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.

翻译：我们采用多臂老虎机模型分析了招聘市场中的统计歧视。短视型企业面临具有异质性可观测特征的求职者。工人技能与特征之间的关联事先未知，因此企业需要对其进行学习。自由放任政策会导致持续低估：少数族裔工人很少被雇用，因此这种低估往往持续存在。即使人口比例出现轻微失衡，也时常导致长期低估。我们提出了两种政策解决方案：一种新型补贴规则（混合机制）和鲁尼规则。研究结果表明，临时性平权措施能有效缓解因数据不足引发的歧视问题。