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Statistical Discrimination and the Implication of Employer-Employee Racial Matches

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  • Yariv Fadlon

Abstract

In this paper, I test the empirical validity of a statistical discrimination model that incorporates employer’s race. I argue that if an employer statistically discriminates less against an employee that shares the same race (matched) than an employee who does not share the same race (mismatched), then the correlation between the employee’s wage and his skill level (AFQT) is stronger for a matched employee than for a mismatched employee. Using data from the NLSY97, which includes information about the racial background of employees and their supervisors, I find evidence that is consistent with a statistical discrimination model for young male employees. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Yariv Fadlon, 2015. "Statistical Discrimination and the Implication of Employer-Employee Racial Matches," Journal of Labor Research, Springer, vol. 36(2), pages 232-248, June.
    • Handle: RePEc:spr:jlabre:v:36:y:2015:i:2:p:232-248
    DOI: 10.1007/s12122-015-9203-2
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    Cited by:

    1. Bond, Timothy N. & Lehmann, Jee-Yeon K., 2018. "Prejudice and racial matches in employment," Labour Economics, Elsevier, vol. 51(C), pages 271-293.
    2. Yariv Fadlon & Sophie Tripp, 2022. "The role of supervisor race and gender on promotion likelihood," Economics Bulletin, AccessEcon, vol. 42(3), pages 1280-1294.
    3. Lawrence, Edward R. & Raithatha, Mehul, 2023. "Gender bias, board diversity, and firm value: Evidence from a natural experiment," Journal of Corporate Finance, Elsevier, vol. 78(C).

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    More about this item

    Keywords

    Statistical discrimination; Employer-Employee data; NLSY97; Wage differentials J31;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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