Do industries shed or create jobs when they adopt new labour-saving technologies? This column shows that manufacturing employment grew along with productivity for a century or more, and only later decreased. It argues that the changing nature of demand was behind this pattern, which led to market saturation. This implies that the main impact of automation in the near future may be a major reallocation of jobs, not necessarily massive job losses.

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On 4-5 July, the ECB hosted the conference, “Challenges in the digital era”, which featured contributions from several leading economists on the effects of digitalisation on the real economy. In this column, we summarise three themes that were keenly debated: labour market implications of digitalisation; effects on inflation, market power and monetary policy; and the productivity promises of digital technologies. 

Work, wages and technology: Past, present and future

David Autor presented work on the geography of labour markets and the work of the past and the future, using US data. He showed that real non-college wages have been falling during the last 40 years, as many mid-skilled, mostly urban, non-college jobs have disappeared. In the period after WWII, high-skilled occupations in urban factories and offices evolved in close association with mid-skilled jobs, where employees were both college and non-college workers.

Instead, in recent decades, trade and automation have caused an occupational realignment by depressing the demand for administrative and clerical jobs; while low-skilled jobs have experienced an inflow of non-college and some college workers, only the more educated workers have moved towards highly paid jobs. Consequently, employment polarisation has increased over time – disproportionately so in cities, where the real wage gap between college and non-college workers has widened and the urban wage premium for non-college workers has fallen.

Yet, opportunity is still concentrated in cities, especially ‘superstar’ cities, although not for less-educated workers. Middle-skilled jobs are scarcer in cities than in rural/suburban areas, representing a critical change compared to a generation ago. Non-metropolitan areas are changing more slowly than the major cities and are getting older, albeit with a relatively stable structure of jobs, skills, and wages.

Similarly, cities account for a large share of employment in ‘new jobs’ as both workers in technology-complemented ‘frontier jobs’ and ‘wealth’ occupations (in-person services to wealthy urban workers, such as yoga instructors and baristas) are disproportionately present in urban areas. However, while workers at the frontier are more educated and earn higher wages than the average worker, ‘wealth jobs’ were paid the average wage in 2015.

On the other hand, ‘last mile’ occupations (jobs with nearly automated tasks and a residual human component) are as represented in cities as in less populated areas and pay low wages. As 38% of last-mile workers are non-college workers, against 26% of the overall workforce, a major future challenge is to rebuild career paths, not just jobs, for non-college workers.

The implications of digital technologies for employment were an important theme. Anna Salomons (Bessen et al. 2019) provided the first estimates of the effects of automation on workers at firms that automate (using Dutch data). While there is a displacement effect, it is far lower relative to mass layoffs and plant closings. 

Displacement works primarily through separations for long-time and older workers, as well as an increase in non-employment following separation, partially due to early retirement; this translates into a decline in earnings, despite no evidence of wage scarring, of about 10% less of one year’s earnings over five years. The effect is pervasive across industries and workers’ skills.

James Bessen (Bessen and Righi 2019) evaluated the outcomes of firms with a high share of software developers in their workforce, who are likely to foster IT-driven innovation in a firm, relative to other firms. IT shocks are associated with a positive effect on productivity, sales and, with the exception of employment in manufacturing, also employment. Additional results point to a declining labour share of revenue as a result of an IT event and to a slowing response after 2002. 

Instead, Gino Gancia discussed recent research (Blanas et al. 2019) which showed that ICT capital investment correlates to sectoral employment gains, but software capital to employment losses. At the firm level, he provided new evidence of adjustment (Bonfiglioli et al., 2019): robot adoption leads to productivity gains for the firm and wage gains for non-production employees, but lower firm employment and no post-adoption increase in sales, suggesting that firms may simply be raising markups. Overall, a common thread is that, at the firm level, automation has overall more negative effects than ICT in general, consistent with recent theories.  

Bessen also took a broader view of the effects of technology on employment, stressing the role of market satiation. For instance, in the US textile industry, productivity growth was associated with higher employment until 1950, as people owned few clothes and demanded more; further price declines failed to boost employment as consumers had already ‘filled their closets’. He showed that computerisation has positive employment effects in non-manufacturing industries but the opposite for manufacturing, where demand is less elastic.

