Ackland, Robert and Jane Falkingham, “Economic Transition and the Profile of Poverty in Kyrgyzstan,” forthcoming in Jane Falkingham, Jeni Klugman, Sheila Marnie, and John Micklewright, “Household Welfare in Central Asia”, (London: McMillan, 1996).
Ahmad, Ehtisham, “Poverty, Inequality, and Public Policy in Transition Economies,” Public Finance (Supplement), Vol. 47 (1992), pp. 94–106.
Alexeev, Michael V., and Clifford G. Gaddy, “Income Distribution in the U.S.S.R. in the 1980s,” Review of Income and Wealth Series 39 (March 1993), pp. 23–36.
Atkinson, Anthony B., and John Micklewright, Economic Transformation in Eastern Europe and the Distribution of Income (Cambridge: Cambridge University Press, 1992).
Bergson, Abram, “Income Inequality under Soviet Socialism,” Journal of Economic Literature, Vol. 22 (September 1984), pp. 1052–1099.
Blackwood, D.L., and Lynch R.G., “The Measurement of Inequality and Poverty: A Policy Maker’s Guide to the Literature”, World Development Vol. 22, No. 4 (1994), pp. 567–578
Cornelius, Peter K., “Cash Benefits and Poverty Alleviation in an Economy in Transition: The Case of Lithuania,” Comparative Economic Studies, Vol. 37 (1995), pp. 49–69.
Cornelius, Peter K., , “Unemployment During Transition: The Experience of the Baltic Countries,” Communist Economies and Economic Transformation, Vol. 7 (1995), pp. 445–64.
Government Finance Statistics Yearbook, (Washington D.C.: International Monetary Fund, 1994).
Hansson, Ardo, and Jeffrey D. Sachs, “Monetary Institutions: A Comparison of the Experience in the Baltics,” Paper presented at a Conference on Central Banks in Eastern Europe and the Newly Independent States, University of Chicago Law School, April 22–23, 1994.
- Search Google Scholar
- Export Citation
)| false “ Hansson, Ardo, and Jeffrey D. Sachs, Monetary Institutions: A Comparison of the Experience in the Baltics,” Paper presented at a Conference on Central Banks in Eastern Europe and the Newly Independent States, University of Chicago Law School, April 22–23, 1994.
International Monetary Fund, “Fund-Supported Programs, Fiscal Policy, and Income Distribution,” Occasional Paper 46 (Washington, D.C.: International Monetary Fund, 1986).
Johnson, Omotunde, and Joanne Salop, “Distributional Aspects of Stabilization Programs in Developing Countries,” Staff Papers, Vol. 27 (1980), pp. 1–23.
Kakwani, Nanak C., “Income Inequality, Welfare, and Poverty. An Illustration Using Ukrainian Data,” Policy Research Working Paper 1411 (Washington, D.C.: The World Bank, 1995).
Kakwani, Nanak C., and N. Podder, “Efficient Estimation of the Lorenz Curve and Associated Inequality Measures from Grouped Observations,” Econometrica, Vol. 44 (March 1976), pp. 137–48.
Milanovic, Branko, “Determinants of Cross-Country Income Inequality. An ‘Augmented’ Kuznets’ Hypothesis,” World Bank Policy Research Working Paper 1246 (Washington, D.C.: The World Bank, 1994).
Milanovic, Branko, “Poverty, Inequality and Social Policy in Transition Economies,” Research Paper 9, Transition Economies Division (Washington, D.C.: The World Bank, 1995).
Morrisson, Christian, “Income Distribution in East European and Western Countries”, Journal of Comparative Economics 8 (1994), pp. 121–138
Saavalainen, Tapio O., “Stabilization in the Baltic Countries: A Comparative Analysis,” IMF Working Paper WP/95/44 (Washington, D.C.: International Monetary Fund, 1995)
Sen A. K., “Informational Bases of Alternative Welfare Approaches: Aggregation and Income Distribution”, Journal of Public Economics 44, No. 2. (March 1974), pp. 219–31
Stodder, James, “Equity-Efficiency Preferences in Poland and the Soviet Union: Order Reversals under the Atkinson Index,” Review of Income and Wealth, Series 37 (September 1991), pp. 287–99.
“Social Safety Nets for Economic Transition: Options and Recent Experiences”, IMF Paper on Policy Analysis and Assessment PPAA/95/4 (Washington D.C.: International Monetary Fund, 1995)
Wiles, P.J.D., and S. Markowski, “Income Distribution under Communism and Capitalism,” Soviet Studies, Part I, Vol. 22 (3), pp. 344–69; Part II, Vol. 22 (4), pp.487–511.
This paper has benefitted from helpful comments by Julian Berengaut, Jeffrey M. Davis, Sanjeev Gupta, Vincent Koen, Adalbert Knöbl, Henri Lorie, John Odling-Smee, Caroline Van Rijckeghem, Eva Srejber, and Jeromin Zettelmeyer. Special thanks are due to the Fund’s resident representatives in Estonia and Latvia, Basil Zavoico and Jukka Paljarvi, who helped us obtain numerous data from the national statistical offices. Valuable research assistance has been provided by Linda Galantin. The authors are responsible for all remaining errors.
