The future of_employment

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 as-
sess 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 ex-
pected 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 relation-
ship with an occupation’s probability of computerisation.
Keywords: Occupational Choice, Technological Change, Wage Inequal-
ity, Employment, Skill Demand
JEL Classiﬁcation: E24, J24, J31, J62, O33.
∗
We thank the Oxford University Engineering Sciences Department and the Oxford Mar-
tin Programme on the Impacts of Future Technology for hosting the “Machines and Employ-
ment” 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 King-
dom, mosb@robots.ox.ac.uk.
1

I. INTRODUCTION
In this paper, we address the question: how susceptible are jobs to computerisa-
tion? 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 out-
comes.
Our paper is motivated by John Maynard Keynes’s frequently cited pre-
diction 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 func-
tions 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 exam-
ple, 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 ad-
1
We refer to computerisation as job automation by means of computer-controlled equip-
ment.
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 reallo-
cating their labour supply from middle-income manufacturing to low-income
service occupations. Arguably, this is because the manual tasks of service occu-
pations 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 occupa-
tions, accompanied by a hollowing-out of middle-income routine jobs.
According to Brynjolfsson and McAfee (2011), the pace of technologi-
cal innovation is still increasing, with more sophisticated software technolo-
gies 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, Comput-
ers 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 execut-
ing 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 modi-
fied 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
3

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. Semi-
nal 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 En-
gineering 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 dif-
ferent occupational characteristics, using O∗NET data, allowing us to examine
the future direction of technological change in terms of its impact on the occu-
pational 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 im-
pact of computerisation on the occupational composition of the labour mar-
ket. 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 liter-
ature has largely focused on task measures from the Dictionary of Occupational
4

Titles (DOT), last revised in 1991, we rely on the 2010 version of the DOT suc-
cessor 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 inf-
ormation-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 oc-
cupations 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 Blin-
der (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 technolog-
ical 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).
5

set the boundaries for economic development, but rather powerful social and
economic interests promoting the technological status quo. This is nicely il-
lustrated by the example of William Lee, inventing the stocking frame knitting
machine in 1589, hoping that it would relieve workers of hand-knitting. Seek-
ing 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 sub-
jects. 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 equip-
ment 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).
6

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 manufac-
turing 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, in-
ventors, consumers and unskilled factory workers largely benefited from mech-
anisation (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 produc-
tion 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 inter-
changeable 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 work-
ers to be stationary while different tasks were moved to them, it was identical in-
terchangeable 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. Follow-
ing 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).
8

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 pro-
cesses 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). Fur-
thermore, 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 arti-
sans (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 com-
pleted. 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.
9

and batch production methods, reduced the demand for unskilled manual work-
ers 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 opera-
tives, electrification allowed many stages of the production process to be au-
tomated, which in turn increased the demand for relatively skilled blue-collar
production workers to operate the machinery. In addition, electrification con-
tributed 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 combina-
tion 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 manage-
rial 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 be-
tween 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 in-
troduced 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 em-
ployment 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.
10

of mechanisation, with dictaphones, calculators, mimeo machines, address ma-
chines, and the predecessor of the computer – the keypunch (Beniger, 1986;
Cortada, 2000). Importantly, these office machines reduced the cost of inform-
ation processing tasks and increased the demand for the complementary factor –
i.e. educated office workers. Yet the increased supply of educated office work-
ers, 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 tech-
nological 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; Au-
tor, 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 self-
service technology (Gordon, 2012). During the 1980s and 1990s, computing
costs declined even more rapidly, on average by 64 percent per year, accompa-
nied 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 fi-
nancial 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 (Gor-
don, 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).
11

The early twentieth century office machines increased the demand for clerking
workers (Chandler, 1977; Goldin and Katz, 1995). In a similar manner, com-
puterisation 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 work-
ers 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 low-
est 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 perspec-
tive, 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 dis-
tribution. 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 oc-
cupations. 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 labour-
saving technology reduces the demand for undifferentiated labour, thus leading
to technological unemployment (Ricardo, 1819). As economists have long un-
derstood, however, an invention that replaces workers by machines will have
effects on all product and factor markets. An increase in the efficiency of pro-
duction 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 rea-
son 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 computerisa-
tion enters more cognitive domains this will become increasingly challenging
(Brynjolfsson and McAfee, 2011). Recent empirical findings are therefore par-
ticularly 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 work-
ers have moved down the occupational ladder, taking on jobs traditionally per-
formed 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, span-
ning across both cognitive and manual tasks (Michaels, et al., 2013). These explanations are
nevertheless equally related to advance in computing and communications technology.
13

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 technologi-
cal 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 counter-
balance the jobs at risk over the next decades.
III. THE TECHNOLOGICAL REVOLUTIONS OF THE TWENTY-FIRST CENTURY
The secular price decline in the real cost of computing has created vast eco-
nomic 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 ap-
plication 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 equip-
ment 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 cate-
gories 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 rule-
based 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 de-
fined 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 hand-
writing recognition is difficult to quantify without data to test on – in particular,
determining whether an algorithm performs well for different styles of writ-
ing 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 contin-
ual 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 hu-
16
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 rou-
tine 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 computerisa-
tion 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 enter-
ing 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 bet-
ter 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 compara-
tive advantage of algorithms: their absence of some human biases. An algo-
rithm 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 per-
formance (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 com-
16

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. Oncolo-
gists 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 medica-
tion 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 exam-
ple 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 algo-
rithms to render many tasks computerisable. A broad class of examples can be
found in condition monitoring and novelty detection, with technology substi-
tuting for closed-circuit TV (CCTV) operators, workers examining equipment
defects, and clinical staff responsible for monitoring the state of patients in in-
tensive 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 digi-
tal 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, reduc-
ing leaks by 40 to 50 percent. In the near future, it will be possible to place inex-
pensive 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 ex-
ample, Apple’s Siri and Google Now rely on natural user interfaces to recognise
spoken words, interpret their meanings, and act on them accordingly. More-
over, a company called SmartAction now provides call computerisation solu-
tions 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 cali-
brated 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 al-
gorithm 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 finan-
cial sector, such automated decision-making has played a role for quite some
time. AI algorithms are able to process a greater number of financial announce-
ments, 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 ex-
ample, advances in ML allow a programmer to leave complex parameter and
design choices to be appropriately optimised by an algorithm (Hoos, 2012). Al-
gorithms 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 bet-
ter 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 approx-
imately 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 rou-
tine tasks of most operatives in manufacturing. Now, however, more advanced
robots are gaining enhanced sensors and manipulators, allowing them to per-
form non-routine manual tasks. For example, General Electric has recently de-
veloped robots to climb and maintain wind turbines, and more flexible surgical
robots with a greater range of motion will soon perform more types of opera-
tions (Robotics-VO, 2013). In a similar manner, the computerisation of logis-
tics 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 technol-
ogy mean that vehicles are likely to soon be augmented with even more ad-
vanced 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 back-
wards, 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 chal-
lenge. 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 tech-
nology will affect a variety of logistics jobs. Agricultural vehicles, forklifts
and cargo-handling vehicles are imminently automatable, and hospitals are al-
ready employing autonomous robots to transport food, prescriptions and sam-
ples (Bloss, 2011). The computerisation of mining vehicles is further being
pursued by companies such as Rio Tinto, seeking to replace labour in Aus-
tralian 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 con-
veyor 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 differ-
ent 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 un-
derstood 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). In-
dustrial 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 increas-
ing set of manual tasks in manufacturing, packing, construction, maintenance,
and agriculture. In addition, robots are already performing many simple ser-
vice 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, commer-
cial cleaning, and elderly care (Robotics-VO, 2013). As robot costs decline and
technological capabilities expand, robots can thus be expected to gradually sub-
stitute for labour in a wide range of low-wage service occupations, where most
US 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.
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 in-
puts 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-rou-
tine tasks.19
Yet the premises about what computers do have recently expanded.
Computer capital can now equally substitute for a wide range of tasks com-
19
The model does not predict any substantial substitution or complementarity with non-
routine 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-to-
scale, 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 computa-
tional capabilities, resulting from technological advances, and a falling market
price of computing, workers in susceptible tasks will thus reallocate to non-
susceptible 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 be-
cause 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 engineer-
ing 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 recog-
nition. Our model thus predicts that the pace at which these bottlenecks can be
overcome will determine the extent of computerisation in the twenty-first cen-
tury.
Hence, in short, while the task model predicts that computers for labour
23

substitution will be confined to routine tasks, our model predicts that comput-
erisation can be extended to any non-routine task that is not subject to any engi-
neering bottlenecks to computerisation. These bottlenecks thus set the bound-
aries for the computerisation of non-routine tasks. Drawing upon the ML and
MR literature, and a workshop held at the Oxford University Engineering Sci-
ences Department, we identify several engineering bottlenecks, corresponding
to three task categories. According to these findings, non-susceptible labor in-
puts can be described as,
(2) LNS =
n
i=1
LPM,i + LC,i + LSI,i
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 allevi-
ated by the simplification of tasks. One generic way of achieving this is to re-
duce 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 al-
lows many construction tasks to be performed by robots under controlled con-
ditions 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, re-
quiring the identification of a plurality of irregular objects and containing many
cluttered spaces which inhibit the mobility of wheeled objects. Conversely, su-
permarkets, factories, warehouses, airports and hospitals have been designed
for large wheeled objects, making it easier for robots to navigate in perform-
ing 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 sin-
gle task, repeated thousands of times a day, that many applications require. A
related challenge is failure recovery – i.e. identifying and rectifying the mis-
takes of the robot when it has, for example, dropped an object. Manipula-
tion 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 com-
pliant dynamics and provide useful tactile feedback. Most industrial manip-
ulation 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 theo-
ries, cooking recipes and jokes, whereas artifacts are objects such as paintings,
sculptures, machinery, and pottery. One process of creating ideas (and simi-
larly 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 drawing-
program, 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 hu-
man 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 real-
time 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 intelli-
gence 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 be-
cause 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 hu-
man 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 ex-
ist, 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 em-
ployment 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 auto-
mated, occupa tions that involve complex perception and manipulation tasks,
creative intelligence tasks, and social intelligence tasks are unlikely to be sub-
stituted 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 computer-
isation 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

Probabilityof
Computerisation
Social Intelligence
Dishwasher
Event
Planner
Public
Relations
0 100
0
1
Probabilityof
Computerisation
Creativity
Court Clerk
Biologist
Fashion
Designer
0 100
0
1
Probabilityof
Computerisation
Perception and manipulation
Telemarketer
Boilermaker
Surgeon
0 100
0
1
FIGURE I. A sketch of how the probability of computerisation might vary as a function of
bottleneck variables.
non-susceptible task characteristics.
IV. MEASURING 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 classification, we used the mean of
28

