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AI and Workforce Dynamics: Unravelling Productivity

Year 2025, Volume: 33 Issue: 63, 143 - 159

Hiroshi Yoshida , Meltem İnce Yenilmez

https://doi.org/10.17233/sosyoekonomi.2025.01.07

Abstract

This study examines how artificial intelligence (AI) affects worker productivity, emphasising AI's capacity to automate jobs, reduce errors, and optimise workflows. It emphasises the need for dynamic reskilling initiatives and company-school cooperation to provide workers with the necessary skills. Using a two-log econometric model, the study examines the association between AI patents and productivity. It observes that the effects of AI differ across industries, with less automation in positions requiring creativity and emotional intelligence. The paper also suggests more research and examines the relationship between productivity and R&D costs, physical assets, and non-AI patents.

Keywords

Artificial Intelligence Worker Productivity Reskilling and Upskilling AI-Related Patents

References

  • Aghion, P. & P. Howitt (1992), “A Model of Growth Through Creative Destruction”, Econometrica, 60(2), 323-351.
  • Agrawal, A. et al. (2019a), “Prediction, Judgment, and Complexity: A Theory of Decision-making and Artificial Intelligence”, in: A. Agrawal et al. (eds.), The Economics of Artificial Intelligence: An Agenda, Chicago: University of Chicago Press.
  • Agrawal, A. et al. (2019b), “Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth”, in: A. Agrawal et al. (eds.), The Economics of Artificial Intelligence: An Agenda, Chicago: University of Chicago Press.
  • Alderucci, D. et al. (2020), “Quantifying the Impact of AI on Productivity and Labour Demand: Evidence from U.S. census Microdata”, in: Paper presented at the Allied Social Science Associations ASSA 2020 Annual Meeting.
  • Ando, M. & F. Kimura (2015), “Globalization and Domestic Operations: Applying the JC/JD Method to Japanese Manufacturing Firms”, Asian Economic Papers, 14, 1-35.
  • Ando, M. & F. Kimura (2017), “Job Creation and Destruction at the Levels of Intra-firm Sections, Firms, and Industries in Globalization: The Case of Japanese Manufacturing Firms”, RIETI Discussion Paper Series, 17-E-100, Tokyo: Research Institute of Economy, Trade and Industry.
  • Antonelli, C. (2009), “The Economics of Innovation: From the Classical Legacies to the Economics of Complexity”, Economics of Innovation and New Technology, 18(7), 611-646.
  • Autor, D. & D. Dorn (2013), “The Growth of Low-skill Service Jobs and the Polarization of the US Labour Market”, The American Economic Review, 103(5), 1553-1597.
  • Autor, D. et al. (2003), “The Skill Content of Recent Technological Change: An Empirical Exploration”, The Quarterly Journal of Economics, 118(4), 1279-1311.
  • Baddeley, M. & D. Barrowclough (2009), Running Regressions: A Practical Guide to Quantitative Research in Economics, Finance and Development Studies, Cambridge: Cambridge Univ. Press.
  • Barbieri, L. et al. (2020), “Testing the Employment and skill impact of new technologies”, in: K. Zimmermann (ed.) Handbook of labour, human resources and population economics, Cham: Springer.
  • Bartelsman, E. et al. (2019), “Productivity, Technological Innovations and Broadband Connectivity: Firm-level evidence for ten European Countries”, Eurasian Business Review, 9, 25-48.
  • Bloom, N. et al. (2020), “Are Ideas Getting Harder to Find?”, American Economic Review, 110(4), 1104-1144.
  • Bresnahan, T. (2019), “Technological Change in ICT in Light of Ideas First Learned about the Machine Tool Industry”, Industrial and Corporate Change, 28, 331-349.
  • Brynjolfsson, E. & A. McAfee (2014), The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, W.W. Norton & Co.
  • Brynjolfsson, E. (1993), “The Productivity Paradox of Information Technology”, Communications of the ACM, 36(12), 66-77.
  • Brynjolfsson, E. et al. (2019), “Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics”, in: A. Agrawal et al. (eds.), The Economics of Artificial Intelligence: An Agenda, Chicago: University of Chicago Press and NBER.
