JobHop: A Large-Scale Dataset of Career Trajectories

2025-05-12

AI Summary

This study addresses the scarcity of high-quality occupational trajectory data for labor market dynamics analysis. Leveraging 2.3 million job histories and 391,000 anonymized resumes from the Flemish Public Employment Service (VDAB), we construct the largest structured occupational trajectory dataset to date. We propose a novel LLM-driven multi-label classification framework that enables high-accuracy, reusable mapping from unstructured resume text to standardized ESCO occupation codes—first of its kind—integrated with privacy-preserving preprocessing and semantic standardization alignment. The dataset is publicly released. Empirical analyses reveal three key findings: (1) occupational transitions exhibit strong path dependence; (2) career interruptions significantly reduce subsequent job stability; and (3) cross-domain occupational mobility concentrates in skill-adjacent occupations. This work provides both a robust empirical foundation and a methodological paradigm for labor market policy design, career counseling, and workforce forecasting.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Philosophy and Ethics of AI: AI & Jobs/Labor

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Lack of comprehensive datasets on real-world career trajectories
• Need for structured career data from unstructured resumes
• Limited insights into labor market mobility and job transitions

Innovation

• LLMs process unstructured resume data
• Multi-label classification maps to ESCO codes
• Dataset enables labor market mobility analysis