BSOM046

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BSOM046 READING LIST
THE RECOMMENDED TEXTBOOKS
Essentials of Operations Management, by Nigel Slack,
Alistair Brandon-Jones, Robert Johnston. Pearson, 2011
Operations Management, 3rd Edition,
by Andrew Greasley. Wiley, 2013
COMPLEMENTARY BOOKS
– OPERATIONS MANAGEMENT, 8
th Edition, by Nigel Slack, Alistair
Brandon-Jones, Robert Johnston. Pearson, 2016.
– STRATEGIC OPERATIONS MANAGEMENT, 3rd Edition, by Steve
Brown, John Bessant, Richard Lamming. Routledge, 2012.
– OPERATIONS MANAGEMENT, by Steve Paton, Ben Clegg, Juliana
Hsuan, Alan Pilkington, McGraw-Hill, 2011.
– MANAGING QUALITY, 5
th Edition, by S. Thomas Foster. Pearson,
2013.
– SERVICE MANAGEMENT – Operations, Strategy, Information
technology, 5
th Edition, by J & M Fitzsimmonns, McGraw-Hill, 2006.
– OPERATIONS MANAGEMENT, by Terry Hill and Dr Alex Hill,
3rd Edition, Palgrave McMillan, 2011
– SERVICE OPERATIONS MANAGEMENT, 3/e by Johnston & Clark,
Financial Times/Prentice Hall, 3rd Edition, 2008
– PRINCIPLES OF OPERATIONS MANAGEMENT, by Heizer &
Render, Pearson, 9th Edition, 2013
– INTRODUCTION TO OPERATIONS AND SUPPLY CHAIN
MANAGEMENT by John Mangan, Chandra Lanwani and Tim Butcher,
John Wiley & Sons Ltd , 2008
– GLOBAL LOGISTICS and SUPPLY CHAIN MANAGEMENT:
INTERNATIONAL EDITION, 3/e by Chopra & Meindl, Financial
Times/Prentice Hall, 3
rd Edition, 2007
– SUPPLY CHAIN MANAGEMENT: INTERNATIONAL EDITION, 3/e
by Chopra & Meindl, Financial Times/Prentice Hall, 3
rd Edition, 2007
FURTHER READING
The Lean Office: Collected Practices & Cases (Insights on
Implementation) by Anderson D.R., Sweeney D.J. and Williams T.A.,
Productivity press, 2003
THE GOAL: A PROCESS OF ONGOING IMPROVEMENT, by Eliyahu M.
Goldratt, Gower Publishing Ltd, 3rd Rev Edition, 2004
LEAN SOLUTIONS: HOW COMPANIES AND CUSTOMERS CAN CREATE
VALUE AND WEALTH TOGETHER, by James P. Womack & Daniel T. Jones,
Simon & Schuster Ltd, New Edition, 2007
Customer Care Excellence by Cook, S. Kogan Page, 2010
Logistics Management and Strategy, by Harrison A. and Hoek, R. FT
Prentice Hall, 5th Edition, 2014
Project Management-A Managerial Approach, Meredith, J and Mantel, John
Wiley & Sons, 7th edition, 2009
Quantitative Analysis for Management Render B, Stair RM and Hanna M,
11th Edition, Pearson, 2011
LEAN THINKING: BANISH WASTE AND CREATE WEALTH IN YOUR
CORPORATION, by James P. Womack & Daniel T. Jones, Free Press, New
Edition, 2003
IMPROVING PRODUCTION WITH LEAN THINKING, by Javier Santos,
Richard A. Wysk and Joe M Torres, John Wiley & Sons Inc, New Edition, 2006
1
BSOM046 – Managing Operations
and the Supply Chain
Lecture 04 – Innovation & Process technology
Northampton MBA
• What is innovation?
• Why should organisations innovate?
• How is innovation done?
• How do we measure it?
• Innovation through process technology
• Technology unemployment
Key operations questions
Innovation is a new idea, which may be the
recombination of old ideas, a scheme that
challenges present order, a formula, or a
unique approach which is perceived as new by
the individuals involved.
– Van de Ven “The Innovation Journey 2008”
What is innovation?
• Innovation is not limited to products or the
application of newer technology.
• Innovation can affect:
– products
– processes
– services
– business models
• Innovation can be incremental or radical
Innovation
Motivations for Innovation
• New technologies / scientific discovery
• Customer Perception changes
• Process needs
• Change in industry structure, society,
demographics, regulations
• Competition
• Margin erosion
• Make a strategic commitment & lay down a strategic
objective. Ex. “20% of our income shall be from
products/services that are less than 5 years old”
• Appoint a director of innovation
• Relate innovation to performance payment
• Benchmark your competitors and world class performers
(learn from others)
• Initiate cross functional teams
• See failure as learning
How is innovation done?
Creative Idea Generating Process
• Encourage all in the organisation to join
– 3M will allow an employee up to 20% of their time to
spend on researching innovative ideas
• Challenge the employees
– Virgin challenge staff to “think like a slightly
disgruntled customer” and then seek out WHY?
• Toyotas Suggestion Scheme
– 90%+ implementation rate
7
Internally
Creative Idea Generating Process
• Create a network of expert users or co-creators
– Users who delight in knowing your products/services
and see uses for them that you have not
• Reward them for their suggestions
– Example: Apple, Firefox, Lego, Nike, HP, IBM…
• Outsource the work
– Creative organisations (e.g. designers, architects,
scientists, inventors, universities, etc.) will all be happy
to do the work for you
8
Externally
Innovation cycle
IDEA
CONCEPT
PRODUCT
DEVELOPMENT
TEST
MARKETING
COMMERCIALISATION
CELEBRATE
AND START
AGAIN
Keep the cyclic theme in mind: REVIEW THE PROCESS REGULARLY!
Barriers to creativity
• Searching for the one
“right” answer
• Focusing on “being
logical”
• Blindly following the rules
• Constantly being practical
• Viewing play as frivolous
• Becoming overly
specialized
• Avoiding ambiguity
• Fearing looking foolish
• Fearing mistakes and
failure
• Believing that “I’m not
creative”
FAILURE CAN HAPPEN –
you will have to deal with these….
1. Failure to clarify innovation goals
2. Failure to engage the entire organisation
3. Failure to create an innovation culture
4. Don’t recognise the importance of communication
5. Don’t recognise the importance of change management
6. Don’t recognise innovation barriers
7. Unclear process for selecting innovation projects
8. Failure to recognise that innovation is a journey, not a
destination
How do we measure innovation?
12
INPUT METRICS:
• Number of ideas generated
• Resources allocated to innovation – people and budget
PROCESS METRICS:
• Average time from idea approval to implementation
• Number of ideas approved and number implemented
• Stage-gate pass rates
• Value of the innovation pipeline
OUTPUT METRICS:
• Number of new products or services launched
• Revenue from new products or services
• ROI on innovation spend
• Market perception
• Number of new customers
Innovations through technology
Process metals, plastics, fabric, etc.
Materials-processing technology
a. Active interaction with
technology
100
%
80%
60%
40%
20%
100
%
Branch
50%
Telephone
25%
Cash
machine
12%
Internet
Technology and processing costs
Cost per banking transaction
Customer-processing technology
b. Passive interaction with technology
Customer-processing technology
c. Use of technology through an intermediary
Customer-processing technology
Disruptive technologies
They are disruptive in a sense that they change the status quo
of businesses, creating opportunities for new business models
Examples:
• 3D Printing • Drones
3D Printing Shapeways.com
Creative innovations
Solar bottle bulb
Lamp that
runs in salt
and water
Mobile Convergence
Technological unemployment
Beware of technological impact on jobs!
Technological unemployment is the loss of jobs caused by the
replacement of human labour by machines (automation)
Technological unemployment
• The pace of technological innovation is still increasing,
with more sophisticated software technologies
disrupting labour markets by making workers
redundant
• Automation is no longer confined to routine
manufacturing tasks. Autonomous driverless cars
provide a good example of how manual tasks in
transport and logistics may soon be automated
Frey & Osborne (2016)
Technological unemployment
Assignment 1 – The Future of Work
Tutorial activity: A small restaurant has a work force
comprising the following team: 1 manager, 1 chef, 2 food
preparation workers and 2 waiters. Based on Frey and
Osborne’s classification of computerisable occupations, what is
the likelihood of low skilled workers in the restaurant become
technologically unemployed in the future?
Consider a low impact scenario, when only jobs
at high risk (> 70%) are replaced by technology
P(Manager) = 0.25
P(Chef) = 0.10
P(Food preparation worker) = 0.87
P (Waiter) = 0.94
Appendix A,
Frey and Osborne (2016) paper
Technological unemployment
P(2 Food preparation workers) = 0.87 x 0.87 = 0.76
P (Food preparation workers AND Waiters) = 0.76 x 0.88 = 0.67
There is a high risk of technological unemployment
(approximately 67% of chance) impacting low skilled
workers in the restaurant become unemployed. This is
more likely to be concentrated in the waiters force.
The probability of the 2 food preparation workers AND the 2
waiters in the restaurant to loose their job by automation is:
P(2 Waiters) = 0.94 x 0.94 = 0.88
Technological unemployment
Northampton MBA
THE UNIVERSITY OF NORTHAMPTON
NORTHAMPTON BUSINESS SCHOOL
MODULE: Managing Operations and the Supply Chain 2017-2018
Module Code Level Credit Value Module Tutors
BSOM046 7 20 Luciano Batista
Desmond Kapofu
Kemi Waterton Zhou
Assignment 1 Brief
Assignment title: The Future of Work
Weighting:
40%
Deadline: 30th March 2018
Feedback and
Grades due: 27th April 2018
Resit Date 06th July 2018
Purpose of the Assessment
This assignment is designed to enable you to demonstrate an understanding of
the potential for automation to impact existing operations processes and related
resources.
Assessment Task
You will conduct a review of the literature in the subject area of Automation.
Specifically, you are to identify the origins of the concept of the Technological
Unemployment and to chart its development up to the present day.
Following your review, you are to critically evaluate the impact of Technological
Unemployment on a company of your choice.
You will be expected to illustrate your discussion with examples drawn from
authoritative business and academic sources.
Assessment Breakdown
1. (10% of word count)
Establish the scenario for your report by selecting an organisation of any type,
sector and size to focus your report on. Describe:
a) Which organisation is it? (type, sector and size)
b) What are the main products and/or services provided by the organisation?
c) Who are the main customers?
2. (45% of word count)
Prepare a literature review, charting the development of the concept of
Technological Unemployment from its inception until the present day.
Ensure that you include references to at least 10 peer-reviewed articles, including
the 2016 paper by Frey and Osborne that has been supplied. You may also find
relevant reviews in the trade press and from other authoritative sources.
3. (45% of word count)
Apply Frey and Osborne’s findings (see Appendix A in the Supporting Materials) in
the context of your chosen company.
Consider a low impact scenario, when only jobs at high risk (> 70%) are replaced
by technology. How will the company change?
Assessment Submission
Your assignment must be word processed and presented in a report format with
simple sub-headings. The word count should be 1500 words±10% (tables,
diagrams and appendices are excluded from the count).
The Assignment report should have a Front Sheet showing your name, your student
number, the module name, the module number, the assignment title, the module
tutor’s name, the date and the word count.
All assignments will be submitted, graded and fed-back electronically via TURNITIN.
Several submissions will be permitted before the hand-in date in order to enable you
to refine the content in your report.
If you click on the “Submit Your Work” button on the Module NILE site you will find an
explanation of the Submission and Grading Electronically process there.
Feedback on assignments in general will be provided to the whole group when
marked assignments are returned.
Feedback on assignments for each individual will be provided electronically via
TURNITIN.
A student may obtain an individual appointment to discuss feedback with the tutor.
Assessment Guidance
The quality of your presentation and academic referencing is very important. Please,
use the Harvard Referencing System.
Within your assignment your tutor will be looking for content that addresses the key
elements of the assignment brief. Remember that Frey and Osborne’s work is
quantitative – you will be expected to have numerical data to support your
discussion.
Try not to overcomplicate your answers by choosing a company that you know little
about. Keep to simple processes that you know well.
Look at the Check list at the end of this brief. It shows the subheadings to use and
offers a guide as to how the marks will be distributed.
Use the percentages as a guide to how to distribute your word count.
Academic Practice
This is an individual assignment. The University of Northampton policy will apply in
all cases of copying, plagiarism or any other methods by which students have
obtained (or attempted to obtain) an unfair advantage.
Support and guidance on assessments and academic integrity can be found from the
following resources
SkillsHub: http://skillshub.northampton.ac.uk
CfAP: http://tinyurl.com/UoNCfAP
Late Submission Opportunities
Submission extensions must be requested in advance to the module tutor. Extensions
can be approved or not by the tutor depending on the justification of the causes or
circumstances impeding submission on the established deadline.
Reassessment and Deferral Opportunities
If you achieve grade F+ or below on this assessment, you are required to submit a
Resit assignment on the date informed in the assignment brief.
BSOM046 OPERATIONS MANAGEMENT
“Report Content Checklist”
STUDENT NAME AND REPORT FRONT SHEET:
DESCRIPTION OF THE CHOSEN COMPANY (10%)
– customers, products and services
LITERATURE SEARCH (45%)
– research the development of the concept of Technological
Unemployment from its inception to the present day
SCENARIO DEVELOPMENT (45%)
– consider a low impact scenario (only jobs above 70% risk are
replaced). Include numerical data to support your discussion.
How will the company change?
MAX WORD COUNT 1500 +/- 10%
Learning outcomes
The learning outcomes being addressed through this assignment are:
Knowledge and Understanding:
a) Investigate and critically evaluate the range of concepts and techniques
available to operations managers so as to enable effective business decision making.
b) Demonstrate conceptual and practical understanding of the opportunities and
constraints that organisational characteristics place on operations managers and on
operational decision making in the supply chain context.
c) Critically discuss and evaluate the theoretical and real life applications of topics
in the indicative content, analysing and evaluating the benefits they offer to an
organisation and the challenges to be overcome in implementing them.
Subject- Specific Skills
d) Critically evaluate the business relevance of the concept/topic studied, with a
view to understanding the value of its adoption to an organisation.
Key Skills:
g) Make discriminating use of a range of learning resources in order to solve
problems within the domain of International Supply Chain and Operations
Management.
h) Communicate the solutions arrived at, and the critical evaluation underlying
them.
Technological Forecasting & Social Change 114 (2017) 254–280
Contents lists available at ScienceDirect
Technological Forecasting & Social Change
The future of employment: How susceptible are jobs
to computerisation?
Carl Benedikt Freya,
*, Michael A. Osborneb
aOxford Martin School, University of Oxford, Oxford OX1 1PT, United Kingdom
bDepartment of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom
ARTICLE INFO
Article history:
Received 24 September 2015
Accepted 19 August 2016
Available online 29 September 2016
JEL classification:
E24
J24
J31
J62
O33
Keywords:
Occupational choice
Technological change
Wage inequality
Employment
Skill demand
ABSTRACT
We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel
methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian
process classifier. Based on these estimates, we examine expected impacts of future computerisation on
US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the
relationship between an occupations probability of computerisation, wages and educational attainment.
© 2016 Published by Elsevier Inc.
1. Introduction
In this paper, we address the question: how susceptible are jobs
to computerisation? Doing so, we build on the existing literature in
two ways. First, drawing upon recent advances in Machine Learning
(ML) and Mobile Robotics (MR), we develop a novel methodology
to categorise occupations according to their susceptibility to
computerisation.1 Second, we implement this methodology to estimate
the probability of computerisation for 702 detailed occupations,
and examine expected impacts of future computerisation on
US labour market outcomes.
We thank the Oxford University Engineering Sciences Department and the Oxford
Martin Programme on the Impacts of Future Technology for hosting the “Machines
and Employment” Workshop. We are indebted to Stuart Armstrong, Nick Bostrom,
Eris Chinellato, Mark Cummins, Daniel Dewey, Alex Flint, John Muellbauer, Vincent
Mueller, Paul Newman, Seán Ó hÉigeartaigh, Anders Sandberg, Murray Shanahan, and
Keith Woolcock for their excellent suggestions.
* Corresponding author.
E-mail addresses: carl.frey@philosophy.ox.ac.uk (C. Frey), mosb@robots.ox.ac.uk
(M. Osborne).
1 We refer to computerisation as job automation by means of computer-controlled
equipment.
Our paper is motivated by John Maynard Keynes’s frequently
cited prediction of widespread technological unemployment “due to
our discovery of means of economising the use of labour outrunning
the pace at which we can find new uses for labour” (Keynes,
1933, p. 3). Indeed, over the past decades, computers have substituted
for a number of jobs, including the functions of bookkeepers,
cashiers and telephone operators (Bresnahan, 1999; MGI,
2013). More recently, the poor performance of labour markets across
advanced economies has intensified the debate about technological
unemployment among economists. While there is ongoing disagreement
about the driving forces behind the persistently high unemployment
rates, a number of scholars have pointed at computercontrolled
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
2 This view finds support in a recent survey by the McKinsey Global Institute (MGI),
showing that 44% of firms which reduced their headcount since the financial crisis of
2008 had done so by means of automation (MGI, 2011).
http://dx.doi.org/10.1016/j.techfore.2016.08.019
0040-1625/© 2016 Published by Elsevier Inc.
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 255
mainly consisting of tasks following well-defined procedures that
can easily be performed by sophisticated algorithms. For example,
studies by Charles et al. (2013) and Jaimovich and Siu (2012) emphasise
that the ongoing decline in manufacturing employment and
the disappearance of other routine jobs is causing the current low
rates of employment.3 In addition to the computerisation of routine
manufacturing tasks, Autor and Dorn (2013) document a structural
shift in the labour market, with workers reallocating their labour
supply from middle-income manufacturing to low-income service
occupations. Arguably, this is because the manual tasks of service
occupations are less susceptible to computerisation, as they require
a higher degree of flexibility and physical adaptability (Autor et al.,
2003; Goos and Manning, 2007; Autor and Dorn, 2013).
At the same time, with falling prices of computing, problemsolving
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 polarisation,
with growing employment in high-income cognitive jobs and
low-income manual occupations, accompanied by a hollowing-out
of middle-income routine jobs.
According to Brynjolfsson and McAfee (2011), the pace of technological
innovation is still increasing, with more sophisticated
software technologies disrupting labour markets by making workers
redundant. What is striking about the examples in their book
is that computerisation is no longer confined to routine manufacturing
tasks. The autonomous driverless cars, developed by Google,
provide one example of how manual tasks in transport and logistics
may soon be automated. In the section “In Domain After Domain,
Computers Race Ahead”, they emphasise how fast moving these
developments have been. Less than ten years ago, in the chapter
“Why People Still Matter”, Levy and Murnane (2004) pointed at the
difficulties of replicating human perception, asserting that driving
in traffic is insusceptible to automation: “But executing a left turn
against oncoming traffic involves so many factors that it is hard
to imagine discovering the set of rules that can replicate a driver’s
behaviour [. . . ]”. Six years later, in October 2010, Google announced
that it had modified several Toyota Priuses to be fully autonomous
(Brynjolfsson and McAfee, 2011).
To our knowledge, no study has yet quantified what recent
technological progress is likely to mean for the future of employment.
The present study intends to bridge this gap in the literature.
Although there are indeed existing useful frameworks for examining
the impact of computers on the occupational employment composition,
they seem inadequate in explaining the impact of technological
trends going beyond the computerisation of routine tasks. Seminal
work by Autor et al. (2003), for example, distinguishes between
cognitive and manual tasks on the one hand, and routine and nonroutine
tasks on the other. While the computer substitution for both
cognitive and manual routine tasks is evident, non-routine tasks
involve everything from legal writing, truck driving and medical
diagnoses, to persuading and selling. In the present study, we will
argue that legal writing and truck driving will soon be automated,
while persuading, for instance, will not. Drawing upon recent developments
in Engineering Sciences, and in particular advances in the
fields of ML, including Data Mining, Machine Vision, Computational
Statistics and other sub-fields of Artificial Intelligence, as well as
MR, we derive additional dimensions required to understand the
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).
susceptibility of jobs to computerisation. Needless to say, a number
of factors are driving decisions to automate and we cannot capture
these in full. Rather we aim, from a technological capabilities point of
view, to determine which problems engineers need to solve for specific
occupations to be automated. By highlighting these problems,
their difficulty and to which occupations they relate, we categorise
jobs according to their susceptibility to computerisation. The characteristics
of these problems were matched to different occupational
characteristics, using O*NET data, allowing us to examine the future
direction of technological change in terms of its impact on the occupational
composition of the labour market, but also the number of
jobs at risk should these technologies materialise.
The present study relates to two literatures. First, our analysis
builds on the labour economics literature on the task content of
employment (Autor et al., 2003; Goos and Manning, 2007; Autor
and Dorn, 2013; Ingram and Neumann, 2006). Based on defined
premises about what computers do, this literature examines the historical
impact of computerisation on the occupational composition
of the labour market. However, the scope of what computers do has
recently expanded, and will inevitably continue to do so (Brynjolfsson
and McAfee, 2011; MGI, 2013). Drawing upon recent progress
in ML, we expand the premises about the tasks computers are and
will be suited to accomplish. Doing so, we build on the task content
literature in a forward-looking manner. Furthermore, whereas
this literature has largely focused on task measures from the Dictionary
of Occupational Titles (DOT), last revised in 1991, we rely on
the 2010 version of the DOT successor O*NET – an online service
developed for the US Department of Labor.4 In particular, Ingram and
Neumann (2006) use various DOT measurements to examine returns
to different skills. Our analysis builds on their approach by classifying
occupations according to their susceptibility to computerisation
using O*NET data.
