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THE FUTURE OF EMPLOYMENT: HOWSUSCEPTIBLE ARE JOBS TOCOMPUTERISATION? Carl Benedikt Frey† and Michael A. Osborne‡September 17, 2013.AbstractWe examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimatethe probability of computerisation for 702 detailed occupations, using aGaussian process classifier. Based on these estimates, we examine expected impacts of future computerisation on US labour market outcomes,with the primary objective of analysing the number of jobs at risk andthe relationship between an occupation’s probability of computerisation,wages and educational attainment. According to our estimates, about 47percent of totalUSemployment is at risk. We further provide evidencethat wages and educational attainment exhibit a strong negative relationship with an occupation’s probability of computerisation.Keywords: Occupational Choice, Technological Change, Wage Inequality, Employment, Skill DemandJELClassification: E24, J24, J31, J62, O33.We thank the Oxford University Engineering Sciences Department and the Oxford Martin Programme on the Impacts of Future Technology for hosting the “Machines and Employment” Workshop. We are indebted to Stuart Armstrong, Nick Bostrom, Eris Chinellato, MarkCummins, Daniel Dewey, David Dorn, Alex Flint, Claudia Goldin, John Muellbauer, VincentMueller, Paul Newman, Seán Ó hÉigeartaigh, Anders Sandberg, Murray Shanahan, and KeithWoolcock for their excellent suggestions.†Oxford Martin School, University of Oxford, Oxford, OX1 1PT, United ent of Engineering Science, University of Oxford, Oxford, OX1 3PJ, United Kingdom, [email protected] 1

I.I NTRODUCTIONIn this paper, we address the question: how susceptible are jobs to computerisation? Doing so, we build on the existing literature in two ways. First, drawingupon recent advances in Machine Learning (ML) and Mobile Robotics (MR),we develop a novel methodology to categorise occupations according to theirsusceptibility to computerisation.1 Second, we implement this methodology toestimate the probability of computerisation for 702 detailed occupations, andexamine expected impacts of future computerisation on US labour market outcomes.Our paper is motivated by John Maynard Keynes’s frequently cited prediction of widespread technological unemployment “due to our discovery ofmeans of economising the use of labour outrunning the pace at which wecan find new uses for labour” (Keynes, 1933, p. 3). Indeed, over the pastdecades, computers have substituted for a number of jobs, including the functions of bookkeepers, cashiers and telephone operators (Bresnahan, 1999; MGI,2013). More recently, the poor performance of labour markets across advancedeconomies has intensified the debate about technological unemployment amongeconomists. While there is ongoing disagreement about the driving forcesbehind the persistently high unemployment rates, a number of scholars havepointed at computer-controlled equipment as a possible explanation for recentjobless growth (see, for example, Brynjolfsson and McAfee, 2011).2The impact of computerisation on labour market outcomes is well-establishedin the literature, documenting the decline of employment in routine intensiveoccupations – i.e. occupations mainly consisting of tasks following well-definedprocedures that can easily be performed by sophisticated algorithms. For example, studies by Charles, et al. (2013) and Jaimovich and Siu (2012) emphasisethat the ongoing decline in manufacturing employment and the disappearanceof other routine jobs is causing the current low rates of employment.3 In ad1We refer to computerisation as job automation by means of computer-controlled equipment.2This view finds support in a recent survey by the McKinsey Global Institute (MGI), showingthat 44 percent of firms which reduced their headcount since the financial crisis of 2008 haddone so by means of automation (MGI, 2011).3Because the core job tasks of manufacturing occupations follow well-defined repetitiveprocedures, they can easily be codified in computer software and thus performed by computers(Acemoglu and Autor, 2011).2

dition to the computerisation of routine manufacturing tasks, Autor and Dorn(2013) document a structural shift in the labour market, with workers reallocating their labour supply from middle-income manufacturing to low-incomeservice occupations. Arguably, this is because the manual tasks of service occupations are less susceptible to computerisation, as they require a higher degreeof flexibility and physical adaptability (Autor, et al., 2003; Goos and Manning,2007; Autor and Dorn, 2013).At the same time, with falling prices of computing, problem-solving skillsare becoming relatively productive, explaining the substantial employment growthin occupations involving cognitive tasks where skilled labour has a comparativeadvantage, as well as the persistent increase in returns to education (Katz andMurphy, 1992; Acemoglu, 2002; Autor and Dorn, 2013). The title “Lousy andLovely Jobs”, of recent work by Goos and Manning (2007), thus captures theessence of the current trend towards labour market polarization, with growingemployment in high-income cognitive jobs and low-income manual occupations, accompanied by a hollowing-out of middle-income routine jobs.According to Brynjolfsson and McAfee (2011), the pace of technological innovation is still increasing, with more sophisticated software technologies disrupting labour markets by making workers redundant. What is strikingabout the examples in their book is that computerisation is no longer confinedto routine manufacturing tasks. The autonomous driverless cars, developed byGoogle, provide one example of how manual tasks in transport and logisticsmay soon be automated. In the section “In Domain After Domain, Computers Race Ahead”, they emphasise how fast moving these developments havebeen. Less than ten years ago, in the chapter “Why People Still Matter”, Levyand Murnane (2004) pointed at the difficulties of replicating human perception,asserting that driving in traffic is insusceptible to automation: “But executing a left turn against oncoming traffic involves so many factors that it is hardto imagine discovering the set of rules that can replicate a driver’s behaviour[. . . ]”. Six years later, in October 2010, Google announced that it had modified several Toyota Priuses to be fully autonomous (Brynjolfsson and McAfee,2011).To our knowledge, no study has yet quantified what recent technologicalprogress is likely to mean for the future of employment. The present studyintends to bridge this gap in the literature. Although there are indeed existing3

