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Knowledge spillovers in U.S. patents: a dynamic patent intensity model with secret common innovation factors

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Abstract
During the past two decades, innovations protected by patents have played a key role in business strategies. This fact enhanced studies of the determinants of patents and the impact of patents on innovation and competitive advantage. Sustaining competitive advantages is as important as creating them. Patents help sustaining competivite advantages by increasing the production cost of competitors, by signaling a better quality of products and by serving as barriers to entry. If patents are rewards for innovation, more R&D should be reflected in more patents applications but this is not the end of the story. There is empirical evidence showing that patents through time are becoming easier to get and more valuable to the firm due to increasing damage awards from infringers. These facts question the constant and static nature of the relationship between R&D and patents. Furthermore, innovation creates important knowledge spillovers due to its imperfect appropriability. Our paper investigates these dynamic effects using U.S. patent data from 1979 to 2000 with alternative model specifications for patent counts. We introduce a general dynamic count panel data model with dynamic observable and unobservable spillovers, which encompasses previous models, is able to control for the endogeneity of R&D and therefore can be consistently estimated by maximum likelihood. Apart from allowing for firm specific fixed and random effects, we introduce a common unobserved component, or secret stock of knowledge, that affects differently the propensity to patent of each firm across sectors due to their different absorptive capacity.

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  • Blazsek, Szabolcs, 2009. "Knowledge spillovers in U.S. patents: a dynamic patent intensity model with secret common innovation factors," UC3M Working papers. Economics we098951, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:werepe:we098951
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    Cited by:

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    3. Klein, Michael A., 2022. "The reward and contract theories of patents in a model of endogenous growth," European Economic Review, Elsevier, vol. 147(C).
    4. Blazsek, Szabolcs & Escribano, Alvaro, 2016. "Patent propensity, R&D and market competition: Dynamic spillovers of innovation leaders and followers," Journal of Econometrics, Elsevier, vol. 191(1), pages 145-163.
    5. Blazsek, Szabolcs & Escribano, Alvaro, 2016. "Score-driven dynamic patent count panel data models," Economics Letters, Elsevier, vol. 149(C), pages 116-119.
    6. Blazsek, Szabolcs & Licht, Adrian, 2018. "Seasonality Detection in Small Samples using Score-Driven Nonlinear Multivariate Dynamic Location Models," UC3M Working papers. Economics 27483, Universidad Carlos III de Madrid. Departamento de Economía.
    7. Waters, James, 2011. "The effect of the Sarbanes-Oxley Act on innovation," MPRA Paper 28072, University Library of Munich, Germany.
    8. Jesús Manuel Plaza Llorente, 2012. "Innovación y caos determinista: un modelo predictivo para Europa," EKONOMIAZ. Revista vasca de Economía, Gobierno Vasco / Eusko Jaurlaritza / Basque Government, vol. 80(02), pages 260-289.
    9. Ben Angelo & Mitchell Johnston, 2023. "Technological innovation and stock returns: Innovative skill versus innovative luck," The Financial Review, Eastern Finance Association, vol. 58(4), pages 811-832, November.
    10. Blazsek, Szabolcs & Licht, Adrian, 2019. "Markov-switching score-driven multivariate models: outlier-robust measurement of the relationships between world crude oil production and US industrial production," UC3M Working papers. Economics 29030, Universidad Carlos III de Madrid. Departamento de Economía.
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    13. Astrid Ayala & Szabolcs Blazsek, 2019. "Score-driven currency exchange rate seasonality as applied to the Guatemalan Quetzal/US Dollar," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 10(1), pages 65-92, March.

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    Keywords

    Secret innovations;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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