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The Impact of Artificial Intelligence on Employment: A Panel Data Analysis for Selected Countries

Year 2025, Volume: 10 Issue: 1, 202 - 233, 28.03.2025
https://doi.org/10.30784/epfad.1621455

Abstract

Various artificial intelligence technologies such as robotics, machine learning, natural language processing, deep learning, and automation have developed rapidly in recent years and their use has become increasingly widespread in all areas that can affect the economy. These technologies have the capacity to optimize production processes, enhance efficiency levels, and play a decisive role in shaping trade and economic growth. Furthermore, they possess significant potential to exert notable impacts on employment and income inequality. The rise of artificial intelligence has sparked widespread debate, particularly regarding its potential impact on employment dynamics. The study analyzes the effect of artificial intelligence on employment in 29 countries from 2017 to 2021 using the System-GMM estimator. The results showed a statistically significant positive effect of artificial intelligence on employment. The analysis also considers the potential impact of labor productivity on employment in relation to artificial intelligence technologies by including an interaction term in the same model. The estimation results show that while the impact of artificial intelligence and labor productivity on employment is positive when considered individually, the interaction term diminishes this positive effect.

Ethical Statement

This study which does not require ethics committee approval and/or legal/specific permission complies with the research and publication ethics.

References

  • Aarvik, P. (2019). Artificial Intelligence – A promising anti-corruption tool in development settings? (U4 Report 2019:1). Retrieved from https://www.u4.no/publications/artificial-intelligence-a-promising-anti-corruption-tool-in-development-settings.pdf
  • Acemoglu, D. and Restrepo, P. (2018b). Artificial intelligence, automation and work. In A. Agrawal, J. Gans and A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 197-236). Chicago: University of Chicago Press.
  • Acemoglu, D. and Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. Retrieved from https://www.aeaweb.org/
  • Acemoglu, D. and Restrepo, P. (2018a). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542. https://doi.org/10.1257/aer.20160696
  • Anderson, T.W. and Hsiao, C. (1982). Formulation and estimation of dynamic models using panel data. Journal of Econometrics, 18(1), 47–82. https://doi.org/10.1016/0304-4076(82)90095-1
  • Arellano, M. and 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. https://doi.org/10.2307/2297968
  • Arellano, M. and Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51. https://doi.org/10.1016/0304-4076(94)01642-D
  • Arntz, M., Gregory, T. and Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A comparative analysis (OECD Social, Employment and Migration Working Papers No. 189). Retrieved from https://www.oecd.org/en/publications/the-risk-of-automation-for-jobs-in-oecd-countries_5jlz9h56dvq7-en.html
  • Asteriou, D. and Hall, S.G. (2007). Applied econometrics: A modern approach (Revised Edition). Hampshire: Palgrave Macmillan.
  • Autor, D.H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3
  • Baltagi, B.H. (2021). Econometric analysis of lanel data (Sixth Edition). Berlin: Springer International Publishing.
  • Bascle, G. (2008). Controlling for endogeneity with instrumental variables in strategic management research. Strategic Organization, 6(3), 285–327. https://doi.org/10.1177/1476127008094339
  • Bessen, J. (2018). AI and jobs: The role of demand (NBER Working Paper No. 24235). Retrieved from https://www.nber.org/system/files/working_papers/w24235/w24235.pdf
  • Blundell, R. and Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 115–143. https://doi.org/10.1016/S0304-4076(98)00009-8
  • Bonsay, J., Cruz, A.P., Firozi, H.C. and Camaro, A.P.J.C. (2021). Artificial intelligence and labor productivity paradox: The economic impact of AI in China, India, Japan, and Singapore. Journal of Economics, Finance and Accounting Studies, 3(2), 120–139. https://doi.org/10.32996/jefas.2021.3.2.13
  • Crafts, N. (2022). Slow real wage growth during the Industrial Revolution: Productivity paradox or pro-rich growth? Oxford Economic Papers, 74(1), 1–13. https://doi.org/10.1093/oep/gpab008
  • Ernst, E. and Mishra, S. (2021). AI efficiency index: Identifying regulatory and policy constraints for resilient national AI ecosystems. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3800783
  • Fossen, F.M., Samaan, D. and Sorgner, A. (2022). How are patented AI, software and robot technologies related to wage changes in the United States? Frontiers in Artificial Intelligence, 5, 869282. https://doi.org/10.3389/frai.2022.869282
  • Fossen, F.M. and Sorgner, A. (2022). New digital technologies and heterogeneous wage and employment dynamics in the United States: Evidence from individual-level data. Technological Forecasting and Social Change, 175, 121381. https://doi.org/10.1016/j.techfore.2021.121381
  • Frey, C.B. and 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
