Cited by 293Artificial Intelligence ApplicationsComputational and Text Analysis MethodsEthics and Social Impacts of AI

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

Korinek, Anton·2023·Journal of Economic Literature

DOI 10.1257/jel.20231736

Open this paper in CoChat — free account

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)

Integrity signals

Registry

Crossref · 10.1257/jel.20231736

Cites

40 of 40 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Attention Is All You Need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Scaling Laws for Neural Language Models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · arXiv · doi:10.48550/arxiv.2001.08361 · Cited by 9,550 · cited 1×
“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).”
Background
On 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.””
Background
Chain-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.”
Background
Experimental 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.”
Background
Sparks 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
Text as Data
Gentzkow, M. A., Kelly, B., & Taddy, M. A. (2019) · Journal of Economic Literature · doi:10.1257/jel.20181020 · Cited by 1,353
Emergent Abilities of Large Language Models
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.07682 · Cited by 1,264

Flagged first, then by citations

Show all 38 references

Research works printed but not linked: 5

Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. 2018. Prediction Machines: The Simple Economics of Artificial Intelligence. Boston, MA: Harvard Business Review Press.
Tabarrok Alexander T., 2023, GMU Working Paper in Economics, 23
Frank, Robert H. 1991. "Positional Externalities." In Strategy and Choice, edited by Richard Zeckhauser, 25-47. Cambridge, MA: MIT Press.
Keynes, John Maynard. 1936. The General Theory of Employment, Interest, and Money. London: Palgrave Macmillan.
Alan, 1950, Mind, 59, 433

