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
Paolo Brunori: University of Firenze and London School of Economics
Pedro Salas-Rojo: London School of Economics

No 02-2024, SERIES from Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro"

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: 277
Date: 2024-08, Revised 2024-08
New Economics Papers: this item is included in nep-big
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