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IACM 2023

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Statistical Discrimination Against Minority Groups

Statistical discrimination relies on people interpreting information about group differences. Across three pre-registered experiments (n = 7,002), we show that information about the demographics of top performers induces incorrect beliefs that lead to discrimination against minority groups because people fail to account for the demographics of the population from which the top performers emerge. Participants are less likely to hire women when they receive information about top performers in a male-dominated candidate pool, even when there are no gender differences in performance, and are less likely to hire better-performing non-White candidates when the racial demographics of the candidate pool reflect the US population. A third experiment replicates the findings with neutral group labels, showing this behavior is a cognitive and not a motivated error.

David Hagmann
The Hong Kong University of Science and Technology
Hong Kong

Sajons Gwendolin
ESCP Business School

Catherine Tinsley
Georgetown University
United States


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