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Weighting Ripley�s K-function to account for the firm dimension in the analysis of spatial concentration

Author

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  • Giuseppe Espa

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  • Diego Giuliani
  • Giuseppe Arbia

Abstract

The spatial concentration of firms has long been a central issue in economics both under the theoretical and the applied point of view due mainly to the important policy implications. A popular approach to its measurement, which does not suffer from the problem of the arbitrariness of the regional boundaries, makes use of micro data and looks at the firms as if they were dimensionless points distributed in the economic space. However in practical circumstances the points (firms) observed in the economic space are far from being dimensionless and are conversely characterized by different dimension in terms of the number of employees, the product, the capital and so on. In the literature, the works that originally introduce such an approach (e.g. Arbia and Espa, 1996; Marcon and Puech, 2003) disregard the aspect of the different firm dimension and ignore the fact that a high degree of spatial concentration may result from both the case of many small points clustering in definite portions of space and from only few large points clustering together (e.g. few large firms). We refer to this phenomena as to clustering of firms and clustering of economic activities. The present paper aims at tackling this problem by adapting the popular Kfunction (Ripley, 1977) to account for the point dimension using the framework of marked point process theory (Penttinen, 2006)

Suggested Citation

  • Giuseppe Espa & Diego Giuliani & Giuseppe Arbia, 2010. "Weighting Ripley�s K-function to account for the firm dimension in the analysis of spatial concentration," Department of Economics Working Papers 1012, Department of Economics, University of Trento, Italia.
  • Handle: RePEc:trn:utwpde:1012
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    Cited by:

    1. Florent Bonneu & Christine Thomas-Agnan, 2015. "Measuring and Testing Spatial Mass Concentration with Micro-geographic Data," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(3), pages 289-316, September.
    2. Juan Tomas Sayago-Gomez & Adam Nowak, 2016. "What is Near and Recent in Crime for a Homeowner? The Cases of Denver and Seattle," Working Papers Working Paper 2016-01, Regional Research Institute, West Virginia University.
    3. Gabriel Lang & Eric Marcon & Florence Puech, 2019. "Distance-Based Measures Of Spatial Concentration: Introducing A Relative Density Function," Working Papers hal-01082178, HAL.
    4. Eric Marcon & Florence Puech, 2012. "A typology of distance-based measures of spatial concentration," Working Papers halshs-00679993, HAL.

    More about this item

    Keywords

    Agglomeration; Marked point processes; Spatial clusters; Spatial econometrics;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • D92 - Microeconomics - - Micro-Based Behavioral Economics - - - Intertemporal Firm Choice, Investment, Capacity, and Financing
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • 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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