Sample Project: Longitudinal Complex Dynamics of Labor Markets Reveal Increasing Polarization

How is the structure of job tasks changing over time?

In this project, we conduct a longitudinal analysis of the structure of labor markets in the US over seven 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 labor market over time. We find a steady rate of both the 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 a written description of the workplace tasks. We also measure increasing polarization between these two classes of jobs, linked by the similarity of tasks over time, that could constrain workers wishing to move to different jobs.  



Key reference

Althobaiti, S., Alabdulkareem, A., Shen, J. H., Rahwan, I., Frank, M., Moro, E., & Rutherford, A. (2022). Longitudinal Complex Dynamics of Labour Markets Reveal Increasing Polarisation. arXiv preprint arXiv:2204.07073.


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