Empoyment

THE FUTURE OF EMPLOYMENT: HOW
SUSCEPTIBLE ARE JOBS TO
COMPUTERISATION?∗
Carl Benedikt Frey† and Michael A. Osborne‡
September 17, 2013
.
Abstract
We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a
Gaussian 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 and the relationship between an occupation’s probability of computerisation, wages and educational attainment. According to our estimates, about 47 percent of total

US

employment is at risk. We further provide evidence

that 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 Demand
JEL

Classiﬁcation: 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, Mark
Cummins, Daniel Dewey, David Dorn, Alex Flint, Claudia Goldin, John Muellbauer, Vincent
Mueller, Paul Newman, Seán Ó hÉigeartaigh, Anders Sandberg, Murray Shanahan, and Keith
Woolcock for their excellent suggestions.
†
Oxford Martin School, University of Oxford, Oxford, OX1 1PT, United Kingdom, carl.frey@oxfordmartin.ox.ac.uk. ‡
Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, United Kingdom, mosb@robots.ox.ac.uk.
∗

1

I.

I NTRODUCTION

In this paper, we address the question: how susceptible are jobs to computerisation? Doing so, we build on the existing literature in two ways. First, drawing upon recent advances in Machine Learning (ML) and Mobile Robotics (MR), we develop a novel methodology to categorise occupations according to their susceptibility to computerisation.1 Second, we implement this methodology to estimate the probability of computerisation for 702 detailed occupations, and examine 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 of means of economising the use of labour outrunning the pace at which we can ﬁnd new uses for labour” (Keynes, 1933, p. 3). Indeed, over the past decades, 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 advanced economies has intensiﬁed the debate about technological unemployment among economists. While there is ongoing disagreement about the driving forces behind the persistently high unemployment rates, a number of scholars have pointed at computer-controlled equipment as a possible explanation for recent jobless growth (see, for example, Brynjolfsson and McAfee, 2011).2
The impact of computerisation on labour market outcomes is well-established in the literature, documenting the decline of employment in routine intensive occupations – i.e. occupations mainly consisting of tasks following well-deﬁned procedures that can easily be performed by sophisticated algorithms. For example, studies by Charles, et al. (2013) and Jaimovich and Siu (2012) emphasise that the ongoing decline in manufacturing employment and the disappearance of other routine jobs is causing the current low rates of employment.3 In ad1

We refer to computerisation as job automation by means of computer-controlled equipment.
2
This view ﬁnds support in a recent survey by the McKinsey Global Institute (MGI), showing that 44 percent of ﬁrms which reduced their headcount since the ﬁnancial crisis of 2008 had done so by means of automation (MGI, 2011).
3
Because the core job tasks of manufacturing occupations follow well-deﬁned repetitive procedures, 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-income service occupations. Arguably, this is because the manual tasks of service occupations are less susceptible to computerisation, as they require a higher degree of 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 skills are becoming relatively productive, explaining the substantial employment growth in occupations involving cognitive tasks where skilled labour has a comparative advantage, as well as the persistent increase in returns to education (Katz and
Murphy, 1992; Acemoglu, 2002; Autor and Dorn, 2013). The title “Lousy and
Lovely Jobs”, of recent work by Goos and Manning (2007), thus captures the essence of the current trend towards labour market polarization, with growing employment 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 striking about the examples in their book is that computerisation is no longer conﬁned to routine manufacturing tasks. The autonomous driverless cars, developed by
Google, provide one example of how manual tasks in transport and logistics may soon be automated. In the section “In Domain After Domain, Computers Race Ahead”, they emphasise how fast moving these developments have been. Less than ten years ago, in the chapter “Why People Still Matter”, Levy and 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 hard to 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 technological progress is likely to mean for the future of employment. The present study intends to bridge this gap in the literature. Although there are indeed existing
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useful frameworks for examining the impact of computers on the occupational employment composition, they seem inadequate in explaining the impact of technological trends going beyond the computerisation of routine tasks. Seminal work by Autor, et al. (2003), for example, distinguishes between cognitive and manual tasks on the one hand, and routine and non-routine tasks on the other. While the computer substitution for both cognitive and manual routine tasks is evident, non-routine tasks involve everything from legal writing, truck driving and medical diagnoses, to persuading and selling. In the present study, we will argue that legal writing and truck driving will soon be automated, while persuading, for instance, will not. Drawing upon recent developments in Engineering Sciences, and in particular advances in the ﬁelds of ML, including
Data Mining, Machine Vision, Computational Statistics and other sub-ﬁelds of
Artiﬁcial Intelligence, as well as MR, we derive additional dimensions required to understand the susceptibility of jobs to computerisation. Needless to say, a number of factors are driving decisions to automate and we cannot capture these in full. Rather we aim, from a technological capabilities point of view, to determine which problems engineers need to solve for speciﬁc occupations to be automated. By highlighting these problems, their difﬁculty and to which occupations they relate, we categorise jobs according to their susceptibility to computerisation. The characteristics of these problems were matched to different occupational characteristics, using O∗NET data, allowing us to examine the 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 risk should these technologies materialise.
The present study relates to two literatures. First, our analysis builds on the labour economics literature on the task content of employment (Autor, et al.,
2003; Goos and Manning, 2007; Autor and Dorn, 2013). Based on deﬁned premises 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 will inevitably continue to do so (Brynjolfsson and McAfee, 2011; MGI, 2013).
Drawing upon recent progress in ML, we expand the premises about the tasks computers are and will be suited to accomplish. Doing so, we build on the task content literature in a forward-looking manner. Furthermore, whereas this literature has largely focused on task measures from the Dictionary of Occupational
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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.4
Accordingly, O∗NET has the advantage of providing more recent information on 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, using
O ∗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 next decade or two. These estimates are based on two deﬁning characteristics of jobs that 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 the characteristics of occupations that can be automated. For example, the work of cashiers, which has largely been substituted by self- service technology, must be performed at speciﬁc work location and requires face-to-face contact. The extent 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 review the literature on the historical relationship between technological progress and employment. Section III describes recent and expected future technological developments. In Section IV, we describe our methodology, and in Section V, we examine the expected impact of these technological developments on labour market outcomes. Finally, in Section VI, we derive some conclusions.
II.

A HISTORY

OF TECHNOLOGICAL REVOLUTIONS AND EMPLOYMENT

The 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 that
4

An exception is Goos, et al. (2009).

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set the boundaries for economic development, but rather powerful social and economic interests promoting the technological status quo. This is nicely illustrated by the example of William Lee, inventing the stocking frame knitting machine in 1589, hoping that it would relieve workers of hand-knitting. Seeking patent protection for his invention, he travelled to London where he had rented a building for his machine to be viewed by Queen Elizabeth I. To his disappointment, the Queen was more concerned with the employment impact of his invention and refused to grant him a patent, claiming that: “Thou aimest high, 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 fear that the invention would make the skills of its artisan members obsolete.5 The guilds’ opposition was indeed so intense that William Lee had to leave Britain.
That guilds systematically tried to weaken market forces as aggregators to maintain the technological status quo is persuasively argued by Kellenbenz
(1974, p. 243), stating that “guilds defended the interests of their members against outsiders, and these included the inventors who, with their new equipment and techniques, threatened to disturb their members’ economic status.”6
As pointed out by Mokyr (1998, p. 11): “Unless all individuals accept the
“verdict” of the market outcome, the decision whether to adopt an innovation is likely to be resisted by losers through non-market mechanism and political activism.” Workers can thus be expected to resist new technologies, insofar that they make their skills obsolete and irreversibly reduce their expected earnings.
The balance between job conservation and technological progress therefore, to a large extent, reﬂects the balance of power in society, and how gains from technological progress are being distributed.
The British Industrial Revolution illustrates this point vividly. While still widely present on the Continent, the craft guild in Britain had, by the time of
5

The term artisan refers to a craftsman who engages in the entire production process of a good, containing almost no division of labour. By guild we mean an association of artisans that control the practice of their craft in a particular town.
6
There is an ongoing debate about the technological role of the guilds. Epstein (1998), for example, has argued that they fulﬁlled an important role in the intergenerational transmission of knowledge. Yet there is no immediate contradiction between such a role and their conservative stand on technological progress: there are clear examples of guilds restraining the diffusion of inventions (see, for example, Ogilvie, 2004).

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the Glorious Revolution of 1688, declined and lost most of its political clout
(Nef, 1957, pp. 26 and 32). With Parliamentary supremacy established over the Crown, legislation was passed in 1769 making the destruction of machinery punishable by death (Mokyr, 1990, p. 257). To be sure, there was still resistance to mechanisation. The “Luddite” riots between 1811 and 1816 were partly a manifestation of the fear of technological change among workers as Parliament revoked a 1551 law prohibiting the use of gig mills in the wool-ﬁnishing trade.
The British government however took an increasingly stern view on groups attempting to halt technological progress and deployed 12,000 men against the rioters (Mantoux, 2006, p. 403-8). The sentiment of the government towards the destruction of machinery was explained by a resolution passed after the
Lancashire riots of 1779, stating that: “The sole cause of great riots was the new machines employed in cotton manufacture; the country notwithstanding has greatly beneﬁted from their erection [and] destroying them in this country would only be the means of transferring them to another [. . . ] to the detriment of the trade of Britain” (cited in Mantoux, 2006, p. 403).
There are at least two possible explanations for the shift in attitudes towards technological progress. First, after Parliamentary supremacy was established over the Crown, the property owning classes became politically dominant in
Britain (North and Weingast, 1989). Because the diffusion of various manufacturing technologies did not impose a risk to the value of their assets, and some property owners stood to beneﬁt from the export of manufactured goods, the artisans simply did not have the political power to repress them. Second, inventors, consumers and unskilled factory workers largely beneﬁted from mechanisation (Mokyr, 1990, p. 256 and 258). It has even been argued that, despite the employment concerns over mechanisation, unskilled workers have been the greatest beneﬁciaries of the Industrial Revolution (Clark, 2008).7 While there
7

Various estimations of the living standards of workers in Britain during the industrialisation exist in the literature. For example, Clark (2008) ﬁnds that real wages over the period 1760 to
1860 rose faster than GDP per capita. Further evidence provided by Lindert and Williamson
(1983) even suggests that real wages nearly doubled between 1820 and 1850. Feinstein (1998), on the other hand, ﬁnds a much more moderate increase, with average working-class living standards improving by less than 15 percent between 1770 and 1870. Finally, Allen (2009a) ﬁnds that over the ﬁrst half of the nineteenth century, the real wage stagnated while output per worker expanded. After the mid nineteenth century, however, real wages began to grow in line with productivity. While this implies that capital owners were the greatest beneﬁciaries of the
Industrial Revolution, there is at the same time consensus that average living standards largely improved. 7

is contradictory evidence suggesting that capital owners initially accumulated a growing share of national income (Allen, 2009a), there is equally evidence of growing real wages (Lindert and Williamson, 1983; Feinstein, 1998). This implies that although manufacturing technologies made the skills of artisans obsolete, gains from technological progress were distributed in a manner that gradually beneﬁted a growing share of the labour force.8
An important feature of nineteenth century manufacturing technologies is that they were largely “deskilling” – i.e. they substituted for skills through the simpliﬁcation of tasks (Braverman, 1974; Hounshell, 1985; James and Skinner,
1985; Goldin and Katz, 1998). The deskilling process occurred as the factory system began to displace the artisan shop, and it picked up pace as production increasingly mechanized with the adoption of steam power (Goldin and
Sokoloff, 1982; Atack, et al., 2008a). Work that had previously been performed by artisans was now decomposed into smaller, highly specialised, sequences, requiring less skill, but more workers, to perform.9 Some innovations were even designed to be deskilling. For example, Eli Whitney, a pioneer of interchangeable parts, described the objective of this technology as “to substitute correct and effective operations of machinery for the skill of the artist which is acquired only by long practice and experience; a species of skill which is not possessed in this country to any considerable extent” (Habakkuk, 1962, p. 22).
Together with developments in continuous-ﬂow production, enabling workers to be stationary while different tasks were moved to them, it was identical interchangeable parts that allowed complex products to be assembled from mass produced individual components by using highly specialised machine tools to
8

The term skill is associated with higher levels of education, ability, or job training. Following Goldin and Katz (1998), we refer to technology-skill or capital-skill complementarity when a new technology or physical capital complements skilled labour relative to unskilled workers.
9
The production of plows nicely illustrates the differences between the artisan shop and the factory. In one artisan shop, two men spent 118 man-hours using hammers, anvils, chisels, hatchets, axes, mallets, shaves and augers in 11 distinct operations to produce a plow. By contrast, a mechanized plow factory employed 52 workers performing 97 distinct tasks, of which 72 were assisted by steam power, to produce a plow in just 3.75 man-hours. The degree of specialization was even greater in the production of men’s white muslin shirts. In the artisan shop, one worker spent 1439 hours performing 25 different tasks to produce 144 shirts. In the factory, it took 188 man-hours to produce the same quantity, engaging 230 different workers performing 39 different tasks, of which more than half required steam power. The workers involved included cutters, turners and trimmers, as well as foremen and forewomen, inspectors, errand boys, an engineer, a ﬁreman, and a watchman (US Department of Labor, 1899).

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a sequence of operations.10 Yet while the ﬁrst assembly-line was documented in 1804, it was not until the late nineteenth century that continuous-ﬂow processes started to be adopted on a larger scale, which enabled corporations such as the Ford Motor Company to manufacture the T-Ford at a sufﬁciently low price for it to become the people’s vehicle (Mokyr, 1990, p. 137). Crucially, the new assembly line introduced by Ford in 1913 was speciﬁcally designed for machinery to be operated by unskilled workers (Hounshell, 1985, p. 239). Furthermore, what had previously been a one-man job was turned into a 29-man worker operation, reducing the overall work time by 34 percent (Bright, 1958).
The example of the Ford Motor Company thus underlines the general pattern observed in the nineteenth century, with physical capital providing a relative complement to unskilled labour, while substituting for relatively skilled artisans (James and Skinner, 1985; Louis and Paterson, 1986; Brown and Philips,
1986; Atack, et al., 2004).11 Hence, as pointed out by Acemoglu (2002, p. 7):
“the idea that technological advances favor more skilled workers is a twentieth century phenomenon.” The conventional wisdom among economic historians, in other words, suggests a discontinuity between the nineteenth and twentieth century in the impact of capital deepening on the relative demand for skilled labour. The modern pattern of capital-skill complementarity gradually emerged in the late nineteenth century, as manufacturing production shifted to increasingly mechanised assembly lines. This shift can be traced to the switch to electricity from steam and water-power which, in combination with continuous-process
10

These machines were sequentially implemented until the production process was completed. Over time, such machines became much cheaper relative to skilled labor. As a result, production became much more capital intensive (Hounshell, 1985).
11
Williamson and Lindert (1980), on the other hand, ﬁnd a relative rise in wage premium of skilled labour over the period 1820 to 1860, which they partly attribute to capital deepening.
Their claim of growing wage inequality over this period has, however, been challenged (Margo,
2000). Yet seen over the long-run, a more reﬁned explanation is that the manufacturing share of the labour force in the nineteenth century hollowed out. This is suggested by recent ﬁndings, revealing a decline of middle-skill artisan jobs in favour of both high-skill white collar workers and low-skill operatives (Gray, 2013; Katz and Margo, 2013). Furthermore, even if the share of operatives was increasing due to organizational change within manufacturing and overall manufacturing growth, it does not follow that the share of unskilled labor was rising in the aggregate economy, because some of the growth in the share of operatives may have come at the expense of a decrease in the share of workers employed as low-skilled farm workers in agriculture (Katz and Margo, 2013). Nevertheless, this evidence is consistent with the literature showing that relatively skilled artisans were replaced by unskilled factory workers, suggesting that technological change in manufacturing was deskilling.

