Explaining International and Intertemporal Variations in Income Inequality

Abstract

This paper explores the propositions that, income inequality is relatively stable within countries; and that it varies significantly among countries. A new and expanded data set provides broad support for both propositions. Drawing on a political economy and capital market imperfection arguments to explain the intertemporal and international variation in inequality, the empirical analysis shows that the predicted variables associated with the first argument (a measure of civil liberties and the initial level of secondary schooling) and the second argument (a measure of financial depth and the initial distribution of land) are indeed important determinants of inequality. This paper explores two propositions regarding income inequality. They are: first, income inequality is relatively stable within countries; and second, it varies significantly across countries.' To illustrate, note that the Gini coefficient in India remained almost constant for forty years (1951-92) with mean 32.6 and standard deviation 2.0.2 In contrast, the variation in Gini coefficients across countries is large: 61.9 in Honduras in 1968 compared with 17.8 in Bulgaria in 1976. If substantiated, these propositions have potentially significant implications for poverty. The significance of the first is obvious - barring any fundamental socio-political change, poverty reduction will depend crucially on the rate of economic growth. Given this, the significance of the second is that in inegalitarian economies the poor will enjoy a smaller share of any national increment in income than in more egalitarian ones. Drawing on a new and expanded data set on inequality (Deininger and Squire, 1996a), the first of the paper's three sections conducts standard statistical tests of the two propositions. The sample comprises 573 observations on the most common measure of inequality - the Gini coefficient - for 49 developed and developing countries covering the period 1947-94. The results broadly confirm our two propositions. Specifically, analysis of variance (ANOVA) shows that about 90% of the total variance in the Gini coefficients