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Interplay between topology and dynamics in the World Trade Web

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  • D. Garlaschelli
  • T. Di Matteo
  • T. Aste
  • G. Caldarelli
  • M. I. Loffredo
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    Abstract

    We present an empirical analysis of the network formed by the trade relationships between all world countries, or World Trade Web (WTW). Each (directed) link is weighted by the amount of wealth flowing between two countries, and each country is characterized by the value of its Gross Domestic Product (GDP). By analysing a set of year-by-year data covering the time interval 1950-2000, we show that the dynamics of all GDP values and the evolution of the WTW (trade flow and topology) are tightly coupled. The probability that two countries are connected depends on their GDP values, supporting recent theoretical models relating network topology to the presence of a `hidden' variable (or fitness). On the other hand, the topology is shown to determine the GDP values due to the exchange between countries. This leads us to a new framework where the fitness value is a dynamical variable determining, and at the same time depending on, network topology in a continuous feedback.

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    File URL: http://arxiv.org/pdf/physics/0701030
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number physics/0701030.

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    Date of creation: Jan 2007
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    Publication status: Published in Eur. Phys. J. B 57,159-164 (2007)
    Handle: RePEc:arx:papers:physics/0701030

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    Web page: http://arxiv.org/

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    Cited by:
    1. Marco Duenas & Giorgio Fagiolo, 2013. "Global Trade Imbalances: A Network Approach," LEM Papers Series 2013/12, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2010. "Complex stock trading network among investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4929-4941.
    3. Marco Duenas & Giorgio Fagiolo, 2011. "Modeling the International-Trade Network: A Gravity Approach," Papers 1112.2867, arXiv.org.
    4. Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2011. "Null Models of Economic Networks: The Case of the World Trade Web," Papers 1112.2895, arXiv.org, revised Sep 2012.
    5. Sandoval, Leonidas & Franca, Italo De Paula, 2012. "Correlation of financial markets in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 187-208.
    6. Giorgio Fagiolo, 2009. "The International-Trade Network: Gravity Equations and Topological Properties," LEM Papers Series 2009/08, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    7. Alexander M. Petersen & Boris Podobnik & Davor Horvatic & H. Eugene Stanley, 2010. "Scale invariant properties of public debt growth," Papers 1002.2491, arXiv.org.
    8. Annika Birch & Tomaso Aste, 2014. "Systemic Losses Due to Counter Party Risk in a Stylized Banking System," Papers 1402.3688, arXiv.org.
    9. Tiziano Squartini & Diego Garlaschelli, 2012. "Triadic motifs and dyadic self-organization in the World Trade Network," Papers 1201.1215, arXiv.org, revised Jan 2012.
    10. Barigozzi, Matteo & Fagiolo, Giorgio & Mangioni, Giuseppe, 2011. "Identifying the community structure of the international-trade multi-network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2051-2066.
    11. Pau Erola & Albert Diaz-Guilera & Sergio Gomez & Alex Arenas, 2012. "Modeling international crisis synchronization in the World Trade Web," Papers 1201.2024, arXiv.org.
    12. Song, Dong-Ming & Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2009. "Statistical properties of world investment networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(12), pages 2450-2460.

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