On Statistical Discrimination as a Failure of Social Learning: A Multiarmed Bandit Approach

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<jats:p> We analyze statistical discrimination in hiring markets using a multiarmed 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 demonstrate that a subsidy rule that is implemented as temporary affirmative action effectively alleviates discrimination stemming from insufficient data. </jats:p><jats:p> This paper was accepted by Nicolas Stier-Moses, Management Science Special Issue on The Human-Algorithm Connection. </jats:p><jats:p> Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant 430-2020-00088] and JST ERATO [Grant JPMJER2301], Japan. </jats:p><jats:p> Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00893 . </jats:p>

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  • Management Science

    Management Science 2024-03-29

    Institute for Operations Research and the Management Sciences (INFORMS)

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