Non-Bayesian Statistical Discrimination

Pol Campos-Mercade, Friederike Mengel

Research output: Contribution to journalArticlepeer-review

Abstract

Models of statistical discrimination typically assume that employers make rational inference from (education) signals. However, there is a large amount of evidence showing that most people do not update their beliefs rationally. We use a model and two experiments to show that employers who are conservative, in the sense of signal neglect, discriminate more against disadvantaged groups than Bayesian employers. We find that such non-Bayesian statistical discrimination deters high-ability workers from disadvantaged groups from pursuing education, further exacerbating initial group inequalities. Excess discrimination caused by employer conservatism is especially important when signals are very informative. Out of the overall hiring gap in our data, around 40% can be attributed to Bayesian statistical discrimination, a further 40% is due to non-Bayesian statistical discrimination, and the remaining 20% is unexplained or potentially taste-based.
Original languageEnglish
Pages (from-to)2549-2567
JournalManagement Science
Volume70
Issue number4
Early online date2024
DOIs
Publication statusPublished - 2024

Subject classification (UKÄ)

  • Economics

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