IDEAS home Printed from https://ideas.repec.org/p/mil/wpdepa/2019-01.html
   My bibliography  Save this paper

The Rush for Patents in the Fourth Industrial Revolution: An Exploration of Patenting Activity at the European Patent Office

Author

Listed:
  • Mario BENASSI
  • Elena GRINZA
  • Francesco RENTOCCHINI

Abstract

Despite the increasing interest related to the Fourth Industrial Revolution (4IR) from practitioners and policy makers, comparatively less attention has come from academia (particularly within the management and economics literature). Our paper aims at filling this gap and provides an in-depth description of the technological trends, geographic distribution, and business-level dynamics of the 4IR in the European Union from patent- and firm-level perspectives. We do so by conducting an empirical assessment of the development of technologies related to the 4IR via the analysis of patents filed at the European Patent Office between 1985 and 2014. We employ a new matched patent-firm data set provided by the Bureau Van Dijk: ORBIS-IP. In line with results from the recent literature, we find evidence of a surge in patenting activity related to the 4IR in the past three decades, particularly in networked devices. Our results also suggest that firms filing 4IR patents have become progressively younger on average. At the same time, we find a steady growth in the average number of 4IR patent applications filed yearly by each company. Further variance decompositions show that the surge in 4IR patent applications is mainly explained by incumbent firms filing more 4IR patent applications over time, rather than new entrants progressively populating the 4IR world. Finally, we uncover a general trend emerging at the firm level, whereby firms tend to specialize in few technological areas and avoid differentiation.

Suggested Citation

  • Mario BENASSI & Elena GRINZA & Francesco RENTOCCHINI, 2019. "The Rush for Patents in the Fourth Industrial Revolution: An Exploration of Patenting Activity at the European Patent Office," Departmental Working Papers 2019-01, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  • Handle: RePEc:mil:wpdepa:2019-01
    as

    Download full text from publisher

    File URL: http://wp.demm.unimi.it/files/wp/2019/DEMM-2019_01wp.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1, March.
    2. Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, December.
    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. Igna, Ioana & Venturini, Francesco, 2023. "The determinants of AI innovation across European firms," Research Policy, Elsevier, vol. 52(2).
    2. Behrens, Vanessa & Viete, Steffen, 2020. "A note on Germany's role in the fourth industrial revolution," Working Papers 09/2020, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung.
    3. Alexander V. Giczy & Nicholas A. Pairolero & Andrew A. Toole, 2022. "Identifying artificial intelligence (AI) invention: a novel AI patent dataset," The Journal of Technology Transfer, Springer, vol. 47(2), pages 476-505, April.

    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. Colin Wessendorf & Alexander Kopka & Dirk Fornahl, 2021. "The impact of the six European Key Enabling Technologies (KETs) on regional knowledge creation," Papers in Evolutionary Economic Geography (PEEG) 2127, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Sep 2021.
    2. Oliver Falck & Johannes Koenen, 2020. "Rohstoff „Daten“: Volkswirtschaflicher Nutzen von Datenbereitstellung – eine Bestandsaufnahme," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 113.
    3. Christian Rammer & Gastón P Fernández & Dirk Czarnitzki, 2021. "Artificial Intelligence and Industrial Innovation: Evidence from Firm-Level Data," Working Papers of Department of Economics, Leuven 674605, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    4. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    5. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    6. DUERNECKER Georg & SANCHEZ MARTINEZ Miguel, 2021. "Structural change and productivity growth in the European Union: Past, present and future," JRC Working Papers on Territorial Modelling and Analysis 2021-09, Joint Research Centre.
    7. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    8. Vasiliki Koniakou, 2023. "From the “rush to ethics” to the “race for governance” in Artificial Intelligence," Information Systems Frontiers, Springer, vol. 25(1), pages 71-102, February.
    9. Andrea Szalavetz, 2019. "Artificial Intelligence-Based Development Strategy in Dependent Market Economies - Any Room amidst Big Power Rivalry?," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 40-54.
    10. Gries, Thomas & Naudé, Wim, 2022. "Modelling artificial intelligence in economics," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 56, pages 1-12.
    11. Fossen, Frank M. & Sorgner, Alina, 2021. "Digitalization of work and entry into entrepreneurship," Journal of Business Research, Elsevier, vol. 125(C), pages 548-563.
    12. Mario Benassi & Elena Grinza & Francesco Rentocchini & Laura Rondi, 2022. "Patenting in 4IR technologies and firm performance [Robots and jobs: evidence from US labor markets]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 31(1), pages 112-136.
    13. Charles M. A. Clark & Aleksandr V. Gevorkyan, 2020. "Artificial Intelligence and Human Flourishing," American Journal of Economics and Sociology, Wiley Blackwell, vol. 79(4), pages 1307-1344, September.
    14. Nicholas Crafts, 2022. "Slow real wage growth during the Industrial Revolution: productivity paradox or pro-rich growth? [Engels’ pause: technical change, capital accumulation, and inequality in the British industrial rev," Oxford Economic Papers, Oxford University Press, vol. 74(1), pages 1-13.
    15. Selale Tuzel & Miao Ben Zhang, 2021. "Economic Stimulus at the Expense of Routine‐Task Jobs," Journal of Finance, American Finance Association, vol. 76(6), pages 3347-3399, December.
    16. Bart van Ark & Anthony J. Venables, 2020. "A Concerted Effort to Tackle the UK Productivity Puzzle," International Productivity Monitor, Centre for the Study of Living Standards, vol. 39, pages 3-15, Fall.
    17. Yuki, Kazuhiro, 2012. "Mechanization, task assignment, and inequality," MPRA Paper 37754, University Library of Munich, Germany.
    18. Nils Grashof & Alexander Kopka, 2023. "Widening or closing the gap? The relationship between artificial intelligence, firm-level productivity and regional clusters," Bremen Papers on Economics & Innovation 2304, University of Bremen, Faculty of Business Studies and Economics.
    19. Jérôme Dugast & Thierry Foucault, 2020. "Equilibrium Data Mining and Data Abundance," Post-Print hal-02933315, HAL.
    20. Andreas Eder & Wolfgang Koller & Bernhard Mahlberg, 2022. "Economy 4.0: employment effects by occupation, industry, and gender," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 49(4), pages 1063-1088, November.

    More about this item

    Keywords

    Fourth Industrial Revolution; Industry 4.0; matched patent-firm data; patent applications; EPO;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

    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:mil:wpdepa:2019-01. 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: DEMM Working Papers (email available below). General contact details of provider: https://edirc.repec.org/data/damilit.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.