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تسجيل مستخدم جديدOn Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach
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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 workers 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, underestimation towards them tends to persist. Even a slight population-ratio imbalance frequently produces 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 mitigate discrimination caused by insufficient data.
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Vineeth S. Varma
, Samson Lasaulce
, Julien Mounthanyvong andn Irinel-Constantin Morarescu
2020
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