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 devel-
opments 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 re-
shapes the occupational composition of the labour market. In the absence of
this experiment, the second preferred option would be to build on the imple-
mentation 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 im-
plemented 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 differ-
ent methodologies to rank and categorise occupations according to their sus-
ceptibility to offshoring (Blinder, 2009; Jensen and Kletzer, 2005, 2010). The
common denominator for these studies is that they rely on O∗NET data in differ-
ent 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 mea-
20
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 automata-
bility 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 availabil-
ity of big data, to be performed by state of the art computer-controlled equip-
ment”. 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 cur-
rently 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 vari-
ables 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 “impor-
tance” and “level” as the predominant pair. We rely on the “level” rating which
corresponds to specific examples about the capabilities required of computer-
controlled 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 or-
anges 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 Variable O∗NET Description
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?
Creative
Intelligence
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
Intelligence
Social
Perceptiveness
Being aware of others’ reactions and understanding why
they react as they do.
Negotiation Bringing others together and trying to reconcile
differences.
Persuasion Persuading others to change their minds or behavior.
Assisting and Caring for
Others
Providing personal assistance, medical attention, emo-
tional 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 au-
tomatability 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 dis-
criminant 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 continuous-
valued ‘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
p(f | K) = N (f; 0, K) =
1
√
det 2πK
exp −
1
2
f⊺
K−1
f .(4)
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 con-
sider 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
2
sufficiently far from training data.
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
p(f∗ | x∗, D) = N f∗; m(f∗ | x∗, D), V (f∗ | x∗, D) ,(5)
where
m(f∗ | x∗, D) = K(x∗, X)K(X, X)−1
y(6)
V (f∗ | x∗, D) = K(x∗, x∗) − K(x∗, X)K(X, X)−1
K(X, x∗) .(7)
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 (Ras-
mussen 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 avail-
able 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 operat-
33

TABLE II. Performance of various classifiers; best performances in bold.
classifier model AUC log-likelihood
exponentiated quadratic 0.894 −163.3
rational quadratic 0.893 −163.7
linear (logit regression) 0.827 −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 regres-
sion), and was hence selected for the remainder of our testing. Note that its
AUC score of nearly 0.9 represents accurate classification: our algorithm suc-
cessfully managed to reproduce our hand-labels specifying whether an occupa-
tion was computerisable. This means that our algorithm verified that our sub-
jective 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 comput-
erisability 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

Probability of
Computerisation
Crampedworkspace
Probability of
Computerisation
Fingerdexterity
Probability of
Computerisation
Manualdexterity
Probability of
Computerisation
Originality
Probability of
Computerisation
Finearts
Probability of
Computerisation
Socialperceptiveness
Probability of
Computerisation
Negotiation
Probability of
Computerisation
Persuasion
Probability of
Computerisation
Assistingand
caringforothers
0 0.5 10 0.5 10 0.5 1
0 0.5 10 0.5 10 0.5 1
0 0.5 10 0.5 10 0.5 1
0
50
100
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
0
50
100
0
50
100
0
20
40
60
80
0
20
40
60
80
0
50
100
FIGURE 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 vari-
ables: 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 prob-
ability 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. EMPLOYMENT IN THE TWENTY-FIRST CENTURY
In this section, we examine the possible future extent of at-risk job computerisa-
tion, 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 de-
mand 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 em-
ployment projections predict US net employment growth across major occupa-
tions, 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 ex-
isted 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

Transportation and Material Moving
Production
Installation, Maintenance, and Repair
Construction and Extraction
Farming, Fishing, and Forestry
Ofﬁce and Administrative Support
Sales and Related
Service
Healthcare Practitioners and Technical
Education, Legal, Community Service, Arts, and Media
Computer, Engineering, and Science
Management, Business, and Financial
Employment
Probability of Computerisation
47% Employment
←−−− High −−−→
19% Employment
←−−− Medium −−−→
33% Employment
←−−− Low −−−→
0 0.2 0.4 0.6 0.8 1
0M
100M
200M
300M
400M
FIGURE 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 probabil-
ity 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 occu-
pations, 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. Fur-
thermore, 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 technologi-
cal 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 com-
puterisation. Yet these findings are largely in line with recent documented tech-
nological 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 dexter-
ity will diminish over time, the pace of labour substitution in service occupa-
tions 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 intel-
ligence, 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 oc-
cupational categories. Third, prefabrication will allow a growing share of con-
struction work to be performed under controlled conditions in factories, which
partly eliminates task variability. This trend is likely to drive the computerisa-
tion of construction work.
In short, our findings suggest that recent developments in ML will put a sub-
stantial 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 em-
ployment can thus partly be interpreted as a technological plateau, with incre-
mental technological improvements successively enabling further labour sub-
stitution. 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 develop-
ments, allowing for more sophisticated pattern recognition, human labour will
still have a comparative advantage in tasks requiring more complex percep-
tion 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, im-
provements 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 manipu-
lation 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 Medium High
Assisting and caring for others 48±20 41±17 34±10
Persuasion 48±7.1 35±9.8 32±7.8
Negotiation 44±7.6 33±9.3 30±8.9
Social perceptiveness 51±7.9 41±7.4 37±5.5
Fine arts 12±20 3.5±12 1.3±5.5
Originality 51±6.5 35±12 32±5.6
Manual dexterity 22±18 34±15 36±14
Finger dexterity 36±10 39±10 40±10
Cramped work space 19±15 37±26 31±20
depend on overcoming the engineering bottlenecks related to creative and so-
cial intelligence. As reported in Table III, the “fine arts”, “originality”, “ne-
gotiation”, “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 oc-
cupations requiring knowledge of human heuristics, and specialist occupations
involving the development of novel ideas and artifacts, are the least suscepti-
ble to computerisation. As a prototypical example of generalist work requir-
ing a high degree of social intelligence, consider the O∗NET tasks reported for
chief executives, involving “conferring with board members, organization offi-
cials, or staff members to discuss issues, coordinate activities, or resolve prob-
lems”, 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 ac-
tors, 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

Averagemedianwage(USD)
0 0.5 1
20k
40k
60k
80k
unweighted
average
weighted by
employment
Bachelor’sdegreeorbetter
0 0.5 1
0%
20%
40%
60%
FIGURE 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, involv-
ing social intelligence, are unlikely to become subject to computerisation in the
near future.
The low susceptibility of engineering and science occupations to computer-
isation, on the other hand, is largely due to the high degree of creative intelli-
gence they require. The O∗NET tasks of mathematicians, for example, involve
“developing new principles and new relationships between existing mathemat-
ical 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 enter-
ing 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 sub-
stitute 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 cate-
gories. 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 bot-
tlenecks 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 oc-
cupations 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 relation-
ship with the probability of computerisation. We note that this prediction im-
plies 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 occu-
pations 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 comput-
erisation 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 cap-
ital prices, making computerisation increasingly proﬁtable (see, for example,
Acemoglu, 2003).
Second, regulatory concerns and political activism may slow down the pro-
cess 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 further-
more 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 technolog-
ical change.24
We avoid making predictions about the legislatory process and
the public acceptance of technological progress, and thus the pace of comput-
erisation.
Third, making predictions about technological progress is notoriously dif-
ﬁcult (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 bot-
tlenecks 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 computerisa-
tion 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 agricul-
tural 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 Manch-
ester 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 re-
cently 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. CONCLUSIONS
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 methodol-
ogy to estimate the probability of computerisation for 702 detailed occupations.
Based on these estimates, we examine expected impacts of future computeri-
sation 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, depend-
ing 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 esti-
mates 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 occu-
pations, 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 surpris-
ingly, 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 de-
mand for skilled labour. While nineteenth century manufacturing technologies
largely substituted for skilled labour through the simplification of tasks (Braver-
man, 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 oc-
cupations. 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.
REFERENCES
Acemoglu, D. (2002). Technical change, inequality, and the labor market. Jour-
nal of Economic Literature, vol. 40, no. 1, pp. 7–72.
Acemoglu, D. (2003). Labor- and capital-augmenting technical change. Jour-
nal of the European Economic Association, vol. 1, no. 1, pp. 1–37.
Acemoglu, D. and Autor, D. (2011). Skills, tasks and technologies: Implica-
tions for employment and earnings. Handbook of labor economics, vol. 4,
pp. 1043–1171.
Acemoglu, D. and Robinson, J. (2012). Why nations fail: the origins of power,
prosperity, and poverty. Random House Digital, Inc.
Ackerman, E. and Guizzo, E. (2011). 5 technologies that will shape the web.
Spectrum, IEEE, vol. 48, no. 6, pp. 40–45.
45

Aghion, P. and Howitt, P. (1994). Growth and unemployment. The Review of
Economic Studies, vol. 61, no. 3, pp. 477–494.
Allen, R.C. (2009a). Engels’ pause: Technical change, capital accumulation,
and inequality in the british industrial revolution. Explorations in Economic
History, vol. 46, no. 4, pp. 418–435.
Allen, R.C. (2009b). The industrial revolution in miniature: The spinning jenny
in Britain, France, and India. The Journal of Economic History, vol. 69,
no. 04, pp. 901–927.
Allen, S.G. (2001). Technology and the wage structure. Journal of Labor
Economics, vol. 19, no. 2, pp. 440–483.
Armstrong, S. and Sotala, K. (2012). How we’re predicting AI – or failing to.
Tech. Rep., Oxford: Future of Humanity Institute, Oxford University.
Atack, J., Bateman, F. and Margo, R.A. (2004). Skill intensity and rising wage
dispersion in nineteenth-century American manufacturing. The Journal of
Economic History, vol. 64, no. 01, pp. 172–192.
Atack, J., Bateman, F. and Margo, R.A. (2008a). Steam power, establishment
size, and labor productivity growth in nineteenth century American manu-
facturing. Explorations in Economic History, vol. 45, no. 2, pp. 185–198.
Atack, J., Haines, M.R. and Margo, R.A. (2008b). Railroads and the rise of the
factory: Evidence for the united states, 1850-70. Tech. Rep., NBER Working
Paper No. 14410, National Bureau of Economic Research.
Atkinson, A.B. (2008). The changing distribution of earnings in OECD coun-
tries. Oxford University Press.
Autor, D. and Dorn, D. (2013). The growth of low skill service jobs and the
polarization of the US labor market. American Economic Review, vol. forth-
coming.
Autor, D., Katz, L.F. and Krueger, A.B. (1998). Computing inequality: have
computers changed the labor market? The Quarterly Journal of Economics,
vol. 113, no. 4, pp. 1169–1213.
46

