A new paper explores the utility of text embeddings in empirical economic analysis, proposing that these embeddings can effectively represent latent topics within documents. The research suggests that using embeddings for tasks like clustering or controlling for confounding factors can yield interpretable results. An application to economic descriptions of U.S. metropolitan areas demonstrated that embedding-based clustering identified distinct economic archetypes and better separated local employment dynamics compared to traditional methods. AI
RANK_REASON Research paper published on arXiv discussing the application of text embeddings in empirical analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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