Biases in inequality of opportunity estimates: measures and solutions

Domenico Moramarco, Paolo Brunori and Pedro Salas Rojo

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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: C30 D31 D63 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2024-09-01
New Economics Papers: this item is included in nep-big and nep-ipr
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