Despite the growing interest in income inequality, cross-country evidence often shows variation between measures and databases, which complicates research and policy evaluation. The objective of the article is to compere the consistency of data on income inequality in postsocialist countries from Central and Eastern Europe and Central Asia for the commonly used measures on the basis of leading databases in this area. Other such analyses typically focus on individual measures, databases or specific countries, which prompted the idea to fill the research gap for a targeted country group. The formulated hypotheses were to test the consistency of the following: development trends, the rankings of countries from the most to the least equal in terms of income, and the values for different measures indicated by databases. The study reveals high correlations in income inequality trends over the long term, particularly among the EU subgroups. Certain consistency was observed in the context of identifying countries with extreme income equality or inequality, and in the rankings between different measures from the same database. However, there was no full consistency, especially in non- EU countries, which highlights the impact of the methodological differences.
This article contributes to the existing body of research on income inequality by providing a broad analysis of the consistency and variability of the related data across different measures and databases, with a particular focus on post-socialist countries. It points to the importance of careful data selection when analyzing income inequality in the indicated group of countries, as individual differences between measures, databases and countries tend to affect the final results of the research.
income inequality, post-socialist countries, statistical analysis
Alvaredo. F., (2011). A note on the relationship between top income shares and the Gini coefficient. Economics Letters, Vol. 110(3). https://doi.org/10.1016/j.econlet.2010.10.008.
Alvaredo, F., Atkinson, A., Chancel, L., Piketty, T., Saez, E. and Zucman, G., (2016). Distributional National Accounts (DINA) Guidelines: Concepts and Methods used in WID.world. Wid.world working paper, No. 2016(2).
Atkinson, A. B., (1970). On the measurement of inequality. Journal of Economic Theory 2(3). In Atkinson, A.B., and Piketty T. (eds), Top Incomes Over the Twentieth Century: A Contrast Between Continental European and English-Speaking Countries. https://doi.org/10.1093/oso/9780199286881.003.0002.
Atkinson, A. B., (2007). Measuring top incomes: methodological issues.
Atkinson, A. B., Brandolini, A., (2010). On analyzing the world distribution of income. World Bank Economic Review, 24(1).
Atkinson, A.B., Piketty, T. and Saez, E., (2011). Top Incomes in the Long Run of History. Journal of Economic Literature, 49(1). DOI: 10.3386/w154.
Ayala, L., Pérez, A. and Prieto-Alaiz, M., (2022). The impact of different data sources on the level and structure of income inequality. SERIEs, 13. https://doi. org/10.1007/s13209-021-00258-0.
Bartles, Ch., Metzing, M., (2019). An integrated approach for a top-corrected income distribution. Journal of Economic Inequality, 17. https://doi.org/10.1007/s10888-018-9394-x.
Bellu, L. G., Liberati, P., (2006). Policy Impacts on Inequality: Inequality and Axioms for its Measurement. EASYPol, 054.
Blanchet, T., Flores, I. and Morgan, M., (2018). The Weight of the Rich: Improving Surveys with Tax Data. WID.world Work. Pap. Ser., 2018/12
Brzeziński, M., Salach, K., (2022). Determinants of inequality in transition countries. IZA World of Labor, 2022(496). https://doi.org/10.15185/izawol.496.
Bukowski, P., Novokmet, F., (2017). Inequality in Poland: Estimating the whole distribution by g- percentile, pp. 1983–2015. LIS Working papers, 731, LIS Cross- National Data Center in Luxembourg.
Corak, M., (2013). Income Inequality, Equality of Opportunity, and Intergenerational Mobility. Journal of Economic Perspectives, 27(3). https://doi.org/10.1257/jep.27.3.79.
Cobham, A., Schlögl, L. and Sumner, A., (2016). Inequality and the Tails: the Palma Proposition and Ratio. Global Policy, 7(1) https://doi.org/10.1111/1758-5899.12320.
Cobhan, A., Sumner, A., (2013). Is it all about the tails? The Palma measure of income inequality. Center for Global Development Working Paper, 2013(308).
Dubois, M., (2016). A note on the normative content of the Atkinson inequality aversion parameter. Post-Print hal-01837118, HAL.
De Maio, F. G., (2007). Income inequality measures. Journal of Epidemiology & Community Health, 61(10). https://doi.org/10.1136/jech.2006.052969.
Eurostat, (2020) Flash estimates of income inequalities and poverty indicators for 2019 (FE 2019) Experimental results. Eurostat.
Farris, F. A., (2010). The Gini index and measures of inequality. The American Mathematical Monthly, 117(10).
