Marta Menéndez

Inequality of Opportunity in Brazil

This paper proposes a method to decompose earnings inequality into a component due to unequal opportunities and a residual term. Drawing on the distinction between ‘circumstance’ and ‘effort’ variables in John Roemer’s work on equality of opportunity, we associate inequality of opportunities with the inequality attributable to circumstances which lie beyond the control of the individual – such as her family background, her race and the region where she was born. We interpret the decomposition as establishing a lower bound on the contribution of opportunities to earnings inequality. We further decompose the effect of opportunities into a direct effect on earnings and an indirect component which works through the “effort” variables. The decomposition is applied to the distributions of male and female earnings in Brazil, in 1996. While the residual term is large, observed circumstances nevertheless account for around a quarter of the value of the Theil index. Parental education is by far the most important circumstance affecting earnings, dwarfing the effects of race and place of birth.

Inequality of Outcomes and Inequality of Opportunities in Brazil

This paper departs from John Roemers theory of equality of opportunities. We seek to determine what part of observed outcome inequality may be attributed to differences in observed circumstances, including family background, and what part is due to personal efforts. We use a micro-econometric technique to simulate what the distribution of outcomes would look like if circumstances were the same for everybody. This technique is applied to Brazilian data from the 1996 household survey, both for earnings and for household incomes. It is shown that observed circumstances are a major source of outcome inequality in Brazil, probably more so than in other countries for which information is available. Nevertheless, the level of inequality after observed circumstances are equalized remains very high in Brazil.

For Richer or for Poorer ? Evidence from Indonesia, South Africa, Spain, and Venezuela

We analyze household income dynamics using longitudinal data from Indonesia, South Africa (KwaZulu-Natal), Spain and Venezuela. In all four countries, households with the lowest reported base-year income experienced the largest absolute income gains. This result is robust to reasonable amounts of measurement error in two of the countries. In three of the four countries, households with the lowest predicted base-year income experienced gains at least as large as their wealthier counterparts. Thus, with one exception, the empirical importance of cumulative advantage, poverty traps, and skill-biased technical change was no greater than structural or macroeconomic changes that favored initially poor households in these four countries.

Household Income Dynamics: A Four-Country Story

In this article, we analyse the dynamics of household per capita incomes using longitudinal data from Indonesia, South Africa, Spain and Venezuela. We find that in all four countries reported initial income and job changes of the head are consistently the most important variables in accounting for income changes, overall and for initially poor households. We also find that changes in income are more important than changes in household size and that changes in labour earnings are more important than changes in other sources of household income.

Escaping From Poverty: Household Income Dynamics in Indonesia, South Africa, Spain, and Venezuela

Opponents of free trade argue that in today’s global economy, unfettered access to foreign capital, technology, and goods primarily benefits a well-connected and highly skilled elite, to the exclusion of the poor, voiceless majority. Are the rich getting richer at the expense of the poorer?

Inequality of Opportunity in Brazil: A Corrigendum

This note acknowledges and corrects a programming error in our paper “Inequality of Opportunity in Brazil” (Review of Income and Wealth, 53(4), 585–618, 2007). Once the error is corrected, our bounds approach to the identification of individual model parameters in the presence of omitted variable biases is much less useful than indicated in the original paper. In the specific context of the measurement of inequality of opportunity, this implies that the decomposition of overall inequality of opportunity into direct and indirect effects is not reliable. However, the parametric approach introduced in our paper remains useful for obtaining a lower-bound estimate of overall ex-ante inequality of opportunity, as proposed by Ferreira and Gignoux (2011).