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Learning through Coworker Referrals

Author

Listed:
  • Albrecht Glitz

    (Universitat Pompeu Fabra)

  • Rune Vejlin

    (Aarhus University)

Abstract

In this paper, we study the role of coworker referrals for labor market outcomes. Using comprehensive Danish administrative data covering the period 1980 to 2005, we first document a strong tendency of workers to follow their former coworkers into the same establishments and provide evidence that these mobility patterns are likely driven by coworker referrals. Treating the presence of a former coworker in an establishment at the time of hiring as a proxy for a referral, we then show that referred workers initially earn 4.6 percent higher wages and are 2.3 percentage points less likely to leave their employers than workers hired through the external market. Consistent with a theoretical framework characterized by higher initial uncertainty in the external market but the possibility of subsequent learning about match-specific productivity, we show that these initial differences gradually decline as tenure increases. We structurally estimate a stylized model using indirect inference and find that the noise of the initial signal about a worker's productivity is 14.5 percent lower in the referral market than in the external market, and that firms learn about their workers' true match-specific productivity with a probability of 48.4 percent per year. Counterfactual simulations show that average wages are lower in the absence of a referral market, primarily because of lower average match productivity in the external market. (Copyright: Elsevier)

Suggested Citation

  • Albrecht Glitz & Rune Vejlin, 2021. "Learning through Coworker Referrals," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 42, pages 37-71, October.
  • Handle: RePEc:red:issued:19-139
    DOI: 10.1016/j.red.2020.10.007
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    Cited by:

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    2. Virág Ilyés & István Boza & László Lőrincz & Rikard H Eriksson, 2023. "How to enter high-opportunity places? The role of social contacts for residential mobility," Journal of Economic Geography, Oxford University Press, vol. 23(2), pages 371-395.
    3. Boza, István & Ilyés, Virág, 2018. "A korábbi munkatársak bérekre gyakorolt hatása [The influence of previous employment on wages]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 726-767.
    4. Ilyés, Virág & Sebők, Anna, 2020. "Egyetemről a munkaerőpiacra. Felsőoktatási ismeretségek hatása a munkaerőpiaci kilátásokra [From university to working life - the effect peers in higher education have on labour-market outcomes]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(10), pages 993-1028.
    5. Yu, Li & Ma, Tiemeng & Wu, Sirong & Lyu, Zhuoyang, 2023. "How does broadband internet affect firm-level labor misallocation: The role of information frictions," China Economic Review, Elsevier, vol. 82(C).
    6. Boza István & Ilyés Virág, 2020. "Decomposition of co-worker wage gains," IZA Journal of Labor Economics, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 9(1), pages 1-31, March.

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    More about this item

    Keywords

    Referrals; Employer Learning; Networks; Wages; Turnover;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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