Inequality of Opportunity in Mexico and its Regions: A Data-Driven Approach

Abstract

This research proposes a first approximation of Inequality of Opportunity (IOp) in Mexico based on a concept of ex-post compensation, fully consistent with Roemer s approach of In-equality of Opportunity. This framework considers the advantage reached by an individual to be determined by the circumstances at origin and by the effort exerted. Following Brunori and Neidhofer (2020), we construct a data-driven procedure using regression trees to identify types based on circumstances. To identify degrees of effort, an algorithm estimates the distribution of outcome in each type based on coefficients of Bernstein polynomials. Our results underline the differences, in terms of opportunities, faced by individuals, based on the territory in which they grew up, the household context, and their sex. We find that the education and the wealth of parents are the principal circumstances that shape their trajectories. Importantly, territorial variables are significant determinants of IOp among the most disadvantaged at origin, but they hold less importance for the most advantaged. IOp is lower in urban areas, in the Northern region and in Mexico City compared to other regions. The Southern region and rural territories are the most unequal. We also estimate that the weight of IOp in terms of total inequality varies according if we adopt an ex-post or an ex-ante approach, which can lead to different conclusions.