Sorted by citations

Items 101–200 of 293

AI Agents and Higher-Order WorkPreprint
Sarkar, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5713646 · Cited by 3
Comparing perceptual judgments in large multimodal models and humans
Dickson, B., Maini, S. S., Sanders, C. A., Nosofsky, R. M., & Tiganj, Z. (2025) · Behavior Research Methods · doi:10.3758/s13428-025-02728-w · Cited by 3
“Advancements in deep neural networks have already been applied to cognitive science, leading to more general models useful in practical applications, such as decision-making in medicine (Hasan et al., 2022; Holmes et al., 2020; Rahgooy et al., 2022) and economics (Horton, 2023; Korinek, 2023).”
Future Ethics. Principles for Governing Bio-Hybrid Robotics in a Post-biological World
Astobiza, A. M., Astakhov, S., & Mestre, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-07448-5_45 · Cited by 2
A taxonomy of AI experiments
Alekseev, A. O., & Strobel, C. (2026) · Journal of Behavioral and Experimental Economics · doi:10.1016/j.socec.2026.102525 · Cited by 2
A Longitudinal Thematic Analysis of Technology Integration in Marketing
Jayawardene, V., Ewing, M. T., Niemann‐Struweg, I., & Cunningham, N. (2026) · Marketing Education Review · doi:10.1080/10528008.2026.2630712 · Cited by 4
The impact of generative artificial intelligence on the process of creating digital content
Milić, S., Stanojević, L., & Bučalina, M. (2025) · Drustveni horizonti · doi:10.5937/drushor2510007m
Reporting Guidelines for Meta‐Analysis in Economics—Updated for AI
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.70116 · Cited by 3
Exploring graduate students’ acceptance and use of generative AI: An application of the UTAUT2 model
Yıldırım-Samuk, Ş., & Altay, İ. F. (2026) · Contemporary Educational Technology · doi:10.30935/cedtech/18745 · Cited by 2
Effect of Digital Optimization Strategy and Communication on Employee Performance
Munandar, D., Winantia, M. B., & Narimawati, U. (2024) · IJBE (Integrated Journal of Business and Economics) · doi:10.33019/ijbe.v8i2.856 · Cited by 2
“This also becomes easier because in the digital era, the development of artificial intelligence makes a significant contribution to economic growth in commercial organizations (Korinek, 2023).”
Background
Investigating the Driving Factors of the Use of Generative AI Among College Students
Tang, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-65884-6_23 · Cited by 2
Data retrieval from local heritage books—Is artificial intelligence the solution?
Stelter, R., & Biehler, R. (2025) · Historical Methods A Journal of Quantitative and Interdisciplinary History · doi:10.1080/01615440.2025.2512744 · Cited by 2
“Not surprisingly, they are already ubiquitous in various research fields such as economics (Korinek 2023).”
Total Recall? Evaluating the Macroeconomic Knowledge of Large Language Models
Crane, D. L., Karra, A., & Soto, P. E. (2025) · Finance and Economics Discussion Series · doi:10.17016/feds.2025.044 · Cited by 2
“The rise of large language models (LLMs) has generated interest in how they can be used for economic analysis and forecasting (e.g., Korinek 2023).”
Generative Artificial Intelligence's Integration for Data Analysis in Conducting Academic Research: Understanding the Perspective of Research Supervisors
Sehmi, A., Sarfraz, I., & Hussain, M. (2025) · Journal of Advanced Academics · doi:10.1177/1932202x251365312 · Cited by 5
“…that use of GenAI for data analysis is still at early stage, as these tools are currently mainly used for literature review, which aligns with Korinek ’ s (2023) ﬁ ndings who argued that GenAI such as ChatGPT has enormous potential in research including data analysis which is dormant until…”
Can Authoritarian Propaganda Compete with the Opposition on Social Media? Experimental Evidence from RussiaPreprint
Rochlitz, M., Schoors, K. J. L., & Zakharov, N. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4433145 · Cited by 2
Evidence-Based Policy or Beauty Contest? An LLM-Based Meta-Analysis of EU Cohesion Policy EvaluationsPreprint
Asatryan, Z., Birkholz, C., & Heinemann, F. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4871648 · Cited by 2
How ChatGPT affects Scientific Publication: an Empirical study on EFL authorsPreprint
Liang, Y., Yang, T., & Zhu, F. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4992755 · Cited by 3
Corporate Green Pledges *Preprint
Bauer, M. D., Huber, D., Renkel, M., Offner, E. A., & Wilms, O. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5027881 · Cited by 2
The Labor Market Impact of Artificial Intelligence: Evidence from US RegionsPreprint
Huang, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5137231 · Cited by 2
GJAE is Preparing for Future Developments in the Scientific System: New Policies Regarding Data and AI
Hüttel, S., Rommel, J., Teuber, R., Banse, M., & Grebitus, C. (2025) · German Journal of Agricultural Economics · doi:10.52825/gjae.v74i.2824 · Cited by 2
“AI has already become an indispensable tool for researchers, for example, in economics (Korinek, 2023), social sciences (Bail, 2024) and in the applied agricultural and food economics domains (Hüttel, Hess, 2024; Fraser et al., 2025).”
AI and the Future of Work in an Aging EconomyPreprint
