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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.

Recent advances in artificial intelligence are primarily driven by machine learning, a prediction technology. Prediction is useful because it is an input into decision-making. In order to appreciate the impact of artificial intelligence on jobs, it is important to understand the relative roles of prediction and decision tasks. We describe and provide examples of how artificial intelligence will affect labor, emphasizing differences between when the automation of prediction leads to automating decisions versus enhancing decision-making by humans.

China is the world's largest user of industrial robots. In 2016, sales of industrial robots in China reached 87,000 units, accounting for around 30 percent of the global market. To put this number in perspective, robot sales in all of Europe and the Americas in 2016 reached 97,300 units (according to data from the International Federation of Robotics). Between 2005 and 2016, the operational stock of industrial robots in China increased at an annual average rate of 38 percent. In this paper, we describe the adoption of robots by China's manufacturers using both aggregate industry-level and firm-level data, and we provide possible explanations from both the supply and demand sides for why robot use has risen so quickly in China. A key contribution of this paper is that we have collected some of the world's first data on firms' robot adoption behaviors with our China Employer-Employee Survey (CEES), which contains the first firm-level data that is representative of the entire Chinese manufacturing sector.

Recent advances in artificial intelligence and robotics have generated a robust debate about the future of work. An analogous debate occurred in the late nineteenth century when mechanization first transformed manufacturing. We analyze an extraordinary dataset from the late nineteenth century, the Hand and Machine Labor study carried out by the US Department of Labor in the mid-1890s. We focus on transitions at the task level from hand to machine production, and on the impact of inanimate power, especially of steam power, on labor productivity. Our analysis sheds light on the ability of modern task-based models to account for the effects of historical mechanization.

OECD work has shown that innovative solutions from social entrepreneurs can help people in the labour market who are vulnerable to the disruption digitalisation and automation bring. See more at http://www.oecd.org/leed-forum/ Governments are key enablers of an equitable digital transition OECD Observer Roundtable on local firms and automation 3 (da Silva) José António Vieira da Silva, Minister of Labour, Solidarity and Social Security, Portugal Interestingly, when it comes to disruptive transformations like automation, digitalisation and artificial intelligence, countries like Portugal, which have prioritised investment in education, skills, the development of digital infrastructures, and university-business interfaces for knowledge transfer and the like, are catching the wave of these new trends and reaping the benefits. The costs of inertia in this field may include increasing inequalities and gaps in social protection, unfair competition, privacy issues relating to data protection, unaddressed health and safety risks and new forms of social exclusion. Visit www.portugal.gov.pt/en/gc21/ministries/labour-solidarity-and-socialsecurity Turin: A city laboratory for innovation OECD Observer Roundtable on local firms and automation 4 (Pisano) Paola Pisano, Deputy Mayor for Innovation, Smart City, Demographic and Statistical Services and Information Systems, City of Torino, Italy The advent of automation will inevitably lead to a radical change in cities and society.

As the current recession ends, many workers will not be returning to the jobs they once held--those jobs are gone. InThe New Division of Labor, Frank Levy and Richard Murnane show how computers are changing the employment landscape and how the right kinds of education can ease the transition to the new job market. The book tells stories of people at work--a high-end financial advisor, a customer service representative, a pair of successful chefs, a cardiologist, an automotive mechanic, the author Victor Hugo, floor traders in a London financial exchange. The authors merge these stories with insights from cognitive science, computer science, and economics to show how computers are enhancing productivity in many jobs even as they eliminate other jobs--both directly and by sending work offshore. At greatest risk are jobs that can be expressed in programmable rules--blue collar, clerical, and similar work that requires moderate skills and used to pay middle-class wages. The loss of these jobs leaves a growing division between those who can and cannot earn a good living in the computerized economy. Left unchecked, the division threatens the nation's democratic institutions. The nation's challenge is to recognize this division and to prepare the population for the high-wage/high-skilled jobs that are rapidly growing in number--jobs involving extensive problem solving and interpersonal communication. Using detailed examples--a second grade classroom, an IBM managerial training program, Cisco Networking Academies--the authors describe how these skills can be taught and how our adjustment to the computerized workplace can begin in earnest.

When industrial robots are adopted by firms in a local labor market, some workers are displaced and become unemployed. Other workers that are not directly affected by automation may however fear that these new technologies might replace their working tasks in the future. This fear of a possible future replacement is important because it negatively affects workers’ job satisfaction at present. This paper studies the extent to which automation affects workers’ job satisfaction, and whether this effect differs for high- versus low-skilled workers. The empirical analysis uses microdata for several thousand workers in Norway from the Working Life Barometer survey for the period 2016–2019, combined with information on the introduction of industrial robots in Norway from the International Federation of Robotics. Our identification strategy exploits variation in the pace of introduction of industrial robots in Norwegian regions and industries since 2007 to instrument workers’ fear of replacement. The results indicate that automation in industrial firms in recent years have induced 40% of the workers that are currently in employment to fear that their work might be replaced by a smart machine in the future. Such fear of future replacement does negatively affect workers’ job satisfaction at present. This negative effect is driven by low-skilled workers, which are those carrying out routine-based tasks, and who are therefore more exposed to the risks of automation.

In this essay, I begin by identifying the reasons that automation has not wiped out a majority of jobs over the decades and centuries. Automation does indeed substitute for labor—as it is typically intended to do. However, automation also complements labor, raises output in ways that leads to higher demand for labor, and interacts with adjustments in labor supply. Journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor. Changes in technology do alter the types of jobs available and what those jobs pay. In the last few decades, one noticeable change has been a “polarization” of the labor market, in which wage gains went disproportionately to those at the top and at the bottom of the income and skill distribution, not to those in the middle; however, I also argue, this polarization and is unlikely to continue very far into future. The final section of this paper reflects on how recent and future advances in artificial intelligence and robotics should shape our thinking about the likely trajectory of occupational change and employment growth. I argue that the interplay between machine and human comparative advantage allows computers to substitute for workers in performing routine, codifiable tasks while amplifying the comparative advantage of workers in supplying problem-solving skills, adaptability, and creativity.

Automation and population aging are two major forces that will shape the nature of works in the future. However, it is not clear how these forces will interact with each other and affect the labor market. This paper examines the interaction effects of computerization and population aging on the labor market. We found that computerization and population aging have large and statistically significant effects on employment growth but not earnings growth. Also, their interaction terms are statistically significant only for employment growth but not for earnings growth.