Rachel Ngai pointed out a similar hump-shaped pattern for female hours in home production from 1870-1960, which rose as higher living standards created a demand for cleaner homes and better food, and then fell once demand was satiated. The increasing share of employment in services and female reallocation from home to market production is also a result of the interaction of structural transformation and demand. 

At this stage of the digitalisation process, as Ngai emphasised, retraining has a central role in minimising employment losses and facilitating transitions within sectors. Similarly, Manuel Trajtenberg stressed the relevance of non-formal education, which should enhance non-cognitive skills, while Romain Duval suggested that labour market institutions and social insurance may have to be rethought. Institutions should shift support to workers, not jobs, and include policies to increase the portability of social rights to facilitate mobility even across borders. Ultimately, whether the development of the current technological process will benefit/exacerbate social challenges is highly dependent on the policies that will be adopted.

Market power in the digital age 

The implications of technology on market power, and market structure more generally, were at the centre of attention. One such implication was on pricing. Alberto Cavallo studied how online competition affects retail pricing behaviour, using daily prices from the Billion Prices Project. Over 2008-2017, median price duration fell from 7 to 3.5 months, with online retailers at the lower end of that range. This effect was not driven by sale prices or retailer composition, or by the sign of the price change. He also found that direct competition with Amazon reduces implied durations by about 20%, suggesting a meaningful role for online competition in the increase in price flexibility.

In addition, he found that online retailers tend to have an especially strong preference for single pricing across locations. This may be due not only to online transparency but also a desire not to risk customer anger at geographical price differentials. Taken together, uniform pricing and more flexibility make prices more sensitive to aggregate national (rather than local) shocks, implying higher pass-through of costs to prices. These findings have important implications for issues such as inflation measurement, price stickiness, price dispersion and welfare.

Chiara Criscuolo provided new evidence on industry concentration, suggesting that of the countries sampled (Belgium, Spain, France, Finland, Italy, the UK, Sweden, the US, and Japan) concentration increased by 5 percentage points on average over 2002-2014. The main drivers of concentration were investment, openness to trade and FDI, digital intensity, and regulation. Of these, intangible investment was a particularly strong predictor of concentration changes, especially strong in globalised, already concentrated and digitally intensive country-industries, consistent with the scalability of intangible capital. Taken at face value, this would imply ‘good’ concentration (driven by firm performance), but more work is needed to detect possible frictions such as rent-seeking behaviour or slow technology diffusion.   

Anton Korinek argued that the rise of superstars (firms with high market shares, rising mark-ups, intangible-capital intensive) is the natural outcome of digital innovation (advances in collection, processing, and provision of information). Digital innovation involves a virtuous circle of cost reductions, innovations, increasing market share and subsequent costs savings passed on to customers. In this process, demand for labour, though initially falling, recovers as output expands. This constellation, however, posed dilemmas for policymakers in terms bodog online casino of anti-trust and macro stabilisation objectives.

Christos Genakos cautioned that the question, “what is the effect of concentration on prices or markups?”, is not well-posed (because of measurement, endogeneity, technological differences across sectors, and so on). He called for more detailed industry studies precisely because the mechanisms at work are not the same across industries. This could help answer questions on sources of market power – whether they arise from network effects (reflecting digital platforms), monopsony power, rent seeking, globalisation, or from particular antitrust regimes. 

Chad Syverson considered what market power, relative to perfect competition, implies for monetary policy pass-through. By lowering the cost of capital, monetary accommodation shifts out firms’ marginal cost (MC) curve. As profit maximising firms produce until marginal revenue (MR) equals MC, the slope of the MR curve determines the new equilibrium. Under perfect competition (i.e. a flat MR curve), a monetary expansion would lead to a larger output expansion than under a monopoly (i.e. a steep MR curve). As such, companies with high market power respond in general less to changes in costs, and hence monetary policy, than perfectly competitive firms.

