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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 corrects the article DOI: 10.1007/s10888-021-09506-6.].

In the initial weeks of the coronavirus disease 2019 (COVID-19) pandemic, employment in several industries was especially vulnerable because of shutdown policies imposed by states, as well as a drop in demand as people engaged in social distancing. This article looks at the demographic characteristics of workers in the initially highly exposed industries, as well as the characteristics and earnings of families with workers in these industries. The article also uses recent Current Population Survey data to look at how various demographic groups have fared in the early weeks of the COVID-19 pandemic between February and April.

To measure the effects of the coronavirus disease 2019 pandemic, the U.S. Bureau of Labor Statistics added questions to the Current Population Survey, the main U.S. labor force survey, starting in May 2020. This article analyzes the results from questions asking people (1) whether they teleworked because of the pandemic and (2) whether they were unable to work because their employers closed or lost business because of the pandemic. We use the data on telework to refine work completed earlier in the pandemic that classified occupations on their suitability for telework. We then apply the revised classification to examine trends in telework and the extent to which working in an occupation suitable for telework shields workers from unemployment. Our results show that the pandemic resulted in a large increase in teleworking, with 33 percent of U.S. workers reporting teleworking because of the coronavirus in the period May-June 2020, before declining to a still substantial 22 percent in the fourth quarter. Rates of lost work varied widely both by an occupation’s suitability for telework and by demographic category.

This article uses multiple surveys and data sourced from administrative records to examine trends in wage inequality from 2003 to 2019. Survey evidence shows that wages were growing more unequal from 2003 to 2013 as wages grew faster among high-wage workers than among low-wage workers. However, from 2013 to 2019, the same surveys show substantial wage gains for workers in the second and third deciles of the wage distribution, particularly among material moving workers and health aides. Administrative tax data also show substantial gains in annual wage and salary earnings income for earners in the lower portion of the earnings distribution in the same years. Wage growth among lower wage workers was large enough to reduce overall wage inequality from 2013 to 2019 in Occupational Employment and Wages Survey data. In tax data, wage growth among lower earning workers was large enough to reduce overall earnings inequality from 2010 to 2018. In data from the Current Population Survey, a plateau was found in overall wage inequality—rather than the clear decline found in the other two data sources—in the later years of the economic expansion.

We merge detailed microdata from the Occupational Employment Statistics survey with establishment founding dates from the BLS Longitudinal Database, which allows us to estimate the occupational and wage distributions of employees by the founding dates of their employing establishments. Overall, we find greater employment levels for older establishments-particularly in education, healthcare, and production occupations-but these differences in employment levels are entirely explained by establishment age. Examining wages, we find that, overall, older establishments pay higher hourly wages than younger establishments, and these differences are not entirely explained by establishment age. We also find noticeable differences in patterns of occupational wages across establishments of different ages. In particular, healthcare occupations have higher wages in younger establishments.

In this article, we describe the details of an alternative estimation method for producing estimates of occupational employment levels and mean wages for the Occupation Employment Statistics survey. In addition, we introduce a bootstrap variance estimation system to accompany the new estimation method. A comparison of the official May 2016 estimates with our model-based estimates shows general agreement between the two sets of estimates.

We document new stylized facts on the occupational mix of businesses in the United States and how their internal organization evolves over their life cycles. Our main empirical finding is that younger businesses have fewer hierarchical layers and lower span of control than comparable older businesses do. Our results suggest that businesses become simultaneously more hierarchical and increase their managerial span of control over their life cycles. We show that this pattern is not entirely driven by selection or differences in size and is pervasive across cohorts and sectors.