The Parts of America Most Susceptible to Automation
Economists expect that millions of American jobs are going to be replaced by automation in the coming decades. But where will those job losses take place? Which areas will be hardest hit?
Much of the focus regarding automation has been on the Rust Belt. There, many workers have been replaced by machines, and the number of factory jobs has slipped as more production is offshored. While a lot of the rhetoric about job loss in the Rust Belt has centered on such outsourcing, one study from Ball State University found that only 13 percent of manufacturing job losses are attributable to trade, and the rest to automation.
A new analysis suggests that the places that are going to be hardest-hit by automation in the coming decades are in fact outside of the Rust Belt. It predicts that areas with high concentrations of jobs in food preparation, office or administrative support, and/or sales will be most affected—places such as Las Vegas and the Riverside-San Bernardino area may be the most vulnerable to automation in upcoming years, with 65 percent of jobs in Las Vegas and 63 percent of jobs in Riverside predicted to be automatable by 2025. Other areas especially vulnerable to automation are El Paso, Orlando, and Louisville.
Still, the authors estimate that almost all large American metropolitan areas may lose more than 55 percent of their current jobs because of automation in the next two decades. “We felt it was really stunning, since we are underestimating the probability of automation,” said Johannes Moenius, the director of the Institute for Spatial Economic Analysis at the University of Redlands, which prepared the report.
Which Regional Economies Are Most Susceptible to Automation?
Moenius and colleagues used a widely cited 2013 study from Oxford University predicting which of roughly 700 common jobs are most susceptible to automation, and then mapped out which metropolitan areas have a high share of those jobs. That study, by the economists Carl Benedikt Frey and Michael A. Osborne, suggested that 47 percent of total U.S. employment is at risk of automation over the next decade or two; they found that telemarketers, insurance underwriters and appraisers, tax preparers, and cashiers were some of the most likely to see their jobs threatened by automation, while the livelihoods of mental-health and substance-abuse social workers, oral surgeons, choreographers, and physicians were more protected.
Frey and Osborne’s estimates cover about 138 million Americans’ jobs. Moenius and his colleagues found that Las Vegas, Riverside, and El Paso all had high numbers of office and administrative-support jobs, food-preparation and -serving jobs, and sales jobs, and thus had the most vulnerability to automation. Moenius estimates that 65.2 percent of jobs in Las Vegas, 63.9 percent in El Paso, and 62.6 percent of jobs in Riverside are susceptible to automation in the next two decades. The automation of transportation and material-moving jobs also contributed to the potential job loss in these places, as well as in Greensboro, North Carolina, where 62.5 percent of jobs are susceptible to automation.
The jobs that the Redlands analysis places new focus on are slightly different from the types of jobs academics once thought would be easily automatable. That’s because before the Frey and Osborne study, scholars had predicted that routine jobs were the most likely to be automated, but Frey and Osborne suggested that advances in computerization have made it likely that non-routine jobs will be automated, too. The power of machine learning means that programmers with large data sets can use them to make machines smarter, allowing them to do non-routine tasks; for example, oncologists are using data from medical journals and patient records to automatically create treatment plans for cancer patients. “It is largely already technologically possible to automate almost any task, provided that sufficient amounts of data are gathered for pattern recognition,” the authors write.
Of course, the Rust Belt will not be immune to automation in coming decades. Metropolitan areas like Detroit, Indianapolis, Cleveland, and Pittsburgh could still see more than half of their jobs computerized, the study suggests. But because so many manufacturing jobs centered in the Midwest have already been automated, those regions are not at the top of the list of the places that currently stand to lose the highest share of jobs. Instead, the brunt of the next automation wave will come in cities with a different type of low-skill job.
What’s particularly striking about the new Redlands report is that the regions that are susceptible to automation are those that already have a high share of low-wage jobs. Previously, automation had hurt middle-class jobs such as those in manufacturing. Now, it’s coming for the lower-income jobs. When those jobs disappear, an entire group of less-educated workers who already weren’t making very much money will be out of work. Moenius worries about the possibility of entire regions in which low earners are competing for increasingly scarce jobs. “I wasn’t in L.A. when the riots happened, but are we worried about this from a social perspective?” he said. “Not for tomorrow, but for 10 years from now? It’s quite frankly frightening.”
There were, however, a few regions of the country where jobs were not as likely to be automated. They included Silicon Valley, North Carolina’s Research Triangle and the Boston area, where a high share of the jobs require more creative and social intelligence, and are thus more difficult to automate.
These areas are currently relatively prosperous, and the Redlands analysis also suggests that America’s growing regional divergence will only continue to worsen. As the Berkeley economist Enrico Moretti wrote in his 2012 book The New Geography of Jobs, high-tech job centers like Silicon Valley are attracting more and more educated and talented people, and are pulling away from the rest of the country. This has implications not only for employment, he wrote, but also for socioeconomic outcomes such as health, family stability, and crime. He put it this way:
“A handful of cities with the ‘right’ industries and a solid base of human capital keep attracting good employers and offering high wages, while at the other extreme, cities with the ‘wrong’ industries and a limited human capital base, are stuck with dead-end jobs and average wages.”
The work by Moenius and his colleagues suggests that this divergence will only continue. While a handful of cities with good jobs and highly educated workers will continue to thrive, other areas are going to see more and more jobs disappear as automated technologies become ever better. This may have much wider implications, politically and socially. People in America’s struggling regions feel left behind economically, as the 2016 election indicated. But the anger that motivated many voters in November may pale in comparison to what comes next, if some regions see two-thirds of their jobs disappear while other areas continue to thrive.