The labour market is undergoing unprecedented change. On the one hand, new technologies, pandemic restrictions, and climate policies cause employers to demand different tasks from workers, while on the other, the workforce is changing as workers adapt and learn new skills. In this project, we use network analysis and agent-based models to analyse and what policies could ease this transition for workers.

Our work includes:

  • Understanding job transitions
  • Occupational mobility and automation
  • The impact of Covid-19 on the labour market
  • Green jobs and the transition to net zero
  • Green Complexity Index

Understanding job transitions

What makes worker change occupations? Work activities, geography, skill requirements, and job vacancy availability factor in occupational mobility. We explore what drives job transitions with a focus on prediction.

Occupational mobility and automation

Concerns about technologies displacing workers led researchers to analyse how susceptible workers of a given occupation is to automation. However, occupation specific estimates of automation ignore occupational mobility and so provide only part of the picture. We develop a data-driven model to analyse how workers move through an empirically derived occupational mobility network in response to automation scenarios and assess the impact automation has on employment at the occupation level.

The impact of Covid-19 on the labour market

The Covid-19 pandemic had a huge impact on employment. For example, restrictions on mobility and social distancing restricted manufacturing workers from the labour supply side, while the transport sector was strongly affected from the demand side. In this project we study the effects the pandemic has and will have on the labour market.

Green jobs and the transition to net zero

Reaching a net zero economy requires a shift from emission-intensive industries, such as oil and coal, to green industries. We explore the positive and negative implications this shift may have on workers and how workers may more easily adapt.

Green Complexity Index

The Green Complexity Index (GCI) allows the ranking of countries in accordance with the number and complexity of green products they export competitively. It shows that countries with higher GCI have higher environmental patenting rates, lower CO2 emissions, and more stringent environmental policies. Through an application of network theory to the export products this approach can also be employed to uncover the Green Adjacent Possible (GAP), which represents the set of technologically proximate green products that in which a country could potentially become competitive.

Key findings:

  • People are significantly more likely to transition into occupations sharing similar work activities. We develop a measure of occupational work-activity similarity, which is more predictive of job-to-job transitions than other measurements based on skills or knowledge.
  • Network structure plays an important role in determining the impact external shocks, such as automation or Covid-19, may have on employment. Occupations in particular areas of the network having few job transition opportunities face a greater increase in unemployment.
  • Low-wage occupations are more vulnerable to adverse supply and demand shocks from the Covid-19 pandemic. 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.

Key publications:

Other outputs

  • The Green Transition Navigator - a map of green competitiveness, which allows you to visualise your country's green strengths and opportunities, or learn more about each green product or category.


Funders include:

Baillie Gifford, BEIS, Conacyt-SENER (Sustentabilidad Energética scholarship), James S McDonnell Foundation

Researchers involved:

Anna Berryman, Joris Bücker, Maria del Rio-Chanona, Doyne Farmer, Matthew Ives, François Lafond, Penny Mealy, Anton Pichler