Automation and Jobs

A report by Oxford Martin School, University of Oxford (The Report), has examined the susceptibility of jobs to computerization. The impact of computerisation on jobs (labour market) is well-established.  It is documented that there will be a decline in routine -intensive occupations, that is, occupations mainly consisting of task following well-defined procedures that can easily be performed by sophisticated algorithms.

At the same time, with falling prices of computers, problem-solving skills are becoming productive, which explains substantial employment growth in occupations involving cognitive tasks where skilled labour has a comparative advantage. According to Brynjolfsson and McAfee (2011), technological innovation is still increasing with more sophisticated technologies disrupting labour by making workers and employees redundant.

According to Autor, et al. (2003) workplace tasks can be categorized as follows:

1. Routine versus non-routines tasks, and
2. Manual versus cognitive tasks.

In short, routine tasks are defined as tasks that follow explicit rules that can be accomplished by machines while, while non-routine tasks are not sufficiently well understood in computer codes. Each of these task categories can, in turn, be of either manual or cognitive in nature, that is, they relate to physical labour or knowledge work.

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.  The difficulty of perception has ramifications for manipulation tasks. This is, in particular, the handling of irregular objects, for which robots are yet to reach human level of aptitude.

A related challenge is failure recovery, that is, 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 form one place to another.

The main challenges to robotic computerization, perception and manipulation, thus largely remain and are unlikely to be fully resolved in the next decade or two.

Creative and Intelligence Tasks

The psychological processes underlying human creativity are difficult to specify. According to Borden (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 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.”

It seems unlikely that occupations requiring a high degree of creative intelligence will be automated in the next decades.

Social Intelligence Tasks

Human social intelligence is important in a wide range of work tasks, such as those involving negotiations, persuasion and care. While algorithms and robots can reproduce some aspects of human social interaction, the real-time recognition of natural human emotion remains a challenging problem, and the ability to respond intelligently to such inputs is even more difficult. Even simplified versions of typical social tasks prove difficult for computers, as is the case in which social interaction is reduced to pure text.

The authors of the Oxford Martin School’s report noted that while sophisticated algorithms and development in MR, building upon big data now allow many non-routine 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 decades or two.

The probability of an occupation being automated can thus be described as a function of these task characteristics.

Measuring Impact of Computerisation

The Report, using 702 detailed occupation information of the US Labour Department’s Standard Occupation Classification (SOC), has developed a model to measure the impact of computerization of various types of occupations.

Table 1 shows the top 20 occupations that are least-computerisable , while Table 2 shows the top 20 occupations that are most-computerisable.

Table 1: Top 20 Least-Computerisable

Rank	Probability	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	33-1011	First-Line Supervisors of Fire Fighting and Prevention Workers
11	0.0039	29-2031	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	29-1060	Physicians and Surgeons
16	0.0042	25-9031	Instructional Coordinators
17	0.0043	19-3039	Psychologists and, All Others
18	0.0044	33-1012	First-Line Supervisors of Police and Detectives
19	0.0044	29-1021	Dentists, General
20	0.0044	25-2021	Elementary School Teachers

 

Table 2: Top 20 Most-Computerisable

 

Rank	Probability	SOC Code	Occupation
1	0.99	41-9041	Telemarketers
2	0.99	23-2093	Title Examiners, Abstractors and Searchers
3	0.99	51-6051	Sewers Hand
4	0.99	15-2091	Mathematical Technicians
5	0.99	13-2053	Insurance Underwriters
6	0.99	49-9064	Watch Repairers
7	0.99	43-5011	Cargo and Freight Agents
8	0.99	13-2082	Tax Preparers
9	0.99	51-9151	Photographic Process Workers
10	0.99	43-4141	New Account Clerks
11	0.99	25-4031	Library Technicians
12	0.99	43-9021	Data Entry Keyers
13	0.98	51-2093	Timing Device Assemblers and Adjusters
14	0.98	43-9041	Insurance Claims and Policy Processing Clerks
15	0.98	43-4011	Brokerage Clerks
16	0.98	43-4151	Order Clerks
17	0.98	13-2072	Loan Officers
18	0.98	27-2023	Umpires, Referees and Other Sport Officials
19	0.98	43-3071	Tellers
20	0.98	51-9194	Etchers and Engravers

 

Please see the whole list of 702 occupations in Appendix of Oxford Martin School’s Report.

Highlights 

The Report’s main conclusions are as follows:

1. It distinguishes high, medium and low risk occupations, depending on their probability of computerisation. It makes 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.
2. It 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.
3. It provides evidence that wages and educational attainment exhibit a strong negative relationship with the probability of computerization.
4. It implies that as technology races ahead, low-skill workers will reallocate to tasks that are non-susceptible to computerization, that is, tasks requiring creative and social intelligence.                                                                    For workers to win the race, however, they will have to acquire creative and social skills.

 

Reference:

1. Carl Benedict Frey, and Micheal A. Osborne (20130, The future employment: How susceptible are jobs to computerisation. Working Paper, Oxford Martin School, University of Oxford.
   1. Brynjolfsson and E. McAffe (2011). Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibility transforming employment and economy. Digital Frontiers Press, Lexington, MA.
2. A. Boden (2003). The creative mind: Myths and mechanisms. Routledge.
3. Autor, F. Levy and R. J. Murnane (2003). The skill content of recent technological change: Am empirical exploration. The Quarterly Journal of Economics. Vol. 118, no.4, pp. 1279-1333.