IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/32768.html
   My bibliography  Save this paper

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 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 companion website, EconDL, with user-friendly demo notebooks, software resources, and a knowledge base that provides technical details and additional applications.

Suggested Citation

  • Melissa Dell, 2024. "Deep Learning for Economists," NBER Working Papers 32768, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:32768
    Note: DAE DEV LS PE POL TWP
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w32768.pdf
    Download Restriction: Access to the full text is generally limited to series subscribers, however if the top level domain of the client browser is in a developing country or transition economy free access is provided. More information about subscriptions and free access is available at http://www.nber.org/wwphelp.html. Free access is also available to older working papers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Beach, Brian & Hanlon, W. Walker, 2022. "Historical Newspaper Data: A Researcher's Guide and Toolkit," CEPR Discussion Papers 17366, C.E.P.R. Discussion Papers.
    3. Ran Abramitzky & Leah Boustan & Katherine Eriksson & James Feigenbaum & Santiago Pérez, 2021. "Automated Linking of Historical Data," Journal of Economic Literature, American Economic Association, vol. 59(3), pages 865-918, September.
    4. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2021. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1849-1864, October.
    5. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    6. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    7. Abhishek Arora & Xinmei Yang & Shao-Yu Jheng & Melissa Dell, 2023. "Linking Representations with Multimodal Contrastive Learning," Papers 2304.03464, arXiv.org, revised Jun 2024.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Samuel Chang & Andrew Kennedy & Aaron Leonard & John List, 2024. "12 Best Practices for Leveraging Generative AI in Experimental Research," Artefactual Field Experiments 00796, The Field Experiments Website.
    2. Feyzollahi, Maryam & Rafizadeh, Nima, 2025. "The adoption of Large Language Models in economics research," Economics Letters, Elsevier, vol. 250(C).
    3. 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.
    4. Garg, Prashant & Fetzer, Thiemo, 2024. "Causal Claims in Economics," I4R Discussion Paper Series 183, The Institute for Replication (I4R).
    5. 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.
    6. Clayton, Christopher & Coppola, Antonio & Maggiori, Matteo & Schreger, Jesse, 2025. "Chokepoints: Identifying Economic Pressure," SocArXiv zsc4x_v1, Center for Open Science.
    7. Paker, Meredith & Stephenson, Judy & Wallis, Patrick, 2025. "Predictive modeling the past," LSE Research Online Documents on Economics 128852, London School of Economics and Political Science, LSE Library.
    8. 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.
    9. Pablo Ottonello & Wenting Song & Sebastian Sotelo, 2024. "An Anatomy of Firms’ Political Speech," Staff Working Papers 24-37, Bank of Canada.
    10. 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.
    11. Tianyu Fan & Mai Wo & Wei Xiang, 2025. "Geopolitical Barriers to Globalization," Papers 2509.12084, arXiv.org, revised Sep 2025.
    12. Tianyu Fan, 2025. "The Labor Market Incidence of New Technologies," Papers 2504.04047, arXiv.org, revised Nov 2025.
    13. Jacob Carlson, 2025. "Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach," Papers 2511.01680, arXiv.org.
    14. Tianyu Fan, 2025. "The Geopolitical Determinants of Economic Growth, 1960-2024," Papers 2507.04833, arXiv.org, revised Nov 2025.
    15. Davide Cipullo & Luca V.A. Colombo & Michele Magnani & Massimiliano Gaetano Onorato, 2025. "Historical Newspaper Markets," CESifo Working Paper Series 12194, CESifo.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    2. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    3. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    5. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    6. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    7. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    8. Aysegül Kayaoglu & Ghassan Baliki & Tilman Brück & Melodie Al Daccache & Dorothee Weiffen, 2023. "How to conduct impact evaluations in humanitarian and conflict settings," HiCN Working Papers 387, Households in Conflict Network.
    9. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
    10. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
    11. Augustine Denteh & Helge Liebert, 2022. "Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment," Papers 2201.07072, arXiv.org, revised Apr 2023.
    12. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
    13. Maximilian Maurice Gail & Phil-Adrian Klotz, 2021. "The Impact of the Agency Model on E-book Prices: Evidence from the UK," MAGKS Papers on Economics 202111, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    14. Lionel Fontagn'e & Francesca Micocci & Armando Rungi, 2024. "The heterogeneous impact of the EU-Canada agreement with causal machine learning," Papers 2407.07652, arXiv.org, revised Apr 2025.
    15. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
    16. Goller, Daniel & Lechner, Michael & Pongratz, Tamara & Wolff, Joachim, 2025. "Active labor market policies for the long-term unemployed: New evidence from causal machine learning," Labour Economics, Elsevier, vol. 94(C).
    17. Brett R. Gordon & Robert Moakler & Florian Zettelmeyer, 2023. "Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement," Marketing Science, INFORMS, vol. 42(4), pages 768-793, July.
    18. Joshua B. Gilbert & Zachary Himmelsbach & James Soland & Mridul Joshi & Benjamin W. Domingue, 2024. "Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory," Papers 2405.00161, arXiv.org, revised Jan 2025.
    19. Garg, Prashant & Fetzer, Thiemo, 2024. "Causal Claims in Economics," OSF Preprints u4vgs, Center for Open Science.
    20. Nathapornpan Uttama & Popkarn Arwatchanakarn, 2023. "How do economic complexity and productive capacities foster foreign direct investment flows? Evidence from the Asian economies," Economics Bulletin, AccessEcon, vol. 43(1), pages 629-643.

    More about this item

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:nbr:nberwo:32768. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.