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Location patterns of service industries in France: A distance-based approach

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  • Barlet, M.
  • Briant, A.
  • Crusson, L.

Abstract

We study the location patterns of business-oriented service and manufacturing industries in France. We develop a new test of localization considering space as continuous. Our test relies on a measure of divergence in the space of density distributions that allows us to assess whether or not the density distribution of bilateral distances between all pairs of plants within an industry significantly departs from randomness. We improve the test proposed by Duranton and Overman (2005), which proves to be biased with respect to the number of plants in the industry under scrutiny. Our test does not suffer from such a bias. This property is crucial for the French case where industrial concentrations of service and manufacturing industries drastically differ. With this distance-based method, we highlight some distinctive locational features of service industries that have not been mentioned in the literature so far. We show that: 1/service industries diverge more often from randomness than manufacturing industries, 2/a majority of diverging service industries are localized at very short distances (less than 4km) whereas a majority of manufacturing industries are localized at longer distances or even dispersed, 3/within a majority of service industries, the largest plants appear localized at shorter distances than other plants, 4/within most service industries, incoming plants reduce localization whereas exiting plants reinforce it over the period 1996–2005.

Suggested Citation

  • Barlet, M. & Briant, A. & Crusson, L., 2013. "Location patterns of service industries in France: A distance-based approach," Regional Science and Urban Economics, Elsevier, vol. 43(2), pages 338-351.
    • Handle: RePEc:eee:regeco:v:43:y:2013:i:2:p:338-351
    DOI: 10.1016/j.regsciurbeco.2012.08.004
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    Cited by:

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    2. Eric Marcon & Florence Puech, 2016. "A typology of distance-based measures of spatial concentration," Post-Print halshs-00679993, HAL.
    3. Ekaterina Aleksandrova & Kristian Behrens & Maria Kuznetsova, 2020. "Manufacturing (co)agglomeration in a transition country: Evidence from Russia," Journal of Regional Science, Wiley Blackwell, vol. 60(1), pages 88-128, January.
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    6. Youwei Tan & Zhihui Gu & Yu Chen & Jiayun Li, 2022. "Industry Linkage and Spatial Co-Evolution Characteristics of Industrial Clusters Based on Natural Semantics—Taking the Electronic Information Industry Cluster in the Pearl River Delta as an Example," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    7. Behrens, Kristian & Bougna, Théophile, 2015. "An anatomy of the geographical concentration of Canadian manufacturing industries," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 47-69.
    8. Gabriel Lang & Eric Marcon & Florence Puech, 2020. "Distance-based measures of spatial concentration: Introducing a relative density function," Post-Print hal-01082178, HAL.
    9. Brad Humphreys & Josh Matti, 2018. "The Spatial Distribution of Urban Consumer Service Firms: Evidence from Yelp Reviews," Working Papers 18-03, Department of Economics, West Virginia University.
    10. Tobias Scholl & Thomas Brenner, 2015. "Optimizing distance-based methods for large data sets," Journal of Geographical Systems, Springer, vol. 17(4), pages 333-351, October.
    11. Shuju Hu & Wei Song & Chenggu Li & Charlie H. Zhang, 2019. "The Evolution of Industrial Agglomerations and Specialization in the Yangtze River Delta from 1990–2018: An Analysis Based on Firm-Level Big Data," Sustainability, MDPI, vol. 11(20), pages 1-21, October.
    12. Jean Dubé & Cédric Brunelle, 2014. "Dots to dots: a general methodology to build local indicators using spatial micro-data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 245-272, August.
    13. Eric Marcon & Florence Puech, 2012. "A typology of distance-based measures of spatial concentration," Working Papers halshs-00679993, HAL.
    14. Shao, Shuai & Tian, Zhihua & Yang, Lili, 2017. "High speed rail and urban service industry agglomeration: Evidence from China's Yangtze River Delta region," Journal of Transport Geography, Elsevier, vol. 64(C), pages 174-183.
    15. 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.
    16. Gabriel Lang & Eric Marcon & Florence Puech, 2019. "Distance-Based Measures Of Spatial Concentration: Introducing A Relative Density Function," Working Papers hal-01082178, HAL.
    17. Wenchao WU & Shaosheng JIN & Suminori TOKUNAGA, 2016. "Testing Localization Of Chinese Food Industries: Evidence From Microgeographic Data," Review of Urban & Regional Development Studies, Wiley Blackwell, vol. 28(3), pages 202-217, November.
    18. Marta R. Casanova & Vicente Orts & José M. Albert, 2017. "Sectoral scope and colocalisation of Spanish manufacturing industries," Journal of Geographical Systems, Springer, vol. 19(1), pages 65-92, January.
    19. S. Usai & F. 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.
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    More about this item

    Keywords

    Business services; Divergence; Localization; Dispersion; Spatial statistics;
    All these keywords.

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • L84 - Industrial Organization - - Industry Studies: Services - - - Personal, Professional, and Business Services

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