The Effect of Artificial Intelligence on The Total Factor Productivity in Iranian Industries

Abrardi, L., Cambini, C., & Rondi, L. (2019). The economics of artificial intelligence: A survey (RSCAS Research Paper No. 2019/58). Robert Schuman Centre for Advanced  Studies. https://doi.org/10.2870/971678

Acemoglu, D. (2022). The simple macroeconomics of AI. Economic Policy, eiae042. https://doi.org/10.1093/epolic/eiae042.

Amini, A., & Hejazi Azad, Z. (2008). The effects of human capital and R&D in TFP growth: The case of Iran. Iranian Journal of Economic Research, 11(35), 1-30

Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. The Review of Economic Studies, 58(2), 277–297.

Autodesk. (2021). Generative design: The future of engineering and design. Retrieved from https://www.autodesk.com/solutions/generative-design

Baily, M. N., & Kane, A. T. (2024). How will AI affect productivity? Brookings Institution. Retrieved from https://www.brookings.edu/articles/how-will-ai-affect-productivity/

Baltagi, A. (2008). Econometrics and Economic Theory. Springer.

Bessen, J. E. (2019). AI and Jobs: The Role of Demand. NBER Working Paper No. 24235.

Bessen, J. E. (2019). Artificial intelligence: A transformational technology for the future of work. Harvard Business Review. Retrieved from https://hbr.org/2019/10/artificial-intelligence-a-transformational-technology-for-the-future-of-work

Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149-158. https://dl.acm.org/doi/10.5555/3287560.3287573.

Blundell, R., & Bond, S. (1998) .Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. Journal of Econometrics, 87(1), 115-143.

Brynjolfsson, E. & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton & Company.

Brynjolfsson, E., & McAfee, A. (2016). Artificial intelligence and its economic impact. Science, 352(6281), 1535-1540. https://doi.org/10.1126/science.aaf1098

Brynjolfsson, E. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 1-25. https://doi.org/10.1257/mac.20170604

Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity. Econometrica, 50(6), 1393-1414.

Choi, T. H., Cheng, T. C. E., & Wu, C. H. (2020). Artificial intelligence in supply chain management: A review of key applications and their implications. Transportation Research Part E: Logistics and Transportation Review, 142, 102118. https://doi.org/10.1016/j.tre.2020.102118

Chui, M., Manyika, J., & Miremadi, M. (2016). "Where machines could replace humans—and where they can’t (yet)." McKinsey Quarterly.

Corrado, C., Halton, C., & Sechel, C. (2005). Intangible capital and economic growth. The Review of Income and Wealth, 51(1), 217-236. https://doi.org/10.1111/j.1475-4991.2005.00161.x

Corrado, C., Hulten, C., & Sichel, D. (2006). "Intangible Capital and U.S. Economic Growth." The Review of Income and Wealth, 52(3), 339-362.

Corrado, C., Haskel, J., Jona-Lasinio, C., & Iommi, M. (2018). Intangible assets in the digital age: The implications for productivity and growth. The Conference Board of Canada. Retrieved from https://www.conferenceboard.ca/e-library/documents/2018/intangible-assets-in-the-digital-age-the-implications-for-productivity-and-growth

Corrado, C., Haskel, J., & Jona-Lasinio, C. (2020). Artificial intelligence and productivity: An intangible assets approach. In Economics of Artificial Intelligence (Vol. 40, pp. 77-95). University of Chicago Press

Corrado, C.,Haskel,J.,& Jona Lasinio,C.)2021(. Artificial intelligence and productivity: an intangible assets approach: Oxford Review of Economic Policy. https://doi.org/10.1093/oxrep/grab018

Czarnitzki, D., Fernández, G. P., & Rammer, C. (2022). Artificial intelligence and firm-level productivity (ZEW Discussion Paper No. 22-005). Centre for European Economic Research (ZEW). https://www.zew.de/en/publications/2022/debate-on-the-impact-of-ai-on-firm-productivity.

Diewert, W. E. (1993). Index Number Theory: A Survey. In Handbook of Applied Econometrics ,2,100-142)

Diewert, W. E. (1976). Exact and Superlative Index Numbers. Journal of Econometrics, 4(2), 115-145. 

Dahlberg, T., Järvinen, J., & Lappalainen, P. (2021). Leveraging AI in financial services: Assessing survival in an era of innovation. Journal of Business Research, 122, 101-109. https://doi.org/10.1016/j.jbusres.2020.08.083

Davenport, T. H., & Ronanki, R. (2018). AI for the real world. Harvard Business Review, 96(1), 108-116. https://hbr.org/2018/01/ai-for-the-real-world

Frey, C. B., & Osborne, M. A. (2017). "The future of employment: How susceptible are jobs to computerization?. Technological Forecasting and Social Change, 114, 254-280.

