INEQUALITY OF OPPORTUNITY IN BRAZIL: A MACRO-REGIONAL PERSPECTIVE WITH CONDITIONAL INFERENCE TREES AND RANDOM FORESTS
Opportunity Inequality; Conditional Inference Trees and Forests; Regional Heterogeneity; Intergenerational Mobility.
This dissertation investigated inequality of opportunity in Brazil and its regions, analyzing how circumstances beyond individuals’ control, such as race, gender, and parents’ education, affect their income in the labor market and how this relationship varies regionally. To this end, conditional inference trees and forests were employed, machine learning methods that reduce the researcher’s subjective intervention in the selection of variables and their interactions, thereby minimizing estimation bias. The analysis used the supplementary microdata from the 2014 National Household Sample Survey (PNAD), the last year with available information on parents’ education. The results indicate that these circumstances beyond individual control explain a significant share of income inequality. Regional heterogeneity was observed both in the most relevant circumstances and in the magnitude of inequality of opportunity, demonstrating that the structure of opportunities is not uniform across the country.