¶ Heterogeneous labor market impacts of the COVID-19 pandemic

pdf link, html link at Sage Journals

¶ BibTeX

@article{cortes2023heterogeneous,
  title={Heterogeneous labor market impacts of the COVID-19 pandemic},
  author={Cortes, Guido Matias and Forsythe, Eliza},
  journal={ILR Review},
  volume={76},
  number={1},
  pages={30--55},
  year={2023},
  publisher={SAGE Publications Sage CA: Los Angeles, CA}
}

¶ Abstract

The authors study the distributional consequences of the COVID-19 pandemic’s impact on employment, both during the onset of the pandemic and over subsequent months. Using cross-sectional and matched longitudinal data from the Current Population Survey, they show that the pandemic has exacerbated pre-existing inequalities. Although employment losses have been widespread, they have been substantially larger—and more persistent—in lower-paying occupations and industries. Hispanics and non-White workers suffered larger increases in job losses, not only because of their over-representation in lower-paying jobs but also because of a disproportionate increase in their job displacement probability relative to non-Hispanic White workers with the same job background. Gaps in year-on-year job displacement probabilities between Black and White workers have widened over the course of the pandemic recession, both overall and conditional on pre-displacement occupation and industry. These gaps are not explained by state-level differences in the severity of the pandemic nor by the associated response in terms of mitigation policies. In addition, evidence suggests that older workers have been retiring at faster rates.

¶ Notes and Excerpts:

This paper focus on longitudinal CPS data to look at labor flows.

Substantial flows to both unemployment and non participation

5% of workers were employed by ‘absent’, and not for vacation or illness. Typically this is less than 0.5%, and BLS says these should likely be reclassified as temporary layoffs

However, nearly one-quarter of individuals who were absent for “other” reasons in April 2020 report being paid by their employer for their time off. We therefore compute an adjusted employment rate (shown by the red, dashed line) that excludes individuals who are classified as employed but 1) were absent from work during the reference week, 2) report being absent for “other” reasons, and 3) report that they were not paid by their employer for their time off.

Few percentage points of difference in first month. Only like a percentage point of difference, though after that.

The official employment rate falls from 60.9% in February 2020 to 51.3% in April. The adjusted employment rate, which historically differs from the official employment rate only marginally, falls farther, from 60.7% in February to 48.9% in April. As of February 2021, both measures have recovered to approximately 57%,

It is important to note that the pandemic induced excess exits toward both unemployment and NILF, with the exit rate to NILF being particularly slow to recover to pre-pandemic levels. Hence, analyses that focus only on individuals classified as unemployed in the CPS will miss an important fraction of the pandemic-related job losses.

They make the same point I noticed about hiring rates.

Compared to the increase in exit rates, hiring has remained comparatively robust during the pandemic. More than 80% of the dramatic initial rise in non-employment is attributable to exits from employment, rather than decreased hiring. This outcome contrasts with the pattern typically observed during recessions, in which a collapse in hiring is usually the dominant driver of increased unemployment rates (see Elsby, Michaels, and Solon 2009; Fujita and Ramey 2009; Shimer 2012).

On the other hand, they say there is a more persistant rate of job loss, especially to NILF.

Food prep and personal care had big exists to non-employment. In general, low-wage occupations had larger exit rates.

These rates are calculated as a share of employment in that occupation one year earlier. This approach puts both inflows and outflows on the same denominator, which makes it easier to compare relative magnitudes.

(I wouldn’t have thought to do that.)

Industries affected seem different than great recession, but demographic affects are similar.

Overall, we observe similar patterns in job loss across demographics as in past recessions, in line with Hoynes, Miller, and Schaller (2012), with the major exceptions being the results for men (which are consistent with the findings of Alon et al. [2021]) and the results for older workers (which are consistent with the findings of Coibion et al. [2020]). The other main difference is that the recovery has disproportionately benefited White workers, who are closer to their pre-pandemic employment rates as compared to Black and Hispanic workers, which is a pattern that was not observed in the aftermath of the Great Recession.

¶ Related papers. Perhaps to read later.

Using data on occupational characteristics from data sets such as O*NET, a number of articles have focused on how the impacts of the pandemic differ across jobs according to the extent to which they can be performed remotely or are likely to be at risk due to social distancing requirements (e.g., Béland, Brodeur, and Wright 2020; Dey, Frazis, Loewenstein, and Sun 2020; Dingel and Neiman 2020; Mongey, Pilossoph, and Weinberg 2021).

Several contributions have made use of non-traditional data sources, such as Cajner et al. (2020), who used data from ADP, a large US payroll processing company; Coibion, Gorodnichenko, and Weber (2020), who used data from the Nielsen Homescan panel; Bartik et al. (2020), who used data from Homebase; and Chetty et al. (2020), who built a database that tracks economic activity at a granular level in real time using anonymized data from various private companies. Alon et al. (2021), Bluedorn et al. (2021), and Albanesi and Kim (2021) examined the gender dimension of the pandemic, while Montenovo et al. (2020) and Couch, Fairlie, and Xu (2020) also used CPS data to explore the heterogeneous employment impacts of the pandemic across demographic groups. Cortes and Forsythe (2020) and Dalton et al. (2021) examined heterogeneity across the individual and establishment wage distribution.