 50-75% (2) 0-75% (3=1+2) 75-100% (4) 100-125% (5) 125-150% (6) 75-150% (7=4+5+6) 150-200% (8) 200% and More (9) All (10) Czechoslovakia 1980, 1988, 1991a and the Czech Republic 1988, 1992 All Households, Unadjusted Income Czechoslovakia 1980 21.2 13.2 34.4 15.7 19.9 15.2 50.8 11.7 3.1 100 1988 19.8 14.6 34.4 15.7 19.8 15.3 50.8 11.9 3.0 100 1991 12.3 14.2 26.5 27.4 15.8 9.3 52.5 9.8 11.1 100 Czech Republic 1988 19.7 15.0 34.7 15.3 19.4 15.5 50.2 12.3 2.8 100 1992 16.7 13.8 30.5 19.5 16.5 13.6 49.6 13.4 6.4 100 All Persons, Equivalent Incomeb Czechoslovakia 1980 5.4 13.4 18.8 31.1 31.1 13.0 75.2 5.4 0.6 100 1988 3.5 14.3 17.8 32.2 31.2 13.1 76.5 5.2 0.5 100 1991 5.7 17.5 23.2 29.9 17.3 10.3 57.5 9.7 9.6 100 Czech Republic 1988 3.1 13.3 16.4 29.9 31.9 14.8 76.6 6.3 0.7 100 1992 6.9 9.5 16.4 17.7 19.3 17.9 54.9 19.1 9.7 100

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Transforming Post-Communist Political Economies Hungary 1987, 1992a All Households, Unadjusted Income 1987 17.7 15.9 33.6 16.4 15.2 11.3 42.9 12.9 10.6 100 1992 17.9 14.5 32.4 16.7 13.9 10.4 41.0 13.3 12.4 100 All Persons, Equivalent Incomeb 1987 3.6 18.4 22.0 28.0 20.3 11.3 59.6 10.9 7.5 100 1992 7.8 19.8 27.6 22.4 19.1 11.2 52.7 11.0 8.6 100 Poland 1987, 1990, 1992 All Households, Unadjusted Income 1987 17.5 16.3 33.8 16.2 16.4 13.7 46.3 14.2 5.8 100 1990 17.3 16.7 34.0 15.9 14.8 12.0 42.7 14.1 9.1 100 1992 15.2 16.3 31.5 18.5 16.2 11.8 46.5 14.2 7.9 100 All Persons, Equivalent Incomeb 1987 4.3 20.2 24.5 25.5 21.8 13.9 61.2 11.2 3.1 100 1990 5.9 20.2 26.1 23.9 19.8 13.4 57.1 11.6 5.2 100 1992 6.3 19.0 25.3 24.7 20.2 13.4 58.3 11.3 5.2 100 a The 1991 Czechoslovakian and 1987 Hungarian surveys differ from the 1980 and 1988 Czechoslovakian and 1992 Hungarian surveys. Thus, trends should be interpreted with caution. b Uses 1.00, .66, .33 equivalence scale and person weights. SOURCES: Czechoslovakia: 1980, 1988 Microcensus; 1991 Survey of Economic Expectations and Attitudes, Institute of Sociology. Czech Republic: 1988, 1992 Microcensus. Hungary: 1987 Household Income Survey; 1992 Hungarian Household Panel Survey. Poland: 1987, 1990, 1992 Household Budget Survey.

