How Susceptible are Jobs to Computerisation?

News articles and reports appear almost daily on the subject of how technological developments in Artificial Intelligence and robotics will cause dramatic changes to employment over the next few decades. (Artificial Intelligence includes techniques such as ‘machine learning’, ‘deep learning’, artificial neural nets and ‘data mining’.) A high proportion of these articles refer back to a 2013 study by Carl Frey and Michael Osborne called ‘The Future of Employment: How Susceptible are Jobs to Computerisation?’ in which they asserted that 47% of total US employment is at risk.

Here, I go back to this original source and provide a summary.

The Method

Starting with a US Department of Labor list of employment categories, Frey and Michael Osborne produced estimates for the probability of computerisation for 702 occupations. (Throughout, reference to ‘computerisation’ means to automation by Artificial Intelligence, which is underpinned by computer technology.) This estimate was derived by assessing occupations in terms of the following factors:

  • Dexterity: The ability to make precisely coordinated movements to grasp, manipulate, or assemble objects.
  • Creative Intelligence: The ability to come up with original ideas, develop creative ways to solve a problem or to compose, produce, and perform works of music, dance, visual arts, drama, and sculpture.
  • Social Intelligence: Being perceptive of others’ reactions and understanding why they react as they do. Being able to negotiate to reconcile differences and persuading others to change their minds or behaviour. Providing personal assistance, medical attention, emotional support, or other personal care to others such as co-workers, customers, or patients.

They then examine the relationship between an occupation’s probability of computerisation and the wages and educational attainments associated with it.

Included in their analysis is a history of the 19th and 20th Centuries in terms of the effect of technological revolutions on employment and contrast this with the expected effect in the 21st Century.

The Results

Whilst the probabilities of automation is listed for all 702 occupations, the results are most succinctly presented in the figure (their ‘Figure III’) below:

Frey and Osborne: The Future of Employment: How Susceptible are Jobs to Computerisation?, Figure III

How Likely is it that your job can be automated?

In the figure, they have organized those 702 occupations into various categories and demarcated based on the probability of computerisation:

  • High: probability over 70%.
  • Medium: probability between 30% and 70%.
  • Low: probability under 30%.

In the table below, I have extracted just some of the 702 probabilities related to some of the categories:

  • Management / financial / legal
  • Engineering and technical
  • Education
  • Healthcare, and
  • Food

…to provide examples that support the above graphs. They clearly show healthcare and education as low-risk categories. Professional engineering jobs are low-risk but technician jobs are spread across the middle-risk and high-risk. Food-related jobs are firmly high-risk. There are a few surprises here for me. ‘Cooks, Restaurant’ and ‘Bicycle repairers’ are going to be almost completely automated and ‘Postsecondary teachers’ are going to be untouched. Will all restaurant meals be microwave-reheated?! Will robots strip down and reassemble bikes? Will online teaching have no impact on teaching roles?

