我们利用多臂老虎机模型分析招聘市场中的统计性歧视。短视的企业面对的是带着异质性可观测特征前来的劳动者。劳动者的技能与其特征之间的关联在事前是未知的,因此企业需要对其进行学习。自由放任会导致永久性的低估:少数群体劳动者很少被雇用,因而这种低估往往持续存在。即使人口比例中存在边际上的不平衡,也常常导致永久性的低估。我们证明,以暂时性平权行动形式实施的补贴规则能有效缓解源于数据不足的歧视。本文由 Nicolas Stier-Moses 接收,收录于"人与算法的联系"特刊。基金资助:本研究得到加拿大社会科学与人文研究理事会[资助号 430-2020-00088]以及日本 JST ERATO[资助号 JPMJER2301]的支持。补充材料:在线附录与数据文件可在 https://doi.org/10.1287/mnsc.2022.00893 获取。
Abstract
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. This paper was accepted by Nicolas Stier-Moses, Special Issue on the Human-Algorithm Connection. 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. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00893 .