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 transitions to low-carbon energy production. 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. Results show that future demand for nearly every type of labor skill and knowledge will increase, while the nature of work shifts from physical to more cognitive activities. 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. Results further show that the socialness of labor, which is important for both innovation and productivity, increases in many more industries than it decreases. Finally, COVID will likely accelerate the adoption of teleworking and slightly decrease the rate of workforce automation. These impacts, combined with a shift to more cognitive worker activities, will likely impact the nature of workforce health and safety with less focus on physical injuries and more on illnesses related to sedentary lifestyles. Overall, results suggest that future workers will need to engage more often in training and skill acquisition, requiring life-long learning and skill maintenance strategies.
Highlights
More than 3 million people globally migrate to cities each week, primarily in search of employment opportunities [1]
To estimate job automation risk at the occupation level I use Frey & Osborne’s metric of automation potential by individual occupation code [29]. Those probabilities area applied to occupation-level employment under each COVID scenario to estimate projected changes in total US workforce susceptible to automation
Elements decreasing the most in importance are largely associated with physical tasks, such as standing, walking, running, or moving objects, and with skills likely to be automated, such as interacting with the public
Summary
More than 3 million people globally migrate to cities each week, primarily in search of employment opportunities [1]. To examine the COVID impact on future worker skills and activities I utilize the ONET dataset [26]. To determine how the future distribution of college degrees changes under each COVID projection scenario I first create a probability distribution function for degrees by occupation derived from empirical data.
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