Second, our study relates to theliterature examining the offshoring
of information/based tasks to foreign worksites (Blinder, 2009;
Blinder and Krueger, 2013; Jensen and Kletzer, 2005, 2010; Oldenski,
2012). This literature consists of different methodologies to rank and
categorise occupations according to their susceptibility to offshoring.
For example, using O*NET data on the nature of work done in different
occupations, Blinder (2009) estimates that 22 to 29% 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 location; and
(b) the job requires face-to-face personal communication. Naturally,
the characteristics of occupations that can be offshored are different
from the characteristics of occupations that can be automated.
For example, the work of cashiers, which has largely been substituted
by self- service technology, must be performed at specific work
location and requires face-to-face contact. The extent of computerisation
is therefore likely to go beyond that of offshoring. Hence, while
the implementation of our methodology is similar to that of Blinder
(2009), we rely on different occupational characteristics.
The remainder of this paper is structured as follows. In Section 2,
we review the literature on the historical relationship between
technological progress and employment. Section 3 describes recent
and expected future technological developments. In Section 4, we
describe our methodology, and in Section 5, we examine the
expected impact of these technological developments on labour
market outcomes. Finally, in Section 6, we derive some conclusions.
2. A history of technological revolutions and employment
The concern over technological unemployment is hardly a
recent phenomenon. Throughout history, the process of creative
4 Goos et al. (2009) provides a notable exception.
256 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
destruction, following technological inventions, has created enormous
wealth, but also undesired disruptions. As stressed by Schumpeter
(1962), it was not the lack of inventive ideas that set the
boundaries for economic development, but rather powerful social
and economic interests promoting the technological status quo. This
is nicely illustrated by the example of William Lee, inventing the
stocking frame knitting machine in 1589, hoping that it would relieve
workers of hand-knitting. Seeking patent protection for his invention,
he travelled to London where he had rented a building for his
machine to be viewed by Queen Elizabeth I. To his disappointment,
the Queen was more concerned with the employment impact of his
invention and refused to grant him a patent, claiming that “Thou
aimest high, Master Lee. Consider thou what the invention could
do to my poor subjects. It would assuredly bring to them ruin by
depriving them of employment, thus making them beggars” (cited
in Acemoglu and Robinson, 2012, p. 182f). Most likely the Queen’s
concern was a manifestation of the hosiers’ guilds fear that the invention
would make the skills of its artisan members obsolete.5 The
guilds’ opposition was indeed so intense that William Lee had to
leave Britain.
That guilds systematically tried to weaken market forces as
aggregators to maintain the technological status quo is persuasively
argued by Kellenbenz (1974, p. 243), stating that “guilds
defended the interests of their members against outsiders, and these
included the inventors who, with their new equipment and techniques,
threatened to disturb their members’ economic status.”6 As
pointed out by Mokyr (1998, p. 11): “Unless all individuals accept
the “verdict” of the market outcome, the decision whether to adopt
an innovation is likely to be resisted by losers through non-market
mechanism and political activism.” Workers can thus be expected
to resist new technologies, insofar that they make their skills obsolete
and irreversibly reduce their expected earnings. The balance
between job conservation and technological progress therefore, to a
large extent, reflects the balance of power in society, and how gains
from technological progress are being distributed.
The British Industrial Revolution illustrates this point vividly.
While still widely present on the Continent, the craft guild in Britain
had, by the time of 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. 403–408). 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).
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).
There are at least two possible explanations for the shift in
attitudes towards technological progress. First, after Parliamentary
supremacy was established over the Crown, the property owning
classes became politically dominant in Britain (North and Weingast,
1989). Because the diffusion of various manufacturing technologies
did not impose a risk to the value of their assets, and some property
owners stood to benefit from the export of manufactured goods, the
artisans simply did not have the political power to repress them. Second,
inventors, consumers and unskilled factory workers largely benefited
from mechanisation (Mokyr, 1990, p. 256 and 258). It has even
been argued that, despite the employment concerns over mechanisation,
unskilled workers have been the greatest beneficiaries of the
Industrial Revolution (Clark, 2008).7 While there 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 (Feinstein, 1998; Lindert and Williamson, 1983).
This implies that although manufacturing technologies made the
skills of artisans obsolete, gains from technological progress were
distributed in a manner that gradually benefited a growing share of
the labour force.8
An important feature of nineteenth century manufacturing technologies
is that they were largely “deskilling” – i.e. they substituted
for skills through the simplification of tasks (Braverman, 1974;
Hounshell, 1985; James and Skinner, 1985; Goldin and Katz, 1998).
The deskilling process occurred as the factory system began to displace
the artisan shop, and it picked up pace as production increasingly
mechanized with the adoption of steam power (Goldin and
Sokoloff, 1982; Atack et al., 2008a). Work that had previously been
performed by artisans was now decomposed into smaller, highly
specialised, sequences, requiring less skill, but more workers, to
perform.9 Some innovations were even designed to be deskilling. For
example, Eli Whitney, a pioneer of interchangeable parts, described
the objective of this technology as “to substitute correct and effective
operations of machinery for the skill of the artist which is acquired
only by long practice and experience; a species of skill which is not
possessed in this country to any considerable extent” (Habakkuk,
1962, p. 22).
Together with developments in continuous-flow production,
enabling workers to be stationary while different tasks were moved
to them, it was identical interchangeable parts that allowed complex
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%
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.
8 The term skill is associated with higher levels of education, ability, or job training.
Following Goldin and Katz (1998), we refer to technology-skill or capital-skill complementarity
when a new technology or physical capital complements skilled labour
relative to unskilled workers.
9 The production of plows nicely illustrates the differences between the artisan
shop and the factory. In one artisan shop, two men spent 118 man-hours using
hammers, anvils, chisels, hatchets, axes, mallets, shaves and augers in 11 distinct
operations to produce a plow. By contrast, a mechanized plow factory employed 52
workers performing 97 distinct tasks, of which 72 were assisted by steam power,
to produce a plow in just 3.75 man-hours. The degree of specialisation 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).
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 257
products to be assembled from mass produced individual components
by using highly specialised machine tools to a sequence of
operations.10 Yet while the first assembly-line was documented in
1804, it was not until the late nineteenth century that continuousflow
processes started to be adopted on a larger scale, which enabled
corporations such as the Ford Motor Company to manufacture the TFord
at a sufficiently low price for it to become the people’s vehicle
(Mokyr, 1990, p. 137). Crucially, the new assembly line introduced by
Ford in 1913 was specifically designed for machinery to be operated
by unskilled workers (Hounshell, 1985, p. 239). Furthermore, what
had previously been a one-man job was turned into a 29-man worker
operation, reducing the overall work time by 34% (Bright, 1958). The
example of the Ford Motor Company thus underlines the general
pattern observed in the nineteenth century, with physical capital
providing a relative complement to unskilled labour, while substituting
for relatively skilled artisans (James and Skinner, 1985; Louis and
Paterson, 1986; Brown and Philips, 1986; Atack et al., 2004).11 Hence,
as pointed out by Acemoglu (2002, p. 7): “the idea that technological
advances favour 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 mechanized assembly lines. This shift can
be traced to the switch to electricity from steam and water-power
which, in combination with continuous-process and batch production
methods, reduced the demand for unskilled manual workers
in many hauling, conveying, and assembly tasks, but increased the
demand for skills (Goldin and Katz, 1998). In short, while factory
assembly lines, with their extreme division of labour, had required
vast quantities of human operatives, electrification allowed many
stages of the production process to be automated, which in turn
increased the demand for relatively skilled blue-collar production
workers to operate the machinery. In addition, electrification contributed
to a growing share of white-collar nonproduction workers
(Goldin and Katz, 1998). Over the course of the nineteenth century,
establishments became larger in size as steam and water
power technologies improved, allowing them to adopt powered
machinery to realise productivity gains through the combination of
enhanced division of labour and higher capital intensity (Atack et
al., 2008a). Furthermore, the transport revolution lowered costs of
shipping goods domestically and internationally as infrastructure
spread and improved (Atack et al., 2008b). The market for artisan
goods early on had largely been confined to the immediate surrounding
area because transport costs were high relative to the value of
10 These machines were sequentially implemented until the production process was
completed. Over time, such machines became much cheaper relative to skilled labour.
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 lowskill
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 labour
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.
the goods produced. With the transport revolution, however, market
size expanded, thereby eroding local monopoly power, which
in turn increased competition and compelled firms to raise productivity
through mechanisation. As establishments became larger and
served geographically expended markets, managerial tasks increased
in number and complexity, requiring more managerial and clerking
employees (Chandler, 1977). This pattern was, by the turn of the
twentieth century, reinforced by electrification, which not only contributed
to a growing share of relatively skilled blue-collar labour, but
also increased the demand for white-collar workers (Goldin and Katz,
1998), who tended to have higher educational attainment (Allen,
2001).12
Since electrification, the story of the twentieth century has been
the race between education and technology (Goldin and Katz, 2009).
The US high school movement coincided with the first industrial
revolution of the office (Goldin and Katz, 1995). While the typewriter
was invented in the 1860s, it was not introduced in the office
until the early twentieth century, when it entered a wave of mechanisation,
with dictaphones, calculators, mimeo machines, address
machines, and the predecessor of the computer – the keypunch
(Beniger, 1986; Cortada, 2000). Importantly, these office machines
reduced the cost of information processing tasks and increased the
demand for the complementary factor – i.e. educated office workers.
Yet the increased supply of educated office workers, following the
high school movement, was associated with a sharp decline in the
wage premium of clerking occupations relative to production workers
(Goldin and Katz, 1995). This was, however, not the result of
deskilling technological change. Clerking workers were indeed relatively
educated. Rather, it was the result of the supply of educated
workers outpacing the demand for their skills, leading educational
wage differentials to compress.
While educational wage differentials in the US narrowed from
1915 to 1980 (Goldin and Katz, 2009), both educational wage differentials
and overall wage inequality have increased sharply since
the 1980s in a number of countries (Krueger, 1993; Murphy et al.,
1998; Atkinson, 2008; Goldin and Katz, 2009). Although there are
clearly several variables at work, consensus is broad that this can be
ascribed to an acceleration in capital-skill complementarity, driven
by the adoption of computers and information technology (Krueger,
1993; Autor et al., 1998; Bresnahan et al., 2002). What is commonly
referred to as the Computer Revolution began with the first commercial
uses of computers around 1960 and continued through the
development of the Internet and e-commerce in the 1990s. As the cost
per computation declined at an annual average of 37% 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% per
year, accompanied by a surge in computational power (Nordhaus,
2007).13 At the same time, bar-code scanners and cash machines were
spreading across the retail and financial industries, and the first personal
computers were introduced in the early 1980s, with their word
processing and spreadsheet functions eliminating copy typist occupations
and allowing repetitive calculations to be automated (Gordon,
2012). This substitution for labour marks a further important reversal.
The early twentieth century office machines increased the demand
for clerking workers (Chandler, 1977; Goldin and Katz, 1995). In a
12 Most likely, the growing share of white-collar workers increased the element
of human interaction in employment. Notably, Michaels et al. (2013) find that the
increase in the employment share of interactive occupations, going hand in hand with
an increase in their relative wage bill share, was particularly strong between 1880 and
1930, which is a period of rapid change in communication and transport technology.
13 Computer power even increased 18% faster on annual basis than predicted by
Moore’s Law, implying a doubling every two years (Nordhaus, 2007).
258 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
similar manner, computerisation augments demand for such tasks,
but it also permits them to be automated (Autor et al., 2003).
The Computer Revolution can go some way in explaining the
growing wage inequality of the past decades. For example, Krueger
(1993) finds that workers using a computer earn roughly earn 10 to
15% 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% after having been flat or declining in the three
prior decades. Furthermore, net changes in US employment were Ushaped
in skill level, meaning that the lowest and highest job-skill
quartile expanded sharply with relative employment declines in the
middle of the distribution.
The expansion in high-skill employment can be explained by
the falling price of carrying out routine tasks by means of computers,
which complements more abstract and creative services. Seen
from a production function perspective, an outward shift in the
supply of routine informational inputs increases the marginal productivity
of workers they are demanded by. For example, text and
data mining has improved the quality of legal research as constant
access to market information has improved the efficiency of managerial
decision-making – i.e. tasks performed by skilled workers
at the higher end of the income distribution. The result has been
an increasingly polarised labour market, with growing employment
in high-income cognitive jobs and low-income manual occupations,
accompanied by a hollowing-out of middle-income routine jobs. This
is a pattern that is not unique to the US and equally applies to a
number of developed economies (Goos et al., 2009).14
How technological progress in the twenty-first century will
impact on labour market outcomes remains to be seen. Throughout
history, technological progress has vastly shifted the composition of
employment, from agriculture and the artisan shop, to manufacturing
and clerking, to service and management occupations. Yet the
concern over technological unemployment has proven to be exaggerated.
The obvious reason why this concern has not materialised
relates to Ricardo’s famous chapter on machinery, which suggests
that labour-saving technology reduces the demand for undifferentiated
labour, thus leading to technological unemployment (Ricardo,
1819). As economists have long understood, however, an invention
that replaces workers by machines will have effects on all product
and factor markets. An increase in the efficiency of production
which reduces the price of one good, will increase real income and
thus increase demand for other goods. Hence, in short, technological
progress has two competing effects on employment (Aghion
and Howitt, 1994). First, as technology substitutes for labour, there
is a destruction effect, requiring workers to reallocate their labour
supply; and second, there is the capitalisation effect, as more companies
enter industries where productivity is relatively high, leading
employment in those industries to expand.
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 the routine-intensity of
employment is thus likely to result from a combination of offshoring and automation.
Furthermore, there is evidence suggesting that improvements in transport and
communication technology have augmented occupations involving human interaction,
spanning across both cognitive and manual tasks (Michaels et al., 2013). These
explanations are nevertheless equally related to advance in computing and communications
technology.
Although the capitalisation effect has been predominant historically,
our discovery of means of economising the use of labour can
outrun the pace at which we can find new uses for labour, as Keynes
(1933) pointed out. The reason why human labour has prevailed
relates to its ability to adopt and acquire new skills by means of education
(Goldin and Katz, 2009). Yet as computerisation enters more
cognitive domains this will become increasingly challenging (Brynjolfsson
and McAfee, 2011). Recent empirical findings are therefore
particularly concerning. For example, Beaudry et al. (2013) document
a decline in the demand for skill over the past decade, even as
the supply of workers with higher education has continued to grow.
They show that high-skilled workers have moved down the occupational
ladder, taking on jobs traditionally performed by low-skilled
workers, pushing low-skilled workers even further down the occupational
ladder and, to some extent, even out of the labour force.
This raises questions about (a) the ability of human labour to win the
race against technology by means of education; and (b) the potential
extent of technological unemployment, as an increasing pace of
technological progress will cause higher job turnover, resulting in
a higher natural rate of unemployment (Lucas and Prescott, 1974;
Davis and Haltiwanger, 1992; Pissarides, 2000). While the present
study is limited to examining the destruction effect of technology, it
nevertheless provides a useful indication of the job growth required
to counterbalance the jobs at risk over the next decades.
3. The technological revolutions of the twenty-first century
The secular price decline in the real cost of computing has created
vast economic incentives for employers to substitute labour for
computer capital.15 Yet the tasks computers are able to perform ultimately
depend upon the ability of a programmer to write a set of
procedures or rules that appropriately direct the technology in each
possible contingency. Computers will therefore be relatively productive
to human labour when a problem can be specified – in the
sense that the criteria for success are quantifiable and can readily
be evaluated (Acemoglu and Autor, 2011). The extent of job computerisation
will thus be determined by technological advances that
allow engineering problems to be sufficiently specified, which sets
the boundaries for the scope of computerisation. In this section,
we examine the extent of tasks computer-controlled equipment can
be expected to perform over the next decades. Doing so, we focus
on advances in fields related to Machine Learning (ML), including
Data Mining, Machine Vision, Computational Statistics and other
sub-fields of Artificial Intelligence (AI), in which efforts are explicitly
dedicated to the development of algorithms that allow cognitive
tasks to be automated. In addition, we examine the application of
ML technologies in Mobile Robotics (MR), and thus the extent of
computerisation in manual tasks.
Our analysis builds on the task categorisation of Autor et al.
(2003), which distinguishes between workplace tasks using a twoby-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 can be
accomplished by machines, while non-routine tasks are not suffi-
ciently well understood to be specified in computer code. Each of
these task categories can, in turn, be of either manual or cognitive
nature – i.e. they relate to physical labour or knowledge work. Historically,
computerisation has largely been confined to manual and
cognitive routine tasks involving explicit 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
15 We refer to computer capital as accumulated computers and computer-controlled
equipment by means of capital deepening.
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 259
were defined as non-routine only a decade ago have now become
computerisable is illustrated by Autor et al. (2003), asserting that
“Navigating a car through city traffic or deciphering the scrawled
handwriting on a personal check – minor undertakings for most
adults – are not routine tasks by our definition.” Today, the problems
of navigating a car and deciphering handwriting are sufficiently well
understood that many related tasks can be specified in computer
code and automated (Veres et al., 2011; Plötz and Fink, 2009).
Recent technological breakthroughs are, in large part, due to
efforts to turn non-routine tasks into well-defined problems. Defining
such problems is helped by the provision of relevant data: this
is highlighted in the case of handwriting recognition by Plötz and
Fink (2009). The success of an algorithm for handwriting recognition
is difficult to quantify without data to test on – in particular,
determining whether an algorithm performs well for different styles
of writing requires data containing a variety of such styles. That is,
data is required to specify the many contingencies a technology must
manage in order to form an adequate substitute for human labour.
With data, objective and quantifiable measures of the success of an
algorithm can be produced, which aid the continual improvement of
its performance relative to humans.
As such, technological progress has been aided by the recent production
of increasingly large and complex datasets, known as big
data.16 For instance, with a growing corpus of human-translated digitalised
text, the success of a machine translator can now be judged
by its accuracy in reproducing observed translations. Data from
United Nations documents, which are translated by human experts
into six languages, allow Google Translate to monitor and improve
the performance of different machine translation algorithms
(Tanner, 2007).
Further, ML algorithms can discover unexpected similarities
between old and new data, aiding the computerisation of tasks for
which big data has newly become available. As a result, computerisation
is no longer confined to routine tasks that can be written as
rule-based software queries, but is spreading to every non-routine
task where big data becomes available (Brynjolfsson and McAfee,
2011). In this section, we examine the extent of future computerisation
beyond routine tasks.
3.1. Computerisation in non-routine cognitive tasks
With the availability of big data, a wide range of non-routine
cognitive tasks are becoming computerisable. That is, further to
the general improvement in technological progress due to big data,
algorithms for big data are rapidly entering domains reliant upon
storing or accessing information. The use of big data is afforded by
one of the chief comparative advantages of computers relative to
human labour: scalability. Little evidence is required to demonstrate
that, in performing the task of labourious computation, networks of
machines scale better than human labour (Campbell-Kelly, 2009). As
such, computers can better manage the large calculations required in
using large datasets. ML algorithms running on computers are now,
in many cases, better able to detect patterns in big data than humans.
Computerisation of cognitive tasks is also aided by another
core comparative advantage of algorithms: their absence of some
human biases. An algorithm can be designed to ruthlessly satisfy
the small range of tasks it is given. Humans, in contrast, must fulfill
a range of tasks unrelated to their occupation, such as sleeping,
necessitating occasional sacrifices in their occupational performance
(Kahneman et al., 1982). The additional constraints under which
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).
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 alunch break. It can thus be argued thatmany 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 completely automated (Phua et al., 2010). In a similar
manner, the comparative advantages of computers arelikely to change
the nature of work across a wide range of industries and occupations.
In health care, diagnostics tasks are already being computerised.
Oncologists at Memorial Sloan-Kettering Cancer Center are, for
example, using IBM’s Watson computer to provide chronic care and
cancer treatment diagnostics. Knowledge from 600,000 medical evidence
reports, 1.5 million patient records and clinical trials, and two
million pages of text from medical journals, are used for benchmarking
and pattern recognition purposes. This allows the computer to
compare each patient’s individual symptoms, genetics, family and
medication history, etc., to diagnose and develop a treatment plan
with the highest probability of success (Cohn, 2013).
In addition, computerisation is entering the domains of legal and
financial services. Sophisticated algorithms are gradually taking on
a number of tasks performed by paralegals, contract and patent
lawyers (Markoff, 2011). More specifically, law firms now rely on
computers that can scan thousands of legal briefs and precedents
to assist in pre-trial research. A frequently cited example is Symantec’s
Clearwell system, which uses language analysis to identify
general concepts in documents, can present the results graphically,
and proved capable of analysing and sorting more than 570,000
documents in two days (Markoff, 2011).
Furthermore, the improvement of sensing technology has made
sensor data one of the most prominent sources of big data (Ackerman
and Guizzo, 2011). Sensor data is often coupled with new ML faultand
anomaly-detection algorithms to render many tasks computerisable.
