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Patent evolution in relation to public/private R&D investment and corporate profitability: Evidence from the United States

Author

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  • Arnold Verbeek

    (K.U. Leuven, Steunpunt O&O Statistieken; IDEA Consult)

  • Koenraad Debackere

    (Faculty of Economics and Applied Economics and Steunpunt O&O Statistieken, K. U. Leuven; Research Division INCENTIM, Faculty of Economics and Applied Economics, K. U. Leuven)

Abstract

Summary In this paper we analyze the (historical) co-evolution of technological development and economic progress (by relating public and private R&D investment, patenting, and corporate profitability). We relate to the work ofSchmookler(1966),Griliches(1990),Pakes&Griliches(1980) andPakes(1986) who all have studied the techno-economic interplay by considering patents as in indicator of technological performance. We use United States industry and government data over the period 1953-1998 (45 years). Co-evolution analysis over this period reveals a strong interdependency among the variables. Patent evolution is strongly related to the development of private R&D and corporate profitability; the levels of public and private R&D expenditure in combination with the level of technological output (i.e. patents) have a strong predictive and explanatory power towards corporate profitability (R2 value of 94.9%). Causality tests reveal a joint determination between R&D investment and corporate profitability (L=2; p

Suggested Citation

  • Arnold Verbeek & Koenraad Debackere, 2006. "Patent evolution in relation to public/private R&D investment and corporate profitability: Evidence from the United States," Scientometrics, Springer;Akadémiai Kiadó, vol. 66(2), pages 279-294, February.
    • Handle: RePEc:spr:scient:v:66:y:2006:i:2:d:10.1007_s11192-006-0021-4
    DOI: 10.1007/s11192-006-0021-4
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    Cited by:

    1. Scott D. Bass & Lukasz A. Kurgan, 2010. "Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 217-241, February.
    2. Copiello, Sergio, 2019. "Peer and neighborhood effects: Citation analysis using a spatial autoregressive model and pseudo-spatial data," Journal of Informetrics, Elsevier, vol. 13(1), pages 238-254.
    3. Konstantakis, Konstantinos N. & Michaelides, Panayotis G., 2017. "Technology and Business Cycles: A Schumpeterian Investigation for the USA," MPRA Paper 80636, University Library of Munich, Germany.
    4. Konstantakis, Konstantinos N. & Michaelides, Panayotis G., 2017. "Does technology cause business cycles in the USA? A Schumpeter-inspired approach," Structural Change and Economic Dynamics, Elsevier, vol. 43(C), pages 15-26, December.
    5. Iraj Daizadeh, 2020. "Trademark filings and patent application count time series are structurally near-identical and cointegrated: Implications for studies in innovation," Papers 2012.10400, arXiv.org.

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