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Location Determinants of Greenfield Foreign Investments in the Enlarged Europe: Evidence from a Spatial Autoregressive Negative Binomial Additive Model

Author

Listed:
  • Roberto Basile

    (Institute for Studies and Economic Analysis, Rome)

  • Luigi Benfratello

    (Department of Economics and Public Finance "G. Prato", University of Torino)

  • Davide Castellani

    (Department of Economics, Finance and Statistics, University of Perugia)

Abstract

This paper addresses two important methodological issues in the analysis of industrial location: spatial dependence and nonlinearities. To this end, we estimate a semi-parametric spatial autoregressive negative binomial model using data on the number of inward greenfield FDI occurred over the 2003-2007 period in 249 European regions. Results support the view that multinational firms' location choices are very spatially dependent, even controlling for a large number of regional characteristics. A spatial lag model with a non-parametric spatial filter allows us to purge the residuals from spatial dependence and yields sensible changes in the magnitude of some estimated coefficients. We also provide robust evidence of nonlinearities. In particular, we find that the effect of agglomeration economies fades down as the density of economic activities reaches some limit value.

Suggested Citation

  • Roberto Basile & Luigi Benfratello & Davide Castellani, 2010. "Location Determinants of Greenfield Foreign Investments in the Enlarged Europe: Evidence from a Spatial Autoregressive Negative Binomial Additive Model," Working papers 10, Former Department of Economics and Public Finance "G. Prato", University of Torino.
  • Handle: RePEc:tur:wpaper:10
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    References listed on IDEAS

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    Cited by:

    1. Liesenfeld, Roman & Richard, Jean-François & Vogler, Jan, 2013. "Analysis of discrete dependent variable models with spatial correlation," Economics Working Papers 2013-01, Christian-Albrechts-University of Kiel, Department of Economics.
    2. Rene Belderbos & Vincent Van Roy & Bart Leten & Bart Thijs, 2014. "Academic Research Strengths and Multinational Firms' Foreign R&D Location Decisions: Evidence from R&D Investments in European Regions," Environment and Planning A, , vol. 46(4), pages 920-942, April.
    3. Daniel Liviano & Josep-Maria Arauzo-Carod, 2020. "Industrial Location and Spatial Dependence: An Empirical Application," Regional Studies, Taylor & Francis Journals, vol. 48(4), pages 727-743, July.
    4. Chandra R. Bhat & Rajesh Paleti & Palvinder Singh, 2014. "A Spatial Multivariate Count Model For Firm Location Decisions," Journal of Regional Science, Wiley Blackwell, vol. 54(3), pages 462-502, June.
    5. Olga M. Karpova & Igor A. Mayburov, 2022. "Assessment of the Influence of Tax and Socio-Economic Factors on the Decision on the Territorial Location of Business in Russia," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 21(2), pages 325-364.
    6. Liviano Solís, Daniel & Arauzo Carod, Josep Maria, 2011. "Industrial Location and Space: New Insights," Working Papers 2072/152137, Universitat Rovira i Virgili, Department of Economics.

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    More about this item

    Keywords

    Multinational firms; greenfield FDI; count data; spatial econometrics; semiparametric econometrics;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F23 - International Economics - - International Factor Movements and International Business - - - Multinational Firms; International Business

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