A broad class of examples can be found in condition monitoring
and novelty detection, with technology substituting for closed-circuit
TV (CCTV) operators, workers examining equipment defects, and clinical
staff responsible for monitoring the state of patients in intensive
care. Here, the fact that computers lack human biases is of great value:
algorithms are free of irrational bias, and their vigilance need not
be interrupted by rest breaks or lapses of concentration. Following
the declining costs of digital sensing and actuation, ML approaches
have successfully addressed condition monitoring applications ranging
from batteries (Saha et al., 2007), to aircraft engines (King et al.,
2009), water quality (Osborne et al., 2012) and intensive care units
(ICUs) (Clifford and Clifton, 2012; Clifton et al., 2012). Sensors can
equally be placed on trucks and pallets to improve companies’ supply
chain management, and used to measure the moisture in a field
of crops to track the flow of water through utility pipes. This allows
for automatic meter reading, eliminating the need for personnel to
gather such information. For example, the cities of Doha, São Paulo,
and Beijing use sensors on pipes, pumps, and other water infrastructure
to monitor conditions and manage water loss, reducing leaks
by 40 to 50%. In the near future, it will be possible to place inexpensive
sensors on light poles, sidewalks, and other public property to
capture sound and images, likely reducing the number of workers
in law enforcement (MGI, 2013).
Advances in user interfaces also enable computers to respond
directly to a wider range of human requests, thus augmenting the
work of highly skilled labour, while allowing some types of jobs
to become fully automated. For example, Apple’s Siri and Google
Now rely on natural user interfaces to recognise spoken words,
interpret their meanings, and act on them accordingly. Moreover,
a company called SmartAction now provides call computerisation
solutions that use ML technology and advanced speech recognition
to improve upon conventional interactive voice response systems,
260 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
realising cost savings of 60 to 80% over an outsourced call center consisting
of human labour (CAA, 2012). Even education, one of the most
labour intensive sectors, will most likely be significantly impacted
by improved user interfaces and algorithms building upon big data.
The recent growth in MOOCs (Massive Open Online Courses) has
begun to generate large datasets detailing how students interact
on forums, their diligence in completing assignments and viewing
lectures, and their ultimate grades (Simonite, 2013; Breslow et al.,
2013). Such information, together with improved user interfaces,
will allow for ML algorithms that serve as interactive tutors, with
teaching and assessment strategies statistically calibrated to match
individual student needs (Woolf, 2010). Big data analysis will also
allow for more effective predictions of student performance, and for
their suitability for post-graduation occupations. These technologies
can equally be implemented in recruitment, most likely resulting in
the streamlining of human resource (HR) departments.
Occupations that require subtle judgement are also increasingly
susceptible to computerisation. To many such tasks, the unbiased
decision making of an algorithm represents a comparative advantage
over human operators. In the most challenging or critical applications,
as in ICUs, algorithmic recommendations may serve as inputs
to human operators; in other circumstances, algorithms will themselves
be responsible for appropriate decision-making. In the financial
sector, such automated decision-making has played a role for
quite some time. AI algorithms are able to process a greater number
of financial announcements, press releases, and other information
than any human trader, and then act faster upon them (Mims, 2010).
Services like Future Advisor similarly use AI to offer personalised
financial advice at larger scale and lower cost. Even the work of software
engineers may soon largely be computerisable. For example,
advances in ML allow a programmer to leave complex parameter
and design choices to be appropriately optimised by an algorithm
(Hoos, 2012). Algorithms can further automatically detect bugs in
software (Hangal and Lam, 2002; Livshits and Zimmermann, 2005;
Kim et al., 2008), with a reliability that humans are unlikely to match.
Big databases of code also offer the eventual prospect of algorithms
that learn how to write programs to satisfy specifications provided
by a human. Such an approach is likely to eventually improve upon
human programmers, in the same way that human-written compilers
eventually proved inferior to automatically optimised compilers.
An algorithm can better keep the whole of a program in working
memory, and is not constrained to human-intelligible code, allowing
for holistic solutions that might never occur to a human. Such algorithmic
improvements over human judgement are likely to become
increasingly common.
Although the extent of these developments remains to be seen,
estimates by MGI (2013) suggests that sophisticated algorithms
could substitute for approximately 140 million full-time knowledge
workers worldwide. Hence, while technological progress throughout
economic history has largely been confined to the mechanisation
of manual tasks, requiring physical labour, technological progress
in the twenty-first century can be expected to contribute to a wide
range of cognitive tasks, which, until now, have largely remained
a human domain. Of course, many occupations being affected by
these developments are still far from fully computerisable, meaning
that the computerisation of some tasks will simply free-up time for
human labour to perform other tasks. Nonetheless, the trend is clear:
computers increasingly challenge human labour in a wide range of
cognitive tasks (Brynjolfsson and McAfee, 2011).
3.2. Computerisation in non-routine manual tasks
Mobile robotics provides a means of directly leveraging ML technologies
to aid the computerisation of a growing scope of manual
tasks. The continued technological development of robotic hardware
is having notable impact upon employment: over the past decades,
industrial robots have taken on the routine tasks of most operatives
in manufacturing. Now, however, more advanced robots are gaining
enhanced sensors and manipulators, allowing them to perform
non-routine manual tasks. For example, General Electric has recently
developed robots to climb and maintain wind turbines, and more
flexible surgical robots with a greater range of motion will soon perform
more types of operations (Robotics-VO, 2013). In a similar manner,
the computerisation of logistics is being aided by the increasing
cost-effectiveness of highly instrumented and computerised cars.
Mass-production vehicles, such as the Nissan LEAF, contain on-board
computers and advanced telecommunication equipment that render
the car a potentially fly-by-wire robot.17 Advances in sensor technology
mean that vehicles are likely to soon be augmented with
even more advanced suites of sensors. These will permit an algorithmic
vehicle controller to monitor its environment to a degree that
exceeds the capabilities of any human driver: they have the ability to
simultaneously look both forwards and backwards, can natively integrate
camera, GPS and LIDAR data, and are not subject to distraction.
Algorithms are thus potentially safer and more effective drivers than
humans.
The big data provided by these improved sensors are offering
solutions to many of the engineering problems that had hindered
robotic development in the past. In particular, the creation
of detailed three dimensional maps of road networks has enabled
autonomous vehicle navigation; most notably illustrated by Google’s
use of large, specialised datasets collected by its driverless cars
(Guizzo, 2011). It is now completely feasible to store representations
of the entire road network on-board a car, dramatically
simplifying the navigation problem. Algorithms that could perform
navigation throughout the changing seasons, particularly after snowfall,
have been viewed as a substantial challenge. However, the big
data approach can answer this by storing records from the last
time snow fell, against which the vehicle’s current environment can
be compared (Churchill and Newman, 2012). ML approaches have
also been developed to identify unprecedented changes to a particular
piece of the road network, such as roadworks (Mathibela et
al., 2012). This emerging technology will affect a variety of logistics
jobs. Agricultural vehicles, forklifts and cargo-handling vehicles
are imminently automatable, and hospitals are already employing
autonomous robots to transport food, prescriptions and samples
(Bloss, 2011). The computerisation of mining vehicles is further being
pursued by companies such as Rio Tinto, seeking to replace labour in
Australian mine-sites.18
With improved sensors, robots are capable of producing goods
with higher quality and reliability than human labour. For example,
El Dulze, a Spanish food processor, now uses robotics to pick up
heads of lettuce from a conveyor belt, rejecting heads that do not
comply with company standards. This is achieved by measuring their
density and replacing them on the belt (IFR, 2012a). Advanced sensors
further allow robots to recognise patterns. Baxter, a 22,000 USD
general-purpose robot, provides a well-known example. The robot
features an LCD display screen displaying a pair of eyes that take on
different expressions depending on the situation. When the robot
is first installed or needs to learn a new pattern, no programming
is required. A human worker simply guides the robot arms through
the motions that will be needed for the task. Baxter then memorises
these patterns and can communicate that it has understood its new
instructions. While the physical flexibility of Baxter is limited to performing
simple operations such as picking up objects and moving
them, different standard attachments can be installed on its arms,
17 A fly-by-wire robot is a robot that is controllable by a remote computer. 18 Rio Tinto’s computerisation efforts are advertised at http://www.mineofthefuture.
com.au.
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 261
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%
annually and are expected to decline at an even faster pace in the
near future (MGI, 2013). Industrial robots, with features enabled by
machine vision and high-precision dexterity, which typically cost
100,000 to 150,000 USD, will be available for 50,000 to 75,000 USD
in the next decade, with higher levels of intelligence and additional
capabilities (IFR, 2012b). Declining robot prices will inevitably place
them within reach of more users. For example, in China, employers
are 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% in 2011 and are expected
to increase further. Globally, industrial robot sales reached a record
166,000 units in 2011, a 40% year-on-year increase (IFR, 2012b).
Most likely, there will be even faster growth ahead as low-priced
general-purpose models, such as Baxter, are adopted in simple manufacturing
and service work.
Expanding technological capabilities and declining costs will
make entirely new uses for robots possible. Robots will likely continue
to take on an increasing set of manual tasks in manufacturing,
packing, construction, maintenance, and agriculture. In addition,
robots are already performing many simple service tasks such as
vacuuming, mopping, lawn mowing, and gutter cleaning – the market
for personal and household service robots is growing by about
20% annually (MGI, 2013). Meanwhile, commercial service robots are
now able to perform more complex tasks in food preparation, health
care, commercial cleaning, and elderly care (Robotics-VO, 2013).
As robot costs decline and technological capabilities expand,
robots can thus be expected to gradually substitute for labour in
a wide range of low-wage service occupations, where most 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.
3.3. The task model revisited
The task model of Autor et al. (2003) has delivered intuitive and
accurate predictions in that (a) computers are more substitutable
for human labour in routine relative to non-routine tasks; and (b) a
greater intensity of routine inputs increases the marginal productivity
of non-routine inputs. Accordingly, computers have served as a
substitute for labour for many routine tasks, while exhibiting strong
complementarities with labour performing cognitive non/routine
tasks.19 Yet the premises about what computers do have recently
expanded. Computer capital can now equally substitute for a wide
range of tasks commonly 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, constantreturns-to-scale,
Cobb-Douglas production function of the form
Q = (LS + C)
1−bL
b
NS, b ∈ [0, 1], (1)
19 The model does not predict any substantial substitution or complementarity with
non-routine manual tasks.
where LS and LNS are susceptible and non-susceptible labour 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-maximising workers, with heterogeneous productivity
endowments in both susceptible and non-susceptible tasks.
Their task supply will respond elastically to relative wage levels,
meaning that workers will reallocate their labour supply according to
their comparative advantage as in Roy (1951). With expanding computational
capabilities, resulting from technological advances, and a
falling market price of computing, workers in susceptible tasks will
thus reallocate to 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 because recent developments in ML and MR, building
upon big data, allow for pattern recognition, and thus enable
computer capital to rapidly substitute for labour across a wide range
of non-routine tasks. Yet some inhibiting engineering bottlenecks
to computerisation persist. Beyond these bottlenecks, however, we
argue that it is largely already technologically possible to automate
almost any task, provided that sufficient amounts of data are gathered
for pattern recognition. Our model thus predicts that the pace at
which these bottlenecks can be overcome will determine the extent
of computerisation in the twenty-first century.
Hence, in short, while the task model predicts that computers
for labour substitution will be confined to routine tasks, our
model predicts that computerisation can be extended to any nonroutine
task that is not subject to any engineering bottlenecks to
computerisation. These bottlenecks thus set the boundaries for the
computerisation of non-routine tasks. Drawing upon the ML and MR
literature, and a workshop held at the Oxford University Engineering
Sciences Department, we identify several engineering bottlenecks,
corresponding to three task categories. According to these findings,
non-susceptible labour inputs can be described as,
LNS = n
i=1
(LPM,i + LC,i + LSI,i) (2)
where LPM, LC and LSI are labour inputs into perception and manipulation
tasks, creative intelligence tasks, and social intelligence tasks.
We note that some related engineering bottlenecks can be partially
alleviated by the simplification of tasks. One generic way of
achieving this is to reduce the variation between task iterations. As
a prototypical example, consider the factory assembly line, turning
the non-routine tasks of the artisan shop into repetitive routine tasks
performed by unskilled factory workers. A more recent example is
the computerisation of non-routine manual tasks in construction.
On-site construction tasks typically demand a high degree of adaptability,
so as to accommodate work environments that are typically
irregularly laid out, and vary according to weather. Prefabrication,
in which the construction object is partially assembled in a factory
before being transported to the construction site, provides a
way of largely removing the requirement for adaptability. It allows
many construction tasks to be performed by robots under controlled
conditions that eliminate task variability – a method that is
becoming increasingly widespread, particularly in Japan (Barlow and
Ozaki, 2005; Linner and Bock, 2012). The extent of computerisation
in the twenty-first century will thus partly depend on innovative
approaches to task restructuring. In the remainder of this section we
examine the engineering bottlenecks related to the above mentioned
task categories, each in turn.
262 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
3.3.1. Perception and manipulation tasks
Robots are still unable to match the depth and breadth of
human perception. While basic geometric identification is reasonably
mature, enabled by the rapid development of sophisticated
sensors and lasers, significant challenges remain for more complex
perception tasks, such as identifying objects and their properties in a
cluttered field of view. As such, tasks that relate to an unstructured
work environment can make jobs less susceptible to computerisation.
For example, most homes are unstructured, requiring the
identification of a plurality of irregular objects and containing many
cluttered spaces which inhibit the mobility of wheeled objects. Conversely,
supermarkets, factories, warehouses, airports and hospitals
have been designed for large wheeled objects, making it easier for
robots to navigate in performing non-routine manual tasks. Perception
problems can, however, sometimes be sidestepped by clever
task design. For example, Kiva Systems, acquired by Amazon.com in
2012, solved the problem of warehouse navigation by simply placing
bar-code stickers on the floor, informing robots of their precise
location (Guizzo, 2008).
The difficulty of perception has ramifications for manipulation
tasks, and, in particular, the handling of irregular objects, for which
robots are yet to reach human levels of aptitude. This has been
evidenced in the development of robots that interact with human
objects and environments. While advances have been made, solutions
tend to be unreliable over the myriad small variations on a
single task, repeated thousands of times a day, that many applications
require. A related challenge is failure recovery – i.e. identifying
and rectifying the mistakes of the robot when it has, for example,
dropped an object. Manipulation is also limited by the difficulties
of planning out the sequence of actions required to move an object
from one place to another. There are yet further problems in designing
manipulators that, like human limbs, are soft, have compliant
dynamics and provide useful tactile feedback. Most industrial manipulation
makes uses of workarounds to these challenges (Brown et
al., 2010), but these approaches are nonetheless limited to a narrow
range of tasks. The main challenges to robotic computerisation, perception
and manipulation, thus largely remain and are unlikely to be
fully resolved in the next decade or two (Robotics-VO, 2013).
3.3.2. 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 broader sense, include concepts, poems, musical compositions,
scientific theories, cooking recipes and jokes, whereas artifacts are
objects such as paintings, sculptures, machinery, and pottery. One
process of creating ideas (and similarly for artifacts) involves making
unfamiliar combinations of familiar ideas, requiring a rich store
of knowledge. The challenge here is to find some reliable means of
arriving at combinations that “make sense.” For a computer to make
a subtle joke, for example, would require a database with a richness
of knowledge comparable to that of humans, and methods of
benchmarking the algorithm’s subtlety.
In principle, such creativity is possible and some approaches
to creativity already exist in the literature. Duvenaud et al. (2013)
provide an example of automating the core creative task required
in order to perform statistics, that of designing models for data.
As to artistic creativity, AARON, a 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 human composers.
In these and many other applications, generating novelty is not
particularly difficult. Instead, the principal obstacle to computerising
creativity is stating our creative values sufficiently clearly that
they can be encoded in a 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.
3.3.3. 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 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 evenmore difficult. Even simplified versions of typical
social tasks prove difficult for computers, as is the case in which
social interaction is reduced to pure text. The social intelligence of
algorithms is partly captured by the Turing test, examining the ability
of amachine to communicate indistinguishably from an actual human.
Since 1990, the Loebner Prize, an annual Turing test competition,
awardsprizes to textualchatprogrammes that areconsidered to be the
most human-like. In each competition, a human judge simultaneously
holdscomputer-based textual interactionswithboth an algorithm and
a human. Based on the responses, the judge is to distinguish between
the two. Sophisticated algorithms have so far failed to convince judges
about their human resemblance. This is largely because there is much
‘common sense’ information possessed by humans, which is difficult
to articulate, that would need to be provided to algorithms if they are
to function in human social settings.
Whole brain emulation, the scanning, mapping and digitalising of
a human brain, is one possible approach to achieving this, but is currently
only a theoretical technology. For brain emulation to become
operational, additional functional understanding is required to recognisewhat
data is relevant, aswell as a roadmap of technologies needed
to implement it. While such roadmaps exist, present implementation
estimates, under certain assumptions, suggest that whole brain
emulation is unlikely to become operational within the next decade
or two (Sandberg and Bostrom, 2008). When or if they do, however,
the employment impact is likely to be vast (Hanson, 2001).
Hence, in short, while sophisticated algorithms and developments
in MR, building upon with big data, now allow many nonroutine
tasks to be automated, occupations that involve complex
perception and manipulation tasks, creative intelligence tasks, and
social intelligence tasks are unlikely to be substituted by computer
capital over the next decade or two. The probability of an occupation
being automated can thus be described as a function of these
task characteristics. As suggested by Fig. 1, the low degree of social
intelligence required by a dishwasher makes this occupation more
susceptible to computerisation than a public relation specialist, for
example. We proceed to examining the susceptibility of jobs to computerisation
as a function of the above described non-susceptible
task characteristics.
4. Measuring the employment impact of computerisation
4.1. 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
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 263
Probability of
Computerisation
Social Intelligence
Dishwasher
Event
Planner
Public
Relations
0 100
1
Probability of
Computerisation
Creativity
Court Clerk
Biologist
Fashion
Designer
1
Probability of
Computerisation
Perception and manipulation
Telemarketer
Boilermaker
Surgeon
0 100 0 100
1
Fig. 1. Bottlenecks to computerisation. Note: This figure provides a sketch of how the probability of computerisation might vary as a function of bottleneck variables.
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 classifi-
cation 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 variables corresponding
to the six-digit SOC classification, we used the mean of
the O*NET aggregate. In addition, we exclude any six-digit SOC occupations
for which O*NET data was missing.20 Doing so, we end up
with a final dataset consisting of 702 occupations.
To assess the employment impact of the described technological
developments in ML, the ideal experiment would provide two
identical autarkic economies, one facing the expanding technological
capabilities we observe, and a secular decline in the price of computerisation,
and the other not. By comparison, it would be straightforward
to examine how computerisation reshapes the occupational
composition of the labour market. In the absence of this experiment,
the second preferred option would be to build on the implementation
strategy of Autor et al. (2003), and test a simple economic
model to predict how demand for workplace tasks responds to developments
in ML and MR technology. However, because our paper is
forward-looking, in the sense that most of the described technological
developments are yet to be implemented across industries on a
broader scale, this option was not available for our purposes.
Instead, our implementation strategy builds on the literature
examining the offshoring of information-based tasks to foreign worksites,
consisting of different methodologies to rank and categorise
occupations according to their susceptibility to offshoring (Blinder,
2009; Jensen and Kletzer, 2005, 2010). The common denominator
for these studies is that they rely on O*NET data in different ways.
While Blinder (2009) eyeballed the O*NET data on each occupation,
paying particular attention to the job description, tasks, and work
activities, to assign an admittedly subjective two-digit index number
of offshorability to each occupation, Jensen and Kletzer (2005)
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% of total employment.
created a purely objective ranking based on standardised and measurable
O*NET variables. Both approaches have obvious drawbacks.
Subjective judgements are often not replicable and may result in the
researcher subconsciously rigging the data to conform to a certain
set of beliefs. 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 measure the offshorability or
automatability of jobs. Accordingly, Blinder (2009) finds that past
attempts to create objective offshorability rankings using O*NET data
have yielded some questionable results, ranking lawyers and judges
among the most tradable occupations, while classifying occupations
such as data entry keyers, telephone operators, and billing clerks as
virtually impossible to move offshore.
To work around some of these drawbacks, we combine and build
upon the two described approaches. First, together with a group of ML
researchers, we subjectively hand-labelled 70 occupations, assigning
1 if automatable, and 0 if not. For our subjective assessments, we draw
upon a workshop held at the Oxford University Engineering Sciences
Department, examining the automatability of a wide range of tasks.
Our label assignments were based on eyeballing the O*NET tasks
and job description of each occupation. This information is particular
to each occupation, as opposed to standardised across different
jobs. The hand-labelling of the occupations was made by answering
the question “Can the tasks of this job be sufficiently specified, conditional
on the availability of big data, to be performed by state of
the art computer-controlled equipment”. Thus, we only assigned a
1 to fully automatable occupations, where we considered all tasks
to be automatable. To the best of our knowledge, we considered the
possibility of task simplification, possibly allowing some currently
non-automatable tasks to be automated. Labels were assigned only
to the occupations about which we were most confident.
Second, we use objective O*NET variables corresponding to the
defined bottlenecks to computerisation. More specifically, we are
interested in variables describing the level of perception and manipulation,
creativity, and social intelligence required to perform it. As
reported in Table 1, we identified nine variables that describe these
attributes. These variables were derived from the O*NET survey,
where the respondents are given multiple scales, with “importance”
and “level” as the predominant pair. We rely on the “level” rating
which corresponds to specific examples about the capabilities
required of 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 oranges in crates
as quickly as possible”; high (level) is described as “Perform openheart
surgery with surgical instruments”. This gives us an indication
of 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).
264 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Table 1
O*NET variables.
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 behaviour.
Assisting and caring for others Providing personal assistance, medical attention, emotional support, or other personal
care to others such as coworkers, customers, or patients.
Note: The O*NET variables chosen are those likely to serve as indicators of bottlenecks to computerisation.
