Non-Bayesian Statistical Discrimination
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/fb1a95f5-08d9-498f-b24d-a4354e9e9f42
- author
- Campos-Mercade, Pol LU and Mengel, Friederike
- organization
- publishing date
- 2024
- 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}},
}