内生劳动力流动网络
Endogenous Labour Flow Networks
摘要：
在过去的十年中，劳动力动态的研究导致了劳动力流动网络（LFN）的引入，作为一种概念化的工作到工作的过渡，并开发数学模型来探索这些网络流的动态。到目前为止，LFN模型依赖于静态网络结构的假设。然而，由于近期事件（工作场所自动化程度提高、COVID-19疫情、对编程技能的需求激增等），本集团的业务表现持续改善。我们正在经历就业环境的急剧变化，这些变化正在改变个人在劳动力市场上的导航方式。在这里，我们开发了一个新的模型，出现LFNs从代理级的行为，消除了假设未来的工作到工作流将沿着相同的路径，他们已经被历史上观察到的必要性。该模型以联合王国的微观数据为依据，生成了具有高度准确性的经验性LFN。我们使用该模型来探讨冲击如何影响就业和工资的基本分布改变了LFN的拓扑结构。该框架是朝着开发模型迈出的关键一步，这些模型可以回答关于不断变化的世界中未来工作的问题。
英文摘要：
In the last decade, the study of labour dynamics has led to the introduction of labour flow networks (LFNs) as a way to conceptualise job-to-job transitions, and to the development of mathematical models to explore the dynamics of these networked flows. To date, LFN models have relied upon an assumption of static network structure. However, as recent events (increasing automation in the workplace, the COVID-19 pandemic, a surge in the demand for programming skills, etc.) have shown, we are experiencing drastic shifts to the job landscape that are altering the ways individuals navigate the labour market. Here we develop a novel model that emerges LFNs from agent-level behaviour, removing the necessity of assuming that future job-to-job flows will be along the same paths where they have been historically observed. This model, informed by microdata for the United Kingdom, generates empirical LFNs with a high level of accuracy. We use the model to explore how shocks impacting the underlying distributions of jobs and wages alter the topology of the LFN. This framework represents a crucial step towards the development of models that can answer questions about the future of work in an ever-changing world.
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