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Transforming Post-Communist Political Economies TABLE 13-2 Comparisons of Gini and Atkinson Measures of Income Inequality for Households (Unadjusted Income) and for Persons (Equivalent Income)a   Gini Coefficient Atkinson Measure   Household Persona Country and Year Household Persona e = 1b e = 2 e = 1 e = 2 Czechoslovakia 1980 .278 .168 .145 .314 .048 .102 1988 .290 .158 .134 .284 .041 .086 Czech Republic 1988 .291 .192 .150 .310 .061 .131 1992 .320 .210 .161 .312 .078 .140 Hungary 1989 .312 .237 n.a. n.a. n.a. n.a. 1992 .361 .295 n.a. n.a. n.a. n.a. Poland 1987 .294 .217 .144 .290 .074 .145 1990 .325 .248 .170 .327 .101 .186 1992 .299 .243 .147 .284 .095 .177 a Uses 1.00, .66, .33 equivalence scale and person weights. b The parameter e represents the weight attached by society to inequality in the income distribution (see text). n.a. = not available SOURCES: Czechoslovakia: 1980, 1988 Microcensus; Czech Republic: 1992 Microcensus. Hungary: 1987 Hungarian Income Survey; 1992 Hungarian Household Panel Survey. Poland: 1987, 1990, 1992 Household Budget Survey. the largest changes in inequality. However, changes in the Atkinson measure for the Czech Republic and Poland mirror the changes noted above in the Gini coefficient, thus assuring us that changes in the income of the lower-income groups do not differ greatly from those observed among the rest of the population. The increase in income inequality observed during the first phase of the transition was accompanied by a decrease in the percentage of people in the middle-income classes (75-150 percent of median equivalent income) (Table 13-1, column 7). Between 1988 and 1992, the decrease was largest in the Czech Republic, from 76.6 to 54.9 percent, followed by Hungary, from 59.6 to 52.7 percent. The change in the percentage of persons in these groups was lowest in Poland, dropping from 61 to 58 percent. The large decline in the middle-income groups in the Czech Republic is correlated with increases in higher-income groups (Table 13-1, columns 8 and 9); in Hungary and Poland, increases in higher- and lower-income groups are correlated with declines in the middle-income groups. While individual income distribution changed considerably, household

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Transforming Post-Communist Political Economies income distribution did not. The change in the fraction of households in the 75 to 150 percent range was negligible in both the Czech Republic and Poland and very small in Hungary. Estimates for the most recent year available in the Luxembourg Income Study suggest that the level of current income inequality for individuals in the Czech Republic is most similar to that found in the Scandinavian economies, while levels in Hungary and Poland are most similar to those in Canada, Australia, and the United Kingdom. It should be noted that, as mentioned earlier, a potential source of bias in our estimates of poverty and income distribution is the underreporting of the informal economy in each country. Every communist Central European country had an unreported "second economy." Estimates of the size and distribution of this second economy suggest that it represented approximately 10 percent of national income, a level similar to that found in Italy. While every income decile received some unreported income, Hungarian analysts have suggested that it went disproportionately to those in the upper-income classes in that country (Eleto and Vita, 1989). Since the collapse of the communist economies, the second economy has been replaced by a range of unreported, informal economic activities. Endre Sik (1994, 1995), a noted Hungarian researcher, estimates the size of these activities in Hungary as a percentage of GDP to be larger (12 to 33 percent of the formal economy) than that of the second economy under communism. The current informal economy is also estimated to provide income to a broader distribution of income groups than did the previous second economy. Sik estimates that those in the lowest quintile receive about 20 percent of the total income generated by the unreported, informal economy, while the highest quintile receives approximately 50 percent; the remainder, approximately 30 percent, is distributed among the other three quintiles (Sik, 1994, 1995). Table 13-3 shows how the inclusion of income from the unreported, informal economy might have affected the 1992 reported Hungarian income distribution. We assume that the informal economy not reported in the household surveys represented 15 percent of total disposable income. That amount of income was then distributed among income quintiles in accordance with Sik's estimated distribution. Persons were ranked by deciles of equivalent disposable income. The incomes of those in the bottom two deciles were increased proportionately by 3 percent (20 percent of the informal economy, which was assumed to be 15 percent of total disposable income). On the same basis, the top two deciles received 50 percent of the income from the informal economy, which increased their incomes 7.5 percent. The remaining 30 percent of the informal economy, 4.5 percent of current disposable income, was added proportionately to the incomes of the middle 60 percent of the population. This exercise was repeated for the theoretical case of the informal economy's being 25