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.
4.2. Classification method
We begin by examining the accuracy of our subjective assessments
of the automatability of 702 occupations. For classification,
we develop an algorithm to provide the label probability given a
previously unseen vector of variables. In the terminology of classification,
the O*NET variables form a feature vector, 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 classifi-
cation algorithm exploits patterns existent in training data to return
the probability P(y∗= 1 | x∗, X, y) of a new, unlabelled, test datum
with features x∗ having class label y∗= 1.
We achieve probabilistic classification by introducing a latent
function f : x → R, known as a discriminant function. Given the
value of the discriminant f∗ at a test point x∗, we assume that the
probability for the class label is given by the logistic
P(y∗ = 1 | f∗) = 1
1 + exp(−f∗)
, (3)
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) = wx,
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
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 are completely
specified by a covariance matrix K
p(f|K) = N (f; 0, K) = 1

det 2pK exp
−1
2 f
K−1 f

. (4)
The covariance matrix is generated by a covariance function j :
X m × Xn → Rm×n; that is, K = j(X, X). The GP model is expressed
by the choice of j; we consider the exponentiated quadratic (squared
exponential) and rational quadratic. Note that we have chosen a
zero mean function, encoding the assumption that P(y∗ = 1) = 1
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) = j(x∗, X) j(X, X)
−1y (6)
V(f∗|x∗, D) = j(x∗, x∗) − j(x∗, X) j(X, X)
−1 j(X, x∗). (7)
Inferring the label posterior p(y∗|x∗, D) is complicated by the nonGaussian
form of the logistic (Eq. (3)). In order to effect inference,
we use the approximate Expectation Propagation algorithm (Minka,
2001).
We tested three Gaussian process classifiers using the GPML
toolbox (Rasmussen and Nickisch, 2010) on our data, built around
exponentiated quadratic, rational quadratic and linear covariances.
Note that the latter is equivalent to logistic regression with a Gaussian
prior taken on the weights w. To validate these classifiers, we
randomly selected a reduced training set of half the available data
D; the remaining data formed a test set. On this test set, we evaluated
how closely the algorithm’s classifications matched the hand
labels according to two metrics (see e.g.Murphy (2012)): the area
under the receiver operating 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
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 265
training set, and the average results tabulated in Table 2. The exponentiated
quadratic model returns (narrowly) the best performance
of the three (clearly outperforming the linear model corresponding
to logistic regression), and was hence selected for the remainder of
our testing. Note that its AUC score of nearly 0.9 represents accurate
classification: our algorithm successfully managed to reproduce our
hand-labels specifying whether an occupation was computerisable.
Thismeans that our algorithm verified that our subjective judgements
were systematically and consistently related to the O*NET variables.
Having validated our approach, we proceed to use classification
to predict the probability of computerisation for all 702 occupations.
For this purpose, we introduce a new label variable, z, denoting
whether an occupation is truly computerisable or not: note that
this can be judged only once an occupation is computerised, at
some indeterminate point in the future. We take, again, a logistic
likelihood,
P(z∗ = 1 | f∗) = 1
1 + exp(−f∗)
. (8)
We implicitly assumed that our hand label, y, is a noise-corrupted
version of the unknown true label, z. Our motivation is that our
hand-labels of computerisability must necessarily be treated as such
noisy measurements. We thus acknowledge that it is by no means
certain that a job is computerisable given our labelling. We define
X∗ ∈ R702×9 as the matrix of O*NET variables for all 702 occupations;
this matrix represents our test features.
We perform a final experiment in which, given training data D,
consisting of our 70 hand-labelled occupations, we aim to predict
z∗ for our test features X∗. This approach firstly allows us to use the
features of the 70 occupations about which we are most certain to
predict for the remaining 632. Further, our algorithm uses the trends
and patterns it has learned from bulk data to correct for what are
likely to be mistaken labels. More precisely, the algorithm provides
a smoothly varying probabilistic assessment of automatability as a
function of the variables. For our Gaussian process classifier, this
function is non-linear, meaning that it flexibly adapts to the patterns
inherent in the training data. Our approach thus allows for more
complex, non-linear, interactions between variables: for example,
perhaps one variable is not of importance unless the value of another
variable is sufficiently large. We report P(z∗ | X∗, D) as the probability
of computerisation henceforth (for a detailed probability ranking, see
the Appendix. Fig. 2 illustrates that this probability is non-linearly
related to the nine O*NET variables selected.
5. Employment in the twenty-first century
In this section, we examine the possible future extent of atrisk
job computerisation, and related labour market outcomes. The
task model predicts that recent developments in ML will reduce
aggregate demand for labour input in tasks that can be routinised
by means of pattern recognition, while increasing the demand for
labour performing tasks that are not susceptible to computerisation.
However, we make no attempt to forecast future changes in the occupational
composition of the labour market. While the 2010–2020
BLS occupational employment projections predict US net employment
growth across major occupations, based on historical staffing
Table 2
Performance of various classifiers.
Classifier model AUC Log-likelihood
Exponentiated quadratic 0.894 −163.3
Rational quadratic 0.893 −163.7
Linear (logit regression) 0.827 −205.0
Note: The best performances are indicated in bold.
patterns, we speculate about technology that is in only the early
stages of development. This means that historical data on the impact
of the technological developments we observe is unavailable.21 We
therefore focus on the impact of computerisation on the mix of jobs
that existed in 2010. Our analysis is thus limited to the substitution
effect of future computerisation.
Turning first to the expected employment impact, reported in
Fig. 3, we distinguish between high, medium and low risk occupations,
depending on their probability of computerisation (thresholding
at probabilities of 0.7 and 0.3). According to our estimate,
47% of total US employment is in the high risk category, meaning
that associated occupations are potentially automatable over some
unspecified number of years, perhaps a decade or two. It shall be
noted that the probability axis can be seen as a rough timeline, where
high probability occupations are likely to be substituted by computer
capital relatively soon. Over the next decades, the extent of
computerisation will be determined by the pace at which the above
described engineering bottlenecks to automation can be overcome.
Seen from this perspective, our findings could be interpreted as two
waves of computerisation, separated by a “technological plateau”.
In the first wave, we find that most workers in transportation and
logistics occupations, together with the bulk of office and administrative
support workers, and labour in production occupations, are
likely to be substituted by computer capital. As computerised cars are
already being developed and the declining cost of sensors makes augmenting
vehicles with advanced sensors increasingly cost-effective,
the automation of transportation and logistics occupations is in line
with the technological developments documented in the literature.
Furthermore, algorithms for big data are already rapidly entering
domains reliant upon storing or accessing information, making it
equally intuitive that office and administrative support occupations
will be subject to computerisation. The computerisation of production
occupations simply suggests a continuation of a trend that has
been observed over the past decades, with industrial robots taking
on the routine tasks of most operatives in manufacturing. As industrial
robots are becoming more advanced, with enhanced senses and
dexterity, they will be able to perform a wider scope of non-routine
manual tasks. From a technological capabilities point of view, the
vast remainder of employment in production occupations is thus
likely to diminish over the next decades.
More surprising, at first sight, is that a substantial share of
employment in services, sales and construction occupations exhibit
high probabilities of computerisation. Yet these findings are largely
in line with recent documented technological developments. First,
the market for personal and household service robots is already
growing by about 20% annually (MGI, 2013). As the comparative
advantage of human labour in tasks involving mobility and dexterity
will diminish over time, the pace of labour substitution in
service occupations is likely to increase even further. Second, while
it seems counterintuitive that sales occupations, which are likely
to require a high degree of social intelligence, will be subject to a
wave of computerisation in the near future, high risk sales occupations
include, for example, cashiers, counter and rental clerks, and
telemarketers. Although these occupations involve interactive tasks,
they do not necessarily require a high degree of social intelligence.
Our model thus seems to do well in distinguishing between individual
occupations within occupational categories. Third, prefabrication
will allow a growing share of construction work to be performed
under controlled conditions in factories, which partly eliminates
task variability. This trend is likely to drive the computerisation of
construction work.
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.
266 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Probability of
Computerisation
Cramped work space
Probability of
Computerisation
Finger dexterity
Probability of
Computerisation
Manual dexterity
Probability of
Computerisation
Originality
Probability of
Computerisation
Fine arts
Probability of
Computerisation
Social perceptiveness
Probability of
Computerisation
Negotiation
Probability of
Computerisation
Persuasion
Probability of
Computerisation
Assisting and
caring for others
0 0.5 1 0 0.5 1 0 0.5 1
0 0.5 1 0 0.5 1 0 0.5 1
0 0.5 1 0 0.5 1 0 0.5 1
50
100
20
40
60
80
20
40
60
80
20
40
60
80
50
100
50
100
20
40
60
80
20
40
60
80
50
100
Fig. 2. Variables’ influence on computerisation. Note: This figure illustrates the distribution of occupational variables as a function of probability of computerisation; each
occupation is a unique point.
In short, our findings suggest that recent developments in ML
will put a substantial share of employment, across a wide range of
occupations, at risk in the near future. According to our estimates,
however, this wave of automation will be followed by a subsequent
slowdown in computers for labour substitution, due to persisting
inhibiting engineering bottlenecks to computerisation. The relatively
slow pace of computerisation across the medium risk category
of employment can thus partly be interpreted as a technological
plateau, with incremental technological improvements successively
enabling further labour substitution. More specifically, the computerisation
of occupations in the medium risk category will mainly
depend on perception and manipulation challenges. This is evident
from Table 2, showing that the “manual dexterity”, “finger dexterity”
and “cramped work space” variables exhibit relatively high
values in the medium risk category. Indeed, even with recent technological
developments, allowing for more sophisticated pattern
recognition, human labour will still have a comparative advantage in
tasks requiring more complex perception and manipulation. Yet with
incremental technological improvements, the comparative advantage
of human labour in perception and manipulation tasks could
eventually diminish. This will require innovative task restructuring,
improvements in ML approaches to perception challenges, and
progress in robotic dexterity to overcome manipulation problems
related to variation between task iterations and the handling of
irregular objects. The gradual computerisation of installation, maintenance,
and repair occupations, which are largely confined to the
medium risk category, and require a high degree of perception and
manipulation capabilities, is a manifestation of this observation.
Our model predicts that the second wave of computerisation will
mainly depend on overcoming the engineering bottlenecks related to
creative and social intelligence. As reported in Table 3, the “fine arts”,
“originality”, “negotiation”, “persuasion”, “social perceptiveness”,
and “assisting and caring for others”, variables, all exhibit relatively
high values in the low risk category. By contrast, we note that the
“manual dexterity”, “finger dexterity” and “cramped work space”
variables take relatively low values. Hence, in short, generalist occupations
requiring knowledge of human heuristics, and specialist
occupations involving the development of novel ideas and artifacts,
are the least susceptible to computerisation. As a prototypical
example of generalist work requiring a high degree of social intelligence,
consider the O*NET tasks reported for chief executives,
involving “conferring with board members, organization officials,
or staff members to discuss issues, coordinate activities, or resolve
problems”, and “negotiating or approving contracts or agreements.”
Our predictions are thus intuitive in that most management, business,
and finance occupations, which are intensive in generalist tasks
requiring social intelligence, are largely confined to the low risk
category. The same is true of most occupations in education, healthcare,
as well as arts and media jobs. The O*NET tasks of actors,
for example, involve “performing humorous and serious interpretations
of emotions, actions, and situations, using body movements,
facial expressions, and gestures”, and “learning about characters in
scripts and their relationships to each other in order to develop
role interpretations.” While these tasks are very different from those
of a chief executive, they equally require profound knowledge of
human heuristics, implying that a wide range of tasks, involving
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 267
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
Fig. 3. Employment affected by computerisation. Note: 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. For ease of visualisation, the plot was produced by
smoothing employment over a sliding window of width 0.1 (in probability).
social intelligence, are unlikely to become subject to computerisation
in the near future.
The low susceptibility of engineering and science occupations to
computerisation, on the other hand, is largely due to the high degree
of creative intelligence they require. The O*NET tasks of mathematicians,
for example, involve “developing new principles and new
relationships between existing mathematical principles to advance
mathematical science” and “conducting research to extend mathematical
knowledge in traditional areas, such as algebra, geometry,
probability, and logic.” Hence, while it is evident that computers are
entering the domains of science and engineering, our predictions
implicitly suggest strong complementarities between computers and
labour in creative science and engineering occupations; although it
is possible that computers will fully substitute for workers in these
occupations over the long-run. This is in line with the findings of
Ingram and Neumann (2006), showing a largely persistent increase
in the returns to cognitive abilities since the 1980s. We also note
that the predictions of our model are strikingly in line with the
Table 3
Variable distributions.
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
Note: Distributions are represented by their mean and standard deviation.
technological trends we observe in the automation of knowledge
work, even within occupational categories. For example, we find that
paralegals and legal assistants – for which computers already substitute
– in the high risk category. At the same time, lawyers, which rely
on labour input from legal assistants, are in the low risk category.
Thus, for the work of lawyers to be fully automated, engineering bottlenecks
to creative and social intelligence will need to be overcome,
implying that the computerisation of legal research will complement
the work of lawyers in the medium term.
To complete the picture of what recent technological progress is
likely to mean for the future of employment, we plot the average
median wage of occupations by their probability of computerisation.
We do the same for skill level, measured by the fraction of
workers having obtained a bachelor’s degree, or higher educational
attainment, within each occupation. Fig. 4 reveals that both wages
and educational attainment exhibit a strong negative relationship
with the probability of computerisation. We note that this prediction
implies a truncation in the current trend towards labour market
polarisation, 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 lowwage
jobs in the near future. By contrast, high-skill and high-wage
occupations are the least susceptible to computer capital.
Our findings were robust to the choice of the 70 occupations that
formed our training data. This was confirmed by the experimental
results tabulated in Table A2: 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.
268 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 Average median wage (USD)
Probability of Computerisation
0 0.5 1
20k
40k
60k
80k
unweighted
average
weighted by
employment
Bachelor’s degree or better
Probability of Computerisation
0 0.5 1
0%
20%
40%
60%
Fig. 4. Computerisation’s dependence on wage and education. Note: Wage and education level as a function of the probability of computerisation; note that both plots share a
legend. The plots were produced by smoothing wage and education level, respectively, over a sliding window of width 0.1 (in probability).
5.1. Limitations
It shall be noted that our predictions are based on expanding the
premises about the tasks that computer-controlled equipment can
be expected to perform. Hence, we focus on estimating the share of
employment that can potentially be substituted by computer capital,
from a technological capabilities point of view, over some unspecified
number of years. We make no attempt to estimate how many
jobs will actually be automated. The actual extent and pace of computerisation
will depend on several additional factors which were
left unaccounted for.
First, labour saving inventions may only be adopted if the access
to cheap labour is scarce or prices of capital are relatively high
(Habakkuk, 1962).22 We do not account for future wage levels, capital
prices or labour shortages. While these factors will impact on the
timeline of our predictions, labour is the scarce factor, implying that
in the long-run wage levels will increase relative to capital prices,
making computerisation increasingly profitable (see, for example,
Acemoglu, 2003).
Second, regulatory concerns and political activism may slow
down the process of computerisation. The states of California and
Nevada are, for example, currently in the process of making legislatory
changes to allow for driverless cars. Similar steps will be needed
in other states, and in relation to various technologies. The extent
and pace of legislatory implementation can furthermore be related
to the public acceptance of technological progress.23 Although resistance
to technological progress has become seemingly less common
since the Industrial Revolution, there are recent examples of resistance
to technological change.24 We avoid making predictions about
the legislatory process and the public acceptance of technological
progress, and thus the pace of computerisation.
Third, making predictions about technological progress is notoriously
difficult (Armstrong and Sotala, 2012).25 For this reason, we
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 reveals a causal relationship between the access
to cheap labour and mechanisation in agricultural production, in terms of sustained
economic transition towards increased mechanisation in areas characterised
by low-wage worker out-migration (Hornbeck and Naidu, 2013). 23 For instance, William Huskisson, former cabinet minister and Member of Parliament
for Liverpool, was killed by a steam locomotive during the opening of the
Liverpool and Manchester Railway. Nonetheless, this well-publicised incident did anything
but dissuade the public from railway transportation technology. By contrast,
airship technology is widely recognised as having been popularly abandoned as a
consequence of the reporting of the Hindenburg disaster.
24 Uber, a start-up company connecting passengers with drivers of luxury vehicles,
has recently faced pressure from 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.
focus on near-term technological breakthroughs in ML and MR, and
avoid making any predictions about the number of years it may take
to overcome various engineering bottlenecks to computerisation.
Finally, we emphasise that since our probability estimates describe
the likelihood of an occupation being fully automated, we do not
capture any within-occupation variation resulting from the computerisation
of tasks that simply free-up time for human labour to
perform other tasks. 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.
6. Conclusions
While computerisation has been historically confined to routine
tasks involving explicit rule-based activities (Autor and Dorn, 2013;
Autor et al., 2003; Goos et al., 2009), algorithms for big data are now
rapidly entering domains reliant upon pattern recognition and can
readily substitute for labour in a wide range of non-routine cognitive
tasks (Brynjolfsson and McAfee, 2011; MGI, 2013). In addition,
advanced robots are gaining enhanced senses and dexterity, allowing
them to perform a broader scope of manual tasks (IFR, 2012b;
Robotics-VO, 2013; MGI, 2013). This is likely to change the nature of
work across industries and occupations.
In this paper, we ask the question: how susceptible are current
jobs to these technological developments? To assess this, we
implement a novel methodology to estimate the probability of computerisation
for 702 detailed occupations. Based on these estimates,
we examine expected impacts of future computerisation on labour
market outcomes, with the primary objective of analysing the number
of jobs at risk and the relationship between an occupation’s
probability of computerisation, wages and educational attainment.
We distinguish between high, medium and low risk occupations,
depending on their probability of computerisation. We make
no attempt to estimate the number of jobs that will actually be
automated, and focus on potential job automatability over some
unspecified number of years. According to our estimates around 47%
of total US employment is in the high risk category. We refer to these
as jobs at risk – i.e. jobs we expect could be automated relatively
soon, perhaps over the next decade or two.
Our model predicts that most workers in transportation and
logistics occupations, together with the bulk of office and administrative
support workers, and labour in production occupations, are at
risk. These findings are consistent with recent technological developments
documented in the literature. More surprisingly, we find that a
substantial share of employment in service occupations, where most
US job growth has occurred over the past decades (Autor and Dorn,
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).
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 269
Finally, we provide evidence that wages and educational attainment
exhibit a strong negative relationship with the probability of
computerisation. We note that this finding implies a discontinuity
between the nineteenth, twentieth and the twenty-first century,
in the impact of capital deepening on the relative demand for
skilled labour. While nineteenth century manufacturing technologies
largely substituted for skilled labour through the simplification
of tasks (Braverman, 1974; Goldin and Katz, 1998; Hounshell, 1985;
James and Skinner, 1985), the Computer Revolution of the twentieth
century caused a hollowing-out of middle-income jobs (Autor and
Dorn, 2013; Goos et al., 2009). Our model predicts a truncation in
the current trend towards labour market polarisation, with computerisation
being principally confined to low-skill and low-wage
occupations. Our findings thus imply that as technology races ahead,
low-skill workers will reallocate to tasks that are non-susceptible
to computerisation – i.e., tasks requiring creative and social intelligence.
For workers to win the race, however, they will have to
acquire creative and social skills.
Appendix A
The table below ranks occupations according to their probability of computerisation (from least- to most-computerisable). Those occupations
used as training data are labelled as either ‘0’ (not computerisable) or ‘1’ (computerisable), respectively. There are 70 such occupations,
10% 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 reachers, except special education
38. 0.0075 25-9021 Farm and home management advisors
39. 0.0077 19-3091 Anthropologists and archeologists
40. 0.0077 25-2054 Special education teachers, secondary school
41. 0.0078 25-2031 Secondary school teachers, except special and career/technical education
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
(continued on next page)
270 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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
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 oorrectional 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 inspectors
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 behavioural 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
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 271
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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
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
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 education
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
(continued on next page)
272 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
205. 0.19 25-3011 Adult basic and secondary education and literacy teachers and instructors
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 network 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 technicians
228. 0.25 41-4011 Sales representatives, wholesale and manufacturing, technical and scientific products
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 labour 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 relay
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
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 equipment
278. 0.42 19-4093 Forest and conservation technicians
279. 0.42 53-1021 First-line supervisors of helpers, labourers, and material movers, hand
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 273
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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
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 tenders
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
(continued on next page)
274 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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
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 workers
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
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
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 275
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
429. 0.78 51-8092 Gas plant operators
430. 0.79 43-5053 Postal service mail sorters, processors, and processing machine operators
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 tenders
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
463. 0.83 47-3011 Helpers–brickmasons, blockmasons, stonemasons, and tile and marble 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 Labourers 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
(continued on next page)
276 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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
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 labourers
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 setters, operators, and tenders
518. 0.88 51-6091 Extruding and forming machine setters, operators, and tenders, synthetic 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 tenders
552. 0.91 51-4081 Multiple machine tool setters, operators, and tenders, metal and plastic
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
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 277
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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, operators, 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
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, operators, 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, operators, and tenders
638. 0.96 53-4011 Locomotive engineers
639. 0.96 1 39-3011 Gaming dealers
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 labour contractors
650. 0.97 51-6061 Textile bleaching and dyeing machine operators and tenders
651. 0.97 51-9081 Dental laboratory technicians
(continued on next page)
278 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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
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
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THE RECOMMENDED TEXTBOOKS
Essentials of Operations Management, by Nigel Slack,
Alistair Brandon-Jones, Robert Johnston. Pearson, 2011
Operations Management, 3rd Edition,
by Andrew Greasley. Wiley, 2013
COMPLEMENTARY BOOKS
– OPERATIONS MANAGEMENT, 8
th Edition, by Nigel Slack, Alistair
Brandon-Jones, Robert Johnston. Pearson, 2016.