This does not mean that lower (but non-zero) market power will result in higher pass-through; this will depend on how firm incentives change with power. Higher power implies a steeper demand curve, but whether it implies a steeper MR curve also depends on whether the demand curve flattens or steepens as output changes, and on the size of the change in the optimal quantity as competition changes.

Growth and productivity: Can digital technologies deliver?

A major theme of the conference was the modern productivity paradox of great innovations, predominantly driven by artificial intelligence (AI), and lacklustre productivity growth. Chad Syverson (Brynjolfsson et al. 2017, 2018) argued that the resolution of this paradox, as with the Solow paradox of the 1980s, lies in the particular nature of the technology at hand (computers then, artificial intelligence now). Such general-purpose technologies (GPTs) are characterised by the ability to improve over time and spawn complementary innovations and hence suffer from implementation lags.

Substantial investments in physical, human, managerial, and intellectual capital need to be made for the productivity effects of these technologies to materialise. Intriguingly, the productivity effects of two disparate GPTs – electricity and IT-  have a remarkably similar behaviour, featuring a very long lag (around three decades) before rapidly accelerating and then slowing down. 

He also suggested that the slowdown is not the result of mismeasurement. That said, there certainly is mismeasurement, in particular due to the intangible nature of digital technologies (data, algorithms, firm-specific human capital, new processes), which are poorly measured. As intangibles are both outputs (investment) and inputs (capital), TFP will be underestimated initially, when the growth rate of this new investment and its return is high, and overestimated later when enough capital has been accumulated, giving rise to a mismeasurement J-curve.

Pat Bajari (Bajari et al. 2019) demonstrated the value of using AI techniques (text analysis and computer vision) and online transactions (rather than posted) prices to augment hedonic models, in order to address the classic problem of estimating quality-adjusted inflation indices and hence address another source of mismeasurement. 

John Fernald and co-authors (Esfahani et al. 2019) documented that the productivity slowdown in advanced economies has been offset, at a global level, by stronger productivity growth in emerging economies. Since labour and product markets in emerging economies are less efficient, they put a drag on world output growth. As such, the authors augmented a standard growth accounting framework to take into account distortions in labour, product and capital markets, including markups. Their main finding is that even though the growth of world labour productivity (ALP) is highly volatile, the country-industry contribution to world growth is much smoother. The bulk of this volatility reflects shifts in the misallocation of capital and, especially, labour in the world economy. 

Diego Comin (Anzoategui et al. 2019) showed evidence of procyclicality in technology-related firm decisions (R&D expenditure, technology adoption) and the protracted effects of the Great Recession on several relevant metrics. He then introduced a mechanism that endogenises growth at business cycles frequencies in an otherwise standard model, with a central role for R&D and technology adoption to drive cyclical fluctuations. The estimated model showed that the productivity slowdown in the Great Recession was primarily the result of a decline in the speed of technology adoption, which was itself caused by the recession. 

Pat Bajari discussed the value of data in prediction (Bajari and Chernozukhov, 2019); there is a misconception that big data is always better, but in fact data richness has diminishing returns, and how data is used to improve models can be important. He further emphasised the role of digital technologies in revolutionising decisions of firms; cheaper CPU power made it possible to deal with big data and, through continuous incremental enhancements in data handling, data science has allowed firms to move from heuristics to scientific decision-making in many different areas (inventory, truck-load and itinerary, human resource management). 

As for specific technologies, Peter Gal (Gal et al. 2019) showed evidence of large productivity gains from broadband internet, cloud computing and back-office integration systems. These effects are in general stronger for more productive firms, with the exception of cloud computing, which allows firms to avoid large fixed costs and is less demanding in terms of complementary investments.

He also presented results on service platforms (Bailin Rivares et al. 2019), showing large within-firm effects from platforms that are ‘aggregators’ of incumbents (e.g. Booking, TripAdvisor) on productivity, profits and employment. Competing ‘disruptors’ (AirBnb, Uber) instead had no effects on incumbent productivity, but the competition shock did reduce markups, wages and employment for affected firms. Productivity in services is typically lower than manufacturing (due to lower uniformity, lower competition, limited scale), and raising productivity in these sectors is crucial given their increasing share of total activity in modern economies. 

