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Non-Bayesian Statistical Discrimination

Campos-Mercade, Pol LU and Mengel, Friederike (2024) In Management Science 70(4). p.2549-2567
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... (More)
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. (Less)
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author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Management Science
volume
70
issue
4
pages
2549 - 2567
publisher
INFORMS Institute for Operations Research and the Management Sciences
external identifiers
  • scopus:85191571043
ISSN
0025-1909
DOI
10.1287/mnsc.2023.4824
language
English
LU publication?
yes
id
fb1a95f5-08d9-498f-b24d-a4354e9e9f42
date added to LUP
2023-01-17 01:57:19
date last changed
2025-04-04 14:51:17
@article{fb1a95f5-08d9-498f-b24d-a4354e9e9f42,
  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.}},
  author       = {{Campos-Mercade, Pol and Mengel, Friederike}},
  issn         = {{0025-1909}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{2549--2567}},
  publisher    = {{INFORMS Institute for Operations Research and the Management Sciences}},
  series       = {{Management Science}},
  title        = {{Non-Bayesian Statistical Discrimination}},
  url          = {{http://dx.doi.org/10.1287/mnsc.2023.4824}},
  doi          = {{10.1287/mnsc.2023.4824}},
  volume       = {{70}},
  year         = {{2024}},
}