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(Optimal) Spatial Aggregation in the Determinants of Industrial Location

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  • Arauzo Carod, Josep Maria
  • Manjón Antolín, Miguel C.

Abstract

Empirical studies on the determinants of industrial location typically use variables measured at the available administrative level (municipalities, counties, etc.). However, this amounts to assuming that the effects these determinants may have on the location process do not extent beyond the geographical limits of the selected site. We address the validity of this assumption by comparing results from standard count data models with those obtained by calculating the geographical scope of the spatially varying explanatory variables using a wide range of distances and alternative spatial autocorrelation measures. Our results reject the usual practice of using administrative records as covariates without making some kind of spatial correction. Keywords: industrial location, count data models, spatial statistics JEL classification: C25, C52, R11, R30

Suggested Citation

  • Arauzo Carod, Josep Maria & Manjón Antolín, Miguel C., 2009. "(Optimal) Spatial Aggregation in the Determinants of Industrial Location," Working Papers 2072/42866, Universitat Rovira i Virgili, Department of Economics.
  • Handle: RePEc:urv:wpaper:2072/42866
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Coll Martínez, Eva & Arauzo Carod, Josep Maria, 2015. "Creative Industries: a Preliminary Insight to their Location Determinants," Working Papers 2072/250133, Universitat Rovira i Virgili, Department of Economics.
    2. 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.
    3. Minghao Li & Stephan J. Goetz & Mark Partridge & David A. Fleming, 2016. "Location determinants of high-growth firms," Entrepreneurship & Regional Development, Taylor & Francis Journals, vol. 28(1-2), pages 97-125, January.
    4. Oscar Martinez Ibañez & Miguel Manjón Antolín & Josep-Maria Arauzo-Carod, 2013. "The Geographical Scope of Industrial Location Determinants: An Alternative Approach," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 104(2), pages 194-214, April.
    5. Kinne, Jan & Resch, Bernd, 2017. "Analysing and predicting micro-location patterns of software firms," ZEW Discussion Papers 17-063, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    6. Josep-Maria Arauzo-Carod & Daniel Liviano-Solís, 2012. "Migration Determinants at a Local Level," ERSA conference papers ersa12p500, European Regional Science Association.
    7. 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.

    More about this item

    Keywords

    Localització industrial; Anàlisi espacial (Estadística); Models economètrics; Economia regional; 332 - Economia regional i territorial. Economia del sòl i de la vivenda;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

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