Inaccurate Statistical Discrimination: An Identification Problem

• 成果类型: Article
• 署名作者: Bohren, J. Aislinn; Haggag, Kareem; Imas, Alex; Pope, Devin G.
• 署名单位: University of Pennsylvania; University of California System; University of California Los Angeles; University of Chicago
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01367
• 发表日期: 2025-05
• 页码: 605-620
• 关键词: motor-vehicle searches; racial bias; attention; beliefs; women

摘要

We study inaccurate beliefs as a source of discrimination. Economists typically characterize discrimination as stemming from a taste-based (preference) or accurate statistical (belief-based) source. Although individuals may have inaccurate beliefs about how relevant characteristics (e.g., productivity, signals) are correlated with group identity, fewer than 7% of empirical discrimination papers in economics consider the possibility of such inaccurate statistical discrimination. Using theory and a labor market experiment, we show that failing to account for inaccurate beliefs leads to a misclassification of source. We outline three methods to identify source: varying observed signals, belief elicitation, and an intervention to target inaccurate beliefs.