The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
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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In the citing paper
“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]
Citations
Cites
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Scaling Laws for Neural Language Models
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“The performance of LLMs improves pre-dictably according to so-called scaling laws, which are empirical regularities that have held for several generations of machine learning models (Kaplan et al. 2020).”
BackgroundOn the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 2×
“The reactions to the release of generative AI tools such as ChatGPT have been sharply divided: One camp of commentators label LLMs as nothing but “stochastic parrots” (Bender et al. 2021) or “advanced autocom-plete.””
BackgroundChain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023) · arXiv · doi:10.48550/arxiv.2303.10130 · Cited by 2,330 · cited 1×
“Eloundou et al. (2023) and Felten et al. (2023) analyze how different occupations and industries will be affected by LLMs. Horton (2023) explores the use of LLMs as simulated economic agents.”
BackgroundExperimental evidence on the productivity effects of generative artificial intelligence
Noy, S., & Zhang, W. (2023) · Science · doi:10.1126/science.adh2586 · Cited by 2,150 · cited 1×
“Noy and Zhang (2023) demonstrate in a controlled experiment that ChatGPT made writers 40 percent faster while improving output quality and helping writers with weaker skills more, thereby reducing output inequality.”
BackgroundSparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526
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Emergent Abilities of Large Language Models
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Cited by
Sorted by citations
Can ChatGPT forecast stock price movements? Return predictability and large language models
Lopez-Lira, A., & Tang, Y. (2026) · Journal of Financial Economics · doi:10.1016/j.jfineco.2026.104335 · Cited by 425
The impact of generative artificial intelligence on socioeconomic inequalities and policy making
Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., Bonnefon, J.-F., Brañas-Garza, P., Butera, L., Douglas, K. M., et al. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae191 · Cited by 371
“There is currently little legislation preventing the use of deepfakes in political campaigns, although there are some steps in this direction.”
Budhathoki, T., Zirar, A., Njoya, E. T., & Timsina, A. (2024) · Studies in Higher Education · doi:10.1080/03075079.2024.2333937 · Cited by 280
“(Korinek 2023, 1281) ChatGPT can be a complementary teaching-learning aid for students (Ali et al. 2024).”
BackgroundGenerative AI in innovation and marketing processes: A roadmap of research opportunities
Cillo, P., & Rubera, G. (2024) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-024-01044-7 · Cited by 256
“For instance, it is estimated that the cost of training GPT-4 is over $100 million (Korinek, 2023 ).”
Human-AI agency in the age of generative AI
Krakowski, S. M. (2025) · Information and Organization · doi:10.1016/j.infoandorg.2025.100560 · Cited by 223
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
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