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Beyond BLS

Beyond BLS briefly summarizes articles, reports, working papers, and other works published outside BLS on broad topics of interest to MLR readers.

August 2022

A look at the U.S. labor force through automation and COVID-19

Summary written by: Alexander Saenz

The first Industrial Revolution was ushered in by the invention of the steam engine in 1698, as machines infiltrated nearly all facets of life in Europe and North America. About a century later, new technologies in manufacturing and production defined the second Industrial Revolution. Digitalization arrived in the mid-1900s with new advancements to society to define the third Industrial Revolution.

In their paper “Race and jobs at risk of being automated in the age of COVID-19” (The Hamilton Project, Brookings Institution, March 4, 2021), Kristen E. Broady, Darlene Booth-Bell, Jason Coupet, and Moriah Macklin establish that we are currently living in a fourth Industrial Revolution, one centered around automation and artificial intelligence. The authors use prior studies to list the occupations that are most and least susceptible to automation and then relate these findings to the demographic data of occupations. Furthermore, in this study, they examine how the coronavirus disease 2019 (COVID-19) pandemic will affect jobs moving forward and offered potential strategic adjustments.

Broady, Booth-Bell Coupet, and Macklin note that other research, based on the Standard Occupational Classification (SOC) system, used machine learning to determine the future potential of an occupation becoming automated. In this study, the authors match these computerization data to occupations by race from the Current Population Survey (CPS). Although the SOC and CPS do not completely overlap, Broady, Booth-Bell, Coupet, and Macklin apply percentages by race from the larger occupational categories to the subcategories that do not have racial data, to provide estimates of the probability of future automation for occupations.

From these occupations, Broady, Booth-Bell, Coupet, and Macklin find that among a subset of 30 jobs (at least 300,000 workers) with the highest risk of automation, Blacks and Hispanics are overrepresented, whereas Asians are underrepresented. Overall, 23.0 percent of the total U.S. workforce is included in this subset of 30 jobs.

Among a subset of 30 jobs with the lowest risk of automation (about 73,000 workers), however, the authors find that these jobs are made up of a low percentage of Blacks, Hispanics, and Asians. In total, 14.0 percent of the U.S. workforce is concentrated in these 30 occupations. The authors then detail specific jobs, such as transportation and food preparation occupations, that are at risk of automation and are filled by a high proportion of Black and Hispanic workers.

Next, Broady and colleagues describe how the COVID-19 pandemic has expedited automation. They explain that the rise of “telepresence,” which they describe as “a form of automation” or “the experience of being present at a real-world location, remote from one’s own immediate physical environment,” will decrease economic hubs, because being situated in a central location will become less necessary. This shift will favor the large businesses that already have more automation in place.

The authors conclude with a few ways to move forward through this fourth revolution, particularly those that do not leave behind the Black and Hispanic workers. The first is investing in education for the Black workers, for example, through historically Black colleges and universities and minority-serving institutions, innovative programs, and organizations such as the United Negro College Fund. Another possibility is through workplace training for unskilled workers or those who need to be reskilled. Many other countries have “developed programs and incentives to ensure workers are able to update their skills to match the demands of the evolving workforce.”