Long-Term COVID-19 Impacts and the U.S. Workforce of 2029

17 Pages Posted: 26 Aug 2021 Last revised: 28 Sep 2021

See all articles by Shade T. Shutters

Shade T. Shutters

Arizona State University (ASU) - School of Complex Adaptive Systems

Date Written: August 25, 2021

Abstract

While ensuring employment opportunities is critical for global progress and stability, workers are now subject to several disruptive trends, including automation, rapid changes in technology and skill requirements, and energy transitions. Yet, these trends seem almost insignificant compared to labor impact of the COVID-19 pandemic. While much has been written about the pandemic’s short-term impacts, this study analyzes anticipated long-term impacts on the labor force of 2029 by comparing original 2029 labor projections to special COVID-adjusted projections recently published by the US Bureau of Labor Statistics. We find that future demand for nearly every type of labor skill and knowledge increases, while work activities shift from physical to more cognitive tasks. Of the nearly three million jobs projected to disappear by 2029 due to COVID, over 91% are among workers without a bachelor’s degree. Among workers with a degree demand shifts primarily from business-related degrees to computer and STEM degrees. We further find that the socialness of labor, which is important for both innovation and productivity, increases in many more industries than it decreases. Finally, an examination of how COVID affects trends in automation and teleworking is mixed across industries. Overall, our results suggest that future workers will need to engage more often in training and skill acquisition, requiring life-long learning and skill maintenance strategies.

Keywords: labor skills, college degree, workforce, labor dynamics, COVID-19, innovation, worker productivity, employment projections

JEL Classification: I26, J11, J21, J24

Suggested Citation

Shutters, Shade T., Long-Term COVID-19 Impacts and the U.S. Workforce of 2029 (August 25, 2021). Available at SSRN: https://ssrn.com/abstract=3911455 or http://dx.doi.org/10.2139/ssrn.3911455

Shade T. Shutters (Contact Author)

Arizona State University (ASU) - School of Complex Adaptive Systems ( email )

PO Box 872701
Tempe, AZ 85287-2701
United States

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