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Testing for Asymmetric Employer Learning and Statistical Discrimination

Author

Listed:
  • Suqin Ge

    (Department of Economics, Virginia Tech.)

  • Andrea Moro

    (Department of Economics, Ca' Foscari University of Venice; Department of Economics, Vanderbilt University)

  • Beibei Zhu

    (Slack)

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.

Suggested Citation

  • Suqin Ge & Andrea Moro & Beibei Zhu, 2018. "Testing for Asymmetric Employer Learning and Statistical Discrimination," Working Papers 2018: 27, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2018:27
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    Cited by:

    1. Sun, Qian, 2024. "Asymmetric employer learning and gender-based statistical discrimination in China," China Economic Review, Elsevier, vol. 87(C).
    2. Lepage, Louis Pierre, 2021. "Endogenous learning, persistent employer biases, and discrimination," CLEF Working Paper Series 34, Canadian Labour Economics Forum (CLEF), University of Waterloo.

    More about this item

    Keywords

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    JEL classification:

    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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