用大模型分析简历数据，发现性别种族差异影响职场晋升效果
Leveraging Large Language Models for Career Mobility Analysis: A Study of Gender, Race, and Job Change Using U.S. Online Resume Profiles
- 用大语言模型改进职业分类准确率，解决原始数据噪声问题
- 22万条职业轨迹显示：内部升职最促晋升，女性黑人获益更少
- 结果在多重检验下仍成立，揭示性别与种族的交叉不平等
本研究基于在线简历数据，对受过大学教育的美国劳动者的职业流动进行大规模分析，探讨性别、种族与职业变动选择如何影响向上流动。针对数据缺失、工资信息不全和职业标签噪声等挑战，采用多种数据处理与人工智能方法应对。特别提出基于大语言模型的职业分类方法FewSOC，其准确率显著高于原始简历中的职业标签。对228,710条职业轨迹的分析表明，企业内部职务变动对向上流动促进作用最强，其次为跨企业职务变动和横向调动；女性及黑人大学毕业生从职业变动中获得的回报显著低于男性和白人同侪。多层次敏感性分析证实这些差异在群体异质性下依然稳健，并揭示出更复杂的交叉性模式。
原文摘要 · Abstract (English)
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.
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