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Transforming Post-Communist Political Economies TABLE 13-3 Simulated Effects of the Informal Economy on Hungarian Incomesa in 1992   Atkinson Decile Pointsb   Estimate Gini e = 1c e=2 P10 P90 Decile Ratiod Reported equivalent disposable incomea .282 .133 .272 54.4 182.6 3.37 Simulated equivalent disposable income  from informal  economy = 15 percente .276 .118 .210 65.8 207.5 3.15 Simulated equivalent disposable income  from informal  economy = 25 percentf .274 .115 .196 72.5 222.0 3.06 a All income measures use 1.00, .67, .33 equivalence scale and person weights. b The decile points are the ratio of the 10th percentile person's income to the median (P10), multiplied by 100, and the 90th percentile person's income (P90) to the median, multiplied by 100. c The parameter e represents the weight attached by society to inequality in the income distribution (see text). d The decile ratio is the ratio of P90 to P10. e Assumes income from the informal economy is 15 percent of total disposable income; see text for simulation details. f Assumes income from the informal economy is 25 percent of total disposable income; see text for simulation details. percent of total disposable income, using the same distribution, but higher values. Persons were then reranked (e.g., a person at the 19th percentile of disposable income could have received a sufficiently high fraction of the informal economy to "pass" a person at the 21st percentile), and the Gini and Atkinson measures were recomputed. We also calculated the percentile points of the distribution (incomes at the 10th and 90th percentiles as a percentage of median income) and the decile ratio (the ratio of the 90th to the 10th percentile income) (Table 13-3). When these estimates of the informal economy were added to the reported survey estimates, the lowest-decile income ratio, the P10 ratio, rose from 54.4 to 72.5 (a 33 percent increase). But the highest-decile ratio, the P90 ratio, also rose, from 182.6 to 222.0 (a 22 percent increase). Thus, both the bottom and the top of the Hungarian distribution improved their positions compared with the middle-income groups, while the Gini coefficient changed hardly at all. Estimates of the distribution of informal income among income quintiles do not yet exist for other Central European countries. These illustrative estimates on the Hungarian income distribution suggest that if the distribution of informal income for the latter countries is similar, the reported income on their

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Transforming Post-Communist Political Economies household surveys may be underestimating the total incomes of both the lowest- and highest-income quintiles. WERE VULNERABLE POPULATIONS DISPROPORTIONATELY AFFECTED DURING THE EARLIEST PHASE OF THE TRANSITION? Vulnerable populations tend to be those with fewer labor market skills or less mobility, such as children, the elderly, women, and those in single-parent families. A comparison of income adjusted for household size shows that in all three countries, children lived in households that had approximately the median income (Table 13-4, column 9), while the elderly received only 72 to 77 percent of the median (Table 13-4, column 10). When the income status of these two groups during the early years of the transition is compared, it becomes evident that the economic status of children improved in Hungary and stayed about the same in Poland and the Czech Republic. The relative income status of the elderly also improved relative to the median in both Poland and the Czech Republic, where pensions were explicitly adjusted to protect them from hyperinflation in the first phase of the transition. This resulted in an improvement in the incomes of pensioners relative to those in other groups, including children, whose real incomes fell with the real wages of their households' wage earners. The attempt to measure the income of vulnerable populations is particularly sensitive to the equivalence scales used to adjust for family size. The former communist countries used a per capita equivalence scale that ignored the economies of scale of the household as an economic unit. The per capita equivalence scale tended to overadjust for the presence of children in the family, thereby making households with children appear poorer than they would have if the equivalence scale described here had been used. The Central European countries that employ per capita income scales estimate large rates of poverty, especially among children, while those using equivalence scales, such as the ones used here, estimate lower poverty rates, particularly among children (Szulc, 1995). Single-parent families constitute the only vulnerable household group that does not include aged members. This group is less well off than almost any other household type, with the possible exception of single women living alone in Western countries (Rainwater and Smeeding, 1995). In contrast with Western countries, however, single-parent families comprise only 3 to 4 percent of all households in the three countries under analysis here. In Central Europe, very few single-parent families live in separate households. They are much more likely to be found in extended families that are counted as "other households with children," particularly in rural areas. These "other" house-

