论文标题
劳动力市场的纵向复杂动态揭示了越来越多的两极分化
Longitudinal Complex Dynamics of Labour Markets Reveal Increasing Polarisation
论文作者
论文摘要
在本文中，我们对美国7年的技术，经济和政策变化进行了纵向分析。我们利用网络科学，自然语言处理和机器学习来揭示劳动力市场的结构变化。尽管在这个时期内有很多技术和经济变化，但我们发现，工作失踪和所需工作任务的转变均稳定。基于工作场所任务的文本描述，机器学习用于将作业分类为主要是认知或物理。我们还衡量了这两个类别的作业之间的两极分化，这些工作是由任务的相似性链接的，随着时间的流逝，这可能会限制希望转向不同工作的工人。
In this paper we conduct a longitudinal analysis of the structure of labour markets in the US over 7 decades of technological, economic and policy change. We make use of network science, natural language processing and machine learning to uncover structural changes in the labour market over time. We find a steady rate of both disappearance of jobs and a shift in the required work tasks, despite much technological and economic change over this time period. Machine learning is used to classify jobs as being predominantly cognitive or physical based on the textual description of the workplace tasks. We also measure increasing polarisation between these two classes of jobs, linked by the similarity of tasks, over time that could constrain workers wishing to move to different jobs.