Ferreira, F. H. G., Lusting, N. and Teles, D., (2015). Appraising Cross-National Income Inequality Databases: An Introduction. Journal of Economic Inequality, 13(4). https://doi.org/10.1007/s10888-015-9316-0.
Goda, T., (2016). Global trends in relative and absolute income inequality. Ecos de Economía, 20(42). DOI: 10.17230/ecos.2015.42.3.
Galbraith, J. K., Choi, J., Halbach, B., Malinowska, A. and Zhang, W., (2016). A Comparison of Major World Inequality Data Sets: LIS, OECD, EU-SILC, WDI, and EHII. Income Inequality Around the World, Vol. 44. https://doi.org/10.1108/S0147-912120160000044008.
Jabkowski, P., (2009). Miary nierówności społecznych – podstawy metodologiczne. W: Podemski, K. (red.), Spór o społeczne znaczenie społecznych nierówności. Wydawnictwo Naukowe Uniwersytetu im. Adama Mickiewicza w Poznaniu.
Neef, T., (2020). What’s New About Income Inequality in Russia (1980-2019)? Trends in Comparison to Eastern Europe. World Inequality Lab – Issue Brief 2020/05.
Jenkins, S. P., (2015). World income inequality databases: an assessment of WIID and SWIID. Journal of Economic Inequality, 2015(13). https://doi.org/10.1007/s10888-015-9305-3.
Jenkins, S. P., (2017). Pareto Models, Top Incomes and Recent Trends in UK Income Inequality. Economica, Vol. 84(334). https://doi.org/10.1111/ecca.12217.
Jędrzejczak, A., Pekasiewicz, D., (2018) Properties of Selected Inequality Measures Based on Quantiles and Their Application to the Analysis of Income Distribution in Poland by Macroregion. Argumenta Oeconomica Cracoviensia, 18. https://doi.org/10.15678/AOC.2018.1803.
Larrimore, J., Burkhauser, R. V. and Armour, P., (2018). Accounting for income inequality in survey and administrative data: Evidence from the US Current Population Survey. Journal of Economic and Social Measurement, 43(1–2).
Latty, K., (2015). A five parameter Atkinson like index featuring relative income effects, with a seven-parameter extension for nonlinear (prioritarian) social welfare functions. Notes on income inequality.
OECD, (2023). Income distribution. OECD Social and Welfare Statistics (database). https://doi.org/10.1787/data-00654-en, https://www.oecd.org/els/soc/IDD-ToR.pdf.
Milanovic, B., (1999). Explaining the Increase in Inequality During the Transition. Economics of Transition, Vol. 7(2). https://doi.org/10.1111/1468-0351.00016.
Novokmet, F., Piketty, T. and Zucman, G., (2018). From Soviets to oligarchs: inequality and property in Russia 1905-2016. The Journal of Economic Inequality, 16. https://doi.org/10.1007/s10888-018-9383-0.
Pkietty T., (2014). Capital in the Twenty-First Century. Harvard University Press.
Pascola, R., Rocha, H., (2017). Inequality measures for wealth distribution: Population vs individuals perspective. Physica A Statistical Mechanics and its Applications, 492. https://doi.org/10.1016/j.physa.2017.11.059.
Ravallion, M., (2015). The Luxembourg Income Study. The Journal of Economic Inequality, 13(4). https://doi.org/10.1007/s10888-015-9298-y.
Sen, A., Foster, J., (1997). On Economic Inequality. Oxford University Press.
Sitthiyot, T., Holasut, K., (2020). A simple method for measuring inequality. Humanities & Social Sciences Communication, 6(112). https://doi.org/10.1057/ s41599-020-0484-6.
Solt, F., (2009). Standardizing the World Income Inequality Database. Social Science Quarterly, Southwestern Social Science Association, 90(2).
Solt, F., (2020). Measuring Income Inequality Across Countries and Over Time: The Standardized World Income Inequality Database. Social Science Quarterly, 101(3).
The Equality Trust, (2011). Income inequality: Trends and Measures. Equality Trust Research Digest. 2. Retrieved from: https://equalitytrust.org.uk/sites/default/files/research-digest-trends-measures-final.pdf.
Trapeznikova, I., (2019). Measuring income inequality. IZA World of Labor (462). https://doi.org/10.15185/izawol.462.
UNU-WIDER, (2022). World Income Inequality Database (WIID). https://doi.org/10.35188/UNU-WIDER/WIID-300622.
Vermeulen, P., (2016). Estimating the top tail of the wealth distribution. American Economic Review, 106(5).
Voitchovsky, S., (2005). Does the Profile of Income Inequality Matter for Economic Growth? Journal of Economic Growth, 10(3). https://doi.org/10.1007/s10887-005-3535-3.