Teaching Textual Analysis to Economics Majors

The proliferation of text-as-data methods in economics research has not yet been matched by curricular innovation at the undergraduate level. This article presents a pedagogical framework for introducing textual analysis to economics majors, using job postings as a case study in labor economics. The tutorial guides students through the complete analytical pipeline — from raw text processing (tokenization, vectorization) to economic applications (topic modeling, similarity, sentiment analysis, and prediction). By providing fully replicable teaching materials and explanations of Python code, this paper equips students with in-demand data science skills, thereby enhancing their ability to conduct novel and policy-relevant research using text data.

by Cheuk Yin Ho

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Ho, C. Y. (2026). Teaching Textual Analysis to Economics Majors. Available online at Journal of Economics Teaching, DOI: 10.58311/jeconteach/5650843b852f5897ebc67e46ce7a5c1271ba0059

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