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The role of distances in the World Trade Web

Author

Listed:
  • Francesco Picciolo
  • Tiziano Squartini
  • Franco Ruzzenenti
  • Riccardo Basosi
  • Diego Garlaschelli

Abstract

In the economic literature, geographic distances are considered fundamental factors to be included in any theoretical model whose aim is the quantification of the trade between countries. Quantitatively, distances enter into the so-called gravity models that successfully predict the weight of non-zero trade flows. However, it has been recently shown that gravity models fail to reproduce the binary topology of the World Trade Web. In this paper a different approach is presented: the formalism of exponential random graphs is used and the distances are treated as constraints, to be imposed on a previously chosen ensemble of graphs. Then, the information encoded in the geographical distances is used to explain the binary structure of the World Trade Web, by testing it on the degree-degree correlations and the reciprocity structure. This leads to the definition of a novel null model that combines spatial and non-spatial effects. The effectiveness of spatial constraints is compared to that of nonspatial ones by means of the Akaike Information Criterion and the Bayesian Information Criterion. Even if it is commonly believed that the World Trade Web is strongly dependent on the distances, what emerges from our analysis is that distances do not play a crucial role in shaping the World Trade Web binary structure and that the information encoded into the reciprocity is far more useful in explaining the observed patterns.

Suggested Citation

  • Francesco Picciolo & Tiziano Squartini & Franco Ruzzenenti & Riccardo Basosi & Diego Garlaschelli, 2012. "The role of distances in the World Trade Web," Papers 1210.3269, arXiv.org, revised Oct 2012.
  • Handle: RePEc:arx:papers:1210.3269
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    References listed on IDEAS

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    1. Tiziano Squartini & Giorgio Fagiolo & Diego Garlaschelli, 2011. "Randomizing world trade. I. A binary network analysis," Papers 1103.1243, arXiv.org, revised Nov 2011.
    2. Gert-Jan M. Linders & Martijn J. Burger & Frank G. van Oort, 2008. "A rather empty world: the many faces of distance and the persistent resistance to international trade," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 1(3), pages 439-458.
    3. Marco Dueñas & Giorgio Fagiolo, 2013. "Modeling the International-Trade Network: a gravity approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 155-178, April.
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    Cited by:

    1. Chiarucci, Riccardo & Ruzzenenti, Franco & Loffredo, Maria I., 2014. "Detecting spatial homogeneity in the World Trade Web with Detrended Fluctuation Analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 1-7.
    2. Assaf Almog & Rhys Bird & Diego Garlaschelli, 2015. "Enhanced Gravity Model of trade: reconciling macroeconomic and network models," Papers 1506.00348, arXiv.org, revised Feb 2019.
    3. Macchiati, Valentina & Mazzarisi, Piero & Garlaschelli, Diego, 2024. "Interbank network reconstruction enforcing density and reciprocity," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    4. Kitamura, Toshihiko & Managi, Shunsuke, 2017. "Driving force and resistance: Network feature in oil trade," Applied Energy, Elsevier, vol. 208(C), pages 361-375.
    5. Riccardo Chiarucci & Franco Ruzzenenti & Maria I. Loffredo, 2013. "Detecting spatial homogeneity in the world trade web with Detrended Fluctuation Analysis," Papers 1308.0526, arXiv.org, revised Nov 2013.
    6. Tanya Ara'ujo & M. Ennes Ferreira, 2016. "The Topology of African Exports: emerging patterns on spanning trees," Papers 1604.03522, arXiv.org.
    7. Araújo, Tanya & Ferreira, Manuel Ennes, 2016. "The topology of African exports: Emerging patterns on spanning trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 962-976.
    8. Tiziano Squartini & Guido Caldarelli & Giulio Cimini & Andrea Gabrielli & Diego Garlaschelli, 2018. "Reconstruction methods for networks: the case of economic and financial systems," Papers 1806.06941, arXiv.org.
    9. Hoppe, K. & Rodgers, G.J., 2015. "A microscopic study of the fitness-dependent topology of the world trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 64-74.
    10. Wang, Xingxing & Li, Huajiao & Yao, Huajun & Chen, Zhihua & Guan, Qing, 2019. "Network feature and influence factors of global nature graphite trade competition," Resources Policy, Elsevier, vol. 60(C), pages 153-161.

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