Autor, D., Levy, F. and Murnane, R.J. (2003). The skill content of recent tech-
nological change: An empirical exploration. The Quarterly Journal of Eco-
nomics, vol. 118, no. 4, pp. 1279–1333.
Barlow, J. and Ozaki, R. (2005). Building mass customised housing through
innovation in the production system: lessons from Japan. Environment and
Planning A, vol. 37, no. 1, pp. 9–20.
Beaudry, P., Green, D.A. and Sand, B.M. (2013). The great reversal in the
demand for skill and cognitive tasks. Tech. Rep., NBER Working Paper No.
18901, National Bureau of Economic Research.
Beniger, J.R. (1986). The control revolution: Technological and economic ori-
gins of the information society. Harvard University Press.
Blinder, A.S. (2009). How many US jobs might be offshorable? World Eco-
nomics, vol. 10, no. 2, p. 41.
Blinder, A.S. and Krueger, A.B. (2013). Alternative measures of offshorability:
a survey approach. Journal of Labor Economics, vol. 31, no. 2, pp. S97–
S128.
Bloss, R. (2011). Mobile hospital robots cure numerous logistic needs. Indus-
trial Robot: An International Journal, vol. 38, no. 6, pp. 567–571.
Boden, M.A. (2003). The creative mind: Myths and mechanisms. Routledge.
Braverman, H. (1974). Labor and monopoly capital: The degradation of work
in the twentieth century. NYU Press.
Breslow, L., Pritchard, D.E., DeBoer, J., Stump, G.S., Ho, A.D. and Seaton, D.
(2013). Studying learning in the worldwide classroom: Research into edx’s
ﬁrst mooc. Research & Practice in Assessment, vol. 8, pp. 13–25.
Bresnahan, T.F. (1999). Computerisation and wage dispersion: an analytical
reinterpretation. The Economic Journal, vol. 109, no. 456, pp. 390–415.
Bresnahan, T.F., Brynjolfsson, E. and Hitt, L.M. (2002). Information technol-
ogy, workplace organization, and the demand for skilled labor: Firm-level
evidence. The Quarterly Journal of Economics, vol. 117, no. 1, pp. 339–376.
47

Bright, J.R. (1958). Automation and management. Division of Research, Grad-
uate School of Business Administration, Harvard University Boston.
Broekens, J., Heerink, M. and Rosendal, H. (2009). Assistive social robots in
elderly care: a review. Gerontechnology, vol. 8, no. 2, pp. 94–103.
Brown, E., Rodenberg, N., Amend, J., Mozeika, A., Steltz, E., Zakin, M.R.,
Lipson, H. and Jaeger, H.M. (2010). Universal robotic gripper based on
the jamming of granular material. Proceedings of the National Academy of
Sciences, vol. 107, no. 44, pp. 18809–18814.
Brown, M. and Philips, P. (1986). Craft labor and mechanization in nineteenth-
century American canning. The Journal of Economic History, vol. 46, no. 3,
pp. 743–56.
Brynjolfsson, E. and McAfee, A. (2011). Race against the machine: How the
digital revolution is accelerating innovation, driving productivity, and irre-
versibly transforming employment and the economy. Digital Frontier Press
Lexington, MA.
Campbell-Kelly, M. (2009). Origin of computing. Scientiﬁc American Maga-
zine, vol. 301, no. 3, pp. 62–69.
Chandler, A.D. (1977). The visible hand: The managerial revolution in Ameri-
can business. Harvard University Press.
Charles, K.K., Hurst, E. and Notowidigdo, M.J. (2013). Manufacturing decline,
housing booms, and non-employment. Tech. Rep., NBER Working Paper No.
Churchill, W. and Newman, P. (2012). Practice makes perfect? managing and
leveraging visual experiences for lifelong navigation. In: Robotics and Au-
tomation (ICRA), 2012 IEEE International Conference on, pp. 4525–4532.
IEEE.
Cisco (2012). Cisco visual networking index. http://www.cisco.com/en/us/
netsol/ns827/networking_solutions_sub_solution.html.
Clark, G. (2008). A farewell to alms: a brief economic history of the world.
Princeton University Press.
48

Clifford, G.D. and Clifton, D. (2012). Wireless technology in disease manage-
ment and medicine. Annual Review of Medicine, vol. 63, pp. 479–492.
Clifton, L., Clifton, D., Pimentel, M., Watkinson, P. and Tarassenko, L. (2012).
Gaussian processes for personalised e-health monitoring with wearable sen-
sors. IEEE Transactions on Biomedical Engineering, vol. 60, pp. 193–197.
Cohn, J. (2013 February 20). The robot will see you now. The Atlantic.
Cortada, J.W. (2000). Before the Computer: IBM, NCR, Burroughs, and Rem-
ington Rand and the Industry They Created, 1865-1956. Princeton University
Press.
Danziger, S., Levav, J. and Avnaim-Pesso, L. (2011). Extraneous factors in
judicial decisions. Proceedings of the National Academy of Sciences, vol.
108, no. 17, pp. 6889–6892.
Davis, S.J. and Haltiwanger, J. (1992). Gross job creation, gross job destruction,
and employment reallocation. The Quarterly Journal of Economics, vol. 107,
no. 3, pp. 819–863.
Duvenaud, D., Lloyd, J.R., Grosse, R., Tenenbaum, J.B. and Ghahramani, Z.
(2013). Structure discovery in nonparametric regression through composi-
tional kernel search. In: Proceedings of the 30th International Conference
on Machine Learning, arXiv:1206.1846 [stat.ML]. Cambridge, UK.
Epstein, S.R. (1998). Craft guilds, apprenticeship, and technological change in
preindustrial europe. Journal of Economic History, vol. 58, pp. 684–713.
Feinstein, C.H. (1998). Pessimism perpetuated: real wages and the standard
of living in Britain during and after the industrial revolution. The Journal of
Economic History, vol. 58, pp. 625–658.
Ge, S.S. (2007). Social robotics: Integrating advances in engineering and com-
puter science. In: The 4th annual international conference organized by
Electrical Engineering/Electronics, Computer, Telecommunication and Inf-
ormation Technology (ECTI) Association, Chiang Rai, Thailand, May 9â,
vol. 12, p. 2007.
49

Goldin, C. and Katz, L.F. (1995). The decline of non-competing groups:
Changes in the premium to education, 1890 to 1940. Tech. Rep., NBER
Working Paper No. 5202, National Bureau of Economic Research.
Goldin, C. and Katz, L.F. (1998). The origins of technology-skill complemen-
tarity. The Quarterly Journal of Economics, vol. 113, no. 3, pp. 693–732.
Goldin, C. and Katz, L.F. (2009). The race between education and technology.
Harvard University Press.
Goldin, C. and Sokoloff, K. (1982). Women, children, and industrialization in
the early republic: Evidence from the manufacturing censuses. The Journal
of Economic History, vol. 42, no. 4, pp. 741–774.
Goos, M. and Manning, A. (2007). Lousy and lovely jobs: The rising polar-
ization of work in Britain. The Review of Economics and Statistics, vol. 89,
no. 1, pp. 118–133.
Goos, M., Manning, A. and Salomons, A. (2009). Job polarization in europe.
The American Economic Review, vol. 99, no. 2, pp. 58–63.
Gordon, R.J. (2012). Is US economic growth over? faltering innovation con-
fronts the six headwinds. Tech. Rep., NBER Working Paper No. 18315, Na-
tional Bureau of Economic Research.
Gray, R. (2013). Taking technology to task: The skill content of technological
change in early twentieth century united states. Explorations in Economic
History.
Guizzo, E. (2008 July 2). Three engineers, hundreds of robots, one ware-
house. IEEE Spectrum. http://spectrum.ieee.org/robotics/robotics-software/
three-engineers-hundreds-of-robots-one-warehouse.
Guizzo, E. (2011 October 18). How google’s self-driving car works. IEEE
Spectrum Online.
Habakkuk, H.J. (1962). American and British technology in the nineteenth
century: The search for labour-saving inventions. University Press.
50

Hangal, S. and Lam, M.S. (2002). Tracking down software bugs using auto-
matic anomaly detection. In: Proceedings of the 24th international confer-
ence on Software engineering, pp. 291–301. ACM.
Hanson, R. (2001). Economic growth given machine intelligence. Technical
Report, University of California, Berkeley.
Hoos, H.H. (2012). Programming by optimization. Communications of the
ACM, vol. 55, no. 2, pp. 70–80.
Hornbeck, R. and Naidu, S. (2013). When the levee breaks: Labor mobility
and economic development in the American south. The American Economic
Review, vol. forthcoming.
Hounshell, D. (1985). From the American system to mass production, 1800-
1932: The development of manufacturing technology in the United States,
vol. 4. JHU Press.
Jaimovich, N. and Siu, H.E. (2012). The trend is the cycle: Job polarization and
jobless recoveries. Tech. Rep., NBER Working Paper No. 18334, National
Bureau of Economic Research.
James, J.A. and Skinner, J.S. (1985). The resolution of the labor-scarcity para-
dox. The Journal of Economic History, vol. 45, no. 3, pp. 513–540.
Jensen, J.B. and Kletzer, L.G. (2005). Tradable services: Understanding the
scope and impact of services offshoring [with comments and discussion]. In:
Brookings trade forum, pp. 75–133. JSTOR.
Jensen, J.B. and Kletzer, L.G. (2010). Measuring tradable services and the
task content of offshorable services jobs. In: Labor in the new economy, pp.
309–335. University of Chicago Press.
Kahneman, D., Slovic, P. and Tversky, A. (1982). Judgment under uncertainty.
Cambridge University Press.
Katz, L.F. and Margo, R.A. (2013). Technical change and the relative demand
for skilled labor: The united states in historical perspective. Tech. Rep.,
NBER Working Paper No. 18752, National Bureau of Economic Research.
51

Katz, L.F. and Murphy, K.M. (1992). Changes in relative wages, 1963–1987:
supply and demand factors. The Quarterly Journal of Economics, vol. 107,
no. 1, pp. 35–78.
Kellenbenz, H. (1974). Technology in the age of the scientiﬁc revolution, 1500-
1700. The Fontana Economic History of Europe, vol. 2, pp. 177–272.
Keynes, J.M. (1933). Economic possibilities for our grandchildren (1930). Es-
says in persuasion, pp. 358–73.
Kim, S., Whitehead, E.J. and Zhang, Y. (2008). Classifying software changes:
Clean or buggy? Software Engineering, IEEE Transactions on, vol. 34, no. 2,
pp. 181–196.
King, S., Bannister, P., Clifton, D. and Tarassenko, L. (2009). Probabilistic
approach to the condition monitoring of aerospace engines. Proceedings
of the Institution of Mechanical Engineers, Part G: Journal of Aerospace
Engineering, vol. 223, no. 5, pp. 533–541.
Krueger, A.B. (1993). How computers have changed the wage structure: ev-
idence from microdata, 1984–1989. The Quarterly Journal of Economics,
vol. 108, no. 1, pp. 33–60.
Levy, F. and Murnane, R.J. (2004). The new division of labor: How computers
are creating the next job market. Princeton University Press.
Lindert, P.H. and Williamson, J.G. (1983). Reinterpreting Britain’s social ta-
bles, 1688–1913. Explorations in Economic History, vol. 20, no. 1, pp. 94–
109.
Linner, T. and Bock, T. (2012). Evolution of large-scale industrialisation and
service innovation in Japanese prefabrication industry. Construction Innova-
tion: Information, Process, Management, vol. 12, no. 2, pp. 156–178.
Livshits, B. and Zimmermann, T. (2005). Dynamine: ﬁnding common error
patterns by mining software revision histories. In: ACM SIGSOFT Software
Engineering Notes, vol. 30, pp. 296–305. ACM.
52

