COVID-19 Economic Impact
- We provide quantitative predictions of first order COVID-19 supply and demand shocks for the US economy. Compared to the pre-COVID period, these shocks would threaten around 20% of the US economy’s GDP, jeopardize 23% of jobs, and reduce total wage in-come by 16%.
- We analyse the economics and epidemiology of different scenarios for a phased restart of the UK economy. Our results suggest that there is a reasonable compromise that yields a relatively small increase in R0 and delivers a substantial boost in economic output.
- del Rio-Chanona, R. Maria, et al. “Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective.” Oxford Review of Economic Policy (2020). Read here
- Pichler, Anton, et al. “Production networks and epidemic spreading: How to restart the UK economy?.” arXiv preprint arXiv:2005.10585 (2020). Read here
Labor Models and Networks
- We study the network structure of the division of labour by analysing discrete work activities. We find that our measure of occupational work-activity similarity is more predictive of job-to-job transitions than existing benchmark measures.
- We develop a data-driven network model to study the impact of automation on employment. We find that the network structure plays an important role in determining unemployment levels, with occupations in particular areas of the network having very few job transition
- Mealy, Penny, R. Maria del Rio-Chanona, and J. Doyne Farmer. “What you do at work matters: New lenses on labour.” What You Do at Work Matters: New Lenses on Labour (March 18, 2018) (2018). Read here
- del Rio-Chanona, R. Maria, et al. “Automation and occupational mobility: A data-driven network model.” Journal of the Royal Society Interface (2021). Read here
Multilayer Networks and Financial Contagion
- We study interconnectedness of the global financial system and its susceptibility to shocks. We study multiple channels of financial contagion using a multilayer network approach.