On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach
This addresses hiring discrimination for minority workers, but is an incremental application of existing models to a social problem.
The paper tackles statistical discrimination in hiring markets using a multi-armed bandit model, showing that laissez-faire leads to persistent underestimation of minority workers, and finds that temporary affirmative actions like a hybrid subsidy rule and the Rooney Rule effectively alleviate this discrimination.
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.