How economics classifies itself: text-based JEL codes and their consistency

Alessio Garau

MPRA Paper from University Library of Munich, Germany

Abstract: Can a language model improve how economists classify their own papers? Only 15% of four million IDEAS/RePEc records carry usable JEL codes, and similar papers often receive different ones. I use a large language model (LLM) to solve this problem and assign three-digit codes from titles and abstracts, evaluating it on 69,503 coded articles published from 1991 to 2023 in the top 100 economics journals. Two tests do not assume that author codes provide the correct classification. Across semantic neighbors identified by a separate embedding model, model codes are 1.8 times as consistent as author codes. Holding codes per paper fixed, a blind check finds that 83% of model codes fit official American Economic Association (AEA) guidelines, compared with 67% of author codes. The classifier expands coverage, and the evaluation framework applies whenever human labels are incomplete or noisy.

Keywords: JEL codes; field classification; generative artificial intelligence; large language models; text as data; semantic similarity (search for similar items in EconPapers)
JEL-codes: A14 C45 C81 (search for similar items in EconPapers)
Date: 2026-07
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:130163

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