Future of work

The authors employ a novel mixed-model prediction approach that leverages expert information with machine learning models to focus on the unexplored effects of automation on job creation. They also gather data on major labour market trends to contextualize the interaction of automation with other relevant future of work trends such as globalization, population aging, urbanization and the rise of the green economy. Using a combination of detailed occupational information from the Occupational Information Network (O*Net) and workshop respondent data for both the US and the UK, the paper maps out how jobs are likely to change, and the resulting implications for skills demand.

The authors discuss the likely dynamics of technological change in different labour markets. They find that education, health care and public sector occupations are likely to grow, while low skilled jobs in fields like construction and agriculture are less likely to suffer poor labour market outcomes. However, because they show heterogenous occupational growth patterns, the authors find that the negative outcomes for lower skilled workers are likely to be less severe than has been previously assumed. The authors conclude that technological change points to opportunities for boosting growth with the caveat that current education and training systems must respond appropriately to these new challenges.