The lecture by R. Maria del Rio Chanona from University of Oxford will take place at the Complexity Science Hub Vienna.
If you are interested in participating, please email to email@example.com.
Recent work has sought to quantify the susceptibility of particular occupations to automation. However, the overall employment prospects for workers depend not only on the automatability of their occupations’ tasks or skills, but also on the alternative jobs they are able to transition into. To better understand the potential impact of automation on employment, we model workers’ movements through an empirically derived occupational mobility network in response to automation shock scenarios. The structure of this network significantly influences aggregate and occupational-specific unemployment impacts in both the short and long term. Some occupations experience greater unemployment not only due to automation but also to workers’ limited transition possibilities. The model’s out-of-equilibrium dynamics also provides new insights into the counter-clockwise cyclicality of the Beveridge curve, which is a macroeconomic phenomenon in the labour market that is yet to be explained.
Maria is a Mathematics DPhil student supervised by Doyne Farmer at the University of Oxford. Before starting her PhD, Maria did her BSc in Physics at Universidad Nacional Autónoma de México (UNAM) 2011-2016, was a research intern at Imperial College London and Ryerson University and worked as a data scientists for two consulting firms (Inno-ba and Pondera). Maria’s research interests are broad. The main work of her PhD thesis focuses on developing a data-driven network model of the labour market to understand the impact of automation on employment. Additionally, Maria has worked on alternative methods to assess forecasts for renewable energy generation and has studied shock propagation in networks. In particular, Maria was a research intern at the International Monetary Fund, where she studied global financial contagion in multilayer network. In general, Maria’s research focuses on complexity economics, networks and shock propagation, agent-based models, and the future of work.