Trends in Inequality of Opportunity for Developing Countries: Does the Economic Indicator Matter?

TLDR: In this article, the authors analyzed the impact of inequality of opportunity on economic inequality in six countries: Brazil, Egypt, Guatemala, India, Peru and South Africa and the periods of time covered vary from 2004 to 2014.

Abstract: The aim of this paper is to shed some light on the behaviour of Inequality of Opportunity (IOp henceforth) in developing countries. The analysis is carried out using microdata collected by national surveys and harmonised by the Luxembourg Income Study (LIS). The LIS database incorporates a wide variety of personal harmonised variables, which allow us to made cross-country comparisons for developing countries. More specifically, we analyse six countries: Brazil, Egypt, Guatemala, India, Peru and South Africa and the periods of time covered vary from 2004 to 2014. In order to analyse the impact of inequality of opportunity we compute relative indicators by comparing IOp with economic inequality for each country analysed. Moreover, to check the robustness of our results we include two sensitivity analyses: first, we test the significance of overtime changes using inferential procedures and second, we assess if different economic indicators lead to different conclusions both in the evolution of IOp and overall inequality and in the relative weights of the circumstances that conform IOp. More specifically, regarding the first aim we focus on the disposable equivalised income to measure IOp and Income Inequality and we test if overtime changes are statistically significant using bootstrapping procedures. With regard to the second objective, to test the robustness of the results we compute IOp and Inequality for four different economic aggregates: Personal Income, Labour Personal Income, Consumption and Monetary Consumption. The empirical results of these analyses lead to two interesting conclusions: most of the overtime changes are found to be statistically significant and the use of a specific economic indicator is not as important as it at first seems, leading in most cases to the same conclusions.