The rising role of intangible assets was another major theme as regards to both productivity and market structure. Janice Eberly showed that intangibles are associated with greater concentration in the US, which could either be the result of changes in technology in a competitive environment or of market power. The heterogeneous nature of this relationship suggests caution when designing policies that aim to promote intangible capital and limit concentration.

Jonathan Haskel suggested that the presence of intangibles substantially changes both our understanding of modern economies and also our measurement thereof; he gave the example of AI as a combination of fast hardware (a tangible), using new software (a measured tangible) which searches databases (unmeasured intangibles). 

He further noted, as did other speakers, the particular nature of financing for intangibles; they are often sunk and highly firm-specific, unlike tangible capital which may be resold. As such, they are unsuitable as collateral for bank financing. Although intangible investment did not take as big a hit in the crisis as did tangible, its rate of growth was hampered, and in fact the slowdown of TFP growth was strongest in countries with slower intangible capital deepening; indeed, he showed evidence that bank lending is skewed towards real estate and away from intangibles.

Romain Duval (Ahn et al. 2019) showed the strong effects of expansionary monetary policy in mitigating the effects of financial frictions on intangible investments. Reinhilde Veugelers singled out the relative dearth of specialised non-bank finance as a major barrier for digital investment in Europe, and showed that businesses in the EU are falling behind in the digital R&D frontier, relative to both their US and Chinese counterparts. The differences with the US at the level of the firm were not major, so the growing digital divide between the EU and US must be driven by composition effects, as old/small firms are significantly less likely to be digitally active. 

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References

Ahn, J, R Duval and C Sever (2019), “Macroeconomic Policy, Product Market Competition, and Growth: The Intangible Investment Channel”, Working Paper.

Anzoategui, D, D Comin, M Gertler and J Martinez (2019), “Endogenous Technology Adoption and R&D as Sources of Business Cycle Persistence”, American Economic Journal: Macroeconomics, forthcoming.

Autor, D (2019), “Work of the Past, Work of the Future”, American Economic Association: Papers and Proceeding 109(5): 1–32.

Bailin Rivares, A, P Gal, V Millot and S Sorbe (2019), “Like it or not? The impact of online platforms on the productivity of incumbent service providers”, OECD Economics Department Working Papers, No. 1548.

Bajari, P, V Chernozhukov, A Hortaçsu and J Suzuki (2019), “The Impact of Big Data on Firm Performance: An Empirical Investigation”, AEA Papers and Proceedings 109: 33-37.

Bajgar, M, C Criscuolo and J Timmis (2019), “Supersize Me: Intangibles and Industry Concentration”, Mimeo.

Bessen, J (2019), “Automation and Jobs: When Technology Boosts Employment”, Economic Policy,forthcoming.

Bessen, J, M Goos, A Salomons and W van den Berge (2019), “Automatic bodog online casino Reaction: What Happens to Workers at Firms that Automate?”, Working Paper.

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Blanas, S, G Gancia and S Y Lee (2019), “Who is Afraid of Machines?”, Working Paper.

Brynjolfsson, E, D Rock and C Syverson (2017), “Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics”, Working Paper.

Brynjolfsson, E, D. Rock and C Syverson (2018), “The Productivity J-Curve: How Intangibles Complement General Purpose Technologies”, Working Paper.

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Esfahani, M, J Fernald and B Hobijn (2019), “World Productivity: 1995-2014”, mimeo.

Gal, P, G Nicoletti, T Renault, S Sorbe and C Timiliotis (2019), “Digitalisation and Productivity: In Search of the Holy Grail – Firm-level Empirical Evidence from EU Countries”, OECD Economics Department Working Papers, No. 1533,

Korinek, A and D X Ng (2019), “Digitization and the Macro-Economics of Superstars”, Mimeo.