Pizzinelli, C., & Tavares, M. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5345347 · Cited by 2
Les humanités numériques, un révélateur de l’écart épistémologique entre l’histoire et l’économie
Gauthier, L. (2024) · Humanités numériques · doi:10.4000/12ypv · Cited by 1
Conceptualizing the limits of the use of generative AI in economic analysis via a deep meaning of habs
Belabes, A. S. (2025) · Journal of King Abdulaziz University-Islamic Economics · doi:10.64064/1658-4244.1001 · Cited by 1
A Knowledge-Augmented Assisted Authoring Method for Power Energy Big Data Retrieval in Report Content
Chen, Y., Shen, H., Xie, B., Wang, J., Zhao, W., Pan, Z., Fu, C., Wang, X., Zhang, Z., & Cai, C. (2025) · International Conference on Artificial Intelligence Technologies and Applications (ICAITA) · doi:10.1109/icaita67588.2025.11137870 · Cited by 1
“Currently, large language model technology has been widely applied in industries such as finance [9-11] , healthcare [12-14] , and computing [15-17] due to its powerful text data processing capabilities.”
Language as Data: A Survey of Natural Language Processing for Economics and Finance
Lucchetti, A. H., & Cajueiro, D. O. (2025) · Journal of Economic Surveys · doi:10.1111/joes.70014 · Cited by 1
Narrative monetary policy expectation in China
Zhu, D., Wang, X., & Zhang, Y. (2025) · China Economic Review · doi:10.1016/j.chieco.2025.102568 · Cited by 1
Operational, marketing and psychological dimensions of food truck entrepreneurship
Sen, S. S., Savitskie, K., Ranganathan, S. K., Kallmuenzer, A., & Brooks, J. R. (., Jr. (2025) · British Food Journal · doi:10.1108/bfj-05-2025-0731 · Cited by 2
A Progress Report on the Economics of Artificial Intelligence: Impacts, Challenges, and Future Directions
Steinmetz, A. (2025) · Journal of Economic Analysis · doi:10.58567/jea04040007 · Cited by 1
Gender bias in Artificial Intelligence
Cuesta, J. (2025) · Journal of Economic Policy Reform · doi:10.1080/17487870.2025.2579039 · Cited by 1
“…Li 2010), information security and privacy (Brinton et al. 2016), decision support systems (Li et al. 2014), design science and economic research (Korinek 2023), and natural language processing CONTACT Jose Cuesta jcuesta@worldbank.org racial bias, as well as those focused primarily on the…”
Benchmarking In-Context Learning Strategies of Large Language Models for Math Reasoning Tasks
Rong, Y., Seßler, K., Gözlüklü, E., & Kasneci, E. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3630117 · Cited by 3
The Capabilities of Large Language Models in Extracting Unstructured Data From Histopathology Reports
Adamson, S., Berry, C., Adler, N. R., Christian, T., Librata, W., & Mar, V. J. (2025) · Australasian Journal of Dermatology · doi:10.1111/ajd.70034 · Cited by 1
“For example, it costs $700,000 per day to operate ChatGPT, and the cost to train version GPT4 was estimated at over $100 million USD before including costs of training staff and data curators [6, 31].”
Large Language Models in Economics
Ash, E., Hansen, S. E., Muvdi, Y., & Marangon, C. (2025) · Springer eBooks · doi:10.1007/978-3-031-88240-1_8 · Cited by 1
Do terrorist attacks polarize politicians? Evidence from the European parliamentary speeches on migration
Jomni, H., & Zakharov, N. (2026) · Journal of Peace Research · doi:10.1093/jopres/xjaf007 · Cited by 1
Simulated cognitive empathy as an emerging tool for socio-ecological research
Richards, D. R., Heays, A., Herzig, A., Lavorel, S., & Vannier, C. (2026) · Environmental Research Communications · doi:10.1088/2515-7620/ae4cfb · Cited by 1
AI‐Driven Inflation Forecasting in the Aftermath of COVID‐19
Jaworski, K. (2026) · Journal of Forecasting · doi:10.1002/for.70148 · Cited by 1
Enhancing GDP nowcasts with ChatGPT: A novel application of PMI news releases
de Bondt, G. J., & Sun, Y. (2026) · Journal of Policy Modeling · doi:10.1016/j.jpolmod.2026.107052 · Cited by 1
The Growth of Digital Transformation in European Companies: Impact of AI Use to Better Decision Making in Organizational Processes
Santos, V., & Malta, P. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-16284-7_11 · Cited by 1
The Computerization of Economics: Three Lessons for Economics
Boumans, M. J., Chassonnery-Zaïgouche, C., Dechaux, P., & Sergi, F. (2023) · OEconomia · doi:10.4000/oeconomia.15684 · Cited by 1
Transformation in the World of Commerce and Economics through AI
Tandon, U., Tandon, A., & Mehrotra, T. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815305180124010012 · Cited by 1
2 Best Practices for Leveraging Generative AI in Experimental ResearchPreprint
Chang, S., Kennedy, A., Leonard, A., & List, J. A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4979191 · Cited by 1
Efficacy of AI and Other Large Language Models in Predicting Stock PricesPreprint
Vidal, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4947135 · Cited by 1
Leveraging Large Language Models to Democratize Access to Costly Financial Datasets for Academic ResearchPreprint
Wang, J., & Wang, V. X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5012660 · Cited by 2
AI AgencyPreprint
Canayaz, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5109326 · Cited by 1
AI Coordination and Self-fulfilling Financial CrisesPreprint
