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

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. This framework considers the advantage reached by an individual to be determined by the circumstances and by the effort exerted. Following Brunori and Neidhofer, we construct a data-driven procedure using regression trees to identify types based on circumstances. To identify effort, an algorithm estimates the distribution of outcome in each type based on coefficients of Bernstein polynomials. We present IOp indicators for both an ex-ante and an ex-post approach. 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 personal characteristics. The education and the wealth of parents, and the area of residence at age of 14 are the principal circumstances that shape the trajectories, besides the skin tone or the region. Importantly, territorial variables are significant among the individuals in relative poor households at age of 14, but they hold less importance for the others. IOp is higher in rural areas, in the South and in the Center compared to other regions.