ARTIFICIAL INTELLIGENCE AND SERVICE, INDUSTRIAL, AND AGRICULTURAL EMPLOYMENT: COMPREHENSIVE INTERNATIONAL MACROECONOMIC EVIDENCE

Year 2024, Volume: 15 Issue: 30, 605 - 629, 30.12.2024

Yahya Algül

https://doi.org/10.36543/kauiibfd.2024.024

Abstract

Recent advancements in artificial intelligence (AI) technology have revived concerns about technological unemployment. Regarding the issue, this study examines the impact of AI on employment rates across 17 leading AI countries from 1998 to 2017 using two panel econometric techniques, DOLS and FMOLS, to ensure robust results. For the first time, as far as is known, the effect of AI on employment in distinct sectors is analyzed separately. By uniquely combining different countries and sectors within a macroeconomic framework, this study provides a more comprehensive understanding through a total of eight estimates. The findings indicate that, according to both DOLS and FMOLS techniques, increased AI innovation raises employment rates in the overall economy and in the service sector, while reducing employment rates in the industrial and agricultural sectors. Consequently, while AI positively impacts overall employment, considering industrial and agricultural sectors, employment policies should be adjusted to meet evolving needs in the AI era.

Keywords

Artificial Intelligence, Technological Unemployment, Patent, Employment Policy

YAPAY ZEKA VE HİZMET, SANAYİ VE TARIM İSTİHDAMI: KAPSAMLI ULUSLARARASI MAKROİKTİSADİ ANALİZ

Abstract

Yapay zeka (YZ) alanındaki son gelişmeler teknolojik işsizlik konusu tekrardan gündeme getirmiştir. Bununla bağlantılı olarak, bu çalışmada YZ’nin istihdam oranları üzerindeki etkisi, YZ teknolojisinde öncü olan 17 ülkenin 1998 ve 2017 yılları verileri kullanılarak ve sonuçların güvenirliliğini güçlendirilmesi adına DOLS ve FMOLS olmak üzere iki farklı teknikle analiz edilmiştir. Dahası, bilindiği kadarıyla literatürde ilk defa YZ’nin farklı sektörlerdeki etkisinin makroekonomik olarak ölçülebilmesi adına dört farklı model ve iki farklı yöntemle sekiz farklı analiz yapılmıştır. Bulgulara göre, hem DOLS hem de FMOLS tekniği için, YZ alanında inovasyon arttıkça, hem ekonominin bütünündeki, hem de sadece hizmetler sektöründeki istihdam oranları artmaktayken, sanayi ve tarım sektörlerinde istihdam oranları düşmektedir. Sonuç olarak, her ne kadar YZ’nin ekonominin bütününde pozitif bir istihdam etkisi yaratabileceği görülse de, sanayi ve tarım sektöründeki olumsuz etkileri göz önüne alınarak, istihdam politikalarının YZ çağındaki değişen ihtiyaçlara göre yeniden dizayn edilmesi tavsiye edilmektedir.

