Clementine van Effenterre, University of Toronto

"Can More Objective Performance Information Overcome Gender Differences in Interview Evaluations"


In spite of the large literature documenting discrimination in various contexts, we know little about the impact of providing additional objective information about candidates' abilities on gender differences in labor market outcomes. We study this question by leveraging data on over 60,000 online interviews for software developers, combined with the quasi-random introduction of a device providing an objective measure of candidates' coding and problem solving performance. Despite gender gaps in the objective measure being much smaller than those in subjective interview evaluations, the improved quality of information does not reduce the gender gap in evaluations. We combine these results with a theoretical model, which has testable predictions for how gender gaps in ratings vary with additional information and across problems of different degrees of difficulty. Our results are not explained by attrition, endogenous matching, or positive selection.

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