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Non-Bayesian Statistical Discrimination

Author

Listed:
  • Pol Campos-Mercade

    (Department of Economics, Lund University, SE-220 07 Lund, Sweden)

  • Friederike Mengel

    (Department of Economics, University of Essex, Colchester CO4 3SQ, United Kingdom; Department of Economics, University of Heidelberg, 69047 Heidelberg, Germany)

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.

Suggested Citation

  • Pol Campos-Mercade & Friederike Mengel, 2024. "Non-Bayesian Statistical Discrimination," Management Science, INFORMS, vol. 70(4), pages 2549-2567, April.
    • Handle: RePEc:inm:ormnsc:v:70:y:2024:i:4:p:2549-2567
    DOI: 10.1287/mnsc.2023.4824
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