Inaccurate Statistical Discrimination: An Identification Problem

Abstract

Discrimination—differential treatment by group identity—is widely studied in economics. Its source is often categorized as taste-based or statistical (beliefbased)— a valuable distinction for policy design and welfare analysis. We argue that in many situations, individuals may have inaccurate beliefs about the relevant characteristics of different groups. This possibility creates an identification problem when isolating the source of discrimination. When not accounted for, we show both theoretically and experimentally that such inaccurate statistical discrimination will be misclassified as taste-based. A review of the empirical discrimination literature in economics reveals the scope of this issue: a small minority of papers—fewer than 7%—consider inaccurate beliefs. We then examine two alternative methodologies for differentiating between these three sources of discrimination—varying the amount of information presented to evaluators and eliciting evaluators’ beliefs. We propose a possible intervention: when presented with accurate information, we show that inaccurate statistical discrimination decreases. (with Kareem Haggag, Alex Imas and Devin Pope).