Non-Bayesian Statistical Discrimination
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Non-Bayesian Statistical Discrimination. / Campos-Mercade, Pol; Mengel, Friederike.
In: SSRN Electronic Journal, 2021.Research output: Contribution to journal › Journal article › Research
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TY - JOUR
T1 - Non-Bayesian Statistical Discrimination
AU - Campos-Mercade, Pol
AU - Mengel, Friederike
PY - 2021
Y1 - 2021
N2 - 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 nonBayesian 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.
AB - 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 nonBayesian 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.
KW - Faculty of Social Sciences
KW - statistical discrimination
KW - conservatism
KW - naive employers
KW - experiments
U2 - 10.2139/ssrn.3843579
DO - 10.2139/ssrn.3843579
M3 - Journal article
JO - SSRN Electronic Journal
JF - SSRN Electronic Journal
SN - 1556-5068
ER -
ID: 286435604