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and batch production methods, reduced the demand for unskilled manual workers in many hauling, conveying, and assembly tasks, but increased the demand for skills (Goldin and Katz, 1998). In short, while factory assembly lines, with their extreme division of labour, had required vast quantities of human operatives, electriﬁcation allowed many stages of the production process to be automated, which in turn increased the demand for relatively skilled blue-collar production workers to operate the machinery. In addition, electriﬁcation contributed to a growing share of white-collar nonproduction workers (Goldin and
Katz, 1998). Over the course of the nineteenth century, establishments became larger in size as steam and water power technologies improved, allowing them to adopt powered machinery to realize productivity gains through the combination of enhanced division of labour and higher capital intensity (Atack, et al.,
2008a). Furthermore, the transport revolution lowered costs of shipping goods domestically and internationally as infrastructure spread and improved (Atack, et al., 2008b). The market for artisan goods early on had largely been conﬁned to the immediate surrounding area because transport costs were high relative to the value of the goods produced. With the transport revolution, however, market size expanded, thereby eroding local monopoly power, which in turn increased competition and compelled ﬁrms to raise productivity through mechanisation.
As establishments became larger and served geographically expended markets, managerial tasks increased in number and complexity, requiring more managerial and clerking employees (Chandler, 1977). This pattern was, by the turn of the twentieth century, reinforced by electriﬁcation, which not only contributed to a growing share of relatively skilled blue-collar labour, but also increased the demand for white-collar workers (Goldin and Katz, 1998), who tended to have higher educational attainment (Allen, 2001).12
Since electriﬁcation, the story of the twentieth century has been the race between education and technology (Goldin and Katz, 2009). The US high school movement coincided with the ﬁrst industrial revolution of the ofﬁce (Goldin and Katz, 1995). While the typewriter was invented in the 1860s, it was not introduced in the ofﬁce until the early twentieth century, when it entered a wave
12

Most likely, the growing share of white-collar workers increased the element of human interaction in employment. Notably, Michaels, et al. (2013) ﬁnd that the increase in the employment share of interactive occupations, going hand in hand with an increase in their relative wage bill share, was particularly strong between 1880 and 1930, which is a period of rapid change in communication and transport technology.

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of mechanisation, with dictaphones, calculators, mimeo machines, address machines, and the predecessor of the computer – the keypunch (Beniger, 1986;
Cortada, 2000). Importantly, these ofﬁce machines reduced the cost of information processing tasks and increased the demand for the complementary factor –
i.e. educated ofﬁce workers. Yet the increased supply of educated ofﬁce workers, following the high school movement, was associated with a sharp decline in the wage premium of clerking occupations relative to production workers
(Goldin and Katz, 1995). This was, however, not the result of deskilling technological change. Clerking workers were indeed relatively educated. Rather, it was the result of the supply of educated workers outpacing the demand for their skills, leading educational wage differentials to compress.
While educational wage differentials in the US narrowed from 1915 to 1980
(Goldin and Katz, 2009), both educational wage differentials and overall wage inequality have increased sharply since the 1980s in a number of countries
(Krueger, 1993; Murphy, et al., 1998; Atkinson, 2008; Goldin and Katz, 2009).
Although there are clearly several variables at work, consensus is broad that this can be ascribed to an acceleration in capital-skill complementarity, driven by the adoption of computers and information technology (Krueger, 1993; Autor, et al., 1998; Bresnahan, et al., 2002). What is commonly referred to as the
Computer Revolution began with the ﬁrst commercial uses of computers around
1960 and continued through the development of the Internet and e-commerce in the 1990s. As the cost per computation declined at an annual average of 37 percent between 1945 and 1980 (Nordhaus, 2007), telephone operators were made redundant, the ﬁrst industrial robot was introduced by General Motors in the 1960s, and in the 1970s airline reservations systems led the way in selfservice technology (Gordon, 2012). During the 1980s and 1990s, computing costs declined even more rapidly, on average by 64 percent per year, accompanied by a surge in computational power (Nordhaus, 2007).13 At the same time, bar-code scanners and cash machines were spreading across the retail and ﬁnancial industries, and the ﬁrst personal computers were introduced in the early
1980s, with their word processing and spreadsheet functions eliminating copy typist occupations and allowing repetitive calculations to be automated (Gordon, 2012). This substitution for labour marks a further important reversal.
13

Computer power even increased 18 percent faster on annual basis than predicted by
Moore’s Law, implying a doubling every two years (Nordhaus, 2007).

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The early twentieth century ofﬁce machines increased the demand for clerking workers (Chandler, 1977; Goldin and Katz, 1995). In a similar manner, computerisation augments demand for such tasks, but it also permits them to be automated (Autor, et al., 2003).
The Computer Revolution can go some way in explaining the growing wage inequality of the past decades. For example, Krueger (1993) ﬁnds that workers using a computer earn roughly earn 10 to 15 percent more than others, but also that computer use accounts for a substantial share of the increase in the rate of return to education. In addition, more recent studies ﬁnd that computers have caused a shift in the occupational structure of the labour market. Autor and Dorn (2013), for example, show that as computerisation erodes wages for labour performing routine tasks, workers will reallocate their labour supply to relatively low-skill service occupations. More speciﬁcally, between 1980 and
2005, the share of US labour hours in service occupations grew by 30 percent after having been ﬂat or declining in the three prior decades. Furthermore, net changes in US employment were U-shaped in skill level, meaning that the lowest and highest job-skill quartile expanded sharply with relative employment declines in the middle of the distribution.
The expansion in high-skill employment can be explained by the falling price of carrying out routine tasks by means of computers, which complements more abstract and creative services. Seen from a production function perspective, an outward shift in the supply of routine informational inputs increases the marginal productivity of workers they are demanded by. For example, text and data mining has improved the quality of legal research as constant access to market information has improved the efﬁciency of managerial decision-making
– i.e. tasks performed by skilled workers at the higher end of the income distribution. The result has been an increasingly polarised labour market, with growing employment in high-income cognitive jobs and low-income manual occupations, accompanied by a hollowing-out of middle-income routine jobs.
This is a pattern that is not unique to the US and equally applies to a number of developed economies (Goos, et al., 2009).14
14

While there is broad consensus that computers substituting for workers in routine-intensive tasks has driven labour market polarisation over the past decades, there are, indeed, alternative explanations. For example, technological advances in computing have dramatically lowered the cost of leaving information-based tasks to foreign worksites (Jensen and Kletzer, 2005; Blinder,
2009; Jensen and Kletzer, 2010; Oldenski, 2012; Blinder and Krueger, 2013). The decline in

12

How technological progress in the twenty-ﬁrst century will impact on labour market outcomes remains to be seen. Throughout history, technological progress has vastly shifted the composition of employment, from agriculture and the artisan shop, to manufacturing and clerking, to service and management occupations. Yet the concern over technological unemployment has proven to be exaggerated. The obvious reason why this concern has not materialised relates to Ricardo’s famous chapter on machinery, which suggests that laboursaving technology reduces the demand for undifferentiated labour, thus leading to technological unemployment (Ricardo, 1819). As economists have long understood, however, an invention that replaces workers by machines will have effects on all product and factor markets. An increase in the efﬁciency of production which reduces the price of one good, will increase real income and thus increase demand for other goods. Hence, in short, technological progress has two competing effects on employment (Aghion and Howitt, 1994). First, as technology substitutes for labour, there is a destruction effect, requiring workers to reallocate their labour supply; and second, there is the capitalisation effect, as more companies enter industries where productivity is relatively high, leading employment in those industries to expand.
Although the capitalisation effect has been predominant historically, our discovery of means of economising the use of labour can outrun the pace at which we can ﬁnd new uses for labour, as Keynes (1933) pointed out. The reason why human labour has prevailed relates to its ability to adopt and acquire new skills by means of education (Goldin and Katz, 2009). Yet as computerisation enters more cognitive domains this will become increasingly challenging
(Brynjolfsson and McAfee, 2011). Recent empirical ﬁndings are therefore particularly concerning. For example, Beaudry, et al. (2013) document a decline in the demand for skill over the past decade, even as the supply of workers with higher education has continued to grow. They show that high-skilled workers have moved down the occupational ladder, taking on jobs traditionally performed by low-skilled workers, pushing low-skilled workers even further down the occupational ladder and, to some extent, even out of the labour force. This the routine-intensity of employment is thus likely to result from a combination of offshoring and automation. Furthermore, there is evidence suggesting that improvements in transport and communication technology have augmented occupations involving human interaction, spanning across both cognitive and manual tasks (Michaels, et al., 2013). These explanations are nevertheless equally related to advance in computing and communications technology.

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raises questions about: (a) the ability of human labour to win the race against technology by means of education; and (b) the potential extent of technological unemployment, as an increasing pace of technological progress will cause higher job turnover, resulting in a higher natural rate of unemployment (Lucas and Prescott, 1974; Davis and Haltiwanger, 1992; Pissarides, 2000). While the present study is limited to examining the destruction effect of technology, it nevertheless provides a useful indication of the job growth required to counterbalance the jobs at risk over the next decades.
III.

T HE

TECHNOLOGICAL REVOLUTIONS OF THE TWENTY- FIRST CENTURY

The secular price decline in the real cost of computing has created vast economic incentives for employers to substitute labour for computer capital.15 Yet the tasks computers are able to perform ultimately depend upon the ability of a programmer to write a set of procedures or rules that appropriately direct the technology in each possible contingency. Computers will therefore be relatively productive to human labour when a problem can be speciﬁed – in the sense that the criteria for success are quantiﬁable and can readily be evaluated (Acemoglu and Autor, 2011). The extent of job computerisation will thus be determined by technological advances that allow engineering problems to be sufﬁciently speciﬁed, which sets the boundaries for the scope of computerisation. In this section, we examine the extent of tasks computer-controlled equipment can be expected to perform over the next decades. Doing so, we focus on advances in ﬁelds related to Machine Learning (ML), including Data Mining, Machine
Vision, Computational Statistics and other sub-ﬁelds of Artiﬁcial Intelligence
(AI), in which efforts are explicitly dedicated to the development of algorithms that allow cognitive tasks to be automated. In addition, we examine the application of ML technologies in Mobile Robotics (MR), and thus the extent of computerisation in manual tasks.
Our analysis builds on the task categorisation of Autor, et al. (2003), which distinguishes between workplace tasks using a two-by-two matrix, with routine versus non-routine tasks on one axis, and manual versus cognitive tasks on the other. In short, routine tasks are deﬁned as tasks that follow explicit rules that
15

We refer to computer capital as accumulated computers and computer-controlled equipment by means of capital deepening.

14

can be accomplished by machines, while non-routine tasks are not sufﬁciently well understood to be speciﬁed in computer code. Each of these task categories can, in turn, be of either manual or cognitive nature – i.e. they relate to physical labour or knowledge work. Historically, computerisation has largely been conﬁned to manual and cognitive routine tasks involving explicit rulebased activities (Autor and Dorn, 2013; Goos, et al., 2009). Following recent technological advances, however, computerisation is now spreading to domains commonly deﬁned as non-routine. The rapid pace at which tasks that were deﬁned as non-routine only a decade ago have now become computerisable is illustrated by Autor, et al. (2003), asserting that: “Navigating a car through city trafﬁc or deciphering the scrawled handwriting on a personal check – minor undertakings for most adults – are not routine tasks by our deﬁnition.” Today, the problems of navigating a car and deciphering handwriting are sufﬁciently well understood that many related tasks can be speciﬁed in computer code and automated (Veres, et al., 2011; Plötz and Fink, 2009).
Recent technological breakthroughs are, in large part, due to efforts to turn non-routine tasks into well-deﬁned problems. Deﬁning such problems is helped by the provision of relevant data: this is highlighted in the case of handwriting recognition by Plötz and Fink (2009). The success of an algorithm for handwriting recognition is difﬁcult to quantify without data to test on – in particular, determining whether an algorithm performs well for different styles of writing requires data containing a variety of such styles. That is, data is required to specify the many contingencies a technology must manage in order to form an adequate substitute for human labour. With data, objective and quantiﬁable measures of the success of an algorithm can be produced, which aid the continual improvement of its performance relative to humans.
As such, technological progress has been aided by the recent production of increasingly large and complex datasets, known as big data.16 For instance, with a growing corpus of human-translated digitalised text, the success of a machine translator can now be judged by its accuracy in reproducing observed translations. Data from United Nations documents, which are translated by hu16

Predictions by Cisco Systems suggest that the Internet trafﬁc in 2016 will be around 1 zettabyte (1 × 1021 bytes) (Cisco, 2012). In comparison, the information contained in all books worldwide is about 480 terabytes (5 × 1014 bytes), and a text transcript of all the words ever spoken by humans would represent about 5 exabytes (5 × 1018 bytes) (UC Berkeley School of
Information, 2003).

15

man experts into six languages, allow Google Translate to monitor and improve the performance of different machine translation algorithms (Tanner, 2007).
Further, ML algorithms can discover unexpected similarities between old and new data, aiding the computerisation of tasks for which big data has newly become available. As a result, computerisation is no longer conﬁned to routine tasks that can be written as rule-based software queries, but is spreading to every non-routine task where big data becomes available (Brynjolfsson and
McAfee, 2011). In this section, we examine the extent of future computerisation beyond routine tasks.
III.A. Computerisation in non-routine cognitive tasks
With the availability of big data, a wide range of non-routine cognitive tasks are becoming computerisable. That is, further to the general improvement in technological progress due to big data, algorithms for big data are rapidly entering domains reliant upon storing or accessing information. The use of big data is afforded by one of the chief comparative advantages of computers relative to human labor: scalability. Little evidence is required to demonstrate that, in performing the task of laborious computation, networks of machines scale better than human labour (Campbell-Kelly, 2009). As such, computers can better manage the large calculations required in using large datasets. ML algorithms running on computers are now, in many cases, better able to detect patterns in big data than humans.
Computerisation of cognitive tasks is also aided by another core comparative advantage of algorithms: their absence of some human biases. An algorithm can be designed to ruthlessly satisfy the small range of tasks it is given.
Humans, in contrast, must fulﬁll a range of tasks unrelated to their occupation, such as sleeping, necessitating occasional sacriﬁces in their occupational performance (Kahneman, et al., 1982). The additional constraints under which humans must operate manifest themselves as biases. Consider an example of human bias: Danziger, et al. (2011) demonstrate that experienced Israeli judges are substantially more generous in their rulings following a lunch break. It can thus be argued that many roles involving decision-making will beneﬁt from impartial algorithmic solutions.
Fraud detection is a task that requires both impartial decision making and the ability to detect trends in big data. As such, this task is now almost com16

pletely automated (Phua, et al., 2010). In a similar manner, the comparative advantages of computers are likely to change the nature of work across a wide range of industries and occupations.
In health care, diagnostics tasks are already being computerised. Oncologists at Memorial Sloan-Kettering Cancer Center are, for example, using IBM’s
Watson computer to provide chronic care and cancer treatment diagnostics.
Knowledge from 600,000 medical evidence reports, 1.5 million patient records and clinical trials, and two million pages of text from medical journals, are used for benchmarking and pattern recognition purposes. This allows the computer to compare each patient’s individual symptoms, genetics, family and medication history, etc., to diagnose and develop a treatment plan with the highest probability of success (Cohn, 2013).
In addition, computerisation is entering the domains of legal and ﬁnancial services. Sophisticated algorithms are gradually taking on a number of tasks performed by paralegals, contract and patent lawyers (Markoff, 2011). More speciﬁcally, law ﬁrms now rely on computers that can scan thousands of legal briefs and precedents to assist in pre-trial research. A frequently cited example is Symantec’s Clearwell system, which uses language analysis to identify general concepts in documents, can present the results graphically, and proved capable of analysing and sorting more than 570,000 documents in two days
(Markoff, 2011).
Furthermore, the improvement of sensing technology has made sensor data one of the most prominent sources of big data (Ackerman and Guizzo, 2011).
Sensor data is often coupled with new ML fault- and anomaly-detection algorithms to render many tasks computerisable. A broad class of examples can be found in condition monitoring and novelty detection, with technology substituting for closed-circuit TV (CCTV) operators, workers examining equipment defects, and clinical staff responsible for monitoring the state of patients in intensive care. Here, the fact that computers lack human biases is of great value: algorithms are free of irrational bias, and their vigilance need not be interrupted by rest breaks or lapses of concentration. Following the declining costs of digital sensing and actuation, ML approaches have successfully addressed condition monitoring applications ranging from batteries (Saha, et al., 2007), to aircraft engines (King, et al., 2009), water quality (Osborne, et al., 2012) and intensive care units (ICUs) (Clifford and Clifton, 2012; Clifton, et al., 2012). Sensors can
17