Keywords

Yapay Zeka, Teknolojik İşsizlik, Patent, İstihdam Politikaları

References

• Acemoglu, D. &Restrepo, P. (2017) , Robots and jobs: evidence from US labor markets. National Bureau of Economic Research Working paper 23285. Cambridge, MA. http://www.nber.org/papers/w23285
• Acemoglu, D., Autor, D., Hazell, J., & Restrepo, P. (2022). Artificial intelligence and jobs: Evidence from online vacancies. Journal of Labor Economics, 40(S1), S293-S340. http://www.nber.org/papers/w28257
• Adalı, Z., Toygar, A., KARATAŞ, A. M., & Yıldırım, U. (2024). Sustainable fisheries and the conservation of marine resources: A stochastic analysis of the fishery balance of African countries. Journal for Nature Conservation, 126653. https://doi.org/10.1016/j.jnc.2024.126653
• Batiz-Lazo, B., Efthymiou, L., Davies, K. (2022). The Spread of Artificial Intelligence and Its Impact on Employment: Evidence from the Banking and Accounting Sectors. In: Business Advancement through Technology Volume II. Palgrave Studies in Cross-disciplinary Business Research, Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-07765-4_7
• Bersvendsen, T., & Ditzen, J. (2020). xthst: Testing for slope homogeneity in Stata. In London Stata Conference (Vol. 7, pp. 1-28). https://ceerp.hw.ac.uk/RePEc/hwc/wpaper/011.pdf
• Bordot, F. (2022). Artificial intelligence, robots and unemployment: Evidence from OECD countries. Journal of Innovation Economics & Management, (1), 117-138. https://doi.org/10.3917/jie.0037.0117
• Botwe, B. O., Antwi, W. K., Arkoh, S., & Akudjedu, T. N. (2021). Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study. Journal of medical radiation sciences, 68(3), 260-268. https://onlinelibrary.wiley.com/doi/10.1002/jmrs.460
• Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The review of economic studies, 47(1), 239-253 https://academic.oup.com/restud/article-abstract/47/1/239/1558204?redirectedFrom=fulltext
• Fallows, J. (2011). The Unreasonable Effectiveness of Operations Research. The Atlantic. Access Date: 17.10.2024. https://www.theatlantic.com/technology/archive/2011/03/the-unreasonable-effectiveness-of-operations-research/72212/
• Brynjolfsson, E., & McAfee, A. (2011). Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy. https://ide.mit.edu/sites/default/files/publications/Brynjolfsson_McAfee_Race_Against_the_Machine.pdf
• Campbell, R. W. (2023). Artificial intelligence in the courtroom: The delivery of justice in the age of machine learning. Revista Forumul Judecatorilor, 15. https://ctlj.colorado.edu/wp-content/uploads/2020/08/2-Campbell_06.25.20.pdf
• Castagno, S., & Khalifa, M. (2020). Perceptions of artificial intelligence among healthcare staff: a qualitative survey study. Frontiers in artificial intelligence, 3, 578983. https://doi.org/10.3389/frai.2020.578983 Eurostat. https://ec.europa.eu/eurostat/cache/digpub/european_economy/bloc-3a.html?lang=en accessed: 10.07.2024
• Floridi, L. (2014). Technological unemployment, leisure occupation, and the human project. Philosophy & Technology, 27, 143-150. https://doi.org/10.1007/s13347-014-0166-7
• Ford, M. (2015). Rise of the Robots (pp. 64-67). New York: Basic books.
• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological forecasting and social change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019
• Furuoka, F. (2012). Unemployment hysteresis in the East Asia‐Pacific region: new evidence from MADF and SURADF tests. Asian‐Pacific Economic Literature, 26(2), 133-143. https://doi.org/10.1111/j.1467-8411.2012.01351.x
• Georgieff, A., & Hyee, R. (2022). Artificial intelligence and employment: new cross-country evidence. Frontiers in artificial intelligence, 5, 832736. https://doi.org/10.1787/1815199X
• Ghazali, A., & Ali, G. (2019). Investigation of key contributors of CO2 emissions in extended STIRPAT model for newly industrialized countries: a dynamic common correlated estimator (DCCE) approach. Energy Reports, 5, 242-252. https://doi.org/10.1016/j.egyr.2019.02.006
• Gregory, T., Salomons, A., & Zierahn, U. (2018). Racing with or against the machine? Evidence from Europe. (July 15, 2016). CESifo Centre for European Economic Research Discussion Paper No: 7247. https://madoc.bib.uni-mannheim.de/41403/1/dp16053.pdf
