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AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity

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  • Xueyuan Gao

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Hua Feng

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Artificial intelligence is profoundly influencing various facets of our lives, indicating its potential to significantly impact sustainability. Nevertheless, capturing the productivity gains stemming from artificial intelligence in macro-level data poses challenges, leading to the question of whether artificial intelligence is reminiscent of the “Solow paradox”. This study employs micro-level manufacturing data to investigate the impact of artificial intelligence on firms’ productivity. The study finds that every 1% increase in artificial intelligence penetration can lead to a 14.2% increase in total factor productivity. This conclusion remains robust even after conducting endogeneity analysis and a series of robustness tests. The study identifies that the positive impact of artificial intelligence on productivity is primarily achieved through the value-added enhancement effect, skill-biased enhancement effect, and technology upgrading effect. Furthermore, the study reveals that the effects of artificial intelligence on productivity vary across different property rights and industry concentration contexts. Additionally, the structure of factor endowments within firms can also influence the productivity gains from artificial intelligence. Our study presents compelling evidence demonstrating the role of artificial intelligence in fostering economic sustainability within the framework of Industry 4.0.

Suggested Citation

  • Xueyuan Gao & Hua Feng, 2023. "AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity," Sustainability, MDPI, vol. 15(11), pages 1-21, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8934-:d:1161805
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    1. Nicholas Bloom & John Van Reenen, 2007. "Measuring and Explaining Management Practices Across Firms and Countries," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(4), pages 1351-1408.
    2. Georg Graetz & Guy Michaels, 2018. "Robots at Work," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 753-768, December.
    3. Chad Syverson, 2011. "What Determines Productivity?," Journal of Economic Literature, American Economic Association, vol. 49(2), pages 326-365, June.
    4. Pekka Ilmakunnas & Mika Maliranta & Jari Vainiomäki, 2004. "The Roles of Employer and Employee Characteristics for Plant Productivity," Journal of Productivity Analysis, Springer, vol. 21(3), pages 249-276, May.
    5. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The Skill Content of Recent Technological Change: An Empirical Exploration," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1279-1333.
    6. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    7. Matt Taddy, 2018. "The Technological Elements of Artificial Intelligence," NBER Working Papers 24301, National Bureau of Economic Research, Inc.
    8. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    9. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    10. Goeldner, Moritz & Herstatt, Cornelius & Tietze, Frank, 2015. "The emergence of care robotics — A patent and publication analysis," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 115-131.
    11. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2018. "Prediction, Judgment, and Complexity: A Theory of Decision-Making and Artificial Intelligence," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 89-110, National Bureau of Economic Research, Inc.
    12. Zhuo, Chengfeng & Chen, Jin, 2023. "Can digital transformation overcome the enterprise innovation dilemma: Effect, mechanism and effective boundary," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    13. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    14. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    15. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    16. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    17. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    18. Rammer, Christian & Fernández, Gastón P. & Czarnitzki, Dirk, 2022. "Artificial intelligence and industrial innovation: Evidence from German firm-level data," Research Policy, Elsevier, vol. 51(7).
    19. Bianchini, Stefano & Müller, Moritz & Pelletier, Pierre, 2022. "Artificial intelligence in science: An emerging general method of invention," Research Policy, Elsevier, vol. 51(10).
    20. Guilherme Luz Tortorella & Diego Fettermann, 2018. "Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies," International Journal of Production Research, Taylor & Francis Journals, vol. 56(8), pages 2975-2987, April.
    21. Stefano Bianchini & Moritz Müller & Pierre Pelletier, 2022. "Artificial intelligence in science: An emerging general method of invention," Post-Print hal-03958025, HAL.
    22. Chong-En Bai & Jiangyong Lu & Zhigang Tao, 2006. "The Multitask Theory of State Enterprise Reform: Empirical Evidence from China," American Economic Review, American Economic Association, vol. 96(2), pages 353-357, May.
    23. Fujii, Hidemichi & Managi, Shunsuke, 2018. "Trends and priority shifts in artificial intelligence technology invention: A global patent analysis," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 60-69.
    24. Charles R. Hulten, 2001. "Total Factor Productivity: A Short Biography," NBER Chapters, in: New Developments in Productivity Analysis, pages 1-54, National Bureau of Economic Research, Inc.
    25. Di Vaio, Assunta & Palladino, Rosa & Hassan, Rohail & Escobar, Octavio, 2020. "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," Journal of Business Research, Elsevier, vol. 121(C), pages 283-314.
    26. Ann Bartel & Casey Ichniowski & Kathryn Shaw, 2007. "How Does Information Technology Affect Productivity? Plant-Level Comparisons of Product Innovation, Process Improvement, and Worker Skills," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(4), pages 1721-1758.
    27. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
    28. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation," NBER Working Papers 24449, National Bureau of Economic Research, Inc.
    29. William D. Nordhaus, 2021. "Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 299-332, January.
    30. Mariani, Marcello M. & Machado, Isa & Nambisan, Satish, 2023. "Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda," Journal of Business Research, Elsevier, vol. 155(PB).
    31. Sungchul Choi & Alex Ng, 2011. "Environmental and Economic Dimensions of Sustainability and Price Effects on Consumer Responses," Journal of Business Ethics, Springer, vol. 104(2), pages 269-282, December.
    32. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue nov.
    33. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2018. "Introduction to "The Economics of Artificial Intelligence: An Agenda"," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 1-19, National Bureau of Economic Research, Inc.
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    11. Brodzicki, Tomasz, 2024. "Heterogeneous Firms and AI Adoption. Dynamic Insights into Market Structure and Global Trade," MPRA Paper 127767, University Library of Munich, Germany, revised 01 Apr 2025.
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    16. Zhiwu Zhang, 2026. "The transformation from human surplus value to AI algorithmic surplus value: logic of the critique of capital in the era of AI," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 13(1), pages 1-14, December.
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    19. Montag, Christian & Riazi, A. Mehdi & Mikros, George & Becker, Benjamin & Ali, Raian, 2025. "On the relevance of Maslow's need theory in the age of artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 219(C).

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