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Generative AI for Economic Research: Use Cases and Implications for Economists

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  • Anton Korinek

Abstract

Generative artificial intelligence (AI) has the potential to revolutionize research. I analyze how large language models (LLMs) such as ChatGPT can assist economists by describing dozens of use cases in six areas: ideation and feedback, writing, background research, data analysis, coding, and mathematical derivations. I provide general instructions and demonstrate specific examples of how to take advantage of each of these, classifying the LLM capabilities from experimental to highly useful. I argue that economists can reap significant productivity gains by taking advantage of generative AI to automate micro-tasks. Moreover, these gains will grow as the performance of AI systems continues to improve. I also speculate on the longer-term implications of AI-powered cognitive automation for economic research. The online resources associated with this paper explain how to get started and will provide regular updates on the latest capabilities of generative AI in economics.

Suggested Citation

  • Anton Korinek, 2023. "Generative AI for Economic Research: Use Cases and Implications for Economists," Journal of Economic Literature, American Economic Association, vol. 61(4), pages 1281-1317, December.
    • Handle: RePEc:aea:jeclit:v:61:y:2023:i:4:p:1281-1317
    DOI: 10.1257/jel.20231736
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    Cited by:

    1. Albanesi, Stefania & Da Silva, António Dias & Jimeno, Juan F. & Lamo, Ana & Wabitsch, Alena, 2023. "New technologies and jobs in Europe," Working Paper Series 2831, European Central Bank.
    2. 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.
    3. Dong, Mengming Michael & Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2024. "A scoping review of ChatGPT research in accounting and finance," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).
    4. Byeungchun Kwon & Taejin Park & Fernando Perez-Cruz & Phurichai Rungcharoenkitkul, 2024. "Large language models: a primer for economists," BIS Quarterly Review, Bank for International Settlements, December.
    5. Shumiao Ouyang & Hayong Yun & Xingjian Zheng, 2024. "How Ethical Should AI Be? How AI Alignment Shapes the Risk Preferences of LLMs," Papers 2406.01168, arXiv.org, revised Aug 2024.
    6. Manish Jha & Jialin Qian & Michael Weber & Baozhong Yang, 2024. "Harnessing Generative AI for Economic Insights," Papers 2410.03897, arXiv.org, revised Feb 2025.
    7. Marc Burri & Daniel Kaufmann & Nima Ostovan, 2024. "AI in economic research: A guide for students and instructors," IRENE Policy Reports 24-03, IRENE Institute of Economic Research.
    8. Prashant Garg & Thiemo Fetzer, 2025. "Causal Claims in Economics," Papers 2501.06873, arXiv.org.
    9. Fetzer, Thiemo & Lambert, Peter John & Feld, Bennet & Garg, Prashant, 2024. "AI-Generated Production Networks: Measurement and Applications to Global Trade," CAGE Online Working Paper Series 733, Competitive Advantage in the Global Economy (CAGE).
    10. Kim Shin Young & Sang-Gun Lee & Ga Youn Hong, 2024. "User satisfaction with the service quality of ChatGPT," Service Business, Springer;Pan-Pacific Business Association, vol. 18(3), pages 417-431, December.
    11. Christian Fieberg & Lars Hornuf & Maximilian Meiler & David J. Streich, 2025. "Using Large Language Models for Financial Advice," CESifo Working Paper Series 11666, CESifo.
    12. Wagner Marco, 2024. "Künstliche Intelligenz: ChatGPT bei EZB-Prognosen," Wirtschaftsdienst, Sciendo, vol. 104(9), pages 592-592.
    13. Cova, Joshua & Schmitz, Luuk, 2024. "A primer for the use of classifier and generative large language models in social science research," OSF Preprints r3qng, Center for Open Science.
    14. Buchanan, Joy & Hickman, William, 2024. "Do people trust humans more than ChatGPT?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 112(C).
    15. Michael Bauer & Daniel Huber & Eric Offner & Marlene Renkel & Ole Wilms & Michael D. Bauer, 2024. "Corporate Green Pledges," CESifo Working Paper Series 11507, CESifo.
    16. Vikram Krishnaveti & Saannidhya Rawat, 2024. "GPT takes the SAT: Tracing changes in Test Difficulty and Math Performance of Students," Papers 2409.10750, arXiv.org.
    17. Julian Junyan Wang & Victor Xiaoqi Wang, 2024. "Leveraging Large Language Models to Democratize Access to Costly Financial Datasets for Academic Research," Papers 2412.02065, arXiv.org.
    18. Rosa-García, Alfonso, 2024. "Student Reactions to AI-Replicant Professor in an Econ101 Teaching Video," MPRA Paper 120135, University Library of Munich, Germany.
    19. Heikkilä, Jussi T. S., 2024. "Human intelligence versus artificial intelligence in classifying economics research articles: exploratory evidence," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 81(7), pages 18-30.
    20. Andrew Leigh, 2024. "Using artificial intelligence for economic research: An agricultural odyssey," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 68(3), pages 521-529, July.
    21. Asatryan, Zareh & Birkholz, Carlo & Heinemann, Friedrich, 2024. "Evidence-based policy or beauty contest? An LLM-based meta-analysis of EU cohesion policy evaluations," ZEW Discussion Papers 24-037, ZEW - Leibniz Centre for European Economic Research.
    22. Mourelatos, Evangelos & Zervas, Panagiotis & Lagios, Dimitris & Tzimas, Giannis, 2024. "Can AI Bridge the Gender Gap in Competitiveness?," GLO Discussion Paper Series 1404, Global Labor Organization (GLO).
    23. Hana Jomni & Nikita Zakharov, 2024. "Do Terrorist Attacks Polarize Politicians? Evidence from the European Parliamentary Speeches on Migration," Discussion Paper Series 50 JEL Classification: D7, Department of International Economic Policy, University of Freiburg, revised Nov 2024.
    24. Cova, Joshua & Schmitz, Luuk, 2024. "A primer for the use of classifier and generative large language models in social science research," OSF Preprints r3qng_v1, Center for Open Science.

    More about this item

    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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