CSH researcher Maria del Rio-Chanona will give an online seminar talk at the Department of Network and Data Science, Central European University.
If you are interested in participating, register via CEU webpage.
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.