Building on the expert assessment carried out by Carl Frey and Michael Osborne in 2013, the paper estimates the risk of automation for individual jobs based on the Survey of Adult Skills (PIAAC). The analysis improves on other international estimates of the individual risk of automation by using a more disaggregated occupational classification and identifying the same automation bottlenecks emerging from the experts’ discussion. Hence, it more closely aligns to the initial assessment of the potential automation deriving from the development of Machine Learning.
The implications for jobs and skills of the developments in Artificial Intelligence and Machine Learning have dominated recent debates on the Future of Work and the changes brought about by digital technologies. Since Frey and Osborne (2013) shocked analysists and policy makers worldwide with a study suggesting that 47% of jobs in the United States are at high risk of being automated, several other researchers and institutions have contributed to the debate, all produced estimates in the high double digits. All these studies stem from an assessment by experts of the risk of automation for a subset of occupational titles, based on the tasks these occupations involved. This allowed identifying the so-called bottlenecks to automation – i.e. the tasks that, given the current state of knowledge, are difficult to automate. These include: social intelligence, such as
the ability to effectively negotiate complex social relationships, including caring for others or recognizing cultural sensitivities; cognitive intelligence, such as creativity and complex reasoning; and perception and manipulation, such as the ability to carry out physical tasks in an unstructured work environment. These bottlenecks were used to compute a risk of automation for occupational titles that were not included in the expert assessment and for countries outside the United States.
More recent studies, exploiting the Survey of Adult Skills (PIAAC), brought the estimates of the share of jobs at risk of automation down significantly. These studies show that there is considerable variation in the tasks involved in jobs having the same occupational title and that accounting for this variation is essential to gauge the extent of the problem. Arntz, Zierhan and Gregory (2016), for instance, put this share to 9% in the United States. While this figure is only a fraction of the estimate provided by Frey and Osborne, it translates to approximately 13 million jobs across the United States, based on 2016 employment figures. As job losses are unlikely to be distributed equally across the country, this would amount to several times the disruption in local economies caused by the 1950s decline of the car industry in Detroit where changes in technology and increased automation, among other factors, caused massive job losses.
The current study aims to go beyond providing an estimate of the share of jobs at high risk of automation by also highlighting the significant changes that jobs will undergo as a result of the adoption of new technologies.
Here are the study’s key findings.
* Across the 32 countries, close to one in two jobs are likely to be significantly affected by automation, based on the tasks they involve. But the degree of risk varies. About 14% of jobs in OECD countries participating in PIAAC are highly automatable (i.e., probability of automation of over 70%). Although smaller than the estimates based on occupational titles obtained applying the method of Frey and Osborne (2013) this is equivalent to over 66 million workers in the 32 countries covered by the study. In addition, another 32% of jobs have a risk of between 50 and 70% pointing to the possibility of significant change in the way these jobs are carried out as a result of automation – i.e. a significant share of tasks, but not all, could be automated, changing the skill requirements for these jobs.
* The variance in automatability across countries is large: 33% of all jobs in Slovakia are highly automatable, while this is only the case with 6% of the jobs in Norway. More generally, jobs in Anglo-Saxon, Nordic countries and the Netherlands are less automatable than jobs in Eastern European countries, South European countries, Germany, Chile and Japan.
* The cross-country variation in automatability, contrary to expectations, is better explained by the differences in the organization of job tasks within economic sectors, than by the differences in the sectoral structure of economies. About 30% of the cross-country variance is explained by cross-country differences in the structure of economic sectors and 70% is explained by the fact that, within these sectors, countries employ different occupational mixes.
* Overall, despite recurrent arguments that automation may start to adversely affect selected highly skilled occupations, this prediction is not supported by the Frey and Osborne (2013) framework of engineering bottlenecks used in this study. If anything, Artificial Intelligence puts more low-skilled jobs at risk than previous waves of technological progress, whereby technology replaced primarily middle-skilled jobs creating labor market polarisation
* A striking novel finding is that the risk of automation is the highest among teenage jobs. The relationship between automation and age is U-shaped, but the peak in automatability among youth jobs is far more pronounced than the peak among senior workers. In this sense, automation is much more likely to result in youth unemployment, than in early retirements.
* This unequal distribution of the risk of automation raises the stakes involved in policies to prepare workers for the new job requirements. In this context, adult learning is a crucial policy instrument for the re-training and up-skilling of workers whose jobs are being affected by technology.
Nedelkoska, L. and G. Quintini (2018), “Automation, skills use and training”, OECD Social, Employment and Migration Working Papers, No. 202, OECD Publishing, Paris, http://dx.doi.org/10.1787/2e2f4eea-en.