Journal article
Occupational mobility and automation: A data-driven network model
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.3MB, Terms of use)
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- Publisher copy:
- 10.1098/rsif.2020.0898rsif20200898
Authors
- Publisher:
- Royal Society
- Journal:
- Journal of the Royal Society Interface More from this journal
- Volume:
- 18
- Issue:
- 174
- Article number:
- 20200898
- Publication date:
- 2021-01-20
- Acceptance date:
- 2020-12-15
- DOI:
- EISSN:
-
1742-5662
- ISSN:
-
1742-5689
- Language:
-
English
- Keywords:
- Pubs id:
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1166329
- Local pid:
-
pubs:1166329
- Deposit date:
-
2021-03-19
- ARK identifier:
Terms of use
- Copyright holder:
- del RIo-Chanona et al.
- Copyright date:
- 2021
- Rights statement:
- © 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.
- Licence:
- CC Attribution (CC BY)
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