Endogenous Learning, Persistent Employer Biases, and Discrimination

Lepage, Louis Pierre

doi:10.2139/ssrn.3640663

Cited by 4 publications

(4 citation statements)

References 98 publications

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“…Pinning down exactly when one or the other arises is a promising direction for future theoretical work. 9 Lepage (2020) shows that selection problems can lead to discrimination in hiring even if employers form beliefs by Bayes' rule, as selection can create skew in the beliefs about ability.…”

Section: Discussionmentioning

confidence: 99%

A Behavioural Theory of Discrimination in Policing

Hübert¹,

Little²

2022

Preprint

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A large economic literature studies whether racial disparities in policing are explained by animus or by beliefs about group crime rates. But what if these beliefs are incorrect? We analyze a model where officers form beliefs using crime statistics, but don’t properly account for the fact that they will detect more crime in more heavily policed communities. This creates a feedback loop where officers over-police groups that they (incorrectly) believe exhibit high crime rates. This inferential mistake can exacerbate discrimination even among officers with no animus and who sincerely believe disparities are driven by real differences in crime rates.

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Section: Discussionmentioning

confidence: 99%

A Behavioural Theory of Discrimination in Policing

Hübert¹,

Little²

2022

Preprint

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“…Current studies show these beliefs can change as employers learn about individual productivity through their hiring (Altonji and Pierret, 2001;Lange, 2007;Arcidiacono et al, 2010;Kahn and Lange, 2014). Lepage (2020) shows employers also learn about group productivity based on their own hiring experiences with workers from different groups. Our results provide evidence of employers learning and updating beliefs about productivity of a group prior to hiring.…”

Section: Introductionmentioning

confidence: 95%

The Labor Market Returns to Unobserved Skills: Evidence from a Gender Quota

Moeeni

Feng

2023

Preprint

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“…But in other situations, there will be little or no feedback on the decisions being made, leading to learning traps in which inaccurate beliefs persist. Inaccurate beliefs may persist in the long run even when employers are perfectly Bayesian if, for example, they face a trade off between learning about the productivity of groups through hiring or maximizing cost-effectiveness (Lepage, 2020). Further, as shown theoretically by Gagnon-Bartsch, Rabin, and Schwartzstein (2018), learning may not correct beliefs if information is filtered through an incorrect model of the world.…”

Section: Introductionmentioning

confidence: 99%

Inaccurate Statistical Discrimination: An Identification Problem

Bohren,

Haggag,

Imas

et al. 2023

Review of Economics and Statistics

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We study inaccurate beliefs as a source of discrimination. Economists typically characterize discrimination as stemming from a taste-based (preference) or accurate statistical (belief-based) source. While individuals may have inaccurate beliefs about how relevant characteristics (e.g., productivity, signals) are correlated with group identity, fewer than 7% of empirical discrimination papers in economics consider the possibility of such inaccurate statistical discrimination. Using theory and a labor market experiment, we show that failing to account for inaccurate beliefs leads to a misclassification of source. We outline three methods to identify source: varying observed signals, belief elicitation, and an intervention to target inaccurate beliefs.

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Endogenous Learning, Persistent Employer Biases, and Discrimination

Cited by 4 publications

References 98 publications

A Behavioural Theory of Discrimination in Policing

A Behavioural Theory of Discrimination in Policing

The Labor Market Returns to Unobserved Skills: Evidence from a Gender Quota

Inaccurate Statistical Discrimination: An Identification Problem

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