  • Georgieva, K. (2024). AI will transform the global economy. Let’s make sure it benefits humanity. Retrieved from https://www.imf.org/en/Blogs/Articles/2024/01/14/ai-will-transform-the-global-economy-lets-make-sure-it-benefits-humanity
  • Gmyrek, P., Berg, J. and Bescond, D. (2023). Generative AI and jobs: A global analysis of potential effects on job quantity and quality (ILO Working Paper No. 96). Retrieved from https://www.unescap.org/sites/default/d8files/event-documents/Presentation%2015%20-%20Pawel%20Gmyrek%2C%20ILO.pdf
  • Gries, T. and Naudé, W. (2018). Artificial intelligence, jobs, inequality and productivity: Does aggregate demand matter? (IZA Discussion Papers No. 12005). Retrieved from https://www.econstor.eu/bitstream/10419/193299/1/dp12005.pdf
  • Gonzales, J.T. (2023). Implications of AI innovation on economic growth: A panel data study. Journal of Economic Structures, 12(1), 13. https://doi.org/10.1186/s40008-023-00307-w
  • 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
  • Hasan, I. and Tucci, C.L. (2010). The innovation–economic growth nexus: Global evidence. Research Policy, 39(10), 1264–1276. https://doi.org/10.1016/j.respol.2010.07.005
  • Hasan, I., Wachtel, P. and Zhou, M. (2009). Institutional development, financial deepening and economic growth: Evidence from China. Journal of Banking & Finance, 33, 157–170. https://doi.org/10.1016/j.jbankfin.2007.11.016
  • Herrendorf, B., Rogerson, R. and Valentinyi, A. (2014). Growth and structural transformation. In P. Aghion and S.N. Durlauf (Eds.), Handbook of economic growth (pp. 855-941). Amsterdam: Elsevier.
  • Lane, M. and Saint-Martin, A. (2021). The impact of Artificial Intelligence on the labour market: What do we know so far? (OECD Social, Employment and Migration Working Papers No. 256). Retrieved from https://www.oecd.org/en/publications/the-impact-of-artificial-intelligence-on-the-labour-market_7c895724-en.html
  • Levine, R., Loayza, N. and Beck, T. (2000). Financial intermediation and growth: Causality and causes. Journal of Monetary Economics, 46, 31–77. https://doi.org/10.1016/S0304-3932(00)00017-9
  • Martens, B. and Tolan, S. (2018). Will this time be different? A review of the literature on the impact of artificial intelligence on employment, incomes and growth (JRC Digital Economy Working Paper No. 2018–08). Retrieved from https://www.ssrn.com/abstract=3290708
  • Maslej, N., Fattorini, L., Perrault, R., Parli, V., Reuel, A., Brynjolfsson, … Clark, J. (2024). The AI index 2024 annual report. Retrieved from https://aiindex.stanford.edu/wp-content/uploads/2024/05/HAI_AI-Index-Report-2024.pdf
  • 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
  • Ng, A. (2017). Why AI is the new electricity. Retrieved from https://www.gsb.stanford.edu/insights/andrew-ng-why-ai-new-electricity
  • Nguyen, Q.P. and 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
  • Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 49(6), 1417-1426. https://doi.org/10.2307/1911408
  • Petropoulos, G. and Kapur, M. (2022). Artificial intelligence: Increasing labour productivity in a responsible way. In Bruegel AISBL and GMF (Eds.), The future of work: A transatlantic perspective on challenges and opportunities (pp. 42-59). Brussel: Bruegel.
  • Piva, M. and Vivarelli, M. (2017). Technological change and employment: Were Ricardo and Marx right? (IZA Discussion Paper No. 10471). Retrieved from https://docs.iza.org/dp10471.pdf
  • Roodman, D. (2006). How to do xtabond2: An introduction to “difference” and “system” GMM in Stata. The Stata Journal, 9(1), 86-136. https://doi.org/10.1177/1536867X09009001
  • Sargan, J. D. (1958). The estimation of economic relationships using instrumental variables. Econometrica, 26(3), 393-415. https://doi.org/10.2307/1907619
  • Sarker, I.H. (2022). AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), 158. https://doi.org/10.1007/s42979-022-01043-x
  • Semadeni, M., Withers, M.C. and Trevis Certo, S. (2014). The perils of endogeneity and instrumental variables in strategy research: Understanding through simulations. Strategic Management Journal, 35(7), 1070–1079. https://doi.org/10.1002/smj.2136
  • Van den Berg, H. (2001). Economic growth and development. New York: McGraw-Hill Irwin.
  • Wang, C., Zheng, M., Bai, X., Li, Y. and Shen, W. (2023). Future of jobs in China under the impact of artificial intelligence. Finance Research Letters, 55, 103798. https://doi.org/10.1016/j.frl.2023.103798
  • Webb, M. (2020). The impact of artificial intelligence on the labor market. Retrieved from https://www.michaelwebb.co/webb_ai.pdf
  • Wolla, S., Schug, M.C. and Wood, W.C. (2019). The economics of artificial intelligence and robotics. Social Education, 83(2), 84-88. Retrieved from https://www.socialstudies.org/
  • Yerdelen-Tatoğlu, F. (2020). Advanced panel data analysis (4th Edition). Istanbul: Beta Publishing and Distribution Inc.
  • Zhang, D., Maslej, N., Brynjolfsson, E., Etchemendy, J., Lyons, T., Manyika, … Perrault, R. (2022). The AI index 2022 annual report. arXiv:2205.03468. Retrieved from https://arxiv.org/abs/2205.03468
  • Zhao, P., Gao, Y. and Sun, X. (2022). How does artificial intelligence affect green economic growth?—Evidence from China. Science of the Total Environment, 834, 155306. http://dx.doi.org/10.1016/j.scitotenv.2022.155306