Green, W. H. (2008). Econometric Analysis. Pearson Education.

Gupta, R., Nair, K., Mishra, M., Ibrahim, B., & Bhardwaj, S. (2024). Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda. International Journal of Information Management Data Insights, 4(1), 100232. 

Jorgenson, D. W. (1963). Capital Theory and Investment Behavior. American Economic Review, 53(2), 247-259.

Jorgenson, D. W. (1966). The Theory of Investment Behavior. In Brookings Papers on Economic Activity, 1966(2), 179-253.

Jorgenson, D. W. (1986). The Contribution of Individual Inputs to Growth. The Review of Economics and Statistics, 68(2), 153-159. 

Jäger, A., Kauffeld, S., & Dörner, T. (2021). Artificial intelligence and productivity: An intangible assets approach. Oxford Review of Economic Policy, 37(2), 324-342. https://doi.org/10.1093/oxrep/grab018

Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M., & Kuusk, A. (2022). Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Systems with Applications, 20(2), 119456. https://doi.org/10.1016/j.eswa.2022.119456.

Jha, R., Srivastava, A., & Gupta, S. (2021). Quality inspection using machine learning: A review of case studies in the manufacturing sector. International Journal of Quality & Reliability Management, 38(8), 1714-1733. https://doi.org/10.1108/IJQRM-06-2020-0211

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Tian, L., & Li, H. (2017). Artificial intelligence in healthcare: Anticipating challenges to ethics, privacy, and bias. American Journal of Industrial Medicine, 60(12), 1114-1121. https://doi.org/10.1002/ajim.22737.

Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Industry 4.0 and the supply chain: A systematic review and future research directions. Computers & Industrial Engineering, 139, 106202. https://doi.org/10.1016/j.cie.2019.106202

Kendrick, J. W. (1976). The Formation and Stocks of Total Capital. New York: Columbia University Press.

Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A Contribution to the Empirics of Economic Growth.The Quarterly Journal of Economics, 107(2), 407-437.

Mátyás, L., & Sevestre, P. (Eds.). (2008). The econometrics of panel data: Fundamentals and recent developments in theory and practice (3rd ed.). Springer.

McKinsey Global Institute. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. Retrieved from https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy

McKinsey Global Institute. (2020). Notes from the AI frontier: Modeling the impact of AI on the world economy. Retrieved from https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy

McKinsey Global Institute. (2021). The productivity imperative: How organizations can adapt to a digital world. Retrieved from https://www.mckinsey.com/business-functions/organization/our-insights/the-productivity-imperative-how-organizations-can-adapt-to-a-digital-world.

Meinen, G., Verbiest, P., & de Wolf, P.-P. (1998). Perpetual inventory method: Service lives, discard patterns, and depreciation methods. Statistics Netherlands, Department of National Accounts.

OECD. (2024). The impact of Artificial Intelligence on productivity, distribution and growth: Key mechanisms, initial evidence and policy challenges. OECD Artificial Intelligence Papers.

O'Mahony, M., & Vecchi, M. (2005). Quantifying the contributions of productivity and real wages to economic growth in the UK: 1970-1991. Applied Economics, 37(14), 1721-1738. 

Romer, P. M. (1990). Endogenous Technological Change. Journal of Political Economy, 98(5), S71-S102.

Porter, M. E., & Heppelmann, J. E. (2014). How Smart, Connected Products Are Transforming Competition. Harvard Business Review, 92(11), 64-88.

Saam, M. (2024). Macroeconomic productivity effects of artificial intelligence. The Economists’ Voice, 21(1), 1-11. https://doi.org/10.1515/ev-2024-0072

Susskind, R., & Susskind, D. (2015). The Future of the Professions: How Technology Will Transform the Work of Human Experts. Harvard University Press

Solow, R. M. (1956). A Contribution to the Theory of Economic Growth. The Quarterly Journal of Economics, 70(1), 65-94.

Solow, R. M. (1957). Technical Change and the Aggregate Production Function. The Review of Economics and Statistics, 39(3), 312-320.

Tian, K., Chen, Y., & Zhang, H. (2021). The impact of artificial intelligence on operational performance: A study of the manufacturing sector. International Journal of Production Economics, 231, 107865. https://doi.org/10.1016/j.ijpe.2020.107865

Varian, H. R. (1992). Microeconomic Analysis. W. W. Norton & Company

World Economic Forum. (2021). How artificial intelligence is transforming the manufacturing industry. Retrieved from https://www.weforum.org/agenda/2021/02/how-ai-is-transforming-manufacturing/

Appendix A: Intangible Capital and TFP: A Theoretical Analysis