New OECD Study Finds Lower Job Threat from Robots

An important new paper published for the OECD predicts that robots and automation will impact far fewer jobs than the now-famous 47% number established by Frey & Osborne.  The OECD paper estimates that 9% of jobs in the United States are at risk of automation.  Nine percent is quite significant, but substantially different than the estimate of nearly 1-in-2 that has dominated recent debate.  Here’s the abstract:

In recent years, there has been a revival of concerns that automation and digitalisation might after all result in a jobless future. The debate has been fueled by studies for the US and Europe arguing that a substantial share of jobs is at “risk of computerisation”. These studies follow an occupation-based approach proposed by Frey and Osborne (2013), i.e. they assume that whole occupations rather than single job-tasks are automated by technology. As we argue, this might lead to an overestimation of job automatibility, as occupations labelled as high-risk occupations often still contain a substantial share of tasks that are hard to automate. Our paper serves two purposes. Firstly, we estimate the job automatibility of jobs for 21 OECD countries based on a task-based approach. In contrast to other studies, we take into account the heterogeneity of workers’ tasks within occupations. Overall, we find that, on average across the 21 OECD countries, 9 % of jobs are automatable. The threat from technological advances thus seems much less pronounced compared to the occupation-based approach. We further find heterogeneities across OECD countries. For instance, while the share of automatable jobs is 6 % in Korea, the corresponding share is 12 % in Austria. Differences between countries may reflect general differences in workplace organisation, differences in previous investments into automation technologies as well as differences in the education of workers across countries.

The OECD authors do warn that automation is likely to have a greater impact on lower skilled workers, a prediction with crucial policy and labor market implications.  As they write:

Even though . . . fewer workplaces are likely to be “at risk” than suspected, differences in automatibility between educational levels are large. This suggests that workers [with less education] likely will bear the brunt of adjustment costs to technological change in terms of requirements for further training and occupational retraining. Moreover, for this group of workers, regaining the competitive advantage over machines by means of upskilling and training may be difficult to achieve, especially since the speed of the current technological revolution appears to exceed the pace of its predecessors. Hence, this study clearly points towards the need to focus more on the potential inequalities and requirements for (re-)training arising from technological change rather than the general threat of unemployment that technological progress might or might not cause.