IDEAS home Printed from https://ideas.repec.org/p/pum/wpaper/2011-02.html

Testing for Clustering of Industries - Evidence from micro geographic data

Author

Listed:
  • Tobias Scholl

    (EBS European Business School)

  • Thomas Brenner

    (Department of Geography, Philipps University Marburg)

Abstract

We present a new statistical method that describes the localization patterns of industries in a continuous space. The proposed method does not divide space into subunits whereby it is not affected by the Modifiable Areal Unit Problem (MAUP). Our method fulfils all five criteria for a spatial statistical test of localization proposed by Duranton and Overman (2005) and improves them with respect to the significance of its results. Additionally, our test allows inference to the localization of highly clustered firms. Furthermore, the algorithm is efficient in its computation, which eases the usage in research.

Suggested Citation

  • Tobias Scholl & Thomas Brenner, 2011. "Testing for Clustering of Industries - Evidence from micro geographic data," Working Papers on Innovation and Space 2011-02, Philipps University Marburg, Department of Geography.
  • Handle: RePEc:pum:wpaper:2011-02
    as

    Download full text from publisher

    File URL: https://repec.geographie.uni-marburg.de/pum/wpaper/wp0211.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyun-Ju Koh & Nadine Riedel, 2009. "Assessing the Localization Pattern of German Manufacturing & Service Industries - A Distance Based Approach," Working Papers 080, Bavarian Graduate Program in Economics (BGPE).
    2. Stefania Vitali & Mauro Napoletano & Giorgio Fagiolo, 2013. "Spatial Localization in Manufacturing: A Cross-Country Analysis," Regional Studies, Taylor & Francis Journals, vol. 47(9), pages 1534-1554, October.
    3. Thomas Klier & Daniel P. McMillen, 2008. "Evolving Agglomeration In The U.S. Auto Supplier Industry," Journal of Regional Science, Wiley Blackwell, vol. 48(1), pages 245-267, February.
    4. repec:spo:wpecon:info:hdl:2441/9932 is not listed on IDEAS
    5. 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.
    6. Glenn Ellison & Edward L. Glaeser & William R. Kerr, 2010. "What Causes Industry Agglomeration? Evidence from Coagglomeration Patterns," American Economic Review, American Economic Association, vol. 100(3), pages 1195-1213, June.
    7. repec:spo:wpmain:info:hdl:2441/9932 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tobias Scholl & Thomas Brenner, 2013. "Detecting Spatial Clustering Using a Firm-Level Index," Working Papers on Innovation and Space 2012-02, Philipps University Marburg, Department of Geography.
    2. Tobias Scholl & Thomas Brenner, 2013. "Optimizing Distance-Based Methods for Big Data Analysis," Working Papers on Innovation and Space 2013-09, Philipps University Marburg, Department of Geography.
    3. Gibbons, Steve & Overman, Henry G. & Patacchini, Eleonora, 2015. "Spatial Methods," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 115-168, Elsevier.
    4. 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.
    5. 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.
    6. 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.
    7. Kristian Behrens, 2016. "Agglomeration and clusters: Tools and insights from coagglomeration patterns," Canadian Journal of Economics, Canadian Economics Association, vol. 49(4), pages 1293-1339, November.
    8. Andreas Behr & Christoph Schiwy & Lucy Hong, 2023. "Do high local customer and supply densities foster firm growth? [Fördern hohe lokale Kunden- und Lieferantendichten das Unternehmenswachstum?]," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 43(2), pages 265-289, August.
    9. Thi Xuan Thu Nguyen & Javier Revilla Diez, 2017. "Multinational enterprises and industrial spatial concentration patterns in the Red River Delta and Southeast Vietnam," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 59(1), pages 101-138, July.
    10. Billings, Stephen B. & Johnson, Erik B., 2012. "A non-parametric test for industrial specialization," Journal of Urban Economics, Elsevier, vol. 71(3), pages 312-331.
    11. Hiroyasu Inoue & Kentaro Nakajima & Yukiko Umeno Saito, 2019. "Localization of collaborations in knowledge creation," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(1), pages 119-140, February.
    12. Sasan Bakhtiari, 2020. "Do manufacturing entrepreneurs in Australia have (or develop) a productivity advantage?," Journal of Productivity Analysis, Springer, vol. 53(3), pages 321-338, June.
    13. Gabriel Lang & Eric Marcon & Florence Puech, 2019. "Distance-Based Measures Of Spatial Concentration: Introducing A Relative Density Function," Working Papers hal-01082178, HAL.
    14. Frank Bickenbach & Eckhardt Bode & Christiane Krieger-Boden, 2013. "Closing the gap between absolute and relative measures of localization, concentration or specialization," Papers in Regional Science, Wiley Blackwell, vol. 92(3), pages 465-479, August.
    15. Eric Marcon & Florence Puech, 2012. "A typology of distance-based measures of spatial concentration," Working Papers halshs-00679993, HAL.
    16. Edilberto Tiago Almeida & Raul Mota Silveira Neto & Jaime Macedo Brito Bastos & Rubens Lopes Pereira Silva, 2021. "Location patterns of service activities in large metropolitan areas: the Case of São Paulo," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 67(2), pages 451-481, October.
    17. Qin, Quande & Yu, Ying & Liu, Yuan & Zhou, Jianqing & Chen, Xiude, 2023. "Industrial agglomeration and energy efficiency: A new perspective from market integration," Energy Policy, Elsevier, vol. 183(C).
    18. Bickenbach, Frank & Bode, Eckhardt & Krieger-Boden, Christiane, 2010. "Structural cohesion in Europe: Stylized facts," Kiel Working Papers 1669, Kiel Institute for the World Economy.
    19. 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.
    20. Tobias Scholl & Thomas Brenner & Martin Wendel, 2016. "Evolving localization patterns of company foundationsEvidence from the German MST-industry," Journal of Evolutionary Economics, Springer, vol. 26(5), pages 1067-1087, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pum:wpaper:2011-02. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Robert Csicsics The email address of this maintainer does not seem to be valid anymore. Please ask Robert Csicsics to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/vamarde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.