This article reviews the literature on automation and its impact on labor markets, wages, factor shares, and productivity. I first introduce the task model and explain why this framework offers a compelling way to think about recent labor market trends and the effects of automation technologies. The task model clarifies that automation technologies operate by substituting capital for labor in a widening range of tasks. This substitution reduces costs, creating a positive productivity effect, but it also reduces employment opportunities for workers displaced from automated tasks, creating a negative displacement effect. I survey the empirical literature and conclude that there is wide qualitative support for the implications of task models and the displacement effects of automation. I conclude by discussing shortcomings of the existing literature and avenues for future research.
This paper uses data from the 2019 Annual Business Survey to document that firms adopting advanced technologies are larger in terms of employment than other firms in their same industry and cohort. Using data from the Longitudinal Business Survey, we show that adopters were already large and growing faster before artificial intelligence, robotics, cloud computing, and specialized software systems became broadly available. These findings support the view that adopters are large because of selection and not because adopting advanced technologies for automation causally expands their employment.
The benefits of new technologies accrue not only to high-skilled labor but also to owners of capital in the form of higher capital incomes. This increases inequality. To make this argument, we develop a tractable theory that links technology to the distribution of income and wealth—and not just that of wages—and use it to study the distributional effects of automation. We isolate a new theoretical mechanism: automation increases inequality by raising returns to wealth. The flip side of such return movements is that automation can lead to stagnant wages and, therefore, stagnant incomes at the bottom of the distribution. We use a multiasset model extension to confront differing empirical trends in returns to productive and safe assets and show that the relevant return measures have increased over time. Automation can account for part of the observed trends in income and wealth inequality.
This paper describes the adoption of automation technologies by US firms across all economic sectors by leveraging a new module introduced in the 2019 Annual Business Survey, conducted by the US Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES). The module collects data from over 300,000 firms on the use of five advanced technologies: AI, robotics, dedicated equipment, specialized software, and cloud computing. The adoption of these technologies remains low (especially for AI and robotics), varies substantially across industries, and concentrates on large and young firms. However, because larger firms are much more likely to adopt them, 12-64% of US workers and 22-72% of manufacturing workers are exposed to these technologies. Firms report a variety of motivations for adoption, including automating tasks previously performed by labor. Consistent with the use of these technologies for automation, adopters have higher labor productivity and lower labor shares. In particular, the use of these technologies is associated with a 11.4% higher labor productivity, which accounts for 20-30% of the difference in labor productivity between large firms and the median firm in an industry. Adopters report that these technologies raised skill requirements and led to greater demand for skilled labor but brought limited or ambiguous effects to their employment levels.
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.
We study the impact of artificial intelligence (AI) on labor markets using establishment-level data on the near universe of online vacancies in the United States from 2010 onward. There is rapid growth in AI-related vacancies over 2010–18 that is driven by establishments whose workers engage in tasks compatible with AI’s current capabilities. As these AI-exposed establishments adopt AI, they simultaneously reduce hiring in non-AI positions and change the skill requirements of remaining postings. While visible at the establishment level, the aggregate impacts of AI-labor substitution on employment and wage growth in more exposed occupations and industries is currently too small to be detectable.
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 study the effects of industrial robots on US labor markets. We show theoretically that robots may reduce employment and wages and that their local impacts can be estimated using variation in exposure to robots—defined from industry-level advances in robotics and local industry employment. We estimate robust negative effects of robots on employment and wages across commuting zones. We also show that areas most exposed to robots after 1990 do not exhibit any differential trends before then, and robots’ impact is distinct from other capital and technologies. One more robot per thousand workers reduces the employment-to-population ratio by 0.2 percentage points and wages by 0.42%.
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.
This paper asks whether scarcity increases violence in markets that lack a centralized authority. We construct a model in which, by raising prices, scarcity fosters violence. Guided by our model, we examine this effect in the Mexican cocaine trade. At a monthly frequency, scarcity created by cocaine seizures in Colombia, Mexico's main cocaine supplier, increases violence in Mexico. The effects are larger in municipalities near the United States, with multiple cartels and with strong support for PAN (the incumbent party). Between 2006 and 2009 the decline in cocaine supply from Colombia could account for 10% to 14% of the increase in violence in Mexico.
We argue that the US tax system is biased against labor and in favor of capital and has become more so in recent years. As a consequence, it has promoted inefficiently high levels of automation. Moving from the US tax system in the 2010s to optimal taxation of capital and labor would raise employment by 4.02% and the labor share by 0.78 percentage points, and restore the optimal level of automation. If moving to optimal taxes is infeasible, more modest reforms can still increase employment by 1.14–1.96%, but in this case efficiency can be increased by imposing an additional automation tax to reduce the equilibrium level of automation. This is because marginal automated tasks do not bring much productivity gains but displace workers, reducing employment below its socially optimal level. We additionally show that reducing labor taxes or combining lower capital taxes with automation taxes can increase employment much more than the uniform reductions in capital taxes enacted between 2000 and 2018.
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.
We provide evidence that democracy has a positive effect on GDP per capita. Our dynamic panel strategy controls for country fixed effects and the rich dynamics of GDP, which otherwise confound the effect of democracy. To reduce measurement error, we introduce a new indicator of democracy that consolidates previous measures. Our baseline results show that democratizations increase GDP per capita by about 20 percent in the long run. We find similar effects using a propensity score reweighting strategy as well as an instrumental-variables strategy using regional waves of democratization. The effects are similar across different levels of development and appear to be driven by greater investments in capital, schooling, and health.