Louis, P. and Paterson, G. (1986). Biased technical change, scale, and fac-
tor substitution in American industry, 1850-1919. The Journal of Economic
History, vol. 46, no. 1.
Lucas, R.E. and Prescott, E.C. (1974). Equilibrium search and unemployment.
Journal of Economic Theory, vol. 7, no. 2, pp. 188–209.
CAA (2012). Roadside assistance customers beneﬁt from smart support dur-
ing peak and after hours. Tech. Rep., SmartAction case study of Canadian
Automobile Association Saskatchewan.
IFR (2012a). 68 robots perform farmer’s work. Tech. Rep., Case study of
Fanuc Robotics Europe S.A., International Federation of Robotics, Septem-
ber 2012.
IFR (2012b August 30). World robotics 2012. Tech. Rep., International Feder-
ation of Robotics.
MGI (2011). An economy that works: Job creation and America’s future. Tech.
Rep., McKinsey Global Institute.
MGI (2013). Disruptive technologies: Advances that will transform life, busi-
ness, and the global economy. Tech. Rep., McKinsey Global Institute.
Mantoux, P. (2006). The industrial revolution in the eighteenth century: An
outline of the beginnings of the modern factory system in England. Taylor &
Francis US.
Margo, R.A. (2000). Introduction to wages and labor markets in the United
States, 1820-1860. In: Wages and Labor Markets in the United States, 1820-
1860, pp. 1–5. University of Chicago Press.
Markoff, J. (2011 March 4). Armies of expensive lawyers replaced by cheaper
software. The New York Times.
Markoff, J. (2012 August 18). Skilled work, without the worker. The New York
Times.
Mathibela, B., Osborne, M.A., Posner, I. and Newman, P. (2012). Can priors
be trusted? learning to anticipate roadworks. In: Intelligent Transportation
53

Systems (ITSC), 2012 15th International IEEE Conference on, pp. 927–932.
IEEE.
Michaels, G., Rauch, F. and Redding, S.J. (2013). Task specialization in US
cities from 1880-2000. Tech. Rep., NBER Working Paper No. 18715, Na-
tional Bureau of Economic Research.
Mims, C. (2010 June 10). AI that picks stocks better than the pros. MIT Tech-
nology Review.
Minka, T.P. (2001). Expectation propagation for approximate Bayesian infer-
ence. In: Proceedings of the Seventeenth conference on Uncertainty in arti-
ﬁcial intelligence, pp. 362–369. Morgan Kaufmann Publishers Inc.
Mokyr, J. (1990). The lever of riches: Technological creativity and economic
progress. Oxford University Press.
Mokyr, J. (1998). The political economy of technological change. Technologi-
cal revolutions in Europe, pp. 39–64.
Murphy, K.M., Riddell, W.C. and Romer, P.M. (1998). Wages, skills, and tech-
nology in the united states and canada. Tech. Rep., NBER Working Paper No.
Murphy, K.P. (2012). Machine learning: a probabilistic perspective. The MIT
Press.
Nef, J.U. (1957). Coal mining and utilization. A history of technology, vol. 3,
pp. 72–88.
Nordhaus, W.D. (2007). Two centuries of productivity growth in computing.
The Journal of Economic History, vol. 67, no. 1, p. 128.
North, D.C. and Weingast, B.R. (1989). Constitutions and commitment: the
evolution of institutions governing public choice in seventeenth-century eng-
land. The Journal of Economic History, vol. 49, no. 04, pp. 803–832.
Ogilvie, S. (2004). Guilds, efﬁciency, and social capital: evidence from German
proto-industry. The Economic History Review, vol. 57, no. 2, pp. 286–333.
54

Oldenski, L. (2012). Offshoring and the polarization of the US labor market.
Tech. Rep., Technical report, Mimeo, Georgetown University.
Osborne, M.A., Garnett, R., Swersky, K. and de Freitas, N. (2012). Prediction
and fault detection of environmental signals with uncharacterised faults. In:
Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12).
Phua, C., Lee, V., Smith, K. and Gayler, R. (2010). A comprehensive survey of
data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.
Picard, R.W. (2010). Affective computing: from laughter to IEEE. Affective
Computing, IEEE Transactions on, vol. 1, no. 1, pp. 11–17.
Pissarides, C.A. (2000). Equilibrium unemployment theory. MIT press.
Plötz, T. and Fink, G.A. (2009). Markov models for offline handwriting recog-
nition: a survey. International Journal on Document Analysis and Recogni-
tion (IJDAR), vol. 12, no. 4, pp. 269–298.
Rasmussen, C.E. and Nickisch, H. (2010). Gaussian processes for machine
learning (GPML) toolbox. The Journal of Machine Learning Research,
vol. 11, pp. 3011–3015.
Rasmussen, C.E. and Williams, C.K.I. (2006). Gaussian Processes for Machine
Learning. MIT Press.
Ricardo, D. (1819). The principles of political economy and taxation. World
Scientific.
Robotics-VO (2013). A Roadmap for US Robotics. From Internet to Robotics.
2013 Edition. Robotics in the United States of America.
Roy, A.D. (1951). Some thoughts on the distribution of earnings. Oxford Eco-
nomic Papers, vol. 3, no. 2, pp. 135–146.
Saha, B., Goebel, K., Poll, S. and Christophersen, J. (2007). An integrated
approach to battery health monitoring using Bayesian regression and state
estimation. In: Autotestcon, 2007 IEEE, pp. 646–653. IEEE.
55

Sandberg, A. and Bostrom, N. (2008). Whole brain emulation: A roadmap,
technical report 2008-3. Tech. Rep., Future of Humanity Institute, Oxford
University.
Scherer, K.R., Bänziger, T. and Roesch, E.B. (2010). Blueprint for Affective
Computing: A Sourcebook and Manual. Oxford University Press.
Schumpeter, J.A. (1962). Capitalism, socialism and democracy. Harper & Row
New York.
Simonite, T. (2013 June 5). As data floods in, massive open online courses
evolve. MIT Technology Review. http://www.technologyreview.com/news/
515396/as-data-floods-in-massive-open-online-courses-evolve.
Tanner, A. (2007 March 27). Google seeks world of instant translations.
Reuters. http://www.reuters.com/article/2007/03/28/us-google-translate-id
usN1921881520070328.
UC Berkeley School of Information (2003). How much information? 2003.
http://www2.sims.berkeley.edu/research/projects/how-much-info-2003.
US Department of Labor (1899). Thirteenth Annual Report of the Commissioner
of Labor. Two Volumes, Government Printing Office, Washington, DC.
Veres, S.M., Molnar, L., Lincoln, N.K. and Morice, C.P. (2011). Autonomous
vehicle control systems – a review of decision making. Proceedings of the
Institution of Mechanical Engineers, Part I: Journal of Systems and Control
Engineering, vol. 225, no. 2, pp. 155–195.
Williamson, J.G. and Lindert, P.H. (1980). American inequality: A macroeco-
nomic history, vol. 53. Academic Press New York.
Woolf, B.P. (2010). Building intelligent interactive tutors: Student-centered
strategies for revolutionizing e-learning. Morgan Kaufmann.
56

APPENDIX
The table below ranks occupations according to their probability of computeri-
sation (from least- to most-computerisable). Those occupations used as training
data are labelled as either ‘0’ (not computerisable) or ‘1’ (computerisable), re-
spectively. There are 70 such occupations, 10 percent of the total number of
occupations.
Computerisable
Rank Probability Label SOC code Occupation
1. 0.0028 29-1125 Recreational Therapists
2. 0.003 49-1011 First-Line Supervisors of Mechanics, Installers, and Repairers
3. 0.003 11-9161 Emergency Management Directors
4. 0.0031 21-1023 Mental Health and Substance Abuse Social Workers
5. 0.0033 29-1181 Audiologists
6. 0.0035 29-1122 Occupational Therapists
7. 0.0035 29-2091 Orthotists and Prosthetists
8. 0.0035 21-1022 Healthcare Social Workers
9. 0.0036 29-1022 Oral and Maxillofacial Surgeons
10. 0.0036 33-1021 First-Line Supervisors of Fire Fighting and Prevention Workers
11. 0.0039 29-1031 Dietitians and Nutritionists
12. 0.0039 11-9081 Lodging Managers
13. 0.004 27-2032 Choreographers
14. 0.0041 41-9031 Sales Engineers
15. 0.0042 0 29-1060 Physicians and Surgeons
16. 0.0042 25-9031 Instructional Coordinators
17. 0.0043 19-3039 Psychologists, All Other
18. 0.0044 33-1012 First-Line Supervisors of Police and Detectives
19. 0.0044 0 29-1021 Dentists, General
20. 0.0044 25-2021 Elementary School Teachers, Except Special Education
21. 0.0045 19-1042 Medical Scientists, Except Epidemiologists
22. 0.0046 11-9032 Education Administrators, Elementary and Secondary School
23. 0.0046 29-1081 Podiatrists
24. 0.0047 19-3031 Clinical, Counseling, and School Psychologists
25. 0.0048 21-1014 Mental Health Counselors
26. 0.0049 51-6092 Fabric and Apparel Patternmakers
27. 0.0055 27-1027 Set and Exhibit Designers
28. 0.0055 11-3121 Human Resources Managers
29. 0.0061 39-9032 Recreation Workers
30. 0.0063 11-3131 Training and Development Managers
31. 0.0064 29-1127 Speech-Language Pathologists
32. 0.0065 15-1121 Computer Systems Analysts
33. 0.0067 0 11-9151 Social and Community Service Managers
34. 0.0068 25-4012 Curators
35. 0.0071 29-9091 Athletic Trainers
36. 0.0073 11-9111 Medical and Health Services Managers
37. 0.0074 0 25-2011 Preschool Teachers, Except Special Education
38. 0.0075 25-9021 Farm and Home Management Advisors
39. 0.0077 19-3091 Anthropologists and Archeologists
57