Yang, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5139405 · Cited by 1
Decoding China's Industrial PoliciesPreprint
Fang, H., Li, M., & Lu, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5259649 · Cited by 1
Artificial Intelligence and Technological Unemployment
Wang, P., & Wong, T.-N. (2025) · National Bureau of Economic Research · doi:10.3386/w33867 · Cited by 2
AI and the Fed
Kazinnik, S., & Brynjolfsson, E. (2025) · National Bureau of Economic Research · doi:10.3386/w33998 · Cited by 1
AI for Fairness? The Role of Generative Tools in Shaping Freelancer SuccessPreprint
Cao, J., & Li, Z. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5346514 · Cited by 1
Structured Data with LLMs Done Right: A Practical Guide to Text Classification, Information Retrieval and Generation with LLMsPreprint
Villota, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5636430 · Cited by 1
Policy Advice by Digital Economic Twins: Using Agent-based Modelling and Artificial IntelligencePreprint
Pobuda, P., & Andrae, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6336459 · Cited by 1
An Impossible Interview with John Stuart MillPreprint
Benedictis, L. D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4640186
The Adoption and Efficacy of Large Language Models: Evidence From Consumer Complaints in the Financial IndustryPreprint
Shin, M., Kim, J., & Shin, J. (2024) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/fdzqg · Cited by 1
ВИКОРИСТАННЯ ВЕЛИКИХ МОВНИХ МОДЕЛЕЙ ДЛЯ АНАЛІЗУ ЧАСОВИХ РЯДІВ У РОЗДРІБНІЙ ТОРГІВЛІ: ПЕРСПЕКТИВИ ТА МОЖЛИВОСТІ
Косован, О., & Datsko, M. (2024) · Цифрова економіка та економічна безпека · doi:10.32782/dees.10-35
The Role of Generative AI-Assisted Literature Reviews in Transforming Academic Research
Vihari, N. S., & Kaur, A. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-1798-3.ch006
Asset Pricing Impacts of Ai in a Model with Heterogeneous Risk AversionPreprint
Gil, H. G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4859670
Asset Pricing Impacts of AI in a Model with Heterogeneous Risk AversionPreprint
Gil, H. G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4864074
Asset Pricing Impacts of Ai in a Model with Heterogeneous Risk AversionPreprint
Gil, H. G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4906991
Artificial Intelligence applications and effects on business communication among marketing professionals
Dadua, N. D. D. (2024) · International Journal of Research Studies in Management · doi:10.5861/ijrsm.2024.1222
Künstliche Intelligenz: ChatGPT bei EZB-Prognosen
Wagner, M. (2024) · Wirtschaftsdienst · doi:10.2478/wd-2024-0154
The Conflict-of-Interest Discount in the Marketplace of IdeasPreprint
Barrios, J. M., Lancieri, F., Levy, J., Singh, S. K., Valletti, T., & Zingales, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4979205 · Cited by 1
Content Bias and Information CompressionPreprint
Liu, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5005665
Unslicing the Pie: AI Innovation and the Labor Share in European RegionsPreprint
Minniti, A., Prettner, K., & Venturini, F. (2024) · Social Science Research Network · doi:10.2139/ssrn.4971431
Generative AI and Finance*Preprint
Eisfeldt, A. L., & Schubert, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4988553
AI Exposure without Labor Data: Measuring AI’s Impact through FirmsPreprint
Liu, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4668083 · Cited by 1
Analysts' Belief Formation in Their Own WordsPreprint
Ke, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5025830
Out of the (Black)Box: AI as Conditional ProbabilityPreprint
Chen, H., Didisheim, A., & Somoza, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5012852
No matter your financial literacy: Simplicity wins when choosing a fund
Gong, Z. F., & Müller, S. (2026) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2026.101188
AI Adoption in Economics ResearchPreprint
Feyzollahi, M., & Rafizadeh, N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5016162
Chinese Housing Market Sentiment Index: A Generative AI Approach and An Application to Monetary Policy TransmissionPreprint
Zhao, Y., & Chen, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5069851
Ai and Firm Green Transformation: Empirical Evidence from ChinaPreprint
Wu, Q., JIANG, Z. A. E., & Huang, F. C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5163075
Economics Research in the Twenty-First Century — 25 Years of Scientific BreakthroughsPreprint
Gomes, O. (2025) · Qeios · doi:10.32388/ka9fg2 · Cited by 1
Economics Research in the Twenty-First Century — 25 Years of Scientific BreakthroughsPreprint
Gomes, O. (2025) · Qeios · doi:10.32388/ka9fg2.2
Economics Research in the Twenty-First Century — 25 Years of Scientific BreakthroughsPreprint
Gomes, O. (2025) · Qeios · doi:10.32388/ka9fg2.3
Rather Be Wrong Than Controversial? Investigating Economic Bias in ChatGPTPreprint
Ashkiani, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5138961
GPT-Language-based Experimental Economics System (GLEES)Preprint
Chen, K., & Wu, D. Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5200496
Narrative Monetary Policy Expectation in ChinaPreprint
Zhang, Y., Wang, X., & Zhu, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5233191
