Testing for Asymmetric Employer Learning and Statistical Discrimination

Suqin Ge, Andrea Moro and Beibei Zhu ()
Additional contact information
Beibei Zhu: Slack

No 2018: 27, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: We test the implications of a statistical discrimination model with asymmetric learning. Firms receive signals of productivity over time and may use race to infer worker's productivity. Incumbent employers have more information about workers productivity than outside employers. Using data from the NLSY79, we find evidence of asymmetric learning. In addition, employers statistically discriminate against non-college educated black workers at time of hiring. We also find that employers directly observe most of the productivity of college graduates at hiring, and learn very little over time about these workers.

Keywords: statistical discrimination; employer learning; asymmetric learning (search for similar items in EconPapers)
JEL-codes: D82 J31 J71 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2018
New Economics Papers: this item is included in nep-bec and nep-lma
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Testing for asymmetric employer learning and statistical discrimination (2021)
Working Paper: Testing for Asymmetric Employer Learning and Statistical Discrimination (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ven:wpaper:2018:27

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