This work presents the trend of Inequality of Opportunity (IOp) and total inequality in wealth in Mexico for the years
2006, 2011 and 2017, and provides estimations using both an ex-ante and ex-post compensation criterion. We resort on
a data-driven approach using supervised machine learning models to run regression trees and random forests that
consider individuals’ circumstances and effort. We find an intensification of both total inequality and IOp between 2006
and 2011, as well as a reduction of these between 2011 and 2017, being absolute IOp slightly higher in 2017 than in
2006. From an ex-ante perspective, the share of IOp within total inequality slightly decreased although using an ex-post
perspective the share remains stable across time. The most important variable in determining IOp is household´s wealth
at age 14, followed by both, father´s and mother´s education. Other variables such as the ability of the parents to speak
an indigenous language proved to have had a lower impact over time.