Statistical Discrimination in Labor Markets: An Experimental Analysis

This article reports results from controlled laboratory experiments designed to study second-moment (that is, risk-based) statistical discrimination in a labor market setting. Since decision makers may not view risk in the same way as economists or statisticians (that is, risk = variance of distribu...

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Bibliographic Details
Published in:Southern economic journal 2009-07, Vol.76 (1), p.16-31
Main Authors: Dickinson, David L., Oaxaca, Ronald L.
Format: Article
Language:English
Subjects:
Analysis
Competition
Control
Economics
Employers
Employment
Employment discrimination
Equal pay
Equilibrium
Experimental economics
Experiments
Gender discrimination
Hiring
Insurance industry
Labor
Labor market
Laboratories
Labour market
Labour policy
Laws, regulations and rules
Payoffs
Productivity
Race
Racial profiling
Risk assessment
Statistical discrimination
Statistical variance
Statistics
Studies
Wage contracts
Wage differential
Wages
Wages & salaries
Workers
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Description
Summary:This article reports results from controlled laboratory experiments designed to study second-moment (that is, risk-based) statistical discrimination in a labor market setting. Since decision makers may not view risk in the same way as economists or statisticians (that is, risk = variance of distribution), we also examine alternative measures of risk: the support of the distribution and the probability of earning less than the expected (maximum) profits for the employer. Our results indicate that employers made statistically discriminatory wage offers consistent with loss aversion in our full sample (though the result is driven by the male employer subsample). If one can transfer these results outside of the laboratory, they indicate that discrimination estimates based only on first-moment (mean-based) discrimination are biased. The public policy implication is that efforts and legislation aimed at reducing discrimination of various sorts face an additional challenge in trying to identify and limit relatively hidden, but significant, forms of statistical discrimination.
ISSN:0038-4038
2325-8012
DOI:10.4284/sej.2009.76.1.16