Longitudinal Complex Dynamics of Labour Markets Reveal Increasing Polarisation

TL;DR

This study analyzes seven decades of US labor market data using network science and machine learning, revealing persistent job turnover, evolving work tasks, and increasing polarization between cognitive and physical jobs.

Contribution

It introduces a longitudinal, data-driven approach combining NLP and network analysis to track structural changes and polarization in labor markets over time.

Findings

Steady job disappearance and task shifts despite technological change

Machine learning classifies jobs into cognitive and physical categories

Increasing polarization constrains worker mobility between job types

Abstract

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.