Mind the Gap: Gender-based Differences in Occupational Embeddings

Large Language Models (LLMs) offer promising alternatives to traditional occupational coding approaches in survey research.Using a German dataset, we examine the extent to which LLM-based occupational coding differs by gender.Our findings reveal systematic disparities: gendered job titles (e.g., "Autor" vs. "Autorin", meaning "male author" vs. "female author") frequently result in diverging occupation codes, even when semantically identical.Across all models, 54%-82% of gendered inputs obtain different Top-5 suggestions.The practical impact, however, depends on the model.GPT includes the correct code most often (62%) but demonstrates female bias (up to +18 pp).IBM is less accurate (51%) but largely balanced.Alibaba, Gemini, and MiniLM achieve about 50% correct-code inclusion, and their small (< 10 pp) and direction-flipping gaps could indicate a sampling noise rather than gender bias.We discuss these findings in the context of fairness and reproducibility in NLP applications for social data.