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High- and low-wage occupations are expanding rapidly relative to middle-wage occupations in both the US and the [European Union] EU. [The authors] study the reallocation of workers from middle-skill occupations towards the tails of the occupational skill distribution by analyzing changes in age structure within and across occupations. Because occupations typically expand by hiring young workers and contract by curtailing such hiring, [the authors] posit that growing occupations will get younger while shrinking occupations will 'get old'. After verifying this proposition, [the authors] apply this observation to local labor markets in the US to test whether markets that were specialized in middle-skilled occupations in 1980 saw a differential movement of both older and younger workers into occupations at the tails of the skill distribution over the subsequent 25 years. Consistent with aggregate trends, employment in initially middle-skill-intensive labor markets hollowed-out between 1980 and 2005. Employment losses among non-college workers in the middle of the occupational skill distribution were almost entirely countered by employment growth in lower-tail occupations. For college workers, employment losses at the middle were offset in roughly equal measures by gains in the upper- and lower-tails of the occupational skill distribution. But gains at the upper-tail were almost entirely limited to young college workers. Consequently, older college workers are increasingly found in lower-skill, lower-paying occupations.

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In this essay, [the author begins] 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, [the author also argues], this polarization 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. [The author argues] 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.

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This report argues that the world now stands on the cusp of a technological revolution in artificial intelligence and robotics that may prove as transformative for economic growth and human potential as were electrification, mass production, and electronic telecommunications in their eras. New and emerging technologies will raise aggregate economic output and boost the wealth of nations. Will these developments enable people to attain higher living standards, better working conditions, greater economic security, and improved health and longevity? The answers to these questions are not predetermined. They depend upon the institutions, investments, and policies that we deploy to harness the opportunities and confront the challenges posed by this new era. How can we move beyond unhelpful prognostications about the supposed end of work and toward insights that will enable policymakers, businesses, and people to better navigate the disruptions that are coming and underway? What lessons should we take from previous epochs of rapid technological change? How is it different this time? And how can we strengthen institutions, make investments, and forge policies to ensure that the labor market of the 21st century enables workers to contribute and succeed?

The MIT Task Force on the Work of the Future was convened in the spring of 2018. Its goals are to understand the relationships between emerging technologies and work, and to explore strategies to enable a future of shared prosperity. To help answer these questions, and to provide a framework for the Task Force's efforts over the next year, this report examines several aspects of the interaction between work and technology.

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[The authors] reassess the effect of state and federal minimum wages on US earnings inequality, attending to two issues that appear to bias earlier work: violation of the assumed independence of state wage levels and state wage dispersion, and errors-in-variables that inflate impact estimates via an analogue of the well known division bias problem. [The authors] find that the minimum wage reduces inequality in the lower tail of the wage distribution (the 50/10 wage ratio), but the impacts are typically less than half as large as those reported in the literature and are almost negligible for males. Nevertheless, the estimated effects extend to wage percentiles where the minimum is nominally non-binding, implying spillovers. [The authors] structurally estimate these spillovers and show that their relative importance grows as the nominal minimum wage becomes less binding. Subsequent analysis underscores, however, that spillovers and measurement error (absent spillovers) have similar implications for the effect of the minimum on the shape of the lower tail of the measured wage distribution. With available precision, we cannot reject the hypothesis that estimated spillovers to non-binding percentiles are due to reporting artifacts. Accepting this null, the implied effect of the minimum wage on the actual wage distribution is smaller than the effect of the minimum wage on the measured wage distribution.

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We offer an integrated explanation and empirical analysis of the polarization of US employment and wages between 1980 and 2005, and the concurrent growth of low skill service occupations. We attribute polarization to the interaction between consumer preferences, which favor variety over specialization, and the falling cost of automating routine, codifiable job tasks. Applying a spatial equilibrium model, we derive, test, and confirm four implications of this hypothesis. Local labor markets that were specialized in routine activities differentially adopted information technology, reallocated low skill labor into service occupations (employment polarization), experienced earnings growth at the tails of the distribution (wage polarization), and received inflows of skilled labor.

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Three years ago, robots, artificial intelligence (AI), and self-driving cars seemed to be coming fast. A widely cited study projected nearly half of all jobs in industrialized countries could soon be performed by robots or AI. In this context, the MIT Task Force on the Work of the Future was commissioned in the spring of 2018, tasked with understanding the relationships between emerging technologies and work, to help shape public discourse around realistic expectations of technology, and to explore strategies to enable a future of shared prosperity.

