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Deep Learning for Economists

Author

Listed:
  • Melissa Dell

Abstract

Deep learning provides powerful methods to impute structured information from large-scale, unstructured text and image datasets. For example, economists might wish to detect the presence of economic activity in satellite images, or to measure the topics or entities mentioned in social media, the congressional record, or firm filings. This review introduces deep neural networks, covering methods such as classifiers, regression models, generative artificial intelligence (AI), and embedding models. Applications include classification, document digitization, record linkage, and methods for data exploration in massive scale text and image corpora. When suitable methods are used, deep learning models can be cheap to tune and can scale affordably to problems involving millions or billions of data points. The review is accompanied by a regularly updated companion website, EconDL (https://econdl.github.io/), with user-friendly demo notebooks, software resources, and a knowledge base that provides technical details and additional applications.

Suggested Citation

  • Melissa Dell, 2025. "Deep Learning for Economists," Journal of Economic Literature, American Economic Association, vol. 63(1), pages 5-58, March.
    • Handle: RePEc:aea:jeclit:v:63:y:2025:i:1:p:5-58
    DOI: 10.1257/jel.20241733
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    Citations

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    Cited by:

    1. Ingar Haaland & Christopher Roth & Stefanie Stantcheva & Johannes Wohlfart, 2025. "Understanding Economic Behavior Using Open-Ended Survey Data," Journal of Economic Literature, American Economic Association, vol. 63(4), pages 1244-1280, December.
    2. Feyzollahi, Maryam & Rafizadeh, Nima, 2025. "The adoption of Large Language Models in economics research," Economics Letters, Elsevier, vol. 250(C).
    3. Yugang He, 2026. "RETRACTED ARTICLE: Beyond GDP: national intelligence quotient as a catalyst for sustainable socioeconomic welfare," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 13(1), pages 1-15, December.
    4. Tsang, Kwok Ping & Yang, Zichao, 2025. "Agree to disagree: Measuring hidden dissent in FOMC meetings," Journal of Economic Dynamics and Control, Elsevier, vol. 180(C).
    5. Anina Harter, 2025. "Legislative institutions and distributive politics: Evidence from Germany’s federal budget committee," Berlin School of Economics Discussion Papers 0075, Berlin School of Economics.
    6. Tianyu Fan, 2025. "The Labor Market Incidence of New Technologies," Papers 2504.04047, arXiv.org, revised Nov 2025.
    7. Yi Lu & Aifan Ling & Chaoqun Wang & Yaxin Xu, 2025. "Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Prediction," Papers 2509.10802, arXiv.org.
    8. Jacob Carlson, 2025. "Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach," Papers 2511.01680, arXiv.org, revised May 2026.
    9. Sukjin Han & Kyungho Lee, 2025. "Copyright and Competition: Estimating Supply and Demand with Unstructured Data," Papers 2501.16120, arXiv.org, revised Sep 2025.
    10. Garg, Prashant & Fetzer, Thiemo, 2024. "Causal Claims in Economics," I4R Discussion Paper Series 183, The Institute for Replication (I4R).
    11. Francesco Columba & Manuel Cugliari & Stefano Di Virgilio, 2026. "Credit Risk Assessment with Stacked Machine Learning," Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems) 73, Bank of Italy, Directorate General for Markets and Payment System.
    12. Jesús Fernández-Villaverde & Galo Nuño & Jesse Perla, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," NBER Working Papers 33117, National Bureau of Economic Research, Inc.
    13. Clayton, Christopher & Coppola, Antonio & Maggiori, Matteo & Schreger, Jesse, 2025. "Chokepoints: Identifying Economic Pressure," SocArXiv zsc4x_v1, Center for Open Science.
    14. Pablo Ottonello & Wenting Song & Sebastian Sotelo, 2024. "An Anatomy of Firms’ Political Speech," NBER Working Papers 32923, National Bureau of Economic Research, Inc.
    15. Tianyu Fan & Mai Wo & Wei Xiang, 2025. "Geopolitical Barriers to Globalization," Papers 2509.12084, arXiv.org, revised Apr 2026.
    16. Tianyu Fan, 2025. "Measuring Geopolitical Alignment and Economic Growth," Papers 2507.04833, arXiv.org, revised Apr 2026.
    17. Davide Cipullo & Luca V.A. Colombo & Michele Magnani & Massimiliano Gaetano Onorato, 2025. "Historical Newspaper Markets," CESifo Working Paper Series 12194, CESifo.
    18. repec:ehl:lserod:128852 is not listed on IDEAS
    19. Samuel Chang & Andrew Kennedy & Aaron Leonard & John A. List, 2024. "12 Best Practices for Leveraging Generative AI in Experimental Research," NBER Working Papers 33025, National Bureau of Economic Research, Inc.
    20. Vegard H. Larsen & Leif Anders Thorsrud, 2026. "Using Transformers and Reinforcement Learning as Narrative Filters in Macroeconomics," CESifo Working Paper Series 12454, CESifo.

    More about this item

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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