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

Author

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  • Garau, Alessio

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.

Suggested Citation

  • Garau, Alessio, 2026. "How economics classifies itself: text-based JEL codes and their consistency," MPRA Paper 130163, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:130163
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    More about this item

    Keywords

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    JEL classification:

    • A14 - General Economics and Teaching - - General Economics - - - Sociology of Economics
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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