Rank Prob.% Occupation Type
6 0.4% Occupational Therapists HEALTH
11 0.4% Dietitians and Nutritionists HEALTH
14 0.4% Sales Engineers HEALTH
15 0.4% Physicians and Surgeons HEALTH
17 0.4% Psychologists, All Other HEALTH
19 0.4% Dentists, General HEALTH
25 0.5% Mental Health Counsellors HEALTH
28 0.6% Human Resources Managers MGMNT
40 0.8% Special Education Teachers, Secondary School EDU
41 0.8% Secondary School Teachers, Except Special and Career/Technical Education EDU
46 0.9% Registered Nurses HEALTH
53 1.1% Mechanical Engineers TECH
54 1.2% Pharmacists HEALTH
63 1.4% Engineers, All Other TECH
70 1.5% Chief Executives MGMNT
77 1.7% Chemical Engineers TECH
79 1.7% Aerospace Engineers TECH
84 1.9% Civil Engineers TECH
82 1.8% Architects, Except Landscape and Naval TECH
98 2.5% Electronics Engineers, Except Computer TECH
104 2.9% Industrial Engineers TECH
112 3.2% Postsecondary Teachers EDU
115 3.5% Lawyers MONEY
120 3.7% Biomedical Engineers TECH
152 6.9% Financial Managers MONEY
153 7% Nuclear Engineers TECH
163 8.4% Childcare Workers EDU
188 14% Optometrists HEALTH
191 15% Kindergarten Teachers, Except Special Education EDU
192 15% Electricians TECH
226 25% Managers, All Other MGMNT
249 35% Plumbers, Pipefitters, and Steamfitters TECH
253 36% Computer Numerically Controlled Machine Tool Programmers, Metal and Plastic TECH
261 38% Electrical and Electronics Repairers, Powerhouse, Substation, and Relay TECH
263 38% Mechanical Engineering Technicians TECH
290 48% Aerospace Engineering and Operations Technicians TECH
317 56% Teacher Assistants EDU
386 70% Avionics Technicians TECH
398 72% Carpenters TECH
422 77% Bartenders FOOD
435 79% Motorcycle Mechanics TECH
441 81% Cooks, Fast Food FOOD
442 81% Word Processors and Typists MONEY
443 81% Electrical and Electronics Drafters TECH
453 82% Sheet Metal Workers TECH
460 83% Cooks, Institution and Cafeteria FOOD
477 84% Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic TECH
489 85% Nuclear Technicians TECH
514 88% Semiconductor Processors TECH
522 89% Bakers FOOD
583 93% Butchers and Meat Cutters FOOD
596 94% Bicycle Repairers TECH
625 95% Postal Service Clerks MONEY
629 96% Office Clerks, General MONEY
641 96% Cooks, Restaurant FOOD
657 97% Cashiers MONEY
671 98% Bookkeeping, Accounting, and Auditing Clerks MONEY
688 98% Brokerage Clerks MONEY
698 99% Insurance Underwriters MONEY

De-Skilling: The First Industrial Revolution

Frey and Osborne provide some historical perspective, looking at the impact of past technological revolutions.

They start with the case of William Lee who invented the stocking frame knitting machine in 1589. But Queen Elizabeth I refused to grant him a patent: “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”.

But by 1688, protection of workers in Britain had declined. The property owning classes were politically dominant and the factory system began to displace the artisan shop. The Luddite riots of 1811-1816 were a prominent example of the fear of technological unemployment. It was the inventors, consumers and unskilled factory workers that benefited from mechanisation. Arguably, unskilled workers have been the greatest beneficiaries of the Industrial Revolution.

An important feature of nineteenth century manufacturing technologies is that they were largely “de-skilling”. 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”.

Up-Skilling: The Second Industrial Revolution

In the late nineteenth century, electricity replaced steam and water-power and manufacturing production shifted over to mechanised assembly lines with continuous-process and batch production methods. This reduced the demand for unskilled manual workers but increased the demand for skills – there was demand for relatively skilled blue-collar production workers to operate the machinery and there was a growing share of white-collar non-production workers.

This shift to more skilled workers continued:

“the idea that technological advances favour more skilled workers is a 20th century phenomenon.”.

“the story of the 20th century has been the race between education and technology”

The Computer Revolution

Office machines reduced the cost of information processing tasks and increased the demand for educated office workers. But the supply of better educated workers filling these roles ended up outpacing the demand for their skills and this led to a sharp decline in the wage premium of clerking occupations.

Educational wage differentials and overall wage inequality have increased sharply since the 1980s. The adoption of computers and information technology explains some of the growing wage inequality of the past decades. Computerisation has eroded wages for (middle-income manufacturing) labour performing routine tasks and so workers have had to switch to relatively low-skill, low-income service occupations, pushing low-skilled workers even further down (and sometimes off) the occupational ladder. 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.

Educational wage differentials and overall wage inequality have increased sharply since the 1980s. The adoption of computers and information technology explains some of the growing wage inequality of the past decades. Computerisation has eroded wages for (middle-income manufacturing) labour performing routine tasks and so workers have had to switch to relatively low-skill, low-income service occupations which are less susceptible to computerisation as they require a higher degree of flexibility and physical adaptability. This has increasingly led to a polarised labour market, with growing employment in the high-income cognitive jobs and low-income manual occupations (the ‘lovely jobs’ and ‘lousy jobs’ as Goos and Manning have called them), accompanied by a hollowing-out of middle-income routine jobs.

Off-shoring is the other big factor affecting wage inequality. It is having a similar effect on jobs as automation. Alan Blinder (who used the same Department of Labor database that Frey and Osborne subsequently used) examined the likelihood of jobs going offshore, and concluded: that 22% to 29% of US jobs are or will be offshorable in the next decade or two.

