Casino dealers and fishermen are both likely to be replaced by machines in coming years. So which city will lose more of its human workforce—Las Vegas, the country’s gambling capital, or Boston, a major fishing hub?

People tend to assume that automation will affect every locale in the same, homogeneous way, says Hyejin Youn, an assistant professor of management and organization at Kellogg. “They have never thought of how this is unequally distributed across cities, across regions in the U.S.” It is a high-stakes question. The knowledge that certain places will lose more jobs could allow workers and industries to better prepare for the change and could help city leaders ensure their local economies are poised to rebound. In new research, Youn and colleagues seek to understand how machines will disrupt the economies of individual cities. By carefully analyzing the workforces of American metropolitan areas, the team calculated what portion of jobs in each area is likely to be automated in coming decades.

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They found that, in general, small cities will have higher portions of their workforce replaced by machines than large cities. The reason: While cities of all sizes have many easily automated jobs (like card dealers, fisherman, cashiers, and accountants), large cities like Boston also have larger shares of managerial and knowledge professions (like lawyers, scientists, and software developers). Since these jobs require knowledge and skills that cannot easily be taught to a machine, they will offset the total impact of automation. In smaller cities, fewer of those offsetting jobs exist.

Based on this finding, Youn says small cities could see an exodus of workers, as well as exacerbated income inequality, since robots are likely to hollow out the middle class there. And large cities are not entirely immune. Las Vegas, for example, has two million people in its metropolitan area, but its economy relies heavily on an industry whose jobs are likely to be automated. “If I’m the policymaker in Las Vegas, I have to think about how to reshape my city’s industry to prepare,” she says. Specialization and Automation The larger a city’s population, the more specialized its jobs tend to be. To illustrate why, Youn thinks about the restaurant industry. In a small town, there are likely a few small restaurants run by a few people who do many things—cook, clean, manage the books, etc. “Some of these tasks are easily enough defined to soon be automatable,” Youn says. By contrast, in a larger city there will likely be some much larger restaurants that require more specialized knowledge and skills—perhaps a marketing team, or a lawyer who specializes in the restaurant industry—that cannot be easily automated.

It was not clear to economists whether a more specialized workforce would lead to more or less automation.

It was not clear to economists, however, whether a more specialized workforce would lead to more or less automation. In some contexts, specialization allows for a greater division of labor, breaking down the production process into distinct, repetitive jobs like you might see on an assembly line. “That makes the person’s task really, really easy to be replaced,” Youn says. But specialization can also have the opposite effect. Although scientists and managers have a highly specific set of skills, robots would struggle to do their jobs. “The kind of knowledge they have is specialized, but it’s also at the frontier, so a machine cannot replace it yet,” Youn says. To quantify the total impact of automation on a given city, Youn teamed up with Massachusetts Institute of Technology researchers Morgan Frank, Lijun Sun, Manuel Cebrian, and Iyad Rahwan. The researchers needed to figure out exactly what portion of a city’s jobs boiled down to routine tasks versus specialized expertise. They used a dataset developed by researchers at Oxford University that estimates the likelihood of a particular job being automated based on the skills it requires. By combining that information with U.S. Bureau of Labor Statistics data on the composition of each city’s workforce, they were able to predict how many workers would be displaced in 380 metropolitan areas across the United States.

Why Automation Will Hit Small Cities Harder The research resulted in an “impact score” for each city, which translates into the average likelihood that a job there will be impacted by automation. The results showed that Boston, with a 54 percent impact score, is among the least susceptible cities to be changed by automation. That likely has to do with the multitude of hospitals and research universities, says Youn. “A lot of the occupations are associated with medicine, startups, and education—things that are not really automatable yet,” she says. The same is true in the two large cities that top the list of those most impervious to robots: Washington, DC, and San Jose, California, in the heart of Silicon Valley.