– STRATEGIC OPERATIONS MANAGEMENT, 3rd Edition, by Steve
Brown, John Bessant, Richard Lamming. Routledge, 2012.
– OPERATIONS MANAGEMENT, by Steve Paton, Ben Clegg, Juliana
Hsuan, Alan Pilkington, McGraw-Hill, 2011.
– MANAGING QUALITY, 5
th Edition, by S. Thomas Foster. Pearson,
2013.
– SERVICE MANAGEMENT – Operations, Strategy, Information
technology, 5
th Edition, by J & M Fitzsimmonns, McGraw-Hill, 2006.
– OPERATIONS MANAGEMENT, by Terry Hill and Dr Alex Hill,
3rd Edition, Palgrave McMillan, 2011
– SERVICE OPERATIONS MANAGEMENT, 3/e by Johnston & Clark,
Financial Times/Prentice Hall, 3rd Edition, 2008
– PRINCIPLES OF OPERATIONS MANAGEMENT, by Heizer &
Render, Pearson, 9th Edition, 2013
– INTRODUCTION TO OPERATIONS AND SUPPLY CHAIN
MANAGEMENT by John Mangan, Chandra Lanwani and Tim Butcher,
John Wiley & Sons Ltd , 2008
– GLOBAL LOGISTICS and SUPPLY CHAIN MANAGEMENT:
INTERNATIONAL EDITION, 3/e by Chopra & Meindl, Financial
Times/Prentice Hall, 3
rd Edition, 2007
– SUPPLY CHAIN MANAGEMENT: INTERNATIONAL EDITION, 3/e
by Chopra & Meindl, Financial Times/Prentice Hall, 3
rd Edition, 2007
FURTHER READING
The Lean Office: Collected Practices & Cases (Insights on
Implementation) by Anderson D.R., Sweeney D.J. and Williams T.A.,
Productivity press, 2003
THE GOAL: A PROCESS OF ONGOING IMPROVEMENT, by Eliyahu M.
Goldratt, Gower Publishing Ltd, 3rd Rev Edition, 2004
LEAN SOLUTIONS: HOW COMPANIES AND CUSTOMERS CAN CREATE
VALUE AND WEALTH TOGETHER, by James P. Womack & Daniel T. Jones,
Simon & Schuster Ltd, New Edition, 2007
Customer Care Excellence by Cook, S. Kogan Page, 2010
Logistics Management and Strategy, by Harrison A. and Hoek, R. FT
Prentice Hall, 5th Edition, 2014
Project Management-A Managerial Approach, Meredith, J and Mantel, John
Wiley & Sons, 7th edition, 2009
Quantitative Analysis for Management Render B, Stair RM and Hanna M,
11th Edition, Pearson, 2011
LEAN THINKING: BANISH WASTE AND CREATE WEALTH IN YOUR
CORPORATION, by James P. Womack & Daniel T. Jones, Free Press, New
Edition, 2003
IMPROVING PRODUCTION WITH LEAN THINKING, by Javier Santos,
Richard A. Wysk and Joe M Torres, John Wiley & Sons Inc, New Edition, 2006
1
BSOM046 – Managing Operations
and the Supply Chain
Lecture 04 – Innovation & Process technology
Northampton MBA
• What is innovation?
• Why should organisations innovate?
• How is innovation done?
• How do we measure it?
• Innovation through process technology
• Technology unemployment
Key operations questions
Innovation is a new idea, which may be the
recombination of old ideas, a scheme that
challenges present order, a formula, or a
unique approach which is perceived as new by
the individuals involved.
– Van de Ven “The Innovation Journey 2008”
What is innovation?
• Innovation is not limited to products or the
application of newer technology.
• Innovation can affect:
– products
– processes
– services
– business models
• Innovation can be incremental or radical
Innovation
Motivations for Innovation
• New technologies / scientific discovery
• Customer Perception changes
• Process needs
• Change in industry structure, society,
demographics, regulations
• Competition
• Margin erosion
• Make a strategic commitment & lay down a strategic
objective. Ex. “20% of our income shall be from
products/services that are less than 5 years old”
• Appoint a director of innovation
• Relate innovation to performance payment
• Benchmark your competitors and world class performers
(learn from others)
• Initiate cross functional teams
• See failure as learning
How is innovation done?
Creative Idea Generating Process
• Encourage all in the organisation to join
– 3M will allow an employee up to 20% of their time to
spend on researching innovative ideas
• Challenge the employees
– Virgin challenge staff to “think like a slightly
disgruntled customer” and then seek out WHY?
• Toyotas Suggestion Scheme
– 90%+ implementation rate
7
Internally
Creative Idea Generating Process
• Create a network of expert users or co-creators
– Users who delight in knowing your products/services
and see uses for them that you have not
• Reward them for their suggestions
– Example: Apple, Firefox, Lego, Nike, HP, IBM…
• Outsource the work
– Creative organisations (e.g. designers, architects,
scientists, inventors, universities, etc.) will all be happy
to do the work for you
8
Externally
Innovation cycle
IDEA
CONCEPT
PRODUCT
DEVELOPMENT
TEST
MARKETING
COMMERCIALISATION
CELEBRATE
AND START
AGAIN
Keep the cyclic theme in mind: REVIEW THE PROCESS REGULARLY!
Barriers to creativity
• Searching for the one
“right” answer
• Focusing on “being
logical”
• Blindly following the rules
• Constantly being practical
• Viewing play as frivolous
• Becoming overly
specialized
• Avoiding ambiguity
• Fearing looking foolish
• Fearing mistakes and
failure
• Believing that “I’m not
creative”
FAILURE CAN HAPPEN –
you will have to deal with these….
1. Failure to clarify innovation goals
2. Failure to engage the entire organisation
3. Failure to create an innovation culture
4. Don’t recognise the importance of communication
5. Don’t recognise the importance of change management
6. Don’t recognise innovation barriers
7. Unclear process for selecting innovation projects
8. Failure to recognise that innovation is a journey, not a
destination
How do we measure innovation?
12
INPUT METRICS:
• Number of ideas generated
• Resources allocated to innovation – people and budget
PROCESS METRICS:
• Average time from idea approval to implementation
• Number of ideas approved and number implemented
• Stage-gate pass rates
• Value of the innovation pipeline
OUTPUT METRICS:
• Number of new products or services launched
• Revenue from new products or services
• ROI on innovation spend
• Market perception
• Number of new customers
Innovations through technology
Process metals, plastics, fabric, etc.
Materials-processing technology
a. Active interaction with
technology
100
%
80%
60%
40%
20%
100
%
Branch
50%
Telephone
25%
Cash
machine
12%
Internet
Technology and processing costs
Cost per banking transaction
Customer-processing technology
b. Passive interaction with technology
Customer-processing technology
c. Use of technology through an intermediary
Customer-processing technology
Disruptive technologies
They are disruptive in a sense that they change the status quo
of businesses, creating opportunities for new business models
Examples:
• 3D Printing • Drones
3D Printing Shapeways.com
Creative innovations
Solar bottle bulb
Lamp that
runs in salt
and water
Mobile Convergence
Technological unemployment
Beware of technological impact on jobs!
Technological unemployment is the loss of jobs caused by the
replacement of human labour by machines (automation)
Technological unemployment
• The pace of technological innovation is still increasing,
with more sophisticated software technologies
disrupting labour markets by making workers
redundant
• Automation is no longer confined to routine
manufacturing tasks. Autonomous driverless cars
provide a good example of how manual tasks in
transport and logistics may soon be automated
Frey & Osborne (2016)
Technological unemployment
Assignment 1 – The Future of Work
Tutorial activity: A small restaurant has a work force
comprising the following team: 1 manager, 1 chef, 2 food
preparation workers and 2 waiters. Based on Frey and
Osborne’s classification of computerisable occupations, what is
the likelihood of low skilled workers in the restaurant become
technologically unemployed in the future?
Consider a low impact scenario, when only jobs
at high risk (> 70%) are replaced by technology
P(Manager) = 0.25
P(Chef) = 0.10
P(Food preparation worker) = 0.87
P (Waiter) = 0.94
Appendix A,
Frey and Osborne (2016) paper
Technological unemployment
P(2 Food preparation workers) = 0.87 x 0.87 = 0.76
P (Food preparation workers AND Waiters) = 0.76 x 0.88 = 0.67
There is a high risk of technological unemployment
(approximately 67% of chance) impacting low skilled
workers in the restaurant become unemployed. This is
more likely to be concentrated in the waiters force.
The probability of the 2 food preparation workers AND the 2
waiters in the restaurant to loose their job by automation is:
P(2 Waiters) = 0.94 x 0.94 = 0.88
Technological unemployment
Northampton MBA
THE UNIVERSITY OF NORTHAMPTON
NORTHAMPTON BUSINESS SCHOOL
MODULE: Managing Operations and the Supply Chain 2017-2018
Module Code Level Credit Value Module Tutors
BSOM046 7 20 Luciano Batista
Desmond Kapofu
Kemi Waterton Zhou
Assignment 1 Brief
Assignment title: The Future of Work
Weighting:
40%
Deadline: 30th March 2018
Feedback and
Grades due: 27th April 2018
Resit Date 06th July 2018
Purpose of the Assessment
This assignment is designed to enable you to demonstrate an understanding of
the potential for automation to impact existing operations processes and related
resources.
Assessment Task
You will conduct a review of the literature in the subject area of Automation.
Specifically, you are to identify the origins of the concept of the Technological
Unemployment and to chart its development up to the present day.
Following your review, you are to critically evaluate the impact of Technological
Unemployment on a company of your choice.
You will be expected to illustrate your discussion with examples drawn from
authoritative business and academic sources.
Assessment Breakdown
1. (10% of word count)
Establish the scenario for your report by selecting an organisation of any type,
sector and size to focus your report on. Describe:
a) Which organisation is it? (type, sector and size)
b) What are the main products and/or services provided by the organisation?
c) Who are the main customers?
2. (45% of word count)
Prepare a literature review, charting the development of the concept of
Technological Unemployment from its inception until the present day.
Ensure that you include references to at least 10 peer-reviewed articles, including
the 2016 paper by Frey and Osborne that has been supplied. You may also find
relevant reviews in the trade press and from other authoritative sources.
3. (45% of word count)
Apply Frey and Osborne’s findings (see Appendix A in the Supporting Materials) in
the context of your chosen company.
Consider a low impact scenario, when only jobs at high risk (> 70%) are replaced
by technology. How will the company change?
Assessment Submission
Your assignment must be word processed and presented in a report format with
simple sub-headings. The word count should be 1500 words±10% (tables,
diagrams and appendices are excluded from the count).
The Assignment report should have a Front Sheet showing your name, your student
number, the module name, the module number, the assignment title, the module
tutor’s name, the date and the word count.
All assignments will be submitted, graded and fed-back electronically via TURNITIN.
Several submissions will be permitted before the hand-in date in order to enable you
to refine the content in your report.
If you click on the “Submit Your Work” button on the Module NILE site you will find an
explanation of the Submission and Grading Electronically process there.
Feedback on assignments in general will be provided to the whole group when
marked assignments are returned.
Feedback on assignments for each individual will be provided electronically via
TURNITIN.
A student may obtain an individual appointment to discuss feedback with the tutor.
Assessment Guidance
The quality of your presentation and academic referencing is very important. Please,
use the Harvard Referencing System.
Within your assignment your tutor will be looking for content that addresses the key
elements of the assignment brief. Remember that Frey and Osborne’s work is
quantitative – you will be expected to have numerical data to support your
discussion.
Try not to overcomplicate your answers by choosing a company that you know little
about. Keep to simple processes that you know well.
Look at the Check list at the end of this brief. It shows the subheadings to use and
offers a guide as to how the marks will be distributed.
Use the percentages as a guide to how to distribute your word count.
Academic Practice
This is an individual assignment. The University of Northampton policy will apply in
all cases of copying, plagiarism or any other methods by which students have
obtained (or attempted to obtain) an unfair advantage.
Support and guidance on assessments and academic integrity can be found from the
following resources
SkillsHub: http://skillshub.northampton.ac.uk
CfAP: http://tinyurl.com/UoNCfAP
Late Submission Opportunities
Submission extensions must be requested in advance to the module tutor. Extensions
can be approved or not by the tutor depending on the justification of the causes or
circumstances impeding submission on the established deadline.
Reassessment and Deferral Opportunities
If you achieve grade F+ or below on this assessment, you are required to submit a
Resit assignment on the date informed in the assignment brief.
BSOM046 OPERATIONS MANAGEMENT
“Report Content Checklist”
STUDENT NAME AND REPORT FRONT SHEET:
DESCRIPTION OF THE CHOSEN COMPANY (10%)
– customers, products and services
LITERATURE SEARCH (45%)
– research the development of the concept of Technological
Unemployment from its inception to the present day
SCENARIO DEVELOPMENT (45%)
– consider a low impact scenario (only jobs above 70% risk are
replaced). Include numerical data to support your discussion.
How will the company change?
MAX WORD COUNT 1500 +/- 10%
Learning outcomes
The learning outcomes being addressed through this assignment are:
Knowledge and Understanding:
a) Investigate and critically evaluate the range of concepts and techniques
available to operations managers so as to enable effective business decision making.
b) Demonstrate conceptual and practical understanding of the opportunities and
constraints that organisational characteristics place on operations managers and on
operational decision making in the supply chain context.
c) Critically discuss and evaluate the theoretical and real life applications of topics
in the indicative content, analysing and evaluating the benefits they offer to an
organisation and the challenges to be overcome in implementing them.
Subject- Specific Skills
d) Critically evaluate the business relevance of the concept/topic studied, with a
view to understanding the value of its adoption to an organisation.
Key Skills:
g) Make discriminating use of a range of learning resources in order to solve
problems within the domain of International Supply Chain and Operations
Management.
h) Communicate the solutions arrived at, and the critical evaluation underlying
them.
Technological Forecasting & Social Change 114 (2017) 254–280
Contents lists available at ScienceDirect
Technological Forecasting & Social Change
The future of employment: How susceptible are jobs
to computerisation?
Carl Benedikt Freya,
*, Michael A. Osborneb
aOxford Martin School, University of Oxford, Oxford OX1 1PT, United Kingdom
bDepartment of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom
ARTICLE INFO
Article history:
Received 24 September 2015
Accepted 19 August 2016
Available online 29 September 2016
JEL classification:
E24
J24
J31
J62
O33
Keywords:
Occupational choice
Technological change
Wage inequality
Employment
Skill demand
ABSTRACT
We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel
methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian
process classifier. Based on these estimates, we examine expected impacts of future computerisation on
US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the
relationship between an occupations probability of computerisation, wages and educational attainment.
© 2016 Published by Elsevier Inc.
1. Introduction
In this paper, we address the question: how susceptible are jobs
to computerisation? Doing so, we build on the existing literature in
two ways. First, drawing upon recent advances in Machine Learning
(ML) and Mobile Robotics (MR), we develop a novel methodology
to categorise occupations according to their susceptibility to
computerisation.1 Second, we implement this methodology to estimate
the probability of computerisation for 702 detailed occupations,
and examine expected impacts of future computerisation on
US labour market outcomes.
We thank the Oxford University Engineering Sciences Department and the Oxford
Martin Programme on the Impacts of Future Technology for hosting the “Machines
and Employment” Workshop. We are indebted to Stuart Armstrong, Nick Bostrom,
Eris Chinellato, Mark Cummins, Daniel Dewey, Alex Flint, John Muellbauer, Vincent
Mueller, Paul Newman, Seán Ó hÉigeartaigh, Anders Sandberg, Murray Shanahan, and
Keith Woolcock for their excellent suggestions.
* Corresponding author.
E-mail addresses: carl.frey@philosophy.ox.ac.uk (C. Frey), mosb@robots.ox.ac.uk
(M. Osborne).
1 We refer to computerisation as job automation by means of computer-controlled
equipment.
Our paper is motivated by John Maynard Keynes’s frequently
cited prediction of widespread technological unemployment “due to
our discovery of means of economising the use of labour outrunning
the pace at which we can find new uses for labour” (Keynes,
1933, p. 3). Indeed, over the past decades, computers have substituted
for a number of jobs, including the functions of bookkeepers,
cashiers and telephone operators (Bresnahan, 1999; MGI,
2013). More recently, the poor performance of labour markets across
advanced economies has intensified the debate about technological
unemployment among economists. While there is ongoing disagreement
about the driving forces behind the persistently high unemployment
rates, a number of scholars have pointed at computercontrolled
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
2 This view finds support in a recent survey by the McKinsey Global Institute (MGI),
showing that 44% of firms which reduced their headcount since the financial crisis of
2008 had done so by means of automation (MGI, 2011).
http://dx.doi.org/10.1016/j.techfore.2016.08.019
0040-1625/© 2016 Published by Elsevier Inc.
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 255
mainly consisting of tasks following well-defined procedures that
can easily be performed by sophisticated algorithms. For example,
studies by Charles et al. (2013) and Jaimovich and Siu (2012) emphasise
that the ongoing decline in manufacturing employment and
the disappearance of other routine jobs is causing the current low
rates of employment.3 In addition to the computerisation of routine
manufacturing tasks, Autor and Dorn (2013) document a structural
shift in the labour market, with workers reallocating their labour
supply from middle-income manufacturing to low-income service
occupations. Arguably, this is because the manual tasks of service
occupations are less susceptible to computerisation, as they require
a higher degree of flexibility and physical adaptability (Autor et al.,
2003; Goos and Manning, 2007; Autor and Dorn, 2013).
At the same time, with falling prices of computing, problemsolving
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 polarisation,
with growing employment in high-income cognitive jobs and
low-income manual occupations, accompanied by a hollowing-out
of middle-income routine jobs.
According to Brynjolfsson and McAfee (2011), the pace of technological
innovation is still increasing, with more sophisticated
software technologies disrupting labour markets by making workers
redundant. What is striking about the examples in their book
is that computerisation is no longer confined to routine manufacturing
tasks. The autonomous driverless cars, developed by Google,
provide one example of how manual tasks in transport and logistics
may soon be automated. In the section “In Domain After Domain,
Computers Race Ahead”, they emphasise how fast moving these
developments have been. Less than ten years ago, in the chapter
“Why People Still Matter”, Levy and Murnane (2004) pointed at the
difficulties of replicating human perception, asserting that driving
in traffic is insusceptible to automation: “But executing a left turn
against oncoming traffic involves so many factors that it is hard
to imagine discovering the set of rules that can replicate a driver’s
behaviour [. . . ]”. Six years later, in October 2010, Google announced
that it had modified several Toyota Priuses to be fully autonomous
(Brynjolfsson and McAfee, 2011).
To our knowledge, no study has yet quantified what recent
technological progress is likely to mean for the future of employment.
The present study intends to bridge this gap in the literature.
Although there are indeed existing useful frameworks for examining
the impact of computers on the occupational employment composition,
they seem inadequate in explaining the impact of technological
trends going beyond the computerisation of routine tasks. Seminal
work by Autor et al. (2003), for example, distinguishes between
cognitive and manual tasks on the one hand, and routine and nonroutine
tasks on the other. While the computer substitution for both
cognitive and manual routine tasks is evident, non-routine tasks
involve everything from legal writing, truck driving and medical
diagnoses, to persuading and selling. In the present study, we will
argue that legal writing and truck driving will soon be automated,
while persuading, for instance, will not. Drawing upon recent developments
in Engineering Sciences, and in particular advances in the
fields of ML, including Data Mining, Machine Vision, Computational
Statistics and other sub-fields of Artificial Intelligence, as well as
MR, we derive additional dimensions required to understand the
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).
susceptibility of jobs to computerisation. Needless to say, a number
of factors are driving decisions to automate and we cannot capture
these in full. Rather we aim, from a technological capabilities point of
view, to determine which problems engineers need to solve for specific
occupations to be automated. By highlighting these problems,
their difficulty and to which occupations they relate, we categorise
jobs according to their susceptibility to computerisation. The characteristics
of these problems were matched to different occupational
characteristics, using O*NET data, allowing us to examine the future
direction of technological change in terms of its impact on the occupational
composition of the labour market, but also the number of
jobs at risk should these technologies materialise.
The present study relates to two literatures. First, our analysis
builds on the labour economics literature on the task content of
employment (Autor et al., 2003; Goos and Manning, 2007; Autor
and Dorn, 2013; Ingram and Neumann, 2006). Based on defined
premises about what computers do, this literature examines the historical
impact of computerisation on the occupational composition
of the labour market. However, the scope of what computers do has
recently expanded, and will inevitably continue to do so (Brynjolfsson
and McAfee, 2011; MGI, 2013). Drawing upon recent progress
in ML, we expand the premises about the tasks computers are and
will be suited to accomplish. Doing so, we build on the task content
literature in a forward-looking manner. Furthermore, whereas
this literature has largely focused on task measures from the Dictionary
of Occupational Titles (DOT), last revised in 1991, we rely on
the 2010 version of the DOT successor O*NET – an online service
developed for the US Department of Labor.4 In particular, Ingram and
Neumann (2006) use various DOT measurements to examine returns
to different skills. Our analysis builds on their approach by classifying
occupations according to their susceptibility to computerisation
using O*NET data.
