JobHop: A Large-Scale Dataset of Career Trajectories

TL;DR

JobHop is a large-scale, publicly available dataset of over 1.67 million career experiences extracted from resumes, enabling detailed analysis of labor market mobility and occupational transitions using advanced NLP techniques.

Contribution

This paper introduces JobHop, a novel large-scale dataset of career trajectories derived from resumes, processed with LLMs and normalized to ESCO codes, filling a gap in labor market data resources.

Findings

Dataset contains 1.67 million experiences from 361,000 resumes.

Analyzes job distributions, career breaks, and transitions.

Demonstrates potential for labor market research and career prediction.

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 normalized to standardized ESCO occupation codes using a multi-label classification model. This results in a rich dataset of over 1.67 million work experiences, extracted from and grouped into more than 361,000 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.