useful frameworks for examining the impact of computers on the occupationalemployment composition, they seem inadequate in explaining the impact oftechnological trends going beyond the computerisation of routine tasks. Seminal work by Autor, et al. (2003), for example, distinguishes between cognitiveand manual tasks on the one hand, and routine and non-routine tasks on theother. While the computer substitution for both cognitive and manual routinetasks is evident, non-routine tasks involve everything from legal writing, truckdriving and medical diagnoses, to persuading and selling. In the present study,we will argue that legal writing and truck driving will soon be automated, whilepersuading, for instance, will not. Drawing upon recent developments in Engineering Sciences, and in particular advances in the fields of ML, includingData Mining, Machine Vision, Computational Statistics and other sub-fields ofArtificial Intelligence, as well as MR, we derive additional dimensions requiredto understand the susceptibility of jobs to computerisation. Needless to say,a number of factors are driving decisions to automate and we cannot capturethese in full. Rather we aim, from a technological capabilities point of view,to determine which problems engineers need to solve for specific occupationsto be automated. By highlighting these problems, their difficulty and to whichoccupations they relate, we categorise jobs according to their susceptibility tocomputerisation. The characteristics of these problems were matched to different occupational characteristics, using O NET data, allowing us to examinethe future direction of technological change in terms of its impact on the occupational composition of the labour market, but also the number of jobs at riskshould these technologies materialise.The present study relates to two literatures. First, our analysis builds on thelabour economics literature on the task content of employment (Autor, et al.,2003; Goos and Manning, 2007; Autor and Dorn, 2013). Based on definedpremises about what computers do, this literature examines the historical impact of computerisation on the occupational composition of the labour market. However, the scope of what computers do has recently expanded, and willinevitably continue to do so (Brynjolfsson and McAfee, 2011; MGI, 2013).Drawing upon recent progress in ML, we expand the premises about the taskscomputers are and will be suited to accomplish. Doing so, we build on the taskcontent literature in a forward-looking manner. Furthermore, whereas this literature has largely focused on task measures from the Dictionary of Occupational4

Titles (DOT), last revised in 1991, we rely on the 2010 version of the DOT successor O NET – an online service developed for the US Department of Labor.4Accordingly, O NET has the advantage of providing more recent informationon occupational work activities.Second, our study relates to the literature examining the offshoring of information-based tasks to foreign worksites (Jensen and Kletzer, 2005; Blinder,2009; Jensen and Kletzer, 2010; Oldenski, 2012; Blinder and Krueger, 2013).This literature consists of different methodologies to rank and categorise occupations according to their susceptibility to offshoring. For example, usingO NET data on the nature of work done in different occupations, Blinder (2009)estimates that 22 to 29 percent of US jobs are or will be offshorable in the nextdecade or two. These estimates are based on two defining characteristics of jobsthat cannot be offshored: (a) the job must be performed at a specific work loca-tion; and (b) the job requires face-to-face personal communication. Naturally,the characteristics of occupations that can be offshored are different from thecharacteristics of occupations that can be automated. For example, the work ofcashiers, which has largely been substituted by self- service technology, mustbe performed at specific work location and requires face-to-face contact. Theextent of computerisation is therefore likely to go beyond that of offshoring.Hence, while the implementation of our methodology is similar to that of Blinder (2009), we rely on different occupational characteristics.The remainder of this paper is structured as follows. In Section II, we reviewthe literature on the historical relationship between technological progress andemployment. Section III describes recent and expected future technologicaldevelopments. In Section IV, we describe our methodology, and in Section V,we examine the expected impact of these technological developments on labourmarket outcomes. Finally, in Section VI, we derive some conclusions.II.A HISTORYOF TECHNOLOGICAL REVOLUTIONS AND EMPLOYMENTThe concern over technological unemployment is hardly a recent phenomenon.Throughout history, the process of creative destruction, following technological inventions, has created enormous wealth, but also undesired disruptions.As stressed by Schumpeter (1962), it was not the lack of inventive ideas that4An exception is Goos, et al. (2009).5

set the boundaries for economic development, but rather powerful social andeconomic interests promoting the technological status quo. This is nicely illustrated by the example of William Lee, inventing the stocking frame knittingmachine in 1589, hoping that it would relieve workers of hand-knitting. Seeking patent protection for his invention, he travelled to London where he hadrented a building for his machine to be viewed by Queen Elizabeth I. To hisdisappointment, the Queen was more concerned with the employment impactof his invention and refused to grant him a patent, claiming that: “Thou aimesthigh, Master Lee. Consider thou what the invention could do to my poor subjects. It would assuredly bring to them ruin by depriving them of employment,thus making them beggars” (cited in Acemoglu and Robinson, 2012, p. 182f).Most likely the Queen’s concern was a manifestation of the hosiers’ guilds fearthat the invention would make the skills of its artisan members obsolete.5 Theguilds’ opposition was indeed so intense that William Lee had to leave Britain.That guilds systematically tried to weaken market forces as aggregators tomaintain the technological status quo is persuasively argued by Kellenbenz(1974, p. 243), stating that “guilds defended the interests of their membersagainst outsiders, and these included the inventors who, with their new equipment and techniques, threatened to disturb their members’ economic status.”6As pointed out by Mokyr (1998, p. 11): “Unless all individuals accept the“verdict” of the market outcome, the decision whether to adopt an innovationis likely to be resisted by losers through non-market mechanism and politicalactivism.” Workers can thus be expected to resist new technologies, insofar thatthey make their skills obsolete and irreversibly reduce their expected earnings.The balance between job conservation and technological progress therefore, toa large extent, reflects the balance of power in society, and how gains fromtechnological progress are being distributed.The British Industrial Revolution illustrates this point vividly. While stillwidely present on the Continent, the craft guild in Britain had, by the time