• Guliyev, H. (2023). Artificial intelligence and unemployment in high-tech developed countries: new insights from dynamic panel data model. Research in Globalization, 7, 100140. https://doi.org/10.1016/j.resglo.2023.100140
• Guliyev, H., Huseynov, N., & Nuriyev, N. (2023). The relationship between artificial intelligence, big data, and unemployment in G7 countries: New insights from dynamic panel data model. World Development Sustainability, 3, 100107. https://doi.org/10.1016/j.wds.2023.100107
• Hamit-Haggar, M. (2012). Greenhouse gas emissions, energy consumption and economic growth: A panel cointegration analysis from Canadian industrial sector perspective. Energy Economics, 34(1), 358-364. https://doi.org/10.1016/j.eneco.2011.06.005
• Jacobs, J. A., & Karen, R. (2019). Technology-driven task replacement and the future of employment. Work and Labor in the Digital Age (Vol. 33, pp. 43-60). Emerald Publishing Limited. https://doi.org/10.1108/S0277-283320190000033004
• Kambur, E., & Akar, C. (2022). Human resource developments with the touch of artificial intelligence: a scale development study. International Journal of Manpower, 43(1), 168-205. https://doi.org/10.1108/IJM-04-2021-0216
• Keskin, H. I., & Kasri, A. (2023). The Future of Workforce: Investigation of The Effect of Artificial Intelligence on Unemployment Using Dynamic Panel Data Analysis. Economics and Administrative Sciences Modern Analysis and Researches.
• Khan, M. W. A., Panigrahi, S. K., & Almuniri, K. S. N. (2019). Investigating the dynamic Impact of CO2 emissions and economic growth on renewable energy production: evidence from FMOLS and DOLS tests. Processes 7: 496. https://bookchapter.org/kitaplar/Economics_and_Administrative_Sciences_Modern_Analysis_and_Researches.pdf#page=82
• Kong, H., Yuan, Y., Baruch, Y., Bu, N., Jiang, X., & Wang, K. (2021). Influences of artificial intelligence (AI) awareness on career competency and job burnout. International Journal of Contemporary Hospitality Management, 33(2), 717-734. http://dx.doi.org/10.1108/IJCHM-07-2020-0789
• Koo, B., Curtis, C., & Ryan, B. (2021). Examining the impact of artificial intelligence on hotel employees through job insecurity perspectives. International Journal of Hospitality Management, 95, 102763. https://doi.org/10.1016/j.ijhm.2020.102763
• Korinek, A., & Stiglitz, J. E. (2018). Artificial intelligence and its implications for income distribution and unemployment. The economics of artificial intelligence: An agenda (pp. 349-390). University of Chicago Press. http://www.nber.org/chapters/c14018
• Kurz, H. D. (2010). Technical progress, capital accumulation and income distribution in Classical economics: Adam Smith, David Ricardo and Karl Marx. The European journal of the history of economic thought, 17(5), 1183-1222. https://www.researchgate.net/profile/Heinz-Kurz-2/publication/227613048_Technical_progress_capital_accumulation_and_income_distribution_in_Classical_economics_Adam_Smith_David_Ricardo_and_Karl_Marx/links/00b7d51824f23afd12000000/Technical-progress-capital-accumulation-and-income-distribution-in-Classical-economics-Adam-Smith-David-Ricardo-and-Karl-Marx.pdf
• Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of econometrics, 108(1), 1-24. https://doi.org/10.1016/S0304-4076(01)00098-7
• Lustrilanang, P., Suwarno, Darusalam, Rizki, L. T., Omar, N., & Said, J. (2023). The role of control of corruption and quality of governance in ASEAN: Evidence from DOLS and FMOLS Test. Cogent Business & Management, 10(1), 2154060. https://doi.org/10.1080/23311975.2022.2154060
• McGinnis, J. O., & Pearce, R. G. (2013). The great disruption: How machine intelligence will transform the role of lawyers in the delivery of legal services. Fordham L. Rev., 82, 3041. https://ir.lawnet.fordham.edu/flr/vol82/iss6/16
• Modeste, N. C. (2016). Trade liberalization and economic growth in guyana: an empirical assessment using DOLS and error correcting methodologies. The Review of Black Political Economy, 43(1), 57-67. https://doi.org/10.1007/s12114-016-9231-z
• Murugesan, A., Patel, S., Viswanathan, V. S., Bhargava, P., & Faraji, N. (2023). Dear medical students-artificial intelligence is not taking away a Radiologist's job. Current Problems in Diagnostic Radiology, 52(1), 1-5. https://doi.org/10.1067/j.cpradiol.2022.08.001
