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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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