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.
“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)
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.
“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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Can environmental courts inhibit corporate greenwash? Evidence from heterogeneous LLMs
Ke, Z., Gao, D., Zhong, X., & Wang, X. (2026) · International Review of Economics & Finance · doi:10.1016/j.iref.2026.105502 · Cited by 14 · see citing works
The Returns to Physical Capital in Knowledge Production: Evidence from Lab Disasters
Baruffaldi, S. H., & Gaessler, F. (2025) · American Economic Journal Applied Economics · doi:10.1257/app.20220629 · Cited by 13 · see citing works
The Impact of Generative Artificial Intelligence on Socioeconomic Inequalities and Policy MakingPreprint
Capraro, V., Lentsch, A., Acemoğlu, D., Akgün, S., Akhmedova, A., Bilancini, E., Bonnefon, J., Brañas‐Garza, P., Butera, L., Douglas, K. M., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4666103 · Cited by 13 · see citing works
Alonso, S. L. N., Ozili, P. K., Sastre-Hernández, B. M., Pacheco, L., & Central Bank of Nigeria, A. (2025) · Quantitative Finance and Economics · doi:10.3934/qfe.2025008 · Cited by 12 · see citing works
The impact of large language models on accounting and future application scenarios
Li, W., Liu, W., Deng, M., Liu, X., & Feng, L. (2025) · Journal of Accounting Literature · doi:10.1108/jal-12-2024-0357 · Cited by 11 · see citing works
Conversations at Scale: Robust AI-led Interviews with a Simple Open-Source PlatformPreprint
Geiecke, F., & Jaravel, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4974382 · Cited by 11 · see citing works
Ma, L., Zhao, X., Jiang, R., Chengke, W., Liao, L., Yang, Z., & Tan, J. (2025) · Buildings · doi:10.3390/buildings15234296 · Cited by 10 · see citing works
User satisfaction with the service quality of ChatGPT
Young, K. S., Lee, S.-G., & Hong, G. Y. (2024) · Service Business · doi:10.1007/s11628-024-00566-y · Cited by 10 · see citing works
“The global AI market size is expected to reach around USD 381.3 billion by 2027, which is five times the AI market size as of 2024 (Korinek 2023; Chintalapati and Pandey 2022).”
InfluentialThe adoption of Large Language Models in economics research
Feyzollahi, M., & Rafizadeh, N. (2025) · Economics Letters · doi:10.1016/j.econlet.2025.112265 · Cited by 10 · see citing works
Firm-Level Litigation Risk: Measurement and EffectsPreprint
Bennett, B., Ham, C., Milbourn, T. T., & Wang, Z. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3247688 · Cited by 10 · see citing works
Harnessing Generative AI for Economic InsightsPreprint
Jha, M., Qian, J., Weber, M., & Yang, B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4976759 · Cited by 10 · see citing works
Examining the Accuracy of AI Detection Software Tools in Education
Halaweh, M., & Refae, G. A. E. (2024) · International Conference on Intelligent Data Science Technologies and Applications (IDSTA) · doi:10.1109/idsta62194.2024.10747004 · Cited by 9 · see citing works
“…Korinek across various use cases, such as ideation, feedback, writing, summarizing text, data analysis, coding, and solving mathematical problems [5], researchers and educators have raised concerns about its potential use for generating unoriginal texts in research papers and assignment reports…”
BackgroundMa, H., Lu, Y., Xiao, Z., Feng, J., Zhang, H., & Yu, J. J. (2025) · Electronics · doi:10.3390/electronics14040742 · Cited by 10 · see citing works
Škare, M., Porada-Rochoń, M., & Celić, R. V. (2025) · Journal of International Students · doi:10.14254/2071-8330.2025/18-4/11 · Cited by 5 · see citing works
AI-Generated Production Networks: Measurement and Applications to Global TradePreprint
Fetzer, T., Lambert, P., Feld, B., & Garg, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5050959 · Cited by 8 · see citing works
Artificial Intelligence–Powered (Finance) Scholarship
Novy‐Marx, R., & Velikov, M. (2026) · Journal of Economic Literature · doi:10.1257/jel.20251821 · Cited by 8 · see citing works
Exploring the impact of digital economy on tourism employment
Li, C., & Tian, J. (2024) · Journal of Hospitality and Tourism Technology · doi:10.1108/jhtt-01-2024-0055 · Cited by 7 · see citing works
Econometric forecasting using ubiquitous news text: Text-enhanced factor model
Seo, B. (2024) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2024.11.001 · Cited by 7 · see citing works
Artificial General Intelligence and Planetary Justice: A Framework for Safe and Just Transitions
Stiefenhofer, P., & Deniz, C. (2025) · Challenges · doi:10.3390/challe16040059 · Cited by 6 · see citing works
“In the digital economy, the concentration of compute resources, data ownership, and technical expertise among a handful of corporations has accelerated AI capabilities while diminishing public accountability and institutional pluralism [26,89].”
