Research Projects

Most U.S. states have workforce development programs that offer firms grants to train their own workers. We create unique data linkages between participating firms, employment, and vacancies to explore the determinants and consequences of such programs. Training grants are more prevalent in markets where firms face greater employee poaching risk, suggesting these programs help overcome a market failure in updating worker skills. After training, firms experience prolonged employment growth and downskilling in their job posts, relative to a matched control group. Training appears to help firms move toward optimal scale and expand opportunities for less skilled workers.

This paper brings together five different datasets to further analyze the effects of the increase in tele-working that occurred during the pandemic: the Business Response Survey (a new BLS Survey of establishments), the Occupational Employment and Wage Statistics (OEWS), the Quarterly Census of Employment and Wages (QCEW), the American Community Survey (ACS), and Zillow. Combining the BRS, the OEWS, and the QCEW, we impute teleworking take-up rates for the universe of business establishments in the U.S, for the period immediately preceding the pandemic and for the summer of 2021. We predict a firm’s decision to relocate or downsize based on whether teleworking by its employees has increased. Increased teleworking results in a reduction in local foot traffic when the number of workers who stop commuting out of the area is smaller than the number workers who no longer commute into an area. Combining information in the ACS about residence with our teleworking estimates for the universe of establishments, we estimate the change in net traffic inflow for every zip code. Armed with these estimates and the QCEW, we then estimate the consequent effects on local employment in the various industries. The negative impact of a reduction in Census track foot traffic was especially strong for employment in accommodation and food services We conclude our analysis by bringing in Zillow data to analyze the effect that the changes brought on by increased working at home has on local rents and home prices. The key explanatory variables in the estimated equations for rents and home prices are predictions by zip code for firm relocations, firm downsizing, and the change in net foot traffic inflow.

This paper expands on previous work analyzing employment changes by employer size during the pandemic by incorporating additional months of new data (October and November 2020) and examining job loss by both employer size class and detailed industries. Continuing trends observed since mid-summer, we observe continued faster job recovery among large employers than among smaller employers. Furthermore, establishments of large employers known to have multiple establishments have fared better than large employers with only a single establishment. Within small employers, we find that employment loss due to closures has declined only a small amount since July, going from 2.8% to 2.3% in November. For large employers, employment loss due to closures has been less than 1% since June.

After matching more than 3 million loans from the $669 billion Paycheck Protection Program (PPP) to administrative wage records, I estimate a dynamic difference-in-difference event study showing robust impacts of the loans on employment, wages, and opening status of establishments 15 months after PPP approval. Between $12,000 and $19,000 of PPP loans are spent per employee-month retained 15 months postapproval, with 43 percent of the PPP going toward wage retention in the baseline model. The smallest employers show the largest impact, explaining disparate results from prior papers focusing on larger employers. Properly accounting for closures is key to understanding the PPP's long-term impact.

This paper examines employment patterns by wage group over the course of the coronavirus pandemic in the United States using microdata from two well-known data sources from the U.S. Bureau of Labor Statistics: the Current Employment Statistics and the Current Population Survey. We find establishments paying the lowest average wages and the lowest wage workers had the steepest decline in employment and experienced the most persistent losses. We disentangle the extent to which the effect observed for low wage workers is due to these workers being concentrated within a few low wage sectors of the economy versus the pandemic affecting low wage workers in a number of sectors across the economy. Our results indicate that the experience of low wage workers is not entirely due to these workers being concentrated in low wage sectors—for many sectors, the lowest wage quintiles in that sector also has had the worst employment outcomes. From April 2020 to May 2021, between 23% and 46% of the decline in employment among the lowest wage establishments was due to within-industry changes. Another important finding is that even for those who remain employed during the pandemic, the probability of becoming part-time for economic reasons increased, especially for low-wage workers.

We provide evidence from a real-world, high-stakes, and empirically-advantageous labor market — the market for NBA basketball players — that employers’ hiring decisions rely too heavily on first-hand experiences with job candidates. Specifically, we find that employers are biased in favor of acquiring players with better-than-usual performances when the employer’s team was playing or preparing to play the player’s original team, with performance information receiving approximately 1.8 times more weight in hiring decisions if it is conveyed through such first-hand experiences. These effects are not predicted by leading behavioral learning theories used to explain similar effects observed in other domains. Instead, our findings point to overattention as a key mechanism through which first-hand experience biases can arise.

