Non-Bayesian Statistical Discrimination

Friederike Mengel (University of Essex)

October 1, 2021, 11:30–12:30


Room Zoom meeting


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 or "irrational" 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 rational statistical discrimination, a further 40% is due to irrational statistical discrimination, and the remaining 20% is unexplained or potentially taste-based.


Friederike Mengel (University of Essex), Non-Bayesian Statistical Discrimination, IAST General Seminar, Toulouse: IAST, October 1, 2021, 11:30–12:30, room Zoom meeting.