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Artificial intelligence (AI), robotics and other forms of 'smart automation' are advancing at a rapid pace and have the potential to bring great benefits to the economy, by boosting productivity and creating new and better products and services. For advanced economies like the US, the EU and Japan, these technologies could also produce a lot of disruption, not least to the jobs market. To explore this further we have analysed a dataset compiled by the OECD that looks in detail at the tasks involved in the jobs of over 200,000 workers across 29 countries (27 from the OECD plus Singapore and Russia). Building on previous research by Frey and Osborne (Oxford University, 2013) [available in VOCEDplus at TD/TNC 123.687] and Arntz, Gregory and Zierahn (OECD, 2016) [available in VOCEDplus at TD/TNC 124.386], we estimated the proportion of existing jobs that might be of high risk of automation by the 2030s for: each of these 29 countries; different industry sectors; occupations within industries; and workers of different genders, ages and education levels.

We also identify how this process might unfold over the period to the 2030s in three overlapping waves: (1) algorithm wave: focused on automation of simple computational tasks and analysis of structured data in areas like finance, information and communications - this is already well underway; (2) augmentation wave: focused on automation of repeatable tasks such as filling in forms, communicating and exchanging information through dynamic technological support, and statistical analysis of unstructured data in semi-controlled environments such as aerial drones and robots in warehouses - this is also underway, but is likely to come to full maturity in the 2020s; and (3) autonomy wave: focused on automation of physical labour and manual dexterity, and problem solving in dynamic real-world situations that require responsive actions, such as in manufacturing and transport (e.g. driverless vehicles) - these technologies are under development already, but may only come to full maturity on an economy-wide scale in the 2030s.

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Employment generation is crucial to spreading the benefits of economic growth broadly and to reducing global poverty. And yet, emerging economies face a contemporary challenge to traditional pathways to employment generation: automation, digitalization, and labor-saving technologies. 1.8 billion jobs - or two-thirds of the current labor force of developing countries - are estimated to be susceptible to automation from today's technological standpoint. Cumulative advances in industrial automation and labor-saving technologies could further exacerbate this trend. Or will they? In this paper we: (i) discuss the literature on automation; and in doing so (ii) discuss definitions and determinants of automation in the context of theories of economic development; (iii) assess the empirical estimates of employment-related impacts of automation; (iv) characterize the potential public policy responses to automation; and (v) highlight areas for further exploration in terms of employment and economic development strategies in developing countries. In an adaption of the Lewis model of economic development, the paper uses a simple framework in which the potential for automation creates 'unlimited supplies of artificial labor' particularly in the agricultural and industrial sectors due to technological feasibility. This is likely to create a push force for labor to move into the service sector, leading to a bloating of service-sector employment and wage stagnation but not to mass unemployment, at least in the short-to-medium term.

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This paper focuses on identifying determinants of 'automatability risk', namely the propensity of EU employees being in jobs with high risk of substitutability by machines, robots or other algorithmic processes, and uncovers its impact on labour market outcomes. Using relevant data on tasks and skill needs in jobs, collected by the European skills and jobs survey (ESJS), jobs are bundled according to their estimated risk of automation. The paper builds on the methodology of previous studies that estimate the latent relationship between 'true' automatability and job tasks (Frey and Osborne, 2013, 2017; Arntz et al., 2016; Nedelkoska and Quintini, 2018) but utilises highly disaggregated job descriptions provided by a subsample of the ESJS, as well as information on jobs' skill requirements. About 14 per cent of EU adult workers are found to face a very high risk of automation. The distribution of high automatability across industries and occupations is also found to be skewed towards routine jobs with low demand for transversal and social skills. The risk of job displacement by machines is higher among males and lower-skilled workers, with little evidence of polarisation. It is prevalent in private sector jobs that fail to provide remedial training to employees, accentuating the vulnerability of at-risk-workers and highlighting the need for stronger lifelong learning policies at EU level.

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Automation will be a boon or a catastrophe depending on whom you listen to. This paper proposes an overlapping-generations model with endogenous school choice in which the quality of a country's education system determines how well skill supply can respond to increased demand from automation and subsequently whether automation will be beneficial or detrimental. In this sense, education quality in the model offers a bridge between the optimistic and pessimistic perspectives on automation. In testing the model's assumptions, the paper finds evidence that educational attainment, cognitive skills, and select noncognitive skills are associated with avoiding automation-prone occupations. Consistent with the model's predictions, census data indicate that countries have historically relied most on these types of occupations at middle-income status. The model and empirical findings suggest that it is middle-income countries that are most vulnerable to automation if their education systems are unable to affect cognitive and noncognitive skills sufficiently. As a result, automation may herald a much different growth model for developing countries: one in which developing these skills is central.

