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A Short-term Intervention for Long-term Fairness in the Labor Market

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  • Lily Hu
  • Yiling Chen

Abstract

The persistence of racial inequality in the U.S. labor market against a general backdrop of formal equality of opportunity is a troubling phenomenon that has significant ramifications on the design of hiring policies. In this paper, we show that current group disparate outcomes may be immovable even when hiring decisions are bound by an input-output notion of "individual fairness." Instead, we construct a dynamic reputational model of the labor market that illustrates the reinforcing nature of asymmetric outcomes resulting from groups' divergent accesses to resources and as a result, investment choices. To address these disparities, we adopt a dual labor market composed of a Temporary Labor Market (TLM), in which firms' hiring strategies are constrained to ensure statistical parity of workers granted entry into the pipeline, and a Permanent Labor Market (PLM), in which firms hire top performers as desired. Individual worker reputations produce externalities for their group; the corresponding feedback loop raises the collective reputation of the initially disadvantaged group via a TLM fairness intervention that need not be permanent. We show that such a restriction on hiring practices induces an equilibrium that, under particular market conditions, Pareto-dominates those arising from strategies that statistically discriminate or employ a "group-blind" criterion. The enduring nature of equilibria that are both inequitable and Pareto suboptimal suggests that fairness interventions beyond procedural checks of hiring decisions will be of critical importance in a world where machines play a greater role in the employment process.

Suggested Citation

  • Lily Hu & Yiling Chen, 2017. "A Short-term Intervention for Long-term Fairness in the Labor Market," Papers 1712.00064, arXiv.org, revised Feb 2018.
  • Handle: RePEc:arx:papers:1712.00064
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    References listed on IDEAS

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    1. Samuel Bowles & Glenn C. Loury & Rajiv Sethi, 2014. "Group Inequality," Journal of the European Economic Association, European Economic Association, vol. 12(1), pages 129-152, February.
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    3. Lily Morse & Mike Horia M. Teodorescu & Yazeed Awwad & Gerald C. Kane, 2022. "Do the Ends Justify the Means? Variation in the Distributive and Procedural Fairness of Machine Learning Algorithms," Journal of Business Ethics, Springer, vol. 181(4), pages 1083-1095, December.
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    5. Lydia T. Liu & Ashia Wilson & Nika Haghtalab & Adam Tauman Kalai & Christian Borgs & Jennifer Chayes, 2019. "The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally," Papers 1910.04123, arXiv.org.
    6. Soheil Ghili & Ehsan Kazemi & Amin Karbasi, 2019. "Eliminating Latent Discrimination: Train Then Mask," Cowles Foundation Discussion Papers 2157, Cowles Foundation for Research in Economics, Yale University.
    7. Sampath Kannan & Mingzi Niu & Aaron Roth & Rakesh Vohra, 2021. "Best vs. All: Equity and Accuracy of Standardized Test Score Reporting," Papers 2102.07809, arXiv.org.

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