Seminar: Occupational mobility and Automation: A data-driven network model

  • Datum: 17.11.2020
  • Uhrzeit: 15:30
  • Vortragende: Maria del Rio Chanona
  • Ort: online
  • Gastgeber: Center for Humans and Machines
  • Kontakt:

Maria del Rio Chanona, University of Oxford

Occupational mobility and Automation: A data-driven network model

The potential impact of automation on the labor market is a topic that has generated significant interest and concern amongst scholars, policymakers, and the broader public. A number of studies have estimated occupation-specific risk profiles by examining how suitable associated skills and tasks are for automation. However, little work has sought to take a more holistic view on the process of labor reallocation and how employment prospects are impacted as displaced workers transition into new jobs. In this paper, Maria del Rio Chanona and her team develop a data-driven model to analyze how workers move through an empirically derived occupational mobility network in response to automation scenarios. At a macro level, their model reproduces the Beveridge curve, a key stylized fact in the labor market. At a micro level, their model provides occupation-specific estimates of changes in short and long-term unemployment corresponding to specific automation shocks. The scientists find that the network structure plays an important role in determining unemployment levels, with occupations in particular areas of the network having few job transition opportunities. 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.

Maria del Rio Chanona is a Mathematics DPhil student 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 scientist for two consulting firms (Inno-ba and Pondera). 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. Maria also studied financial contagion using multiplex networks during a research internship at the International Monetary Fund. Maria has recently studied the economic impact of the COVID-19 pandemic.

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