Inaccurate Statistical Discrimination: An Identification Problem

Aislinn Bohren, Kareem Haggag, Alex Imas and Devin Pope

No 13790, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: Discrimination, defined as differential treatment by group identity, is widely studied in economics. Its source is often categorized as taste-based or statistical (belief-based)---a valuable distinction for policy design and welfare analysis. However, in many situations individuals may have inaccurate beliefs about the relevant characteristics of different groups. This paper demonstrates that this possibility creates an identification problem when isolating the source of discrimination. A review of the empirical discrimination literature in economics reveals that a small minority of papers---fewer than 7%---consider inaccurate beliefs. We show both theoretically and experimentally that, if not accounted for, such inaccurate statistical discrimination will be misclassified as taste-based. We then examine three alternative methodologies for differentiating between different sources of discrimination: varying the amount of information presented to evaluators, eliciting their beliefs, and presenting them with accurate information. Importantly, the latter can be used to differentiate whether inaccurate beliefs are due to a lack of information or motivated factors.

Keywords: Discrimination; Inaccurate beliefs; Model misspecification (search for similar items in EconPapers)
JEL-codes: D90 J71 (search for similar items in EconPapers)
Date: 2019-06
New Economics Papers: this item is included in nep-exp and nep-lma
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Citations: View citations in EconPapers (34)

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Related works:
Working Paper: Inaccurate Statistical Discrimination: An Identification Problem (2020)
Working Paper: Inaccurate Statistical Discrimination: An Identification Problem (2019)
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