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Knowledge flows, the influence of national R&D structure and the moderating role of public–private cooperation

The Journal of Technology Transfer volume 41, pages152–172(2016)Cite this article

Abstract

This paper analyses country-specific determinants of knowledge flows with a view to uncover the role of cross-organizational interactions. Using a sample of some 600,000 patents from the EU27 member states in the period 1990–2007, we take backward citations as dependent variable and find that technological sophistication and research size have a positive effect on knowledge flows. While a national bias towards applied research and development has a negative impact, individual public–private cooperation has a moderating effect due to the generation of scientific knowledge by public institutions. The present study contributes to the debate concerning the direction of R&D investments and provides empirical support to policies aimed at the enhancement of public–private cooperation.

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Fig. 1
Fig. 2

Notes

  1. 1.

    Interestingly, if we include also non-university public research organizations, the share of business funding of R&D in the higher education and government sectors is found to increase in Europe, and is higher than in US (De Backer et al. 2008).

  2. 2.

    Other forms of cooperation encompass private-private and public-public. We focus on public–private cooperation because of its inter-sectoral nature, as opposed to the intra-sectoral style of private-private and of public-public cooperation. In a study on knowledge flows measured by patent citations, inter-sectoral cooperation appears as the most appealing option because it entails accounting for the diversity of attitudes and cultural differences towards openness and intellectual property (Stevens et al. 2013). Actually, in studies about patents the most analysed inter-sectorial co-patenting activity is university-firm patents, which makes sense because it is associated with higher market value (Belderbos et al. 2013). Conversely, public-public co-ownership is very marginal and often subsumed within one category of single-authorship (Callaert et al. 2013).

  3. 3.

    An international consortium of researchers from the University of Newcastle, Incentim (KU Leuven Research and Development), and the Centre for Science and Technology Studies (CWTS) (Leiden University) implemented the data collection.

  4. 4.

    Patents featuring a non-EU27 co-applicant were counted twice. For the econometric analysis, we use as a weight variable the share of number of applicants to avoid double counting.

  5. 5.

    Since we dropped many observations due to missing national data, there may be a bias. We deal with this issue at the end of the results section.

  6. 6.

    To test Hypothesis 1, we tried to include additional national characteristics such as full-time R&D personnel, human resources in science and technology, value of high-tech exports, gross domestic product, etc. We were particularly interested in full-time R&D personnel to closely replicate other works on national innovative capacity (see references in Sect. 2), however, they were excluded because of high multi-collinearity. To test Hypothesis 2, we used share of the sum of higher education plus government expenditure rather than higher education only, which is sensible because in some countries, both are heavily intertwined and the results are similar. However, because we found less theoretical support for this procedure, we prefer to present the results for higher education only. Finally, we tried country fixed effects but they were highly collinear.

  7. 7.

    This specification requires the transformation of the dependent variable nbackcit into log(nbackcit + 1).

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Acknowledgments

Marina Ranga participated in the brainstorming that led to this paper and the thorough revision of an earlier version. Catalina Martínez provided us with data to update the database. Both reviewed a previous version of the manuscript. Anabel Fernández-Mesa, Alberto Marzucchi and Francesco Rentocchini helped with the econometric estimations. Richard Woolley shared insights on the structural composition of research expenditure. Elena M. Tur and François Perruchas offered technical assistance. The research was carried out with funding from project GV/2012/018 of the Valencian Regional Government. Previous versions of the paper were presented at the 2013 Eu-SPRI Forum Conference, the 2013 ISSI Conference and the 2013 IPTS Workshop on ‘Harnessing the Powers of Patent Data’. The authors acknowledge helpful comments from the audiences, especially Jorge Niosi and Julio Raffo. Joaquín M. Azagra-Caro is grateful to René van Bavel and Xabier Goenaga for their institutional support, and to the international consortium that produced the database, especially Henry Etzkowitz, Marina Ranga and members of Incentim and CWTS, led, respectively, by Bart Van Looy and Robert J.W. Tijssen.

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Affiliations

  1. INGENIO (CSIC-UPV), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain
    • Joaquín M. Azagra-Caro
    •  & Davide Consoli
  2. European Commission, Joint Research Centre (JRC)-Institute for Prospective Technological Studies (IPTS), Edificio Expo, C/Inca Garcilaso 3, 41092, Seville, Spain
    • Joaquín M. Azagra-Caro
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  1. Joaquín M. Azagra-Caro
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Correspondence to Joaquín M. Azagra-Caro.

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The initial data construction was conducted within ERAWATCH, a joint initiative of the European Commission’s Directorate General for Research and the Joint Research Centre—Institute for Prospective Technological Studies. The views expressed in this article are those of the author and do not necessarily reflect those of the European Commission. Neither the European Commission nor anyone acting on its behalf is responsible for the use that might be made of the information.

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Azagra-Caro, J.M., Consoli, D. Knowledge flows, the influence of national R&D structure and the moderating role of public–private cooperation. J Technol Transf 41, 152–172 (2016). https://doi.org/10.1007/s10961-014-9382-7

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Keywords

  • Knowledge flows
  • National innovative capacity
  • Patents
  • Citations

JEL Classification

  • O31
  • O33
  • O34