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, we develop a datadriven model to analyze how workers move through an empirically derived occupational mobility network in response to automation scenarios. At a macro
level, our model reproduces the Beveridge curve, a key stylized fact in the labor market. At a micro level, our model provides occupation-specific estimates
of changes in short and long-term unemployment corresponding to specific automation shocks. We 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 an automation scenario
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