Chen, Y., Fang, H., Zhao, Y., & Zhao, Z. (2024) · National Bureau of Economic Research · doi:10.3386/w32327 · Cited by 6 · see citing works
Using Large Language Models for Financial AdvicePreprint
Fieberg, C., Hornuf, L., & Streich, D. J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4850039 · Cited by 7 · see citing works
How Ethical Should AI Be? How AI Alignment Shapes the Risk Preferences of LLMsPreprint
Ouyang, S., Yun, H., & Zheng, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4851711 · Cited by 6 · see citing works
Automatic Product Classification in International Trade: Machine Learning and Large Language Models
de Artiñano, I. M., Depetris, F. R., & Martincus, C. V. (2025) · Review of International Economics · doi:10.1111/roie.70009 · Cited by 7 · see citing works
Shen, C., Pan, W., & Shen, X. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02192-1 · Cited by 5 · see citing works
Artificial intelligence and technological unemployment
Wang, P., & Wong, T.-N. (2026) · Journal of Monetary Economics · doi:10.1016/j.jmoneco.2026.103905 · Cited by 7 · see citing works
Bridging or Widening the Gap? The Role of AI in Shaping Global Research Performance
Ni, R., Qu, Y., & Wang, J. (2026) · Journal of Chinese Political Science · doi:10.1007/s11366-026-09931-2 · Cited by 5 · see citing works
Wisdom or Whims? Decoding Retail Strategies with Social Media and AIPreprint
Chen, S., Peng, L., & Zhou, D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4867401 · Cited by 5 · see citing works
Using Large Language Models for Financial AdvicePreprint
Fieberg, C., Hornuf, L., Meiler, M., & Streich, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5133294 · Cited by 5 · see citing works
Roget, F. M., González, X. A. R., & Mourão, L. P. (2025) · AI & Society · doi:10.1007/s00146-025-02756-2 · Cited by 5 · see citing works
ChatGPT as economics tutor: Capabilities and limitations
Bröse, N., Spielmann, C., & Tode, C. (2026) · International Review of Economics Education · doi:10.1016/j.iree.2025.100337 · Cited by 4 · see citing works
“This approach to prompting is also recommended by, e.g., L. Mollick and E. Mollick (2023) and Korinek (2023).”
Qiao, Y., Min, X., Ge, S., Zhu, X., & Yang, N. (2026) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2026.122522 · Cited by 7 · see citing works
Generative AI and Asset Management
Sheng, J., Sun, Z., Yang, B., & Zhang, A. L. (2026) · Review of Financial Studies · doi:10.1093/rfs/hhag050 · Cited by 5 · see citing works
Unleashing generative AI: funding implications and insights from China
Qian, J. (2024) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2381549 · Cited by 4 · see citing works
“LLMs excel at summarization, demonstrating proficiency in extracting the most pertinent information from a given text passage (Korinek, 2023).”
InfluentialDominic, N., & Pardamean, B. (2024) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech63123.2024.10780864 · Cited by 4 · see citing works
“An online, commercial LLM such as ChatGPT (with GPT-4 version) can also effectively automate microtasks for economists [3], perhaps due to its capabilities for approximating human preferences [4].”
Applications of GPT in Political Science Research: Extracting Information from Unstructured Text
Lee, K., Paci, S., Park, J., You, H. Y., & Zheng, S. (2025) · PS Political Science & Politics · doi:10.1017/s1049096525000046 · Cited by 6 · see citing works
The Impact of AI on Corporate Green Transformation: Empirical Evidence from China
Jiang, Z.-E., Huang, F. C., & Wu, Q. (2025) · Sustainability · doi:10.3390/su17177782 · Cited by 4 · see citing works
Heikkilä, J. (2024) · Journal of Documentation · doi:10.1108/jd-05-2024-0104 · Cited by 4 · see citing works
Asatryan, Z., Birkholz, C., & Heinemann, F. (2024) · International Tax and Public Finance · doi:10.1007/s10797-024-09875-4 · Cited by 5 · see citing works
“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 4 · see citing works
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 · see citing works
Salman, H., Ahmad, M. A., Ibrahim, R. B., & Mahmood, J. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3417 · Cited by 3 · see citing works
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 3 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
Generative AI: A new general-purpose technology for growth and research
Vujović, D. (2025) · Ekonomika preduzeca · doi:10.5937/ekopre2502097v · Cited by 3 · see citing works
Tavoletti, E., Stephens, R. D., Dong, L., & Taras, V. (2025) · Management Research Review · doi:10.1108/mrr-11-2024-0913 · Cited by 4 · see citing works
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 · see citing works
Wu, X., Whittington, D., Chen, Y. J., & Zuo, R. (2024) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2425874 · Cited by 3 · see citing works
“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 · see citing works
- 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 · 2018: 1
- 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
- Social Sciences › Safety Research › 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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