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Occupational mobility and automation: a data-driven network model
Journal of The Royal Society Interface ( IF 3.9 ) Pub Date : 2021-01-01 , DOI: 10.1098/rsif.2020.0898
R Maria Del Rio-Chanona 1, 2 , Penny Mealy 1, 3, 4, 5 , Mariano Beguerisse-Díaz 2 , François Lafond 1, 2 , J Doyne Farmer 1, 2, 6
Affiliation  

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.

中文翻译:

职业流动性和自动化:数据驱动的网络模型

自动化对劳动力市场的潜在影响是一个引起学者、政策制定者和广大公众极大兴趣和关注的话题。许多研究通过检查相关技能和任务对自动化的适用程度,估计了特定于职业的风险状况。然而,很少有工作试图更全面地了解劳动力重新分配的过程以及随着失业工人过渡到新工作岗位对就业前景的影响。在本文中,我们开发了一个数据驱动模型来分析工人如何通过经验派生的职业流动网络以响应自动化场景。在宏观层面,我们的模型再现了贝弗里奇曲线,这是劳动力市场中一个关键的程式化事实。在微观层面,我们的模型提供了与特定自动化冲击相对应的短期和长期失业变化的特定职业估计。我们发现网络结构在确定失业水平方面起着重要作用,网络特定区域的职业几乎没有工作转换机会。在低工资职业比高工资职业更可能被自动化的自动化场景中,网络效应也更有可能增加低工资职业的长期失业。
更新日期:2021-01-01
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