Adopting new technology creates winners and losers and will continue to do so. Involvement of all stakeholders - including workers, businesses, investors, educational and social organizations, and government - can minimize the damage done to individuals and communities and help ensure that the jobs of the future offer benefits that are shared by all. [The authors] explore this inclusive approach by examining the institutional frameworks around work, including how education and training programs can be made more effective and inclusive, as well as new ways of empowering workers who may never have the protections afforded by traditional union structures. This report begins by documenting and diagnosing the challenges facing the U.S. labor market. Next, [the authors] survey the technological frontier to draw lessons about the pace and direction of change and its likely impacts on employment, skill demands, and opportunity. Finally, [the authors] synthesize insights from work and technology to consider how our policies and institutions should innovate to leverage technological and economic opportunities while surmounting the substantial challenges that lie ahead.

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A central organizing framework of the voluminous recent literature studying changes in the returns to skills and the evolution of earnings inequality is what the authors refer to as the canonical model. This model operationalizes the supply and demand for skills by assuming two distinct skill groups that perform two different and imperfectly substitutable tasks or produce two imperfectly substitutable goods. In this paper the authors argue that despite its notable successes, the canonical model is largely silent on a number of central empirical developments of the last three decades. The authors argue that it is valuable to consider a richer framework for analyzing how recent changes in the earnings and employment distribution in the United States and other advanced economies are shaped by the interactions among worker skills, job tasks, evolving technologies, and shifting trading opportunities.

The authors propose an alternate model and further consider how the evolution of technology in task-based settings may be endogenized.

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This paper analyses the state of the US labour market over the past three decades to inform policymaking on two fronts. The first is to document and place in historical and international context the trajectory of the US labour market, focusing on the evolving earnings, employment rates, and labour market opportunities for workers with low, moderate, and high levels of education. The second is to illuminate the key forces shaping this trajectory, including the slowing rate of four-year college degree attainment among young adults, particularly males, shifts in the gender and racial composition of the workforce, changes in technology, international trade and the international offshoring of jobs, which affect job opportunities and skill demands, and changes in US labour market institutions affecting wage setting, including labour unions and minimum wage legislation. The paper offers the following conclusions: employment growth is polarising, with job opportunities concentrated in relatively high-skill, high-wage jobs and low-skill, low-wage jobs; and employment losses during the recession have been far more severe in middle-skilled white- and blue-collar jobs than in either high-skill, white-collar jobs or in low-skill service occupations. Rising demand for highly educated workers, combined with lagging supply, is contributing to higher levels of earnings inequality. Demand for middle-skill jobs is declining, and consequently, workers that do not obtain postsecondary education face a contracting set of job opportunities. Perhaps most alarmingly, males as a group have adapted comparatively poorly to the changing labour market. Show less

While the utopian vision of the current Information Age was that computerization would flatten economic hierarchies by democratizing information, the opposite has occurred. Information, it turns out, is merely an input into a more consequential economic function, decision-making, which is the province of elite experts. The unique opportunity that [artificial intelligence] AI offers to the labor market is to extend the relevance, reach, and value of human expertise. Because of AI's capacity to weave information and rules with acquired experience to support decision-making, it can be applied to enable a larger set of workers possessing complementary knowledge to perform some of the higher-stakes decision-making tasks that are currently arrogated to elite experts, e.g., medical care to doctors, document production to lawyers, software coding to computer engineers, and undergraduate education to professors. My thesis is not a forecast but an argument about what is possible: AI, if used well, can assist with restoring the middle-skill, middle-class heart of the US labor market that has been hollowed out by automation and globalization.

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Prior to the onset of the [Coronavirus Disease 2019] COVID crisis, the industrialized world was undergoing rapid employment growth, so much so that The Economist magazine declared in May 2019, 'Most of the rich world is enjoying a jobs boom of unprecedented scope' (The Economist 2019). Despite these encouraging trends, a cross-national Pew Research Center survey conducted in 2018 found that majorities of citizens in advanced and emerging economies anticipated that robots and computers would probably or definitely take over many jobs, exacerbating inequality, and making it more difficult to find work (Wike and Stokes 2018). The COVID crisis has upended these predictions, bringing to an end the longest economic expansion in US history and causing a worldwide spike in unemployment. Ironically, technological advances generally, and automation specifically, had almost nothing to do with this reversal of fortune. Should we now stop worrying about technological unemployment and focus instead on conventional threats? Or are all prior bets simply off? In our view, the answer to both questions is no. The current COVID crisis makes the trajectory of automation's impact on employment more readily discernible, and what we see provides no grounds for setting aside our concerns. The COVID crisis appears poised to reshape labor markets along at least four axes: telepresence, urban de-densification, employment concentration in large firms, and general automation forcing. Although these changes will have longrun efficiency benefits, they will exacerbate economic pain in the short and medium terms for the least economically secure workers in our economy, particularly those in the rapidly growing but never-highly-paid personal services sector.

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