We measure unfair health inequality in the UK using a novel data- driven empirical approach. We explain health variability as the result of circumstances beyond individual control and health-related behaviours. We do this using model-based recursive partitioning, a supervised machine learning...

Despite a recent surge in the number of studies attempting to measure inequality of opportunity in various countries, methodological differences have so far prevented meaningful international comparisons. This paper presents a comparison of ex-ante measures of inequality of economic opportunity...

We axiomatically characterize two classes of poverty measures which are sensitive to inequality of opportunity – one a strict subset of the other. The proposed indices are sensitive not only to income shortfalls from the poverty line, but also to differences in opportunities faced by people...

Estimates of the level of inequality of opportunity have traditionally been interpreted as lower bounds due to the downward bias resulting from the partial observability of circumstances that affect individual outcome. We show that such estimates may also suffer from upward bias as a consequence...

This paper asks whether prioritarianism – the view that social welfare orderings should give explicit priority to the worse-off – is consistent with the normative theory of equality of opportunity. We show that there are inherent tensions between some of the axioms underpinning...

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...