While the liberalization of prices (including factor prices) has probably the most direct impact on the dispersion of income, there are, of course, numerous other channels through which the distribution of income can be affected. These channels include, inter alia, tax and expenditure policies, monetary and exchange rate policies, and trade policies. For a discussion of these channels and the conceptual problems to measure the effects of certain policies on income distribution, see, for example, Johnson and Salop (1980) and IMF (1986).
For a review of these programs, see, for example, Hansson and Sachs (1994); Lainela and Sutela (1994); and Saavalainen (1995). On individual country experiences, see Hansen et al. (1994); Knöbl et al. (1994); and Wolf et al. (1994).
There have been various approaches to estimate Lorenz curves from grouped observations (e.g. Kakwani and Podder, 1976; and Villasenor and Arnold, 1989). However, these approaches do not always work well. Certain groupings of the data can yield distorted estimates.
However, this result should be regarded with caution because, as is well known, an unambiguous ranking of income distributions across countries requires that the Lorenz curves do not intersect. Otherwise, alternative income distributions might rank differently depending on the precise shape of the households’ common utility function.
These results are in line with those derived by Kakwani (1995), who estimated a separate continuously differentiable function fitting the different data points.
The decile ratios reported in table 1 refer to the ratio of gross income at the top decile relative to the median (P90) over the gross income at the bottom decile relative to the median (P10).
Notwithstanding these improvements of the household surveys, a number of important problems still remain. For a discussion, see Cornelius (1995a).
The egalitarian effect of queueing was probably largest in the lower and middle range of the income distribution. In contrast, privileges such as access to special shops and preferential treatment in ordinary shops restaurants or cafeterias for executives, foreign currency, housing, official cars, reserved hospital and holiday-resort facilities, benefitted mainly people at the upper end of the distribution, estimated at 0.2 to 0.3 percent of the total population. According to Morrisson (1994), whose estimates are based on rather generous assumptions about the value of such benefits, the pre-reform Gini coefficients in various Central and Eastern European economies could have been distorted downward by a maximum of 3 to 4 percentage points.
This seems especially likely in the case of Latvia, where according to the household surveys the mean income per month per head amounted to only US$36 in 1994—compared with an average monthly wage of nearly US$200.
However, according to a representative Multipurpose Poverty Survey conducted in the Kyrgyz Republic in the fall of 1993, the actual increase in the dispersion of income seems to have been much larger than suggested by official data. Based on this survey, a Gini of 0.678 was estimated, implying that the distribution had widened by 37 Gini points. For details, see Ackland and Falkingham (forthcoming 1996).
Based on data from the 1980s, Milanovic (1994) has estimated the average Gini coefficient for the OECD at 0.312.
In Estonia, the percentage of earnings in the lowest decile relative to the median (P10) amounted to 53.7 percent in 1989. Earnings in the top decile relative to the median (P90) were estimated at 172.8 percent. In Latvia, these ratios were estimated at 53.5 percent and 173.6 percent, repectively; in Lithuania they were estimated at 53.9 and 178.7 percent, respectively (Atkinson and Micklewright (1992, table UE 6). In this context, it is interesting to note that earnings in the Baltic countries have been far more dispersed than in other former centrally planned economies. Atkinson and Micklewright (1992, p. 80), for example, report decile ratios of 2.5, 2.6 and 2.8 for former Czechoslovakia, Hungary, and Poland, respectively.
Whereas the Baltic states moved rapidly in liberalizing prices of goods and services, the authorities initially continued to intervene in the labor market. In order to deal with a sharp deterioration in their terms of trade and to break the momentum of inflation expectations, the authorities in Estonia and Latvia imposed a tax on excessive wage increases in the state sector in 1992, while Lithuania implemented a statutory incomes policy. These wage controls have contributed to a significant adjustment in real incomes, which was regarded as indispensable in light of the severe supply shock caused by the sharp rise in imported energy prices (Cornelius, 1995b) Consequently, the wage controls, which inevitably had important distortionary effects, were converted into voluntary guidelines in early 1993.
Cornelius (1995a) has estimated that in the case of Lithuania cash benefits have reduced the poverty gap by only about one percentage point. With perfect targeting of the poor, a three and a half times larger reduction could have been achieved. However, as Ahmad (1992) argues, detailed means-testing is likely to be administratively cumbersome so that the actual impact of social assistance reforms would probably be considerably smaller.
Ceteris paribus, the distribution of disposable income becomes more equal with higher average tax rates. However, since the average tax rate may be changed without changing the tax elasticity or the tax progressivity, the comparison of the pre-tax and post-tax Lorenz curves as a measure of the redistributive effects of direct taxes should be regarded with considerable caution.
In 1992, the average expenditures for social security and welfare in a sample of 19 high income countries were about 12 percent of GDP and the corresponding figure in a sample of 29 middle income countries was about 5 percent of GDP (Government Finance Statistics Yearbook, 1994).
For a discussion of reform options for the social safety net in transition economies see “Social Safety Nets for Economic Transition” (1995).