Suppr超能文献

职业流动性与自动化:一种数据驱动的网络模型。

Occupational mobility and automation: a data-driven network model.

作者信息

Del Rio-Chanona R Maria, Mealy Penny, Beguerisse-Díaz Mariano, Lafond François, Farmer J Doyne

机构信息

Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Oxford, UK.

Mathematical Institute, University of Oxford, Oxford, UK.

出版信息

J R Soc Interface. 2021 Jan;18(174):20200898. doi: 10.1098/rsif.2020.0898. Epub 2021 Jan 20.

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.

摘要

自动化对劳动力市场的潜在影响是一个在学者、政策制定者和广大公众中引起了极大兴趣和关注的话题。一些研究通过考察相关技能和任务对自动化的适配程度,估算了特定职业的风险状况。然而,很少有研究试图从更全面的视角审视劳动力重新分配过程,以及当被取代的工人转向新工作时就业前景是如何受到影响的。在本文中,我们构建了一个数据驱动的模型,以分析工人如何响应自动化情景,在一个基于实证得出的职业流动网络中进行流动。在宏观层面,我们的模型再现了贝弗里奇曲线,这是劳动力市场一个关键的典型事实。在微观层面,我们的模型针对特定的自动化冲击,提供了短期和长期失业变化的特定职业估计。我们发现,网络结构在决定失业水平方面起着重要作用,网络中特定区域的职业拥有的工作转换机会很少。在低薪职业比高薪职业更有可能实现自动化的自动化情景中,网络效应也更有可能增加低薪职业的长期失业率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe56/7879770/b4669572f5a7/rsif20200898-g1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验