Second, our study relates to theliterature examining the offshoring
of information/based tasks to foreign worksites (Blinder, 2009;
Blinder and Krueger, 2013; Jensen and Kletzer, 2005, 2010; Oldenski,
2012). This literature consists of different methodologies to rank and
categorise occupations according to their susceptibility to offshoring.
For example, using O*NET data on the nature of work done in different
occupations, Blinder (2009) estimates that 22 to 29% 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 location; and
(b) the job requires face-to-face personal communication. Naturally,
the characteristics of occupations that can be offshored are different
from the characteristics of occupations that can be automated.
For example, the work of cashiers, which has largely been substituted
by self- service technology, must be performed at specific work
location and requires face-to-face contact. The extent of computerisation
is therefore likely to go beyond that of offshoring. Hence, while
the implementation of our methodology is similar to that of Blinder
(2009), we rely on different occupational characteristics.
The remainder of this paper is structured as follows. In Section 2,
we review the literature on the historical relationship between
technological progress and employment. Section 3 describes recent
and expected future technological developments. In Section 4, we
describe our methodology, and in Section 5, we examine the
expected impact of these technological developments on labour
market outcomes. Finally, in Section 6, we derive some conclusions.
2. A history of technological revolutions and employment
The concern over technological unemployment is hardly a
recent phenomenon. Throughout history, the process of creative
4 Goos et al. (2009) provides a notable exception.
256 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
destruction, following technological inventions, has created enormous
wealth, but also undesired disruptions. As stressed by Schumpeter
(1962), it was not the lack of inventive ideas that set the
boundaries for economic development, but rather powerful social
and economic interests promoting the technological status quo. This
is nicely illustrated by the example of William Lee, inventing the
stocking frame knitting machine in 1589, hoping that it would relieve
workers of hand-knitting. Seeking patent protection for his invention,
he travelled to London where he had rented a building for his
machine to be viewed by Queen Elizabeth I. To his disappointment,
the Queen was more concerned with the employment impact of his
invention and refused to grant him a patent, claiming that “Thou
aimest high, Master Lee. Consider thou what the invention could
do to my poor subjects. It would assuredly bring to them ruin by
depriving them of employment, thus making them beggars” (cited
in Acemoglu and Robinson, 2012, p. 182f). Most likely the Queen’s
concern was a manifestation of the hosiers’ guilds fear that the invention
would make the skills of its artisan members obsolete.5 The
guilds’ opposition was indeed so intense that William Lee had to
leave Britain.
That guilds systematically tried to weaken market forces as
aggregators to maintain the technological status quo is persuasively
argued by Kellenbenz (1974, p. 243), stating that “guilds
defended the interests of their members against outsiders, and these
included the inventors who, with their new equipment and techniques,
threatened to disturb their members’ economic status.”6 As
pointed out by Mokyr (1998, p. 11): “Unless all individuals accept
the “verdict” of the market outcome, the decision whether to adopt
an innovation is likely to be resisted by losers through non-market
mechanism and political activism.” Workers can thus be expected
to resist new technologies, insofar that they make their skills obsolete
and irreversibly reduce their expected earnings. The balance
between job conservation and technological progress therefore, to a
large extent, reflects the balance of power in society, and how gains
from technological progress are being distributed.
The British Industrial Revolution illustrates this point vividly.
While still widely present on the Continent, the craft guild in Britain
had, by the time of 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. 403–408). 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).
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).
There are at least two possible explanations for the shift in
attitudes towards technological progress. First, after Parliamentary
supremacy was established over the Crown, the property owning
classes became politically dominant in Britain (North and Weingast,
1989). Because the diffusion of various manufacturing technologies
did not impose a risk to the value of their assets, and some property
owners stood to benefit from the export of manufactured goods, the
artisans simply did not have the political power to repress them. Second,
inventors, consumers and unskilled factory workers largely benefited
from mechanisation (Mokyr, 1990, p. 256 and 258). It has even
been argued that, despite the employment concerns over mechanisation,
unskilled workers have been the greatest beneficiaries of the
Industrial Revolution (Clark, 2008).7 While there 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 (Feinstein, 1998; Lindert and Williamson, 1983).
This implies that although manufacturing technologies made the
skills of artisans obsolete, gains from technological progress were
distributed in a manner that gradually benefited a growing share of
the labour force.8
An important feature of nineteenth century manufacturing technologies
is that they were largely “deskilling” – i.e. they substituted
for skills through the simplification of tasks (Braverman, 1974;
Hounshell, 1985; James and Skinner, 1985; Goldin and Katz, 1998).
The deskilling process occurred as the factory system began to displace
the artisan shop, and it picked up pace as production increasingly
mechanized with the adoption of steam power (Goldin and
Sokoloff, 1982; Atack et al., 2008a). Work that had previously been
performed by artisans was now decomposed into smaller, highly
specialised, sequences, requiring less skill, but more workers, to
perform.9 Some innovations were even designed to be deskilling. For
example, Eli Whitney, a pioneer of interchangeable parts, described
the objective of this technology as “to substitute correct and effective
operations of machinery for the skill of the artist which is acquired
only by long practice and experience; a species of skill which is not
possessed in this country to any considerable extent” (Habakkuk,
1962, p. 22).
Together with developments in continuous-flow production,
enabling workers to be stationary while different tasks were moved
to them, it was identical interchangeable parts that allowed complex
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%
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.
8 The term skill is associated with higher levels of education, ability, or job training.
Following Goldin and Katz (1998), we refer to technology-skill or capital-skill complementarity
when a new technology or physical capital complements skilled labour
relative to unskilled workers.
9 The production of plows nicely illustrates the differences between the artisan
shop and the factory. In one artisan shop, two men spent 118 man-hours using
hammers, anvils, chisels, hatchets, axes, mallets, shaves and augers in 11 distinct
operations to produce a plow. By contrast, a mechanized plow factory employed 52
workers performing 97 distinct tasks, of which 72 were assisted by steam power,
to produce a plow in just 3.75 man-hours. The degree of specialisation 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).
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 257
products to be assembled from mass produced individual components
by using highly specialised machine tools to a sequence of
operations.10 Yet while the first assembly-line was documented in
1804, it was not until the late nineteenth century that continuousflow
processes started to be adopted on a larger scale, which enabled
corporations such as the Ford Motor Company to manufacture the TFord
at a sufficiently low price for it to become the people’s vehicle
(Mokyr, 1990, p. 137). Crucially, the new assembly line introduced by
Ford in 1913 was specifically designed for machinery to be operated
by unskilled workers (Hounshell, 1985, p. 239). Furthermore, what
had previously been a one-man job was turned into a 29-man worker
operation, reducing the overall work time by 34% (Bright, 1958). The
example of the Ford Motor Company thus underlines the general
pattern observed in the nineteenth century, with physical capital
providing a relative complement to unskilled labour, while substituting
for relatively skilled artisans (James and Skinner, 1985; Louis and
Paterson, 1986; Brown and Philips, 1986; Atack et al., 2004).11 Hence,
as pointed out by Acemoglu (2002, p. 7): “the idea that technological
advances favour 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 mechanized assembly lines. This shift can
be traced to the switch to electricity from steam and water-power
which, in combination with continuous-process and batch production
methods, reduced the demand for unskilled manual workers
in many hauling, conveying, and assembly tasks, but increased the
demand for skills (Goldin and Katz, 1998). In short, while factory
assembly lines, with their extreme division of labour, had required
vast quantities of human operatives, electrification allowed many
stages of the production process to be automated, which in turn
increased the demand for relatively skilled blue-collar production
workers to operate the machinery. In addition, electrification contributed
to a growing share of white-collar nonproduction workers
(Goldin and Katz, 1998). Over the course of the nineteenth century,
establishments became larger in size as steam and water
power technologies improved, allowing them to adopt powered
machinery to realise productivity gains through the combination of
enhanced division of labour and higher capital intensity (Atack et
al., 2008a). Furthermore, the transport revolution lowered costs of
shipping goods domestically and internationally as infrastructure
spread and improved (Atack et al., 2008b). The market for artisan
goods early on had largely been confined to the immediate surrounding
area because transport costs were high relative to the value of
10 These machines were sequentially implemented until the production process was
completed. Over time, such machines became much cheaper relative to skilled labour.
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 lowskill
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 labour
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.
the goods produced. With the transport revolution, however, market
size expanded, thereby eroding local monopoly power, which
in turn increased competition and compelled firms to raise productivity
through mechanisation. As establishments became larger and
served geographically expended markets, managerial tasks increased
in number and complexity, requiring more managerial and clerking
employees (Chandler, 1977). This pattern was, by the turn of the
twentieth century, reinforced by electrification, which not only contributed
to a growing share of relatively skilled blue-collar labour, but
also increased the demand for white-collar workers (Goldin and Katz,
1998), who tended to have higher educational attainment (Allen,
2001).12
Since electrification, the story of the twentieth century has been
the race between education and technology (Goldin and Katz, 2009).
The US high school movement coincided with the first industrial
revolution of the office (Goldin and Katz, 1995). While the typewriter
was invented in the 1860s, it was not introduced in the office
until the early twentieth century, when it entered a wave of mechanisation,
with dictaphones, calculators, mimeo machines, address
machines, and the predecessor of the computer – the keypunch
(Beniger, 1986; Cortada, 2000). Importantly, these office machines
reduced the cost of information processing tasks and increased the
demand for the complementary factor – i.e. educated office workers.
Yet the increased supply of educated office workers, following the
high school movement, was associated with a sharp decline in the
wage premium of clerking occupations relative to production workers
(Goldin and Katz, 1995). This was, however, not the result of
deskilling technological change. Clerking workers were indeed relatively
educated. Rather, it was the result of the supply of educated
workers outpacing the demand for their skills, leading educational
wage differentials to compress.
While educational wage differentials in the US narrowed from
1915 to 1980 (Goldin and Katz, 2009), both educational wage differentials
and overall wage inequality have increased sharply since
the 1980s in a number of countries (Krueger, 1993; Murphy et al.,
1998; Atkinson, 2008; Goldin and Katz, 2009). Although there are
clearly several variables at work, consensus is broad that this can be
ascribed to an acceleration in capital-skill complementarity, driven
by the adoption of computers and information technology (Krueger,
1993; Autor et al., 1998; Bresnahan et al., 2002). What is commonly
referred to as the Computer Revolution began with the first commercial
uses of computers around 1960 and continued through the
development of the Internet and e-commerce in the 1990s. As the cost
per computation declined at an annual average of 37% 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% per
year, accompanied by a surge in computational power (Nordhaus,
2007).13 At the same time, bar-code scanners and cash machines were
spreading across the retail and financial industries, and the first personal
computers were introduced in the early 1980s, with their word
processing and spreadsheet functions eliminating copy typist occupations
and allowing repetitive calculations to be automated (Gordon,
2012). This substitution for labour marks a further important reversal.
The early twentieth century office machines increased the demand
for clerking workers (Chandler, 1977; Goldin and Katz, 1995). In a
12 Most likely, the growing share of white-collar workers increased the element
of human interaction in employment. Notably, Michaels et al. (2013) find that the
increase in the employment share of interactive occupations, going hand in hand with
an increase in their relative wage bill share, was particularly strong between 1880 and
1930, which is a period of rapid change in communication and transport technology.
13 Computer power even increased 18% faster on annual basis than predicted by
Moore’s Law, implying a doubling every two years (Nordhaus, 2007).
258 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
similar manner, computerisation augments demand for such tasks,
but it also permits them to be automated (Autor et al., 2003).
The Computer Revolution can go some way in explaining the
growing wage inequality of the past decades. For example, Krueger
(1993) finds that workers using a computer earn roughly earn 10 to
15% 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% after having been flat or declining in the three
prior decades. Furthermore, net changes in US employment were Ushaped
in skill level, meaning that the lowest and highest job-skill
quartile expanded sharply with relative employment declines in the
middle of the distribution.
The expansion in high-skill employment can be explained by
the falling price of carrying out routine tasks by means of computers,
which complements more abstract and creative services. Seen
from a production function perspective, an outward shift in the
supply of routine informational inputs increases the marginal productivity
of workers they are demanded by. For example, text and
data mining has improved the quality of legal research as constant
access to market information has improved the efficiency of managerial
decision-making – i.e. tasks performed by skilled workers
at the higher end of the income distribution. The result has been
an increasingly polarised labour market, with growing employment
in high-income cognitive jobs and low-income manual occupations,
accompanied by a hollowing-out of middle-income routine jobs. This
is a pattern that is not unique to the US and equally applies to a
number of developed economies (Goos et al., 2009).14
How technological progress in the twenty-first century will
impact on labour market outcomes remains to be seen. Throughout
history, technological progress has vastly shifted the composition of
employment, from agriculture and the artisan shop, to manufacturing
and clerking, to service and management occupations. Yet the
concern over technological unemployment has proven to be exaggerated.
The obvious reason why this concern has not materialised
relates to Ricardo’s famous chapter on machinery, which suggests
that labour-saving technology reduces the demand for undifferentiated
labour, thus leading to technological unemployment (Ricardo,
1819). As economists have long understood, however, an invention
that replaces workers by machines will have effects on all product
and factor markets. An increase in the efficiency of production
which reduces the price of one good, will increase real income and
thus increase demand for other goods. Hence, in short, technological
progress has two competing effects on employment (Aghion
and Howitt, 1994). First, as technology substitutes for labour, there
is a destruction effect, requiring workers to reallocate their labour
supply; and second, there is the capitalisation effect, as more companies
enter industries where productivity is relatively high, leading
employment in those industries to expand.
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 the routine-intensity of
employment is thus likely to result from a combination of offshoring and automation.
Furthermore, there is evidence suggesting that improvements in transport and
communication technology have augmented occupations involving human interaction,
spanning across both cognitive and manual tasks (Michaels et al., 2013). These
explanations are nevertheless equally related to advance in computing and communications
technology.
Although the capitalisation effect has been predominant historically,
our discovery of means of economising the use of labour can
outrun the pace at which we can find new uses for labour, as Keynes
(1933) pointed out. The reason why human labour has prevailed
relates to its ability to adopt and acquire new skills by means of education
(Goldin and Katz, 2009). Yet as computerisation enters more
cognitive domains this will become increasingly challenging (Brynjolfsson
and McAfee, 2011). Recent empirical findings are therefore
particularly concerning. For example, Beaudry et al. (2013) document
a decline in the demand for skill over the past decade, even as
the supply of workers with higher education has continued to grow.
They show that high-skilled workers have moved down the occupational
ladder, taking on jobs traditionally performed by low-skilled
workers, pushing low-skilled workers even further down the occupational
ladder and, to some extent, even out of the labour force.
This raises questions about (a) the ability of human labour to win the
race against technology by means of education; and (b) the potential
extent of technological unemployment, as an increasing pace of
technological progress will cause higher job turnover, resulting in
a higher natural rate of unemployment (Lucas and Prescott, 1974;
Davis and Haltiwanger, 1992; Pissarides, 2000). While the present
study is limited to examining the destruction effect of technology, it
nevertheless provides a useful indication of the job growth required
to counterbalance the jobs at risk over the next decades.
3. The technological revolutions of the twenty-first century
The secular price decline in the real cost of computing has created
vast economic incentives for employers to substitute labour for
computer capital.15 Yet the tasks computers are able to perform ultimately
depend upon the ability of a programmer to write a set of
procedures or rules that appropriately direct the technology in each
possible contingency. Computers will therefore be relatively productive
to human labour when a problem can be specified – in the
sense that the criteria for success are quantifiable and can readily
be evaluated (Acemoglu and Autor, 2011). The extent of job computerisation
will thus be determined by technological advances that
allow engineering problems to be sufficiently specified, which sets
the boundaries for the scope of computerisation. In this section,
we examine the extent of tasks computer-controlled equipment can
be expected to perform over the next decades. Doing so, we focus
on advances in fields related to Machine Learning (ML), including
Data Mining, Machine Vision, Computational Statistics and other
sub-fields of Artificial Intelligence (AI), in which efforts are explicitly
dedicated to the development of algorithms that allow cognitive
tasks to be automated. In addition, we examine the application of
ML technologies in Mobile Robotics (MR), and thus the extent of
computerisation in manual tasks.
Our analysis builds on the task categorisation of Autor et al.
(2003), which distinguishes between workplace tasks using a twoby-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 can be
accomplished by machines, while non-routine tasks are not suffi-
ciently well understood to be specified in computer code. Each of
these task categories can, in turn, be of either manual or cognitive
nature – i.e. they relate to physical labour or knowledge work. Historically,
computerisation has largely been confined to manual and
cognitive routine tasks involving explicit 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
15 We refer to computer capital as accumulated computers and computer-controlled
equipment by means of capital deepening.
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 259
were defined as non-routine only a decade ago have now become
computerisable is illustrated by Autor et al. (2003), asserting that
“Navigating a car through city traffic or deciphering the scrawled
handwriting on a personal check – minor undertakings for most
adults – are not routine tasks by our definition.” Today, the problems
of navigating a car and deciphering handwriting are sufficiently well
understood that many related tasks can be specified in computer
code and automated (Veres et al., 2011; Plötz and Fink, 2009).
Recent technological breakthroughs are, in large part, due to
efforts to turn non-routine tasks into well-defined problems. Defining
such problems is helped by the provision of relevant data: this
is highlighted in the case of handwriting recognition by Plötz and
Fink (2009). The success of an algorithm for handwriting recognition
is difficult to quantify without data to test on – in particular,
determining whether an algorithm performs well for different styles
of writing requires data containing a variety of such styles. That is,
data is required to specify the many contingencies a technology must
manage in order to form an adequate substitute for human labour.
With data, objective and quantifiable measures of the success of an
algorithm can be produced, which aid the continual improvement of
its performance relative to humans.
As such, technological progress has been aided by the recent production
of increasingly large and complex datasets, known as big
data.16 For instance, with a growing corpus of human-translated digitalised
text, the success of a machine translator can now be judged
by its accuracy in reproducing observed translations. Data from
United Nations documents, which are translated by human experts
into six languages, allow Google Translate to monitor and improve
the performance of different machine translation algorithms
(Tanner, 2007).
Further, ML algorithms can discover unexpected similarities
between old and new data, aiding the computerisation of tasks for
which big data has newly become available. As a result, computerisation
is no longer confined to routine tasks that can be written as
rule-based software queries, but is spreading to every non-routine
task where big data becomes available (Brynjolfsson and McAfee,
2011). In this section, we examine the extent of future computerisation
beyond routine tasks.
3.1. Computerisation in non-routine cognitive tasks
With the availability of big data, a wide range of non-routine
cognitive tasks are becoming computerisable. That is, further to
the general improvement in technological progress due to big data,
algorithms for big data are rapidly entering domains reliant upon
storing or accessing information. The use of big data is afforded by
one of the chief comparative advantages of computers relative to
human labour: scalability. Little evidence is required to demonstrate
that, in performing the task of labourious computation, networks of
machines scale better than human labour (Campbell-Kelly, 2009). As
such, computers can better manage the large calculations required in
using large datasets. ML algorithms running on computers are now,
in many cases, better able to detect patterns in big data than humans.
Computerisation of cognitive tasks is also aided by another
core comparative advantage of algorithms: their absence of some
human biases. An algorithm can be designed to ruthlessly satisfy
the small range of tasks it is given. Humans, in contrast, must fulfill
a range of tasks unrelated to their occupation, such as sleeping,
necessitating occasional sacrifices in their occupational performance
(Kahneman et al., 1982). The additional constraints under which
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).
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 alunch break. It can thus be argued thatmany 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 completely automated (Phua et al., 2010). In a similar
manner, the comparative advantages of computers arelikely to change
the nature of work across a wide range of industries and occupations.
In health care, diagnostics tasks are already being computerised.
Oncologists at Memorial Sloan-Kettering Cancer Center are, for
example, using IBM’s Watson computer to provide chronic care and
cancer treatment diagnostics. Knowledge from 600,000 medical evidence
reports, 1.5 million patient records and clinical trials, and two
million pages of text from medical journals, are used for benchmarking
and pattern recognition purposes. This allows the computer to
compare each patient’s individual symptoms, genetics, family and
medication history, etc., to diagnose and develop a treatment plan
with the highest probability of success (Cohn, 2013).
In addition, computerisation is entering the domains of legal and
financial services. Sophisticated algorithms are gradually taking on
a number of tasks performed by paralegals, contract and patent
lawyers (Markoff, 2011). More specifically, law firms now rely on
computers that can scan thousands of legal briefs and precedents
to assist in pre-trial research. A frequently cited example is Symantec’s
Clearwell system, which uses language analysis to identify
general concepts in documents, can present the results graphically,
and proved capable of analysing and sorting more than 570,000
documents in two days (Markoff, 2011).
Furthermore, the improvement of sensing technology has made
sensor data one of the most prominent sources of big data (Ackerman
and Guizzo, 2011). Sensor data is often coupled with new ML faultand
anomaly-detection algorithms to render many tasks computerisable.
