Nonrelocatable Occupations at Increased Risk During Pandemics: United States, 2018
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Abstract
Objectives. To characterize which occupations in the United States could likely work from home during a pandemic such as COVID-19.
Methods. I merged 2018 US Bureau of Labor Statistics (BLS) national employment and wage data with measures ranking the importance of computer use at work and the importance of working with or performing for the public from the BLS O*NET survey.
Results. Approximately 25% (35.6 million) of US workers are employed in occupations (such as technology, administrative, financial, and engineering) that could be done from home; the remaining 75% work in occupations (including health care, manufacturing, retail, and food services) that are challenging to do from home.
Conclusions. Most US workers are employed in occupations that cannot be done at home, putting 108.4 million workers at increased risk for adverse health outcomes related to working during a pandemic. These workers tend to be lower paid. The stress experienced by lower-income groups, coupled with job insecurity, could result in a large burden of mental health disorders in the United States in addition to increased cases of COVID-19 from workplace transmission.
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SciScore for 10.1101/2020.03.21.20031336: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations related to the data used here must be acknowledged. BLS data does not count self-employed (which includes a variety of workers ranging from gig economy workers to highly trained independent consultants, for …
SciScore for 10.1101/2020.03.21.20031336: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations related to the data used here must be acknowledged. BLS data does not count self-employed (which includes a variety of workers ranging from gig economy workers to highly trained independent consultants, for example), undocumented, contingent, military, and domestic workers. This undercoverage of the working population in the BLS survey could affect conclusions presented here. O*NET relies on employee and employer self-report, so is subject to inherent bias and misclassification during collection. Further, data collected by O*NET is aggregated on the occupational level, and I further aggregated data into quadrants, meaning that within-occupation and within-quadrant variation isn’t accounted for in this analysis.35 This will lead to misclassification both within the occupations, and within each quadrant. The O*NET metrics used in this analysis were measures of the importance of using a computer for work and importance of interacting with the public, which differs from the frequency of using a computer or frequency of interacting with the public. Therefore, some jobs for which computer use is rated as very important, may not actually require use of a computer very frequently, and jobs where interaction with the public is rated as important may not actually interact with the public frequently. This further lead to misclassification in the analysis for who could work from home most easily.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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