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The Role of R&D Collaboration Networks on Regional Innovation Performance

Author

Listed:
  • Corinne Autant-Bernard

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

  • Cilem Selin Hazir

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

  • James Lesage

    (McCoy College of Business Administration Finance and Economics Department - Texas State University)

Abstract
In this study, we consider R&D collaboration networks as a mechanism that modifies knowledge flows in space, and hence as another source of interaction among regional innovation processes. Our objective is to understand the relative role of spatial neighbors and network neighbors on patenting performance of regions. We make use of data on R&D collaborations supported by the European Union's Framework Programs (FP) and empirically investigate the patent activity of 213 European regions in the field of ICT during 2003-2009. Concerning the short length of the time frame we adopt a static modeling strategy and specify a spatial Durbin Model. As spatial neighbors intersect with network neighbors we decompose neighbor regions into three sets: spatially proximate regions that are not collaboration partners, spatially proximate regions that are collaboration partners, and distant collaboration partners. We express the weight matrix as a convex combination of these three sets and by means of gridding we compare how model fit changes as we move from a purely space based view to a purely network based view to express the dependence structure. The weight matrix that performs the best accords 60% weight to distant collaboration partners, 30% weight to proximate collaboration partners and 10% weight to proximate regions with whom there is no FP collaboration. This result reveals that the interaction (proximate and distant) among European regions within FP networks in the field of ICT is key for understanding dependence among their patenting performances.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Corinne Autant-Bernard & Cilem Selin Hazir & James Lesage, 2014. "The Role of R&D Collaboration Networks on Regional Innovation Performance," Post-Print halshs-01090736, HAL.
    • Handle: RePEc:hal:journl:halshs-01090736
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    1. Nicolas Debarsy & James P Lesage, 2019. "Using Convex Combinations of Spatial Weights in Spatial Autoregressive Models," Post-Print halshs-03509810, HAL.
    2. Debarsy, Nicolas & Dossougoin, Cyrille & Ertur, Cem & Gnabo, Jean-Yves, 2018. "Measuring sovereign risk spillovers and assessing the role of transmission channels: A spatial econometrics approach," Journal of Economic Dynamics and Control, Elsevier, vol. 87(C), pages 21-45.
    3. Laurent R. Bergé, 2017. "Network proximity in the geography of research collaboration," Papers in Regional Science, Wiley Blackwell, vol. 96(4), pages 785-815, November.
    4. Clément Gorin, 2017. "Accessibility, absorptive capacity and innovation in European urban areas," Working Papers halshs-01584111, HAL.
    5. Matteo Lippi Bruni & Irene Mammi, 2017. "Spatial effects in hospital expenditures: A district level analysis," Health Economics, John Wiley & Sons, Ltd., vol. 26(S2), pages 63-77, September.
    6. James Paul LeSage & Manfred M. Fischer, 2020. "Cross-sectional dependence model specifications in a static trade panel data setting," Journal of Geographical Systems, Springer, vol. 22(1), pages 5-46, January.
    7. Oerlemans, Leon & Chan, K.Y. & Knoben, Joris & Vermeulen, P.A.M., 2018. "Structural and geographical conditions for exploitative innovation : Evidence from South African manufacturing firms," Other publications TiSEM 4abcf615-a0d4-49ef-ba25-c, Tilburg University, School of Economics and Management.
    8. Demidova, Olga, 2021. "Methods of spatial econometrics and evaluation of government programs effectiveness," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 107-134.
    9. Nicola Cortinovis & Frank van Oort, 2017. "Between spilling over and boiling down: network-mediated spillovers, absorptive capacity and productivity in European regions," Tinbergen Institute Discussion Papers 17-118/VI, Tinbergen Institute.

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    More about this item

    Keywords

    Networks; R&D; Innovation; Regional Science;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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