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Randomizing world trade. I. A binary network analysis

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  • Tiziano Squartini
  • Giorgio Fagiolo
  • Diego Garlaschelli

Abstract

The international trade network (ITN) has received renewed multidisciplinary interest due to recent advances in network theory. However, it is still unclear whether a network approach conveys additional, nontrivial information with respect to traditional international-economics analyses that describe world trade only in terms of local (first-order) properties. In this and in a companion paper, we employ a recently proposed randomization method to assess in detail the role that local properties have in shaping higher-order patterns of the ITN in all its possible representations (binary/weighted, directed/undirected, aggregated/disaggregated by commodity) and across several years. Here we show that, remarkably, the properties of all binary projections of the network can be completely traced back to the degree sequence, which is therefore maximally informative. Our results imply that explaining the observed degree sequence of the ITN, which has not received particular attention in economic theory, should instead become one the main focuses of models of trade.

Suggested Citation

  • Tiziano Squartini & Giorgio Fagiolo & Diego Garlaschelli, 2011. "Randomizing world trade. I. A binary network analysis," Papers 1103.1243, arXiv.org, revised Nov 2011.
  • Handle: RePEc:arx:papers:1103.1243
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    File URL: http://arxiv.org/pdf/1103.1243
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    References listed on IDEAS

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    1. Stéphane Villeneuve & Erik Ekstrom, 2006. "On the Value of Optimal Stopping Games," Post-Print hal-00173182, HAL.
    2. HuyËn Pham & Nizar Touzi & Jaksa Cvitanic, 1999. "A closed-form solution to the problem of super-replication under transaction costs," Finance and Stochastics, Springer, vol. 3(1), pages 35-54.
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    Cited by:

    1. Lomi, Alessandro & Conaldi, Guido & Tonellato, Marco & Pallotti, Francesca, 2014. "Participation motifs and the emergence of organization in open productions," Structural Change and Economic Dynamics, Elsevier, vol. 29(C), pages 40-57.
    2. Marco Dueñas & Giorgio Fagiolo, 2014. "Global Trade Imbalances: A Network Approach," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 17(03n04), pages 1-29.
    3. Assaf Almog & Rhys Bird & Diego Garlaschelli, 2015. "Enhanced Gravity Model of trade: reconciling macroeconomic and network models," Papers 1506.00348, arXiv.org, revised Mar 2017.
    4. Gao, Cuixia & Sun, Mei & Shen, Bo, 2015. "Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis," Applied Energy, Elsevier, pages 542-554.
    5. Zhang, Xiaohang & Cui, Huiyuan & Zhu, Ji & Du, Yu & Wang, Qi & Shi, Wenhua, 2017. "Measuring the dissimilarity of multiplex networks: An empirical study of international trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 380-394.
    6. 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.
    7. Xu, Helian & Cheng, Long, 2016. "The QAP weighted network analysis method and its application in international services trade," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 91-101.
    8. 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.
    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. 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.
    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. Luu, Duc Thi & Lux, Thomas & Yanovski, Boyan, 2017. "Structural correlations in the Italian overnight money market: An analysis based on network configuration models," Economics Working Papers 2017-02, Christian-Albrechts-University of Kiel, Department of Economics.

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