Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2026

Abstract

We present a large-scale analysis of career mobility of college-educated U.S. workers using online resume profiles to investigate how gender, race, and job change options are associated with upward mobility. This study addresses key research questions of how the job changes affect their upward career mobility, and how the outcomes of upward career mobility differ by gender and race. We address data challenges – such as missing demographic attributes, missing wage data, and noisy occupation labels – through various data processing and Artificial Intelligence (AI) methods. In particular, we develop a large language models (LLMs) based occupation classification method known as FewSOC that achieves accuracy significantly higher than the original occupation labels in the resume dataset. Analysis of 228,710 career trajectories reveals that intra-firm occupation change has been found to facilitate upward mobility most strongly, followed by inter-firm occupation change and inter-firm lateral move. Women and Black college graduates experience significantly lower returns from job changes than men and White peers. Multilevel sensitivity analyses confirm that these disparities are robust to cluster-level heterogeneity and reveal additional intersectional patterns.

Keywords

Career Mobility Analysis, Crowdsourcing, Gender and Racial Disparity, Large Language Models, Occupation, Occupation Classification, Upward Mobility

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

EPJ Data Science

Volume

15

Issue

1

First Page

1

Last Page

33

Identifier

10.1140/epjds/s13688-025-00607-0

Publisher

SpringerOpen

Copyright Owner and License

Authors-CC-BY

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Additional URL

https://doi.org//10.1140/epjds/s13688-025-00607-0

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