Maria del Rio-Chanona

I am a JSMF Postdoctoral Research Fellow at the Complexity Science Hub Vienna and the Growth Lab at the Harvard Kennedy School. I did a PhD in Mathematics at the Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.

My research draws from network science and agent-based modelling and focuses on labour economics, the future of work, green transition, and the economic impact of the Covid-19 pandemic.

delrio [dot] chanona [at]

Current Research

List of publications

The Great Resignation

  • We use text analysis to investigate the changes in work- and quit-related posts between 2018 and 2021 on Reddit. Our main finding is that mental health and work-related distress topics disproportionally increased among quit-related posts since the onset of the pandemic, likely contributing to the Great Resignation.  


  • del Rio-Chanona, R. Maria, et al. “Mental health concerns prelude the Great Resignation: Evidence from Social Media” arXiv preprint arXiv:2208.07926 (2020). Read here

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 developed a model to predict the economic impact of the pandemic on UK economy. We show that the model predicted aggregate dynamics very well, and sectoral dynamics to a large extent.


  • 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. “In and out of lockdown: Propagation of supply and demand shocks in a dynamic input-output model” arXiv preprint arXiv:2102.09608 (2021). Read here

Labor Markets 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

Previous research projects

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.


  • Korniyenko, Yevgeniya, et al. “Evolution of the global financial network and contagion: A new approach.” International Monetary Fund, 2018. Read here
  • del Rio-chanona, R. Maria, et al. “The Multiplex Nature of Global Financial Contagions” Applied Network Science, (2020). Read here