  • Cockburn, I. et al. (2019), “The Impact of Artificial Intelligence on Innovation”, in: A. Agrawal et al. (eds.), The economics of artificial intelligence: an agenda, Chicago: University of Chicago Press and NBER.
  • Davis, S.J. et al. (1996), Job Creation and Destruction, MIT Press, Cambridge, MA.
  • Frank, M.R. et al. (2019), “Toward Understanding the Impact of Artificial Intelligence on Labour”, Proceedings of the National Academy of Sciences of the United States of America (PNAS), 116(14), 6531-6539.
  • Goldfarb, A. et al. (2020), Could Machine Learning be a General-Purpose Technology? Evidence from Online Job Postings, Available at SSRN: <https://ssrn.com/abstract=3468822>.
  • Gordon, R.J. (2016), The Rise and Fall of American Growth: The US Standard of Living since the Civil War, Princeton: Princeton University Press.
  • Gordon, R.J. (2018), “Why Has Economic Growth Slowed When Innovation Appears to be Accelerating?”, NBER Working Paper 24554, National Bureau for Economic Research.
  • Graetz, G. & G. Michaels (2018), “Robots at Work”, The Review of Economics and Statistics, 100(5), 753-768.
  • Gries, T. & W. Naudié (2018), “Artificial Intelligence, Jobs, Inequality and Productivity: Does Aggregate Demand Matter?”, IZA DP No. 12005, Bonn.
  • Griliches, Z. (1990), “Patent Statistics as Economic Indicators: A Survey”, Journal of Economic Literature, 18(4), 1661-707.
  • Gujarati, N.D. (2006), Essentials of Econometrics, United States Military Academy.
  • Hausman, J. et al. (1984), “Econometric Models for Count Data with an Application to the Patents-R&D Relationship”, Econometrica, 52, 909-938.
  • Hsiao, C. (2003), Analysis of Panel Data, Cambridge University Press.
  • Jones, B.F. (2009), “The Burden of Knowledge and the ‘Death of the Renaissance Man’: Is Innovation Getting Harder?”, Review of Economic Studies, 76(1), 283-317.
  • Josten, C. & G. Lordan (2020), “Robots at Work: Automatable and Non-Automatable Jobs”, in: K. Zimmermann (ed.), Handbook of labour, human resources and population economics, Cham: Springer.
  • Joutz, F.L. & A.T. Gardner (1996), “Economic Growth, Energy Prices and Technological Innovation”, Southern Economic Journal, 62(3), 653-666.
  • Kodama, N. & T. Inui (2015), “The Impact of Globalization on Establishment-level Employment Dynamics in Japan”, Asian Economic Papers, 14.
  • Kortum, S. & J. Lerner (1998), “Does Venture Capital Spur Innovation?”, Discussion Paper, NBER Working Paper No. 6846.
  • Kortum, S. & J. Lerner (2001), “Does Venture Capital Spur Innovation?”, in: Entrepreneurial inputs and outcomes: New studies of entrepreneurship in the United States (Advances in the Study of Entrepreneurship, Innovation and Economic Growth, Vol. 13: 1-4), Emerald Group Publishing Limited, Leeds.
  • Liu, Y. & B. Ni (2018), “Outward FDI and Firm-level Employment Dynamics in Japan”, RIETI Discussion Paper Series, 18-E-069, RIETI.
  • Liu, Y. (2018), “Job Creation and Destruction in Japan: Evidence from Division level Employment Data”, Journal of Asian Economics, 58, 59-71.
  • Los, B. et al. (2014), “The demand for skills 1995-2008. A Global Supply Chain Perspective”, OECD Economics Department Working Papers, 1141.
  • Machin, S. & J. Van Reenen (1998), “Technology and Changes in Skill Structure: Evidence from Seven OECD countries”, Q J Econ, 113(4), 1215-1244.
  • Piva, M. & M. Vivarelli (2018), “Is Innovation Destroying Jobs? Firm-level Evidence from the EU”, Sustainability, 10(4), 1279.
  • Romer, P.M. (1990), “Endogenous Technological Change”, Journal of Political Economy, 98(5), 71-102.
  • Solow, R. (1957), “Technical Change and the Aggregate Production Function”, Review of Economics and Statistics, 39, 312-320.
  • Vivarelli, M. (1995), The Economics of Technology and Employment: Theory and Empirical Evidence, Aldershot: Edward Elgar.
  • Vivarelli, M. (2013), “Technology, Employment and Skills: An Interpretative Framework”, Eurasian Business Review, 3(1), 66-89.
  • Webb, M. (2019), The Impact of Artificial Intelligence on the Labour Market, Available at SSRN: <https://ssrn.com/abstract=3482150>.
  • Zhang, J. et al. (2022), “Moving towards Vertically Integrated Artificial Intelligence Development”, Digit. Med., 5, 143.

