Kyle Herkenhoff and Gordon Phillips How does access to consumer credit affect the job finding behavior of displaced workers? Are these workers looking for jobs at larger and more productive firms? What is the impact of consumer credit on the amount of time it takes to find a job? In recent work with Ethan Cohen-Cole we explore these questions by building a new data set of individual credit reports (from TransUnion) merged with administrative earnings data. We describe our approach and our results in this post. To answer these questions convincingly, we have to overcome the problem that a person’s credit is determined endogenously: For example, let’s say two individuals lose their job. Individual A has one credit card and individual B has four credit cards. Why does
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How does access to consumer credit affect the job finding behavior of displaced workers? Are these workers looking for jobs at larger and more productive firms? What is the impact of consumer credit on the amount of time it takes to find a job? In recent work with Ethan Cohen-Cole we explore these questions by building a new data set of individual credit reports (from TransUnion) merged with administrative earnings data. We describe our approach and our results in this post.
To answer these questions convincingly, we have to overcome the problem that a person’s credit is determined endogenously: For example, let’s say two individuals lose their job. Individual A has one credit card and individual B has four credit cards. Why does individual B have more credit? Perhaps individual B earned more before being laid off, or perhaps individual B is simply more responsible. To address these concerns about the endogeneity of credit, we use two natural experiments.
Our main approach is to exploit the fact that credit limits differ as a result of variation in account ages, as originally shown by Gross and Souleles (2002). We use this fact to isolate plausibly exogenous differences in credit limits among workers who are mass-displaced, in the merged data based on methods pioneered by Jacobson et al. (1993). Our results are striking: those with a 10 percent increase in credit access (measured as unused credit to prior annual income) take about 0.33 to 0.53 weeks longer to find a job (.5 is half a week and thus corresponds to 3 to 4 days). Among job finders, they had a 0.61 percent to 1.34 percent greater annual earnings replacement rate (the replacement rate is the ratio of current earnings one year after layoff to earnings in the year before layoff). Moreover, job finders are more likely to work for more productive firms. Here, we define a productive firm to be one that is in the top 25 percent of wages paid per worker (a common proxy for labor productivity).
To provide additional validation for our findings, we exploit regional differences in credit access. We use the fact that some metropolitan statistical areas have different housing supply elasticities, and therefore have different house price growth rates. This empirical approach was popularized by Saiz (2010). When house prices increase in a region, we empirically observe a significant rise in both secured credit access (e.g., mortgages) and unsecured credit access (e.g., credit cards). Of course wealthier households may search differently, so we directly control for access to housing wealth, and therefore isolate the impact of consumer credit access on job search behavior. We find that in response to a 10 percent increase in an individual’s credit limit to income ratio, the typical worker takes 1.8 weeks longer to find a job and finds a job that replaces 1.7 percent more of their prior wage.
What is the intuition behind these results? If someone loses their job and their back is against the wall, for instance, they have taken out a mortgage and maxed out their credit cards, that person is much more likely to take a low paying, low productivity job, immediately, rather than hold out and wait for a better job. Another way of looking at these results is that if an individual has enough credit to buy groceries and maintain their overall consumption, they can take longer to find a better fitting job.
Our findings have several important policy implications. In particular, any type of government policy that affects access to credit will change the search durations, wages, and subsequent types of jobs that individuals take. This is relevant for mortgage modifications, principal reductions, or any type of credit subsidy. One relevant example is that if the central bank lowers the interest rate, this will affect job finding behavior. The idea is that providing some “breathing room" for severely credit constrained individuals will enable them to conduct a more thorough search for a job, and they will, on average, find a better fitting job.
Even though workers will have longer unemployment durations when interest rates are lowered, they are better off on average. A side effect of lowering the interest rate is therefore that workers take longer to find a job, so the unemployment rate may be temporarily elevated when interest rates fall. However, workers are searching for jobs that are a better fit, which is obviously a good thing for them and the economy. As a consequence, our results and our theory imply that rather than focusing on unemployment rates, a central bank attempting to implement maximum sustainable employment should look at the wages of new hires to evaluate the effectiveness of monetary policy on labor markets.
Another implication of our research is that credit acts a lot like unemployment insurance (UI). Several other papers, including Chetty (2008) and Nekoei and Weber (2015) have studied the impact of UI on durations and subsequent wages. They find that increased UI protracts unemployment durations and workers generally find higher paying jobs (this is true in the United States and Europe, although European estimates are sometimes insignificant or negative Schmieder et al. (2013)). Our work suggests that consumer credit may, in certain cases, be a viable substitute for unemployment insurance. To evaluate these statements in a scientific way, in follow up work, we are studying whether or not it may be optimal to hand unemployed households a credit card rather than simply giving them a UI check (Braxton et al. 2019). We find that if the U.S. government cuts UI, the credit market will contract, and U.S. households will be worse off. Another way of saying this is that private forms of self-insurance (such as credit cards), are complements to public forms of insurance (e.g. UI).
There is a flip side of increased credit access, however. Those who borrow while waiting to find a job, but do not conduct a successful search, are then in a position in which they still are unemployed and now have debt. Moreover, households may borrow before they lose their job, in which case they move into unemployment with a large amount of existing debt. This may force job seekers to take lower wage jobs more quickly than if they had never borrowed at all. In Herkenhoff (2019) one of us explores the impact of credit access on wage inequality, and finds that through this channel, wage dispersion increases mildly. However, in this context, credit access is still welfare improving.
LSE Series on Heterogeneity
Kyle Herkenhoff is a senior economist in the Federal Reserve Bank of New York’s Research and Statistics Group.
Gordon Phillips is the Laurence F. Whittemore Professor of Business Administration and faculty director of the Center for Private Equity and Venture Capital at Dartmouth’s Tuck School of Business.
How to cite this post:
Kyle Herkenhoff and Gordon Phillips, “How Does Credit Access Affect Job-Search Outcomes and Sorting?,” Federal Reserve Bank of New York Liberty Street Economics, March 4, 2020, https://libertystreeteconomics.newyorkfed.org/2020/02/how-does-credit-access-affect-job-search-outcomes-and-sorting.html.
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.