• Mutascu, M. (2021). Artificial intelligence and unemployment: New insights. Economic Analysis and Policy, 69, 653-667. https://doi.org/10.1016/j.eap.2021.01.012
• Ngoma, J. B., & Yang, L. (2024). Does economic performance matter for forest conversion in Congo Basin tropical forests? FMOLS-DOLS approaches. Forest Policy and Economics, 162, 103199. https://doi.org/10.1016/j.forpol.2024.103199
• Nguyen, Q. P., & Vo, D. H. (2022). Artificial intelligence and unemployment: An international evidence. Structural Change and Economic Dynamics, 63, 40-55. https://doi.org/10.1016/j.strueco.2022.09.003 OECD, Patents By Technology. https://stats.oecd.org/Index.aspx?DataSetCode=PATS_IPC# Accessed: 19.03.2024
• Pedroni, P. (2001). Fully modified OLS for heterogeneous cointegrated panels. Nonstationary panels, panel cointegration, and dynamic panels (pp. 93-130). Emerald Group Publishing Limited. https://core.ac.uk/download/pdf/6223847.pdf
• Pesapane, F., Codari, M., & Sardanelli, F. (2018). Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. European radiology experimental, 2, 1-10. https://doi.org/10.1186/s41747-018-0061-6
• Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers 572504. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=572504
• Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of econometrics, 142(1), 50-93. https://doi.org/10.1016/j.jeconom.2007.05.010
• Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias‐adjusted LM test of error cross-section independence. The econometrics journal, 11(1), 105-127 https://www.jstor.org/stable/23116064
• Phillips, P. C., & Hansen, B. E. (1990). Statistical inference in instrumental variables regression with I (1) processes. The review of economic studies, 57(1), 99-125. https://www.jstor.org/stable/2297545
• Rahman, M. M., Hosan, S., Karmaker, S. C., Chapman, A. J., & Saha, B. B. (2021). The effect of remittance on energy consumption: Panel cointegration and dynamic causality analysis for South Asian countries. Energy, 220, 119684. https://doi.org/10.1016/j.energy.2020.119684
• Remus, D., & Levy, F. (2017). Can robots be lawyers: Computers, lawyers, and the practice of law. Geo. J. Legal Ethics, 30, 501.
• Rifkin, J. (1995). The end of work. New York: Putnam Book.
• Rowland, C. E., Delehanty, J. B., Dwyer, C. L., & Medintz, I. L. (2017). Growing applications for bioassembled Förster resonance energy transfer cascades. Materials Today, 20(3), 131-141. https://doi.org/10.1016/j.mattod.2016.09.013
• Saikkonen, P. (1992). Estimation and testing of cointegrated systems by an autoregressive approximation. Econometric theory, 8(1), 1-27. https://www.jstor.org/stable/3532143
• Stock, J. H., & Watson, M. W. (1993). A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica. 783-820. https://www.jstor.org/stable/2951763
• Sulaiman, C., Abdul-Rahim, A. S., & Ofozor, C. A. (2020). Does wood biomass energy use reduce CO2 emissions in European Union member countries? Evidence from 27 members. Journal of Cleaner Production, 253, 119996. https://doi.org/10.1016/j.jclepro.2020.119996
• Tajaldeen, A., & Alghamdi, S. (2020). Evaluation of radiologist’s knowledge about the Artificial Intelligence in diagnostic radiology: a survey-based study. Acta Radiologica Open, 9(7), 2058460120945320. https://doi.org/10.1177/2058460120945320
• Tatoğlu, Y. F. (2020). Panel Zaman Serileri Analizi Stata Uygulamalı (3. Baskı). Beta Basim Yayim
• Taylor, M. P., & Sarno, L. (1998). The behavior of real exchange rates during the post-Bretton Woods period. Journal of international Economics, 46(2), 281-312. https://doi.org/10.1016/S0022-1996(97)00054-8
• Webb, M. (2019). The impact of artificial intelligence on the labor market. SSRN 3482150.
• Westerlund, J. (2005). New simple tests for panel cointegration. Econometric Reviews, 24(3), 297-316. https://doi.org/10.1080/07474930500243019 World Bank, World Development Indicators(WDI). https://databank.worldbank.org/source/world-development-indicators# 19.03.2024.
• Yakar, D., Ongena, Y. P., Kwee, T. C., & Haan, M. (2022). Do people favor artificial intelligence over physicians? A survey among the general population and their view on artificial intelligence in medicine. Value in Health, 25(3), 374-381. https://doi.org/10.1016/j.jval.2021.09.004