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On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach

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  • Junpei Komiyama
  • Shunya Noda

Abstract

We analyze statistical discrimination in hiring markets using a multi-armed bandit model. Myopic firms face workers arriving with heterogeneous observable characteristics. The association between the worker's skill and characteristics is unknown ex ante; thus, firms need to learn it. Laissez-faire causes perpetual underestimation: minority workers are rarely hired, and therefore, the underestimation tends to persist. Even a marginal imbalance in the population ratio frequently results in perpetual underestimation. We propose two policy solutions: a novel subsidy rule (the hybrid mechanism) and the Rooney Rule. Our results indicate that temporary affirmative actions effectively alleviate discrimination stemming from insufficient data.

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  • Junpei Komiyama & Shunya Noda, 2020. "On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach," Papers 2010.01079, arXiv.org, revised Jul 2023.
    • Handle: RePEc:arx:papers:2010.01079
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    Cited by:

    1. Paula Onuchic, 2022. "Recent Contributions to Theories of Discrimination," Papers 2205.05994, arXiv.org, revised Jun 2023.

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