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The aim of this report is to detail how the impact of ageing populations, the adoption of new automation technologies and rising inequality will likely combine to give rise to new business risks and opportunities. This work is based on secondary market research, analysis of financial information available or provided to Bain and Company and a range of interviews with industry participants. Chapter one explores the impact of ageing populations and the end of plentiful labor. The baby boomer generation powered a long but temporary surge in labor force growth. Now this group is moving into retirement, and labor force growth is slowing. Chapter two examines how automation may solve one problem by increasing productivity and powering growth but creates another by potentially eliminating millions of jobs and suppressing wages for many workers. Chapter three looks at how rising inequality could threaten growth. Chapter four traces how developments are likely to unfold in the turbulent 2020s. Chapter five considers the outlook if governments intervene more actively in the marketplace to address economic imbalances. Their options include tax, labor market and regulatory interventions. Chapter six focuses on the practical business implications of these trends for leadership teams.

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Automation and artificial intelligence (AI) are changing the nature of work. In this discussion paper, part of our ongoing research on the impact of technology on the economy, business, and society, we present new findings on the coming shifts in demand for workforce skills and how work is organized within companies, as people increasingly interact with machines in the workplace. We quantify time spent on 25 core workplace skills today and in the future for the United States and five European countries (France, Germany, Italy, Spain, and the UK), with a particular focus on five sectors: banking and insurance, energy and mining, healthcare, manufacturing, and retail. The main themes of the discussion are: How will demand for workforce skills change with automation?; Shifting skill requirements in five sectors; How will organizations adapt?; Building the workforce of the future.

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By 2030, according to a McKinsey Global Institute report published in 2017, 'Jobs lost, jobs gained: workforce transitions in a time of automation' [available in VOCEDplus at TD/TNC 130.485], as many as 375 million workers - or roughly 14 per cent of the global workforce - may need to switch occupational categories as digitization, automation, and advances in artificial intelligence (AI) disrupt the world of work. The kinds of skills companies require will shift, with profound implications for the career paths individuals will need to pursue. This article discusses the potential size of this challenge, arguing that, so far, growing awareness of the scale of the task ahead has yet to translate into action in terms of both public and corporate spending on labour force training.

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A truly successful skills system could bring vast benefits to Northern Ireland and Scotland. Both are on the cusp of significant changes, as the impact of automation and technological change combines with an ageing population. This could carry significant dividends, driving productivity and boosting economic growth, and bringing new opportunities for individuals and employers. But there are also big risks. As jobs are reshaped by new technology, people who do not have the right skills risk being unable to obtain secure, well-paying work or even being pushed out of the labour market altogether. Technological and demographic change could narrow social inequalities, if we prepare and respond correctly. The skills system must be at the heart of readying our societies to seize these new opportunities successfully and mitigate the risks.

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This study focuses on the risk of automation and its interaction with training and the use of skills at work. Building on the expert assessment carried out by Carl Frey and Michael Osborne in 2013 [report available in VOCEDplus at TD/TNC 123.687], the paper estimates the risk of automation for individual jobs based on the Survey of Adult Skills (PIAAC). The analysis improves on other international estimates of the individual risk of automation by using a more disaggregated occupational classification and identifying the same automation bottlenecks emerging from the experts' discussion. Hence, it more closely aligns to the initial assessment of the potential automation deriving from the development of Machine Learning. Furthermore, this study investigates the same methodology using national data from Germany and United Kingdom, providing insights into the robustness of the results.

The risk of automation is estimated for the 32 OECD countries that have participated in the Survey of Adult Skills (PIAAC) so far. Beyond the share of jobs likely to be significantly disrupted by automation of production and services, the accent is put on characteristics of these jobs and the characteristics of the workers who hold them. The risk is also assessed against the use of ICT at work and the role of training in helping workers transit to new career opportunities.

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The onset of the digital revolution has resulted in technological advances that are constantly evolving. A key element of concern to policymakers is the impact that these changes will have on the world of work and employment. This report reviews the history of the digital revolution to date, placing it in the context of other periods of marked technological advances and examining how technological change interacts with changes in institutions. Digital technologies have considerable disruptive potential, including making production much more flexible and information more readily available. While the information technology sector has been most affected to date, other sectors are rapidly changing with the diffusion of new technology. The report also examines three key vectors of change: automation of work, the incorporation of digital technology into processes, and the coordination of economic transactions through the digital networks known as 'platforms'. Show less