Study: Firms often use automation to control certain workers’ wages

Source: MIT News - AI

Automation, Wage Premiums, and U.S. Inequality

When we hear about automation and artificial intelligence replacing jobs, it may seem like a tsunami of technology is going 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, firms have often used automation to replace employees who specifically receive a “wage premium,” earning higher salaries than other comparable workers. In practice, that means automation has frequently reduced the earnings of non‑college‑educated workers who had obtained better salaries than most employees with similar qualifications.

Key Implications

1. Inequality: Automation has affected the growth in U.S. income inequality even more than many observers realize.
2. Productivity: Automation has yielded a mediocre productivity boost, plausibly because firms focus on controlling wages rather than on tech‑driven 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, firms could automate efficiently. But they have not, by emphasizing it 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 % of the growth in income inequality from 1980 to 2016, and that about 10 percentage points derive specifically from firms replacing workers who had been earning a wage premium. This inefficient targeting has offset 60–90 % of the productivity gains from automation during the 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 (Institute Professor at MIT) and Pascual Restrepo (Associate Professor of Economics at Yale University).

Inequality Implications

• Since the 2010s, Acemoglu and Restrepo have produced many studies on automation’s effects on employment, wages, productivity, and firm growth. Their findings consistently suggest that post‑1980 automation impacts are larger than many scholars have believed.
• Data sources include U.S. Census Bureau statistics, the American Community Survey, industry numbers, and more. The researchers analyzed 500 detailed demographic groups, sorted by five education levels, gender, age, and ethnic background, and linked these to changes in 49 U.S. industries for a granular view of automation’s workforce effects.
• The analysis allowed the scholars to estimate not only the overall number of jobs erased by automation, but also how much of that was driven by firms specifically targeting the wage premium.
• Key finding: Within groups of workers affected by automation, the biggest effects occur for workers in the 70th–95th percentile of the salary range, indicating that higher‑earning employees bear much of the brunt of this process.
• 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 Simon Johnson (MIT) and James Robinson (University of Chicago).

“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 basic choice for firm managers that often goes overlooked. Imagine a type of automation—call‑center technology, for instance—that might actually be inefficient for a business. Even so, managers have an incentive to adopt it, reduce wages, and oversee a less productive business with increased net profits.

• Greater profitability ≠ increased productivity.

“Those two things are different,” says Acemoglu. “You can reduce costs while reducing productivity.”

The observation echoes the late MIT economist Robert M. Solow, who in 1987 wrote, “You can see the computer age everywhere but in the productivity statistics.”

Acemoglu adds:

“If managers can reduce productivity by 1 % 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 imply that less automation is always better; certain types of automation can boost productivity dramatically. The key takeaway is that how automation is deployed matters enormously for both inequality and overall economic performance.

Ty and feed a virtuous cycle in which a firm makes more money and hires more workers.

But currently, 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.”

“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.”