The Automation of Routine Tasks

Frey and Osborne consider cutting the jobs cake in two ways:

  • Between routine and non-routine jobs, and
  • Between cognitive and non-cognitive jobs.

Previously, the tasks that have been automated have been routine, non-cognitive ones. Routine tasks are ones that follow explicit rules – behaviour that can be codified (and then coded). New Machine Learning technologies open up routine, cognitive tasks to automation and computers will quickly become more productive than human labour in these tasks. Non-routine tasks, whether cognitive on non-cognitive, are more difficult to codify and their automation would have to follow later – gradually, as the technology develops.

But Machine Learning improves the ability of robots to perceive the world around them and so it also helps automate routine, non-cognitive (manual) tasks that have not been possible previously.

Robots are becoming more advanced, and cheaper too (Rethink Robotics’s ‘Baxter’ only costs about $20,000). They can already perform many simple service tasks such as vacuuming, mopping, lawn mowing, and gutter cleaning and will likely continue to take on an increasing set of manual tasks in manufacturing, packing, construction, maintenance, and agriculture. It must be expected that they can gradually replace human labour in a wide range of low-wage service occupations – which is where most US job growth has occurred over the past decades.

The Automation of Non-Routine Tasks

More advanced application of Machine Learning and Big Data will allow non-routine tasks to be automated. Once technology has mastered a task, machines can rapidly exceed human labour in both capability and scale. Machine Learning algorithms running on computers are commonly better able to detect patterns in big data than humans. And they are not subject to human bias. Fraud detection is already almost completely automated. IBM’s Watson is being applied to medical diagnoses. Symantec’s Clearwell acquisition (now Veritas ‘eDiscovery’) can extract general concepts from thousands of legal documents. And this intelligence is made more accessible with improved voice Human-Computer Interfaces such as Apple’s Siri and Google Now.

Education is one sector that will be affected by this. Universities are experimenting with MOOCs (Massive Open Online Courses). From what they are learning about how students react to these online courses, they will be able to create interactive tutors that adjust their teaching to match each individual student needs.

And there are ways of automating non-routine manual tasks not through new technology but just by restructuring the tasks. For example, in the construction industry, on-site tasks typically demand a high degree of adaptability. But prefabrication in a factory before transportation to the site provides a way of largely removing the requirement for adaptability.

Employment in the Twenty-First Century

Over the years, the concern over technological unemployment has proven to be exaggerated because increased productivity has led to increased demand for goods, enabled by the better skills of the workforce. But Frey and Osborne cite Brynjolfsson and McAfee: as computerisation enters more cognitive domains, it will become increasingly difficult for workers to outpace the machines.

Frey and Osborne’s headline is that 47% of total US employment is in the ‘high risk’ category; this will affect most workers in production, transportation and logistics and office administrative support in a first wave of changes.

Wary of the difficulties of making predictions, they have restricted themselves to just analysing the likelihood of jobs that currently exist being automated as a result of near-term technological breakthroughs in Machine Learning and Robotics. Regarding timescales of the effects, they only go as far as saying ‘perhaps a decade or two’ for the first wave to take effect. And they are not wanting to forecast future changes in the occupational composition of the labour market or how many jobs will actually be automated. Many jobs will disappear completely but many roles will be modified because the offloading of automated tasks just frees-up time for human labour to perform other tasks. For example, while it is evident that much computer programming can be automated, Frey and Osborne say there are ‘strong complementarities’ in science and engineering between the power of computers and the high degree of creative intelligence of the scientists and engineers.

Beyond this first wave, they say there will be slowdown in labour substitution, which will then be driven by incremental technological improvements. All told, a ‘substantial share’ of employment, across a wide range of occupations, is at risk in the near future.

There is a strong negative correlation between a job’s risk of automation and wages/educational attainment. For example, paralegals and legal assistants are in the high risk category whereas the highly-paid, highly-educated lawyers are in the low risk category.

This marks a profound change in the balance of jobs. Whereas the nineteenth century manufacturing technologies largely substituted for skilled labour through the simplification of tasks and the Computer Revolution of the twentieth century caused a hollowing-out of middle-income jobs (splitting the jobs market into high-wage, high-skill and low-wage, low-skill occupations), Frey and Osborne predict that, as technology races ahead, the Machine Learning and Robotics revolution will take out the bottom of the market, requiring the low-skill workers to acquire creative and social skills and reallocate to tasks that are non-susceptible to computerisation!

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