Automation, skills use and training

Ljubica Nedelkoska, Glenda Quintini · OECD social employment and migration working papers · 8 Mar 2018

Where this sits in the debate

Will AI destroy more jobs than it creates?

Direction of the finding as read from the abstract by an AI model (AI-written).

Key takeaway AI-written

This OECD study estimates the risk of automation for individual jobs in 32 countries using the Survey of Adult Skills (PIAAC), building on the Frey and Osborne expert assessment with a finer occupational classification. It also examines which workers hold high-risk jobs and how training and ICT use relate to that risk.

The paper in three steps AI-written

Written by an AI agent from the paper's abstract, which is the primary source and is quoted below.

The question

What share of jobs in OECD countries face a high risk of automation, and how does that risk relate to skills use and training?

The approach

An OECD study that applies the Frey and Osborne expert assessment to individual jobs in the Survey of Adult Skills (PIAAC) for 32 countries, with a finer occupational classification and robustness checks on German and UK data.

The findings

The study estimates the share of jobs likely to be significantly disrupted by automation in each country and describes the jobs and workers most at risk, relating risk to ICT use at work and to training.

How to read it: observational. Analysis of real-world firm, worker, or market data — realistic settings; causal claims depend on the identification strategy.

Limitations and counterpoints AI-written

Estimates rest on expert judgments of technical feasibility from 2013 and do not model adoption, costs or new jobs; the abstract does not report the headline shares.

Abstract

This study focuses on the risk of automation and its interaction with training and the use of skills at work. 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. Furthermore, this study investigates the same methodology using national data from Germany and United Kingdom, providing insights into the robustness of the results. The risk of automation is estimated for the 32 OECD countries that have participated in the Survey of Adult Skills (PIAAC) so far. Beyond the share of jobs likely to be significantly disrupted by automation of production and services, the accent is put on characteristics of these jobs and the characteristics of the workers who hold them. The risk is also assessed against the use of ICT at work and the role of training in helping workers transit to new career opportunities.

About this publication

Working paper. A working paper — circulated for discussion before formal peer review, the standard first outlet in economics.

Published: 2018 (105 months ago)
Citations: 909 (OpenAlex)

909 citations recorded by OpenAlex — too few 2018 papers in this collection for a fair comparison.