Women (compared to men) and individuals from minority ethnic groups (compared to the majority group) face unfavourable labour market outcomes in many economies^1,2, but the extent to which discrimination is responsible for these effects, and the channels through which they occur, remain unclear^3,4. Although correspondence tests⁵—in which researchers send fictitious CVs that are identical except for the randomized minority trait to be tested (for example, names that are deemed to sound ‘Black’ versus those deemed to sound ‘white’)—are an increasingly popular method to quantify discrimination in hiring practices^6,7, they can usually consider only a few applicant characteristics in select occupations at a particular point in time. To overcome these limitations, here we develop an approach to investigate hiring discrimination that combines tracking of the search behaviour of recruiters on employment websites and supervised machine learning to control for all relevant jobseeker characteristics that are visible to recruiters. We apply this methodology to the online recruitment platform of the Swiss public employment service and find that rates of contact by recruiters are 4–19% lower for individuals from immigrant and minority ethnic groups, depending on their country of origin, than for citizens from the majority group. Women experience a penalty of 7% in professions that are dominated by men, and the opposite pattern emerges for men in professions that are dominated by women. We find no evidence that recruiters spend less time evaluating the profiles of individuals from minority ethnic groups. Our methodology provides a widely applicable, non-intrusive and cost-efficient tool that researchers and policy-makers can use to continuously monitor hiring discrimination, to identify some of the drivers of discrimination and to inform approaches to counter it.

在许多经济体中，女性（与男性相比）和少数族裔群体（与多数群体相比）面临不利的劳动力市场结果^1,2，但歧视对这些影响的影响程度以及它们发生的渠道，尚不清楚^3,4。尽管对应测试^5——研究人员发送虚构的简历，除了要测试的随机少数族裔特征（例如，被认为听起来“黑人”的名字与听起来“白人”的名字）之外，其他都是相同的——越来越受欢迎量化招聘实践中歧视的方法^6,7，他们通常只能考虑在特定时间点选择职业中的少数申请人特征。为了克服这些限制，我们在这里开发了一种调查招聘歧视的方法，该方法将跟踪招聘网站上招聘人员的搜索行为和监督机器学习相结合，以控制招聘人员可见的所有相关求职者特征。我们将这种方法应用于瑞士公共就业服务的在线招聘平台，发现移民和少数族裔群体的招聘人员的联系率比来自瑞士的公民低 4-19%，具体取决于他们的原籍国。多数群体。女性在男性主导的职业中会受到 7% 的惩罚，在女性主导的职业中，男性出现了相反的模式。我们没有发现任何证据表明招聘人员花费更少的时间来评估少数族裔群体的个人资料。我们的方法提供了一种广泛适用、非侵入性且具有成本效益的工具，研究人员和政策制定者可以使用该工具来持续监控招聘歧视，找出一些歧视的驱动因素，并为应对方法提供信息。