Statistical Discrimination in a Search Equilibrium Model: Racial Wage and Employment Disparities in the US

Bruno Decreuse and Linas Tarasonis

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Abstract: In the US, black workers spend more time in unemployment, lose their jobs more rapidly, and earn lower wages than white workers. This paper quantifies the contributions of statistical discrimination, as portrayed by negative stereotyping and screening discrimination, to such employment and wage dis- parities. We develop an equilibrium search model of statistical discrimination with learning based on Moscarini (2005) and estimate it by indirect inference. We show that statistical discrimination alone cannot simultaneously explain the observed differences in residual wages and monthly job loss probabilities between black and white workers. However, a model with negative stereotyping, larger unemployment valuation and faster learning about the quality of matches for black workers can account for these facts. One implication of our findings is that black workers have larger returns to tenure.

Keywords: learning; screening discrimination; job search; indirect inference (search for similar items in EconPapers)
Date: 2016-06
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01327961
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Statistical Discrimination in a Search Equilibrium Model: Racial Wage and Employment Disparities in the US (2021)
Working Paper: Statistical Discrimination in a Search Equilibrium Model: Racial Wage and Employment Disparities in the US (2021)
Working Paper: Statistical Discrimination in a Search Equilibrium Model: Racial Wage and Employment Disparities in the US (2020)
Working Paper: Statistical Statistical Discrimination in a Search Equilibrium Model: Racial Wage and Employment Disparities in the US (2016)
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