What Transferable Skills Do Workers Need to Future-Proof Their Careers? a Dynamic Network Analysis of the Skills That Support Upward Mobility in a Changing Labor Market.

In recent decades, accelerating technological change and the increasing digitalization of the economy have transformed the work of many individuals. Previous research has shown that workers are not equally able to adapt to changing labor markets, suggesting that some of them lack the transferable skills to change occupation. However, we still know very little about the actual skills that help workers move between occupations, especially without losing wages or qualifications. In this paper, I use the UK Labor Force Survey (LFS) to model the mobility flows of workers between occupations in each month as a directed dynamic network between 2012 and 2019. I then leverage a rich dataset of 60 million online job advertisements in the UK collected over the same period to identify the skills required in occupations over time, as described by employers. Rather than using apriori defined skill categories, I identify latent skill profiles in the job postings using topic modeling and develop new indicators to measure the extent to which occupations require a diverse set of skills and transferable skills. In a final step, I use the unique combination of LFS and job advertisement data to analyze which skill dimensions of occupations best predict workers' upward mobility between occupations at the same point in time. More specifically, I use a dynamic network model, the Stochastic Actor-oriented Model, to predict the strength of mobility ties between occupations based on their skill profiles. This paper provides the first insights into the skills that workers need to adapt to a changing labor market, using a fully data-driven approach that directly models the dynamic nature of mobility flows between occupations.