In this paper we propose the use of machine learning methods to estimate inequality of opportunity. We illustrate how our proposed methods - conditional inference regression trees and forests - represent a substantial improvement over existing estimation approaches. First, they reduce the risk...

A growing literature has tried to measure the extent to which individuals have equal opportunities to acquire income. At the same time, policy makers have doubled down on efforts to go beyond income when measuring well- being. We attempt to bridge these two areas by measuring the extent to which...

A growing literature has tried to measure the extent to which individuals have equal opportunities to acquire income. At the same time, policy makers have doubled down on efforts to go beyond income when measuring well-being. We attempt to bridge these two areas by measuring the extent to which...

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...

This paper proposes a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. It illustrates how these methods represent a substantial improvement over existing empirical approaches to measure inequality of opportunity. First, the new methods...

This paper proposes a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. It illustrates how these methods represent a substantial improvement over existing empirical approaches to measure inequality of opportunity. First, the new methods...