Yapay Zekânın İstihdam Üzerindeki Etkisi: Seçilmiş Ülkelere Yönelik Panel Veri Analizi

Year 2025, Volume: 10 Issue: 1, 202 - 233, 28.03.2025
https://doi.org/10.30784/epfad.1621455

Abstract

Robotik, makine öğrenimi, doğal dil işleme, derin öğrenme ve otomasyon gibi çeşitli yapay zekâ teknolojileri son yıllarda hızla gelişmiş ve ekonomiyi etkileyebilecek tüm alanlarda kullanımları giderek yaygınlaşmıştır. Bu teknolojiler, üretim süreçlerini optimize etme, verimlilik düzeylerini yükseltme ve ticaret ile ekonomik büyüme üzerinde belirleyici bir rol oynama kapasitesine sahiptir. Bunun yanı sıra, istihdam ve gelir eşitsizliği üzerinde de kayda değer etkiler yaratabilme potansiyeli bulunmaktadır. Yapay zekânın yükselişi, özellikle istihdam dinamikleri üzerindeki potansiyel etkisi konusunda yaygın tartışmalara yol açmıştır. Çalışma, yapay zekânın 2017-2021 yılları arasında 29 ülkede istihdam üzerindeki etkisini Sistem-GMM tahmincisini kullanarak analiz etmektedir. Sonuçlar, yapay zekânın istihdam üzerinde istatistiksel olarak anlamlı pozitif bir etkisi olduğunu göstermiştir. Analiz, aynı modele bir etkileşim terimi dahil ederek yapay zekâ teknolojileriyle ilişkili olarak işgücü verimliliğinin istihdam üzerindeki potansiyel etkisini de dikkate almaktadır. Tahmin sonuçları, yapay zekâ ve işgücü verimliliğinin ayrı ayrı ele alındığında istihdam üzerindeki etkisinin pozitif olduğunu, etkileşim teriminin ise bu pozitif etkiyi azalttığını göstermektedir.

