自动化与职业流动：一个数据驱动的网络模型

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摘要翻译：
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的自动化程度来估计特定职业的风险状况。然而，相对较少的工作试图对劳动力重新分配的过程以及随着流离失所的工人过渡到新的工作，就业前景如何受到影响采取更全面的观点。在本文中，我们开发了一个新的数据驱动模型来分析工人如何通过经验推导的职业流动网络来响应自动化场景，这些场景增加了一些职业的劳动力需求，减少了另一些职业的劳动力需求。在宏观层面上，我们的模型再现了劳动力市场中被称为贝弗里奇曲线的一个关键的程式化事实，并为解释该曲线的逆时针周期提供了新的见解。在微观层面上，我们的模型提供了与给定自动化冲击相对应的短期和长期失业变化的职业特定估计。我们发现，网络结构在决定失业水平方面起着重要作用，网络中特定领域的职业很少有工作转换机会。这些见解可以有效地应用于帮助设计更有效率和更有效的政策，以帮助工人适应不断变化的劳动力市场性质。
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英文标题：
《Automation and occupational mobility: A data-driven network model》
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作者：
R. Maria del Rio-Chanona, Penny Mealy, Mariano Beguerisse-D\'iaz,
  Francois Lafond and J. Doyne Farmer
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
  The potential impact of automation on the labor 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 the automatability of associated skills and tasks. However, relatively little work has sought to take a more holistic view on the process of labor reallocation and how employment prospects are impacted as displaced workers transition into new jobs. In this paper, we develop a new data-driven model to analyze how workers move through an empirically derived occupational mobility network in response to automation scenarios which increase labor demand for some occupations and decrease it for others. At the macro level, our model reproduces a key stylized fact in the labor market known as the Beveridge curve and provides new insights for explaining the curve's counter-clockwise cyclicality. At the micro level, our model provides occupation-specific estimates of changes in short and long-term unemployment corresponding to a given automation shock. We find that the network structure plays an important role in determining unemployment levels, with occupations in particular areas of the network having very few job transition opportunities. Such insights could be fruitfully applied to help design more efficient and effective policies aimed at helping workers adapt to the changing nature of the labor market.
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PDF链接：
https://arxiv.org/pdf/1906.04086

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关键词：网络模型 自动化 unemployment Occupational Quantitative model more 驱动 how network