Investigating the effect of artificial intelligence in reforming the macroeconomic structure of Iran
Subject Areas :Seyyed Hasan Shojaei 1 , Mehrzad Ebrahimi 2 , Hashem Zare 3
1 - PhD student, Department of Management and Economics, Islamic Azad University, Shiraz branch, Iran
2 - Assistant Professor, Department of Management and Economics, Islamic Azad University, Shiraz branch, Iran
3 - Assistant Professor, Department of Management and Economics, Islamic Azad University, Shiraz branch, Iran
Keywords: artificial intelligence, macroeconomics, gross domestic product, inflation rate, unemployment rate, investment, Iran's economy, economic structure reform,
Abstract :
The purpose of this research is to investigate the effect of using artificial intelligence in reforming the macroeconomic structure of Iran. As an emerging technology, AI can transform the economy by improving production processes, reducing costs, and increasing productivity. This research analyzes the impact of artificial intelligence on macroeconomic indicators such as GDP, inflation, unemployment and investment. The research method is based on quantitative analysis and econometrics, and the data are collected from domestic and international sources, including the Central Bank of Iran and the World Bank. After the data normality test, suitable econometric models were selected and analyzes were performed in EViews7 software. The findings show that artificial intelligence has a positive and significant effect on Iran's economy. Specifically, the coefficient of impact of artificial intelligence on GDP is 0.85 and on unemployment rate -0.60, which indicates an increase in production and a decrease in unemployment, respectively. Also, its effect on inflation is -0.35 and indicates a decrease in inflation. The positive effect of 0.40 on investment indicates the growth of investments. These results confirm that artificial intelligence plays an effective role in improving Iran's economic indicators. In other words, artificial intelligence has significant effects on Iran's macroeconomic indicators. The analysis indicates that the use of artificial intelligence can increase the GDP, so that its coefficient of influence on the GDP is positive and significant. Also, the effect of artificial intelligence on the unemployment rate has also been significant and has led to a decrease in this rate, which indicates the positive role of this technology in creating new jobs and improving the labor market situation. Regarding the inflation rate, the results show that the use of artificial intelligence can lead to a decrease in inflation, which can be justified due to increased productivity and reduced production costs. Finally, the effect of artificial intelligence on investment was also investigated and it was found that this technology can stimulate new investments and increase the amount of investment in the country's economy. The research results confirm that artificial intelligence can be effective in correcting macroeconomic indicators and help Iran's economic growth.
کاشفی صادق. (1403). تحلیل داده؛ مزیتی پنهان یا گنجی دسترسناپذیر. قابل دسترس در asretarakonesh.ir.
Acemoglu, D., D. Autor, J. Hazell and P. Restrepo (2022), Artificial Intelligence and Jobs: Evidence from Online Vacancies, Journal of Labor Economics, 40(S1), S293-S340.
Aghion, P., B. F. Jones and C. I. Jones (2019), Artificial Intelligence and Economic Growth, in A. Agrawal, J. Gans and A. Goldfarb (eds.), The Economics of Artificial Intelligence: An Agenda, University of Chicago Press.
Aghion, P., B. F. Jones and C. I. Jones (2019), Artificial Intelligence and Economic Growth, in A. Agrawal, J. Gans and A. Goldfarb (eds.), The Economics of Artificial Intelligence: An Agenda, University of Chicago Press.
Akerlof, G. 2020. Sins of omission and the practice of economics, Journal of Economic Literature, vol. 58, no. 2, 405–18
Arora, M., & Mittal, A. (2024). Employees’ change in perception when artificial intelligence integrates with human resource management: a mediating role of AI-tech trust. Benchmarking: An International Journal. DOI: 10.1108/bij-11-2023-0795.
Aslan M.F., Sabanci K. A novel proposal for deep learning-based diabetes prediction: converting clinical data to image data. Diagnostics. 2023;13:796. doi: 10.3390/diagnostics13040796.
Atashbar, T. (2021) R4: A transformer machine fine-tuned with the IMF public knowledge, V4.4.2
Azizi, Jafar and Jabbari, Afsaneh, The Role of Artificial Intelligence in Macroeconomics (July 29, 2024). Available at SSRN: https://ssrn.com/abstract=4854582
Babina, T., A. Fedyk, A. He and J. Hodson (2024), Artificial intelligence, firm growth, and product innovation, Journal of Financial Economics, 151, 103745.
Chen, M., Joseph, A., Kumhof, M., Pan, X., Shi, R. and Zhou, X., (2021) Deep reinforcement learning in a monetary model. arXiv preprint arXiv:2104.09368.
Corrado, C., J. Haskel and C. Jona-Lasinio (2021), Artificial intelligence and productivity: An intangible assets approach, Oxford Review of Economic Policy, 37(3), 435-458.
Dow, S. C. 2007. Variety of methodological approach in economics, Journal of Economic Surveys, vol. 21, no. 3, 447–65. doi:10.1111/j.1467-6419.2007.00510.x
Dow, S. C. 2019. Pluralist economics: is it scientific?, pp. 13–30 in Decker, S., Elsner, W., and Flechtner, S. (eds.), Advancing Pluralism in Teaching Economics: International Perspectives on a Textbook Science, London, Routledge
Espin, J., Palmas, S., Carrasco-Rueda, F., Riemer, K., Allen, P. E., Berkebile, N., Hecht, K. A., Kastner-Wilcox, K., Núñez-Regueiro, M. M., Prince, C., Rios, C., Ross, E., Sangha, B., Tyler, T., Ungvari-Martin, J., Villegas, M., Cataldo, T. T. and Bruna, E. M. 2017. A persistent lack of international representation on editorial boards in environmental biology, PLoS Biology, vol. 15, no. 12, e2002760
F¨orster, M., Klier, M., Kluge, K. & Sigler, I. (2020). Fostering Human Agency: A Process for the Design of User-Centric XAI Systems. ICIS 2020 Research Papers.