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To be sure, many others disagree with the view that the widespread adoption of AI will lead to massive unemployment that, in turn, will necessitate major new taxpayer-funded income support programs. Roe (2018) references a number of senior executives in corporate strategy and technology management positions who argue that AI primarily changes the nature of work rather than causing widespread unemployment. Furthermore, while some jobs will certainly be lost as AI takes on skills formerly attributed to humans, new jobs will emerge. Warren Buffet, the CEO of Berkshire Hathaway and an immensely successful investor, cautions that the trend towards automation replacing low-skilled labour is not new. He notes that in 1800, 80 percent of people were employed on farms. Two hundred years later, it was 2 to 3 percent. Productivity improvements meant that fewer people were needed to work on farms, and therefore were free to pursue other vocations (Clifford, 2017b).

The research of economic historians largely supports the claims that in the past, automation has primarily led to changes in the mix of occupations rather than to mass unemployment; it has also led to increases in real income levels rather than an expansion of poverty. Nevertheless, a number of prominent technology experts argue that the AI experience will be different from the historical experience with automation. For example, Parada (2017) contends that it is catastrophically wrong to draw an analogy between AI and previous waves of automation. Specifically, he argues that automation in the past was primarily about mechanical power replacing human muscle. The AI revolution will be nothing like that. When robots become as smart and capable as human beings, there will be nothing left for people to do because machines will be both stronger and smarter than humans. Parada (2017) makes what seems to be the extreme claim that intelligent robots will be cheaper, faster, and more reliable than humans, and that no capitalist in her right mind will continue to employ humans. He goes on to assert that unless society figures out how to distribute the fruits of robot labour fairly, it will be an era of mass joblessness and mass poverty. Frey (2019) offers a more modest but still startling estimate that 47 percent of US jobs could be automated due to AI.

Others have made a similar, if less extreme, argument that AI represents a “different” technological innovation, and that the labour market experiences associated with other major epochs of technological change may not apply in the case of AI. Microsoft founder Bill Gates does not believe that AI will prove to be a bad thing for society. However, he is in the camp that believes that job displacement will be sufficiently widespread that government will need to direct financial assistance to the many workers who will be hurt by AI adoption with some of that assistance directed at re-education and income support programs (Clifford, 2017b).

The purpose of this essay is to assess the argument that AI’s effects on employment will be significantly different from previous episodes of automation. Specifically, we review some expert opinion on how producers are likely to use AI, as well as the limited available evidence on how AI has affected employment up until now. We also consider whether there are reasons to believe that AI will be adopted at a much faster rate than other major innovations so that retraining workers for occupations that are complementary to AI is impractical. Our broad conclusion is that future employment effects and adoption rates of AI are unlikely to be much different from the broad historical experiences of other General Purpose Technologies (GPTs).

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International trade law has been oblivious to social inclusion. It is not the reason for the weakening of the U.S. economy and entrenchments of poverty, but it is nevertheless blamed for them, including the shuttering of factories, joblessness, and even homelessness. bodog poker review Although it is not primarily to blame, it is not wholly innocent either. International trade law plays a powerful role in fomenting the conditions under which people may thrive, implicating social equality and inclusion. This Article addresses why international trade law needs to be structured in ways that support social inclusion if society is to turn the tide against rising neo-nationalism, racism, and authoritarianism. The impacts of trade and rapid technological change on income inequality and the security of work have become politically salient issues in the United States and Europe. They have led to the rise of nativist political parties that threaten to upset the international institutional framework. The outcome could be dire. The Article shows how international economic law can and should be retooled. By doing so, it can: (i) help combat harmful tax competition, avoidance, and evasion; (ii) aid domestic social security and job retraining; (iii) support labor protection; (iv) deter social dumping; and (v) enable industrial policy experimentation for development.

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Our key findings:

• Automation technologies including artificial intelligence and robotics will generate significant benefits for users, businesses, and economies, lifting productivity and economic growth. The extent to which these technologies displace workers will depend on the pace of their development and adoption, economic growth, and growth in demand for work. Even as it causes declines in some occupations, automation will change many more—60 percent of occupations have at least 30 percent of constituent work activities that could be automated. It will also create new occupations that do not exist today, much as technologies of the past have done.

