In 1930, John Maynard Keynes famously predicted the rapid technological progress of the next 100 years, but also conjectured that this would translate into widespread “technological unemployment:”
“We are being afflicted with a new disease of which some readers may not have heard the name, but of which they will hear a great deal in the years to come — namely, technological unemployment.”
More than two decades later, Wassily Leontief would foretell of similar problems for workers:
“Labor will become less and less important. . . More and more workers will be replaced by machines. I do not see that new industries can employ everybody who wants a job”(Leontief, 1952).
Though these predictions did not come true in the decades that followed, there is renewed concern that with the striking advances in automation, robotics, and artificial intelligence, we are on the verge of —or perhaps we are already— seeing them realized (e.g., Brynjolfsson and McAfee, 2012; Ford, 2016). The mounting evidence that the automation of a range of low-skill and medium-skill occupations has contributed to wage inequality and employment polarization (e.g., Autor, Levy and Murnane, 2003; Goos and Manning, 2007; Michaels, Natraj and Van Reenen, 2014) adds to these worries.
These concerns notwithstanding, we have little systematic evidence of the equilibrium impact of these new technologies, and especially of robots, on employment and wages. One line of research (exemplified by Frey and Osbourne, 2013) investigates how feasible it is to automate existing jobs given current and presumed technological advances. Based on the tasks that workers perform, Frey and Osborne (2013) classify 702 occupations by how susceptible they are to automation. They conclude that over the next two decades, 47 percent of US workers are at the risk of automation. Using a related methodology, McKinsey puts the same number at 45 percent, while the World Bank estimates that 57 percen of the jobs in the OECD could be automated over the next two decades (World Development Report, 2016). Even if these studies were on target on what can be technologically feasible,1 these numbers do not correspond to the equilibrium impact of automation on employment and wages. First, even if the presumed technological advances materialize, there is no guarantee that firms would choose to automate; that would depend on the costs of substituting machines for labor and how much wages change in response to this threat. Second, the labor market impacts of new technologies depend not only on where they hit but also on the adjustment in other parts of the economy. For example, other sectors and occupations might expand to soak up the labor freed from the tasks that are now performed by machines and productivity improvements due to new machines may even expand employment in affected industries (Acemoglu and Restrepo, 2016).
In this paper we move beyond these feasibility studies and estimate the equilibrium impact of one type of automation technology, industrial robots, on US labor markets. The International Federation of Robotics—IFR for short—defines an industrial robot as “an automatically controlled, reprogrammable, and multipurpose [machine]” (IFR, 2014). That is, industrial robots are machines that do not need a human operator and that can be programmed to perform several manual tasks such as welding, painting, assembling, handling materials, or packaging. Textile looms, elevators, cranes, transportation bands or coffee makers are not industrial robots as they have a unique purpose, cannot be reprogrammed to perform other tasks, and/or require a human operator. Although this definition excludes other types of capital that may also replace labor—most notably software and human-operated machines—it enables an internationally and temporally comparable measurement of industrial robots, which are argued to have already deeply impacted the labor market and expected to transform it in the decades to come.
by Daron Acemoglu, MIT and Pascual Restrepo, Yale and Boston University | Read more: (pdf)