IDEAS home Printed from https://ideas.repec.org/a/aea/jeclit/v63y2025i1p5-58.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/jel.20241733
    Download Restriction: no

    File URL: https://doi.org/10.3886/E210922V1
    Download Restriction: no

    File URL: https://www.aeaweb.org/articles/attachments?retrieve=DdN6DBqHlca3lE3Scn44Y3Y5dzGCrhS1
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.

    File URL: https://libkey.io/10.1257/jel.20241733?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:jeclit:v:63:y:2025:i:1:p:5-58. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.