Yapay Zekâ ve İşgücü Dı̇namı̇klerı̇: Üretkenlı̇ğı̇n Çözülmesı̇

Year 2025, Volume: 33 Issue: 63, 143 - 159

Hiroshi Yoshida , Meltem İnce Yenilmez

https://doi.org/10.17233/sosyoekonomi.2025.01.07

Abstract

Bu çalışma, yapay zekanın (YZ) çalışan verimliliğini nasıl etkilediğini incelemekte ve YZ'nın işleri otomatikleştirme, hataları azaltma ve iş akışlarını optimize etme kapasitesini vurgulamaktadır. Çalışanlara gerekli becerileri kazandırmak için yeniden beceri kazandırma girişimlerine ve şirket-okul iş birliğine duyulan ihtiyaç vurgulanmaktadır. Çalışma, iki loglu model kullanılarak yapay zekâ patentleri ve üretkenlik arasındaki ilişkiyi incelemektedir. YZ'nın etkilerinin sektörler arasında farklılık gösterdiğini, yaratıcılık ve duygusal zekâ gerektiren pozisyonlarda daha az otomasyon olduğunu gözlemlemektedir. Çalışma aynı zamanda verimlilik ile Ar-Ge maliyetleri, fiziksel varlıklar ve yapay zekâ dışı patentler arasındaki ilişkiyi de incelemektedir.

Keywords

Yapay Zekâ İşçi Üretkenliği Yeniden Beceri Kazandırma ve Yükseltme Yapay Zekâ ile İlgili Patentler