• While about half of all work activities globally have the technical potential to be automated by adapting currently demonstrated technologies, the proportion of work actually displaced by 2030 will likely be lower, because of technical, economic, and social factors that affect adoption. Our scenarios across 46 countries suggest that between almost zero and one third of work activities could be displaced by 2030, with a midpoint of 15 percent. The proportion varies widely across countries, with advanced economies more affected by automation than developing ones, reflecting higher wage rates and thus economic incentives to automate.

• Even with automation, the demand for work and workers could increase as economies grow, partly fueled by productivity growth enabled by technological progress. Rising incomes and consumption especially in developing countries, increasing health care for aging societies, investment in infrastructure and energy, and other trends will create demand for work that could help offset the displacement of workers. Additional investments such as in infrastructure and construction, beneficial in their own right, could be needed to reduce the risk of job shortages in some advanced economies.

• Even if there is enough work to ensure full employment by 2030, major transitions lie ahead that could match or even exceed the scale of historical shifts out of agriculture and manufacturing. Our scenarios suggest that by 2030, 75 million to 375 million workers (3 to 14 percent of the global workforce) will need to switch occupational categories. Moreover, all workers will need to adapt, as their occupations evolve alongside increasingly capable machines. Some of that adaptation will require higher educational attainment, or spending more time on activities that require social and emotional skills, creativity, high-level cognitive capabilities and other skills relatively hard to automate.

• Income polarization could continue in the United States and other advanced economies, where demand for high-wage occupations may grow the most while middle-wage occupations decline— assuming current wage structures persist. Increased investment and productivity growth from automation could spur enough growth to ensure full employment, but only if most displaced workers find new work within one year. If reemployment is slow, frictional unemployment will likely rise in the short-term and wages could face downward pressure. These wage trends are not universal: in China and other emerging economies, middle-wage occupations such as service and construction jobs will likely see the most net job growth, boosting the emerging middle class.

• To achieve good outcomes, policy makers and business leaders will need to embrace automation’s benefits and, at the same time, address the worker transitions brought about by these technologies. Ensuring robust demand growth and economic dynamism is a priority: history shows that economies that are not expanding do not generate job growth. Mid career job training will be essential, as will enhancing labor market dynamism and enabling worker redeployment. These changes will challenge current educational and workforce training models, as well as business approaches to skill-building. Another priority is rethinking and strengthening transition and income support for workers caught in the crosscurrents of automation.

Summary of Findings:

The technology-driven world in which we live is a world filled with promise but also challenges. Cars that drive themselves, machines that read X-rays, and algorithms that respond to customer service inquiries are all manifestations of powerful new forms of automation. Yet even as these technologies increase productivity and improve our lives, their use will substitute for some work activities humans currently perform—a development
that has sparked much public concern. This research builds on MGI’s January 2017 report on automation and its impact on work activities. We assess the number and types of jobs that might be created under different scenarios through 2030, and compare that to work that could be displaced by automation.2 The results reveal a rich mosaic of potential shifts in occupations in the years ahead, with important implications for workforce skills and wages. The analysis covers 46 countries that comprise almost 90 percent of global GDP. We focus on six countries that span income levels (China, Germany, India, Japan, Mexico, and the United States). For each, we modeled the potential net employment changes for more than 800 occupations, based on different scenarios for the pace of automation adoption and for future labor demand. The intent of this research is not to forecast. Rather, we present a set of scenarios (necessarily incomplete) to serve as a guide, as we anticipate and prepare for the future of work. This research is by
no means the final word on this topic; ongoing research is required. Indeed, in Box E2 at the end of this summary, we highlight some of the potential limitations of the research presented in this report.

Our findings suggest that several trends that may serve as catalysts of future labor demand could create demand for millions of jobs by 2030. These trends include caring for others in aging societies, raising energy efficiency and meeting climate challenges, producing goods and services for the expanding consuming class, especially in developing countries, not to mention the investment in technology, infrastructure, and buildings needed in all countries. Taken from another angle, we also find that a growing and dynamic economy—in part fueled by technology itself and its contributions to productivity—would create jobs. These jobs would result from growth in current occupations due to demand and the creation of new types of occupations that may not have existed before, as has happened historically. This job growth (jobs gained) could more than offset the jobs lost to automation. None of this will happen by itself—it will require businesses and governments to seize opportunities to boost job creation and for labor markets to function well. The workforce transitions ahead will be enormous. We estimate that as many as 375 million workers globally (14 percent of the global workforce) will likely need to transition to new occupational categories and learn new skills, in the event of rapid automation adoption. If their transition to new jobs is slow, unemployment could rise and dampen wage growth.

