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Regional Knowledge Production in Nanomaterials: A Spatial Filtering Approach

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  • Christoph Grimpe

    () (Centre for European Economic Research (ZEW), Mannheim, Germany; Katholieke Universiteit Leuven, Belgium; University of Zurich, Switzerland)

  • Roberto Patuelli

    () (Institute for Economic Research (IRE), University of Lugano, Switzerland; The Rimini Centre for Economic Analysis (RCEA), Italy)

Abstract

Nanomaterials are seen as a key technology for the 21st Century, and much is expected of them in terms of innovation and economic growth. They could open the way to many radically new applications, which would form the basis of innovative products. As nanomaterials are still in their infancy, universities, public research institutes and private businesses seem to play a vital role in the innovation process. Existing literature points to the importance of knowledge spillovers between these actors and suggests that the opportunities for these depend on proximity, with increasing distance being detrimental to the extent that spillovers can be realised. Due to the technological complexity, however, proximity could also be less important as relevant nanomaterials research is globally dispersed. Hence in this paper, we analyse the effects of co-location of R&D activities on nanomaterial patenting. Based on European Patent Office data at the German district level (NUTS-3), we estimate two negative binomial models in a knowledge production function framework and include a spatial filtering approach to adjust for spatial autocorrelation. Our results indicate that there is a significant positive effect of both public and private R&D on the production of nanomaterial patents. Moreover, we find a positive interaction between them which hints at the importance of their co-location for realising the full potential of an emerging technology like nanomaterials.

Suggested Citation

  • Christoph Grimpe & Roberto Patuelli, 2008. "Regional Knowledge Production in Nanomaterials: A Spatial Filtering Approach," Working Paper series 29_08, Rimini Centre for Economic Analysis, revised Apr 2009.
  • Handle: RePEc:rim:rimwps:29_08
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Gianni Guastella & Frank G. van Oort, 2015. "Regional Heterogeneity and Interregional Research Spillovers in European Innovation: Modelling and Policy Implications," Regional Studies, Taylor & Francis Journals, vol. 49(11), pages 1772-1787, November.
    2. repec:zbw:rwirep:0311 is not listed on IDEAS
    3. Dirk Engel & Timo Mitze & Roberto Patuelli & Janina Reinkowski, 2013. "Does Cluster Policy Trigger R&D Activity? Evidence from German Biotech Contests," European Planning Studies, Taylor & Francis Journals, vol. 21(11), pages 1735-1759, November.
    4. Timo Mitze & Falk Strotebeck, 2012. "What Drives Regional Cooperative Behavior in German Biotechnology? Embedding Social Network Analysis in a Regression Framework," ERSA conference papers ersa12p629, European Regional Science Association.
    5. Roberto Patuelli & Andrea Vaona & Christoph Grimpe, 2010. "The German East‐West Divide In Knowledge Production: An Application To Nanomaterial Patenting," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 101(5), pages 568-582, December.
    6. repec:elg:eechap:14395_24 is not listed on IDEAS
    7. Joana Mendonça & Christoph Grimpe, 2016. "Skills and regional entrepreneurship capital formation: a comparison between Germany and Portugal," The Journal of Technology Transfer, Springer, vol. 41(6), pages 1440-1456, December.
    8. Yongwan Chun & Daniel A. Griffith & Monghyeon Lee & Parmanand Sinha, 2016. "Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters," Journal of Geographical Systems, Springer, vol. 18(1), pages 67-85, January.
    9. Janina Reinkowski, 2014. "Empirical Essays in the Economics of Ageing and the Economics of Innovation," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 53.
    10. Daisuke Murakami & Daniel Griffith, 2015. "Random effects specifications in eigenvector spatial filtering: a simulation study," Journal of Geographical Systems, Springer, vol. 17(4), pages 311-331, October.
    11. Mitze, Timo & Strotebeck, Falk, 2017. "Modeling interregional research collaborations in German biotechnology using industry directory data: A quantitative social network analysis," MPRA Paper 83392, University Library of Munich, Germany.
    12. James LeSage & Carlos Llano-Verduras, 2014. "Forecasting spatially dependent origin and destination commodity flows," Empirical Economics, Springer, vol. 47(4), pages 1543-1562, December.

    More about this item

    Keywords

    nanomaterials; patents; co-location; spatial autocorrelation; spatial filtering;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • 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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