Społeczno-ekonomiczne determinanty rozwoju zastosowań technologii generatywnej sztucznej inteligencji
Prokopowicz, D. (2024) · Uniwersyteckie Czasopismo Socjologiczne · doi:10.21697/ucs.2024.34.2.03
Using GenAI Tools to Engage Students with Economics ResearchPreprint
Daniels, G. E., Kakar, V., Lee, M., & Tombari, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5271856
Asset Pricing Impacts of AI in a Model with Heterogeneous Risk AversionPreprint
Gil, H. G., & Mihai, M. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5277572
A Taxonomy of AI ExperimentsPreprint
Alekseev, A., & Strobel, C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5291490
AI and the FedPreprint
Kazinnik, S., & Brynjolfsson, E. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5354077
Topic Labeling Using Large Language ModelsPreprint
Bogachek, O. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5369321 · Cited by 1
Labor Market Adjustment to Generative AI: The Good, the Bad, and the UglyPreprint
Rafizadeh, N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5367192
SOTA large language models’ contribution on the design of economic policies
Zhao, Z. (2025) · Advances in Engineering Technology Research · doi:10.56028/aetr.14.1.1569.2025
Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope?
Baily, M. N., Byrne, D. M., Kane, A. T., & Soto, P. E. (2025) · Finance and Economics Discussion Series · doi:10.17016/feds.2025.053 · Cited by 11
AI agents for cash management in payment systems
Aldasoro, I., & Desai, A. (2026) · The Journal of Financial Market Infrastructures · doi:10.21314/jfmi.2026.008
“In contrast, new models with ‘reasoning’ capabilities mitigate some of these limitations—to a certain extent—by employing techniques that enable step-by-step analysis (Wei et al. 2022; Korinek 2023, 2025).”
The Current State of Knowledge on the Role of Research Technologies in Innovation
Barbosu, S., & Teodoridis, F. (2025) · Springer eBooks · doi:10.1007/978-3-031-98341-2_2
La IA y la nueva era de la investigación económica
Estupiñán, J. M. T., & Danna-Buitrago, J. P. (2025) · APUNTES DEL CENES · doi:10.19053/uptc.01203053.v44.n80.2025.18993
“Proteger las ideas: ser conscientes de la propiedad intelectual y tomar medidas para proteger las ideas generadas con la ayuda de la IA (Korinek, 2024b).”
AI-Based Forecasting and Market Expectations: A Self-Fulfilling Prophecy?
Beretta, E., Dianova, V. G., Pankratz, G., & Schollmeyer, U. (2025) · Review of Political Economy · doi:10.1080/09538259.2025.2562206
Generative AI and Income Growth: Early Evidence on Global Data
Venturini, F. (2025) · Gospodarka Narodowa · doi:10.33119/gn/203715
“GenAI expands the scope of technological innovation and the development of novel ideas [ Korinek, 2023 ].”
Does AI lead to significant skilling or deskilling of academics? Results from a large-scale surveyPreprint
Muscio, A., & Shibayama, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5572865
Algorithmic Narratives: A Minskian Analysis of Financial Media Discourse in the Age of Generative AI (2015-2025)Preprint
Pilkington, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5601795
Nurturing Knowledge
Candiotto, L., Growiec, J., Kellmeyer, P., Rabinowitch, I., & Livermore, M. A. (2025) · Oxford University Press eBooks · doi:10.1093/9780198945215.003.0176
Managing migration: Female mayors and the intake of refugees
Schirner, S. (2025) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2025.107306
The decline of age-friendly jobs in China: Evidence from online job vacancies
Feng, S., Liu, J., & Lu, J. (2025) · Economic Modelling · doi:10.1016/j.econmod.2025.107399
Multilingual Sentiment Analysis in Economics and Translation-Induced DistortionsPreprint
Magro, M. M., & Pinter, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5899382
Development of Multilingual AI Legal Assistants for Real-Time Legal Aid Delivery, Document Drafting, and Procedural Guidance in Underserved Judicial Systems
Adain, A. A., Alasadi, A. Y., Hameed, M., Mehd, R., Hamza, A. A., Hameed, D. W., & Mustafa, A. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11292048
“Of these, language-adaptive legal AI—hinging on transformer-based models and cross-lingual embeddings—has been at the forefront of democratizing procedural information and streamlining drafting workflows in legal services [3].”
GPT takes the SAT: Tracing changes in Test Difficulty and Students' Math PerformancePreprint
Rawat, S., & Suresh, V. K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4915452
Business Economics Forecasting Using LLMs: A Reflective StudyPreprint
Ganguli, S., & Mercado, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5511969
Unveiling Plant‐Product Productivity via First‐Order Conditions: Robust Replication of Orr (2022)
Hong, J., & Luparello, D. (2025) · Journal of Applied Econometrics · doi:10.1002/jae.70033
An LLM-Powered Framework for Privacy-Preserving and Scalable Labor Market Analysis
Ji, W., & Ying, Z. (2025) · Mathematics · doi:10.3390/math14010053

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)
DOI: 10.1257/jel.20231736
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 ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks