Metro Area Job Automation Potential Map

The automation of jobs is expected to accelerate in the coming years, and it's likely to play out differently across metro regions.

Generally speaking, regional economies with more highly-educated workforces in technology, healthcare and similar industries are expected to be more resilient to any job displacement. Economists have offered varying predictions on automation's effects on the economy. One highly-cited paper by University of Oxford professors Carl Frey and Michael Osborn estimates about 47 percent of total U.S. employment is at risk. For the study, a probability of automation was calculated for each occupation by evaluating the extent to which its work activities required “creativity, social intelligence and perception, and manipulation.”

Governing utilized the Oxford study definitions to calculate the share of jobs that could potentially be automated within the 50 largest metro areas. Automation probabilities for each individual occupation were compared with corresponding metro area occupational employment statistics published by the Department of Labor. Approximately 65 percent of Las Vegas area jobs were found to be susceptible to automation, the highest of any metro area. San Jose-Sunnyvale-Santa Clara, Calif., Durham-Chapel Hill, N.C., and other regions with large tech industries registered the lowest shares of employment vulnerable to automation.

Larger markers represent regions with higher percentages of jobs at risk of being automated 

About the data

University of Oxford researchers calculated the approximate automation potential for 702 occupations, which are listed in the report appendix. These estimated probabilities were compared with the Labor Department's most recent Occupational Employment Statistics, current as of May 2016. A small number of jobs, less than 10 percent of metro area employment, were not assigned probabilities in the Oxford study and were excluded from our calculations. Please note that these estimates refer to jobs thought to potentially be automated given their work activities, not actual numbers of jobs expected to be lost.

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