Occupational mobility and automation

Summary

In this project, we develop a data-driven model to analyse how workers move through an empirically derived occupational mobility network in response to automation scenarios.

Key researchers

  • R. Maria del Rio-Chanona, Institute for New Economic Thinking at the Oxford Martin School, Mathematical Institute
  • Penny Mealy, School of Geography and Environment, and Smith School of Environment and Enterprise, University of Oxford, Oxford, UK, and Soda Laboratories, Monash Business School
  • Mariano Beguerisse-Díaz, Mathematical Institute
  • François Lafond, Institute for New Economic Thinking at the Oxford Martin School, Mathematical Institute
  • J. Doyne Farmer, Institute for New Economic Thinking at the Oxford Martin School, Mathematical Institute, Santa Fe Institute

Project background and aims

The potential impact of automation on the labour market is a topic that has generated significant interest and concern amongst scholars, policymakers and the broader public.

A number of studies have estimated occupation-specific risk profiles by examining how suitable associated skills and tasks are for automation. However, little work has sought to take a more holistic view on the process of labour reallocation and how employment prospects are impacted as displaced workers transition into new jobs.

In this article, we develop a data-driven model to analyse how workers move through an empirically derived occupational mobility network in response to automation scenarios. At a macro level, our model reproduces the Beveridge curve, a key stylized fact in the labour market. At a micro level, our model provides occupation-specific estimates of changes in short and long-term unemployment corresponding to specific automation shocks.

We find that the network structure plays an important role in determining unemployment levels, with occupations in particular areas of the network having few job transition opportunities. In an automation scenario where low wage occupations are more likely to be automated than high wage occupations, the network effects are also more likely to increase the long-term unemployment of low-wage occupations.

Output