equally be placed on trucks and pallets to improve companies’ supply chain management, and used to measure the moisture in a ﬁeld of crops to track the ﬂow of water through utility pipes. This allows for automatic meter reading, eliminating the need for personnel to gather such information. For example, the cities of Doha, São Paulo, and Beijing use sensors on pipes, pumps, and other water infrastructure to monitor conditions and manage water loss, reducing leaks by 40 to 50 percent. In the near future, it will be possible to place inexpensive sensors on light poles, sidewalks, and other public property to capture sound and images, likely reducing the number of workers in law enforcement
(MGI, 2013).
Advances in user interfaces also enable computers to respond directly to a wider range of human requests, thus augmenting the work of highly skilled labour, while allowing some types of jobs to become fully automated. For example, Apple’s Siri and Google Now rely on natural user interfaces to recognise spoken words, interpret their meanings, and act on them accordingly. Moreover, a company called SmartAction now provides call computerisation solutions that use ML technology and advanced speech recognition to improve upon conventional interactive voice response systems, realising cost savings of 60 to
80 percent over an outsourced call center consisting of human labour (CAA,
2012). Even education, one of the most labour intensive sectors, will most likely be signiﬁcantly impacted by improved user interfaces and algorithms building upon big data. The recent growth in MOOCs (Massive Open Online
Courses) has begun to generate large datasets detailing how students interact on forums, their diligence in completing assignments and viewing lectures, and their ultimate grades (Simonite, 2013; Breslow, et al., 2013). Such information, together with improved user interfaces, will allow for ML algorithms that serve as interactive tutors, with teaching and assessment strategies statistically calibrated to match individual student needs (Woolf, 2010). Big data analysis will also allow for more effective predictions of student performance, and for their suitability for post-graduation occupations. These technologies can equally be implemented in recruitment, most likely resulting in the streamlining of human resource (HR) departments.
Occupations that require subtle judgement are also increasingly susceptible to computerisation. To many such tasks, the unbiased decision making of an algorithm represents a comparative advantage over human operators. In the most
18

challenging or critical applications, as in ICUs, algorithmic recommendations may serve as inputs to human operators; in other circumstances, algorithms will themselves be responsible for appropriate decision-making. In the ﬁnancial sector, such automated decision-making has played a role for quite some time. AI algorithms are able to process a greater number of ﬁnancial announcements, press releases, and other information than any human trader, and then act faster upon them (Mims, 2010). Services like Future Advisor similarly use
AI to offer personalised ﬁnancial advice at larger scale and lower cost. Even the work of software engineers may soon largely be computerisable. For example, advances in ML allow a programmer to leave complex parameter and design choices to be appropriately optimised by an algorithm (Hoos, 2012). Algorithms can further automatically detect bugs in software (Hangal and Lam,
2002; Livshits and Zimmermann, 2005; Kim, et al., 2008), with a reliability that humans are unlikely to match. Big databases of code also offer the eventual prospect of algorithms that learn how to write programs to satisfy speciﬁcations provided by a human. Such an approach is likely to eventually improve upon human programmers, in the same way that human-written compilers eventually proved inferior to automatically optimised compilers. An algorithm can better keep the whole of a program in working memory, and is not constrained to human-intelligible code, allowing for holistic solutions that might never occur to a human. Such algorithmic improvements over human judgement are likely to become increasingly common.
Although the extent of these developments remains to be seen, estimates by
MGI (2013) suggests that sophisticated algorithms could substitute for approximately 140 million full-time knowledge workers worldwide. Hence, while technological progress throughout economic history has largely been conﬁned to the mechanisation of manual tasks, requiring physical labour, technological progress in the twenty-ﬁrst century can be expected to contribute to a wide range of cognitive tasks, which, until now, have largely remained a human domain. Of course, many occupations being affected by these developments are still far from fully computerisable, meaning that the computerisation of some tasks will simply free-up time for human labour to perform other tasks.
Nonetheless, the trend is clear: computers increasingly challenge human labour in a wide range of cognitive tasks (Brynjolfsson and McAfee, 2011).

19

III.B. Computerisation in non-routine manual tasks
Mobile robotics provides a means of directly leveraging

ML

technologies to

aid the computerisation of a growing scope of manual tasks. The continued technological development of robotic hardware is having notable impact upon employment: over the past decades, industrial robots have taken on the routine tasks of most operatives in manufacturing. Now, however, more advanced robots are gaining enhanced sensors and manipulators, allowing them to perform non-routine manual tasks. For example, General Electric has recently developed robots to climb and maintain wind turbines, and more ﬂexible surgical robots with a greater range of motion will soon perform more types of operations (Robotics-VO, 2013). In a similar manner, the computerisation of logistics is being aided by the increasing cost-effectiveness of highly instrumented and computerised cars. Mass-production vehicles, such as the Nissan LEAF, contain on-board computers and advanced telecommunication equipment that render the car a potentially ﬂy-by-wire robot.17 Advances in sensor technology mean that vehicles are likely to soon be augmented with even more advanced suites of sensors. These will permit an algorithmic vehicle controller to monitor its environment to a degree that exceeds the capabilities of any human driver: they have the ability to simultaneously look both forwards and backwards, can natively integrate camera, GPS and LIDAR data, and are not subject to distraction. Algorithms are thus potentially safer and more effective drivers than humans.
The big data provided by these improved sensors are offering solutions to many of the engineering problems that had hindered robotic development in the past. In particular, the creation of detailed three dimensional maps of road networks has enabled autonomous vehicle navigation; most notably illustrated by Google’s use of large, specialised datasets collected by its driverless cars
(Guizzo, 2011). It is now completely feasible to store representations of the entire road network on-board a car, dramatically simplifying the navigation problem. Algorithms that could perform navigation throughout the changing seasons, particularly after snowfall, have been viewed as a substantial challenge. However, the big data approach can answer this by storing records from the last time snow fell, against which the vehicle’s current environment can
17

A ﬂy-by-wire robot is a robot that is controllable by a remote computer.

20

be compared (Churchill and Newman, 2012). ML approaches have also been developed to identify unprecedented changes to a particular piece of the road network, such as roadworks (Mathibela, et al., 2012). This emerging technology will affect a variety of logistics jobs. Agricultural vehicles, forklifts and cargo-handling vehicles are imminently automatable, and hospitals are already employing autonomous robots to transport food, prescriptions and samples (Bloss, 2011). The computerisation of mining vehicles is further being pursued by companies such as Rio Tinto, seeking to replace labour in Australian mine-sites.18
With improved sensors, robots are capable of producing goods with higher quality and reliability than human labour. For example, El Dulze, a Spanish food processor, now uses robotics to pick up heads of lettuce from a conveyor belt, rejecting heads that do not comply with company standards. This is achieved by measuring their density and replacing them on the belt (IFR,
2012a). Advanced sensors further allow robots to recognise patterns. Baxter, a
22,000 USD general-purpose robot, provides a well-known example. The robot features an LCD display screen displaying a pair of eyes that take on different expressions depending on the situation. When the robot is ﬁrst installed or needs to learn a new pattern, no programming is required. A human worker simply guides the robot arms through the motions that will be needed for the task. Baxter then memorises these patterns and can communicate that it has understood its new instructions. While the physical ﬂexibility of Baxter is limited to performing simple operations such as picking up objects and moving them, different standard attachments can be installed on its arms, allowing Baxter to perform a relatively broad scope of manual tasks at low cost (MGI, 2013).
Technological advances are contributing to declining costs in robotics. Over the past decades, robot prices have fallen about 10 percent annually and are expected to decline at an even faster pace in the near future (MGI, 2013). Industrial robots, with features enabled by machine vision and high-precision dexterity, which typically cost 100,000 to 150,000 USD, will be available for
50,000 to 75,000 USD in the next decade, with higher levels of intelligence and additional capabilities (IFR, 2012b). Declining robot prices will inevitably place them within reach of more users. For example, in China, employers are
18

Rio Tinto’s computerisation efforts are advertised at http://www.mineofthefuture.com.au.

21

increasingly incentivised to substitute robots for labour, as wages and living standards are rising – Foxconn, a Chinese contract manufacturer that employs
1.2 million workers, is now investing in robots to assemble products such as the Apple iPhone (Markoff, 2012). According to the International Federation of Robotics, robot sales in China grew by more than 50 percent in 2011 and are expected to increase further. Globally, industrial robot sales reached a record
166,000 units in 2011, a 40 percent year-on-year increase (IFR, 2012b). Most likely, there will be even faster growth ahead as low-priced general-purpose models, such as Baxter, are adopted in simple manufacturing and service work.
Expanding technological capabilities and declining costs will make entirely new uses for robots possible. Robots will likely continue to take on an increasing set of manual tasks in manufacturing, packing, construction, maintenance, and agriculture. In addition, robots are already performing many simple service tasks such as vacuuming, mopping, lawn mowing, and gutter cleaning – the market for personal and household service robots is growing by about 20 percent annually (MGI, 2013). Meanwhile, commercial service robots are now able to perform more complex tasks in food preparation, health care, commercial cleaning, and elderly care (Robotics-VO, 2013). As robot costs decline and technological capabilities expand, robots can thus be expected to gradually substitute for labour in a wide range of low-wage service occupations, where most job growth has occurred over the past decades (Autor and Dorn, 2013). This means that many low-wage manual jobs that have been previously protected from computerisation could diminish over time.
US

III.C.

The task model revisited

The task model of Autor, et al. (2003) has delivered intuitive and accurate predictions in that: (a) computers are more substitutable for human labour in routine relative to non-routine tasks; and (b) a greater intensity of routine inputs increases the marginal productivity of non-routine inputs. Accordingly, computers have served as a substitute for labour for many routine tasks, while exhibiting strong complementarities with labour performing cognitive non-routine tasks.19 Yet the premises about what computers do have recently expanded.
Computer capital can now equally substitute for a wide range of tasks com19

The model does not predict any substantial substitution or complementarity with nonroutine manual tasks.

22

monly deﬁned as non-routine (Brynjolfsson and McAfee, 2011), meaning that the task model will not hold in predicting the impact of computerisation on the task content of employment in the twenty-ﬁrst century. While focusing on the substitution effects of recent technological progress, we build on the task model by deriving several factors that we expect will determine the extent of computerisation in non-routine tasks.
The task model assumes for tractability an aggregate, constant-returns-toscale, Cobb-Douglas production function of the form

(1)

Q = (LS + C)1−β Lβ ,
NS

β ∈ [0, 1],

where LS and LNS are susceptible and non-susceptible labor inputs and C is computer capital. Computer capital is supplied perfectly elastically at market price per efﬁciency unit, where the market price is falling exogenously with time due to technological progress. It further assumes income-maximizing workers, with heterogeneous productivity endowments in both susceptible and non-susceptible tasks. Their task supply will respond elastically to relative wage levels, meaning that workers will reallocate their labour supply according to their comparative advantage as in Roy (1951). With expanding computational capabilities, resulting from technological advances, and a falling market price of computing, workers in susceptible tasks will thus reallocate to nonsusceptible tasks.
The above described simple model differs from the task model of Autor, et al. (2003), in that LNS is not conﬁned to routine labour inputs. This is because recent developments in ML and MR, building upon big data, allow for pattern recognition, and thus enable computer capital to rapidly substitute for labour across a wide range of non-routine tasks. Yet some inhibiting engineering bottlenecks to computerisation persist. Beyond these bottlenecks, however, we argue that it is largely already technologically possible to automate almost any task, provided that sufﬁcient amounts of data are gathered for pattern recognition. Our model thus predicts that the pace at which these bottlenecks can be overcome will determine the extent of computerisation in the twenty-ﬁrst century.
Hence, in short, while the task model predicts that computers for labour
23

substitution will be conﬁned to routine tasks, our model predicts that computerisation can be extended to any non-routine task that is not subject to any engineering bottlenecks to computerisation. These bottlenecks thus set the boundaries for the computerisation of non-routine tasks. Drawing upon the ML and
MR literature, and a workshop held at the Oxford University Engineering Sciences Department, we identify several engineering bottlenecks, corresponding to three task categories. According to these ﬁndings, non-susceptible labor inputs can be described as,

n

(2)

LNS =

LPM,i + LC,i + LSI,i i=1 where LPM , LC and LSI are labour inputs into perception and manipulation tasks, creative intelligence tasks, and and social intelligence tasks.
We note that some related engineering bottlenecks can be partially alleviated by the simpliﬁcation of tasks. One generic way of achieving this is to reduce the variation between task iterations. As a prototypical example, consider the factory assembly line, turning the non-routine tasks of the artisan shop into repetitive routine tasks performed by unskilled factory workers. A more recent example is the computerisation of non-routine manual tasks in construction.
On-site construction tasks typically demand a high degree of adaptability, so as to accommodate work environments that are typically irregularly laid out, and vary according to weather. Prefabrication, in which the construction object is partially assembled in a factory before being transported to the construction site, provides a way of largely removing the requirement for adaptability. It allows many construction tasks to be performed by robots under controlled conditions that eliminate task variability – a method that is becoming increasingly widespread, particularly in Japan (Barlow and Ozaki, 2005; Linner and Bock,
2012). The extent of computerisation in the twenty-ﬁrst century will thus partly depend on innovative approaches to task restructuring. In the remainder of this section we examine the engineering bottlenecks related to the above mentioned task categories, each in turn.
Perception and manipulation tasks. Robots are still unable to match the depth and breadth of human perception. While basic geometric identiﬁcation is
24

reasonably mature, enabled by the rapid development of sophisticated sensors and lasers, signiﬁcant challenges remain for more complex perception tasks, such as identifying objects and their properties in a cluttered ﬁeld of view. As such, tasks that relate to an unstructured work environment can make jobs less susceptible to computerisation. For example, most homes are unstructured, requiring the identiﬁcation of a plurality of irregular objects and containing many cluttered spaces which inhibit the mobility of wheeled objects. Conversely, supermarkets, factories, warehouses, airports and hospitals have been designed for large wheeled objects, making it easier for robots to navigate in performing non-routine manual tasks. Perception problems can, however, sometimes be sidestepped by clever task design. For example, Kiva Systems, acquired by
Amazon.com in 2012, solved the problem of warehouse navigation by simply placing bar-code stickers on the ﬂoor, informing robots of their precise location
(Guizzo, 2008).
The difﬁculty of perception has ramiﬁcations for manipulation tasks, and, in particular, the handling of irregular objects, for which robots are yet to reach human levels of aptitude. This has been evidenced in the development of robots that interact with human objects and environments. While advances have been made, solutions tend to be unreliable over the myriad small variations on a single task, repeated thousands of times a day, that many applications require. A related challenge is failure recovery – i.e. identifying and rectifying the mistakes of the robot when it has, for example, dropped an object. Manipulation is also limited by the difﬁculties of planning out the sequence of actions required to move an object from one place to another. There are yet further problems in designing manipulators that, like human limbs, are soft, have compliant dynamics and provide useful tactile feedback. Most industrial manipulation makes uses of workarounds to these challenges (Brown, et al., 2010), but these approaches are nonetheless limited to a narrow range of tasks. The main challenges to robotic computerisation, perception and manipulation, thus largely remain and are unlikely to be fully resolved in the next decade or two
(Robotics-VO, 2013).
Creative intelligence tasks. The psychological processes underlying human creativity are difﬁcult to specify. According to Boden (2003), creativity is the ability to come up with ideas or artifacts that are novel and valuable. Ideas, in a
25