We use the Current Employment Statistics survey microdata for the private sector to calculate employment changes since February 2020 by employer size. We find that, for employers with 1 to 9 employees, the largest component of employment change since February is closings (either temporary or permanent) in all months. For employers with 10 or more employees, the largest component of employment change since February is within employers that have continued to report nonzero employment to the survey, rather than within those reporting zero employment or from imputed closures from nonrespondents to the survey. In percentage terms, the greatest overall employment losses shifted to larger and larger employers each month from March through July. However, the largest employers recovered employment faster than smaller employers from July to September. By September, the largest cumulative employment losses were for employers with 50 to 499 employees, with employment losses of 6.5 percent since February. Meanwhile, by September, employers with 1 to 9 employees had employment losses of 3.3 percent since February.

The behavior of noncoresident family members motivates much of the literature on consumption smoothing, risk-sharing, and informal networks, yet little is known empirically on the topic due to a lack of data simultaneously observing multiple households in an extended family. This study utilizes genealogically linked longitudinal data to examine how extended family networks insure against financial risks from severely limiting health conditions. We find that nonhealth consumption of unmarried households declines in response to worsening health, whereas married households smooth expenditures in a way that is consistent with full insurance. Families mitigate losses by reallocating home production, drawing down home equity, holding formal health insurance, collecting social security, and receiving transfers from noncoresident relatives. We illustrate that the costs of health shocks are transmitted throughout family networks, and that noncoresident children draw down their assets and consumption when responding to a parent’s health decline.

Using new data from the 2021 Business Response Survey, a large, nationally representative survey of U.S. private sector businesses, this article presents unique estimates of telework patterns observed during the coronavirus disease 2019 (COVID-19) pandemic. We find that, between July and September 2021, 13 percent of all U.S. private sector jobs involved teleworking full time and 9 percent involved teleworking some of the time. Telework was less common in establishments that increased base wages during the pandemic. The share of establishments that increased telework was larger among establishments that started offering flexible work hours or compressed work schedules after the pandemic hit. Telework was also associated with reductions in workplace square footage and relocation. Within each industry sector, low-paying establishments had a smaller share of jobs that involved telework.

We use the Current Employment Statistics survey microdata for the private sector to calculate employment changes since February 2020 by employer size. We find that, for employers with 1 to 9 employees, the largest component of employment change since February is closings (either temporary or permanent) in all months. For employers with 10 or more employees, the largest component of employment change since February is within employers that have continued to report nonzero employment to the survey, rather than within those reporting zero employment or from imputed closures from nonrespondents to the survey. In percentage terms, the greatest overall employment losses shifted to larger and larger employers each month from March through July. However, the largest employers recovered employment faster than smaller employers from July to September. By September, the largest cumulative employment losses were for employers with 50 to 499 employees, with employment losses of 6.5 percent since February. Meanwhile, by September, employers with 1 to 9 employees had employment losses of 3.3 percent since February.

How successful are jobseekers in finding jobs? How many applications does it take to get an interview? How likely is a job offer after an interview? Are job offers accepted or turned down? Data on job search are typically not available for large representative samples or do not address all of these questions. However, data have become available that quantify job-seeking activity at a specific time during a person’s unemployment spell.

This Beyond the Numbers article explores aspects of job search using data from a supplement to the Current Population Survey (CPS) in May and September 2018 which obtained information about the job search of those who were not employed and asked whether people applied for and received unemployment insurance (UI) benefits. The CPS is a monthly survey of about 60,000 households that provides data on employment and unemployment in the United States, including the national unemployment rate. In the supplement, questions about job search were asked of those who were without a job and had looked for work recently. In this analysis, we restrict the sample to those who were unemployed at the time of the survey and had looked for work in the past 4 weeks.

Using microdata from the Current Employment Statistics survey and the Current Population Survey, I illustrate how the local spread of coronavirus disease 2019 (COVID-19) has differentially affected industry employment. Industries that are not very telework friendly are more likely to have job loss related to its spread. In addition, COVID-19’s spread appears to be most correlated with temporary job loss, which could partially explain employment numbers improving slightly in May and June 2020.