The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees - Paolo Brunori; Paul Hufe; Gerszon Daniel Mahler (ifo Institute - Leibniz Institute for Economic Research at the University of Munich, 2018)
We propose a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. In particular, we illustrate how these methods represent a substantial improvement over existing empirical approaches to measure in equality of opportunity. First, they minimize the risk of arbitrary and ad-hoc model selection. Second, they provide a standardized way of trading off upward and downward biases in inequality of opportunity estimations. Finally, regression trees can be graphically represented; their structure is immediate to read and easy to understand. This will make the measurement of inequality of opportunity more easily comprehensible to a large audience. These advantages are illustrated by an empirical application based on the 2011 wave of the European Union Statistics on Income and Living Conditions.
Keywords: D31; D63; C38; 330; Equality of opportunity; machine learning; random forests.