Referral-Networks in Frictional Labor Markets

Benjamin W Raymond, Purdue University

Abstract

This dissertation is composed of three essays using labor search models to explore the role of referral-networks in the labor market. The first, “The Stabilizing Effect of Referral-Networks on the Labor Market,” examines how the use of informal connections (i.e. referral-networks) affects the severity and duration of recessions. To do so, I develop a search-and-matching model in which there are two hiring methods, formal channels and informal channels, and workers endogenously adjust their network of informal contacts in response to shocks and government policy. I show referral-networks have a stabilizing effect on the labor market, reducing the severity of adverse economic shocks and accelerating post-recession recovery. Counterfactuals demonstrate the government must exercise caution when enacting policies intended to expedite economic recovery. Policies that generically improve worker-firm matching prolong recovery by 8 months, as they facilitate relatively more matches between workers and low-productivity firms during recessions. In contrast, policies aimed at reducing the costs of network-formation or increasing referral-network prevalence facilitate more matches between workers and high-productivity firms, expediting recovery by 3-6 months. The second chapter, “The Impact of Referral-Networks on Sectoral Reallocation,” investigates a new explanation for the long-run decline in sectoral switching– the increased prevalence of referral-networks. Using data from the Current Population Survey (CPS), I first document empirically significant increase in the use of referral-networks in the job-search process by the unemployed. Moreover, this increase is concurrent with the decline in sectoral switching. The CPS is then used to estimate the effect of using referral-networks on the likelihood of an individual switching sectors at a various levels of industry classifications. For all aggregations, using referral-networks significantly reduces the probability a worker switches sectors. After controlling for demographics, these estimates imply an increase in the prevalence of referral-network use could explain as much as 5% to 40% of the decline in sectoral switching. To better illustrate the policy implications of this finding, a discrete time sectoralswitching model is constructed using a search and matching framework with labor market referrals. The estimated model estimates a referral-switching elasticity of about -.12, which is within the empirically estimated range of -.05 to -.22 for the 2-sector industry aggregation, demonstrating that the increased of the prevalence of referrals overtime can explain about 20% of the decline in US sectoral switching. Welfare results indicate that referrals are a “benign” cause of the decline, i.e. welfare declines upon effectively banning the use of referral-networks. These results have important implications for policymakers. They suggest that the cause of the decline in sectoral switching (and more generally job-changing) is the result of improved matching efficiency over time rather than market inefficiency. The third chapter, “Does Job-Finding Using Informal Connections Reduce Mismatch?,” presents evidence that nonpecuniary benefits of a job, such as hours, commute time, and work environment, are a salient factor in a worker’s decision to either accept or reject the offer. Using data form the Survey of Consumer Expectations (SCE), I document three empirical facts on the use of referral-networks and mismatch. First, not all referrals reduce perceived mismatch as reported by workers. For high-skill workers, referrals from former coworkers tend to reduce perceived nonpecuniary-mismatch. For low-skill workers, referrals from friends and family tend to increase perceived non-pecuniary mismatch.

Degree

Ph.D.

Advisors

Zhang, Purdue University.

Subject Area

Economics|Labor economics|Labor relations

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