How Will Automation Affect Different U.S. Cities?
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Careers Apr 10, 2018

How Will Automa­tion Affect Dif­fer­ent U.S. Cities?

Jobs in small cities will like­ly be hit hard­est. Check how your com­mu­ni­ty and pro­fes­sion will fare.

How will automation affect jobs and cities?

Michael Meier

Based on the research of

Morgan Frank

Manuel Cebrian

Hyejin Youn

Lijun Sun

Iyad Rahwan

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. 

By con­trast, small­er cities tend to have larg­er shares of cashiers, retail sales­peo­ple, recep­tion­ists, and food-ser­vice work­ers — the types of jobs that can be more eas­i­ly auto­mat­ed. Las Vegas, a metro area less than half the size of Boston, could see 68 per­cent of its jobs auto­mat­ed. Worse still are the pre­dic­tions for tiny cities like Mayagüez, Puer­to Rico, where 73 per­cent of the work­force is like­ly to be automated. 

You don’t nor­mal­ly see physi­cists in real­ly small towns,” Youn says. You don’t see CEOs in real­ly small towns.” 

But even CEOs and physi­cists are not entire­ly in the clear. Youn’s esti­mates are based on the skills that today’s machine-learn­ing experts pre­dict robots can take over. As arti­fi­cial intel­li­gence and machine learn­ing empow­er machines to move beyond sim­ple, repet­i­tive tasks, more and more jobs will be up for grabs. 

I don’t know how long it will take,” Youn says, but if they become real­ly gen­er­al­ized machines, it will be a dif­fer­ent story.” 

What’s a Small City to Do?

Youn expects the more intense job loss will push peo­ple to leave small­er cities in search of work. And where do you find a new job? A larg­er city,” she says. So urban­iza­tion will con­tin­ue to hap­pen, aggra­vat­ed by this automa­tion hit.” 

Youn also notes that, besides white-col­lar work­ers, only one oth­er class of jobs will like­ly avoid automa­tion: extreme­ly low-wage posi­tions, such as jan­i­tors. There’s lit­tle incen­tive to auto­mate, because automa­tion requires cap­i­tal,” and that labor is already very cheap, Youn explains. 

There­fore, for­mer­ly mid­dle-class work­ers whose jobs have been auto­mat­ed will be pushed to find work at the far extremes of the wage dis­tri­b­u­tion, since those kinds of jobs — CEOs on one end and jan­i­tors on the oth­er — will be the only human posi­tions left. And because the easy-to-auto­mate mid-range jobs are more preva­lent in small­er cities, inequal­i­ty will rise faster there. 

Wor­ry­ing as these con­se­quences may be for small cities, Youn thinks there is a sil­ver lin­ing: pol­i­cy­mak­ers now have a bet­ter, more nuanced idea of what to expect. 

Had the researchers found that automa­tion was com­plete­ly ran­dom, with no rela­tion to city size, there would be no way to pre­dict how a par­tic­u­lar city might fare. 

But there is a sys­tem­at­ic dif­fer­en­ti­a­tion,” Youn says. And since it’s sys­tem­at­ic, we can pre­pare for it.” That is, small cities might pre­emp­tive­ly set up job-retrain­ing pro­grams, or cre­ate incen­tives to attract new, high-tech indus­tries where work­ers can­not eas­i­ly be replaced by machines, as opposed to focus­ing on cre­at­ing jobs that could be auto­mat­ed in the near future. 

Her find­ings have left Youn want­i­ng to take an even more detailed look at how automa­tion impacts a com­pa­ny or a job. For exam­ple, while email and dig­i­tal sched­ul­ing have replaced some work­ers, she notes that they have also spurred pro­duc­tiv­i­ty and inno­va­tion for remain­ing work­ers, which may improve a company’s bot­tom line. Dis­en­tan­gling the con­trast­ing roles that tech­nol­o­gy plays could help pre­dict which indus­tries machines will dis­rupt next. 

Every task will be some form of col­lab­o­ra­tion with a machine,” she says, as machines get smarter and can assist with increas­ing­ly spe­cial­ized work. The ques­tion is: How will this either help or destroy jobs?” 

Featured Faculty

Hyejin Youn

Assistant Professor of Management & Organizations

About the Writer

Jake J. Smith is a writer and radio producer in Chicago.

About the Research

Frank, Morgan, Manuel Cebrian, Hyejin Youn, Lijun Sun, and Iyad Rahwan. 2018. “Small Cities Face Greater Impact from Automation.” Journal of the Royal Society Interface. 15(139).

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