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Transforming Post-Communist Political Economies TABLE 13-4 Ratio of Group Median Equivalent Income to National Median   Czech Republic Hungary Poland   Income Ratioa Change  in Ratio Income Ratioa Change  in Ratio Income Ratioa Change  in Ratio Household Type 1988 1992 1988-1992 1987 1992 1987-1992 1987 1990 1992 1987-1992 Households with Head Under Age 60 One-person household 1.05 0.98 −7 1.08 0.87 −21 0.96 0.95 1.17 22 Couples without children 1.22 1.13 −9 1.18 0.94 −24 1.22 1.15 1.32 17 Couples with children 1.01 1.03 2 1.06 1.25 19 0.99 0.96 1.01 5 One-parent families 0.86 0.88 2     0.77 0.80 0.85 5   Other households with children 1.08 1.03 -5 0.98 1.00 2 n.a. n.a. 0.92 n.a. Other households without children 1.16 1.12 −4 1.08 0.91 −17 1.17 1.16 1.10 −6 Households with Head over Age 60 One-person household 0.58 0.75 17 0.72 0.60 −12 0.72b 0.72b 0.88b 16 Two-person household 0.80 0.87 7 n.a. 0.75 n.a.   Other 1.03 0.98 −5 0.79 0.85 6 0.79 0.94 0.89 −5 Individuals Children under 18 0.98 1.01 3 1.00 1.18 18 0.98c 0.99c 0.94 −4 Elderly over 60 0.72 0.85 13 0.81 0.74 −7 0.77 0.75 0.94 17 a Ratio of median equivalent income of group to national median equivalent income. b Polish estimates are for one- or two-person households with a head age 60 or over. c Children were defined as 16 and under in Poland in 1987 and 1990. * = Too few in sample. n.a. = not available SOURCE: Data from Luxembourg Income Study.

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Transforming Post-Communist Political Economies holds comprise 13 percent of all households in the Czech Republic and Poland and 20 percent in Hungary. The income status of other household types relative to the national median income changed very little in the Czech Republic during this period (Table 13-4). In Hungary, the relative income of households without children declined, while in Poland it improved. No obvious pattern of vulnerability among different household types is evident across the countries. In fact, the more disaggregated the data become, the more unique the household transition in each country appears. Because women receive lower wages than men in both Western and Central Europe, they are also considered more vulnerable than male workers during economic transitions. Registered unemployment rates are higher for women than for men in the Czech Republic and Poland, but they are lower in Hungary (Employment Observatory, 1994). In all three countries, labor force participation rates for both men and women have fallen, but further for women than for men. Elderly women in single-person households are perhaps the most vulnerable group, receiving only approximately two-thirds the income of their male peers. But these households constitute only approximately 4 percent of the households in the three countries. Living together continues both as a result of tradition (predominantly in rural areas) and out of necessity (single mothers, older women), but it does not appear to be increasing. Because of the slow deregulation of utility prices and the abandonment of rights to flats by previous ''state" owners, housing costs have remained at reasonable levels in these countries, not rising above 15 to 20 percent of total expenses (Struyk, 1995). It is also clear that urban populations are experiencing a relative income advantage during the current transition. The most recent survey data indicate that in all three countries, the income status of both the young and the old was in most cases better in the capital city and the region surrounding it than outside the capital region. The variance in income between the capital region and all other regions was at its lowest level in the Czech Republic (no difference for children and 11 percent higher in the capital for the elderly) and at its highest between Budapest and Eastern Hungary (36 percent larger for children and 26 percent larger for the elderly in the capital). Regional differences in Poland were less severe than those in Hungary (26 percent higher for children and 17 percent higher for the elderly in Warsaw compared with the South Eastern Region). One reason for this differential between urban and rural areas is consistently lower unemployment rates in the capital city than in the country as a whole (Milanovic, 1996a). In fact, one of the outcomes of the initial period of economic transition has been a decline in the importance of demographic variables (such as age and gender) as explanatory variables in income distribution. Education and financial capital are increasingly important in explaining earnings and income