broader sense, include concepts, poems, musical compositions, scientiﬁc theories, cooking recipes and jokes, whereas artifacts are objects such as paintings, sculptures, machinery, and pottery. One process of creating ideas (and similarly for artifacts) involves making unfamiliar combinations of familiar ideas, requiring a rich store of knowledge. The challenge here is to ﬁnd some reliable means of arriving at combinations that “make sense.” For a computer to make a subtle joke, for example, would require a database with a richness of knowledge comparable to that of humans, and methods of benchmarking the algorithm’s subtlety. In principle, such creativity is possible and some approaches to creativity already exist in the literature. Duvenaud, et al. (2013) provide an example of automating the core creative task required in order to perform statistics, that of designing models for data. As to artistic creativity, AARON, a drawingprogram, has generated thousands of stylistically-similar line-drawings, which have been exhibited in galleries worldwide. Furthermore, David Cope’s EMI software composes music in many different styles, reminiscent of speciﬁc human composers.
In these and many other applications, generating novelty is not particularly difﬁcult. Instead, the principal obstacle to computerising creativity is stating our creative values sufﬁciently clearly that they can be encoded in an program
(Boden, 2003). Moreover, human values change over time and vary across cultures. Because creativity, by deﬁnition, involves not only novelty but value, and because values are highly variable, it follows that many arguments about creativity are rooted in disagreements about value. Thus, even if we could identify and encode our creative values, to enable the computer to inform and monitor its own activities accordingly, there would still be disagreement about whether the computer appeared to be creative. In the absence of engineering solutions to overcome this problem, it seems unlikely that occupations requiring a high degree of creative intelligence will be automated in the next decades.
Social intelligence tasks. Human social intelligence is important in a wide range of work tasks, such as those involving negotiation, persuasion and care.
To aid the computerisation of such tasks, active research is being undertaken within the ﬁelds of Affective Computing (Scherer, et al., 2010; Picard, 2010), and Social Robotics (Ge, 2007; Broekens, et al., 2009). While algorithms and
26

robots can now reproduce some aspects of human social interaction, the realtime recognition of natural human emotion remains a challenging problem, and the ability to respond intelligently to such inputs is even more difﬁcult. Even simpliﬁed versions of typical social tasks prove difﬁcult for computers, as is the case in which social interaction is reduced to pure text. The social intelligence of algorithms is partly captured by the Turing test, examining the ability of a machine to communicate indistinguishably from an actual human. Since
1990, the Loebner Prize, an annual Turing test competition, awards prizes to textual chat programmes that are considered to be the most human-like. In each competition, a human judge simultaneously holds computer-based textual interactions with both an algorithm and a human. Based on the responses, the judge is to distinguish between the two. Sophisticated algorithms have so far failed to convince judges about their human resemblance. This is largely because there is much ‘common sense’ information possessed by humans, which is difﬁcult to articulate, that would need to be provided to algorithms if they are to function in human social settings.
Whole brain emulation, the scanning, mapping and digitalising of a human brain, is one possible approach to achieving this, but is currently only a theoretical technology. For brain emulation to become operational, additional functional understanding is required to recognise what data is relevant, as well as a roadmap of technologies needed to implement it. While such roadmaps exist, present implementation estimates, under certain assumptions, suggest that whole brain emulation is unlikely to become operational within the next decade or two (Sandberg and Bostrom, 2008). When or if they do, however, the employment impact is likely to be vast (Hanson, 2001).
Hence, in short, while sophisticated algorithms and developments in MR, building upon with big data, now allow many non-routine tasks to be automated, occupa tions that involve complex perception and manipulation tasks, creative intelligence tasks, and social intelligence tasks are unlikely to be substituted by computer capital over the next decade or two. The probability of an occupation being automated can thus be described as a function of these task characteristics. As suggested by Figure I, the low degree of social intelligence required by a dishwasher makes this occupation more susceptible to computerisation than a public relation specialist, for example. We proceed to examining the susceptibility of jobs to computerisation as a function of the above described
27

Public
Event
Relations
0 Planner
0
100

1 Court Clerk

0 Biologist
0

Social Intelligence

1

Fashion
Designer

Creativity

100

Probability of
Computerisation

Dishwasher

Probability of
Computerisation

Probability of
Computerisation

1

Telemarketer
Boilermaker

0
0

Surgeon
100

Perception and manipulation

F IGURE I. A sketch of how the probability of computerisation might vary as a function of bottleneck variables.

non-susceptible task characteristics.
IV.

M EASURING

THE EMPLOYMENT IMPACT OF COMPUTERISATION

IV.A. Data sources and implementation strategy
To implement the above described methodology, we rely on O∗NET, an online service developed for the US Department of Labor. The 2010 version of O∗NET contains information on 903 detailed occupations, most of which correspond closely to the Labor Department’s Standard Occupational Classiﬁcation (SOC).

The O∗NET data was initially collected from labour market analysts, and has since been regularly updated by surveys of each occupation’s worker population and related experts, to provide up-to-date information on occupations as they evolve over time. For our purposes, an important feature of O∗NET is that it deﬁnes the key features of an occupation as a standardised and measurable set of variables, but also provides open-ended descriptions of speciﬁc tasks to each occupation. This allows us to: (a) objectively rank occupations according to the mix of knowledge, skills, and abilities they require; and (b) subjectively categorise them based on the variety of tasks they involve.
The close SOC correspondence of O∗NET allows us to link occupational

characteristics to 2010 Bureau of Labor Statistics (BLS) employment and wage data. While the O∗NET occupational classiﬁcation is somewhat more detailed, distinguishing between Auditors and Accountants, for example, we aggregate these occupations to correspond to the six-digit 2010 SOC system, for which employment and wage ﬁgures are reported. To obtain unique O∗NET vari-

ables corresponding to the six-digit

SOC

28

classiﬁcation, we used the mean of

the O∗NET aggregate. In addition, we exclude any six-digit SOC occupations for which O∗NET data was missing.20 Doing so, we end up with a ﬁnal dataset consisting of 702 occupations.
To assess the employment impact of the described technological developments in ML, the ideal experiment would provide two identical autarkic economies, one facing the expanding technological capabilities we observe, and a secular decline in the price of computerisation, and the other not. By comparison, it would be straightforward to examine how computerisation reshapes the occupational composition of the labour market. In the absence of this experiment, the second preferred option would be to build on the implementation strategy of Autor, et al. (2003), and test a simple economic model to predict how demand for workplace tasks responds to developments in ML and MR technology. However, because our paper is forward-looking, in the sense that most of the described technological developments are yet to be implemented across industries on a broader scale, this option was not available for our purposes.
Instead, our implementation strategy builds on the literature examining the offshoring of information-based tasks to foreign worksites, consisting of different methodologies to rank and categorise occupations according to their susceptibility to offshoring (Blinder, 2009; Jensen and Kletzer, 2005, 2010). The common denominator for these studies is that they rely on O∗NET data in different ways. While Blinder (2009) eyeballed the O∗NET data on each occupation, paying particular attention to the job description, tasks, and work activities, to assign an admittedly subjective two-digit index number of offshorability to each occupation, Jensen and Kletzer (2005) created a purely objective ranking based on standardised and measurable O∗NET variables. Both approaches have obvi-

ous drawbacks. Subjective judgments are often not replicable and may result in the researcher subconsciously rigging the data to conform to a certain set of be-

liefs. Objective rankings, on the other hand, are not subject to such drawbacks, but are constrained by the reliability of the variables that are being used. At this stage, it shall be noted that O∗NET data was not gathered to speciﬁcally mea20

The missing occupations consist of “All Other” titles, representing occupations with a wide range of characteristics which do not ﬁt into one of the detailed O∗NET-SOC occupations.
O ∗ NET data is not available for this type of title. We note that US employment for the 702 occupations we considered is 138.44 million. Hence our analysis excluded 4.628 million jobs, equivalent to 3 percent of total employment.

29

sure the offshorability or automatability of jobs. Accordingly, Blinder (2009) ﬁnds that past attempts to create objective offshorability rankings using O∗NET data have yielded some questionable results, ranking lawyers and judges among the most tradable occupations, while classifying occupations such as data entry keyers, telephone operators, and billing clerks as virtually impossible to move offshore. To work around some of these drawbacks, we combine and build upon the two described approaches. First, together with a group of ML researchers, we subjectively hand-labelled 70 occupations, assigning 1 if automatable, and 0 if not. For our subjective assessments, we draw upon a workshop held at the
Oxford University Engineering Sciences Department, examining the automatability of a wide range of tasks. Our label assignments were based on eyeballing the O∗NET tasks and job description of each occupation. This information is

particular to each occupation, as opposed to standardised across different jobs.
The hand-labelling of the occupations was made by answering the question
“Can the tasks of this job be sufﬁciently speciﬁed, conditional on the availability of big data, to be performed by state of the art computer-controlled equipment”. Thus, we only assigned a 1 to fully automatable occupations, where we considered all tasks to be automatable. To the best of our knowledge, we considered the possibility of task simpliﬁcation, possibly allowing some currently non-automatable tasks to be automated. Labels were assigned only to the occupations about which we were most conﬁdent.
Second, we use objective O∗NET variables corresponding to the deﬁned

bottlenecks to computerisation. More speciﬁcally, we are interested in variables describing the level of perception and manipulation, creativity, and social intelligence required to perform it. As reported in Table I, we identiﬁed nine variables that describe these attributes. These variables were derived from the
O ∗NET survey, where the respondents are given multiple scales, with “importance” and “level” as the predominant pair. We rely on the “level” rating which corresponds to speciﬁc examples about the capabilities required of computercontrolled equipment to perform the tasks of an occupation. For instance, in relation to the attribute “Manual Dexterity”, low (level) corresponds to “Screw a light bulb into a light socket”; medium (level) is exempliﬁed by “Pack oranges in crates as quickly as possible”; high (level) is described as “Perform open-heart surgery with surgical instruments”. This gives us an indication of
30

TABLE I. O∗NET variables that serve as indicators of bottlenecks to computerisation.
Computerisation
bottleneck

O ∗ NET

Perception and Manipulation

Finger
Dexterity

The ability to make precisely coordinated movements of the ﬁngers of one or both hands to grasp, manipulate, or assemble very small objects.

Manual
Dexterity

The ability to quickly move your hand, your hand together with your arm, or your two hands to grasp, manipulate, or assemble objects.

Cramped Work Space,
Awkward Positions

How often does this job require working in cramped work spaces that requires getting into awkward positions?

Originality

The ability to come up with unusual or clever ideas about a given topic or situation, or to develop creative ways to solve a problem.

Fine Arts

Knowledge of theory and techniques required to compose, produce, and perform works of music, dance, visual arts, drama, and sculpture.

Social
Perceptiveness

Being aware of others’ reactions and understanding why they react as they do.

Negotiation

Bringing others together and trying to reconcile differences. Creative
Intelligence

Social
Intelligence

Variable

O ∗ NET

Description

Persuasion

Persuading others to change their minds or behavior.

Assisting and Caring for
Others

Providing personal assistance, medical attention, emotional support, or other personal care to others such as coworkers, customers, or patients.

the level of “Manual Dexterity” computer-controlled equipment would require to perform a speciﬁc occupation. An exception is the “Cramped work space” variable, which measures the frequency of unstructured work.
Hence, in short, by hand-labelling occupations, we work around the issue that O∗NET data was not gathered to speciﬁcally measure the automatability of jobs in a similar manner to Blinder (2009). In addition, we mitigate some of the subjective biases held by the researchers by using objective O∗NET variables to

correct potential hand-labelling errors. The fact that we label only 70 of the full
702 occupations, selecting those occupations whose computerisation label we

are highly conﬁdent about, further reduces the risk of subjective bias affecting our analysis. To develop an algorithm appropriate for this task, we turn to probabilistic classiﬁcation.

31

IV.B. Classiﬁcation method
We begin by examining the accuracy of our subjective assessments of the automatability of 702 occupations. For classiﬁcation, we develop an algorithm to provide the label probability given a previously unseen vector of variables.
In the terminology of classiﬁcation, the O∗NET variables form a feature vec-

tor, denoted x ∈ R9 . O∗NET hence supplies a complete dataset of 702 such feature vectors. A computerisable label is termed a class, denoted y ∈ {0, 1}.

For our problem, y = 1 (true) implies that we hand-labelled as computerisable the occupation described by the associated nine O∗NET variables contained in x ∈ R9 . Our training data is D = (X, y), where X ∈ R70×9 is a matrix of

variables and y ∈ {0, 1}70 gives the associated labels. This dataset contains information about how y varies as a function of x: as a hypothetical example,

it may be the case that, for all occupations for which x1 > 50, y = 1. A probabilistic classiﬁcation algorithm exploits patterns existent in training data to return the probability P (y∗ = 1 | x∗ , X, y) of a new, unlabelled, test datum with features x∗ having class label y∗ = 1.
We achieve probabilistic classiﬁcation by introducing a latent function f : x → R, known as a discriminant function. Given the value of the discriminant f∗ at a test point x∗ , we assume that the probability for the class label is given by the logistic
(3)

P (y∗ = 1 | f∗ ) =

1
,
1 + exp(−f∗ )

and P (y∗ = 0 | f∗ ) = 1 − P (y∗ = 1 | f∗ ). For f∗ > 0, y∗ = 1 is more

probable than y∗ = 0. For our application, f can be thought of as a continuousvalued ‘automatability’ variable: the higher its value, the higher the probability of computerisation.
We test three different models for the discriminant function, f , using the best performing for our further analysis. Firstly, logistic (or logit) regression, which adopts a linear model for f , f (x) = w ⊺ x, where the un-known weights w are often inferred by maximising their probability in light of the training data. This simple model necessarily implies a simple monotonic relationship between features and the probability of the class taking a particular value.
Richer models are provided by Gaussian process classiﬁers (Rasmussen and

32

Williams, 2006). Such classiﬁers model the latent function f with a Gaussian process (GP): a non-parametric probability distribution over functions.
A GP is deﬁned as a distribution over the functions f : X → R such that the

distribution over the possible function values on any ﬁnite subset of X (such as
X) is multivariate Gaussian. For a function f (x), the prior distribution over its values f on a subset x ⊂ X are completely speciﬁed by a covariance matrix K
(4)

p(f | K) = N (f ; 0, K) = √

1 det 2πK

exp −

1 ⊺ −1 f K f .
2

The covariance matrix is generated by a covariance function κ : X × X → R; that is, K = κ(X, X). The GP model is expressed by the choice of κ; we consider the exponentiated quadratic (squared exponential) and rational quadratic.
Note that we have chosen a zero mean function, encoding the assumption that
P (y∗ = 1) = 1 sufﬁciently far from training data.
2
Given training data D, we use the GP to make predictions about the function values f∗ at input x∗ . With this information, we have the predictive equations
(5)

p(f∗ | x∗ , D) = N f∗ ; m(f∗ | x∗ , D), V (f∗ | x∗ , D) ,

where
(6)
(7)

m(f∗ | x∗ , D) = K(x∗ , X)K(X, X)−1y
V (f∗ | x∗ , D) = K(x∗ , x∗ ) − K(x∗ , X)K(X, X)−1K(X, x∗ ) .

