Learning through Coworker Referrals

Albrecht Glitz () and Rune Vejlin

No 12270, IZA Discussion Papers from Institute of Labor Economics (IZA)

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 the model using two different sets of auxiliary parameters 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 3.9 percent lower in the absence of a referral market, primarily because of lower average productivity in the external market.

Keywords: referrals; employer learning; networks; wages; turnover (search for similar items in EconPapers)
JEL-codes: J31 J63 J64 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2019-04
New Economics Papers: this item is included in nep-lab
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published - published in: Review of Economic Dynamics, 2021, 42, 37-71

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