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Transforming Post-Communist Political Economies differentials (Vecernik, 1994). Under the communist economies, wage income was strongly correlated with age and household size (because of policies related to equal wages per worker and very high labor force participation rates) and only weakly related to education. But with the breakdown of the command economy, a much higher premium has been placed on secondary and university education and on the ownership of capital. Between 1989 and 1993, food and housing costs rose slightly as a share of total expenditures in Central Europe. In Poland, however, other real consumption increases suggest that the substantial import liberalization had the desired effect of lowering the dollar costs of consumer durables. Since 1989, consumption of consumer durable goods has increased in every country, and in some instances these changes have been far from trivial (Gorecki et al., 1995). In Poland, the percentage of households owning cars has increased by one-third or more. While many of the cars are second-hand, they still represent a substantial purchase. The number of Polish worker households with video cassette recorders has also increased substantially, from almost nil in 1989 to over 50 percent of households with nonfarm workers by 1992. Similar increases can be noted among Polish mixed (farmer-worker) households, and to a lesser extent among farmer households. Much of the same pattern emerges in Slovakia, with most of the changes occurring between 1991 and 1994 (Bednarik et al., 1995). Although smaller consumption increases were seen in the Czech Republic and Hungary between 1989 and 1991, the consumption of nearly all consumer durables increased there as well (Vecernik et al., 1995; Sik et al., 1995). Central European consumers also increased their purchases of other luxury durables, such as color rather than black-and-white televisions, during the transition. In Hungary, the number of consumer durables purchased by households without members in the labor force (a group dominated by pensioners) actually increased over the period. With the increases in their real benefit levels, Polish and Slovak pensioners were also able to increase substantially their consumption of cars, washing machines, and freezers. Czech consumption data, when disaggregated by income, show that while those in the lowest-income population groups consumed less than the average in the country (especially families with children), pensioner households tended to increase their consumption of non-necessities, such as leisure goods (Garner, 1995). Thus, even the most vulnerable populations have found the means to take advantage of the increased availability of consumer goods since the transition began. DISCUSSION The above analyses suggest that as the national economies of the Czech Republic, Hungary, and Poland declined, the poverty rate and income inequal-

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Transforming Post-Communist Political Economies ity of individuals increased. The changes in the relative income of two vulnerable groups, the young and the old, were mixed. There was improvement for both groups in the Czech Republic, improvement for children in Hungary, and improvement for the elderly in Poland. But given their considerable limitations, the data can only be suggestive of income changes; by themselves they cannot be used conclusively. The sources of bias in the survey data have been mentioned above. The surveys exclude a small but important segment of the population—the institutionalized, the military, and the police. Since 1989, the surveys also have had an increasing nonresponse rate. The effect of this increasing trend on the data is not yet known. Most important, the full effect of the informal economy is not captured in any of the Central European surveys; as income from this economy grows, its exclusion from the surveys will become increasingly important. Without more research, the magnitude of these biases cannot be accurately estimated. Adding to the known biases of the income survey data is the incompleteness of the concept of income for measuring welfare (van de Walle, 1996). While income is an important factor in welfare, other concepts, such as the measurement of time allocation, life expectancy, and the subjective sense of personal well-being, are very important. Private transfers of time and resources among social and family networks may also be very important in the transition period. The governments of Central Europe need reliable household income data to help target social safety net transfers to the most vulnerable individuals and households. In both the Czech Republic and Poland, social safety net programs have been used explicitly to minimize the adverse effects of the economic transition on pensioners (Blanchard et al., 1994). The sustainability of current pension levels is, however, in doubt (Sachs, 1995). If social safety nets decline in the future, the need for reliable household income data to guide the allocation of shrinking resources will become even more important. The Central European countries studied in this chapter have had a long tradition of taking sophisticated household income surveys. The economic transition, however, has made the quality and quantity of the data an issue. More work needs to be done to improve the quality of the data, and more resources will be needed to improve the quantity and timeliness of the surveys. Future research should also focus on the many indicators of well-being that are not captured by income measures, no matter how accurate they may be. The current data raise a number of intriguing questions. For instance, how closely linked are the economic transitions of the macro, national economies and the micro household economies? What adjustments do households make to insulate themselves from major declines in the macro economies? Do households pool their resources, and if so, how does this happen? Are the

