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Measuring and Testing Spatial Mass Concentration with Micro-geographic Data

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  • Florent Bonneu
  • Christine Thomas-Agnan

Abstract

We address the question of measuring and testing industrial spatial concentration based on micro-geographic data with distance-based methods. We discuss the basic requirements for such measures and we propose four additional requirements. We also discuss the null assumptions classically used for testing aggregation of a particular sector and propose an alternative point of view. Our general index measure involves a cumulative and a non-cumulative version. This allows us to propose an alternative version of the Duranton-Overman index with a proper baseline as well as a cumulative version of this same index. We present simulations to evaluate the respective powers of this new approach and the classical ones.

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  • 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.
    • Handle: RePEc:taf:specan:v:10:y:2015:i:3:p:289-316
    DOI: 10.1080/17421772.2015.1062124
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    7. Eric Marcon & Florence Puech, 2009. "Measures of the Geographic Concentration of Industries: Improving Distance-Based Methods," Working Papers halshs-00372617, HAL.
    8. Eric Marcon & Florence Puech, 2010. "Measures of the geographic concentration of industries: improving distance-based methods," Journal of Economic Geography, Oxford University Press, vol. 10(5), pages 745-762, September.
    9. Arbia, G. & Espa, G. & Giuliani, D. & Mazzitelli, A., 2012. "Clusters of firms in an inhomogeneous space: The high-tech industries in Milan," Economic Modelling, Elsevier, vol. 29(1), pages 3-11.
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    11. Grabarnik, Pavel & Myllymäki, Mari & Stoyan, Dietrich, 2011. "Correct testing of mark independence for marked point patterns," Ecological Modelling, Elsevier, vol. 222(23), pages 3888-3894.
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    Cited by:

    1. A. Tidu & S. Usai & Frederick Guy, 2021. "Agglomeration in manufacturing and services: an experimental application of a distance-based measure to Sardinia," Working Paper CRENoS 202110, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    2. Eric Marcon & Florence Puech, 2016. "A typology of distance-based measures of spatial concentration," Post-Print halshs-00679993, HAL.
    3. Franz-Josef Bade & Eckhardt Bode & Eleonora Cutrini, 2015. "Spatial fragmentation of industries by functions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(1), pages 215-250, January.
    4. Rawaa Laajimi & Julie Le Gallo & Saloua Benammou, 2020. "What Geographical Concentration of Industries in the Tunisian Sahel? Empirical Evidence Using Distance‐Based Measures," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 111(5), pages 738-757, December.
    5. Gabriel Lang & Eric Marcon & Florence Puech, 2020. "Distance-based measures of spatial concentration: Introducing a relative density function," Post-Print hal-01082178, HAL.
    6. Daniel A. Griffith & Yongwan Chun & Jan Hauke, 2022. "A Moran eigenvector spatial filtering specification of entropy measures," Papers in Regional Science, Wiley Blackwell, vol. 101(1), pages 259-279, February.
    7. Marcon, Eric & Puech, Florence, 2017. "A typology of distance-based measures of spatial concentration," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 56-67.
    8. S. Usai & Frederick Guy & A. Tidu, 2022. "Measuring spatial dispersion: an experimental test on the M-index," Working Paper CRENoS 202206, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    9. Gabriel Lang & Eric Marcon & Florence Puech, 2020. "Distance-based measures of spatial concentration: introducing a relative density function," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 243-265, April.

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