A broad class of examples can be found in condition monitoring
and novelty detection, with technology substituting for closed-circuit
TV (CCTV) operators, workers examining equipment defects, and clinical
staff responsible for monitoring the state of patients in intensive
care. Here, the fact that computers lack human biases is of great value:
algorithms are free of irrational bias, and their vigilance need not
be interrupted by rest breaks or lapses of concentration. Following
the declining costs of digital sensing and actuation, ML approaches
have successfully addressed condition monitoring applications ranging
from batteries (Saha et al., 2007), to aircraft engines (King et al.,
2009), water quality (Osborne et al., 2012) and intensive care units
(ICUs) (Clifford and Clifton, 2012; Clifton et al., 2012). Sensors can
equally be placed on trucks and pallets to improve companies’ supply
chain management, and used to measure the moisture in a field
of crops to track the flow of water through utility pipes. This allows
for automatic meter reading, eliminating the need for personnel to
gather such information. For example, the cities of Doha, São Paulo,
and Beijing use sensors on pipes, pumps, and other water infrastructure
to monitor conditions and manage water loss, reducing leaks
by 40 to 50%. In the near future, it will be possible to place inexpensive
sensors on light poles, sidewalks, and other public property to
capture sound and images, likely reducing the number of workers
in law enforcement (MGI, 2013).
Advances in user interfaces also enable computers to respond
directly to a wider range of human requests, thus augmenting the
work of highly skilled labour, while allowing some types of jobs
to become fully automated. For example, Apple’s Siri and Google
Now rely on natural user interfaces to recognise spoken words,
interpret their meanings, and act on them accordingly. Moreover,
a company called SmartAction now provides call computerisation
solutions that use ML technology and advanced speech recognition
to improve upon conventional interactive voice response systems,
260 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
realising cost savings of 60 to 80% over an outsourced call center consisting
of human labour (CAA, 2012). Even education, one of the most
labour intensive sectors, will most likely be significantly impacted
by improved user interfaces and algorithms building upon big data.
The recent growth in MOOCs (Massive Open Online Courses) has
begun to generate large datasets detailing how students interact
on forums, their diligence in completing assignments and viewing
lectures, and their ultimate grades (Simonite, 2013; Breslow et al.,
2013). Such information, together with improved user interfaces,
will allow for ML algorithms that serve as interactive tutors, with
teaching and assessment strategies statistically calibrated to match
individual student needs (Woolf, 2010). Big data analysis will also
allow for more effective predictions of student performance, and for
their suitability for post-graduation occupations. These technologies
can equally be implemented in recruitment, most likely resulting in
the streamlining of human resource (HR) departments.
Occupations that require subtle judgement are also increasingly
susceptible to computerisation. To many such tasks, the unbiased
decision making of an algorithm represents a comparative advantage
over human operators. In the most challenging or critical applications,
as in ICUs, algorithmic recommendations may serve as inputs
to human operators; in other circumstances, algorithms will themselves
be responsible for appropriate decision-making. In the financial
sector, such automated decision-making has played a role for
quite some time. AI algorithms are able to process a greater number
of financial announcements, press releases, and other information
than any human trader, and then act faster upon them (Mims, 2010).
Services like Future Advisor similarly use AI to offer personalised
financial advice at larger scale and lower cost. Even the work of software
engineers may soon largely be computerisable. For example,
advances in ML allow a programmer to leave complex parameter
and design choices to be appropriately optimised by an algorithm
(Hoos, 2012). Algorithms can further automatically detect bugs in
software (Hangal and Lam, 2002; Livshits and Zimmermann, 2005;
Kim et al., 2008), with a reliability that humans are unlikely to match.
Big databases of code also offer the eventual prospect of algorithms
that learn how to write programs to satisfy specifications provided
by a human. Such an approach is likely to eventually improve upon
human programmers, in the same way that human-written compilers
eventually proved inferior to automatically optimised compilers.
An algorithm can better keep the whole of a program in working
memory, and is not constrained to human-intelligible code, allowing
for holistic solutions that might never occur to a human. Such algorithmic
improvements over human judgement are likely to become
increasingly common.
Although the extent of these developments remains to be seen,
estimates by MGI (2013) suggests that sophisticated algorithms
could substitute for approximately 140 million full-time knowledge
workers worldwide. Hence, while technological progress throughout
economic history has largely been confined to the mechanisation
of manual tasks, requiring physical labour, technological progress
in the twenty-first century can be expected to contribute to a wide
range of cognitive tasks, which, until now, have largely remained
a human domain. Of course, many occupations being affected by
these developments are still far from fully computerisable, meaning
that the computerisation of some tasks will simply free-up time for
human labour to perform other tasks. Nonetheless, the trend is clear:
computers increasingly challenge human labour in a wide range of
cognitive tasks (Brynjolfsson and McAfee, 2011).
3.2. Computerisation in non-routine manual tasks
Mobile robotics provides a means of directly leveraging ML technologies
to aid the computerisation of a growing scope of manual
tasks. The continued technological development of robotic hardware
is having notable impact upon employment: over the past decades,
industrial robots have taken on the routine tasks of most operatives
in manufacturing. Now, however, more advanced robots are gaining
enhanced sensors and manipulators, allowing them to perform
non-routine manual tasks. For example, General Electric has recently
developed robots to climb and maintain wind turbines, and more
flexible surgical robots with a greater range of motion will soon perform
more types of operations (Robotics-VO, 2013). In a similar manner,
the computerisation of logistics is being aided by the increasing
cost-effectiveness of highly instrumented and computerised cars.
Mass-production vehicles, such as the Nissan LEAF, contain on-board
computers and advanced telecommunication equipment that render
the car a potentially fly-by-wire robot.17 Advances in sensor technology
mean that vehicles are likely to soon be augmented with
even more advanced suites of sensors. These will permit an algorithmic
vehicle controller to monitor its environment to a degree that
exceeds the capabilities of any human driver: they have the ability to
simultaneously look both forwards and backwards, can natively integrate
camera, GPS and LIDAR data, and are not subject to distraction.
Algorithms are thus potentially safer and more effective drivers than
humans.
The big data provided by these improved sensors are offering
solutions to many of the engineering problems that had hindered
robotic development in the past. In particular, the creation
of detailed three dimensional maps of road networks has enabled
autonomous vehicle navigation; most notably illustrated by Google’s
use of large, specialised datasets collected by its driverless cars
(Guizzo, 2011). It is now completely feasible to store representations
of the entire road network on-board a car, dramatically
simplifying the navigation problem. Algorithms that could perform
navigation throughout the changing seasons, particularly after snowfall,
have been viewed as a substantial challenge. However, the big
data approach can answer this by storing records from the last
time snow fell, against which the vehicle’s current environment can
be compared (Churchill and Newman, 2012). ML approaches have
also been developed to identify unprecedented changes to a particular
piece of the road network, such as roadworks (Mathibela et
al., 2012). This emerging technology will affect a variety of logistics
jobs. Agricultural vehicles, forklifts and cargo-handling vehicles
are imminently automatable, and hospitals are already employing
autonomous robots to transport food, prescriptions and samples
(Bloss, 2011). The computerisation of mining vehicles is further being
pursued by companies such as Rio Tinto, seeking to replace labour in
Australian mine-sites.18
With improved sensors, robots are capable of producing goods
with higher quality and reliability than human labour. For example,
El Dulze, a Spanish food processor, now uses robotics to pick up
heads of lettuce from a conveyor belt, rejecting heads that do not
comply with company standards. This is achieved by measuring their
density and replacing them on the belt (IFR, 2012a). Advanced sensors
further allow robots to recognise patterns. Baxter, a 22,000 USD
general-purpose robot, provides a well-known example. The robot
features an LCD display screen displaying a pair of eyes that take on
different expressions depending on the situation. When the robot
is first installed or needs to learn a new pattern, no programming
is required. A human worker simply guides the robot arms through
the motions that will be needed for the task. Baxter then memorises
these patterns and can communicate that it has understood its new
instructions. While the physical flexibility of Baxter is limited to performing
simple operations such as picking up objects and moving
them, different standard attachments can be installed on its arms,
17 A fly-by-wire robot is a robot that is controllable by a remote computer. 18 Rio Tinto’s computerisation efforts are advertised at http://www.mineofthefuture.
com.au.
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 261
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%
annually and are expected to decline at an even faster pace in the
near future (MGI, 2013). Industrial robots, with features enabled by
machine vision and high-precision dexterity, which typically cost
100,000 to 150,000 USD, will be available for 50,000 to 75,000 USD
in the next decade, with higher levels of intelligence and additional
capabilities (IFR, 2012b). Declining robot prices will inevitably place
them within reach of more users. For example, in China, employers
are 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% in 2011 and are expected
to increase further. Globally, industrial robot sales reached a record
166,000 units in 2011, a 40% year-on-year increase (IFR, 2012b).
Most likely, there will be even faster growth ahead as low-priced
general-purpose models, such as Baxter, are adopted in simple manufacturing
and service work.
Expanding technological capabilities and declining costs will
make entirely new uses for robots possible. Robots will likely continue
to take on an increasing set of manual tasks in manufacturing,
packing, construction, maintenance, and agriculture. In addition,
robots are already performing many simple service tasks such as
vacuuming, mopping, lawn mowing, and gutter cleaning – the market
for personal and household service robots is growing by about
20% annually (MGI, 2013). Meanwhile, commercial service robots are
now able to perform more complex tasks in food preparation, health
care, commercial cleaning, and elderly care (Robotics-VO, 2013).
As robot costs decline and technological capabilities expand,
robots can thus be expected to gradually substitute for labour in
a wide range of low-wage service occupations, where most 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.
3.3. The task model revisited
The task model of Autor et al. (2003) has delivered intuitive and
accurate predictions in that (a) computers are more substitutable
for human labour in routine relative to non-routine tasks; and (b) a
greater intensity of routine inputs increases the marginal productivity
of non-routine inputs. Accordingly, computers have served as a
substitute for labour for many routine tasks, while exhibiting strong
complementarities with labour performing cognitive non/routine
tasks.19 Yet the premises about what computers do have recently
expanded. Computer capital can now equally substitute for a wide
range of tasks commonly 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, constantreturns-to-scale,
Cobb-Douglas production function of the form
Q = (LS + C)
1−bL
b
NS, b ∈ [0, 1], (1)
19 The model does not predict any substantial substitution or complementarity with
non-routine manual tasks.
where LS and LNS are susceptible and non-susceptible labour 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-maximising workers, with heterogeneous productivity
endowments in both susceptible and non-susceptible tasks.
Their task supply will respond elastically to relative wage levels,
meaning that workers will reallocate their labour supply according to
their comparative advantage as in Roy (1951). With expanding computational
capabilities, resulting from technological advances, and a
falling market price of computing, workers in susceptible tasks will
thus reallocate to 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 because recent developments in ML and MR, building
upon big data, allow for pattern recognition, and thus enable
computer capital to rapidly substitute for labour across a wide range
of non-routine tasks. Yet some inhibiting engineering bottlenecks
to computerisation persist. Beyond these bottlenecks, however, we
argue that it is largely already technologically possible to automate
almost any task, provided that sufficient amounts of data are gathered
for pattern recognition. Our model thus predicts that the pace at
which these bottlenecks can be overcome will determine the extent
of computerisation in the twenty-first century.
Hence, in short, while the task model predicts that computers
for labour substitution will be confined to routine tasks, our
model predicts that computerisation can be extended to any nonroutine
task that is not subject to any engineering bottlenecks to
computerisation. These bottlenecks thus set the boundaries for the
computerisation of non-routine tasks. Drawing upon the ML and MR
literature, and a workshop held at the Oxford University Engineering
Sciences Department, we identify several engineering bottlenecks,
corresponding to three task categories. According to these findings,
non-susceptible labour inputs can be described as,
LNS = n
i=1
(LPM,i + LC,i + LSI,i) (2)
where LPM, LC and LSI are labour inputs into perception and manipulation
tasks, creative intelligence tasks, and social intelligence tasks.
We note that some related engineering bottlenecks can be partially
alleviated by the simplification of tasks. One generic way of
achieving this is to reduce the variation between task iterations. As
a prototypical example, consider the factory assembly line, turning
the non-routine tasks of the artisan shop into repetitive routine tasks
performed by unskilled factory workers. A more recent example is
the computerisation of non-routine manual tasks in construction.
On-site construction tasks typically demand a high degree of adaptability,
so as to accommodate work environments that are typically
irregularly laid out, and vary according to weather. Prefabrication,
in which the construction object is partially assembled in a factory
before being transported to the construction site, provides a
way of largely removing the requirement for adaptability. It allows
many construction tasks to be performed by robots under controlled
conditions that eliminate task variability – a method that is
becoming increasingly widespread, particularly in Japan (Barlow and
Ozaki, 2005; Linner and Bock, 2012). The extent of computerisation
in the twenty-first century will thus partly depend on innovative
approaches to task restructuring. In the remainder of this section we
examine the engineering bottlenecks related to the above mentioned
task categories, each in turn.
262 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
3.3.1. Perception and manipulation tasks
Robots are still unable to match the depth and breadth of
human perception. While basic geometric identification is reasonably
mature, enabled by the rapid development of sophisticated
sensors and lasers, significant challenges remain for more complex
perception tasks, such as identifying objects and their properties in a
cluttered field of view. As such, tasks that relate to an unstructured
work environment can make jobs less susceptible to computerisation.
For example, most homes are unstructured, requiring the
identification of a plurality of irregular objects and containing many
cluttered spaces which inhibit the mobility of wheeled objects. Conversely,
supermarkets, factories, warehouses, airports and hospitals
have been designed for large wheeled objects, making it easier for
robots to navigate in performing non-routine manual tasks. Perception
problems can, however, sometimes be sidestepped by clever
task design. For example, Kiva Systems, acquired by Amazon.com in
2012, solved the problem of warehouse navigation by simply placing
bar-code stickers on the floor, informing robots of their precise
location (Guizzo, 2008).
The difficulty of perception has ramifications for manipulation
tasks, and, in particular, the handling of irregular objects, for which
robots are yet to reach human levels of aptitude. This has been
evidenced in the development of robots that interact with human
objects and environments. While advances have been made, solutions
tend to be unreliable over the myriad small variations on a
single task, repeated thousands of times a day, that many applications
require. A related challenge is failure recovery – i.e. identifying
and rectifying the mistakes of the robot when it has, for example,
dropped an object. Manipulation is also limited by the difficulties
of planning out the sequence of actions required to move an object
from one place to another. There are yet further problems in designing
manipulators that, like human limbs, are soft, have compliant
dynamics and provide useful tactile feedback. Most industrial manipulation
makes uses of workarounds to these challenges (Brown et
al., 2010), but these approaches are nonetheless limited to a narrow
range of tasks. The main challenges to robotic computerisation, perception
and manipulation, thus largely remain and are unlikely to be
fully resolved in the next decade or two (Robotics-VO, 2013).
3.3.2. 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 broader sense, include concepts, poems, musical compositions,
scientific theories, cooking recipes and jokes, whereas artifacts are
objects such as paintings, sculptures, machinery, and pottery. One
process of creating ideas (and similarly for artifacts) involves making
unfamiliar combinations of familiar ideas, requiring a rich store
of knowledge. The challenge here is to find some reliable means of
arriving at combinations that “make sense.” For a computer to make
a subtle joke, for example, would require a database with a richness
of knowledge comparable to that of humans, and methods of
benchmarking the algorithm’s subtlety.
In principle, such creativity is possible and some approaches
to creativity already exist in the literature. Duvenaud et al. (2013)
provide an example of automating the core creative task required
in order to perform statistics, that of designing models for data.
As to artistic creativity, AARON, a 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 human composers.
In these and many other applications, generating novelty is not
particularly difficult. Instead, the principal obstacle to computerising
creativity is stating our creative values sufficiently clearly that
they can be encoded in a 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.
3.3.3. 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 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 evenmore difficult. Even simplified versions of typical
social tasks prove difficult for computers, as is the case in which
social interaction is reduced to pure text. The social intelligence of
algorithms is partly captured by the Turing test, examining the ability
of amachine to communicate indistinguishably from an actual human.
Since 1990, the Loebner Prize, an annual Turing test competition,
awardsprizes to textualchatprogrammes that areconsidered to be the
most human-like. In each competition, a human judge simultaneously
holdscomputer-based textual interactionswithboth an algorithm and
a human. Based on the responses, the judge is to distinguish between
the two. Sophisticated algorithms have so far failed to convince judges
about their human resemblance. This is largely because there is much
‘common sense’ information possessed by humans, which is difficult
to articulate, that would need to be provided to algorithms if they are
to function in human social settings.
Whole brain emulation, the scanning, mapping and digitalising of
a human brain, is one possible approach to achieving this, but is currently
only a theoretical technology. For brain emulation to become
operational, additional functional understanding is required to recognisewhat
data is relevant, aswell as a roadmap of technologies needed
to implement it. While such roadmaps exist, present implementation
estimates, under certain assumptions, suggest that whole brain
emulation is unlikely to become operational within the next decade
or two (Sandberg and Bostrom, 2008). When or if they do, however,
the employment impact is likely to be vast (Hanson, 2001).
Hence, in short, while sophisticated algorithms and developments
in MR, building upon with big data, now allow many nonroutine
tasks to be automated, occupations that involve complex
perception and manipulation tasks, creative intelligence tasks, and
social intelligence tasks are unlikely to be substituted by computer
capital over the next decade or two. The probability of an occupation
being automated can thus be described as a function of these
task characteristics. As suggested by Fig. 1, the low degree of social
intelligence required by a dishwasher makes this occupation more
susceptible to computerisation than a public relation specialist, for
example. We proceed to examining the susceptibility of jobs to computerisation
as a function of the above described non-susceptible
task characteristics.
4. Measuring the employment impact of computerisation
4.1. 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
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 263
Probability of
Computerisation
Social Intelligence
Dishwasher
Event
Planner
Public
Relations
0 100
1
Probability of
Computerisation
Creativity
Court Clerk
Biologist
Fashion
Designer
1
Probability of
Computerisation
Perception and manipulation
Telemarketer
Boilermaker
Surgeon
0 100 0 100
1
Fig. 1. Bottlenecks to computerisation. Note: This figure provides a sketch of how the probability of computerisation might vary as a function of bottleneck variables.
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 classifi-
cation 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 variables corresponding
to the six-digit SOC classification, we used the mean of
the O*NET aggregate. In addition, we exclude any six-digit SOC occupations
for which O*NET data was missing.20 Doing so, we end up
with a final dataset consisting of 702 occupations.
To assess the employment impact of the described technological
developments in ML, the ideal experiment would provide two
identical autarkic economies, one facing the expanding technological
capabilities we observe, and a secular decline in the price of computerisation,
and the other not. By comparison, it would be straightforward
to examine how computerisation reshapes the occupational
composition of the labour market. In the absence of this experiment,
the second preferred option would be to build on the implementation
strategy of Autor et al. (2003), and test a simple economic
model to predict how demand for workplace tasks responds to developments
in ML and MR technology. However, because our paper is
forward-looking, in the sense that most of the described technological
developments are yet to be implemented across industries on a
broader scale, this option was not available for our purposes.
Instead, our implementation strategy builds on the literature
examining the offshoring of information-based tasks to foreign worksites,
consisting of different methodologies to rank and categorise
occupations according to their susceptibility to offshoring (Blinder,
2009; Jensen and Kletzer, 2005, 2010). The common denominator
for these studies is that they rely on O*NET data in different ways.
While Blinder (2009) eyeballed the O*NET data on each occupation,
paying particular attention to the job description, tasks, and work
activities, to assign an admittedly subjective two-digit index number
of offshorability to each occupation, Jensen and Kletzer (2005)
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% of total employment.
created a purely objective ranking based on standardised and measurable
O*NET variables. Both approaches have obvious drawbacks.
Subjective judgements are often not replicable and may result in the
researcher subconsciously rigging the data to conform to a certain
set of beliefs. 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 measure the offshorability or
automatability of jobs. Accordingly, Blinder (2009) finds that past
attempts to create objective offshorability rankings using O*NET data
have yielded some questionable results, ranking lawyers and judges
among the most tradable occupations, while classifying occupations
such as data entry keyers, telephone operators, and billing clerks as
virtually impossible to move offshore.
To work around some of these drawbacks, we combine and build
upon the two described approaches. First, together with a group of ML
researchers, we subjectively hand-labelled 70 occupations, assigning
1 if automatable, and 0 if not. For our subjective assessments, we draw
upon a workshop held at the Oxford University Engineering Sciences
Department, examining the automatability of a wide range of tasks.
Our label assignments were based on eyeballing the O*NET tasks
and job description of each occupation. This information is particular
to each occupation, as opposed to standardised across different
jobs. The hand-labelling of the occupations was made by answering
the question “Can the tasks of this job be sufficiently specified, conditional
on the availability of big data, to be performed by state of
the art computer-controlled equipment”. Thus, we only assigned a
1 to fully automatable occupations, where we considered all tasks
to be automatable. To the best of our knowledge, we considered the
possibility of task simplification, possibly allowing some currently
non-automatable tasks to be automated. Labels were assigned only
to the occupations about which we were most confident.
Second, we use objective O*NET variables corresponding to the
defined bottlenecks to computerisation. More specifically, we are
interested in variables describing the level of perception and manipulation,
creativity, and social intelligence required to perform it. As
reported in Table 1, we identified nine variables that describe these
attributes. These variables were derived from the O*NET survey,
where the respondents are given multiple scales, with “importance”
and “level” as the predominant pair. We rely on the “level” rating
which corresponds to specific examples about the capabilities
required of 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 oranges in crates
as quickly as possible”; high (level) is described as “Perform openheart
surgery with surgical instruments”. This gives us an indication
of 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).
264 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Table 1
O*NET variables.
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 behaviour.
Assisting and caring for others Providing personal assistance, medical attention, emotional support, or other personal
care to others such as coworkers, customers, or patients.
Note: The O*NET variables chosen are those likely to serve as indicators of bottlenecks to computerisation.
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.
