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Generative AI for Economic Research: Use Cases and Implications for Economists
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. (JEL A11, C45, D83, I23, O33)
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“The application of LLMs in economics, particularly ChatGPT, is a relatively unexplored area.”
From Can ChatGPT forecast stock price movements? Return predictability and large language models (2026) · cites this paper as Korinek (2023)
In the citing paper
“There is cause for optimism: AI can complement workers by making them more efficient, helping them to produce higher quality work, or enabling them to take on new value-adding tasks (72–74).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [74]
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“This exercise of using LLM to run automated textual analysis contributes to a new and quickly growing field in economics that uses LLMs, in particular in turning text into numerical data (for a review, see Korinek, 2023).”
Behavioral Economics of AI: LLM Biases and CorrectionsPreprint
Bini, P., Cong, L., Huang, X., & Jin, L. J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5213130 · Cited by 6
Replicate and Extend: An Approach for Term Paper Assignments in Introductory Econometrics
Holian, M. J. (2025) · The American Economist · doi:10.1177/05694345251334557 · Cited by 3
Salman, H., Ahmad, M. A., Ibrahim, R. B., & Mahmood, J. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3417 · Cited by 3
One size does not fit all: customizing teaching and learning strategies with Generative AI
Núñez, N. A., Fernández-Concha, R., & Cornejo-Meza, G. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1699228 · Cited by 4
Guidance for the Use of AI in the Meta‐Analysis of Economics Research
Cook, N., Bartos̆, F., Bom, P. R. D., Gechert, S., Kantová, K., Geyer‐Klingeberg, J., Havránek, T., Iršová, Z., Lušková, M., Opatrný, M., et al. (2026) · Journal of Economic Surveys · doi:10.1111/joes.70105 · Cited by 3
“This document must, by necessity, remain static; however, we will strive to update and revise this as the evolution of AI dictates, similarly to Korinek (2023).”
Using artificial intelligence for economic research: An agricultural odyssey
Leigh, A. (2024) · Australian Journal of Agricultural and Resource Economics · doi:10.1111/1467-8489.12567 · Cited by 3
Advancements in Generative AI: Applications and Challenges in the Modern Era
(2024) · International Journal of Science and Engineering Applications · doi:10.7753/ijsea1308.1023 · Cited by 6
Generative AI: A new general-purpose technology for growth and research
Vujović, D. (2025) · Ekonomika preduzeca · doi:10.5937/ekopre2502097v · Cited by 4
Tavoletti, E., Stephens, R. D., Dong, L., & Taras, V. (2025) · Management Research Review · doi:10.1108/mrr-11-2024-0913 · Cited by 4
Întorsureanu, I., Voicu-Dorobanţu, R., Nisioiu, C., & Ploae, C. (2024) · ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH · doi:10.24818/18423264/58.2.24.08 · Cited by 3
Wu, X., Whittington, D., Chen, Y. J., & Zuo, R. (2024) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2425874 · Cited by 3
“Furthermore, generative AI can be utilized for data analysis (Xu et al., 2024) and has implications for economic research (Korinek, 2023).”
Motoki, F. Y. S., Monteiro, J. J., Malagueño, R., & Rodrigues, V. R. D. S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4595896 · Cited by 4
Metadata
- Authors
- Korinek, A.
- Affiliations
- Brookings Institution (US)
- Corresponding author
- Korinek, Anton
- ORCID
- Korinek, Anton
- Venue
- Journal of Economic Literature
- Publisher
- American Economic Association
- Published
- 2023-12-01
- Volume
- 61
- Issue
- 4
- Pages
- 1281-1317
- ISSN
- 0022-0515 (ISSN-L)
- OpenAlex
- W4389615177
- Cited by
- 293
- References
- 38
- Citations by year
- 2026: 111 · 2025: 109 · 2024: 65 · 2023: 7
- FWCI
- 27.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Economic Literature (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- generative AI, large language models, ChatGPT, economic research, research productivity, cognitive automation, academic writing, background research, data analysis, coding, mathematical derivation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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