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Crisis contagion in the world trade network

Author

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  • Célestin Coquidé

    (UTINAM - Univers, Transport, Interfaces, Nanostructures, Atmosphère et environnement, Molécules (UMR 6213) - INSU - CNRS - Institut national des sciences de l'Univers - CNRS - Centre National de la Recherche Scientifique - UFC - Université de Franche-Comté - UBFC - Université Bourgogne Franche-Comté [COMUE])

  • José Lages

    (UTINAM - Univers, Transport, Interfaces, Nanostructures, Atmosphère et environnement, Molécules (UMR 6213) - INSU - CNRS - Institut national des sciences de l'Univers - CNRS - Centre National de la Recherche Scientifique - UFC - Université de Franche-Comté - UBFC - Université Bourgogne Franche-Comté [COMUE])

  • Dima Shepelyansky

    (ICQ - Cohérence Quantique (LPT) - LPT - Laboratoire de Physique Théorique - IRSAMC - Institut de Recherche sur les Systèmes Atomiques et Moléculaires Complexes - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique, LPT - Laboratoire de Physique Théorique - IRSAMC - Institut de Recherche sur les Systèmes Atomiques et Moléculaires Complexes - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique)

Abstract

We present a model of worldwide crisis contagion based on the Google matrix analysis of the world trade network obtained from the UN Comtrade database. The fraction of bankrupted countries exhibits an \textit{on-off} phase transition governed by a bankruptcy threshold $\kappa$ related to the trade balance of the countries. For $\kappa>\kappa_c$, the contagion is circumscribed to less than 10\% of the countries, whereas, for $\kappa

Suggested Citation

  • Célestin Coquidé & José Lages & Dima Shepelyansky, 2020. "Crisis contagion in the world trade network," Post-Print hal-02482438, HAL.
    • Handle: RePEc:hal:journl:hal-02482438
    DOI: 10.1007/s41109-020-00304-z
    Note: View the original document on HAL open archive server: https://hal.science/hal-02482438
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    References listed on IDEAS

    as
    1. Matthew Elliott & Benjamin Golub & Matthew O. Jackson, 2014. "Financial Networks and Contagion," American Economic Review, American Economic Association, vol. 104(10), pages 3115-3153, October.
    2. Leonardo Ermann & Dima L. Shepelyansky, 2011. "Google matrix of the world trade network," Papers 1103.5027, arXiv.org.
    3. Luca De Benedictis & Lucia Tajoli, 2011. "The World Trade Network," The World Economy, Wiley Blackwell, vol. 34(8), pages 1417-1454, August.
    4. M. Serrano & Marián Boguñá & Alessandro Vespignani, 2007. "Patterns of dominant flows in the world trade web," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 2(2), pages 111-124, December.
    5. Tsuyoshi Deguchi & Katsuhide Takahashi & Hideki Takayasu & Misako Takayasu, 2014. "Hubs and Authorities in the World Trade Network Using a Weighted HITS Algorithm," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-16, July.
    6. Leonardo Ermann & Klaus M. Frahm & Dima L. Shepelyansky, 2018. "Google matrix of Bitcoin network," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 91(6), pages 1-13, June.
    7. M. Angeles Serrano & Marian Boguna & Alessandro Vespignani, 2007. "Patterns of dominant flows in the world trade web," Papers 0704.1225, arXiv.org.
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    Cited by:

    1. Célestin Coquidé & José Lages & Dima Shepelyansky, 2020. "Interdependence of sectors of economic activities for world countries from the reduced Google matrix analysis of WTO data," Post-Print hal-02132487, HAL.
    2. Li, Jingwei & Li, Shouwei, 2023. "Immunization of systemic risk in trade–investment networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

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