Testing for Asymmetric Employer Learning and Statistical Discrimination

Posted: 27 Sep 2018

See all articles by Suqin Ge

Suqin Ge

Virginia Tech - Department of Economics

Andrea Moro

Vanderbilt University - College of Arts and Science - Department of Economics

Beibei Zhu

Independent

Date Written: June 25, 2018

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

JEL Classification: J71, D82, J31

Suggested Citation

Ge, Suqin and Moro, Andrea and Zhu, Beibei, Testing for Asymmetric Employer Learning and Statistical Discrimination (June 25, 2018). Available at SSRN: https://ssrn.com/abstract=3243966

Suqin Ge

Virginia Tech - Department of Economics ( email )

Department of Economics
Virginia Tech
Blacksburg, VA 24061
United States

Andrea Moro (Contact Author)

Vanderbilt University - College of Arts and Science - Department of Economics ( email )

Box 1819 Station B
Nashville, TN 37235
United States

Beibei Zhu

Independent ( email )

No Address Available

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