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Misperception and informativeness in statistical discrimination

Author

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  • Matteo Escud'e
  • Paula Onuchic
  • Ludvig Sinander
  • Quitz'e Valenzuela-Stookey

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

  • Matteo Escud'e & Paula Onuchic & Ludvig Sinander & Quitz'e Valenzuela-Stookey, 2025. "Misperception and informativeness in statistical discrimination," Papers 2508.20053, arXiv.org.
    • Handle: RePEc:arx:papers:2508.20053
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    References listed on IDEAS

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    1. Chen Yeh & Claudia Macaluso & Brad Hershbein, 2022. "Monopsony in the US Labor Market," American Economic Review, American Economic Association, vol. 112(7), pages 2099-2138, July.
    2. repec:cdl:econwp:qt59s4j4m4 is not listed on IDEAS
    3. Phelps, Edmund S, 1972. "The Statistical Theory of Racism and Sexism," American Economic Review, American Economic Association, vol. 62(4), pages 659-661, September.
    4. Amanda Agan & Sonja Starr, 2018. "Ban the Box, Criminal Records, and Racial Discrimination: A Field Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 191-235.
    5. Rothstein, J.M.Jesse M., 2004. "College performance predictions and the SAT," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 297-317.
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