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Irrational Statistical Discrimination

25 November 2020
2:00 pm

In the absence of complete information about worker characteristics, employers might systematically discriminate against workers from given identity groups. Such statistical discrimination is typically modeled assuming that employers are rational Bayesian updaters. However, a large literature shows that most people fail to update rationally. We use a model and a lab experiment to show that if employers are naive, in the sense of signal neglect, workers from disadvantaged groups will be discriminated against more often than when employers are rational. Furthermore, this makes discriminated workers less likely to pursue education. We further document in an online experiment that people are naive when updating about others. This leads employers to overdiscriminate disadvantaged groups especially when the signals are very informative. Our results suggest that what sometimes appear to be taste-based discrimination may instead stem from wrong belief updating.


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Friederike Mengel, University of Essex