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Published October 17, 2019 | Published + Accepted Version
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A Characterization of "Phelpsian" Statistical Discrimination

Abstract

We establish that statistical discrimination is possible if and only if it is impossible to uniquely identify the signal structure observed by an employer from a realized empirical distribution of skills. The impossibility of statistical discrimination is shown to be equivalent to the existence of a fair, skill-dependent remuneration for every set of tasks every signal-dependent optimal assignment of workers to tasks. Finally, we connect this literature to Bayesian persuasion, establishing that if the possibility of discrimination is absent, then the optimal signalling problem results in a linear payoff function (as well as a kind of converse).

Additional Information

Echenique thanks the NSF for support through the grants SES-1558757 and CNS1518941. We are grateful to Leeat Yariv for comments on a previous draft. 1 We follow the interpretation of Phelps' model due to Aigner and Cain (1977). Arrow's theory of statistical discrimination relies on a coordination failure, and is quite different from Phelps'. Statistical discrimination stands in contrast with taste-based discrimination, as in Becker (1957). arXiv copy submitted on 3 Aug 2018.

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Published - 1808.01351.pdf

Accepted Version - sswp1440.pdf

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