Mathematical modelling of knowledge flow in local economic networks

Project ID: 2531ad1498

(You will need this ID for your application)

UCL Lead department: Centre for Advanced Spatial Analysis (CASA)

Project Summary:

From social networks to transport networks, protein networks to economic networks, networks are ubiquitous in daily life. This project will focus on local economic networks, and specifically inter-firm and inter-industry supply chain linkages and labour flows. While a wide literature has demonstrated that these networks constructed at a national scale capture the broad ‘economic landscape’ on which regions diversify in a path dependent manner into new economic activities, much less is known about such networks at a local scale and their role in knowledge exchange.

This project aims to investigate two inter-related problems. First, it seeks to uncover the ‘generating processes’ that underlie the creation of local economic networks. In practice, this means building statistical models that capture the processes by which new links and new nodes emerge in local networks, and investigating the extent to which there is a ‘global’ model in the sense that regions by and large follow a similar underlying model, or there is wide divergence in generating models (which may be related to local factors).

Second, it seeks to better understand resilience in local networks, taking an ‘evolutionary’ approach which sees a regions’ resilience as a function of its ability to adapt to a shock. In recent work, we found that UK regions are more resilient to economic shocks such as the financial crisis if they have greater potential to move skilled workers between related sectors (Straulino et al, 2024). This project would investigate the drivers of resilience at a local scale, probing the rewiring of local economic networks in response to shocks on a significantly more micro scale than previously possible.

This project would connect to existing policy collaborations including national/local government.

This project would suit a candidate with a strong quantitative background (maths, physics, engineering, computer science) and an interest in economics.