Biases in inequality of opportunity estimates: measures and solutions

Domenico Moramarco (), Paolo Brunori and Pedro Salas-Rojo
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Domenico Moramarco: University of Bari - Department of Economics and Finance
Paolo Brunori: University of Firenze and LSE - International Inequalities Institute
Pedro Salas-Rojo: LSE - International Inequalities Institute

No 675, Working Papers from ECINEQ, Society for the Study of Economic Inequality

Abstract: In this paper we discuss some limitations of using survey data to measure inequality of opportunity. First, we highlight a link between the two fundamental principles of the theory of equal opportunities -- compensation and reward -- and the concepts of power and confidence levels in hypothesis testing. This connection can be used to address, for example, whether a sample has sufficient observations to appropriately measure inequality of opportunity. Second, we propose a set of tools to normatively assess inequality of opportunity estimates in any type partition. We apply our proposal to Conditional Inference Trees, a machine learning technique that has received growing attention in the literature. Finally, guided by such tools, we suggest that standard tree-based partitions can be manipulated to reduce the risk of compensation and reward principles.Our methodological contribution is complemented with an application using a quasi-administrative sample of Italian PhD graduates. We find a substantial level of labor income inequality among two cohorts of PhD graduates (2012 and 2014), with a significant portion explained by circumstances beyond their control.

Keywords: Equality of opportunity; Machine learning; PhD graduates; Compensation; Reward (search for similar items in EconPapers)
JEL-codes: C38 D31 D63 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2024-09
New Economics Papers: this item is included in nep-big and nep-mac
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