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A Characterization of "Phelpsian" Statistical Discrimination

Chambers, Christopher P. and Echenique, Federico (2018) A Characterization of "Phelpsian" Statistical Discrimination. Social Science Working Paper, 1440. California Institute of Technology , Pasadena, CA. (Unpublished)

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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).

Item Type:Report or Paper (Working Paper)
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URLURL TypeDescription Paper ItemJournal Article
Chambers, Christopher P.0000-0001-8253-0328
Echenique, Federico0000-0002-1567-6770
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.
Group:Social Science Working Papers
Funding AgencyGrant Number
Subject Keywords:Statistical discrimination; Bayesian persuasion; Employment discrimination,Test scores
Series Name:Social Science Working Paper
Issue or Number:1440
Classification Code:JEL: D8, D1
Record Number:CaltechAUTHORS:20191017-160253683
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99347
Deposited By: Katherine Johnson
Deposited On:17 Oct 2019 23:10
Last Modified:29 Jul 2021 21:28

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