Abstract
The potential impact of automation on the labour 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 labour reallocation and how employment prospects are impacted as displaced workers transition into new jobs. In this article, we develop a data-driven model to analyse 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 labour 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.
Highlights
In response to widespread concern about the potential impact of automation on the labour market [1,2,3,4,5], significant effort has been devoted towards analysing how susceptible a given occupation is to computerization [6,7,8]
Even though the immediate risk of automation is predicted to be larger for statistical technicians, accounting for possible occupational transitions and labour demand reallocation and could see childcare workers facing a greater risk of unemployment
In the Results section, we present analytical approximations to the dynamics of the model and make more explicit how the flow of workers depends on the occupational mobility network structure
Summary
In response to widespread concern about the potential impact of automation on the labour market [1,2,3,4,5], significant effort has been devoted towards analysing how susceptible a given occupation is to computerization [6,7,8]. Estimates developed by Frey and Osborne [8] suggest that statistical technicians are more likely than childcare workers, for example, to be replaced by software technology Should such forecasts eventuate, and statistical technicians find themselves out of a job, their existing skills could allow them to transition into a range of ‘safer’ occupations with lower automation risk and growing demand. Even though the immediate risk of automation is predicted to be larger for statistical technicians, accounting for possible occupational transitions and labour demand reallocation and could see childcare workers facing a greater risk of unemployment To study these important, but overlooked indirect labour displacement effects, this article develops a new data-driven model of the labour market
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
