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Investment decision and the spatial dimension : Evidence from firm level data

Author

Listed:
  • Luisito BERTINELLI

    (UNIVERSITE CATHOLIQUE DE LOUVAIN, Institut de Recherches Economiques et Sociales (IRES) and CORE)

  • Rosella NICOLINI

    (UNIVERSITE CATHOLIQUE DE LOUVAIN, Institut de Recherches Economiques et Sociales (IRES) and CODE, Universitat de Barcelona)

Abstract

This paper proposes to investigate the effect of spillovers on location decision of firms. We develop an analysis merging the geographer toolbox with the standard econometric techniques. For a chosen sample of sectors, through the spatial data analysis, we test the existence of positive spatial autocorrelation for R&D investments that lead R&D expenditure to cluster. Moreover, we succeed in detecting how far the local environment may influence the firm decisions in R&D investments. Data confirm tha tthe proximity to other firms investing in R&D may produce positive externalities. Finally, the diversity vs. specialization debate is tackled.

Suggested Citation

  • Luisito BERTINELLI & Rosella NICOLINI, 2002. "Investment decision and the spatial dimension : Evidence from firm level data," LIDAM Discussion Papers IRES 2002007, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    • Handle: RePEc:ctl:louvir:2002007
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    File URL: http://sites.uclouvain.be/econ/DP/IRES/2002-7.pdf
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    References listed on IDEAS

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    1. Feldman, Maryann P. & Audretsch, David B., 1999. "Innovation in cities:: Science-based diversity, specialization and localized competition," European Economic Review, Elsevier, vol. 43(2), pages 409-429, February.
    2. Kenneth Arrow, 1962. "Economic Welfare and the Allocation of Resources for Invention," NBER Chapters, in: The Rate and Direction of Inventive Activity: Economic and Social Factors, pages 609-626, National Bureau of Economic Research, Inc.
    3. Paul Krugman, 1992. "Geography and Trade," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262610868, December.
    4. Bruno Cassiman & Reinhilde Veugelers, 1998. "R&D cooperation and spillovers: Some empirical evidence," Economics Working Papers 328, Department of Economics and Business, Universitat Pompeu Fabra.
    5. Glaeser, Edward L & Hedi D. Kallal & Jose A. Scheinkman & Andrei Shleifer, 1992. "Growth in Cities," Journal of Political Economy, University of Chicago Press, vol. 100(6), pages 1126-1152, December.
      • Edward L. Glaeser & Hedi D. Kallal & Jose A. Scheinkman & Andrei Shleifer, 1991. "Growth in Cities," NBER Working Papers 3787, National Bureau of Economic Research, Inc.
      • Glaeser, Edward Ludwig & Kallal, Hedi D. & Scheinkman, Jose A. & Shleifer, Andrei, 1992. "Growth in Cities," Scholarly Articles 3451309, Harvard University Department of Economics.
    6. Audretsch, David B, 1998. "Agglomeration and the Location of Innovative Activity," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 14(2), pages 18-29, Summer.
    7. Wallsten, Scott J., 2001. "An empirical test of geographic knowledge spillovers using geographic information systems and firm-level data," Regional Science and Urban Economics, Elsevier, vol. 31(5), pages 571-599, September.
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    Citations

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    Cited by:

    1. Reinhold Kosfeld, 2012. "Identifying Clusters within R&D Intensive Industries Using Local Spatial Methods," ERSA conference papers ersa12p232, European Regional Science Association.
    2. Yufang Shi & Yufeng Jiang & Can Xie & Cong Li, 2024. "Regional Innovation and Sustainable Development Interplay: Analyzing the Spatial Externalities of Domestic Demand in the New Development Paradigm," Sustainability, MDPI, vol. 16(6), pages 1-15, March.
    3. Alessandra Cassar & Rosella Nicolini, 2008. "Spillovers and growth in a local interaction model," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(2), pages 291-306, June.

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    Keywords

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
    • 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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