This paper addresses the problem of the normative evaluation of income tax systems and income tax reforms. While most of the existing criteria, framed in the utilitarian tradition, are uniquely based on information about individual incomes, this paper, building upon the opportunity egalitarian...

The paper proposes an analysis of the recent distributional dynamics in Uganda. This analysis is performed by endorsing an opportunity egalitarian perspective, in order to evaluate the outcome dynamics of specific groups of the population and infer the role of growth in the evolution of...

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 with...

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...

We show that measures of inequality of opportunity (IOP) fully consistent with Roemer (1998)'s IOP theory can be straightforwardly estimated by adopting a machine learning approach, and apply our novel method to analyse the development of IOP in Germany during the last three decades. Hereby, we...

We show that measures of inequality of opportunity (IOP) fully consistent with Roemer (1998)'s IOP theory can be straightforwardly estimated by adopting a machine learning approach, and apply our novel method to analyse the development of IOP in Germany during the last three decades. Hereby, we...

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...

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...

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 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...