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.
Notes
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).
According to ISTAT, Italy has 13 big municipalities: Turin, Genoa, Milan, Verona, Venice, Bulogne, Florence, Rome, Naples, Bari, Palermo, Messina, Catania.
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.
References
Adams, J. D., & Jaffe, A. B. (1996). Bounding the effects of R&D: An Investigation using matched establishment-firm data. Rand Journal of Economics, 27(4), 700–721.
Aiello, F., & Cardamone, P. (2008). R&D Spillovers and firms’ performance in Italy: Evidence from a flexible production function. Empirical Economics, 34(1), 143–166.
Andersson, M., & Gråsjö, U. (2009). Spatial dependence and the representation of space in empirical models. The Annals of Regional Science, 43(1), 159–180.
Anselin, L. (1988). Spatial econometrics: Methods and models. Boston: Kluwer.
Anselin, L., & Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. In D. Giles & A. Ullah (Eds.), Handbook of applied economic statistics (pp. 237–289). New York: Marcel Dekker.
Anselin, L., & Florax, R. (1995). Small sample properties of tests for spatial dependence in regression models: Some further results. In L. Anselin & R. Florax (Eds.), New directions in spatial econometrics (pp. 75–95). New York: Springer.
Arraiz, I., Drukker, D. M., Kelejian, H. H., & Prucha, I. R. (2010). A spatial Cliff-Ord-type model with heteroskedastic innovations: Small and large sample results. Journal of Regional Science, 50(2), 592–614.
Audretsch, D. B., & Feldman, M. (1996). R&D spillovers and the geography of innovation and production. American Economic Review, 86(3), 630–640.
Belderbos, R., Carree, M., Diederen, B., Lokshin, B., & Veugelers, R. (2004). Heterogeneity in R&D cooperation strategies. International Journal of Industrial Organization, 22(8–9), 1237–1263.
Capitalia, (2003). Indagine sulle imprese manifatturiere. Rapporto sull’industria italiana e sulla politica industriale.
Carboni, O. A. (2011). R&D subsidies and private R&D expenditures: Evidence from Italian manufacturing data. International Review of Applied Economics, 25(4), 419–439.
Carboni, O. A. (2012). A spatial analysis of R&D: The role of industry proximity. CRENoS WP. 2012-04.
Cassiman, B., & Veugelers, R. (2002). R&D cooperation and spillovers: Some empirical evidence from Belgium. American Economic Review, 92, 1169–1184.
Cliff, A., & Ord, J. K. (1981). Spatial processes: Models and applications. London: Pion.
Coe, D. T., & Helpman, E. (1995). International R&D spillovers. European Economic Review, 39, 859–887.
Cunningham, S.W. & Werker C. (2012). Proximity and collaboration in European nanotechnology, Papers in Regional Science, forthcoming.
D’Aspremont, C., & Jacquemin, A. (1988). Cooperative and noncooperative R&D in duopoly with spillovers. American Economic Review, 78(5), 1133–1137.
Drukker, D. M., P. Egger, & Prucha I. R. (2010) On two-step estimation of a spatial autoregressive model with autoregressive autoregressive disturbances and endogenous regressors, Technical report, Department of Economics, University of Maryland.
Drukker, D. M., Peng, H., Prucha, I. R., & Raciborski, R. (2011). Creating and managing spatial-weighting matrices using the spmat command. Technical Report, Stata.
Giovanni Abramo, G., D’Angelo, C. A., Di Costa, F., & Solazzi, M. (2011). The role of information asymmetry in the market for university–industry research collaboration. Journal of Technology Transfer, 36(1), 84–100.
Griliches, Z. (1979). Issues in assessing the contribution of R&D to productivity growth. Bell Journal of Economics, 10, 92–116.
Haining, R. P. (2003). Spatial data analysis. Cambridge: Cambridge University Press.
Harris, R., Moffat, J., & Kravtsova, V. (2011). In Search of ‘W’. Spatial Economic Analysis, 6(3), 249–270.
Hordijk, L. (1974). Spatial correlation in the disturbances of spatial correlation in the disturbances of a linear interregional model. Regional and Urban Economics, 4, 117–140.
ISTAT. (2004). Il nuovo sistema input-output. Roma: Istat.
Kelejian, H. H., & Prucha, I. R. (1998). A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. Journal of Real Estate Finance and Economics, 17(1), 99–121.
Kelejian, H. H., & Prucha, I. R. (2010). Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Econometrics, 140(1), 53–130.
Keller, W. (2002). Trade and the transmission of technology. Journal of Economic Growth, 7, 5–24.
Leenders, R. Th. A. J. (2002). Modelling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24, 21–47.
LeSage, J. P., & Pace, R. K. (2009). Introduction to spatial econometrics. Boca Raton, FL: CRC Press Taylor & Francis Group.
Los, B., & Verspagen, B. (2000). R&D spillovers and productivity: Evidence from U.S. manufacturing microdata. Empirical Economics, 25, 127–148.
Macdissi, C., & Negassi, S. (2002). International R&D spillovers: An empirical study. Economics of Innovation and New Technologies, 11(2), 77–91.
Marrocu, E., Paci, R., & Usai, S. (2011). The complementary effects of proximity dimensions on knowledge spillovers. WP CREnoS 2011/21.
Medda, G. & Piga, C. A. (2007). Technological Spillovers and Productivity in Italian Manufacturing Firms. Loughborough University, WP 2007-16.
Moran, P. (1950). A test for serial independence of residuals. Biometrika, 37, 178–181.
Morrison, C. J., & Siegel, D. S. (1999). Scale economies and industry agglomeration externalities: A dynamic cost function approach. American Economic Review, 89(1), 272–290.
Orlando, M. J. (2004). Measuring spillovers from industrial R&D: On the importance of geographic and technological proximity. The Rand Journal of Economics, 35(4), 777–786.
Peri, G. (2005). Determinants of knowledge flows and their effect on innovation. Review of Economics and Statistics, 87(2), 308–322.
Piga, C. A., & Poyago-Theotoky, J. (2005). Endogenous R&D spillovers and locational choice. Regional Science and Urban Economics, 35, 127–139.
Pisati, M. (2001). Tools for spatial data analysis. Stata Technical Bulletin STB-60, March.
Ponds, R., Frank van Oort, F., & Frenken, K. (2007). The geographical and institutional proximity of research collaboration. Papers in Regional Science, 86(3), 423–444.
Scherer, F. M. (1982). Inter-industrial technology flows and productivity growth. The Review of Economics and Statistics, 64, 627–634.
Terleckyj, N. E. (1974). Effects of R&D on the productivity growth of industries: An exploratory study. Washington, DC: National Planning Association.
Terleckyj, N. E. (1980). Direct and indirect effects of industries research and development on the productivity growth of industries. In W. Kendrick & B. Vaccara (Eds.), New development in productivity measurement. New York: National Bureau of Economic Research.
Wolff, E. N., & Nadiri, M. I. (1993). Spillover effects, linkage structure, and research and development. Structural Change and Economic Dynamics, 4, 315–331.
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10961-012-9279-2