useful frameworks for examining the impact of computers on the occupational
employment composition, they seem inadequate in explaining the impact of
technological trends going beyond the computerisation of routine tasks. Semi-
nal work by Autor, et al. (2003), for example, distinguishes between cognitive
and manual tasks on the one hand, and routine and non-routine tasks on the
other. While the computer substitution for both cognitive and manual routine
tasks is evident, non-routine tasks involve everything from legal writing, truck
driving and medical diagnoses, to persuading and selling. In the present study,
we will argue that legal writing and truck driving will soon be automated, while
persuading, for instance, will not. Drawing upon recent developments in En-
gineering Sciences, and in particular advances in the fields of ML, including
Data Mining, Machine Vision, Computational Statistics and other sub-fields of
Artificial Intelligence, as well as MR, we derive additional dimensions required
to understand the susceptibility of jobs to computerisation. Needless to say,
a number of factors are driving decisions to automate and we cannot capture
these in full. Rather we aim, from a technological capabilities point of view,
to determine which problems engineers need to solve for specific occupations
to be automated. By highlighting these problems, their difficulty and to which
occupations they relate, we categorise jobs according to their susceptibility to
computerisation. The characteristics of these problems were matched to dif-
ferent occupational characteristics, using O∗NET data, allowing us to examine
the future direction of technological change in terms of its impact on the occu-
pational composition of the labour market, but also the number of jobs at risk
should these technologies materialise.
The present study relates to two literatures. First, our analysis builds on the
labour economics literature on the task content of employment (Autor, et al.,
2003; Goos and Manning, 2007; Autor and Dorn, 2013). Based on defined
premises about what computers do, this literature examines the historical im-
pact of computerisation on the occupational composition of the labour mar-
ket. However, the scope of what computers do has recently expanded, and will
inevitably continue to do so (Brynjolfsson and McAfee, 2011; MGI, 2013).
Drawing upon recent progress in ML, we expand the premises about the tasks
computers are and will be suited to accomplish. Doing so, we build on the task
content literature in a forward-looking manner. Furthermore, whereas this liter-
ature has largely focused on task measures from the Dictionary of Occupational
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