Statistical discrimination, employer learning, and employment gap by race and education

Seik Kim, Hwa Ryung Lee

Research output: Contribution to journalArticle

Abstract

Tests of statistical discrimination require evaluation records provided by employers or variables that employers do not observe directly but are observed by researchers. As such variables are difficult to obtain, this paper develops a strategy that uses variables available in usual data sets. This paper derives testable implications for statistical discrimination by exploiting the heterogeneity in employer learning processes. Evidence from analysis using the March Current Population Survey for 1971-2016 is consistent with the theoretical predictions. The empirical findings are not explained by alternative hypotheses, such as human capital theory, taste-based discrimination, or search and matching models.

Original languageEnglish
Pages (from-to)5-27
Number of pages23
JournalKorean Economic Review
Volume36
Issue number1
Publication statusPublished - 2020 Jan 1

Keywords

  • Employer Learning
  • Statistical Discrimination
  • Unemployment Rate

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)

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