References

  • Aarvik, P. (2019). Artificial Intelligence – A promising anti-corruption tool in development settings? (U4 Report 2019:1). Retrieved from https://www.u4.no/publications/artificial-intelligence-a-promising-anti-corruption-tool-in-development-settings.pdf
  • Acemoglu, D. and Restrepo, P. (2018b). Artificial intelligence, automation and work. In A. Agrawal, J. Gans and A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 197-236). Chicago: University of Chicago Press.
  • Acemoglu, D. and Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. Retrieved from https://www.aeaweb.org/
  • Acemoglu, D. and Restrepo, P. (2018a). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542. https://doi.org/10.1257/aer.20160696
  • Anderson, T.W. and Hsiao, C. (1982). Formulation and estimation of dynamic models using panel data. Journal of Econometrics, 18(1), 47–82. https://doi.org/10.1016/0304-4076(82)90095-1
  • Arellano, M. and 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. https://doi.org/10.2307/2297968
  • Arellano, M. and Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51. https://doi.org/10.1016/0304-4076(94)01642-D
  • Arntz, M., Gregory, T. and Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A comparative analysis (OECD Social, Employment and Migration Working Papers No. 189). Retrieved from https://www.oecd.org/en/publications/the-risk-of-automation-for-jobs-in-oecd-countries_5jlz9h56dvq7-en.html
  • Asteriou, D. and Hall, S.G. (2007). Applied econometrics: A modern approach (Revised Edition). Hampshire: Palgrave Macmillan.
  • Autor, D.H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3
  • Baltagi, B.H. (2021). Econometric analysis of lanel data (Sixth Edition). Berlin: Springer International Publishing.
  • Bascle, G. (2008). Controlling for endogeneity with instrumental variables in strategic management research. Strategic Organization, 6(3), 285–327. https://doi.org/10.1177/1476127008094339
  • Bessen, J. (2018). AI and jobs: The role of demand (NBER Working Paper No. 24235). Retrieved from https://www.nber.org/system/files/working_papers/w24235/w24235.pdf
  • Blundell, R. and Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 115–143. https://doi.org/10.1016/S0304-4076(98)00009-8
  • Bonsay, J., Cruz, A.P., Firozi, H.C. and Camaro, A.P.J.C. (2021). Artificial intelligence and labor productivity paradox: The economic impact of AI in China, India, Japan, and Singapore. Journal of Economics, Finance and Accounting Studies, 3(2), 120–139. https://doi.org/10.32996/jefas.2021.3.2.13
  • Crafts, N. (2022). Slow real wage growth during the Industrial Revolution: Productivity paradox or pro-rich growth? Oxford Economic Papers, 74(1), 1–13. https://doi.org/10.1093/oep/gpab008
  • Ernst, E. and Mishra, S. (2021). AI efficiency index: Identifying regulatory and policy constraints for resilient national AI ecosystems. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3800783
  • Fossen, F.M., Samaan, D. and Sorgner, A. (2022). How are patented AI, software and robot technologies related to wage changes in the United States? Frontiers in Artificial Intelligence, 5, 869282. https://doi.org/10.3389/frai.2022.869282
  • Fossen, F.M. and Sorgner, A. (2022). New digital technologies and heterogeneous wage and employment dynamics in the United States: Evidence from individual-level data. Technological Forecasting and Social Change, 175, 121381. https://doi.org/10.1016/j.techfore.2021.121381
  • Frey, C.B. and 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
  • Georgieva, K. (2024). AI will transform the global economy. Let’s make sure it benefits humanity. Retrieved from https://www.imf.org/en/Blogs/Articles/2024/01/14/ai-will-transform-the-global-economy-lets-make-sure-it-benefits-humanity
  • Gmyrek, P., Berg, J. and Bescond, D. (2023). Generative AI and jobs: A global analysis of potential effects on job quantity and quality (ILO Working Paper No. 96). Retrieved from https://www.unescap.org/sites/default/d8files/event-documents/Presentation%2015%20-%20Pawel%20Gmyrek%2C%20ILO.pdf
  • Gries, T. and Naudé, W. (2018). Artificial intelligence, jobs, inequality and productivity: Does aggregate demand matter? (IZA Discussion Papers No. 12005). Retrieved from https://www.econstor.eu/bitstream/10419/193299/1/dp12005.pdf
  • Gonzales, J.T. (2023). Implications of AI innovation on economic growth: A panel data study. Journal of Economic Structures, 12(1), 13. https://doi.org/10.1186/s40008-023-00307-w
  • 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
  • Hasan, I. and Tucci, C.L. (2010). The innovation–economic growth nexus: Global evidence. Research Policy, 39(10), 1264–1276. https://doi.org/10.1016/j.respol.2010.07.005