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Transforming Post-Communist Political Economies same populations vulnerable at the beginning of economic decline and over the long term, or does vulnerability shift with the movement to a more market economy? Are there lessons from the economic transition among households in Central Europe that will eventually be applicable to households in other parts of the world? How good is income as an indicator of well-being during major economic transitions? The data in this chapter have raised more questions than they have answered, and therefore leave a substantial challenge for subsequent research. REFERENCES Atkinson, A.B. 1975 The Economics of Inequality. Oxford, England: Clarendon Press. Atkinson, A.B., and J. Micklewright 1992 The Economic Transformation of Eastern Europe and the Distribution of Income. Cambridge, England: Cambridge University Press. Atkinson, A.B., L. Rainwater, and T. Smeeding 1995 Income Distribution in OECD Countries: Evidence from the Luxembourg Income Study. Paris: Organization for Economic Cooperation and Development. September/ October. Bednarik, R., J. Filipova, and S. Valna 1995 LIS Country Paper: Slovakia. Paper presented at the Luxembourg Income Study Conference on Economic Hardship and Social Protection in East-Central Europe, Walferdange, Luxembourg, July. Blanchard, O.J., K. Froot, and J. Sachs 1994 The Transition in Eastern Europe, Volume 1 and 2. Chicago: The University of Chicago Press. Eleto, O., and L. Vita 1989 A Micro-Simulation Experiment for the Estimation of the Possible Effects of Income from the Underground Economy on the Income Distribution: Methods and Results. International Statistical Institute, 47th Session, Paris. August 29 to September 6. Employment Observatory 1994 Central and Eastern Europe employment trends and developments. Alphametrics 6 (October). Förster, M. 1993 Poverty in OECD Countries. Social Policy Studies Paper #10, October. Paris: Organization for Economic Cooperation and Development. Garner, T. 1995 Moving to a Better World? Coping and Behaviors During Economic Uncertainty: A View from Eastern Europe. Paper presented at the workshop, Economic Transformation: Households and Health, September 7-8, 1995, National Academy of Sciences, Washington, DC. Garner, T., W. Okrasa, T. Smeeding, and B. Torrey 1991 Household Surveys of Economic Status in Eastern Europe: An Evaluation. Paper presented at the Bureau of Labor Statistics and EUROSTAT Conference on Economic Statistics for Economies in Transition, February, Washington, DC. Gorecki, B., I. Topinska, and M. Wisniewski 1995 Poland Country Paper. Paper presented at the Luxembourg Income Study Conference on Economic Hardship and Social Protection in East-Central Europe, July, Walferdange, Luxembourg.

