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

Seik Kim, University of Washington

Previous papers on testing for statistical discrimination and employer learning 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, experience, and some variables on which discrimination is based, such as race, gender, and education. Evidence from analysis using the March Current Population Survey for 1977-2010 supports statistical discrimination and employer learning. The empirical findings are not explained by alternative hypotheses, such as human capital theory, search and matching models, and the theory of taste-based discrimination.

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Presented in Poster Session 9