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Biases in inequality of opportunity estimates: measures and solutions

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  • Moramarco, Domenico
  • Brunori, Paolo
  • Salas Rojo, Pedro

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.

Suggested Citation

  • Moramarco, Domenico & Brunori, Paolo & Salas Rojo, Pedro, 2024. "Biases in inequality of opportunity estimates: measures and solutions," LSE Research Online Documents on Economics 125442, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:125442
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    File URL: http://eprints.lse.ac.uk/125442/
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    References listed on IDEAS

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    1. Marc Fleurbaey & Vito Peragine, 2013. "Ex Ante Versus Ex Post Equality of Opportunity," Economica, London School of Economics and Political Science, vol. 80(317), pages 118-130, January.
    2. Xavier Ramos & Dirk gaer, 2016. "Approaches To Inequality Of Opportunity: Principles, Measures And Evidence," Journal of Economic Surveys, Wiley Blackwell, vol. 30(5), pages 855-883, December.
    3. Paolo Brunori & Vito Peragine & Laura Serlenga, 2019. "Upward and downward bias when measuring inequality of opportunity," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 52(4), pages 635-661, April.
    4. Juan Carlos Escanciano & Joel Robert Terschuur, 2022. "Machine Learning Inference on Inequality of Opportunity," Papers 2206.05235, arXiv.org, revised Oct 2023.
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    More about this item

    Keywords

    equality of opportunity; machine learning; PhD graduates; compensation; reward;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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