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Patenting in 4IR technologies and firm performance
[Robots and jobs: evidence from US labor markets]

Author

Listed:
  • Mario Benassi
  • Elena Grinza
  • Francesco Rentocchini
  • Laura Rondi

Abstract

We investigate whether firm performance is related to the accumulated stock of technological knowledge associated with the Fourth Industrial Revolution (4IR) and, if so, whether the firm’s history in 4IR technology development affects such a relationship. We exploit a rich longitudinal matched patent-firm data set on the population of large firms that filed 4IR patents at the European Patent Office (EPO) between 2009 and 2014, while reconstructing their patent stocks from 1985 onward. To identify 4IR patents, we use a novel two-step procedure proposed by EPO (2020, Patents and the Fourth Industrial Revolution: The Global Technology Trends Enabling the Data-Driven Economy, European Patent Office), based on Cooperative Patent Classification codes and on a full-text patent search. Our results show a positive and significant relationship between firms’ stocks of 4IR patents and labor and total factor productivity. We also find that firms with a long history in 4IR patent filings benefit more from the development of 4IR technological capabilities than later applicants. Conversely, we find that firm profitability is not significantly related to the stock of 4IR patents, which suggests that the returns from 4IR technological developments may be slow to be cashed in. Finally, we find that the positive relationship with productivity is stronger for 4IR-related wireless technology and for artificial intelligence, cognitive computing, and big data analytics.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:indcch:v:31:y:2022:i:1:p:112-136.
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    File URL: http://hdl.handle.net/10.1093/icc/dtab041
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    1. Davide Antonioli & Alberto Marzucchi & Francesco Rentocchini & Simone Vannuccini, 2022. "Robot Adoption and Innovation Activities (last revised: December 2023)," Munich Papers in Political Economy 21, Munich School of Politics and Public Policy and the School of Management at the Technical University of Munich.
    2. NINDL Elisabeth & NAPOLITANO Lorenzo & CONFRARIA Hugo & RENTOCCHINI Francesco & FAKO Peter & GAVIGAN James & TUEBKE Alexander, 2024. "The 2024 EU Industrial R&D Investment Scoreboard," JRC Research Reports JRC140129, Joint Research Centre.
    3. Cambini, Carlo & Grinza, Elena & Sabatino, Lorien, 2023. "Ultra-fast broadband access and productivity: Evidence from Italian firms," International Journal of Industrial Organization, Elsevier, vol. 86(C).
    4. Antonioli, Davide & Marzucchi, Alberto & Rentocchini, Francesco & Vannuccini, Simone, 2024. "Robot adoption and product innovation," Research Policy, Elsevier, vol. 53(6).
    5. Zhang, Wenwen & Fu, Shuai & Chiu, Yi-Bin & Hsiao, Cody Yu-Ling, 2025. "Artificial intelligence, digital inclusive finance, and financial performance: Dynamic threshold insights from renewable energy enterprises," Energy Economics, Elsevier, vol. 148(C).
    6. Marioni, Larissa da Silva & Rincon-Aznar, Ana & Venturini, Francesco, 2024. "Productivity performance, distance to frontier and AI innovation: Firm-level evidence from Europe," Journal of Economic Behavior & Organization, Elsevier, vol. 228(C).
    7. Guderian, Carsten C. & Posth, Jan-Alexander & Grob, Linus, 2023. "Investment decisions and passive portfolio construction utilizing patent analytics: A multi-case study on COVID-19 treatment technologies," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 66-87.
    8. Emmanuel Umoru Haruna & Yong Jun Baek, 2025. "Does technological innovation influence productive capacities in the Asia–Pacific region? Evidence from a dynamic model approach," International Economics and Economic Policy, Springer, vol. 22(4), pages 1-26, October.
    9. Mauro Caselli & Edwin Fourrier-Nicolai & Andrea Fracasso & Sergio Scicchitano, 2024. "Digital Technologies and Firms’ Employment and Training," CESifo Working Paper Series 11056, CESifo.
    10. Stan Metcalfe, 2024. "Joseph Schumpeter, Alfred Marshall and the nature of restless capitalism," MIOIR Working Paper Series 2024-02, The Manchester Institute of Innovation Research (MIoIR), The University of Manchester.
    11. Parteka, Aleksandra & Kordalska, Aleksandra, 2023. "Artificial intelligence and productivity: global evidence from AI patent and bibliometric data," Technovation, Elsevier, vol. 125(C).
    12. Zhai, Shaoxuan & Liu, Zhenpeng, 2023. "Artificial intelligence technology innovation and firm productivity: Evidence from China," Finance Research Letters, Elsevier, vol. 58(PB).
    13. Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).

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