Model uncertainty and measures of inequality of opportunity

Inequality of opportunity has great normative importance. This has led to a literature on measuring the part of overall inequality that is due to circumstances outside of a person’s control. We contribute to such studies by evaluating the implications of uncertainty about circumstance variables and linear versus nonlinear transmission of circumstances on inequality of opportunity estimates. Applying linear Bayesian model averaging methods and three ensemble tree-based machine learning approaches to data from 31 European Union countries, we find that ignoring model uncertainty can lead to substantial overstatement of levels of inequality of opportunity.