IDEAS home Printed from https://ideas.repec.org/p/ssa/lemwps/2021-41.html
   My bibliography  Save this paper

The direction of technical change in AI and the trajectory effects of government funding

Author

Listed:
  • Martina Iori
  • Arianna Martinelli
  • Andrea Mina

Abstract

Government funding of innovation can have a significant impact not only on the rate of technical change, but also on its direction. In this paper, we examine the role that government grants and government departments played in the development of artificial intelligence (AI), an emergent general purpose technology with the potential to revolutionize many aspects of the economy and society. We analyze all AI patents filed at the US Patent and Trademark Office and develop network measures that capture each patent's influence on all possible sequences of follow-on innovation. By identifying the effect of patents on technological trajectories, we are able to account for the long-term cumulative impact of new knowledge that is not captured by standard patent citation measures. We show that patents funded by government grants, but above all patents filed by federal agencies and state departments, profoundly influenced the development of AI. These long-term effects were especially significant in early phases, and weakened over time as private incentives took over. These results are robust to alternative specifications and controlling for endogeneity.

Suggested Citation

  • Martina Iori & Arianna Martinelli & Andrea Mina, 2021. "The direction of technical change in AI and the trajectory effects of government funding," LEM Papers Series 2021/41, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  • Handle: RePEc:ssa:lemwps:2021/41
    as

    Download full text from publisher

    File URL: http://www.lem.sssup.it/WPLem/files/2021-41.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lucrezia Fanti & Dario Guarascio & Massimo Moggi, 2022. "From Heron of Alexandria to Amazon’s Alexa: a stylized history of AI and its impact on business models, organization and work," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(3), pages 409-440, September.
    2. Sofia Patsali & Michele Pezzoni & Jackie Krafft, 2023. "Healthcare Procurement and Firm Innovation: Evidence from AI-powered Equipment," GREDEG Working Papers 2023-05, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gilbert Cette & Lorraine Koehl & Thomas Philippon, 2019. "The Labor Share in the Long Term: A Decline?," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 510-511-5, pages 35-51.
    2. Eleni Giouli & Pisinas Yorgos & Anna-Maria Kanzola, 2021. "Human Capital and Production Structure: Evidence from Greece," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 7, January -.
    3. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    4. Gabriel López-Martínez & Francisco Eduardo Haz-Gómez & José Eulogio Real Deus, 2023. "Are You Really Your Own Boss? Flexi-Vulnerability and False Consciousness of Autonomy in the Digital Labor Culture of Riders," Social Sciences, MDPI, vol. 12(8), pages 1-18, July.
    5. Colombo, Emilio & Mercorio, Fabio & Mezzanzanica, Mario, 2019. "AI meets labor market: Exploring the link between automation and skills," Information Economics and Policy, Elsevier, vol. 47(C), pages 27-37.
    6. Alejandro Micco, 2019. "The Impact of Automation in Developed Countries," Working Papers wp480, University of Chile, Department of Economics.
    7. 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.
    8. Zhu, Jun & Zhang, Jingting & Feng, Yiqing, 2022. "Hard budget constraints and artificial intelligence technology," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    9. Gregory Casey & Ryo Horii, 2019. "A Multi-factor Uzawa Growth Theorem and Endogenous Capital-Augmenting Technological Change," ISER Discussion Paper 1051, Institute of Social and Economic Research, Osaka University.
    10. Belitski, Maksim & Korosteleva, Julia & Piscitello, Lucia, 2023. "Digital affordances and entrepreneurial dynamics: New evidence from European regions," Technovation, Elsevier, vol. 119(C).
    11. Fran Stewart & Kathryn Kelley, 2020. "Connecting Hands and Heads: Retooling Engineering Technology for the “Smart†Manufacturing Workplace," Economic Development Quarterly, , vol. 34(1), pages 31-45, February.
    12. Ana L. ABELIANSKY & Eda ALGUR & David E. BLOOM & Klaus PRETTNER, 2020. "The future of work: Meeting the global challenges of demographic change and automation," International Labour Review, International Labour Organization, vol. 159(3), pages 285-306, September.
    13. Ping Chen & Jiawei Gao & Zheng Ji & Han Liang & Yu Peng, 2022. "Do Artificial Intelligence Applications Affect Carbon Emission Performance?—Evidence from Panel Data Analysis of Chinese Cities," Energies, MDPI, vol. 15(15), pages 1-16, August.
    14. Yining Zhang & Zhong Wu, 2021. "Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    15. Kexu Wu & Zhiwei Tang & Longpeng Zhang, 2022. "Population Aging, Industrial Intelligence and Export Technology Complexity," Sustainability, MDPI, vol. 14(20), pages 1-24, October.
    16. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Wuest, Thorsten & Stahre, Johan, 2020. "Smart Maintenance: a research agenda for industrial maintenance management," International Journal of Production Economics, Elsevier, vol. 224(C).
    17. Matthias Firgo & Peter Mayerhofer & Michael Peneder & Philipp Piribauer & Peter Reschenhofer, 2018. "Beschäftigungseffekte der Digitalisierung in den Bundesländern sowie in Stadt und Land," WIFO Studies, WIFO, number 61633, April.
    18. Mohamed Salem Ahmed Ibrahim Alhosani & Kamarul Bahari Yaakub, 2021. "Investigating the Relationship Between Total Quality Management and Primary School Academic Performance with Innovation as a Mediator Using SEM," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 7, January -.
    19. Ghimire, Ramesh & Skinner, Jim & Carnathan, Mike, 2020. "Who perceived automation as a threat to their jobs in metro Atlanta: Results from the 2019 Metro Atlanta Speaks survey," Technology in Society, Elsevier, vol. 63(C).
    20. Gebs, Mehdi & Nabi, Mahmoud Sami, 2021. "The economic impacts of digitalization through an extended input-output model: theory and application to Tunisia," MPRA Paper 113299, University Library of Munich, Germany.

    More about this item

    Keywords

    R&D; Technical change; Government subsidies; Technology policy; General purpose technology.;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ssa:lemwps:2021/41. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/labssit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.