Job Referrals and Skills

Benjamin Lester, David Rivers and Giorgio Topa

No 306, 2018 Meeting Papers from Society for Economic Dynamics

Abstract: We exploit a novel data set to study the extent to which firms rely on referrals when hiring for positions that require different levels of skill. The data come from the 2013-16 waves of a special supplement to the Survey of Consumer Expectations that focuses on job search behavior and outcomes. Using a measure of the skill content of different occupations that is widely used in the literature, we find that referrals are used most for low- and high-skill jobs, and less for intermediate-skill jobs. We develop a model that is consistent with these findings, in which referrals help firms to overcome two types of frictions: search frictions that make it difficult to find potential workers, and information frictions that make it difficult to identify the best worker from this set. According to our theory, the primary benefit of referrals for filling low-skill jobs is related to search frictions, while the primary benefit of referrals for filling high-skill jobs is related to information frictions. We explore the implications for inequality, and the differential effects of new technologies on hiring across skill levels.

Date: 2018
New Economics Papers: this item is included in nep-hrm
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