The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach

We show that measures of inequality of opportunity fully consistent with Roemer (1998)'s inequality of opportunity theory can be straightforwardly estimated adopting a machine learning approach. Following Roemer, inequality of opportunity is generally defined as inequality between individuals exerting the same degree of effort but characterized by different exogenous circumstances. Due to difficulties of measuring effort, most empirical contributions so far identified groups of individuals sharing same circumstances, and then measured inequality of opportunity as between-group inequality, without considering the effort exerted. Our approach uses regression trees to identify groups of individuals characterized by identical circumstances, and a polynomial approximation to estimate the degree of effort exerted. To apply our method, we take advantage of information contained in 25 waves of the German Socio-Economic Panel. We show that in Germany inequality of opportunity declined immediately after the reunification, surged in the first decade of the century, and slightly declined again after 2010. The level of estimated unequal opportunity is today just above the level recorded in 1992.