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R&D networks and regional knowledge production in Europe: Evidence from a space‐time model

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  • Iris Wanzenböck
  • Philipp Piribauer

Abstract

In this study we estimate space‐time impacts of the embeddedness in R&D networks on regional knowledge production using a dynamic spatial panel data model with non‐linear effects for 229 European NUTS 2 regions in the period 1998–2010. Embeddedness refers to the positioning in networks where nodes represent regions that are linked by joint R&D projects funded by EU Framework Programmes. We find evidence that increasing embeddedness in EU funded R&D networks leads to positive immediate impacts on regional knowledge production, and that regions with lower levels of own knowledge endowments more likely exploit the positive effects. However, the long‐term impacts of a region's embeddedness in these R&D networks are comparatively small. En este estudio se estiman los impactos espacio‐temporales del arraigo en las redes de I+D en la producción de conocimiento regional utilizando un modelo dinámico espacial de datos de panel con efectos no lineales para 229 regiones europeas NUTS 2 en el período 1998–2010. El arraigo se refiere al posicionamiento en redes donde los nodos representan regiones que están vinculadas por proyectos conjuntos de I+D financiados por los Programas Marco de la UE. Se encontró evidencia de que el aumento del arraigo en las redes de I +D financiadas por la UE genera impactos inmediatos positivos en la producción de conocimiento regional, y que las regiones con niveles más bajos de dotación de conocimiento propio explotan más los efectos positivos. Sin embargo, los impactos a largo plazo del arraigo de una región en estas redes de I+D son comparativamente pequeños. 本稿では、1998年から2010年までの欧州の第二種地域統計分類単位(NUTS2)に分類される229の地域の、非線形効果を用いたダイナミック空間パネルデータモデルを使用して、地域の知識生産に対する研究開発ネットワークにおける埋め込み(embeddedness)の時空間波及効果を推計する。埋め込み(embeddedness)とは、ノードがEUフレームワークプログラムの助成により行われた研究開発ジョイントプロジェクトにより繋がれた地域を表しているネットワーク上のポジショニングを指す。EUの助成による研究開発ネットワークが地域の知識生産に直接的な正の効果をもたらし、自己の知的資本(knowledge endowments)レベルがより低い地域には、その正の効果を上手く利用する傾向があることを示すエビデンスが認められた。しかし、研究開発ネットワークのひとつの地域の埋め込みが及ぼす長期的な波及効果は、比較的小さいものである。

Suggested Citation

  • Iris Wanzenböck & Philipp Piribauer, 2018. "R&D networks and regional knowledge production in Europe: Evidence from a space‐time model," Papers in Regional Science, Wiley Blackwell, vol. 97(S1), pages 1-24, March.
  • Handle: RePEc:bla:presci:v:97:y:2018:i:s1:p:s1-s24
    DOI: 10.1111/pirs.12236
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    Cited by:

    1. Eduardo Gonçalves & Henrique Silva Costa & Rosa Livia Montenegro & Tulio Chiarini & Juliana Gonçalves Taveira, 2023. "Green technology co-patenting networks: international dynamics," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1603-1627, April.
    2. Emanuela Marrocu & Raffaele Paci & Stefano Usai, 2022. "Direct and indirect effects of universities on European regional productivity," Papers in Regional Science, Wiley Blackwell, vol. 101(5), pages 1105-1133, October.
    3. Weidenfeld, Adi & Makkonen, Teemu & Clifton, Nick, 2021. "From interregional knowledge networks to systems," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    4. Weidenfeld, Adi & Clifton, Nick, 2025. "Investigating the systemic nature of knowledge networks of regions," Technological Forecasting and Social Change, Elsevier, vol. 215(C).
    5. Anna Bottasso & Maurizio Conti & Simone Robbiano & Marta Santagata, 2022. "Roads to innovation: Evidence from Italy," Journal of Regional Science, Wiley Blackwell, vol. 62(4), pages 981-1005, September.
    6. Martina Neuländtner & Thomas Scherngell, 2020. "Geographical or relational: What drives technology-specific R&D collaboration networks?," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 65(3), pages 743-773, December.
    7. Cristian Barra & Nazzareno Ruggiero, 2022. "How do dimensions of institutional quality improve Italian regional innovation system efficiency? The Knowledge production function using SFA," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 591-642, April.
    8. Li, Tingzhu & Du, Debin, 2025. "Cross-border R&D, absorptive capacity and innovation performance," Structural Change and Economic Dynamics, Elsevier, vol. 73(C), pages 460-471.
    9. Haschka, Rouven E. & Herwartz, Helmut, 2020. "Innovation efficiency in European high-tech industries: Evidence from a Bayesian stochastic frontier approach," Research Policy, Elsevier, vol. 49(8).

    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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