JobHop: A Large-Scale Dataset of Career Trajectories

Understanding labor market dynamics is essential for policymakers, employers, and job seekers. However, comprehensive datasets that capture real-world career trajectories are scarce. In this paper, we introduce JobHop, a large-scale public dataset derived from anonymized resumes provided by VDAB, the public employment service in Flanders, Belgium. Utilizing Large Language Models (LLMs), we process unstructured resume data to extract structured career information, which is then mapped to standardized ESCO occupation codes using a multi-label classification model. This results in a rich dataset of over 2.3 million work experiences, extracted from and grouped into more than 391,000 user resumes and mapped to standardized ESCO occupation codes, offering valuable insights into real-world occupational transitions. This dataset enables diverse applications, such as analyzing labor market mobility, job stability, and the effects of career breaks on occupational transitions. It also supports career path prediction and other data-driven decision-making processes. To illustrate its potential, we explore key dataset characteristics, including job distributions, career breaks, and job transitions, demonstrating its value for advancing labor market research.
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This article contributes to the broader collection of external ESCO publications, showcasing the use of ESCO within various methodologies or its presentation in both European and International contexts. As ESCO becomes increasingly used in applications and research projects across Europe and beyond, it is valuable to collect such sources and share best practices by diverse stakeholders. Therefore, this collection of external publications strengthens the exchange of knowledge within the ESCO community and can contribute to mutual learning in the field of skills, occupations and qualifications among European and international actors. If you are interested in sharing your publication, please write to [email protected]