4.2. Classification method
We begin by examining the accuracy of our subjective assessments
of the automatability of 702 occupations. For classification,
we develop an algorithm to provide the label probability given a
previously unseen vector of variables. In the terminology of classification,
the O*NET variables form a feature vector, 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 classifi-
cation algorithm exploits patterns existent in training data to return
the probability P(y∗= 1 | x∗, X, y) of a new, unlabelled, test datum
with features x∗ having class label y∗= 1.
We achieve probabilistic classification by introducing a latent
function f : x → R, known as a discriminant function. Given the
value of the discriminant f∗ at a test point x∗, we assume that the
probability for the class label is given by the logistic
P(y∗ = 1 | f∗) = 1
1 + exp(−f∗)
, (3)
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) = wx,
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
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 are completely
specified by a covariance matrix K
p(f|K) = N (f; 0, K) = 1

det 2pK exp
−1
2 f
K−1 f

. (4)
The covariance matrix is generated by a covariance function j :
X m × Xn → Rm×n; that is, K = j(X, X). The GP model is expressed
by the choice of j; we consider the exponentiated quadratic (squared
exponential) and rational quadratic. Note that we have chosen a
zero mean function, encoding the assumption that P(y∗ = 1) = 1
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) = j(x∗, X) j(X, X)
−1y (6)
V(f∗|x∗, D) = j(x∗, x∗) − j(x∗, X) j(X, X)
−1 j(X, x∗). (7)
Inferring the label posterior p(y∗|x∗, D) is complicated by the nonGaussian
form of the logistic (Eq. (3)). In order to effect inference,
we use the approximate Expectation Propagation algorithm (Minka,
2001).
We tested three Gaussian process classifiers using the GPML
toolbox (Rasmussen and Nickisch, 2010) on our data, built around
exponentiated quadratic, rational quadratic and linear covariances.
Note that the latter is equivalent to logistic regression with a Gaussian
prior taken on the weights w. To validate these classifiers, we
randomly selected a reduced training set of half the available data
D; the remaining data formed a test set. On this test set, we evaluated
how closely the algorithm’s classifications matched the hand
labels according to two metrics (see e.g.Murphy (2012)): the area
under the receiver operating 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
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 265
training set, and the average results tabulated in Table 2. The exponentiated
quadratic model returns (narrowly) the best performance
of the three (clearly outperforming the linear model corresponding
to logistic regression), and was hence selected for the remainder of
our testing. Note that its AUC score of nearly 0.9 represents accurate
classification: our algorithm successfully managed to reproduce our
hand-labels specifying whether an occupation was computerisable.
Thismeans that our algorithm verified that our subjective judgements
were systematically and consistently related to the O*NET variables.
Having validated our approach, we proceed to use classification
to predict the probability of computerisation for all 702 occupations.
For this purpose, we introduce a new label variable, z, denoting
whether an occupation is truly computerisable or not: note that
this can be judged only once an occupation is computerised, at
some indeterminate point in the future. We take, again, a logistic
likelihood,
P(z∗ = 1 | f∗) = 1
1 + exp(−f∗)
. (8)
We implicitly assumed that our hand label, y, is a noise-corrupted
version of the unknown true label, z. Our motivation is that our
hand-labels of computerisability must necessarily be treated as such
noisy measurements. We thus acknowledge that it is by no means
certain that a job is computerisable given our labelling. We define
X∗ ∈ R702×9 as the matrix of O*NET variables for all 702 occupations;
this matrix represents our test features.
We perform a final experiment in which, given training data D,
consisting of our 70 hand-labelled occupations, we aim to predict
z∗ for our test features X∗. This approach firstly allows us to use the
features of the 70 occupations about which we are most certain to
predict for the remaining 632. Further, our algorithm uses the trends
and patterns it has learned from bulk data to correct for what are
likely to be mistaken labels. More precisely, the algorithm provides
a smoothly varying probabilistic assessment of automatability as a
function of the variables. For our Gaussian process classifier, this
function is non-linear, meaning that it flexibly adapts to the patterns
inherent in the training data. Our approach thus allows for more
complex, non-linear, interactions between variables: for example,
perhaps one variable is not of importance unless the value of another
variable is sufficiently large. We report P(z∗ | X∗, D) as the probability
of computerisation henceforth (for a detailed probability ranking, see
the Appendix. Fig. 2 illustrates that this probability is non-linearly
related to the nine O*NET variables selected.
5. Employment in the twenty-first century
In this section, we examine the possible future extent of atrisk
job computerisation, and related labour market outcomes. The
task model predicts that recent developments in ML will reduce
aggregate demand for labour input in tasks that can be routinised
by means of pattern recognition, while increasing the demand for
labour performing tasks that are not susceptible to computerisation.
However, we make no attempt to forecast future changes in the occupational
composition of the labour market. While the 2010–2020
BLS occupational employment projections predict US net employment
growth across major occupations, based on historical staffing
Table 2
Performance of various classifiers.
Classifier model AUC Log-likelihood
Exponentiated quadratic 0.894 −163.3
Rational quadratic 0.893 −163.7
Linear (logit regression) 0.827 −205.0
Note: The best performances are indicated in bold.
patterns, we speculate about technology that is in only the early
stages of development. This means that historical data on the impact
of the technological developments we observe is unavailable.21 We
therefore focus on the impact of computerisation on the mix of jobs
that existed in 2010. Our analysis is thus limited to the substitution
effect of future computerisation.
Turning first to the expected employment impact, reported in
Fig. 3, we distinguish between high, medium and low risk occupations,
depending on their probability of computerisation (thresholding
at probabilities of 0.7 and 0.3). According to our estimate,
47% of total US employment is in the high risk category, meaning
that associated occupations are potentially automatable over some
unspecified number of years, perhaps a decade or two. It shall be
noted that the probability axis can be seen as a rough timeline, where
high probability occupations are likely to be substituted by computer
capital relatively soon. Over the next decades, the extent of
computerisation will be determined by the pace at which the above
described engineering bottlenecks to automation can be overcome.
Seen from this perspective, our findings could be interpreted as two
waves of computerisation, separated by a “technological plateau”.
In the first wave, we find that most workers in transportation and
logistics occupations, together with the bulk of office and administrative
support workers, and labour in production occupations, are
likely to be substituted by computer capital. As computerised cars are
already being developed and the declining cost of sensors makes augmenting
vehicles with advanced sensors increasingly cost-effective,
the automation of transportation and logistics occupations is in line
with the technological developments documented in the literature.
Furthermore, algorithms for big data are already rapidly entering
domains reliant upon storing or accessing information, making it
equally intuitive that office and administrative support occupations
will be subject to computerisation. The computerisation of production
occupations simply suggests a continuation of a trend that has
been observed over the past decades, with industrial robots taking
on the routine tasks of most operatives in manufacturing. As industrial
robots are becoming more advanced, with enhanced senses and
dexterity, they will be able to perform a wider scope of non-routine
manual tasks. From a technological capabilities point of view, the
vast remainder of employment in production occupations is thus
likely to diminish over the next decades.
More surprising, at first sight, is that a substantial share of
employment in services, sales and construction occupations exhibit
high probabilities of computerisation. Yet these findings are largely
in line with recent documented technological developments. First,
the market for personal and household service robots is already
growing by about 20% annually (MGI, 2013). As the comparative
advantage of human labour in tasks involving mobility and dexterity
will diminish over time, the pace of labour substitution in
service occupations is likely to increase even further. Second, while
it seems counterintuitive that sales occupations, which are likely
to require a high degree of social intelligence, will be subject to a
wave of computerisation in the near future, high risk sales occupations
include, for example, cashiers, counter and rental clerks, and
telemarketers. Although these occupations involve interactive tasks,
they do not necessarily require a high degree of social intelligence.
Our model thus seems to do well in distinguishing between individual
occupations within occupational categories. Third, prefabrication
will allow a growing share of construction work to be performed
under controlled conditions in factories, which partly eliminates
task variability. This trend is likely to drive the computerisation of
construction work.
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.
266 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Probability of
Computerisation
Cramped work space
Probability of
Computerisation
Finger dexterity
Probability of
Computerisation
Manual dexterity
Probability of
Computerisation
Originality
Probability of
Computerisation
Fine arts
Probability of
Computerisation
Social perceptiveness
Probability of
Computerisation
Negotiation
Probability of
Computerisation
Persuasion
Probability of
Computerisation
Assisting and
caring for others
0 0.5 1 0 0.5 1 0 0.5 1
0 0.5 1 0 0.5 1 0 0.5 1
0 0.5 1 0 0.5 1 0 0.5 1
50
100
20
40
60
80
20
40
60
80
20
40
60
80
50
100
50
100
20
40
60
80
20
40
60
80
50
100
Fig. 2. Variables’ influence on computerisation. Note: This figure illustrates the distribution of occupational variables as a function of probability of computerisation; each
occupation is a unique point.
In short, our findings suggest that recent developments in ML
will put a substantial share of employment, across a wide range of
occupations, at risk in the near future. According to our estimates,
however, this wave of automation will be followed by a subsequent
slowdown in computers for labour substitution, due to persisting
inhibiting engineering bottlenecks to computerisation. The relatively
slow pace of computerisation across the medium risk category
of employment can thus partly be interpreted as a technological
plateau, with incremental technological improvements successively
enabling further labour substitution. More specifically, the computerisation
of occupations in the medium risk category will mainly
depend on perception and manipulation challenges. This is evident
from Table 2, showing that the “manual dexterity”, “finger dexterity”
and “cramped work space” variables exhibit relatively high
values in the medium risk category. Indeed, even with recent technological
developments, allowing for more sophisticated pattern
recognition, human labour will still have a comparative advantage in
tasks requiring more complex perception and manipulation. Yet with
incremental technological improvements, the comparative advantage
of human labour in perception and manipulation tasks could
eventually diminish. This will require innovative task restructuring,
improvements in ML approaches to perception challenges, and
progress in robotic dexterity to overcome manipulation problems
related to variation between task iterations and the handling of
irregular objects. The gradual computerisation of installation, maintenance,
and repair occupations, which are largely confined to the
medium risk category, and require a high degree of perception and
manipulation capabilities, is a manifestation of this observation.
Our model predicts that the second wave of computerisation will
mainly depend on overcoming the engineering bottlenecks related to
creative and social intelligence. As reported in Table 3, the “fine arts”,
“originality”, “negotiation”, “persuasion”, “social perceptiveness”,
and “assisting and caring for others”, variables, all exhibit relatively
high values in the low risk category. By contrast, we note that the
“manual dexterity”, “finger dexterity” and “cramped work space”
variables take relatively low values. Hence, in short, generalist occupations
requiring knowledge of human heuristics, and specialist
occupations involving the development of novel ideas and artifacts,
are the least susceptible to computerisation. As a prototypical
example of generalist work requiring a high degree of social intelligence,
consider the O*NET tasks reported for chief executives,
involving “conferring with board members, organization officials,
or staff members to discuss issues, coordinate activities, or resolve
problems”, and “negotiating or approving contracts or agreements.”
Our predictions are thus intuitive in that most management, business,
and finance occupations, which are intensive in generalist tasks
requiring social intelligence, are largely confined to the low risk
category. The same is true of most occupations in education, healthcare,
as well as arts and media jobs. The O*NET tasks of actors,
for example, involve “performing humorous and serious interpretations
of emotions, actions, and situations, using body movements,
facial expressions, and gestures”, and “learning about characters in
scripts and their relationships to each other in order to develop
role interpretations.” While these tasks are very different from those
of a chief executive, they equally require profound knowledge of
human heuristics, implying that a wide range of tasks, involving
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 267
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
Fig. 3. Employment affected by computerisation. Note: 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. For ease of visualisation, the plot was produced by
smoothing employment over a sliding window of width 0.1 (in probability).
social intelligence, are unlikely to become subject to computerisation
in the near future.
The low susceptibility of engineering and science occupations to
computerisation, on the other hand, is largely due to the high degree
of creative intelligence they require. The O*NET tasks of mathematicians,
for example, involve “developing new principles and new
relationships between existing mathematical principles to advance
mathematical science” and “conducting research to extend mathematical
knowledge in traditional areas, such as algebra, geometry,
probability, and logic.” Hence, while it is evident that computers are
entering the domains of science and engineering, our predictions
implicitly suggest strong complementarities between computers and
labour in creative science and engineering occupations; although it
is possible that computers will fully substitute for workers in these
occupations over the long-run. This is in line with the findings of
Ingram and Neumann (2006), showing a largely persistent increase
in the returns to cognitive abilities since the 1980s. We also note
that the predictions of our model are strikingly in line with the
Table 3
Variable distributions.
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
Note: Distributions are represented by their mean and standard deviation.
technological trends we observe in the automation of knowledge
work, even within occupational categories. For example, we find that
paralegals and legal assistants – for which computers already substitute
– in the high risk category. At the same time, lawyers, which rely
on labour input from legal assistants, are in the low risk category.
Thus, for the work of lawyers to be fully automated, engineering bottlenecks
to creative and social intelligence will need to be overcome,
implying that the computerisation of legal research will complement
the work of lawyers in the medium term.
To complete the picture of what recent technological progress is
likely to mean for the future of employment, we plot the average
median wage of occupations by their probability of computerisation.
We do the same for skill level, measured by the fraction of
workers having obtained a bachelor’s degree, or higher educational
attainment, within each occupation. Fig. 4 reveals that both wages
and educational attainment exhibit a strong negative relationship
with the probability of computerisation. We note that this prediction
implies a truncation in the current trend towards labour market
polarisation, 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 lowwage
jobs in the near future. By contrast, high-skill and high-wage
occupations are the least susceptible to computer capital.
Our findings were robust to the choice of the 70 occupations that
formed our training data. This was confirmed by the experimental
results tabulated in Table A2: 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.
268 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 Average median wage (USD)
Probability of Computerisation
0 0.5 1
20k
40k
60k
80k
unweighted
average
weighted by
employment
Bachelor’s degree or better
Probability of Computerisation
0 0.5 1
0%
20%
40%
60%
Fig. 4. Computerisation’s dependence on wage and education. Note: Wage and education level as a function of the probability of computerisation; note that both plots share a
legend. The plots were produced by smoothing wage and education level, respectively, over a sliding window of width 0.1 (in probability).
5.1. Limitations
It shall be noted that our predictions are based on expanding the
premises about the tasks that computer-controlled equipment can
be expected to perform. Hence, we focus on estimating the share of
employment that can potentially be substituted by computer capital,
from a technological capabilities point of view, over some unspecified
number of years. We make no attempt to estimate how many
jobs will actually be automated. The actual extent and pace of computerisation
will depend on several additional factors which were
left unaccounted for.
First, labour saving inventions may only be adopted if the access
to cheap labour is scarce or prices of capital are relatively high
(Habakkuk, 1962).22 We do not account for future wage levels, capital
prices or labour shortages. While these factors will impact on the
timeline of our predictions, labour is the scarce factor, implying that
in the long-run wage levels will increase relative to capital prices,
making computerisation increasingly profitable (see, for example,
Acemoglu, 2003).
Second, regulatory concerns and political activism may slow
down the process of computerisation. The states of California and
Nevada are, for example, currently in the process of making legislatory
changes to allow for driverless cars. Similar steps will be needed
in other states, and in relation to various technologies. The extent
and pace of legislatory implementation can furthermore be related
to the public acceptance of technological progress.23 Although resistance
to technological progress has become seemingly less common
since the Industrial Revolution, there are recent examples of resistance
to technological change.24 We avoid making predictions about
the legislatory process and the public acceptance of technological
progress, and thus the pace of computerisation.
Third, making predictions about technological progress is notoriously
difficult (Armstrong and Sotala, 2012).25 For this reason, we
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 reveals a causal relationship between the access
to cheap labour and mechanisation in agricultural production, in terms of sustained
economic transition towards increased mechanisation in areas characterised
by low-wage worker out-migration (Hornbeck and Naidu, 2013). 23 For instance, William Huskisson, former cabinet minister and Member of Parliament
for Liverpool, was killed by a steam locomotive during the opening of the
Liverpool and Manchester Railway. Nonetheless, this well-publicised incident did anything
but dissuade the public from railway transportation technology. By contrast,
airship technology is widely recognised as having been popularly abandoned as a
consequence of the reporting of the Hindenburg disaster.
24 Uber, a start-up company connecting passengers with drivers of luxury vehicles,
has recently faced pressure from 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.
focus on near-term technological breakthroughs in ML and MR, and
avoid making any predictions about the number of years it may take
to overcome various engineering bottlenecks to computerisation.
Finally, we emphasise that since our probability estimates describe
the likelihood of an occupation being fully automated, we do not
capture any within-occupation variation resulting from the computerisation
of tasks that simply free-up time for human labour to
perform other tasks. 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.
6. Conclusions
While computerisation has been historically confined to routine
tasks involving explicit rule-based activities (Autor and Dorn, 2013;
Autor et al., 2003; Goos et al., 2009), algorithms for big data are now
rapidly entering domains reliant upon pattern recognition and can
readily substitute for labour in a wide range of non-routine cognitive
tasks (Brynjolfsson and McAfee, 2011; MGI, 2013). In addition,
advanced robots are gaining enhanced senses and dexterity, allowing
them to perform a broader scope of manual tasks (IFR, 2012b;
Robotics-VO, 2013; MGI, 2013). This is likely to change the nature of
work across industries and occupations.
In this paper, we ask the question: how susceptible are current
jobs to these technological developments? To assess this, we
implement a novel methodology to estimate the probability of computerisation
for 702 detailed occupations. Based on these estimates,
we examine expected impacts of future computerisation on labour
market outcomes, with the primary objective of analysing the number
of jobs at risk and the relationship between an occupation’s
probability of computerisation, wages and educational attainment.
We distinguish between high, medium and low risk occupations,
depending on their probability of computerisation. We make
no attempt to estimate the number of jobs that will actually be
automated, and focus on potential job automatability over some
unspecified number of years. According to our estimates around 47%
of total US employment is in the high risk category. We refer to these
as jobs at risk – i.e. jobs we expect could be automated relatively
soon, perhaps over the next decade or two.
Our model predicts that most workers in transportation and
logistics occupations, together with the bulk of office and administrative
support workers, and labour in production occupations, are at
risk. These findings are consistent with recent technological developments
documented in the literature. More surprisingly, we find that a
substantial share of employment in service occupations, where most
US job growth has occurred over the past decades (Autor and Dorn,
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).
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 269
Finally, we provide evidence that wages and educational attainment
exhibit a strong negative relationship with the probability of
computerisation. We note that this finding implies a discontinuity
between the nineteenth, twentieth and the twenty-first century,
in the impact of capital deepening on the relative demand for
skilled labour. While nineteenth century manufacturing technologies
largely substituted for skilled labour through the simplification
of tasks (Braverman, 1974; Goldin and Katz, 1998; Hounshell, 1985;
James and Skinner, 1985), the Computer Revolution of the twentieth
century caused a hollowing-out of middle-income jobs (Autor and
Dorn, 2013; Goos et al., 2009). Our model predicts a truncation in
the current trend towards labour market polarisation, with computerisation
being principally confined to low-skill and low-wage
occupations. Our findings thus imply that as technology races ahead,
low-skill workers will reallocate to tasks that are non-susceptible
to computerisation – i.e., tasks requiring creative and social intelligence.
For workers to win the race, however, they will have to
acquire creative and social skills.
Appendix A
The table below ranks occupations according to their probability of computerisation (from least- to most-computerisable). Those occupations
used as training data are labelled as either ‘0’ (not computerisable) or ‘1’ (computerisable), respectively. There are 70 such occupations,
10% 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 reachers, except special education
38. 0.0075 25-9021 Farm and home management advisors
39. 0.0077 19-3091 Anthropologists and archeologists
40. 0.0077 25-2054 Special education teachers, secondary school
41. 0.0078 25-2031 Secondary school teachers, except special and career/technical education
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
(continued on next page)
270 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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
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 oorrectional 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 inspectors
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 behavioural 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
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 271
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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
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
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 education
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
(continued on next page)
272 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
205. 0.19 25-3011 Adult basic and secondary education and literacy teachers and instructors
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 network 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 technicians
228. 0.25 41-4011 Sales representatives, wholesale and manufacturing, technical and scientific products
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 labour 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 relay
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
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 equipment
278. 0.42 19-4093 Forest and conservation technicians
279. 0.42 53-1021 First-line supervisors of helpers, labourers, and material movers, hand
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 273
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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
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 tenders
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
(continued on next page)
274 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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
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 workers
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
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
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 275
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
429. 0.78 51-8092 Gas plant operators
430. 0.79 43-5053 Postal service mail sorters, processors, and processing machine operators
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 tenders
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
463. 0.83 47-3011 Helpers–brickmasons, blockmasons, stonemasons, and tile and marble 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 Labourers 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
(continued on next page)
276 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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
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 labourers
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 setters, operators, and tenders
518. 0.88 51-6091 Extruding and forming machine setters, operators, and tenders, synthetic 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 tenders
552. 0.91 51-4081 Multiple machine tool setters, operators, and tenders, metal and plastic
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
C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280 277
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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, operators, 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
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, operators, 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, operators, and tenders
638. 0.96 53-4011 Locomotive engineers
639. 0.96 1 39-3011 Gaming dealers
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 labour contractors
650. 0.97 51-6061 Textile bleaching and dyeing machine operators and tenders
651. 0.97 51-9081 Dental laboratory technicians
(continued on next page)
278 C. Frey, M. Osborne / Technological Forecasting & Social Change 114 (2017) 254–280
Appendix Table (continued)
Computerisable
Rank Probability Label SOC code Occupation
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
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
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