Inferring the label posterior p(y∗ | x∗ , D) is complicated by the non-Gaussian form of the logistic (3). In order to effect inference, we use the approximate
Expectation Propagation algorithm (Minka, 2001).
We tested three Gaussian process classiﬁers using the GPML toolbox (Rasmussen and Nickisch, 2010) on our data, built around exponentiated quadratic, rational quadratic and linear covariances. Note that the latter is equivalent to logistic regression with a Gaussian prior taken on the weights w. To validate these classiﬁers, we randomly selected a reduced training set of half the available data D; the remaining data formed a test set. On this test set, we evaluated how closely the algorithm’s classiﬁcations matched the hand labels according to two metrics (see e.g. Murphy (2012)): the area under the receiver operat33

TABLE II. Performance of various classiﬁers; best performances in bold.

classiﬁer model

AUC

exponentiated quadratic rational quadratic linear (logit regression)

log-likelihood

0.894
0.893
0.827

−163.3
−163.7
−205.0

ing characteristic curve (AUC), which is equal to one for a perfect classiﬁer, and one half for a completely random classiﬁer, and the log-likelihood, which should ideally be high. This experiment was repeated for one hundred random selections of training set, and the average results tabulated in Table II. The exponentiated quadratic model returns (narrowly) the best performance of the three (clearly outperforming the linear model corresponding to logistic regression), and was hence selected for the remainder of our testing. Note that its
AUC score of nearly 0.9 represents accurate classiﬁcation: our algorithm successfully managed to reproduce our hand-labels specifying whether an occupation was computerisable. This means that our algorithm veriﬁed that our subjective judgements were systematically and consistently related to the O∗NET

variables.
Having validated our approach, we proceed to use classiﬁcation to predict the probability of computerisation for all 702 occupations. For this purpose, we introduce a new label variable, z, denoting whether an occupation is truly computerisable or not: note that this can be judged only once an occupation is computerised, at some indeterminate point in the future. We take, again, a logistic likelihood,
(8)

P (z∗ = 1 | f∗ ) =

1
.
1 + exp(−f∗ )

We implicitly assumed that our hand label, y, is a noise-corrupted version of the unknown true label, z. Our motivation is that our hand-labels of computerisability must necessarily be treated as such noisy measurements. We thus acknowledge that it is by no means certain that a job is computerisable given our labelling. We deﬁne X∗ ∈ R702×9 as the matrix of O∗NET variables for all

702 occupations; this matrix represents our test features.
We perform a ﬁnal experiment in which, given training data D, consisting
34

50

0

60

40
20

0.5
1
Probability of
Computerisation

0

0

50

0.5
1
Probability of
Computerisation

20
0

Cramped work space

Finger dexterity

40

60
40
20
0

0

0.5
1
Probability of
Computerisation

0

0.5
1
Probability of
Computerisation

40
20

0.5
1
Probability of
Computerisation

80

60

60

0
0

80

0.5
1
Probability of
Computerisation

80

0
0

1
0.5
Probability of
Computerisation

0

100

50

20

0.5
1
Probability of
Computerisation

Originality

100

40

0
0

Fine arts

Social perceptiveness

60

0
0

Manual dexterity

80
Negotiation

80
Persuasion

Assisting and caring for others

100

0

0.5
1
Probability of
Computerisation

100

50

0

F IGURE II. The distribution of occupational variables as a function of probability of computerisation; each occupation is a unique point.

35

of our 70 hand-labelled occupations, we aim to predict z ∗ for our test features
X∗ . This approach ﬁrstly allows us to use the features of the 70 occupations about which we are most certain to predict for the remaining 632. Further, our algorithm uses the trends and patterns it has learned from bulk data to correct for what are likely to be mistaken labels. More precisely, the algorithm provides a smoothly varying probabilistic assessment of automatability as a function of the variables. For our Gaussian process classiﬁer, this function is non-linear, meaning that it ﬂexibly adapts to the patterns inherent in the training data. Our approach thus allows for more complex, non-linear, interactions between variables: for example, perhaps one variable is not of importance unless the value of another variable is sufﬁciently large. We report P (z ∗ | X∗ , D) as the probability of computerisation henceforth (for a detailed probability ranking, see
Appendix). Figure II illustrates that this probability is non-linearly related to the nine O∗NET variables selected.
V.

E MPLOYMENT

IN THE TWENTY- FIRST CENTURY

In this section, we examine the possible future extent of at-risk job computerisation, and related labour market outcomes. The task model predicts that recent developments in ML will reduce aggregate demand for labour input in tasks that can be routinised by means of pattern recognition, while increasing the demand for labour performing tasks that are not susceptible to computerisation.
However, we make no attempt to forecast future changes in the occupational composition of the labour market. While the 2010-2020 BLS occupational employment projections predict US net employment growth across major occupations, based on historical stafﬁng patterns, we speculate about technology that is in only the early stages of development. This means that historical data on the impact of the technological developments we observe is unavailable.21 We therefore focus on the impact of computerisation on the mix of jobs that existed in 2010. Our analysis is thus limited to the substitution effect of future computerisation. Turning ﬁrst to the expected employment impact, reported in Figure III, we distinguish between high, medium and low risk occupations, depending on their
21

It shall be noted that the BLS projections are based on what can be referred to as changes in normal technological progress, and not on any breakthrough technologies that may be seen as conjectural.

36

Management, Business, and Financial
Computer, Engineering, and Science
Education, Legal, Community Service, Arts, and Media
Healthcare Practitioners and Technical
Service
Sales and Related
Ofﬁce and Administrative Support
Farming, Fishing, and Forestry
Construction and Extraction
Installation, Maintenance, and Repair
Production
Transportation and Material Moving
400M
← − Low − −
−−
−→
33% Employment

← − Medium − −
−−
−→
19% Employment

← − High − −
−−
−→
47% Employment

Employment

300M

200M

100M

0M
0

0.2

0.6
0.4
Probability of Computerisation

0.8

1

F IGURE III. The distribution of BLS 2010 occupational employment over the probability of computerisation, along with the share in low, medium and high probability categories. Note that the total area under all curves is equal to total US employment.

37

probability of computerisation (thresholding at probabilities of 0.7 and 0.3).
According to our estimate, 47 percent of total US employment is in the high risk category, meaning that associated occupations are potentially automatable over some unspeciﬁed number of years, perhaps a decade or two. It shall be noted that the probability axis can be seen as a rough timeline, where high probability occupations are likely to be substituted by computer capital relatively soon.
Over the next decades, the extent of computerisation will be determined by the pace at which the above described engineering bottlenecks to automation can be overcome. Seen from this perspective, our ﬁndings could be interpreted as two waves of computerisation, separated by a “technological plateau”. In the ﬁrst wave, we ﬁnd that most workers in transportation and logistics occupations, together with the bulk of ofﬁce and administrative support workers, and labour in production occupations, are likely to be substituted by computer capital. As computerised cars are already being developed and the declining cost of sensors makes augmenting vehicles with advanced sensors increasingly cost-effective, the automation of transportation and logistics occupations is in line with the technological developments documented in the literature. Furthermore, algorithms for big data are already rapidly entering domains reliant upon storing or accessing information, making it equally intuitive that ofﬁce and administrative support occupations will be subject to computerisation. The computerisation of production occupations simply suggests a continuation of a trend that has been observed over the past decades, with industrial robots taking on the routine tasks of most operatives in manufacturing. As industrial robots are becoming more advanced, with enhanced senses and dexterity, they will be able to perform a wider scope of non-routine manual tasks. From a technological capabilities point of view, the vast remainder of employment in production occupations is thus likely to diminish over the next decades.
More surprising, at ﬁrst sight, is that a substantial share of employment in services, sales and construction occupations exhibit high probabilities of computerisation. Yet these ﬁndings are largely in line with recent documented technological developments. First, the market for personal and household service robots is already growing by about 20 percent annually (MGI, 2013). As the comparative advantage of human labour in tasks involving mobility and dexterity will diminish over time, the pace of labour substitution in service occupations is likely to increase even further. Second, while it seems counterintuitive
38

that sales occupations, which are likely to require a high degree of social intelligence, will be subject to a wave of computerisation in the near future, high risk sales occupations include, for example, cashiers, counter and rental clerks, and telemarketers. Although these occupations involve interactive tasks, they do not necessarily require a high degree of social intelligence. Our model thus seems to do well in distinguishing between individual occupations within occupational categories. Third, prefabrication will allow a growing share of construction work to be performed under controlled conditions in factories, which partly eliminates task variability. This trend is likely to drive the computerisation of construction work.
In short, our ﬁndings suggest that recent developments in ML will put a substantial share of employment, across a wide range of occupations, at risk in the near future. According to our estimates, however, this wave of automation will be followed by a subsequent slowdown in computers for labour substitution, due to persisting inhibiting engineering bottlenecks to computerisation. The relatively slow pace of computerisation across the medium risk category of employment can thus partly be interpreted as a technological plateau, with incremental technological improvements successively enabling further labour substitution. More speciﬁcally, the computerisation of occupations in the medium risk category will mainly depend on perception and manipulation challenges.
This is evident from Table III, showing that the “manual dexterity”, “ﬁnger dexterity” and “cramped work space” variables exhibit relatively high values in the medium risk category. Indeed, even with recent technological developments, allowing for more sophisticated pattern recognition, human labour will still have a comparative advantage in tasks requiring more complex perception and manipulation. Yet with incremental technological improvements, the comparative advantage of human labour in perception and manipulation tasks could eventually diminish. This will require innovative task restructuring, improvements in ML approaches to perception challenges, and progress in robotic dexterity to overcome manipulation problems related to variation between task iterations and the handling of irregular objects. The gradual computerisation of installation, maintenance, and repair occupations, which are largely conﬁned to the medium risk category, and require a high degree of perception and manipulation capabilities, is a manifestation of this observation.
Our model predicts that the second wave of computerisation will mainly
39

TABLE III. Distribution (mean and standard deviation) of values for each variable.

Variable

Probability of Computerisation
Low

Assisting and caring for others
Persuasion
Negotiation
Social perceptiveness
Fine arts
Originality
Manual dexterity
Finger dexterity
Cramped work space

Medium

High

48±20
48±7.1
44±7.6
51±7.9
12±20
51±6.5
22±18
36±10
19±15

41±17
35±9.8
33±9.3
41±7.4
3.5±12
35±12
34±15
39±10
37±26

34±10
32±7.8
30±8.9
37±5.5
1.3±5.5
32±5.6
36±14
40±10
31±20

depend on overcoming the engineering bottlenecks related to creative and social intelligence. As reported in Table III, the “ﬁne arts”, “originality”, “negotiation”, “persuasion”, “social perceptiveness”, and “assisting and caring for others”, variables, all exhibit relatively high values in the low risk category. By contrast, we note that the “manual dexterity”, “ﬁnger dexterity” and “cramped work space” variables take relatively low values. Hence, in short, generalist occupations requiring knowledge of human heuristics, and specialist occupations involving the development of novel ideas and artifacts, are the least susceptible to computerisation. As a prototypical example of generalist work requiring a high degree of social intelligence, consider the O∗NET tasks reported for chief executives, involving “conferring with board members, organization ofﬁcials, or staff members to discuss issues, coordinate activities, or resolve problems”, and “negotiating or approving contracts or agreements.” Our predictions are thus intuitive in that most management, business, and ﬁnance occupations, which are intensive in generalist tasks requiring social intelligence, are largely conﬁned to the low risk category. The same is true of most occupations in education, healthcare, as well as arts and media jobs. The O∗NET tasks of actors, for example, involve “performing humorous and serious interpretations of emotions, actions, and situations, using body movements, facial expressions, and gestures”, and “learning about characters in scripts and their relationships to each other in order to develop role interpretations.” While these tasks are very different from those of a chief executive, they equally require profound

40

Bachelor’s degree or better

Average median wage (USD)

80k
60k
40k
20k
0

0.5

1

60%

average weighted by employment 40%

unweighted

20%
0%
0

Probability of Computerisation

0.5

1

Probability of Computerisation

F IGURE IV. Wage and education level as a function of the probability of computerisation; note that both plots share a legend.

knowledge of human heuristics, implying that a wide range of tasks, involving social intelligence, are unlikely to become subject to computerisation in the near future.
The low susceptibility of engineering and science occupations to computerisation, on the other hand, is largely due to the high degree of creative intelligence they require. The O∗NET tasks of mathematicians, for example, involve

“developing new principles and new relationships between existing mathematical principles to advance mathematical science” and “conducting research to extend mathematical knowledge in traditional areas, such as algebra, geometry, probability, and logic.” Hence, while it is evident that computers are entering the domains of science and engineering, our predictions implicitly suggest strong complementarities between computers and labour in creative science and engineering occupations; although it is possible that computers will fully substitute for workers in these occupations over the long-run. We note that the predictions of our model are strikingly in line with the technological trends we observe in the automation of knowledge work, even within occupational categories. For example, we ﬁnd that paralegals and legal assistants – for which computers already substitute – in the high risk category. At the same time, lawyers, which rely on labour input from legal assistants, are in the low risk

category. Thus, for the work of lawyers to be fully automated, engineering bottlenecks to creative and social intelligence will need to be overcome, implying that the computerisation of legal research will complement the work of lawyers in the medium term.
To complete the picture of what recent technological progress is likely to
41

mean for the future of employment, we plot the average median wage of occupations by their probability of computerisation. We do the same for skill level, measured by the fraction of workers having obtained a bachelor’s degree, or higher educational attainment, within each occupation. Figure IV reveals that both wages and educational attainment exhibit a strong negative relationship with the probability of computerisation. We note that this prediction implies a truncation in the current trend towards labour market polarization, with growing employment in high and low-wage occupations, accompanied by a hollowing-out of middle-income jobs. Rather than reducing the demand for middle-income occupations, which has been the pattern over the past decades, our model predicts that computerisation will mainly substitute for low-skill and low-wage jobs in the near future. By contrast, high-skill and high-wage occupations are the least susceptible to computer capital.
Our ﬁndings were robust to the choice of the 70 occupations that formed our training data. This was conﬁrmed by the experimental results tabulated in
Table II: a GP classiﬁer trained on half of the training data was demonstrably able to accurately predict the labels of the other half, over one hundred different partitions. That these predictions are accurate for many possible partitions of the training set suggests that slight modiﬁcations to this set are unlikely to lead to substantially different results on the entire dataset.
V.A.

Limitations

It shall be noted that our predictions are based on expanding the premises about the tasks that computer-controlled equipment can be expected to perform.
Hence, we focus on estimating the share of employment that can potentially be substituted by computer capital, from a technological capabilities point of view, over some unspeciﬁed number of years. We make no attempt to estimate how many jobs will actually be automated. The actual extent and pace of computerisation will depend on several additional factors which were left unaccounted for. First, labour saving inventions may only be adopted if the access to cheap labour is scarce or prices of capital are relatively high (Habakkuk, 1962).22 We
22

For example, case study evidence suggests that mechanisation in eighteenth century cotton production initially only occurred in Britain because wage levels were much higher relative to prices of capital than in other countries (Allen, 2009b). In addition, recent empirical research

42

do not account for future wage levels, capital prices or labour shortages. While these factors will impact on the timeline of our predictions, labour is the scarce factor, implying that in the long-run wage levels will increase relative to capital prices, making computerisation increasingly proﬁtable (see, for example,
Acemoglu, 2003).
Second, regulatory concerns and political activism may slow down the process of computerisation. The states of California and Nevada are, for example, currently in the process of making legislatory changes to allow for driverless cars. Similar steps will be needed in other states, and in relation to various technologies. The extent and pace of legislatory implementation can furthermore be related to the public acceptance of technological progress.23 Although resistance to technological progress has become seemingly less common since the Industrial Revolution, there are recent examples of resistance to technological change.24 We avoid making predictions about the legislatory process and the public acceptance of technological progress, and thus the pace of computerisation.
Third, making predictions about technological progress is notoriously difﬁcult (Armstrong and Sotala, 2012).25 For this reason, we focus on near-term technological breakthroughs in ML and MR, and avoid making any predictions about the number of years it may take to overcome various engineering bottlenecks to computerisation. Finally, we emphasise that since our probability estimates describe the likelihood of an occupation being fully automated, we do not capture any within-occupation variation resulting from the computerisation of tasks that simply free-up time for human labour to perform other tasks. reveals a causal relationship between the access to cheap labour and mechanisation in agricultural production, in terms of sustained economic transition towards increased mechanisation in areas characterised by low-wage worker out-migration (Hornbeck and Naidu, 2013).
23
For instance, William Huskisson, former cabinet minister and Member of Parliament for
Liverpool, was killed by a steam locomotive during the opening of the Liverpool and Manchester Railway. Nonetheless, this well-publicised incident did anything but dissuade the public from railway transportation technology. By contrast, airship technology is widely recognised as having been popularly abandoned as a consequence of the reporting of the Hindenburg disaster.
24
Uber, a start-up company connecting passengers with drivers of luxury vehicles, has recently faced pressure from from local regulators, arising from tensions with taxicab services.
Furthermore, in 2011 the UK Government scrapped a 12.7 billion GBP project to introduce electronic patient records after resistance from doctors.
25
Marvin Minsky famously claimed in 1970 that “in from three to eight years we will have a machine with the general intelligence of an average human being”. This prediction is yet to materialise. 43

Although it is clear that the impact of productivity gains on employment will vary across occupations and industries, we make no attempt to examine such effects. VI.

