ABSTRACT / The recent COVID-19 pandemic has shown that the economy changes rapidly and that reaching an equilibrium state can take years, if reached at all. To understand the economy during normal and pandemic times, we need non-equilibrium data-driven models that focus on transitory periods. In the first part of this talk, I will present work we did during the early days of the pandemic, where we predicted the lockdown economic shocks, built a network model of production and forecasted the pandemic's impact on the UK economy. In the second part of this talk, I will present a data-driven network model of the labor market. In this model, workers move through an empirically derived occupational mobility network in response to automation scenarios. We find that the network structure plays an essential role in determining unemployment levels, with occupations in particular areas of the network having few job transition opportunities. Furthermore, in automation scenarios 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. Finally, I will conclude by discussing possible future projects.
BIO / Maria is a JSMF Postdoctoral Fellow at the Complexity Science Hub Vienna. Her 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. Maria has a PhD in Mathematics from Oxford University, where she was part of the Complexity Economics group of the Institute for New Economic Thinking at the Oxford Martin School. She has worked alongside international policy organizations, including the International Monetary Fund and the International Labour Organisation. Before her postgraduate studies, Maria did her undergraduate studies in Physics at UNAM, Mexico.