Inaccurate Statistical Discrimination: An Identification Problem

J. Aislinn Bohren, Kareem Haggag, Alex Imas and Devin G. Pope
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J. Aislinn Bohren: University of Pennsylvania
Kareem Haggag: UCLA
Alex Imas: University of Chicago
Devin G. Pope: University of Chicago

The Review of Economics and Statistics, 2025, vol. 107, issue 3, 605-620

Abstract: 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.

Date: 2025
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