C ONCLUSIONS

While computerisation has been historically conﬁned to routine tasks involving explicit rule-based activities (Autor, et al., 2003; Goos, et al., 2009; Autor and
Dorn, 2013), algorithms for big data are now rapidly entering domains reliant upon pattern recognition and can readily substitute for labour in a wide range of non-routine cognitive tasks (Brynjolfsson and McAfee, 2011; MGI, 2013). In addition, advanced robots are gaining enhanced senses and dexterity, allowing them to perform a broader scope of manual tasks (IFR, 2012b; Robotics-VO,
2013; MGI, 2013). This is likely to change the nature of work across industries and occupations.
In this paper, we ask the question: how susceptible are current jobs to these technological developments? To assess this, we implement a novel methodology to estimate the probability of computerisation for 702 detailed occupations.
Based on these estimates, we examine expected impacts of future computerisation on labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupation’s probability of computerisation, wages and educational attainment.
We distinguish between high, medium and low risk occupations, depending on their probability of computerisation. We make no attempt to estimate the number of jobs that will actually be automated, and focus on potential job automatability over some unspeciﬁed number of years. According to our estimates around 47 percent of total US employment is in the high risk category. We refer to these as jobs at risk – i.e. jobs we expect could be automated relatively soon, perhaps over the next decade or two.
Our model predicts that most workers in transportation and logistics occupations, together with the bulk of ofﬁce and administrative support workers, and labour in production occupations, are at risk. These ﬁndings are consistent with recent technological developments documented in the literature. More surprisingly, we ﬁnd that a substantial share of employment in service occupations, where most US job growth has occurred over the past decades (Autor and Dorn,
44

2013), are highly susceptible to computerisation. Additional support for this ﬁnding is provided by the recent growth in the market for service robots (MGI,
2013) and the gradually diminishment of the comparative advantage of human labour in tasks involving mobility and dexterity (Robotics-VO, 2013).
Finally, we provide evidence that wages and educational attainment exhibit a strong negative relationship with the probability of computerisation. We note that this ﬁnding implies a discontinuity between the nineteenth, twentieth and the twenty-ﬁrst century, in the impact of capital deepening on the relative demand for skilled labour. While nineteenth century manufacturing technologies largely substituted for skilled labour through the simpliﬁcation of tasks (Braverman, 1974; Hounshell, 1985; James and Skinner, 1985; Goldin and Katz, 1998), the Computer Revolution of the twentieth century caused a hollowing-out of middle-income jobs (Goos, et al., 2009; Autor and Dorn, 2013). Our model predicts a truncation in the current trend towards labour market polarisation, with computerisation being principally conﬁned to low-skill and low-wage occupations. Our ﬁndings thus imply that as technology races ahead, low-skill workers will reallocate to tasks that are non-susceptible to computerisation –
i.e., tasks requiring creative and social intelligence. For workers to win the race, however, they will have to acquire creative and social skills.
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56

A PPENDIX
The table below ranks occupations according to their probability of computerisation (from least- to most-computerisable). Those occupations used as training data are labelled as either ‘0’ (not computerisable) or ‘1’ (computerisable), respectively. There are 70 such occupations, 10 percent of the total number of occupations. Computerisable
Rank

Probability

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.

0.0028
0.003
0.003
0.0031
0.0033
0.0035
0.0035
0.0035
0.0036
0.0036
0.0039
0.0039
0.004
0.0041
0.0042
0.0042
0.0043
0.0044
0.0044
0.0044
0.0045
0.0046
0.0046
0.0047
0.0048
0.0049
0.0055
0.0055
0.0061
0.0063
0.0064
0.0065
0.0067
0.0068
0.0071
0.0073
0.0074
0.0075
0.0077

Label

0

0

0

0

SOC

code

29-1125
49-1011
11-9161
21-1023
29-1181
29-1122
29-2091
21-1022
29-1022
33-1021
29-1031
11-9081
27-2032
41-9031
29-1060
25-9031
19-3039
33-1012
29-1021
25-2021
19-1042
11-9032
29-1081
19-3031
21-1014
51-6092
27-1027
11-3121
39-9032
11-3131
29-1127
15-1121
11-9151
25-4012
29-9091
11-9111
25-2011
25-9021
19-3091

Occupation
Recreational Therapists
First-Line Supervisors of Mechanics, Installers, and Repairers
Emergency Management Directors
Mental Health and Substance Abuse Social Workers
Audiologists
Occupational Therapists
Orthotists and Prosthetists
Healthcare Social Workers
Oral and Maxillofacial Surgeons
First-Line Supervisors of Fire Fighting and Prevention Workers
Dietitians and Nutritionists
Lodging Managers
Choreographers
Sales Engineers
Physicians and Surgeons
Instructional Coordinators
Psychologists, All Other
First-Line Supervisors of Police and Detectives
Dentists, General
Elementary School Teachers, Except Special Education
Medical Scientists, Except Epidemiologists
Education Administrators, Elementary and Secondary School
Podiatrists
Clinical, Counseling, and School Psychologists
Mental Health Counselors
Fabric and Apparel Patternmakers
Set and Exhibit Designers
Human Resources Managers
Recreation Workers
Training and Development Managers
Speech-Language Pathologists
Computer Systems Analysts
Social and Community Service Managers
Curators
Athletic Trainers
Medical and Health Services Managers
Preschool Teachers, Except Special Education
Farm and Home Management Advisors
Anthropologists and Archeologists

57

Computerisable
Rank

Probability

40.
41.

0.0077
0.0078

42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.

0.0081
0.0081
0.0085
0.0088
0.009
0.0094
0.0095
0.0095
0.01
0.01
0.01
0.011
0.012
0.012
0.012
0.012
0.013
0.013
0.014
0.014
0.014
0.014
0.014
0.014
0.015
0.015
0.015
0.015
0.015
0.015
0.015
0.016
0.016
0.016
0.016
0.017
0.017
0.017
0.018
0.018
0.018
0.018
0.019
0.02
0.021
0.021

Label

SOC

code

25-2054
25-2031
0

0

0

0
0

0

21-2011
19-1032
21-1012
25-2032
29-1111
21-1015
25-3999
19-4092
39-5091
17-2121
11-9033
17-2141
29-1051
13-1081
19-1022
19-3032
27-2022
11-2022
19-2043
11-2021
21-1013
17-2199
13-1151
43-1011
19-1029
11-2031
27-1014
15-1111
11-1011
11-9031
27-2041
51-1011
41-3031
19-1031
25-2053
17-2041
11-9041
17-2011
11-9121
17-2081
17-1011
31-2021
17-2051
29-1199
19-1013
19-2032

Occupation
Special Education Teachers, Secondary School
Secondary School Teachers, Except Special and Career/Technical Education
Clergy
Foresters
Educational, Guidance, School, and Vocational Counselors
Career/Technical Education Teachers, Secondary School
Registered Nurses
Rehabilitation Counselors
Teachers and Instructors, All Other
Forensic Science Technicians
Makeup Artists, Theatrical and Performance
Marine Engineers and Naval Architects
Education Administrators, Postsecondary
Mechanical Engineers
Pharmacists
Logisticians
Microbiologists
Industrial-Organizational Psychologists
Coaches and Scouts
Sales Managers
Hydrologists
Marketing Managers
Marriage and Family Therapists
Engineers, All Other
Training and Development Specialists
First-Line Supervisors of Ofﬁce and Administrative Support Workers
Biological Scientists, All Other
Public Relations and Fundraising Managers
Multimedia Artists and Animators
Computer and Information Research Scientists
Chief Executives
Education Administrators, Preschool and Childcare Center/Program
Music Directors and Composers
First-Line Supervisors of Production and Operating Workers
Securities, Commodities, and Financial Services Sales Agents
Conservation Scientists
Special Education Teachers, Middle School
Chemical Engineers
Architectural and Engineering Managers
Aerospace Engineers
Natural Sciences Managers
Environmental Engineers
Architects, Except Landscape and Naval
Physical Therapist Assistants
Civil Engineers
Health Diagnosing and Treating Practitioners, All Other
Soil and Plant Scientists
Materials Scientists

58

Computerisable
Rank

Probability

Label

88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.

0.021
0.021
0.021
0.021
0.022
0.022
0.023
0.023
0.025
0.025
0.025
0.027
0.027
0.028
0.028
0.028

104.
105.

0.029
0.029

17-2112
53-1031

106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126.
127.
128.
129.
130.
131.
132.
133.
134.

0.029
0.03
0.03
0.03
0.03
0.03
0.032
0.033
0.033
0.035
0.035
0.035
0.035
0.037
0.037
0.037
0.038
0.038
0.039
0.039
0.04
0.04
0.041
0.041
0.042
0.042
0.043
0.045
0.045

29-2056
11-3051
17-3026
15-1142
15-1141
11-3061
25-1000
19-2041
21-1011
23-1011
27-1012
15-2031
11-3021
27-1021
17-2031
13-1121
29-1131
27-3043
11-2011
19-3094
13-2071
19-3099
19-2011
53-5031
15-1132
27-1013
29-2053
17-1012
21-1091

0

0
0

0

0

SOC

code

17-2131
27-1022
29-1123
27-4021
27-2012
27-1025
29-1023
27-1011
33-1011
21-2021
17-2072
19-1021
29-1011
31-2011
21-1021
17-2111

Occupation
Materials Engineers
Fashion Designers
Physical Therapists
Photographers
Producers and Directors
Interior Designers
Orthodontists
Art Directors
First-Line Supervisors of Correctional Ofﬁcers
Directors, Religious Activities and Education
Electronics Engineers, Except Computer
Biochemists and Biophysicists
Chiropractors
Occupational Therapy Assistants
Child, Family, and School Social Workers
Health and Safety Engineers, Except Mining Safety Engineers and Inspectors
Industrial Engineers
First-Line Supervisors of Transportation and Material-Moving Machine and Vehicle Operators
Veterinary Technologists and Technicians
Industrial Production Managers
Industrial Engineering Technicians
Network and Computer Systems Administrators
Database Administrators
Purchasing Managers
Postsecondary Teachers
Environmental Scientists and Specialists, Including Health
Substance Abuse and Behavioral Disorder Counselors
Lawyers
Craft Artists
Operations Research Analysts
Computer and Information Systems Managers
Commercial and Industrial Designers
Biomedical Engineers
Meeting, Convention, and Event Planners
Veterinarians
Writers and Authors
Advertising and Promotions Managers
Political Scientists
Credit Counselors
Social Scientists and Related Workers, All Other
Astronomers
Ship Engineers
Software Developers, Applications
Fine Artists, Including Painters, Sculptors, and Illustrators
Psychiatric Technicians
Landscape Architects
Health Educators

59

Computerisable
Rank

Probability

135.
136.
137.
138.
139.
140.
141.
142.
143.
144.
145.
146.
147.
148.
149.
150.
151.
152.
153.
154.
155.
156.
157.
158.
159.
160.
161.
162.
163.
164.
165.
166.
167.
168.
169.
170.
171.
172.
173.
174.
175.
176.
177.
178.
179.
180.
181.
182.
183.

0.047
0.047
0.047
0.048
0.049
0.055
0.055
0.055
0.057
0.058
0.059
0.06
0.061
0.064
0.066
0.066
0.067
0.069
0.07
0.071
0.074
0.075
0.076
0.077
0.08
0.08
0.082
0.083
0.084
0.085
0.091
0.097
0.098
0.099
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.11
0.11
0.11
0.13
0.13
0.13
0.13
0.13

Label

0

0

0

0

0

0
0

SOC

code

15-2021
27-1023
11-9013
33-2022
29-2041
27-3041
29-1024
29-9799
39-7012
29-2061
19-3041
23-1022
19-1011
39-9041
53-1011
29-1126
27-3021
11-3031
17-2161
11-9021
27-2042
41-1012
39-1021
19-1012
13-1041
33-3031
27-1024
11-9051
39-9011
39-9031
11-9071
49-9051
33-3051
41-3041
35-1011
39-2011
27-3011
17-2071
19-2031
29-2054
19-2012
39-5012
27-3022
53-2021
27-2031
29-2033
15-1133
13-1111
29-2051

Occupation
Mathematicians
Floral Designers
Farmers, Ranchers, and Other Agricultural Managers
Forest Fire Inspectors and Prevention Specialists
Emergency Medical Technicians and Paramedics
Editors
Prosthodontists
Healthcare Practitioners and Technical Workers, All Other
Travel Guides
Licensed Practical and Licensed Vocational Nurses
Sociologists
Arbitrators, Mediators, and Conciliators
Animal Scientists
Residential Advisors
Aircraft Cargo Handling Supervisors
Respiratory Therapists
Broadcast News Analysts
Financial Managers
Nuclear Engineers
Construction Managers
Musicians and Singers
First-Line Supervisors of Non-Retail Sales Workers
First-Line Supervisors of Personal Service Workers
Food Scientists and Technologists
Compliance Ofﬁcers
Fish and Game Wardens
Graphic Designers
Food Service Managers
Childcare Workers
Fitness Trainers and Aerobics Instructors
Gaming Managers
Electrical Power-Line Installers and Repairers
Police and Sheriff’s Patrol Ofﬁcers
Travel Agents
Chefs and Head Cooks
Animal Trainers
Radio and Television Announcers
Electrical Engineers
Chemists
Respiratory Therapy Technicians
Physicists
Hairdressers, Hairstylists, and Cosmetologists
Reporters and Correspondents
Air Trafﬁc Controllers
Dancers
Nuclear Medicine Technologists
Software Developers, Systems Software
Management Analysts
Dietetic Technicians

60

Computerisable
Rank

Probability

Label

184.
185.
186.
187.
188.
189.
190.
191.
192.
193.
194.
195.
196.
197.
198.
199.
200.

0.13
0.13
0.13
0.13
0.14
0.14
0.14
0.15
0.15
0.16
0.16
0.16
0.17
0.17
0.17
0.17
0.17

19-3051
21-1093
25-3021
27-4014
29-1041
17-2151
29-1071
25-2012
47-2111
17-2171
43-9031
11-1021
29-9011
33-2011
13-2061
47-1011
25-2022

201.
202.
203.
204.
205.

0.18
0.18
0.18
0.18
0.19

27-3031
49-9092
49-9095
53-2011
25-3011

206.
207.
208.

0.2
0.2
0.21

19-1041
39-4831
15-1179

209.
210.
211.
212.
213.
214.
215.
216.
217.
218.
219.
220.
221.
222.
223.
224.
225.
226.
227.

0.21
0.21
0.21
0.22
0.22
0.22
0.23
0.23
0.23
0.23
0.23
0.24
0.24
0.25
0.25
0.25
0.25
0.25
0.25

15-2011
33-9011
39-6012
15-1799
15-2041
17-2061
19-3022
13-1199
13-2051
29-2037
29-2031
13-1011
17-3029
19-3092
29-9012
21-1092
17-3025
11-9199
53-3011

228.

0.25

0

SOC

code

41-4011

Occupation
Urban and Regional Planners
Social and Human Service Assistants
Self-Enrichment Education Teachers
Sound Engineering Technicians
Optometrists
Mining and Geological Engineers, Including Mining Safety Engineers
Physician Assistants
Kindergarten Teachers, Except Special Education
Electricians
Petroleum Engineers
Desktop Publishers
General and Operations Managers
Occupational Health and Safety Specialists
Fireﬁghters
Financial Examiners
First-Line Supervisors of Construction Trades and Extraction Workers
Middle School Teachers, Except Special and Career/Technical Education
Public Relations Specialists
Commercial Divers
Manufactured Building and Mobile Home Installers
Airline Pilots, Copilots, and Flight Engineers
Adult Basic and Secondary Education and Literacy Teachers and Instructors
Epidemiologists
Funeral Service Managers, Directors, Morticians, and Undertakers
Information Security Analysts, Web Developers, and Computer Network Architects
Actuaries
Animal Control Workers
Concierges
Computer Occupations, All Other
Statisticians
Computer Hardware Engineers
Survey Researchers
Business Operations Specialists, All Other
Financial Analysts
Radiologic Technologists and Technicians
Cardiovascular Technologists and Technicians
Agents and Business Managers of Artists, Performers, and Athletes
Engineering Technicians, Except Drafters, All Other
Geographers
Occupational Health and Safety Technicians
Probation Ofﬁcers and Correctional Treatment Specialists
Environmental Engineering Technicians
Managers, All Other
Ambulance Drivers and Attendants, Except Emergency Medical Technicians
Sales Representatives, Wholesale and Manufacturing, Technical and
Scientiﬁc Products

61

Computerisable
Rank

Probability

Label

229.
230.
231.
232.
233.
234.
235.
236.
237.
238.
239.
240.
241.
242.
243.
244.
245.
246.
247.
248.
249.
250.
251.
252.
253.

