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We document that between 50% and 70% of changes in the U.S. wage structure over the last four decades are accounted for by relative wage declines of worker groups specialized in routine tasks in industries experiencing rapid automation. We develop a conceptual framework where tasks across industries are allocated to different types of labor and capital. Automation technologies expand the set of tasks performed by capital, displacing certain worker groups from jobs for which they have comparative advantage. This framework yields a simple equation linking wage changes of a demographic group to the task displacement it experiences. We report robust evidence in favor of this relationship and show that regression models incorporating task displacement explain much of the changes in education wage differentials between 1980 and 2016. The negative relationship between wage changes and task displacement is unaffected when we control for changes in market power, deunionization, and other forms of capital deepening and technology unrelated to automation. We also propose a methodology for evaluating the full general equilibrium effects of automation, which incorporate induced changes in industry composition and ripple effects due to task reallocation across different groups. Our quantitative evaluation explains how major changes in wage inequality can go hand‐in‐hand with modest productivity gains. 

Abstract We argue theoretically and document empirically that aging leads to greater (industrial) automation, because it creates a shortage of middle-aged workers specializing in manual production tasks. We show that demographic change is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across U.S. commuting zones. We also document more automation innovation in countries undergoing faster aging. Our directed technological change model predicts that the response of automation technologies to aging should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation and that productivity should improve and the labor share should decline relatively in industries that are more amenable to automation. The evidence supports all four of these predictions. 

We extend the canonical model of skill-biased technical change by modeling the allocation of tasks to factors and allowing for automation and the creation of new tasks. In our model, factor prices depend on the set of tasks they perform. Automation can reduce real wages and generate sizable changes in inequality associated with small productivity gains. New tasks can increase or reduce inequality depending on whether they are performed by skilled or unskilled workers. Industry-level data suggest that automation significantly contributed to the rising skill premium, while new tasks reduced inequality in the past but have contributed to inequality recently. 

We study the firm-level implications of robot adoption in France. Of 55,390 firms in our sample, 598 adopted robots between 2010 and 2015, but these firms accounted for 20 percent of manufacturing employment. Adopters experienced significant declines in labor shares, the share of production workers in employment, and increases in value added and productivity. They expand their overall employment as well. However, this expansion comes at the expense of competitors, leading to an overall negative association between adoption and employment. Robot adoption has a large impact on the labor share because adopters are larger and grow faster than their competitors. 

We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. At the center of our framework is the allocation of tasks to capital and labor—the task content of production. Automation, which enables capital to replace labor in tasks it was previously engaged in, shifts the task content of production against labor because of a displacement effect. As a result, automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity. The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage. The introduction of new tasks changes the task content of production in favor of labor because of a reinstatement effect, and always raises the labor share and labor demand. We show how the role of changes in the task content of production—due to automation and new tasks—can be inferred from industry-level data. Our empirical decomposition suggests that the slower growth of employment over the last three decades is accounted for by an acceleration in the displacement effect, especially in manufacturing, a weaker reinstatement effect, and slower growth of productivity than in previous decades.