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Transforming Post-Communist Political Economies Gottschalk, P., and T. Smeeding 1995 Cross-National Comparisons of Levels and Trends in Inequality. Unpublished paper, May. Milanovic, B. 1996a Poverty, Inequality, and Social Policy in Transition Economics. Research Paper Series, Paper No. 9, February, World Bank, Washington, DC. 1996b Income, Inequality and Poverty During the Transition. Research Paper Series, Paper No. 11, March, World Bank, Washington, DC. Rainwater, L., and T. Smeeding 1995 Doing Poorly: The Real Income of American Children in a Comparative Perspective. LIS Working Paper No. 127, August, Luxembourg: LIS. Sachs, J. 1995 Postcommunist parties and the politics of entitlements. Transition: The Newsletter About Reforming Economies 6(3): 1-4. Sik, E. 1994 From the multicolored to the black and white economy: The Hungarian second economy and the transformation. International Journal of Urban and Regional Research 18(1):46-70. 1995 Measuring the Unregistered Economy in Post-Communist Transformation. Eurosocial Report Series No. 52. Vienna, Austria: European Centre for Social Welfare Policy and Research. Sik, E., Z. Berencsi, and G.I. Toth 1995 Aspects and Trends of Social Inequality in Hungary. Paper presented at the Luxembourg Income Study Conference on "Economic Hardship and Social Protection in East-Central Europe," July, Walferdange, Luxembourg. Endre Sik, Budapest, Hungary. Struyk, R. (ed.) 1995 Economic Restructuring in the Former Soviet Bloc: Evidence from the Housing. Paper presented at the Luxembourg Income Study Conference on Economic Hardship and Social Protection in East-Central Europe, July, Walferdange, Luxembourg. Szulc, A. 1995 How Many Losers? The Polish Poverty Statistics Reconsidered. Unpublished paper, July. van de Walle, D. 1996 Common pitfalls in measuring welfare during transition. Transition 7(78):5-6. Vecernik, J. 1994 Changing Earnings Inequality under the Economic Transformation: The Czech and Slovak Republics, 1984-1992, Unpublished paper, June. 1995 Changes in Earnings Inequality Under the Economic Transformation: The Czech and Slovak Republics, 1984-1992. Paper presented to the LIS Russian Workshop, July 13. Vecernik, J., S. Perusicova, T. Zuda, and S. Howard 1995 Household Incomes and Social Policies: The Czech Republic in the Period 1989-1995. Paper presented at the Luxembourg Income Study Conference on "Economic Hardship and Social Protection in East-Central Europe," July, Walferdange, Luxembourg.

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Transforming Post-Communist Political Economies ANNEX TABLE 13-1 Selected Characteristics of Central and Eastern European Household Surveys Country Czechoslovakia/Czech (CR) and Slovak (SR) Republics Hungary Hungary Poland Name of survey Microcensus Household Income Survey Household Panel Survey Household Budget Survey Year of survey examined 1980, 1984, 1988 Czechoslovakia; 1992 CR and SR, separately 1983, 1987 1992 1987, 1990, 1992 Frequency every 3 to 5 years every 5 years annually quarterly/annually Household sample size 102,637 Czechoslovakia; 15,677 CR 15,221 SR 14,790 2,059 10,800 Population coverage Noninstitutional population living in households; persons absent for longer than 6 months excluded in 1992 Noninstitutional populations living in private households, including selfemployed All individuals with a noninstitutional address Noninstitutional population, excluding nonfarm selfemployed, police, military, Communist Party administration Sample design Two-stage stratified sample from the population census, stratified by size of locality and by number of flats within the census tract Drawn from "uniform system of household surveys," a dispropor- tionate random sample of 1980 census tracts Four-stage stratification from 1990 census, with additional subsample for Budapest. Two-stage, two-phase rotation sampling

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Transforming Post-Communist Political Economies Reference person Head of household, defined as male if complete family, parent if incomplete Head of household Head of household defined as oldest active male; if no active members, head is oldest male, Head of household, defined as person whose income is major source of livelihood for household Primary unit of collection Economic/consumer household is observational unit, while address is sample unit; all households at address included in sample; persons are asked to declare whether they are members of a common household./consumer Economic/consumer household is observational unit, while address is sample unit; all households at address included in sample; households are all persons living together who share a common budget Economic/consumer household is observational unit, while address is sample unit; household is defined to be all persons living under the same roof, sharing income and expenditures Economic/consumer(identified as a household); if not a single person, then a group of persons living together who share a household budget. Non-response rates In 1993, nonresponse rate was 15.7 percent in CR, 7 percent in SR. Total nonresponse rate was 3 percent. Nonresponse rate was 17 percent Nonresponse rate was 30.9 percent of households selected to participate for the first time in 1992.