Feng, Z., Narasimhan, H., & Parkes, D. C. (2018, July). Deep learning for revenue-optimal auctions with budgets. In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (pp. 354–362).
Hoover, K. D. 2023. The struggle for the soul of macroeconomics, Journal of Economic Methodology, vol. 30, no. 2, 80–9. doi:10.1080/1350178X.2021.2010281
Jindal K., Upadhyay R., Padhy P.K., Longo L. In: Artificial Intelligence-Based Brain-Computer Interface. Bajaj V., Sinha G., editors. Academic Press; 2022. 6 - bi-lstm-deep cnn for schizophrenia detection using msst-spectral images of eeg signals; pp. 145–162.
Leandro, Maciel., Plamen, Angelov., Fernando, Gomide. (2024). Guest editorial: Special issue: Applications of artificial intelligence and machine learning in business, finance and economics. REGE - Revista de Gestão, doi: 10.1108/rege-04-2024-209.
Lu, Y., & Zhou, Y. (2021). A review on the economics of artificial intelligence. Journal of Economic Surveys, 35 (4), 1045–1072.
Lundberg, S. and Stearns, J. 2019. Women in economics: stalled progress, Journal of Economic Perspectives, vol. 33, no. 1, 3–22
Powdthavee, N., Riyanto, Y. E. and Knetsch, J. L. 2018. Lower-rated publications do lower academics’ judgments of publication lists: evidence from a survey experiment of economists, Journal of Economic Psychology, vol. 66, 33–44
Rotbe, Sayna; Wei Hsuan Tseng, Beatriz Merino-Barbancho, Muhammad Salman Haleem, Luis Montesinos, Leandro Pecchia, Giuseppe Fico, Alessio Botta. (2024). Evaluating impact of movement on diabetes via artificial intelligence and smart devices systematic literature review. Expert Systems with Applications Available online 9 August 2024, 125058
Saam, Marianne. (2024). The Impact of Artificial Intelligence on Productivity and Employment – How Can We Assess It and What Can We Observe?. Volume 59, 2024 · Number 1 · pp. 22–27.
Savarimuthu, B. T. R., Corbett, J., Yasir, M. & Lakshmi, V. (2020). Using Machine Learning to Improve the Sustainability of the Online Review Market. ICIS 2020 Research Papers.
Shi, R. A., (2021a) Learning from Zero: How to Make Consumption-Saving Decisions in a Stochastic Environment with an AI Algorithm. arXiv preprint arXiv:2105.10099.
Singh L., Khanna M., Garg H., Singh D. Emperor penguin opti- mization algorithm- and bacterial foraging optimization algorithm-based novel feature selection approach for glaucoma classification from fundus images. Soft Comput. 2023;28:1–37. doi: 10.1007/s00500-023-08449-6.
Srinivasu PN, Ahmed S, Hassaballah M, Almusallam N. An explainable Artificial Intelligence software system for predicting diabetes. Heliyon. 2024 Aug 10;10(16):e36112. doi: 10.1016/j.heliyon.2024.e36112. PMID: 39253141; PMCID: PMC11381601.
Stecher, P., Pohl, M. & Turowski, K. (2020). Enterprise architecture’s effects on organizations’ ability to adopt artificial intelligence - A Resource-based perspective. ECIS 2020 Research Papers.
Tasin I., Nabil T.U., Islam S., Khan R. Diabetes prediction using machine learning and explainable AI techniques. Healthcare Technology Letters. 2023;10:1–10.
Van Der Velden B.H., Kuijf H.J., Gilhuijs K.G., Viergever M.A. Explainable artificial intelligence (xai) in deep learning-based medical image analysis. Med. Image Anal. 2022;79 doi: 10.1016/j.media.2022.102470.
Veloso, M., Balch, T., Borrajo, D., Reddy, P., & Shah, S. (2021). Artificial intelligence research in finance: discussion and examples. Oxford Review of Economic Policy, 37 (3), 564–584.
Venumuddala, V. & Kamath, R. (2020). Actualizing affordances – Story of Indian Information Technology (IT) Industry delivering Artificial Intelligence based solutions. ICIS 2020 Research Papers.
Wang L., Mu Y., Zhao J., Wang X., Che H. IGRNet: a deep learning model for non-invasive, real-time diagnosis of prediabetes through electrocardiograms. Sensors. 2020;20:2556. doi: 10.3390/s20092556.
Wright, J. 2023. The hierarchy in economics and its implications, Economics and Philosophy, 1–22. doi: 10.1017/S0266267123000032
Zolas, Nicholas, Zachary Kroff, Erik Brynjolfsson, Kristina McElheran, David N. Beede, Cathy Buffington, Nathan Goldschlag, Lucia Foster, and Emin Dinlersoz. 2021. “Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey.” NBER Working Paper 28290, National Bureau of Economic Research, Cambridge, MA. https://www.nber.org/papers/w28290.
