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

arXiv: https://arxiv.org/abs/2511.12010 · HTML: https://mlsanity.com/paper/2511.12010/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 15 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Batch Prompting: Efficient Inference with Large Language Model APIs](https://mlsanity.com/paper/2301.08721/) (2023)
- [Public Perception of Generative AI on Twitter: An Empirical Study Based on Occupation and Usage](https://mlsanity.com/paper/2305.09537/) (2023)
- [Avoiding bias when inferring race using name-based approaches](https://mlsanity.com/paper/2104.12553/) (2021)
- [What the Language You Tweet Says About Your Occupation](https://mlsanity.com/paper/1701.06233/) (2017)
- [Inferring gender from name: a large scale performance evaluation study](https://mlsanity.com/paper/2308.12381/) (2023)
- [Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions](https://mlsanity.com/paper/2110.00078/) (2021)
- [Gender Gaps in Online Social Connectivity, Promotion and Relocation Reports on LinkedIn](https://mlsanity.com/paper/2308.13296/) (2023)
- [Professional Gender Gaps Across US Cities](https://mlsanity.com/paper/1801.09429/) (2018)
- [Monitoring Gender Gaps via LinkedIn Advertising Estimates: the case study of Italy](https://mlsanity.com/paper/2303.05862/) (2023)

## Abstract (arXiv)

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.