References

  • Aghion, P. & P. Howitt (1992), “A Model of Growth Through Creative Destruction”, Econometrica, 60(2), 323-351.
  • Agrawal, A. et al. (2019a), “Prediction, Judgment, and Complexity: A Theory of Decision-making and Artificial Intelligence”, in: A. Agrawal et al. (eds.), The Economics of Artificial Intelligence: An Agenda, Chicago: University of Chicago Press.
  • Agrawal, A. et al. (2019b), “Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth”, in: A. Agrawal et al. (eds.), The Economics of Artificial Intelligence: An Agenda, Chicago: University of Chicago Press.
  • Alderucci, D. et al. (2020), “Quantifying the Impact of AI on Productivity and Labour Demand: Evidence from U.S. census Microdata”, in: Paper presented at the Allied Social Science Associations ASSA 2020 Annual Meeting.
  • Ando, M. & F. Kimura (2015), “Globalization and Domestic Operations: Applying the JC/JD Method to Japanese Manufacturing Firms”, Asian Economic Papers, 14, 1-35.
  • Ando, M. & F. Kimura (2017), “Job Creation and Destruction at the Levels of Intra-firm Sections, Firms, and Industries in Globalization: The Case of Japanese Manufacturing Firms”, RIETI Discussion Paper Series, 17-E-100, Tokyo: Research Institute of Economy, Trade and Industry.
  • Antonelli, C. (2009), “The Economics of Innovation: From the Classical Legacies to the Economics of Complexity”, Economics of Innovation and New Technology, 18(7), 611-646.
  • Autor, D. & D. Dorn (2013), “The Growth of Low-skill Service Jobs and the Polarization of the US Labour Market”, The American Economic Review, 103(5), 1553-1597.
  • Autor, D. et al. (2003), “The Skill Content of Recent Technological Change: An Empirical Exploration”, The Quarterly Journal of Economics, 118(4), 1279-1311.
  • Baddeley, M. & D. Barrowclough (2009), Running Regressions: A Practical Guide to Quantitative Research in Economics, Finance and Development Studies, Cambridge: Cambridge Univ. Press.
  • Barbieri, L. et al. (2020), “Testing the Employment and skill impact of new technologies”, in: K. Zimmermann (ed.) Handbook of labour, human resources and population economics, Cham: Springer.
  • Bartelsman, E. et al. (2019), “Productivity, Technological Innovations and Broadband Connectivity: Firm-level evidence for ten European Countries”, Eurasian Business Review, 9, 25-48.
  • Bloom, N. et al. (2020), “Are Ideas Getting Harder to Find?”, American Economic Review, 110(4), 1104-1144.
  • Bresnahan, T. (2019), “Technological Change in ICT in Light of Ideas First Learned about the Machine Tool Industry”, Industrial and Corporate Change, 28, 331-349.
  • Brynjolfsson, E. & A. McAfee (2014), The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, W.W. Norton & Co.
  • Brynjolfsson, E. (1993), “The Productivity Paradox of Information Technology”, Communications of the ACM, 36(12), 66-77.
  • Brynjolfsson, E. et al. (2019), “Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics”, in: A. Agrawal et al. (eds.), The Economics of Artificial Intelligence: An Agenda, Chicago: University of Chicago Press and NBER.
  • Cockburn, I. et al. (2019), “The Impact of Artificial Intelligence on Innovation”, in: A. Agrawal et al. (eds.), The economics of artificial intelligence: an agenda, Chicago: University of Chicago Press and NBER.
  • Davis, S.J. et al. (1996), Job Creation and Destruction, MIT Press, Cambridge, MA.
  • Frank, M.R. et al. (2019), “Toward Understanding the Impact of Artificial Intelligence on Labour”, Proceedings of the National Academy of Sciences of the United States of America (PNAS), 116(14), 6531-6539.
  • Goldfarb, A. et al. (2020), Could Machine Learning be a General-Purpose Technology? Evidence from Online Job Postings, Available at SSRN: <https://ssrn.com/abstract=3468822>.
  • Gordon, R.J. (2016), The Rise and Fall of American Growth: The US Standard of Living since the Civil War, Princeton: Princeton University Press.
  • Gordon, R.J. (2018), “Why Has Economic Growth Slowed When Innovation Appears to be Accelerating?”, NBER Working Paper 24554, National Bureau for Economic Research.
  • Graetz, G. & G. Michaels (2018), “Robots at Work”, The Review of Economics and Statistics, 100(5), 753-768.
  • Gries, T. & W. Naudié (2018), “Artificial Intelligence, Jobs, Inequality and Productivity: Does Aggregate Demand Matter?”, IZA DP No. 12005, Bonn.
  • Griliches, Z. (1990), “Patent Statistics as Economic Indicators: A Survey”, Journal of Economic Literature, 18(4), 1661-707.
  • Gujarati, N.D. (2006), Essentials of Econometrics, United States Military Academy.
  • Hausman, J. et al. (1984), “Econometric Models for Count Data with an Application to the Patents-R&D Relationship”, Econometrica, 52, 909-938.
  • Hsiao, C. (2003), Analysis of Panel Data, Cambridge University Press.
  • Jones, B.F. (2009), “The Burden of Knowledge and the ‘Death of the Renaissance Man’: Is Innovation Getting Harder?”, Review of Economic Studies, 76(1), 283-317.
  • Josten, C. & G. Lordan (2020), “Robots at Work: Automatable and Non-Automatable Jobs”, in: K. Zimmermann (ed.), Handbook of labour, human resources and population economics, Cham: Springer.
  • Joutz, F.L. & A.T. Gardner (1996), “Economic Growth, Energy Prices and Technological Innovation”, Southern Economic Journal, 62(3), 653-666.
  • Kodama, N. & T. Inui (2015), “The Impact of Globalization on Establishment-level Employment Dynamics in Japan”, Asian Economic Papers, 14.
  • Kortum, S. & J. Lerner (1998), “Does Venture Capital Spur Innovation?”, Discussion Paper, NBER Working Paper No. 6846.
  • Kortum, S. & J. Lerner (2001), “Does Venture Capital Spur Innovation?”, in: Entrepreneurial inputs and outcomes: New studies of entrepreneurship in the United States (Advances in the Study of Entrepreneurship, Innovation and Economic Growth, Vol. 13: 1-4), Emerald Group Publishing Limited, Leeds.
  • Liu, Y. & B. Ni (2018), “Outward FDI and Firm-level Employment Dynamics in Japan”, RIETI Discussion Paper Series, 18-E-069, RIETI.
  • Liu, Y. (2018), “Job Creation and Destruction in Japan: Evidence from Division level Employment Data”, Journal of Asian Economics, 58, 59-71.
  • Los, B. et al. (2014), “The demand for skills 1995-2008. A Global Supply Chain Perspective”, OECD Economics Department Working Papers, 1141.
  • Machin, S. & J. Van Reenen (1998), “Technology and Changes in Skill Structure: Evidence from Seven OECD countries”, Q J Econ, 113(4), 1215-1244.
  • Piva, M. & M. Vivarelli (2018), “Is Innovation Destroying Jobs? Firm-level Evidence from the EU”, Sustainability, 10(4), 1279.
  • Romer, P.M. (1990), “Endogenous Technological Change”, Journal of Political Economy, 98(5), 71-102.
  • Solow, R. (1957), “Technical Change and the Aggregate Production Function”, Review of Economics and Statistics, 39, 312-320.
  • Vivarelli, M. (1995), The Economics of Technology and Employment: Theory and Empirical Evidence, Aldershot: Edward Elgar.
  • Vivarelli, M. (2013), “Technology, Employment and Skills: An Interpretative Framework”, Eurasian Business Review, 3(1), 66-89.
  • Webb, M. (2019), The Impact of Artificial Intelligence on the Labour Market, Available at SSRN: <https://ssrn.com/abstract=3482150>.
  • Zhang, J. et al. (2022), “Moving towards Vertically Integrated Artificial Intelligence Development”, Digit. Med., 5, 143.
There are 46 citations in total.

