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.
After matching over 3 million loans from the $669 billion Paycheck Protection Program to administrative wage records, I estimate a doubly robust dynamic difference-in-difference event study showing
robust, causal impacts of the loans on employment, wages, and opening status of establishments 7 months after PPP approval. Doing back-of-the-envelope calculations, I find a range of $20,000 to $34,000 of PPP spent per employee-month retained, with about 24% of the PPP money going towards wage retention in the baseline model. Small and low-wage establishments show the largest impact from PPP.
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.
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.
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.
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