Understanding Disparities in Unemployment Insurance Recipiency (with Hesong Yang)

Report prepared for the Department of Labor Chief Evaluation Office Summer Data Challenge on Equity and Underserved Communities

Blog from DOL: https://blog.dol.gov/2022/02/10/were-using-data-to-better-understand-our-work-and-create-more-equitable-programs-and-policies



Abstract: Using data from before and during the Covid-19 pandemic, we show that the expansion of benefi ts under the CARES Act only modestly increased self-reported UI recipiency among UI eligible workers, from 27% in 2018 to 36% in 2020/2021. We find that the same demographic groups that historically are less likely to report receiving benefi ts (less educated, younger, and racial and ethnic minorities) continued to be less likely to receive benefi ts during the pandemic. In addition we fi nd non-heterosexual workers are also substantially less likely to report receiving benefi ts. The overarching reason for these disparities is di fferences in beliefs about eligibility, resulting in likely-eligible workers not applying for benefi ts. We show that union members and individuals who live in states with historically higher recipiency rates are less likely to be misinformed about eligibility, suggesting a role for policy and informational interventions to improve recipiency rates.

“Computerization of White Collar Jobs” (with Marcus Dillender), 2022

Abstract: We investigate the impact of computerization of white-collar jobs on wages and employment. Using online job postings from 2007 and 2010–2016 for office and administrative support (OAS) jobs, we show that when firms adopt new software at the job-title level they increase the skills required of job applicants. Furthermore, firms change the task content of such jobs, broadening them to include tasks associated with higher-skill office functions. We aggregate these patterns to the local labor-market level, instrumenting for local technology adoption with national measures. We find that a 1 standard deviation increase in OAS technology usage reduces employment in OAS occupations by about 1 percentage point and increases wages for college graduates in OAS jobs by over 3 percent. We find negative wage spillovers, with wages falling for both workers with and without a college degree. These results are consistent with technological adoption inducing a realignment in task assignment across occupations, leading office support occupations to become higher skill. We argue relative wage gains for OAS workers indicates that factor-augmenting features of OAS technological change dominate task-substituting features. In addition, while we find that total employment increases, these gains primarily accrue to college-educated women.

Upjohn Institute working paper: 19-310,  NBER Working Paper 29866

U of I News Bureau: “Paper: Economy benefits when secretarial jobs require more computer skills” (December 2019)

“Youth Hiring and Labor Market Tightness” (January 2022) (forthcoming AEA P&P)

Abstract: It is well-known that recessions can lead to long-term scarring for young workers. I show that employers hire fewer young workers when there are few job openings per unemployed job seeker, while hiring rates for workers with more than 10 years of potential experience are much less cyclically volatile. During the COVID-19 pandemic, youth employment rates rebounded particularly quickly compared with other groups and historic patterns. I show this is consistent with the historic relationship between tightness and youth hiring rates, suggesting youth scarring from the COVID-19 pandemic may be less severe compared with previous recessions.

“Recruiting Intensity, Hires, and Vacancies: Evidence from Firm-Level Data” (with Russell Weinstein), 2021

Abstract: We investigate employer recruiting behavior, using detailed firm-level data from a national survey of employers hiring recent college graduates. We show employers adjust recruiting effort, hiring standards, and compensation with the business cycle, beliefs about tightness, and their own hiring plans. We then show that firms expending greater recruiting effort hire more individuals per vacancy. The results suggest that when firms want to increase hires they adjust vacancies and recruiting intensity per vacancy. If true more broadly in the labor market, it may help explain the breakdown in the standard matching function during the Great Recession. For the firms in our sample, the difference in firm vacancy yields between 2011 and 2015 would have more than doubled if recruiting effort had been constant. Finally, we estimate that our measure of recruiting effort can explain 61% of the elasticity of the vacancy yield with respect to hires in our data.

“Searching, Recalls, and Tightness: An Interim Report on the COVID Labor Market” (NBER WP 28083) with Lisa Kahn, Fabian Lange, and David Wiczer, 2020

Abstract: We report on the state of the labor market midway through the COVID recession, focusing particularly on measuring market tightness. As we 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. We measure tightness combining job seeker information from the CPS and vacancy postings from Burning Glass Technologies. To parse the former, we 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, we 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, we 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.

Occupational Job Ladders and Displaced Workers

Abstract: I investigate how movements up and down an occupational job ladder lead to earnings gains and losses for both displaced and non-displaced workers. I find both types of workers exhibit similar rates of upward and downward mobility, and relative occupational wages before mobility strongly predict the direction of mobility. I decompose the difference in wage changes between displaced and non-displaced individuals, and show that occupational mobility can explain about 10% of the gap, indicating that occupational mobility is not the primary source of wage losses for displaced workers.

Impacts of the COVID-19 Pandemic and the CARES Act on Earnings and Inequality” (May 2021 update), joint with Matias Cortes


Using data from the Current Population Surveys, we investigate the aggregate and distributional consequences of the Covid-19 pandemic and the associated public policy response on labor earnings and unemployment benefits in the United States up until February 2021. We find that year-on-year changes in labor earnings for employed individuals were not atypical during the pandemic months, regardless of their initial position in the earnings distribution. The incidence of job loss, however, was, and continues to be, substantially higher among low earners, leading to a dramatic increase in labor income inequality among the set of individuals who were employed prior to the onset of the pandemic. By providing very high replacement rates for individuals displaced from low-paying jobs, the initial public policy response was successful in reversing the regressive nature of the pandemic’s impacts. We estimate, however, that recipiency rates for displaced low earners were relatively low. Moreover, from September onwards, when policy changes led to a decline in benefit levels, earnings changes became much more regressive, even after factoring in benefits.

Previous version: “Impacts of the COVID-19 Pandemic and the CARES Act on Earnings and Inequality” (IZA DP No. 13643)