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Statistical Discrimination, Employer Learning, and Employment Differentials by Race, Gender, and Education


  • Seik Kim


Previous papers on testing for statistical discrimination require variables that employers do not observe directly, but are observed by researchers or data on employer-provided performance measures. This paper develops a test that does not rely on these specific variables. The proposed test can be performed with individual-level cross-section data on employment status, potential experience, and some variables on which discrimination is based, such as race, gender, and education. This paper shows that if employers statistically discriminate among unexperienced workers, but learn about their productivity over time, then the unemployment rates for discriminated groups will be higher than those for non-discriminated groups at the time of labor market entry and that the unemployment rates for discriminated groups will decline faster than those for non-discriminated groups with experience. Using the March CPS for 1977-2010, the preliminary results suggest that employers statistically discriminate on the basis of race and education, but not on the basis of gender.

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  • Seik Kim, 2011. "Statistical Discrimination, Employer Learning, and Employment Differentials by Race, Gender, and Education," Working Papers UWEC-2011-12, University of Washington, Department of Economics.
  • Handle: RePEc:udb:wpaper:uwec-2011-12

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