Computerisable
40. 0.0077 25-2054 Special Education Teachers, Secondary School
41. 0.0078 25-2031 Secondary School Teachers, Except Special and Career/Technical Edu-
cation
42. 0.0081 0 21-2011 Clergy
43. 0.0081 19-1032 Foresters
44. 0.0085 21-1012 Educational, Guidance, School, and Vocational Counselors
45. 0.0088 25-2032 Career/Technical Education Teachers, Secondary School
46. 0.009 0 29-1111 Registered Nurses
47. 0.0094 21-1015 Rehabilitation Counselors
48. 0.0095 25-3999 Teachers and Instructors, All Other
49. 0.0095 19-4092 Forensic Science Technicians
50. 0.01 39-5091 Makeup Artists, Theatrical and Performance
51. 0.01 17-2121 Marine Engineers and Naval Architects
52. 0.01 11-9033 Education Administrators, Postsecondary
53. 0.011 17-2141 Mechanical Engineers
54. 0.012 29-1051 Pharmacists
55. 0.012 13-1081 Logisticians
56. 0.012 19-1022 Microbiologists
57. 0.012 19-3032 Industrial-Organizational Psychologists
58. 0.013 27-2022 Coaches and Scouts
59. 0.013 11-2022 Sales Managers
60. 0.014 19-2043 Hydrologists
61. 0.014 11-2021 Marketing Managers
62. 0.014 0 21-1013 Marriage and Family Therapists
63. 0.014 17-2199 Engineers, All Other
64. 0.014 13-1151 Training and Development Specialists
65. 0.014 43-1011 First-Line Supervisors of Ofﬁce and Administrative Support Workers
66. 0.015 19-1029 Biological Scientists, All Other
67. 0.015 11-2031 Public Relations and Fundraising Managers
68. 0.015 27-1014 Multimedia Artists and Animators
69. 0.015 15-1111 Computer and Information Research Scientists
70. 0.015 0 11-1011 Chief Executives
71. 0.015 0 11-9031 Education Administrators, Preschool and Childcare Center/Program
72. 0.015 27-2041 Music Directors and Composers
73. 0.016 51-1011 First-Line Supervisors of Production and Operating Workers
74. 0.016 41-3031 Securities, Commodities, and Financial Services Sales Agents
75. 0.016 19-1031 Conservation Scientists
76. 0.016 25-2053 Special Education Teachers, Middle School
77. 0.017 17-2041 Chemical Engineers
78. 0.017 11-9041 Architectural and Engineering Managers
79. 0.017 17-2011 Aerospace Engineers
80. 0.018 11-9121 Natural Sciences Managers
81. 0.018 17-2081 Environmental Engineers
82. 0.018 17-1011 Architects, Except Landscape and Naval
83. 0.018 31-2021 Physical Therapist Assistants
84. 0.019 0 17-2051 Civil Engineers
85. 0.02 29-1199 Health Diagnosing and Treating Practitioners, All Other
86. 0.021 19-1013 Soil and Plant Scientists
87. 0.021 19-2032 Materials Scientists
58

Computerisable
88. 0.021 17-2131 Materials Engineers
89. 0.021 0 27-1022 Fashion Designers
90. 0.021 29-1123 Physical Therapists
91. 0.021 27-4021 Photographers
92. 0.022 27-2012 Producers and Directors
93. 0.022 27-1025 Interior Designers
94. 0.023 29-1023 Orthodontists
95. 0.023 27-1011 Art Directors
96. 0.025 33-1011 First-Line Supervisors of Correctional Ofﬁcers
97. 0.025 21-2021 Directors, Religious Activities and Education
98. 0.025 17-2072 Electronics Engineers, Except Computer
99. 0.027 19-1021 Biochemists and Biophysicists
100. 0.027 29-1011 Chiropractors
101. 0.028 31-2011 Occupational Therapy Assistants
102. 0.028 21-1021 Child, Family, and School Social Workers
103. 0.028 17-2111 Health and Safety Engineers, Except Mining Safety Engineers and In-
spectors
104. 0.029 17-2112 Industrial Engineers
105. 0.029 53-1031 First-Line Supervisors of Transportation and Material-Moving Machine
and Vehicle Operators
106. 0.029 29-2056 Veterinary Technologists and Technicians
107. 0.03 11-3051 Industrial Production Managers
108. 0.03 17-3026 Industrial Engineering Technicians
109. 0.03 15-1142 Network and Computer Systems Administrators
110. 0.03 15-1141 Database Administrators
111. 0.03 11-3061 Purchasing Managers
112. 0.032 25-1000 Postsecondary Teachers
113. 0.033 19-2041 Environmental Scientists and Specialists, Including Health
114. 0.033 0 21-1011 Substance Abuse and Behavioral Disorder Counselors
115. 0.035 0 23-1011 Lawyers
116. 0.035 27-1012 Craft Artists
117. 0.035 15-2031 Operations Research Analysts
118. 0.035 11-3021 Computer and Information Systems Managers
119. 0.037 27-1021 Commercial and Industrial Designers
120. 0.037 17-2031 Biomedical Engineers
121. 0.037 0 13-1121 Meeting, Convention, and Event Planners
122. 0.038 29-1131 Veterinarians
123. 0.038 27-3043 Writers and Authors
124. 0.039 11-2011 Advertising and Promotions Managers
125. 0.039 19-3094 Political Scientists
126. 0.04 13-2071 Credit Counselors
127. 0.04 19-3099 Social Scientists and Related Workers, All Other
128. 0.041 19-2011 Astronomers
129. 0.041 53-5031 Ship Engineers
130. 0.042 15-1132 Software Developers, Applications
131. 0.042 27-1013 Fine Artists, Including Painters, Sculptors, and Illustrators
132. 0.043 29-2053 Psychiatric Technicians
133. 0.045 0 17-1012 Landscape Architects
134. 0.045 21-1091 Health Educators
59

Computerisable
135. 0.047 15-2021 Mathematicians
136. 0.047 27-1023 Floral Designers
137. 0.047 11-9013 Farmers, Ranchers, and Other Agricultural Managers
138. 0.048 33-2022 Forest Fire Inspectors and Prevention Specialists
139. 0.049 29-2041 Emergency Medical Technicians and Paramedics
140. 0.055 27-3041 Editors
141. 0.055 29-1024 Prosthodontists
142. 0.055 0 29-9799 Healthcare Practitioners and Technical Workers, All Other
143. 0.057 39-7012 Travel Guides
144. 0.058 29-2061 Licensed Practical and Licensed Vocational Nurses
145. 0.059 19-3041 Sociologists
146. 0.06 23-1022 Arbitrators, Mediators, and Conciliators
147. 0.061 19-1011 Animal Scientists
148. 0.064 39-9041 Residential Advisors
149. 0.066 53-1011 Aircraft Cargo Handling Supervisors
150. 0.066 29-1126 Respiratory Therapists
151. 0.067 27-3021 Broadcast News Analysts
152. 0.069 11-3031 Financial Managers
153. 0.07 17-2161 Nuclear Engineers
154. 0.071 11-9021 Construction Managers
155. 0.074 27-2042 Musicians and Singers
156. 0.075 41-1012 First-Line Supervisors of Non-Retail Sales Workers
157. 0.076 39-1021 First-Line Supervisors of Personal Service Workers
158. 0.077 19-1012 Food Scientists and Technologists
159. 0.08 0 13-1041 Compliance Officers
160. 0.08 33-3031 Fish and Game Wardens
161. 0.082 27-1024 Graphic Designers
162. 0.083 11-9051 Food Service Managers
163. 0.084 0 39-9011 Childcare Workers
164. 0.085 39-9031 Fitness Trainers and Aerobics Instructors
165. 0.091 11-9071 Gaming Managers
166. 0.097 49-9051 Electrical Power-Line Installers and Repairers
167. 0.098 33-3051 Police and Sheriff’s Patrol Officers
168. 0.099 41-3041 Travel Agents
169. 0.1 0 35-1011 Chefs and Head Cooks
170. 0.1 39-2011 Animal Trainers
171. 0.1 27-3011 Radio and Television Announcers
172. 0.1 0 17-2071 Electrical Engineers
173. 0.1 19-2031 Chemists
174. 0.1 29-2054 Respiratory Therapy Technicians
175. 0.1 0 19-2012 Physicists
176. 0.11 0 39-5012 Hairdressers, Hairstylists, and Cosmetologists
177. 0.11 27-3022 Reporters and Correspondents
178. 0.11 53-2021 Air Traffic Controllers
179. 0.13 27-2031 Dancers
180. 0.13 29-2033 Nuclear Medicine Technologists
181. 0.13 15-1133 Software Developers, Systems Software
182. 0.13 13-1111 Management Analysts
183. 0.13 29-2051 Dietetic Technicians
60

Computerisable
184. 0.13 19-3051 Urban and Regional Planners
185. 0.13 21-1093 Social and Human Service Assistants
186. 0.13 25-3021 Self-Enrichment Education Teachers
187. 0.13 27-4014 Sound Engineering Technicians
188. 0.14 29-1041 Optometrists
189. 0.14 17-2151 Mining and Geological Engineers, Including Mining Safety Engineers
190. 0.14 29-1071 Physician Assistants
191. 0.15 25-2012 Kindergarten Teachers, Except Special Education
192. 0.15 47-2111 Electricians
193. 0.16 17-2171 Petroleum Engineers
194. 0.16 43-9031 Desktop Publishers
195. 0.16 11-1021 General and Operations Managers
196. 0.17 29-9011 Occupational Health and Safety Specialists
197. 0.17 33-2011 Firefighters
198. 0.17 13-2061 Financial Examiners
199. 0.17 47-1011 First-Line Supervisors of Construction Trades and Extraction Workers
200. 0.17 25-2022 Middle School Teachers, Except Special and Career/Technical Educa-
tion
201. 0.18 27-3031 Public Relations Specialists
202. 0.18 49-9092 Commercial Divers
203. 0.18 49-9095 Manufactured Building and Mobile Home Installers
204. 0.18 53-2011 Airline Pilots, Copilots, and Flight Engineers
205. 0.19 25-3011 Adult Basic and Secondary Education and Literacy Teachers and In-
structors
206. 0.2 19-1041 Epidemiologists
207. 0.2 39-4831 Funeral Service Managers, Directors, Morticians, and Undertakers
208. 0.21 15-1179 Information Security Analysts, Web Developers, and Computer Net-
work Architects
209. 0.21 15-2011 Actuaries
210. 0.21 33-9011 Animal Control Workers
211. 0.21 0 39-6012 Concierges
212. 0.22 15-1799 Computer Occupations, All Other
213. 0.22 15-2041 Statisticians
214. 0.22 17-2061 Computer Hardware Engineers
215. 0.23 19-3022 Survey Researchers
216. 0.23 13-1199 Business Operations Specialists, All Other
217. 0.23 13-2051 Financial Analysts
218. 0.23 29-2037 Radiologic Technologists and Technicians
219. 0.23 29-2031 Cardiovascular Technologists and Technicians
220. 0.24 13-1011 Agents and Business Managers of Artists, Performers, and Athletes
221. 0.24 17-3029 Engineering Technicians, Except Drafters, All Other
222. 0.25 19-3092 Geographers
223. 0.25 29-9012 Occupational Health and Safety Technicians
224. 0.25 21-1092 Probation Officers and Correctional Treatment Specialists
225. 0.25 17-3025 Environmental Engineering Technicians
226. 0.25 11-9199 Managers, All Other
227. 0.25 53-3011 Ambulance Drivers and Attendants, Except Emergency Medical Tech-
nicians
228. 0.25 41-4011 Sales Representatives, Wholesale and Manufacturing, Technical and
Scientific Products
61