0.26
0.27
0.27
0.27
0.28
0.28
0.28
0.29
0.29
0.3
0.3
0.3
0.3
0.31
0.31
0.31
0.33
0.34
0.34
0.34
0.35
0.35
0.35
0.36
0.36

254.

0.36

49-2022

255.
256.
257.
258.
259.
260.
261.

0.37
0.37
0.37
0.37
0.37
0.37
0.38

51-9051
53-7061
39-4021
47-5081
27-2011
53-7111
49-2095

262.
263.
264.
265.
266.
267.
268.
269.
270.
271.
272.
273.
274.

0.38
0.38
0.38
0.38
0.39
0.39
0.39
0.39
0.39
0.4
0.4
0.4
0.41

0

0

0
0

1

0

SOC

code

25-2023
53-5021
31-2012
49-9062
41-1011
27-2021
39-1011
39-5094
13-1022
19-4021
31-9092
19-1023
35-2013
13-1078
33-9021
27-4032
13-2099
33-3021
29-2055
29-1124
47-2152
53-2031
29-2032
33-3011
51-4012

17-1022
17-3027
53-7064
27-3091
31-1011
51-6093
47-4021
43-3041
25-9011
23-1023
49-3042
29-2799
45-2041

Occupation
Career/Technical Education Teachers, Middle School
Captains, Mates, and Pilots of Water Vessels
Occupational Therapy Aides
Medical Equipment Repairers
First-Line Supervisors of Retail Sales Workers
Athletes and Sports Competitors
Gaming Supervisors
Skincare Specialists
Wholesale and Retail Buyers, Except Farm Products
Biological Technicians
Medical Assistants
Zoologists and Wildlife Biologists
Cooks, Private Household
Human Resources, Training, and Labor Relations Specialists, All Other
Private Detectives and Investigators
Film and Video Editors
Financial Specialists, All Other
Detectives and Criminal Investigators
Surgical Technologists
Radiation Therapists
Plumbers, Pipeﬁtters, and Steamﬁtters
Flight Attendants
Diagnostic Medical Sonographers
Bailiffs
Computer Numerically Controlled Machine Tool Programmers, Metal and Plastic
Telecommunications Equipment Installers and Repairers, Except Line
Installers
Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders
Cleaners of Vehicles and Equipment
Funeral Attendants
Helpers–Extraction Workers
Actors
Mine Shuttle Car Operators
Electrical and Electronics Repairers, Powerhouse, Substation, and Relay
Surveyors
Mechanical Engineering Technicians
Packers and Packagers, Hand
Interpreters and Translators
Home Health Aides
Upholsterers
Elevator Installers and Repairers
Gaming Cage Workers
Audio-Visual and Multimedia Collections Specialists
Judges, Magistrate Judges, and Magistrates
Mobile Heavy Equipment Mechanics, Except Engines
Health Technologists and Technicians, All Other
Graders and Sorters, Agricultural Products

62

Computerisable
Rank

Probability

Label

SOC

275.
276.
277.

0.41
0.41
0.41

1

51-2041
23-1012
49-2094

278.
279.

0.42
0.42

19-4093
53-1021

280.
281.
282.
283.
284.
285.
286.
287.
288.
289.
290.
291.
292.
293.
294.
295.
296.
297.
298.
299.
300.
301.
302.
303.
304.
305.
306.
307.
308.
309.
310.
311.
312.
313.
314.
315.
316.
317.
318.
319.
320.
321.

0.43
0.43
0.43
0.44
0.45
0.46
0.47
0.47
0.47
0.48
0.48
0.48
0.48
0.48
0.49
0.49
0.49
0.49
0.49
0.5
0.5
0.5
0.51
0.51
0.52
0.52
0.53
0.53
0.54
0.54
0.54
0.54
0.54
0.55
0.55
0.55
0.55
0.56
0.57
0.57
0.57
0.57

39-3093
19-2099
19-3011
19-3093
51-9082
43-4031
13-1141
31-1013
29-2012
33-2021
17-3021
27-1026
47-5031
15-1131
33-9091
17-2021
47-5061
49-9052
43-5031
53-7033
49-9799
23-2091
41-9011
31-9091
51-6041
17-3011
47-5012
47-4041
39-4011
47-5041
39-1012
31-9011
41-3011
49-3022
53-2012
43-4051
27-4011
25-9041
45-1011
19-4031
47-3015
13-1051

0

1

code

Occupation
Structural Metal Fabricators and Fitters
Judicial Law Clerks
Electrical and Electronics Repairers, Commercial and Industrial Equipment
Forest and Conservation Technicians
First-Line Supervisors of Helpers, Laborers, and Material Movers,
Hand
Locker Room, Coatroom, and Dressing Room Attendants
Physical Scientists, All Other
Economists
Historians
Medical Appliance Technicians
Court, Municipal, and License Clerks
Compensation, Beneﬁts, and Job Analysis Specialists
Psychiatric Aides
Medical and Clinical Laboratory Technicians
Fire Inspectors and Investigators
Aerospace Engineering and Operations Technicians
Merchandise Displayers and Window Trimmers
Explosives Workers, Ordnance Handling Experts, and Blasters
Computer Programmers
Crossing Guards
Agricultural Engineers
Roof Bolters, Mining
Telecommunications Line Installers and Repairers
Police, Fire, and Ambulance Dispatchers
Loading Machine Operators, Underground Mining
Installation, Maintenance, and Repair Workers, All Other
Court Reporters
Demonstrators and Product Promoters
Dental Assistants
Shoe and Leather Workers and Repairers
Architectural and Civil Drafters
Rotary Drill Operators, Oil and Gas
Hazardous Materials Removal Workers
Embalmers
Continuous Mining Machine Operators
Slot Supervisors
Massage Therapists
Advertising Sales Agents
Automotive Glass Installers and Repairers
Commercial Pilots
Customer Service Representatives
Audio and Video Equipment Technicians
Teacher Assistants
First-Line Supervisors of Farming, Fishing, and Forestry Workers
Chemical Technicians
Helpers–Pipelayers, Plumbers, Pipeﬁtters, and Steamﬁtters
Cost Estimators

63

Computerisable
Rank

Probability

Label

322.
323.

0.57
0.57

33-3052
37-1012

324.
325.
326.
327.
328.
329.
330.
331.
332.
333.
334.
335.
336.
337.
338.
339.
340.
341.
342.

0.58
0.59
0.59
0.59
0.59
0.59
0.59
0.6
0.6
0.6
0.61
0.61
0.61
0.61
0.61
0.61
0.61
0.61
0.61

13-2052
49-9044
25-4013
47-5042
11-3071
49-3092
49-3023
33-3012
27-4031
51-3023
49-2096
31-2022
39-3092
13-1161
43-4181
51-8031
19-4099
51-3093
51-4122

343.
344.
345.
346.
347.
348.
349.
350.
351.
352.
353.
354.
355.
356.
357.
358.
359.
360.
361.
362.
363.
364.
365.
366.
367.
368.

0.62
0.62
0.62
0.63
0.63
0.63
0.63
0.63
0.64
0.64
0.64
0.64
0.64
0.64
0.65
0.65
0.65
0.65
0.65
0.65
0.65
0.66
0.66
0.66
0.66
0.66

0

1

1

SOC

code

53-5022
47-2082
47-2151
19-2042
49-9012
31-9799
35-1012
47-4011
51-9031
49-9071
23-1021
43-5081
51-8012
47-2132
19-4061
51-4041
15-1150
25-4021
49-2097
49-9021
53-7041
37-2021
51-9198
43-9111
37-2011
49-3051

Occupation
Transit and Railroad Police
First-Line Supervisors of Landscaping, Lawn Service, and
Groundskeeping Workers
Personal Financial Advisors
Millwrights
Museum Technicians and Conservators
Mine Cutting and Channeling Machine Operators
Transportation, Storage, and Distribution Managers
Recreational Vehicle Service Technicians
Automotive Service Technicians and Mechanics
Correctional Ofﬁcers and Jailers
Camera Operators, Television, Video, and Motion Picture
Slaughterers and Meat Packers
Electronic Equipment Installers and Repairers, Motor Vehicles
Physical Therapist Aides
Costume Attendants
Market Research Analysts and Marketing Specialists
Reservation and Transportation Ticket Agents and Travel Clerks
Water and Wastewater Treatment Plant and System Operators
Life, Physical, and Social Science Technicians, All Other
Food Cooking Machine Operators and Tenders
Welding, Soldering, and Brazing Machine Setters, Operators, and Tenders
Motorboat Operators
Tapers
Pipelayers
Geoscientists, Except Hydrologists and Geographers
Control and Valve Installers and Repairers, Except Mechanical Door
Healthcare Support Workers, All Other
First-Line Supervisors of Food Preparation and Serving Workers
Construction and Building Inspectors
Cutters and Trimmers, Hand
Maintenance and Repair Workers, General
Administrative Law Judges, Adjudicators, and Hearing Ofﬁcers
Stock Clerks and Order Fillers
Power Distributors and Dispatchers
Insulation Workers, Mechanical
Social Science Research Assistants
Machinists
Computer Support Specialists
Librarians
Electronic Home Entertainment Equipment Installers and Repairers
Heating, Air Conditioning, and Refrigeration Mechanics and Installers
Hoist and Winch Operators
Pest Control Workers
Helpers–Production Workers
Statistical Assistants
Janitors and Cleaners, Except Maids and Housekeeping Cleaners
Motorboat Mechanics and Service Technicians

64

Computerisable
Rank

Probability

369.
370.
371.
372.
373.

0.67
0.67
0.67
0.67
0.67

374.
375.
376.
377.
378.
379.
380.
381.
382.
383.
384.
385.
386.
387.
388.
389.
390.
391.
392.
393.
394.
395.
396.
397.
398.
399.
400.
401.
402.
403.
404.
405.
406.
407.
408.
409.
410.
411.
412.
413.
414.
415.
416.

0.67
0.68
0.68
0.68
0.68
0.68
0.69
0.69
0.69
0.7
0.7
0.7
0.7
0.71
0.71
0.71
0.71
0.71
0.71
0.72
0.72
0.72
0.72
0.72
0.72
0.72
0.73
0.73
0.73
0.73
0.73
0.74
0.74
0.74
0.74
0.75
0.75
0.75
0.75
0.75
0.75
0.76
0.76

Label

1

1
0

1

SOC

code

51-9196
51-4071
19-2021
53-3021
33-9092
49-9041
43-5052
47-5071
47-2011
17-3013
29-2021
53-3033
37-2012
51-9122
43-4061
49-3093
51-3092
49-2091
49-3011
53-2022
51-8093
47-4799
29-2081
51-6011
39-3091
31-9095
47-3016
53-7121
49-9031
47-2031
27-3012
51-6063
11-3011
47-2121
51-2021
49-3031
49-2011
39-9021
27-4012
47-3013
11-9131
47-2044
47-2141
53-6061
17-3022
49-3041
25-4011
51-9011

Occupation
Paper Goods Machine Setters, Operators, and Tenders
Foundry Mold and Coremakers
Atmospheric and Space Scientists
Bus Drivers, Transit and Intercity
Lifeguards, Ski Patrol, and Other Recreational Protective Service Workers
Industrial Machinery Mechanics
Postal Service Mail Carriers
Roustabouts, Oil and Gas
Boilermakers
Mechanical Drafters
Dental Hygienists
Light Truck or Delivery Services Drivers
Maids and Housekeeping Cleaners
Painters, Transportation Equipment
Eligibility Interviewers, Government Programs
Tire Repairers and Changers
Food Batchmakers
Avionics Technicians
Aircraft Mechanics and Service Technicians
Airﬁeld Operations Specialists
Petroleum Pump System Operators, Reﬁnery Operators, and Gaugers
Construction and Related Workers, All Other
Opticians, Dispensing
Laundry and Dry-Cleaning Workers
Amusement and Recreation Attendants
Pharmacy Aides
Helpers–Roofers
Tank Car, Truck, and Ship Loaders
Home Appliance Repairers
Carpenters
Public Address System and Other Announcers
Textile Knitting and Weaving Machine Setters, Operators, and Tenders
Administrative Services Managers
Glaziers
Coil Winders, Tapers, and Finishers
Bus and Truck Mechanics and Diesel Engine Specialists
Computer, Automated Teller, and Ofﬁce Machine Repairers
Personal Care Aides
Broadcast Technicians
Helpers–Electricians
Postmasters and Mail Superintendents
Tile and Marble Setters
Painters, Construction and Maintenance
Transportation Attendants, Except Flight Attendants
Civil Engineering Technicians
Farm Equipment Mechanics and Service Technicians
Archivists
Chemical Equipment Operators and Tenders

65

Computerisable
Rank

Probability

Label

417.
418.
419.
420.
421.
422.
423.
424.
425.
426.
427.

0.76
0.76
0.77
0.77
0.77
0.77
0.77
0.77
0.77
0.78
0.78

428.
429.
430.

0.78
0.78
0.79

43-9011
51-8092
43-5053

431.
432.
433.
434.
435.
436.
437.
438.
439.
440.
441.
442.
443.
444.
445.

0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.8
0.8
0.81
0.81
0.81
0.81
0.81

53-3032
39-5093
47-2081
49-9098
49-3052
51-2011
45-4022
47-2042
39-5011
47-5011
35-2011
43-9022
17-3012
17-3024
51-9192

446.
447.
448.
449.
450.
451.
452.
453.
454.
455.
456.
457.
458.
459.
460.
461.
462.

0.81
0.81
0.81
0.82
0.82
0.82
0.82
0.82
0.82
0.82
0.83
0.83
0.83
0.83
0.83
0.83
0.83

1
0

1
1

1

SOC

code

49-2092
45-4021
19-4091
49-9094
37-3013
35-3011
13-1023
35-9021
45-3021
31-9093
51-4031

11-9141
43-6013
51-6021
51-2031
49-2098
49-9045
39-2021
47-2211
47-2072
47-2021
45-3011
47-2221
53-4021
53-4031
35-2012
53-5011
51-9023

Occupation
Electric Motor, Power Tool, and Related Repairers
Fallers
Environmental Science and Protection Technicians, Including Health
Locksmiths and Safe Repairers
Tree Trimmers and Pruners
Bartenders
Purchasing Agents, Except Wholesale, Retail, and Farm Products
Dishwashers
Hunters and Trappers
Medical Equipment Preparers
Cutting, Punching, and Press Machine Setters, Operators, and Tenders,
Metal and Plastic
Computer Operators
Gas Plant Operators
Postal Service Mail Sorters, Processors, and Processing Machine Operators
Heavy and Tractor-Trailer Truck Drivers
Shampooers
Drywall and Ceiling Tile Installers
Helpers–Installation, Maintenance, and Repair Workers
Motorcycle Mechanics
Aircraft Structure, Surfaces, Rigging, and Systems Assemblers
Logging Equipment Operators
Floor Layers, Except Carpet, Wood, and Hard Tiles
Barbers
Derrick Operators, Oil and Gas
Cooks, Fast Food
Word Processors and Typists
Electrical and Electronics Drafters
Electro-Mechanical Technicians
Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders
Property, Real Estate, and Community Association Managers
Medical Secretaries
Pressers, Textile, Garment, and Related Materials
Engine and Other Machine Assemblers
Security and Fire Alarm Systems Installers
Refractory Materials Repairers, Except Brickmasons
Nonfarm Animal Caretakers
Sheet Metal Workers
Pile-Driver Operators
Brickmasons and Blockmasons
Fishers and Related Fishing Workers
Structural Iron and Steel Workers
Railroad Brake, Signal, and Switch Operators
Railroad Conductors and Yardmasters
Cooks, Institution and Cafeteria
Sailors and Marine Oilers
Mixing and Blending Machine Setters, Operators, and Tenders

66

Computerisable
Rank

Probability

Label

463.

