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Referral hiring and wage formation in a market with adverse selection

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  • Dariel, Aurelie
  • Riedl, Arno
  • Siegenthaler, Simon

Abstract

The widespread use of employee referrals raises questions regarding how they affect labor market outcomes. Does referral hiring lead to a more efficient allocation of workers compared to when hiring is possible only on a competitive market? To utilize the social links of their employees, are employers willing to pay a wage premium? We develop a model and provide results from a laboratory experiment to address these questions. We find that employers often hire via referrals, which in turn mitigates adverse selection and elevates wages. Importantly, employers anticipate the future value of hiring high-productivity employees—which consists of gaining access to valuable social links—and are thus willing to take the risk of offering wage premiums when hiring on the competitive market. We also find that employers' risk aversion and the dynamic nature of the hiring process can help account for the inefficiency remaining in the labor market.

Suggested Citation

  • Dariel, Aurelie & Riedl, Arno & Siegenthaler, Simon, 2021. "Referral hiring and wage formation in a market with adverse selection," Games and Economic Behavior, Elsevier, vol. 130(C), pages 109-130.
  • Handle: RePEc:eee:gamebe:v:130:y:2021:i:c:p:109-130
    DOI: 10.1016/j.geb.2021.08.005
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    More about this item

    Keywords

    Adverse selection; Wage formation; Asymmetric information; Referral hiring; Social links;
    All these keywords.

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)

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