When we hear about automation and artificial intelligence replacing jobs, it can look like a tsunami of technology is about to wipe out workers broadly, in the name of greater efficiency. But a study co-authored by an MIT economist shows markedly different dynamics in the U.S. since 1980.
Rather than implement automation in pursuit of maximal productivity, companies have often used automation to replace workers who specifically receive a “wage premium”—earning higher salaries than other comparable workers. In practice, that means automation has steadily reduced the earnings of non-college-educated workers who had obtained higher salaries than most employees with similar qualifications.
This finding has at least two big implications. For one thing, automation has affected the growth in U.S. income inequality much more than many observers realize. At the same time, automation has yielded mediocre productivity gains, plausibly because companies have focused on controlling wages rather than finding more tech-driven ways to improve efficiency and long-term growth.
“There has been an inefficient targeting of automation,” says MIT’s Daron Acemoglu, co-author of a published paper detailing the study’s results. “The higher the wage of the worker in a particular industry or occupation or task, the more attractive automation becomes to firms.” In theory, he notes, companies could automate efficiently. But they haven’t, by emphasizing automation as a tool for shedding salaries—which helps their own internal short-term numbers without building an optimal path for growth.
The study estimates that automation is responsible for 52 percent of the growth in income inequality from 1980 to 2016, and that about 10 percentage points derive specifically from companies replacing workers who had been earning a wage premium. This inefficient targeting of certain workers has offset 60–90 percent of the productivity gains from automation over that period.
“It’s one of the possible reasons productivity improvements have been relatively muted in the U.S., despite the fact that we’ve had an amazing number of new patents and an amazing number of new technologies,” Acemoglu says. “Then you look at the productivity statistics, and they are fairly pitiful.”
The paper, “Automation and Rent Dissipation: Implications for Wages, Inequality, and Productivity,” appears in the May print issue of the Quarterly Journal of Economics. The authors are Acemoglu, who is an Institute Professor at MIT, and Pascual Restrepo, an associate professor of economics at Yale University.
Inequality implications
Dating back to the 2010s, Acemoglu and Restrepo have combined to conduct many studies on automation and its effects on employment, wages, productivity, and firm growth. In general, their findings have suggested that the effects of automation on the workforce after 1980 are more significant than many other scholars have believed.
To conduct the present study, the researchers used data from many sources, including U.S. Census Bureau statistics, data from the bureau’s American Community Survey, industry numbers, and more. Acemoglu and Restrepo analyzed 500 detailed demographic groups, sorted by five levels of education, as well as gender, age, and ethnic background. The study links this information to an analysis of changes in 49 U.S. industries, for a granular look at how automation affected the workforce.
Ultimately, the analysis allowed the scholars to estimate not just the overall number of jobs erased due to automation, but how much of that consisted of companies very specifically trying to remove the wage premium accruing to some of their workers.
Among other findings, the study shows that within groups of workers affected by automation, the largest effects occur for workers in the 70th–95th percentile of the wage range, indicating that higher-earning workers bear much of the brunt of this process.
And as the analysis indicates, about one-fifth of the overall growth in income inequality is attributable to this sole factor.
“I think that is a big number,” says Acemoglu, who shared the 2024 Nobel Prize in economic sciences with his longtime collaborators Simon Johnson of MIT and James Robinson of the University of Chicago.
He adds: “Automation, of course, is an engine of economic growth and we’re going to use it, but it does create very large inequalities between capital and labor, and between different labor groups, and hence it may have been a much bigger contributor to the increase in inequality in the United States over the last several decades.”
The productivity puzzle
The study also illuminates a fundamental choice for firm managers, but one that often gets missed. Imagine a kind of automation—call-center technology, for example—that may actually be inefficient for a business. Even so, firm managers have an incentive to adopt it, reduce wages, and oversee a less productive business with increased net income.
Writ large, some version of this appears to have been happening to the U.S. economy since 1980: Greater profitability is not the same as increased productivity.
“Those two things are different,” says Acemoglu. “You can reduce costs while reducing productivity.”
Indeed, the present study by Acemoglu and Restrepo calls to mind an observation by the late MIT economist Robert M. Solow, who in 1987 wrote, “You can see the computer age everywhere but in the productivity statistics.”
In that vein, Acemoglu observes, “If managers can reduce productivity by 1 percent but increase profits, many of them might be happy with that. It depends on their priorities and values. So the other important implication of our paper is that good automation at the margins is being bundled with not-so-good automation.”
To be clear, the study does not necessarily suggest that less automation is always better. Certain types of automation can boost productivity and feed a virtuous cycle in which a firm makes more money and hires more workers.
But presently, Acemoglu believes, the complexities of automation are not yet recognized clearly enough. Perhaps seeing the broad historical pattern of U.S. automation since 1980 will help people better grasp the trade-offs involved—and not just economists, but firm managers, workers, and technologists.
“The important thing is whether it becomes incorporated into people’s thinking and where we land in terms of the overall holistic assessment of automation, in terms of inequality, productivity, and labor market effects,” Acemoglu says. “So we hope this study moves the dial there.”
Or, as he concludes, “We could be missing out on potentially even better productivity gains by calibrating the type and extent of automation more carefully, and in a more productivity-enhancing way. It’s all a choice, 100 percent.”
ztoog.com