IDEAS home Printed from https://ideas.repec.org/p/cam/camdae/0601.html

Spatial Dispersion of Peering Clusters in the European Internet

Author

Listed:
  • D'Ignazio, A.
  • Giovannetti, E.

Abstract

We study the role played by geographical distance in the peering decisions between Internet Service Providers. Firstly, we assess whether or not the Internet industry shows clustering in peering; we then concentrate on the dynamics of the agglomeration process by studying the effects of bilateral distance in changing the morphology of existing peering patterns. Our results show a dominance of random spatial patterns in peering agreements. The sign of the effect of distance on the peering decision, driving the agglomeration/dispersion process, depends, however, on the initial level of clustering. We show that clustered patterns will disperse in the long run.

Suggested Citation

  • D'Ignazio, A. & Giovannetti, E., 2006. "Spatial Dispersion of Peering Clusters in the European Internet," Cambridge Working Papers in Economics 0601, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:0601
    Note: IO
    as

    Download full text from publisher

    File URL: https://files.econ.cam.ac.uk/repec/cam/pdf/cwpe0601.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Spagnolo, Giancarlo, 1999. "Social relations and cooperation in organizations," Journal of Economic Behavior & Organization, Elsevier, vol. 38(1), pages 1-25, January.
    2. Dominic Power & Mats Lundmark, 2004. "Working through Knowledge Pools: Labour Market Dynamics, the Transference of Knowledge and Ideas, and Industrial Clusters," Urban Studies, Urban Studies Journal Limited, vol. 41(5-6), pages 1025-1044, May.
    3. Glaeser, Edward L & Hedi D. Kallal & Jose A. Scheinkman & Andrei Shleifer, 1992. "Growth in Cities," Journal of Political Economy, University of Chicago Press, vol. 100(6), pages 1126-1152, December.
      • Edward L. Glaeser & Hedi D. Kallal & Jose A. Scheinkman & Andrei Shleifer, 1991. "Growth in Cities," NBER Working Papers 3787, National Bureau of Economic Research, Inc.
      • Glaeser, Edward Ludwig & Kallal, Hedi D. & Scheinkman, Jose A. & Shleifer, Andrei, 1992. "Growth in Cities," Scholarly Articles 3451309, Harvard University Department of Economics.
    4. D'Ignazio, A. & Giovannetti, E., 2004. "From Exogenous to Endogenous Networks: Internet Applications," Cambridge Working Papers in Economics 0445, Faculty of Economics, University of Cambridge.
    5. Fujita,Masahisa & Thisse,Jacques-François, 2013. "Economics of Agglomeration," Cambridge Books, Cambridge University Press, number 9781107001411, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. D’Ignazio, Alessio & Giovannetti, Emanuele, 2014. "Continental differences in the clusters of integration: Empirical evidence from the digital commodities global supply chain networks," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 486-497.

    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. Gao, Ting, 2004. "Regional industrial growth: evidence from Chinese industries," Regional Science and Urban Economics, Elsevier, vol. 34(1), pages 101-124, January.
    2. Rui Baptista & Joana Mendonça, 2010. "Proximity to knowledge sources and the location of knowledge-based start-ups," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 45(1), pages 5-29, August.
    3. Yang, Yong, 2012. "Agglomeration density and tourism development in China: An empirical research based on dynamic panel data model," Tourism Management, Elsevier, vol. 33(6), pages 1347-1359.
    4. Matthias Firgo & Peter Mayerhofer, 2015. "Wissens-Spillovers und regionale Entwicklung - welche strukturpolitische Ausrichtung optimiert des Wachstum?," Working Paper Reihe der AK Wien - Materialien zu Wirtschaft und Gesellschaft 144, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik.
    5. Edward L. Glaeser, 1998. "Are Cities Dying?," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 139-160, Spring.
    6. Lara Agostini & Federico Caviggioli & Francesco Galati & Barbara Bigliardi, 2020. "A social perspective of knowledge-based innovation: mobility and agglomeration. Introduction to the special section," The Journal of Technology Transfer, Springer, vol. 45(5), pages 1309-1323, October.
    7. GUILLAIN, Rachel & HURIOT, Jean-Marie, 1999. "How information shapes cities: theory and facts," LATEC - Document de travail - Economie (1991-2003) 1999-05, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    8. William Bowen & Mark Atlas & Sugie Lee, 2009. "Industrial agglomeration and the regional scientific explanation of perceived environmental injustice," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(4), pages 1013-1031, December.
    9. Theodore Tsekeris & Klimis Vogiatzoglou, 2014. "Public infrastructure investments and regional specialization: empirical evidence from Greece," Regional Science Policy & Practice, Wiley Blackwell, vol. 6(3), pages 265-289, August.
    10. Belleflamme, Paul & Picard, Pierre & Thisse, Jacques-Francois, 2000. "An Economic Theory of Regional Clusters," Journal of Urban Economics, Elsevier, vol. 48(1), pages 158-184, July.
    11. Alberto Franco Pozzolo, 2004. "Research and Development, Regional Spillovers and the Location of Economic Activities," Manchester School, University of Manchester, vol. 72(4), pages 463-482, July.
    12. Albin Skog & Örjan Sölvell, 2020. "The dark side of agglomeration, sustained wealth and transposition of trading institutions—the case of Bordeaux in the 18th and 19th centuries," Journal of Economic Geography, Oxford University Press, vol. 20(1), pages 67-91.
    13. Sverre J. Herstad, 2018. "Beyond ‘related variety’: how inflows of skills shape innovativeness in different industries," European Planning Studies, Taylor & Francis Journals, vol. 26(2), pages 396-420, February.
    14. Martijn J. Smit, 2017. "Innovation through new blood," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(3), pages 543-578, May.
    15. Cazzuffi, Chiara & Pereira-López, Mariana & Soloaga, Isidro, 2017. "Local poverty reduction in Chile and Mexico: The role of food manufacturing growth," Food Policy, Elsevier, vol. 68(C), pages 160-185.
    16. Jonathan Jones, 2017. "Agglomeration economies and the location of foreign direct investment: A meta-analysis," Journal of Regional Science, Wiley Blackwell, vol. 57(5), pages 731-757, November.
    17. Van Long, N. & Soubeyran, A., 1996. "R&D Spillovers and Location Choice Under Cournot Rivalry," G.R.E.Q.A.M. 96a35, Universite Aix-Marseille III.
    18. Gaël Le Boulch, 2001. "The Dynamic Concept of Territory in a Globalized World," Post-Print halshs-00140288, HAL.
    19. Mano, Yukichi & Otsuka, Keijiro, 2000. "Agglomeration Economies and Geographical Concentration of Industries: A Case Study of Manufacturing Sectors in Postwar Japan," Journal of the Japanese and International Economies, Elsevier, vol. 14(3), pages 189-203, September.
    20. repec:bla:reviec:v:17:y:2009:i:si:p:261-279 is not listed on IDEAS
    21. Yuriy V. Pavlov, 2022. "Systematisation of factors behind the agglomeration effect," Journal of New Economy, Ural State University of Economics, vol. 22(4), pages 116-138, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

    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:cam:camdae:0601. 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: Jake Dyer (email available below). General contact details of provider: https://www.econ.cam.ac.uk/ .

    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.