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Misperception and Informativeness in Statistical Discrimination

Author

Listed:
  • Escudé, Matteo
  • Onuchic, Paula
  • Sinander, Ludvig
  • Valenzuela-Stookey, Quitzé

Abstract

We study the interplay of information and prior (mis)perceptions in a Phelps--Aigner--Cain-type model of statistical discrimination in the labor market. We decompose the effect on average pay of an increase in how informative observables are about workers' skill into a non-negative instrumental component, reflecting increased surplus due to better matching of workers with tasks, and a perception-correcting component capturing how extra information diminishes the importance of prior misperceptions about the distribution of skills in the worker population. We sign the perception-correcting term: it is non-negative (non-positive) if the population was ex-ante under-perceived (over-perceived). We then consider the implications for pay gaps between equally-skilled populations that differ in information, perceptions, or both, and identify conditions under which improving information narrows pay gaps.

Suggested Citation

  • Escudé, Matteo & Onuchic, Paula & Sinander, Ludvig & Valenzuela-Stookey, Quitzé, 2025. "Misperception and Informativeness in Statistical Discrimination," CEPR Discussion Papers 20603, Centre for Economic Policy Research.
    • Handle: RePEc:cpr:ceprdp:20603
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    Cited by:

    1. Mimosa Distefano & Lorenzo Incoronato & Anna Raute, 2026. "Female-targeted hiring subsidies, firm learning, and women’s employment," CEP Discussion Papers dp2209, Centre for Economic Performance, LSE.
    2. Francesco Bilotta & Luca Braghieri & Collin Raymond & Mark Whitmeyer, 2026. "Agreement and Diversity in Interpretation," Papers 2607.05558, arXiv.org.

    More about this item

    Keywords

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    JEL classification:

    • D60 - Microeconomics - - Welfare Economics - - - General
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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