Indeed, while this report is titled Jobs lost, jobs gained, it could have been, Jobs lost, jobs changed, jobs gained; in many ways a big part of this story is about how more occupations will change than will be lost as machines affect portions of occupations and people increasingly work alongside them. Societal choices will determine whether all three of these coming workforce transitions are smooth, or whether bodog sportsbook review unemployment and income inequality rise. History shows numerous examples of countries that have successfully ridden the wave
of technological change by investing in their workforce and adapting policies, institutions, and business models to the new era. It is our hope that this report prompts leaders in that direction once again.

AUTOMATION COULD DISPLACE A SIGNIFICANT SHARE OF WORK GLOBALLY TO 2030; 15 PERCENT IS THE MIDPOINT OF OUR SCENARIO RANGE

In our prior report on automation, we found that about half the activities people are paid to do globally could theoretically be automated using currently demonstrated technologies. Very few occupations—less than 5 percent—consist entirely of activities that can be fully automated. However, in about 60 percent of occupations, at least one-third of the constituent activities could be automated, implying substantial workplace transformations and changes for all workers. All this is based on our assessments of current technological capability—an ever evolving frontier

While technical feasibility of automation is important, it is not the only factor that will influence the pace and extent of automation adoption. Other factors include the cost of developing and deploying automation solutions for specific uses in the workplace, the labor market dynamics (including quality and quantity of labor and associated wages), the benefits of automation beyond labor substitution, and regulatory and social acceptance. Taking into account these factors, our new research estimates that between almost zero and
30 percent of the hours worked globally could be automated by 2030, depending on the speed of adoption. In this report we mainly use the midpoint of our scenario range, which is 15 percent of current activities automated. Results differ significantly by country, reflecting the mix of activities currently performed by workers and prevailing wage rates. They range from 9 percent in India to 26 percent in Japan in the midpoint adoption rate scenario This is on par with the scale of the great employment shifts of the past, such as out of agriculture or manufacturing evidence on technology and employment is reassuring.

Jacques Bughin, Director, McKinsey Global Institute
Senior Partner, McKinsey & Company Brussels

James Manyika, Chairman and Director, McKinsey Global Institute
Senior Partner, McKinsey & Company
San Francisco 

 Jonathan Woetzel, Director, McKinsey Global Institute
Senior Partner, McKinsey & Company
Shanghai 

Susan Lund, Michael Chui, Parul Batra, Ryan Ko, Saurabh Sanghvi 

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• Encourage unions to offer cross border services such as collective representation, benefits, training, and other workplace services. In so doing, unions would become stronger and more competitive.

• Consider each chapter as part of a coherent whole and review each chapter for consistency with labour and employment objectives, including ensuring that regulatory coherence provisions or the investor-state dispute settlement mechanism do not impede the ability to regulate to protect workers’ rights.

• Jointly explore how each country can ensure that its workers have the education and training to take advantage of 21 st Century job opportunities, and have access to health insurance and pensions that are not at risk if they lose a job.
• Recent trade agreements allow highly skilled workers to work temporarily on projects in other countries. But less skilled workers deserve similar provisions. We believe policymakers should create a mechanism either within the agreement or as a side agreement that allows for temporary movement of low skilled workers in areas outside agriculture and hospitality such as barbers, masseuses, and home health aides. These individuals should be allowed to offer services across borders.

Susan Ariel Aaronson is Research Professor of International Affairs at George Washington University.

Kimberly Ann Elliott is a Senior Fellow with the Center for Global Development and the author or co-author of numerous books and articles on trade policy and globalization, economic sanctions, and food security.  Previously, she was with the Peterson Institute for International Economics.

The views expressed here are the authors’ own. 

***

© Washington International Trade Association. 

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