Computerisable
229. 0.26 25-2023 Career/Technical Education Teachers, Middle School
230. 0.27 53-5021 Captains, Mates, and Pilots of Water Vessels
231. 0.27 31-2012 Occupational Therapy Aides
232. 0.27 49-9062 Medical Equipment Repairers
233. 0.28 41-1011 First-Line Supervisors of Retail Sales Workers
234. 0.28 0 27-2021 Athletes and Sports Competitors
235. 0.28 39-1011 Gaming Supervisors
236. 0.29 39-5094 Skincare Specialists
237. 0.29 13-1022 Wholesale and Retail Buyers, Except Farm Products
238. 0.3 19-4021 Biological Technicians
239. 0.3 31-9092 Medical Assistants
240. 0.3 0 19-1023 Zoologists and Wildlife Biologists
241. 0.3 35-2013 Cooks, Private Household
242. 0.31 13-1078 Human Resources, Training, and Labor Relations Specialists, All Other
243. 0.31 33-9021 Private Detectives and Investigators
244. 0.31 27-4032 Film and Video Editors
245. 0.33 13-2099 Financial Specialists, All Other
246. 0.34 33-3021 Detectives and Criminal Investigators
247. 0.34 29-2055 Surgical Technologists
248. 0.34 29-1124 Radiation Therapists
249. 0.35 0 47-2152 Plumbers, Pipeﬁtters, and Steamﬁtters
250. 0.35 0 53-2031 Flight Attendants
251. 0.35 29-2032 Diagnostic Medical Sonographers
252. 0.36 33-3011 Bailiffs
253. 0.36 51-4012 Computer Numerically Controlled Machine Tool Programmers, Metal
and Plastic
254. 0.36 49-2022 Telecommunications Equipment Installers and Repairers, Except Line
Installers
255. 0.37 51-9051 Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders
256. 0.37 53-7061 Cleaners of Vehicles and Equipment
257. 0.37 39-4021 Funeral Attendants
258. 0.37 47-5081 Helpers–Extraction Workers
259. 0.37 27-2011 Actors
260. 0.37 53-7111 Mine Shuttle Car Operators
261. 0.38 49-2095 Electrical and Electronics Repairers, Powerhouse, Substation, and Re-
lay
262. 0.38 1 17-1022 Surveyors
263. 0.38 17-3027 Mechanical Engineering Technicians
264. 0.38 53-7064 Packers and Packagers, Hand
265. 0.38 27-3091 Interpreters and Translators
266. 0.39 31-1011 Home Health Aides
267. 0.39 51-6093 Upholsterers
268. 0.39 47-4021 Elevator Installers and Repairers
269. 0.39 43-3041 Gaming Cage Workers
270. 0.39 25-9011 Audio-Visual and Multimedia Collections Specialists
271. 0.4 0 23-1023 Judges, Magistrate Judges, and Magistrates
272. 0.4 49-3042 Mobile Heavy Equipment Mechanics, Except Engines
273. 0.4 29-2799 Health Technologists and Technicians, All Other
274. 0.41 45-2041 Graders and Sorters, Agricultural Products
62

Computerisable
275. 0.41 51-2041 Structural Metal Fabricators and Fitters
276. 0.41 1 23-1012 Judicial Law Clerks
277. 0.41 49-2094 Electrical and Electronics Repairers, Commercial and Industrial Equip-
ment
278. 0.42 19-4093 Forest and Conservation Technicians
279. 0.42 53-1021 First-Line Supervisors of Helpers, Laborers, and Material Movers,
Hand
280. 0.43 39-3093 Locker Room, Coatroom, and Dressing Room Attendants
281. 0.43 19-2099 Physical Scientists, All Other
282. 0.43 0 19-3011 Economists
283. 0.44 19-3093 Historians
284. 0.45 51-9082 Medical Appliance Technicians
285. 0.46 43-4031 Court, Municipal, and License Clerks
286. 0.47 13-1141 Compensation, Benefits, and Job Analysis Specialists
287. 0.47 31-1013 Psychiatric Aides
288. 0.47 29-2012 Medical and Clinical Laboratory Technicians
289. 0.48 33-2021 Fire Inspectors and Investigators
290. 0.48 17-3021 Aerospace Engineering and Operations Technicians
291. 0.48 27-1026 Merchandise Displayers and Window Trimmers
292. 0.48 47-5031 Explosives Workers, Ordnance Handling Experts, and Blasters
293. 0.48 15-1131 Computer Programmers
294. 0.49 33-9091 Crossing Guards
295. 0.49 17-2021 Agricultural Engineers
296. 0.49 47-5061 Roof Bolters, Mining
297. 0.49 49-9052 Telecommunications Line Installers and Repairers
298. 0.49 43-5031 Police, Fire, and Ambulance Dispatchers
299. 0.5 53-7033 Loading Machine Operators, Underground Mining
300. 0.5 49-9799 Installation, Maintenance, and Repair Workers, All Other
301. 0.5 23-2091 Court Reporters
302. 0.51 41-9011 Demonstrators and Product Promoters
303. 0.51 31-9091 Dental Assistants
304. 0.52 51-6041 Shoe and Leather Workers and Repairers
305. 0.52 17-3011 Architectural and Civil Drafters
306. 0.53 47-5012 Rotary Drill Operators, Oil and Gas
307. 0.53 47-4041 Hazardous Materials Removal Workers
308. 0.54 39-4011 Embalmers
309. 0.54 47-5041 Continuous Mining Machine Operators
310. 0.54 39-1012 Slot Supervisors
311. 0.54 31-9011 Massage Therapists
312. 0.54 41-3011 Advertising Sales Agents
313. 0.55 49-3022 Automotive Glass Installers and Repairers
314. 0.55 53-2012 Commercial Pilots
315. 0.55 43-4051 Customer Service Representatives
316. 0.55 27-4011 Audio and Video Equipment Technicians
317. 0.56 25-9041 Teacher Assistants
318. 0.57 45-1011 First-Line Supervisors of Farming, Fishing, and Forestry Workers
319. 0.57 19-4031 Chemical Technicians
320. 0.57 47-3015 Helpers–Pipelayers, Plumbers, Pipefitters, and Steamfitters
321. 0.57 1 13-1051 Cost Estimators
63

Computerisable
322. 0.57 33-3052 Transit and Railroad Police
323. 0.57 37-1012 First-Line Supervisors of Landscaping, Lawn Service, and
Groundskeeping Workers
324. 0.58 13-2052 Personal Financial Advisors
325. 0.59 49-9044 Millwrights
326. 0.59 25-4013 Museum Technicians and Conservators
327. 0.59 47-5042 Mine Cutting and Channeling Machine Operators
328. 0.59 0 11-3071 Transportation, Storage, and Distribution Managers
329. 0.59 49-3092 Recreational Vehicle Service Technicians
330. 0.59 49-3023 Automotive Service Technicians and Mechanics
331. 0.6 33-3012 Correctional Ofﬁcers and Jailers
332. 0.6 27-4031 Camera Operators, Television, Video, and Motion Picture
333. 0.6 51-3023 Slaughterers and Meat Packers
334. 0.61 49-2096 Electronic Equipment Installers and Repairers, Motor Vehicles
335. 0.61 31-2022 Physical Therapist Aides
336. 0.61 39-3092 Costume Attendants
337. 0.61 1 13-1161 Market Research Analysts and Marketing Specialists
338. 0.61 43-4181 Reservation and Transportation Ticket Agents and Travel Clerks
339. 0.61 51-8031 Water and Wastewater Treatment Plant and System Operators
340. 0.61 19-4099 Life, Physical, and Social Science Technicians, All Other
341. 0.61 51-3093 Food Cooking Machine Operators and Tenders
342. 0.61 51-4122 Welding, Soldering, and Brazing Machine Setters, Operators, and Ten-
ders
343. 0.62 1 53-5022 Motorboat Operators
344. 0.62 47-2082 Tapers
345. 0.62 47-2151 Pipelayers
346. 0.63 19-2042 Geoscientists, Except Hydrologists and Geographers
347. 0.63 49-9012 Control and Valve Installers and Repairers, Except Mechanical Door
348. 0.63 31-9799 Healthcare Support Workers, All Other
349. 0.63 35-1012 First-Line Supervisors of Food Preparation and Serving Workers
350. 0.63 47-4011 Construction and Building Inspectors
351. 0.64 51-9031 Cutters and Trimmers, Hand
352. 0.64 49-9071 Maintenance and Repair Workers, General
353. 0.64 23-1021 Administrative Law Judges, Adjudicators, and Hearing Ofﬁcers
354. 0.64 43-5081 Stock Clerks and Order Fillers
355. 0.64 51-8012 Power Distributors and Dispatchers
356. 0.64 47-2132 Insulation Workers, Mechanical
357. 0.65 19-4061 Social Science Research Assistants
358. 0.65 51-4041 Machinists
359. 0.65 15-1150 Computer Support Specialists
360. 0.65 25-4021 Librarians
361. 0.65 49-2097 Electronic Home Entertainment Equipment Installers and Repairers
362. 0.65 49-9021 Heating, Air Conditioning, and Refrigeration Mechanics and Installers
363. 0.65 53-7041 Hoist and Winch Operators
364. 0.66 37-2021 Pest Control Workers
365. 0.66 51-9198 Helpers–Production Workers
366. 0.66 43-9111 Statistical Assistants
367. 0.66 37-2011 Janitors and Cleaners, Except Maids and Housekeeping Cleaners
368. 0.66 49-3051 Motorboat Mechanics and Service Technicians
64

Computerisable
369. 0.67 51-9196 Paper Goods Machine Setters, Operators, and Tenders
370. 0.67 51-4071 Foundry Mold and Coremakers
371. 0.67 19-2021 Atmospheric and Space Scientists
372. 0.67 1 53-3021 Bus Drivers, Transit and Intercity
373. 0.67 33-9092 Lifeguards, Ski Patrol, and Other Recreational Protective Service Work-
ers
374. 0.67 49-9041 Industrial Machinery Mechanics
375. 0.68 43-5052 Postal Service Mail Carriers
376. 0.68 47-5071 Roustabouts, Oil and Gas
377. 0.68 47-2011 Boilermakers
378. 0.68 17-3013 Mechanical Drafters
379. 0.68 29-2021 Dental Hygienists
380. 0.69 1 53-3033 Light Truck or Delivery Services Drivers
381. 0.69 0 37-2012 Maids and Housekeeping Cleaners
382. 0.69 51-9122 Painters, Transportation Equipment
383. 0.7 43-4061 Eligibility Interviewers, Government Programs
384. 0.7 49-3093 Tire Repairers and Changers
385. 0.7 51-3092 Food Batchmakers
386. 0.7 49-2091 Avionics Technicians
387. 0.71 49-3011 Aircraft Mechanics and Service Technicians
388. 0.71 53-2022 Airfield Operations Specialists
389. 0.71 51-8093 Petroleum Pump System Operators, Refinery Operators, and Gaugers
390. 0.71 47-4799 Construction and Related Workers, All Other
391. 0.71 29-2081 Opticians, Dispensing
392. 0.71 51-6011 Laundry and Dry-Cleaning Workers
393. 0.72 39-3091 Amusement and Recreation Attendants
394. 0.72 31-9095 Pharmacy Aides
395. 0.72 47-3016 Helpers–Roofers
396. 0.72 53-7121 Tank Car, Truck, and Ship Loaders
397. 0.72 49-9031 Home Appliance Repairers
398. 0.72 47-2031 Carpenters
399. 0.72 27-3012 Public Address System and Other Announcers
400. 0.73 51-6063 Textile Knitting and Weaving Machine Setters, Operators, and Tenders
401. 0.73 11-3011 Administrative Services Managers
402. 0.73 47-2121 Glaziers
403. 0.73 51-2021 Coil Winders, Tapers, and Finishers
404. 0.73 49-3031 Bus and Truck Mechanics and Diesel Engine Specialists
405. 0.74 49-2011 Computer, Automated Teller, and Office Machine Repairers
406. 0.74 39-9021 Personal Care Aides
407. 0.74 27-4012 Broadcast Technicians
408. 0.74 47-3013 Helpers–Electricians
409. 0.75 11-9131 Postmasters and Mail Superintendents
410. 0.75 47-2044 Tile and Marble Setters
411. 0.75 47-2141 Painters, Construction and Maintenance
412. 0.75 53-6061 Transportation Attendants, Except Flight Attendants
413. 0.75 1 17-3022 Civil Engineering Technicians
414. 0.75 49-3041 Farm Equipment Mechanics and Service Technicians
415. 0.76 25-4011 Archivists
416. 0.76 51-9011 Chemical Equipment Operators and Tenders
65

