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

TL;DR

This study uses large language models to analyze U.S. online resumes, revealing how gender, race, and job change influence career mobility, with notable disparities affecting women and Black workers.

Contribution

It introduces FewSOC, a novel LLM-based occupation classification method, and provides large-scale insights into demographic disparities in career mobility.

Findings

Intra-firm job changes most strongly promote upward mobility.

Women and Black graduates experience lower returns from job changes.

Disparities are robust across different data clusters.

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.