Dynamics of Skills Demand and Job Transition Opportunities: a Machine Learning Approach

Patricia Prüfer (CentERdata, Tilburg University)
Pradeep Kumar (CentERdata, Tilburg University)
Marcia Den Uijl (CentERdata, Tilburg University)

Abstract: What are consequences of the ongoing digitalization and automatization for the labor market? We analyze to which extent types of occupations and skill requirements change over time and how these insights can be used to substantiate demand for reskilling of several groups of employees. To answer these questions, we make use of a novel approach in which we combine unstructured data from the Internet with structured data from labor market forecasts. Based on a dataset of 95% of all job vacancies in the Netherlands over a 6-year period with 7.7 million data points, we show which skills are particularly important for which type of profession. Besides, we provide job transition opportunities for employees from shrinking sector or occupations to sectors and professions not affected negatively by technological change. Our results suggest that the labor market is undergoing a transitions from degree-based to skill-based demand. This has consequences for both the participants and the institutions connected to the labor market.

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