Computerisable
417. 0.76 49-2092 Electric Motor, Power Tool, and Related Repairers
418. 0.76 45-4021 Fallers
419. 0.77 19-4091 Environmental Science and Protection Technicians, Including Health
420. 0.77 49-9094 Locksmiths and Safe Repairers
421. 0.77 37-3013 Tree Trimmers and Pruners
422. 0.77 35-3011 Bartenders
423. 0.77 13-1023 Purchasing Agents, Except Wholesale, Retail, and Farm Products
424. 0.77 1 35-9021 Dishwashers
425. 0.77 0 45-3021 Hunters and Trappers
426. 0.78 31-9093 Medical Equipment Preparers
427. 0.78 51-4031 Cutting, Punching, and Press Machine Setters, Operators, and Tenders,
Metal and Plastic
428. 0.78 43-9011 Computer Operators
429. 0.78 51-8092 Gas Plant Operators
430. 0.79 43-5053 Postal Service Mail Sorters, Processors, and Processing Machine Oper-
ators
431. 0.79 53-3032 Heavy and Tractor-Trailer Truck Drivers
432. 0.79 39-5093 Shampooers
433. 0.79 47-2081 Drywall and Ceiling Tile Installers
434. 0.79 49-9098 Helpers–Installation, Maintenance, and Repair Workers
435. 0.79 49-3052 Motorcycle Mechanics
436. 0.79 51-2011 Aircraft Structure, Surfaces, Rigging, and Systems Assemblers
437. 0.79 45-4022 Logging Equipment Operators
438. 0.79 47-2042 Floor Layers, Except Carpet, Wood, and Hard Tiles
439. 0.8 39-5011 Barbers
440. 0.8 47-5011 Derrick Operators, Oil and Gas
441. 0.81 1 35-2011 Cooks, Fast Food
442. 0.81 43-9022 Word Processors and Typists
443. 0.81 1 17-3012 Electrical and Electronics Drafters
444. 0.81 17-3024 Electro-Mechanical Technicians
445. 0.81 51-9192 Cleaning, Washing, and Metal Pickling Equipment Operators and Ten-
ders
446. 0.81 11-9141 Property, Real Estate, and Community Association Managers
447. 0.81 43-6013 Medical Secretaries
448. 0.81 51-6021 Pressers, Textile, Garment, and Related Materials
449. 0.82 51-2031 Engine and Other Machine Assemblers
450. 0.82 49-2098 Security and Fire Alarm Systems Installers
451. 0.82 49-9045 Refractory Materials Repairers, Except Brickmasons
452. 0.82 39-2021 Nonfarm Animal Caretakers
453. 0.82 1 47-2211 Sheet Metal Workers
454. 0.82 47-2072 Pile-Driver Operators
455. 0.82 47-2021 Brickmasons and Blockmasons
456. 0.83 45-3011 Fishers and Related Fishing Workers
457. 0.83 47-2221 Structural Iron and Steel Workers
458. 0.83 53-4021 Railroad Brake, Signal, and Switch Operators
459. 0.83 53-4031 Railroad Conductors and Yardmasters
460. 0.83 35-2012 Cooks, Institution and Cafeteria
461. 0.83 53-5011 Sailors and Marine Oilers
462. 0.83 51-9023 Mixing and Blending Machine Setters, Operators, and Tenders
66

Computerisable
463. 0.83 47-3011 Helpers–Brickmasons, Blockmasons, Stonemasons, and Tile and Mar-
ble Setters
464. 0.83 47-4091 Segmental Pavers
465. 0.83 47-2131 Insulation Workers, Floor, Ceiling, and Wall
466. 0.83 51-5112 Printing Press Operators
467. 0.83 53-6031 Automotive and Watercraft Service Attendants
468. 0.83 47-4071 Septic Tank Servicers and Sewer Pipe Cleaners
469. 0.83 39-6011 Baggage Porters and Bellhops
470. 0.83 41-2012 Gaming Change Persons and Booth Cashiers
471. 0.83 51-4023 Rolling Machine Setters, Operators, and Tenders, Metal and Plastic
472. 0.83 47-2071 Paving, Surfacing, and Tamping Equipment Operators
473. 0.84 51-4111 Tool and Die Makers
474. 0.84 17-3023 Electrical and Electronics Engineering Technicians
475. 0.84 47-2161 Plasterers and Stucco Masons
476. 0.84 51-4192 Layout Workers, Metal and Plastic
477. 0.84 51-4034 Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal
and Plastic
478. 0.84 33-9032 Security Guards
479. 0.84 51-6052 Tailors, Dressmakers, and Custom Sewers
480. 0.84 53-7073 Wellhead Pumpers
481. 0.84 43-9081 Proofreaders and Copy Markers
482. 0.84 33-3041 Parking Enforcement Workers
483. 0.85 53-7062 Laborers and Freight, Stock, and Material Movers, Hand
484. 0.85 41-4012 Sales Representatives, Wholesale and Manufacturing, Except Technical
and Scientiﬁc Products
485. 0.85 1 43-5041 Meter Readers, Utilities
486. 0.85 51-8013 Power Plant Operators
487. 0.85 51-8091 Chemical Plant and System Operators
488. 0.85 47-5021 Earth Drillers, Except Oil and Gas
489. 0.85 19-4051 Nuclear Technicians
490. 0.86 43-6011 Executive Secretaries and Executive Administrative Assistants
491. 0.86 51-8099 Plant and System Operators, All Other
492. 0.86 35-3041 Food Servers, Nonrestaurant
493. 0.86 51-7041 Sawing Machine Setters, Operators, and Tenders, Wood
494. 0.86 53-4041 Subway and Streetcar Operators
495. 0.86 31-9096 Veterinary Assistants and Laboratory Animal Caretakers
496. 0.86 51-9032 Cutting and Slicing Machine Setters, Operators, and Tenders
497. 0.86 41-9022 Real Estate Sales Agents
498. 0.86 1 51-4011 Computer-Controlled Machine Tool Operators, Metal and Plastic
499. 0.86 49-9043 Maintenance Workers, Machinery
500. 0.86 43-4021 Correspondence Clerks
501. 0.87 45-2090 Miscellaneous Agricultural Workers
502. 0.87 45-4011 Forest and Conservation Workers
503. 0.87 51-4052 Pourers and Casters, Metal
504. 0.87 47-2041 Carpet Installers
505. 0.87 47-2142 Paperhangers
506. 0.87 13-1021 Buyers and Purchasing Agents, Farm Products
507. 0.87 51-7021 Furniture Finishers
508. 0.87 35-2021 Food Preparation Workers
67

Computerisable
509. 0.87 47-2043 Floor Sanders and Finishers
510. 0.87 1 53-6021 Parking Lot Attendants
511. 0.87 47-4051 Highway Maintenance Workers
512. 0.88 47-2061 Construction Laborers
513. 0.88 43-5061 Production, Planning, and Expediting Clerks
514. 0.88 51-9141 Semiconductor Processors
515. 0.88 17-1021 Cartographers and Photogrammetrists
516. 0.88 51-4051 Metal-Reﬁning Furnace Operators and Tenders
517. 0.88 51-9012 Separating, Filtering, Clarifying, Precipitating, and Still Machine Set-
ters, Operators, and Tenders
518. 0.88 51-6091 Extruding and Forming Machine Setters, Operators, and Tenders, Syn-
thetic and Glass Fibers
519. 0.88 47-2053 Terrazzo Workers and Finishers
520. 0.88 51-4194 Tool Grinders, Filers, and Sharpeners
521. 0.88 49-3043 Rail Car Repairers
522. 0.89 51-3011 Bakers
523. 0.89 1 31-9094 Medical Transcriptionists
524. 0.89 47-2022 Stonemasons
525. 0.89 53-3022 Bus Drivers, School or Special Client
526. 0.89 1 27-3042 Technical Writers
527. 0.89 49-9096 Riggers
528. 0.89 47-4061 Rail-Track Laying and Maintenance Equipment Operators
529. 0.89 51-8021 Stationary Engineers and Boiler Operators
530. 0.89 1 51-6031 Sewing Machine Operators
531. 0.89 1 53-3041 Taxi Drivers and Chauffeurs
532. 0.9 1 43-4161 Human Resources Assistants, Except Payroll and Timekeeping
533. 0.9 29-2011 Medical and Clinical Laboratory Technologists
534. 0.9 47-2171 Reinforcing Iron and Rebar Workers
535. 0.9 47-2181 Roofers
536. 0.9 53-7021 Crane and Tower Operators
537. 0.9 53-6041 Trafﬁc Technicians
538. 0.9 53-6051 Transportation Inspectors
539. 0.9 51-4062 Patternmakers, Metal and Plastic
540. 0.9 51-9195 Molders, Shapers, and Casters, Except Metal and Plastic
541. 0.9 13-2021 Appraisers and Assessors of Real Estate
542. 0.9 53-7072 Pump Operators, Except Wellhead Pumpers
543. 0.9 49-9097 Signal and Track Switch Repairers
544. 0.91 39-3012 Gaming and Sports Book Writers and Runners
545. 0.91 49-9063 Musical Instrument Repairers and Tuners
546. 0.91 39-7011 Tour Guides and Escorts
547. 0.91 49-9011 Mechanical Door Repairers
548. 0.91 51-3091 Food and Tobacco Roasting, Baking, and Drying Machine Operators
and Tenders
549. 0.91 53-7071 Gas Compressor and Gas Pumping Station Operators
550. 0.91 29-2071 Medical Records and Health Information Technicians
551. 0.91 51-9121 Coating, Painting, and Spraying Machine Setters, Operators, and Ten-
ders
552. 0.91 51-4081 Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plas-
tic
68