0.83

47-3011

464.
465.
466.
467.
468.
469.
470.
471.
472.
473.
474.
475.
476.
477.

0.83
0.83
0.83
0.83
0.83
0.83
0.83
0.83
0.83
0.84
0.84
0.84
0.84
0.84

47-4091
47-2131
51-5112
53-6031
47-4071
39-6011
41-2012
51-4023
47-2071
51-4111
17-3023
47-2161
51-4192
51-4034

478.
479.
480.
481.
482.
483.
484.

0.84
0.84
0.84
0.84
0.84
0.85
0.85

33-9032
51-6052
53-7073
43-9081
33-3041
53-7062
41-4012

485.
486.
487.
488.
489.
490.
491.
492.
493.
494.
495.
496.
497.
498.
499.
500.
501.
502.
503.
504.
505.
506.
507.
508.

0.85
0.85
0.85
0.85
0.85
0.86
0.86
0.86
0.86
0.86
0.86
0.86
0.86
0.86
0.86
0.86
0.87
0.87
0.87
0.87
0.87
0.87
0.87
0.87

1

1

SOC

code

43-5041
51-8013
51-8091
47-5021
19-4051
43-6011
51-8099
35-3041
51-7041
53-4041
31-9096
51-9032
41-9022
51-4011
49-9043
43-4021
45-2090
45-4011
51-4052
47-2041
47-2142
13-1021
51-7021
35-2021

Occupation
Helpers–Brickmasons, Blockmasons, Stonemasons, and Tile and Marble Setters
Segmental Pavers
Insulation Workers, Floor, Ceiling, and Wall
Printing Press Operators
Automotive and Watercraft Service Attendants
Septic Tank Servicers and Sewer Pipe Cleaners
Baggage Porters and Bellhops
Gaming Change Persons and Booth Cashiers
Rolling Machine Setters, Operators, and Tenders, Metal and Plastic
Paving, Surfacing, and Tamping Equipment Operators
Tool and Die Makers
Electrical and Electronics Engineering Technicians
Plasterers and Stucco Masons
Layout Workers, Metal and Plastic
Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic
Security Guards
Tailors, Dressmakers, and Custom Sewers
Wellhead Pumpers
Proofreaders and Copy Markers
Parking Enforcement Workers
Laborers and Freight, Stock, and Material Movers, Hand
Sales Representatives, Wholesale and Manufacturing, Except Technical and Scientiﬁc Products
Meter Readers, Utilities
Power Plant Operators
Chemical Plant and System Operators
Earth Drillers, Except Oil and Gas
Nuclear Technicians
Executive Secretaries and Executive Administrative Assistants
Plant and System Operators, All Other
Food Servers, Nonrestaurant
Sawing Machine Setters, Operators, and Tenders, Wood
Subway and Streetcar Operators
Veterinary Assistants and Laboratory Animal Caretakers
Cutting and Slicing Machine Setters, Operators, and Tenders
Real Estate Sales Agents
Computer-Controlled Machine Tool Operators, Metal and Plastic
Maintenance Workers, Machinery
Correspondence Clerks
Miscellaneous Agricultural Workers
Forest and Conservation Workers
Pourers and Casters, Metal
Carpet Installers
Paperhangers
Buyers and Purchasing Agents, Farm Products
Furniture Finishers
Food Preparation Workers

67

Computerisable
Rank

Probability

Label

509.
510.
511.
512.
513.
514.
515.
516.
517.

0.87
0.87
0.87
0.88
0.88
0.88
0.88
0.88
0.88

518.

0.88

51-6091

519.
520.
521.
522.
523.
524.
525.
526.
527.
528.
529.
530.
531.
532.
533.
534.
535.
536.
537.
538.
539.
540.
541.
542.
543.
544.
545.
546.
547.
548.

0.88
0.88
0.88
0.89
0.89
0.89
0.89
0.89
0.89
0.89
0.89
0.89
0.89
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.91
0.91
0.91
0.91
0.91

47-2053
51-4194
49-3043
51-3011
31-9094
47-2022
53-3022
27-3042
49-9096
47-4061
51-8021
51-6031
53-3041
43-4161
29-2011
47-2171
47-2181
53-7021
53-6041
53-6051
51-4062
51-9195
13-2021
53-7072
49-9097
39-3012
49-9063
39-7011
49-9011
51-3091

549.
550.
551.

0.91
0.91
0.91

53-7071
29-2071
51-9121

552.

0.91

51-4081

1

1

1

1
1
1

SOC

code

47-2043
53-6021
47-4051
47-2061
43-5061
51-9141
17-1021
51-4051
51-9012

Occupation
Floor Sanders and Finishers
Parking Lot Attendants
Highway Maintenance Workers
Construction Laborers
Production, Planning, and Expediting Clerks
Semiconductor Processors
Cartographers and Photogrammetrists
Metal-Reﬁning Furnace Operators and Tenders
Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders
Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers
Terrazzo Workers and Finishers
Tool Grinders, Filers, and Sharpeners
Rail Car Repairers
Bakers
Medical Transcriptionists
Stonemasons
Bus Drivers, School or Special Client
Technical Writers
Riggers
Rail-Track Laying and Maintenance Equipment Operators
Stationary Engineers and Boiler Operators
Sewing Machine Operators
Taxi Drivers and Chauffeurs
Human Resources Assistants, Except Payroll and Timekeeping
Medical and Clinical Laboratory Technologists
Reinforcing Iron and Rebar Workers
Roofers
Crane and Tower Operators
Trafﬁc Technicians
Transportation Inspectors
Patternmakers, Metal and Plastic
Molders, Shapers, and Casters, Except Metal and Plastic
Appraisers and Assessors of Real Estate
Pump Operators, Except Wellhead Pumpers
Signal and Track Switch Repairers
Gaming and Sports Book Writers and Runners
Musical Instrument Repairers and Tuners
Tour Guides and Escorts
Mechanical Door Repairers
Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders
Gas Compressor and Gas Pumping Station Operators
Medical Records and Health Information Technicians
Coating, Painting, and Spraying Machine Setters, Operators, and Tenders
Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic

68

Computerisable
Rank

Probability

Label

553.
554.

0.91
0.91

53-4013
49-2093

555.
556.

0.91
0.91

35-9011
51-4191

557.
558.
559.
560.

0.91
0.91
0.91
0.91

19-4041
49-3021
51-7032
51-4021

561.
562.
563.
564.
565.
566.
567.
568.
569.

0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92

43-9071
29-2052
43-4131
53-7031
41-3021
51-7011
51-9123
47-4031
51-4193

570.
571.
572.
573.
574.
575.
576.
577.
578.
579.
580.
581.
582.
583.
584.

0.92
0.92
0.92
0.92
0.93
0.93
0.93
0.93
0.93
0.93
0.93
0.93
0.93
0.93
0.93

41-2031
35-3021
51-9399
47-3012
51-9193
51-2091
47-5013
53-7011
49-3053
53-4012
53-7063
51-4061
49-2021
51-3021
51-9041

585.
586.
587.
588.
589.
590.

0.93
0.93
0.93
0.93
0.94
0.94

591.
592.
593.
594.
595.

0.94
0.94
0.94
0.94
0.94

1
1
1

0

SOC

code

53-7081
13-2081
51-4022
53-7051
13-2011
51-4032
43-9051
35-3031
51-3022
13-2031
47-2051

Occupation
Rail Yard Engineers, Dinkey Operators, and Hostlers
Electrical and Electronics Installers and Repairers, Transportation
Equipment
Dining Room and Cafeteria Attendants and Bartender Helpers
Heat Treating Equipment Setters, Operators, and Tenders, Metal and
Plastic
Geological and Petroleum Technicians
Automotive Body and Related Repairers
Patternmakers, Wood
Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic
Ofﬁce Machine Operators, Except Computer
Pharmacy Technicians
Loan Interviewers and Clerks
Dredge Operators
Insurance Sales Agents
Cabinetmakers and Bench Carpenters
Painting, Coating, and Decorating Workers
Fence Erectors
Plating and Coating Machine Setters, Operators, and Tenders, Metal and Plastic
Retail Salespersons
Combined Food Preparation and Serving Workers, Including Fast Food
Production Workers, All Other
Helpers–Carpenters
Cooling and Freezing Equipment Operators and Tenders
Fiberglass Laminators and Fabricators
Service Unit Operators, Oil, Gas, and Mining
Conveyor Operators and Tenders
Outdoor Power Equipment and Other Small Engine Mechanics
Locomotive Firers
Machine Feeders and Offbearers
Model Makers, Metal and Plastic
Radio, Cellular, and Tower Equipment Installers and Repairs
Butchers and Meat Cutters
Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders
Refuse and Recyclable Material Collectors
Tax Examiners and Collectors, and Revenue Agents
Forging Machine Setters, Operators, and Tenders, Metal and Plastic
Industrial Truck and Tractor Operators
Accountants and Auditors
Drilling and Boring Machine Tool Setters, Operators, and Tenders,
Metal and Plastic
Mail Clerks and Mail Machine Operators, Except Postal Service
Waiters and Waitresses
Meat, Poultry, and Fish Cutters and Trimmers
Budget Analysts
Cement Masons and Concrete Finishers

69

Computerisable
Rank

Probability

596.
597.
598.
599.
600.
601.
602.
603.
604.
605.
606.

0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94

607.
608.
609.
610.
611.
612.
613.
614.
615.
616.
617.
618.
619.
620.

0.94
0.94
0.94
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95

621.
622.
623.
624.

0.95
0.95
0.95
0.95

625.
626.
627.
628.
629.
630.
631.
632.
633.
634.

0.95
0.95
0.96
0.96
0.96
0.96
0.96
0.96
0.96
0.96

635.
636.
637.

0.96
0.96
0.96

638.
639.

0.96
0.96

Label

1

1

1

1

SOC

code

49-3091
49-9091
51-4121
43-5021
43-4111
35-2015
53-7032
47-3014
43-4081
51-9197
41-9091
37-1011
45-2011
23-2011
39-5092
43-5111
51-6062
43-3011
51-8011
33-9031
43-4121
47-2073
51-5113
45-2021
51-4072
51-2022
51-9191
37-3011
51-4033
43-5051
51-9071
43-5032
43-4171
43-9061
11-3111
43-2011
35-3022
47-5051
43-6014
17-3031
51-7031
51-6064

1

53-4011
39-3011

Occupation
Bicycle Repairers
Coin, Vending, and Amusement Machine Servicers and Repairers
Welders, Cutters, Solderers, and Brazers
Couriers and Messengers
Interviewers, Except Eligibility and Loan
Cooks, Short Order
Excavating and Loading Machine and Dragline Operators
Helpers–Painters, Paperhangers, Plasterers, and Stucco Masons
Hotel, Motel, and Resort Desk Clerks
Tire Builders
Door-to-Door Sales Workers, News and Street Vendors, and Related
Workers
First-Line Supervisors of Housekeeping and Janitorial Workers
Agricultural Inspectors
Paralegals and Legal Assistants
Manicurists and Pedicurists
Weighers, Measurers, Checkers, and Samplers, Recordkeeping
Textile Cutting Machine Setters, Operators, and Tenders
Bill and Account Collectors
Nuclear Power Reactor Operators
Gaming Surveillance Ofﬁcers and Gaming Investigators
Library Assistants, Clerical
Operating Engineers and Other Construction Equipment Operators
Print Binding and Finishing Workers
Animal Breeders
Molding, Coremaking, and Casting Machine Setters, Operators, and
Tenders, Metal and Plastic
Electrical and Electronic Equipment Assemblers
Adhesive Bonding Machine Operators and Tenders
Landscaping and Groundskeeping Workers
Grinding, Lapping, Polishing, and Bufﬁng Machine Tool Setters, Operators, and Tenders, Metal and Plastic
Postal Service Clerks
Jewelers and Precious Stone and Metal Workers
Dispatchers, Except Police, Fire, and Ambulance
Receptionists and Information Clerks
Ofﬁce Clerks, General
Compensation and Beneﬁts Managers
Switchboard Operators, Including Answering Service
Counter Attendants, Cafeteria, Food Concession, and Coffee Shop
Rock Splitters, Quarry
Secretaries and Administrative Assistants, Except Legal, Medical, and
Executive
Surveying and Mapping Technicians
Model Makers, Wood
Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders
Locomotive Engineers
Gaming Dealers

70

Computerisable
Rank

Probability

640.
641.
642.
643.
644.
645.
646.
647.
648.
649.
650.
651.
652.

0.96
0.96
0.96
0.96
0.97
0.97
0.97
0.97
0.97
0.97
0.97
0.97
0.97

653.
654.
655.
656.
657.
658.
659.
660.
661.
662.
663.
664.
665.
666.
667.
668.
669.
670.
671.
672.
673.
674.
675.
676.
677.
678.

0.97
0.97
0.97
0.97
0.97
0.97
0.97
0.97
0.97
0.97
0.97
0.97
0.97
0.97
0.97
0.97
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98

679.
680.
681.
682.
683.
684.
685.
686.

0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98

Label

1

1

1

1

1
1

1

SOC

code

49-9093
35-2014
39-3031
43-3021
53-6011
51-7042
51-2092
51-6042
51-2023
13-1074
51-6061
51-9081
51-9021
51-9022
37-3012
45-4023
51-9083
41-2011
49-9061
39-3021
51-5111
41-2021
43-4071
41-9021
43-2021
19-4011
43-3051
43-4041
35-9031
41-9012
51-9061
43-3031
43-6012
27-4013
53-3031
13-1031
41-2022
13-2041
51-4035
43-5071
43-3061
51-9111
51-9194
43-3071
27-2023
13-1032
13-2072

Occupation
Fabric Menders, Except Garment
Cooks, Restaurant
Ushers, Lobby Attendants, and Ticket Takers
Billing and Posting Clerks
Bridge and Lock Tenders
Woodworking Machine Setters, Operators, and Tenders, Except Sawing
Team Assemblers
Shoe Machine Operators and Tenders
Electromechanical Equipment Assemblers
Farm Labor Contractors
Textile Bleaching and Dyeing Machine Operators and Tenders
Dental Laboratory Technicians
Crushing, Grinding, and Polishing Machine Setters, Operators, and
Tenders
Grinding and Polishing Workers, Hand
Pesticide Handlers, Sprayers, and Applicators, Vegetation
Log Graders and Scalers
Ophthalmic Laboratory Technicians
Cashiers
Camera and Photographic Equipment Repairers
Motion Picture Projectionists
Prepress Technicians and Workers
Counter and Rental Clerks
File Clerks
Real Estate Brokers
Telephone Operators
Agricultural and Food Science Technicians
Payroll and Timekeeping Clerks
Credit Authorizers, Checkers, and Clerks
Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop
Models
Inspectors, Testers, Sorters, Samplers, and Weighers
Bookkeeping, Accounting, and Auditing Clerks
Legal Secretaries
Radio Operators
Driver/Sales Workers
Claims Adjusters, Examiners, and Investigators
Parts Salespersons
Credit Analysts
Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic
Shipping, Receiving, and Trafﬁc Clerks
Procurement Clerks
Packaging and Filling Machine Operators and Tenders
Etchers and Engravers
Tellers
Umpires, Referees, and Other Sports Ofﬁcials
Insurance Appraisers, Auto Damage
Loan Ofﬁcers

71

Computerisable
Rank

Probability

687.
688.
689.
690.
691.
692.
693.
694.
695.
696.
697.
698.
699.
700.
701.
702.

0.98
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99

Label

1

1

SOC

code

43-4151
43-4011
43-9041
51-2093
43-9021
25-4031
43-4141
51-9151
13-2082
43-5011
49-9064
13-2053
15-2091
51-6051
23-2093
41-9041

Occupation
Order Clerks
Brokerage Clerks
Insurance Claims and Policy Processing Clerks
Timing Device Assemblers and Adjusters
Data Entry Keyers
Library Technicians
New Accounts Clerks
Photographic Process Workers and Processing Machine Operators
Tax Preparers
Cargo and Freight Agents
Watch Repairers
Insurance Underwriters
Mathematical Technicians
Sewers, Hand
Title Examiners, Abstractors, and Searchers
Telemarketers

72

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