Referral-Networks in Frictional Labor Markets

2019-08-15T18:47:48Z (GMT) by Benjamin W Raymond
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 sectoral-switching 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.

Given these empirical facts, I construct a search-and-matching model of the labor market similar to Buhrmann [2018a] where workers and firms are given types on a unit interval and suffer increasingly greater productivity losses depending on distance between the firm's type and the worker's type. I augment this baseline model with mismatch along two dimensions -- skill and nonpecuniary preferences-- and calibrate it to the US economy. Results show nonpecuniary preferences can generate more dispersion in skill-mismatch for very low-skill workers and very high-skill workers. Moreover, while referral-networks generally improve aggregate mismatch, they have a heterogeneous affect on nonpecuniary mismatch by type. For low-skill (high-skill) workers, referral-networks increase (decrease) nonpecuniary mismatch.

Overall, the results from this dissertation serve as a guide for policymakers. While government intervention may be deemed necessary in recessions, it is vital to understand the role specific matching channels serve in the economy in order for a policy to achieve the desired result. Understanding that referrals generate more high-productivity matches suggests policymakers should investigate policies aimed at improving network formation and functionality. Similarly, distinguishing between formal and informal methods of job finding are key to understanding recent labor market phenomenon. The second chapter shows informal channels have become more ubiquitous in order to facilitate matching. While this change creates patterns in the data that seem concerning, a closer investigation reveals this seems to be a result of the market simply adapting to be more efficient. Finally, understanding why people use formal and informal channels is vital to understanding worker-firm mismatch on a micro-level. While high-skill workers use informal channels to find better matches, low-skill seem to use them to find any match faster. In essence, the findings of this dissertation emphasize the need for policymakers to understand the nuanced behavior of job seekers and the differing goals of various job-finding methods. One cannot simply treat all job-finding as the same, especially if a particular method is widely used and leads to significantly different outcomes, and expect to implement efficient policy. Thus, it is important to understand how certain job-finding methods differ on a micro level and apply these finding to macro policy.

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CC BY 4.0