ArticleJournal of the Royal Society, Interface2021
Occupational mobility and automation: a data-driven network model.
Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Organizations, teams and job mobility: a social microdynamics approach inspired by a large US organization.Royal Society open science · 2025Article
- Skill dependencies uncover nested human capital.Nature human behaviour · 2025Article
- Endogenous labour flow networks.EPJ data science · 2025Article
- Complex systems perspective in assessing risks in artificial intelligence.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024Article
- Mapping job fitness and skill coherence into wages: an economic complexity analysis.Scientific reports · 2024Article
- Exposure to automation explains religious declines.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- The dynamic resilience of urban labour networks.Royal Society open science · 2023Article
- Organizational Labor Flow Networks and Career Forecasting.Entropy (Basel, Switzerland) · 2023Article
- Distributional labour challenges and opportunities for decarbonizing the US power system.Nature climate change · 2023Article
- Forecasting the propagation of pandemic shocks with a dynamic input-output model.Journal of economic dynamics & control · 2022Article
- The new paradigm of economic complexity.Research policy · 2022Article
- Occupational mobility and automation: a data-driven network model.Journal of the Royal Society, Interface · 2021Article
- Who perceived automation as a threat to their jobs in metro Atlanta: Results from the 2019 Metro Atlanta Speaks survey.Technology in society · 2020Article
- Supply and demand shocks in the COVID-19 pandemic: an industry and occupation perspective.Oxford review of economic policy · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.