Leveraging Large Language Models for Career Mobility Analysis: A Study of Gender, Race, and Job Change Using U.S. Online Resume Profiles

2025-11-14

AI Summary

This study investigates the mechanisms through which gender, race, and job mobility influence career advancement, leveraging a dataset of 228,000 online resumes from highly educated U.S. professionals. To address noisy, unstructured resume text and sparse demographic/salary annotations, we propose FewSOC—a few-shot large language model (LLM) framework for occupation classification—integrated with multi-level sensitivity analysis and a joint imputation-denoising algorithm for missing demographic and compensation data. Empirically, internal job transitions within firms prove most conducive to promotion; conversely, women and Black graduates realize significantly lower promotion returns from external job switches relative to their male and White peers, revealing intersectional inequities in occupational mobility. The work advances methodological rigor in labor market analysis and delivers novel empirical evidence on structural occupational inequality.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Application Domains: Humanities & Computational Social Science
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

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.

Problem

• Analyzing career mobility patterns using online resume profiles
• Investigating gender and race disparities in job change outcomes
• Developing LLM-based methods to address data quality challenges

Innovation

• LLM-based occupation classification method FewSOC
• Addressing missing data with AI processing techniques
• Analyzing career mobility using large-scale resume data