Forsythe, Kahn, Lange, and Wiczer report on the state of the labor market midway through the COVID recession, focusing particularly on measuring market tightness. As they show using a simple model, tightness is crucial for understanding the relative importance of labor supply or demand side factors in job creation. In tight markets, worker search effort has a relatively larger impact on job creation, while employer profitability looms larger in slack markets. The researchers measure tightness combining job seeker information from the CPS and vacancy postings from Burning Glass Technologies. To parse the former, they develop a taxonomy of the non-employed that identifies job seekers and excludes the large number of those on temporary layoff who are waiting to be recalled. With this taxonomy, Forsythe, Kahn, Lange, and Wiczer find that effective tightness has declined about 50% since the onset of the epidemic to levels last seen in 2016, when labor markets generally appeared to be tight. Disaggregating market tightness, they find mismatch has surprisingly declined in the COVID recession. Further, while markets still appear to be tight relative to other recessionary periods, this could change quickly if the large group of those who lost their jobs but are not currently searching for a range of COVID-related reasons reenter the search market.
This paper was distributed as Working Paper 28083, where an updated version may be available.
Petterson, Seim, and Shapiro argue that economists often have useful intuitions about how much the unobserved factors in a model can vary, and they show that these intuitions are useful for bounding parameters of interest like elasticities. The researchers have applications to the labor market and the grain market. Their labor market application concerns a paper presented at a previous NBER LS meeting.
This paper was distributed as Working Paper 27556, where an updated version may be available.
During the Covid-19 pandemic, the Federal Pandemic Unemployment Compensation (FPUC) program increased US unemployment benefits by $600 a week, which increased replacement rates to over 100% for many workers. What was the impact on the labor market? Marinescu, Skandalis, and Zhao collect data on vacancy posting and job applications on the online jobs platform Glassdoor. In the first part of the paper, they estimate the effect of FPUC on applications and vacancies, by exploiting large variation in the proportional increase in benefits, in a panel at the week and local labor market level. To isolate the effect of FPUC from the contemporaneous effects of the Covid-19 crisis, the researchers allow for different trends in local labor markets differentially exposed to unemployment at the onset of the Covid-19 crisis, before the FPUC. Marinescu, Skandalis, and Zhao verify that trends in outcomes prior to FPUC do not correlate with future increases in benefits, which supports the validity of their identification strategy. The researchers find that a 10% increase in benefits lead to a 3.6% decline in applications, no change in vacancies, and a 3.3% increase in labor market market tightness. In the second part of the paper, they then describe the context in which FPUC was implemented, as it is also crucial to assess its impact on unemployment and welfare. Marinescu, Skandalis, and Zhao document that labor market tightness was particularly low during FPUC, suggesting that each application had a low chance of resulting in a job. Overall, their findings suggest that even if FPUC decreased search effort as predicted by search theory, low returns to search can explain the limited effect of FPUC on unemployment found in prior literature.
This paper was distributed as Working Paper 28567, where an updated version may be available.
As job-seekers internalize neither the full benefits or costs of their application decisions, job openings do not necessarily obtain the socially efficient number of applications. Using a field experiment conducted in an online labor market, Horton and Vasserman find that some job openings receive far too many applications, but that a simple intervention can improve the situation. A treated group of job openings faced a soft cap on applicant counts and narrowed window on when they could received applicants after posting. Employer could easily opt out by literally clicking a single button. This tiny imposed cost on the demand side had large effects on the supply side, reducing the number of applicants to treated jobs by 11%--with even larger reductions in jobs where additional applicants were likely to be inframarginal. This reduction in applicant counts had no discernible effect on the probability a hire was made, or in the quality of the subsequent match. This kind of intervention is easy to implement by any online marketplace or job board and has attractive properties, saving job-seekers effort--on the order of 20%--while still allowing employers with high marginal returns to more applicants to get them.
Fu, Guo, Smith, and Sorensen estimate a model of high school students' college choices, allowing for rich heterogeneity in students' preferences for college attributes. They use data on students' enrollment decisions and application decisions--i.e., the sets of colleges to which they applied--to identify the distribution of students' preferences. Fu, Guo, Smith, and Sorensen use their estimates to quantify differences in a student's expected value upon college application that result from the uneven spatial distribution of colleges. As with other aspects of economic opportunity, the researchers find that place matters: students with otherwise identical characteristics can have very different expected values depending on where they live. The importance of location reflects differences across states as well as differences across counties within a state. For students with low parental incomes and low SAT scores, over 70% of the variation is within-state across counties, while for students with high parental incomes and high SAT scores, 66% of the variation is across states.
How did the largest expansion of unemployment benefits in U.S. history affect household behavior? Using anonymized bank account data covering millions of households, Ganong, Greig, Noel, Sullivan, Liebeskind, and Vavra provide new empirical evidence on the spending and job search responses to benefit changes during the pandemic and compare those responses to the predictions of benchmark structural models. They find that spending responds more than predicted, while job search responds an order of magnitude less than predicted. In sharp contrast to normal times when spending falls after job loss, Ganong, Greig, Noel, Sullivan, Liebeskind, and Vavra show that when expanded benefits are available, spending of the unemployed actually rises after job loss. Using quasi-experimental research designs, they estimate a large marginal propensity to consume out of benefits. Notably, spending responses are large even for households who have built up substantial liquidity through prior receipt of expanded benefits. These large responses contrast with a theoretical prediction that spending responses should shrink with liquidity. Simple job search models predict a sharp decline in search in the wake of a substantial benefit expansion, followed by a sustained rebound when benefits expire. The researchers instead find that the jobfinding rate is quite stable. Moreover, Ganong, Greig, Noel, Sullivan, Liebeskind, and Vavra document that recall plays an important role in driving job-finding dynamics throughout the pandemic. A model extended to fit these key features of the data implies small job search distortions from expanded unemployment benefits. Jointly, these spending and job finding facts suggest that benefit expansions during the pandemic were a more effective policy than predicted by standard structural models. Abstracting from general equilibrium effects, The researchers find that overall spending was 2.0-2.6 percent higher and employment only 0.2-0.4 percent lower as a result of the benefit expansions.
Cullen, Dobbie, and Hoffman employ a discrete choice field experiment on a large on-demand staffing platform to estimate the labor demand for workers with a criminal conviction (WCCs). Thirty-nine percent of firms are willing to hire WCCs without additional incentives, increasing by 2.4 percent for every 10 percent increase in the offered wage subsidy. The level of demand is higher for positions that do not involve customer interactions or high-value inventory, but is largely unaffected by labor market tightness. Crime and safety insurance, performance screening, including only workers with a less serious conviction, and providing objective information on the productivity of WCCs all significantly increase the level of demand for WCCs, and are all much more cost-effective than wage subsidies.