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It is widely accepted that women are underrepresented in academia in generaland economics in particular. This paper introduces a test to detect anunder-researched form of hiring bias: implicit quotas. I derive a test underthe Null of random hiring that requires no information about individual hiresunder some assumptions. I derive the asymptotic distribution of this teststatistic and, as an alternative, propose a parametric bootstrap procedure thatsamples from the exact distribution. This test can be used to analyze a varietyof other hiring settings. I analyze the distribution of female professors atGerman universities across 50 different disciplines. I show that thedistribution of women, given the average number of women in the respectivefield, is highly unlikely to result from a random allocation of women acrossdepartments and more likely to stem from an implicit quota of one or two womenon the department level. I also show that a large part of the variation in theshare of women across STEM and non-STEM disciplines could be explained by atwo-women quota on the department level. These findings have importantimplications for the potential effectiveness of policies aimed at reducingunderrepresentation and providing evidence of how stakeholders perceive andevaluate diversity.

Testing the Presence of Implicit Hiring Quotas with Application to German Universities