Computerisable
553. 0.91 53-4013 Rail Yard Engineers, Dinkey Operators, and Hostlers
554. 0.91 49-2093 Electrical and Electronics Installers and Repairers, Transportation
Equipment
555. 0.91 35-9011 Dining Room and Cafeteria Attendants and Bartender Helpers
556. 0.91 51-4191 Heat Treating Equipment Setters, Operators, and Tenders, Metal and
Plastic
557. 0.91 19-4041 Geological and Petroleum Technicians
558. 0.91 49-3021 Automotive Body and Related Repairers
559. 0.91 51-7032 Patternmakers, Wood
560. 0.91 51-4021 Extruding and Drawing Machine Setters, Operators, and Tenders, Metal
and Plastic
561. 0.92 43-9071 Ofﬁce Machine Operators, Except Computer
562. 0.92 29-2052 Pharmacy Technicians
563. 0.92 43-4131 Loan Interviewers and Clerks
564. 0.92 53-7031 Dredge Operators
565. 0.92 41-3021 Insurance Sales Agents
566. 0.92 51-7011 Cabinetmakers and Bench Carpenters
567. 0.92 51-9123 Painting, Coating, and Decorating Workers
568. 0.92 47-4031 Fence Erectors
569. 0.92 51-4193 Plating and Coating Machine Setters, Operators, and Tenders, Metal
and Plastic
570. 0.92 41-2031 Retail Salespersons
571. 0.92 35-3021 Combined Food Preparation and Serving Workers, Including Fast Food
572. 0.92 51-9399 Production Workers, All Other
573. 0.92 47-3012 Helpers–Carpenters
574. 0.93 51-9193 Cooling and Freezing Equipment Operators and Tenders
575. 0.93 51-2091 Fiberglass Laminators and Fabricators
576. 0.93 47-5013 Service Unit Operators, Oil, Gas, and Mining
577. 0.93 53-7011 Conveyor Operators and Tenders
578. 0.93 49-3053 Outdoor Power Equipment and Other Small Engine Mechanics
579. 0.93 53-4012 Locomotive Firers
580. 0.93 53-7063 Machine Feeders and Offbearers
581. 0.93 51-4061 Model Makers, Metal and Plastic
582. 0.93 49-2021 Radio, Cellular, and Tower Equipment Installers and Repairs
583. 0.93 51-3021 Butchers and Meat Cutters
584. 0.93 51-9041 Extruding, Forming, Pressing, and Compacting Machine Setters, Oper-
ators, and Tenders
585. 0.93 53-7081 Refuse and Recyclable Material Collectors
586. 0.93 1 13-2081 Tax Examiners and Collectors, and Revenue Agents
587. 0.93 51-4022 Forging Machine Setters, Operators, and Tenders, Metal and Plastic
588. 0.93 1 53-7051 Industrial Truck and Tractor Operators
589. 0.94 1 13-2011 Accountants and Auditors
590. 0.94 51-4032 Drilling and Boring Machine Tool Setters, Operators, and Tenders,
Metal and Plastic
591. 0.94 43-9051 Mail Clerks and Mail Machine Operators, Except Postal Service
592. 0.94 0 35-3031 Waiters and Waitresses
593. 0.94 51-3022 Meat, Poultry, and Fish Cutters and Trimmers
594. 0.94 13-2031 Budget Analysts
595. 0.94 47-2051 Cement Masons and Concrete Finishers
69

Computerisable
596. 0.94 49-3091 Bicycle Repairers
597. 0.94 49-9091 Coin, Vending, and Amusement Machine Servicers and Repairers
598. 0.94 51-4121 Welders, Cutters, Solderers, and Brazers
599. 0.94 1 43-5021 Couriers and Messengers
600. 0.94 43-4111 Interviewers, Except Eligibility and Loan
601. 0.94 35-2015 Cooks, Short Order
602. 0.94 53-7032 Excavating and Loading Machine and Dragline Operators
603. 0.94 47-3014 Helpers–Painters, Paperhangers, Plasterers, and Stucco Masons
604. 0.94 43-4081 Hotel, Motel, and Resort Desk Clerks
605. 0.94 51-9197 Tire Builders
606. 0.94 41-9091 Door-to-Door Sales Workers, News and Street Vendors, and Related
Workers
607. 0.94 37-1011 First-Line Supervisors of Housekeeping and Janitorial Workers
608. 0.94 45-2011 Agricultural Inspectors
609. 0.94 1 23-2011 Paralegals and Legal Assistants
610. 0.95 39-5092 Manicurists and Pedicurists
611. 0.95 43-5111 Weighers, Measurers, Checkers, and Samplers, Recordkeeping
612. 0.95 51-6062 Textile Cutting Machine Setters, Operators, and Tenders
613. 0.95 43-3011 Bill and Account Collectors
614. 0.95 51-8011 Nuclear Power Reactor Operators
615. 0.95 33-9031 Gaming Surveillance Officers and Gaming Investigators
616. 0.95 43-4121 Library Assistants, Clerical
617. 0.95 47-2073 Operating Engineers and Other Construction Equipment Operators
618. 0.95 51-5113 Print Binding and Finishing Workers
619. 0.95 45-2021 Animal Breeders
620. 0.95 51-4072 Molding, Coremaking, and Casting Machine Setters, Operators, and
Tenders, Metal and Plastic
621. 0.95 1 51-2022 Electrical and Electronic Equipment Assemblers
622. 0.95 51-9191 Adhesive Bonding Machine Operators and Tenders
623. 0.95 37-3011 Landscaping and Groundskeeping Workers
624. 0.95 51-4033 Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Oper-
ators, and Tenders, Metal and Plastic
625. 0.95 43-5051 Postal Service Clerks
626. 0.95 51-9071 Jewelers and Precious Stone and Metal Workers
627. 0.96 43-5032 Dispatchers, Except Police, Fire, and Ambulance
628. 0.96 43-4171 Receptionists and Information Clerks
629. 0.96 43-9061 Office Clerks, General
630. 0.96 11-3111 Compensation and Benefits Managers
631. 0.96 1 43-2011 Switchboard Operators, Including Answering Service
632. 0.96 35-3022 Counter Attendants, Cafeteria, Food Concession, and Coffee Shop
633. 0.96 47-5051 Rock Splitters, Quarry
634. 0.96 43-6014 Secretaries and Administrative Assistants, Except Legal, Medical, and
Executive
635. 0.96 17-3031 Surveying and Mapping Technicians
636. 0.96 51-7031 Model Makers, Wood
637. 0.96 51-6064 Textile Winding, Twisting, and Drawing Out Machine Setters, Opera-
tors, and Tenders
638. 0.96 53-4011 Locomotive Engineers
639. 0.96 1 39-3011 Gaming Dealers
70

Computerisable
640. 0.96 49-9093 Fabric Menders, Except Garment
641. 0.96 35-2014 Cooks, Restaurant
642. 0.96 39-3031 Ushers, Lobby Attendants, and Ticket Takers
643. 0.96 43-3021 Billing and Posting Clerks
644. 0.97 53-6011 Bridge and Lock Tenders
645. 0.97 51-7042 Woodworking Machine Setters, Operators, and Tenders, Except Sawing
646. 0.97 51-2092 Team Assemblers
647. 0.97 51-6042 Shoe Machine Operators and Tenders
648. 0.97 51-2023 Electromechanical Equipment Assemblers
649. 0.97 1 13-1074 Farm Labor Contractors
650. 0.97 51-6061 Textile Bleaching and Dyeing Machine Operators and Tenders
651. 0.97 51-9081 Dental Laboratory Technicians
652. 0.97 51-9021 Crushing, Grinding, and Polishing Machine Setters, Operators, and
Tenders
653. 0.97 51-9022 Grinding and Polishing Workers, Hand
654. 0.97 37-3012 Pesticide Handlers, Sprayers, and Applicators, Vegetation
655. 0.97 45-4023 Log Graders and Scalers
656. 0.97 51-9083 Ophthalmic Laboratory Technicians
657. 0.97 1 41-2011 Cashiers
658. 0.97 49-9061 Camera and Photographic Equipment Repairers
659. 0.97 39-3021 Motion Picture Projectionists
660. 0.97 51-5111 Prepress Technicians and Workers
661. 0.97 41-2021 Counter and Rental Clerks
662. 0.97 1 43-4071 File Clerks
663. 0.97 41-9021 Real Estate Brokers
664. 0.97 43-2021 Telephone Operators
665. 0.97 19-4011 Agricultural and Food Science Technicians
666. 0.97 43-3051 Payroll and Timekeeping Clerks
667. 0.97 1 43-4041 Credit Authorizers, Checkers, and Clerks
668. 0.97 35-9031 Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop
669. 0.98 41-9012 Models
670. 0.98 51-9061 Inspectors, Testers, Sorters, Samplers, and Weighers
671. 0.98 43-3031 Bookkeeping, Accounting, and Auditing Clerks
672. 0.98 43-6012 Legal Secretaries
673. 0.98 27-4013 Radio Operators
674. 0.98 53-3031 Driver/Sales Workers
675. 0.98 1 13-1031 Claims Adjusters, Examiners, and Investigators
676. 0.98 41-2022 Parts Salespersons
677. 0.98 1 13-2041 Credit Analysts
678. 0.98 51-4035 Milling and Planing Machine Setters, Operators, and Tenders, Metal
and Plastic
679. 0.98 43-5071 Shipping, Receiving, and Traffic Clerks
680. 0.98 43-3061 Procurement Clerks
681. 0.98 51-9111 Packaging and Filling Machine Operators and Tenders
682. 0.98 51-9194 Etchers and Engravers
683. 0.98 43-3071 Tellers
684. 0.98 27-2023 Umpires, Referees, and Other Sports Officials
685. 0.98 13-1032 Insurance Appraisers, Auto Damage
686. 0.98 1 13-2072 Loan Officers
71

Computerisable
687. 0.98 43-4151 Order Clerks
688. 0.98 43-4011 Brokerage Clerks
689. 0.98 43-9041 Insurance Claims and Policy Processing Clerks
690. 0.98 51-2093 Timing Device Assemblers and Adjusters
691. 0.99 1 43-9021 Data Entry Keyers
692. 0.99 25-4031 Library Technicians
693. 0.99 43-4141 New Accounts Clerks
694. 0.99 51-9151 Photographic Process Workers and Processing Machine Operators
695. 0.99 13-2082 Tax Preparers
696. 0.99 43-5011 Cargo and Freight Agents
697. 0.99 49-9064 Watch Repairers
698. 0.99 1 13-2053 Insurance Underwriters
699. 0.99 15-2091 Mathematical Technicians
700. 0.99 51-6051 Sewers, Hand
701. 0.99 23-2093 Title Examiners, Abstractors, and Searchers
702. 0.99 41-9041 Telemarketers
72

The future of_employment

More Related Content

What's hot

Similar to The future of_employment

More from Rafael Villas B

Recently uploaded

The future of_employment