Details

Primary Language English
Subjects Microeconomic Theory, Employment, Labor Economics
Journal Section Articles
Authors

Hiroshi Yoshida 0000-0003-1643-5220

Meltem İnce Yenilmez 0000-0002-4689-3196

Early Pub Date January 1, 2025
Publication Date
Submission Date April 23, 2024
Acceptance Date December 8, 2024
Published in Issue Year 2025 Volume: 33 Issue: 63

Cite

APA Yoshida, H., & İnce Yenilmez, M. (2025). AI and Workforce Dynamics: Unravelling Productivity. Sosyoekonomi, 33(63), 143-159. https://doi.org/10.17233/sosyoekonomi.2025.01.07
AMA Yoshida H, İnce Yenilmez M. AI and Workforce Dynamics: Unravelling Productivity. Sosyoekonomi. January 2025;33(63):143-159. doi:10.17233/sosyoekonomi.2025.01.07
Chicago Yoshida, Hiroshi, and Meltem İnce Yenilmez. “AI and Workforce Dynamics: Unravelling Productivity”. Sosyoekonomi 33, no. 63 (January 2025): 143-59. https://doi.org/10.17233/sosyoekonomi.2025.01.07.
EndNote Yoshida H, İnce Yenilmez M (January 1, 2025) AI and Workforce Dynamics: Unravelling Productivity. Sosyoekonomi 33 63 143–159.
IEEE H. Yoshida and M. İnce Yenilmez, “AI and Workforce Dynamics: Unravelling Productivity”, Sosyoekonomi, vol. 33, no. 63, pp. 143–159, 2025, doi: 10.17233/sosyoekonomi.2025.01.07.
ISNAD Yoshida, Hiroshi - İnce Yenilmez, Meltem. “AI and Workforce Dynamics: Unravelling Productivity”. Sosyoekonomi 33/63 (January 2025), 143-159. https://doi.org/10.17233/sosyoekonomi.2025.01.07.
JAMA Yoshida H, İnce Yenilmez M. AI and Workforce Dynamics: Unravelling Productivity. Sosyoekonomi. 2025;33:143–159.
MLA Yoshida, Hiroshi and Meltem İnce Yenilmez. “AI and Workforce Dynamics: Unravelling Productivity”. Sosyoekonomi, vol. 33, no. 63, 2025, pp. 143-59, doi:10.17233/sosyoekonomi.2025.01.07.
Vancouver Yoshida H, İnce Yenilmez M. AI and Workforce Dynamics: Unravelling Productivity. Sosyoekonomi. 2025;33(63):143-59.