  • Hasan, I., Wachtel, P. and Zhou, M. (2009). Institutional development, financial deepening and economic growth: Evidence from China. Journal of Banking & Finance, 33, 157–170. https://doi.org/10.1016/j.jbankfin.2007.11.016
  • Herrendorf, B., Rogerson, R. and Valentinyi, A. (2014). Growth and structural transformation. In P. Aghion and S.N. Durlauf (Eds.), Handbook of economic growth (pp. 855-941). Amsterdam: Elsevier.
  • Lane, M. and Saint-Martin, A. (2021). The impact of Artificial Intelligence on the labour market: What do we know so far? (OECD Social, Employment and Migration Working Papers No. 256). Retrieved from https://www.oecd.org/en/publications/the-impact-of-artificial-intelligence-on-the-labour-market_7c895724-en.html
  • Levine, R., Loayza, N. and Beck, T. (2000). Financial intermediation and growth: Causality and causes. Journal of Monetary Economics, 46, 31–77. https://doi.org/10.1016/S0304-3932(00)00017-9
  • Martens, B. and Tolan, S. (2018). Will this time be different? A review of the literature on the impact of artificial intelligence on employment, incomes and growth (JRC Digital Economy Working Paper No. 2018–08). Retrieved from https://www.ssrn.com/abstract=3290708
  • Maslej, N., Fattorini, L., Perrault, R., Parli, V., Reuel, A., Brynjolfsson, … Clark, J. (2024). The AI index 2024 annual report. Retrieved from https://aiindex.stanford.edu/wp-content/uploads/2024/05/HAI_AI-Index-Report-2024.pdf
  • 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
  • Ng, A. (2017). Why AI is the new electricity. Retrieved from https://www.gsb.stanford.edu/insights/andrew-ng-why-ai-new-electricity
  • Nguyen, Q.P. and 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
  • Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 49(6), 1417-1426. https://doi.org/10.2307/1911408
  • Petropoulos, G. and Kapur, M. (2022). Artificial intelligence: Increasing labour productivity in a responsible way. In Bruegel AISBL and GMF (Eds.), The future of work: A transatlantic perspective on challenges and opportunities (pp. 42-59). Brussel: Bruegel.
  • Piva, M. and Vivarelli, M. (2017). Technological change and employment: Were Ricardo and Marx right? (IZA Discussion Paper No. 10471). Retrieved from https://docs.iza.org/dp10471.pdf
  • Roodman, D. (2006). How to do xtabond2: An introduction to “difference” and “system” GMM in Stata. The Stata Journal, 9(1), 86-136. https://doi.org/10.1177/1536867X09009001
  • Sargan, J. D. (1958). The estimation of economic relationships using instrumental variables. Econometrica, 26(3), 393-415. https://doi.org/10.2307/1907619
  • Sarker, I.H. (2022). AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), 158. https://doi.org/10.1007/s42979-022-01043-x
  • Semadeni, M., Withers, M.C. and Trevis Certo, S. (2014). The perils of endogeneity and instrumental variables in strategy research: Understanding through simulations. Strategic Management Journal, 35(7), 1070–1079. https://doi.org/10.1002/smj.2136
  • Van den Berg, H. (2001). Economic growth and development. New York: McGraw-Hill Irwin.
  • Wang, C., Zheng, M., Bai, X., Li, Y. and Shen, W. (2023). Future of jobs in China under the impact of artificial intelligence. Finance Research Letters, 55, 103798. https://doi.org/10.1016/j.frl.2023.103798
  • Webb, M. (2020). The impact of artificial intelligence on the labor market. Retrieved from https://www.michaelwebb.co/webb_ai.pdf
  • Wolla, S., Schug, M.C. and Wood, W.C. (2019). The economics of artificial intelligence and robotics. Social Education, 83(2), 84-88. Retrieved from https://www.socialstudies.org/
  • Yerdelen-Tatoğlu, F. (2020). Advanced panel data analysis (4th Edition). Istanbul: Beta Publishing and Distribution Inc.
  • Zhang, D., Maslej, N., Brynjolfsson, E., Etchemendy, J., Lyons, T., Manyika, … Perrault, R. (2022). The AI index 2022 annual report. arXiv:2205.03468. Retrieved from https://arxiv.org/abs/2205.03468
  • Zhao, P., Gao, Y. and Sun, X. (2022). How does artificial intelligence affect green economic growth?—Evidence from China. Science of the Total Environment, 834, 155306. http://dx.doi.org/10.1016/j.scitotenv.2022.155306
There are 49 citations in total.

Details

Primary Language English
Subjects Panel Data Analysis, Applied Macroeconometrics, Employment, Macroeconomics (Other)
Journal Section Makaleler
Authors

Cemre Nur Çetin 0000-0002-7396-7859

Erol Kutlu 0000-0003-0600-5534

Publication Date March 28, 2025
Submission Date January 17, 2025
Acceptance Date March 27, 2025
Published in Issue Year 2025 Volume: 10 Issue: 1

Cite

APA Çetin, C. N., & Kutlu, E. (2025). The Impact of Artificial Intelligence on Employment: A Panel Data Analysis for Selected Countries. Ekonomi Politika Ve Finans Araştırmaları Dergisi, 10(1), 202-233. https://doi.org/10.30784/epfad.1621455