THE FUTURE OF EMPLOYMENT: HOW SUSCEPTIBLE ARE JOBS TO COMPUTERIZATION?
We examine how susceptible jobs are to computerization. To assess this, we begin by implementing a novel methodology to estimate the probability of computerization for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, we examine expected impacts of future computerization on US labor market outcomes, with the primary objective of analyzing the number of jobs at risk and the relationship between an occupation’s probability of computerization, wages and educational attainment. According to our estimates, about 47 percent of total US employment is at risk. We further provide evidence that wages and educational attainment exhibit a strong negative relationship with an occupation’s probability of computerization.
We examine how susceptible jobs are to computerization. To assess this, we begin by implementing a novel methodology to estimate the probability of computerization for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, we examine expected impacts of future computerization on US labor market outcomes, with the primary objective of analyzing the number of jobs at risk and the relationship between an occupation’s probability of computerization, wages and educational attainment. According to our estimates, about 47 percent of total US employment is at risk. We further provide evidence that wages and educational attainment exhibit a strong negative relationship with an occupation’s probability of computerization.
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challenging or critical applications, as in ICUs, algorithmic recommendations<br />
may serve as inputs to human operators; in other circumstances, algorithms<br />
will themselves be responsible for appropriate decision-making. In the financial<br />
sector, such automated decision-making has played a role for quite some<br />
time. AI algorithms are able to process a greater number of financial announcements,<br />
press releases, and other information than any human trader, and then<br />
act faster upon them (Mims, 2010). Services like Future Advisor similarly use<br />
AI to offer personalised financial advice at larger scale and lower cost. Even<br />
the work of software engineers may soon largely be computerisable. For example,<br />
advances in ML allow a programmer to leave complex parameter and<br />
design choices to be appropriately optimised by an algorithm (Hoos, 2012). Algorithms<br />
can further automatically detect bugs in software (Hangal and Lam,<br />
2002; Livshits and Zimmermann, 2005; Kim, et al., 2008), with a reliability<br />
that humans are unlikely to match. Big databases of code also offer the eventual<br />
prospect of algorithms that learn how to write programs to satisfy specifications<br />
provided by a human. Such an approach is likely to eventually improve upon<br />
human programmers, in the same way that human-written compilers eventually<br />
proved inferior to automatically optimised compilers. An algorithm can better<br />
keep the whole of a program in working memory, and is not constrained to<br />
human-intelligible code, allowing for holistic solutions that might never occur<br />
to a human. Such algorithmic improvements over human judgement are likely<br />
to become increasingly common.<br />
Although the extent of these developments remains to be seen, estimates by<br />
MGI (2013) suggests that sophisticated algorithms could substitute for approximately<br />
140 million full-time knowledge workers worldwide. Hence, while<br />
technological progress throughout economic history has largely been confined<br />
to the mechanisation of manual tasks, requiring physical labour, technological<br />
progress in the twenty-first century can be expected to contribute to a wide<br />
range of cognitive tasks, which, until now, have largely remained a human<br />
domain. Of course, many occupations being affected by these developments<br />
are still far from fully computerisable, meaning that the computerisation of<br />
some tasks will simply free-up time for human labour to perform other tasks.<br />
Nonetheless, the trend is clear: computers increasingly challenge human labour<br />
in a wide range of cognitive tasks (Brynjolfsson and McAfee, 2011).<br />
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