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Spatial and industry proximity in collaborative research: evidence from Italian manufacturing firms

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  • Oliviero Carboni

Abstract

This paper attempts to check the existence of geographic and industry distance effects, alongside other microeconomic determinants, on firms’ decisions to engage in R&D collaboration. Physical distance is defined by geographical coordinates while the measure of industry distance is based on the trade intensity between sectors. The model specified here refers to the combined spatial autoregressive model with autoregressive disturbances and it is estimated through the spatial two stage least square procedure. The results show that both geographical and industry proximity, positively affect the decision to cooperate in R&D. Copyright Springer Science+Business Media New York 2013

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  • Oliviero Carboni, 2013. "Spatial and industry proximity in collaborative research: evidence from Italian manufacturing firms," The Journal of Technology Transfer, Springer, vol. 38(6), pages 896-910, December.
    • Handle: RePEc:kap:jtecht:v:38:y:2013:i:6:p:896-910
    DOI: 10.1007/s10961-012-9279-2
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    Cited by:

    1. Anna M. Ferragina & Giulia Nunziante, 2018. "Are Italian firms performances influenced by innovation of domestic and foreign firms nearby in space and sectors?," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 45(3), pages 335-360, September.
    2. Loet Leydesdorff & Igone Porto-Gomez, 2019. "Measuring the expected synergy in Spanish regional and national systems of innovation," The Journal of Technology Transfer, Springer, vol. 44(1), pages 189-209, February.
    3. Oliviero A. Carboni & Claudio Detotto, 2016. "The economic consequences of crime in Italy," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 43(1), pages 122-140, January.
    4. Giorgio Calcagnini & Ilario Favaretto & Germana Giombini & Francesco Perugini & Rosalba Rombaldoni, 2016. "The role of universities in the location of innovative start-ups," The Journal of Technology Transfer, Springer, vol. 41(4), pages 670-693, August.
    5. repec:cup:judgdm:v:14:y:2019:i:3:p:299-308 is not listed on IDEAS
    6. Luigi Aldieri & Gennaro Guida & Maxim Kotsemir & Concetto Paolo Vinci, 2019. "An investigation of impact of research collaboration on academic performance in Italy," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 2003-2040, July.
    7. Aiello, Francesco & Castiglione, Concetta, 2014. "Being efficient to stay strong in a weak economy. The case of calabrian manufacturing firms," MPRA Paper 54366, University Library of Munich, Germany.
    8. Cardamone, Paola, 2014. "R&D, spatial proximity and productivity at firm level: evidence from Italy," MPRA Paper 57149, University Library of Munich, Germany.
    9. Glenda Kruss & Mariette Visser, 2017. "Putting university–industry interaction into perspective: a differentiated view from inside South African universities," The Journal of Technology Transfer, Springer, vol. 42(4), pages 884-908, August.
    10. Tommaso Agasisti & Cristian Barra & Roberto Zotti, 2019. "Research, knowledge transfer, and innovation: The effect of Italian universities’ efficiency on local economic development 2006−2012," Journal of Regional Science, Wiley Blackwell, vol. 59(5), pages 819-849, November.
    11. Carboni, Oliviero A., 2013. "Heterogeneity in R&D collaboration: An empirical investigation," Structural Change and Economic Dynamics, Elsevier, vol. 25(C), pages 48-59.
    12. Adalgiso Amendola & Cristian Barra & Roberto Zotti, 2020. "Does graduate human capital production increase local economic development? An instrumental variable approach," Journal of Regional Science, Wiley Blackwell, vol. 60(5), pages 959-994, November.
    13. Douglas S. Noonan & Joanna Woronkowicz & Jessica Sherrod Hale, 2021. "More than STEM: spillovers from higher education institution infrastructure investments in the arts," The Journal of Technology Transfer, Springer, vol. 46(6), pages 1784-1813, December.
    14. Miao He & Guibing He & Jiaxin Chen & Yuan Wang, 2019. "Sense of control matters: A long spatial distance leads to a short-term investment preference," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(3), pages 299-308, May.
    15. Saxena, Gunjan, 2015. "Imagined relational capital: An analytical tool in considering small tourism firms' sociality," Tourism Management, Elsevier, vol. 49(C), pages 109-118.
    16. Iacobucci, Donato & Perugini, Francesco, 2023. "Innovation performance in traditional industries: Does proximity to universities matter," Technological Forecasting and Social Change, Elsevier, vol. 189(C).

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    More about this item

    Keywords

    Spatial weights; Spatial dependence; Spatial models; R&D; C31; R15; O10; O31;
    All these keywords.

    JEL classification:

    • 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
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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