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

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.

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Notes

  1. In this analysis South comprises 9 regions (Lazio, Abruzzo, Molise, Campania, Puglia, Basilicata, Calabria, Sicilia, Sardegna). North comprises 10 regions (Lombardia, Piemonte, Liguria, Trentino, Friuli, Veneto, Emilia, Toscana, Umbria, Marche).

  2. According to ISTAT, Italy has 13 big municipalities: Turin, Genoa, Milan, Verona, Venice, Bulogne, Florence, Rome, Naples, Bari, Palermo, Messina, Catania.

  3. Further discussions of spatial-weighting matrices and the parameter space for the spatial-autoregressive parameter can be found in Kelejian and Prucha (2010) and Drukker et al. (2011).

  4. Some studies measure distance between firms by considering inter-sectorial flows of intermediate goods. Other works employ patents of innovations to construct technology spaces. Adams and Jaffe (1996) and Orlando (2004) employ a measure of geographical distance between firms, while Macdissi and Negassi (2002) model the external technological spillover on the basis of firms’ resources devoted to cooperation and capital flows.

  5. For a discussion of the estimation theory for the implemented spatial two stage least square estimator (GS2SLS) see Kelejian and Prucha (1998, 2010), Arraiz et al. (2010) and Drukker et al. (2010).

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Acknowledgments

I am indebted to David M. Drukker, Maurizio Pisati and Rafal Raciborski for helpful suggestions for the spatial analysis. I also thank Giuseppe Medda and Claudio Detotto for valuable comments on the matrix construction. All the usual disclaims apply.

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Correspondence to Oliviero A. Carboni.

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Carboni, O.A. Spatial and industry proximity in collaborative research: evidence from Italian manufacturing firms. J Technol Transf 38, 896–910 (2013). https://doi.org/10.1007/s10961-012-9279-2

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  • DOI: https://doi.org/10.1007/s10961-012-9279-2

Keywords

  • Spatial weights
  • Spatial dependence
  • Spatial models
  • R&D

JEL Classification

  • C31
  • R15
  • O10
  • O31