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The double role of GDP in shaping the structure of the International Trade Network

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  • Assaf Almog
  • Tiziano Squartini
  • Diego Garlaschelli

Abstract

The International Trade Network (ITN) is the network formed by trade relationships between world countries. The complex structure of the ITN impacts important economic processes such as globalization, competitiveness, and the propagation of instabilities. Modeling the structure of the ITN in terms of simple macroeconomic quantities is therefore of paramount importance. While traditional macroeconomics has mainly used the Gravity Model to characterize the magnitude of trade volumes, modern network theory has predominantly focused on modeling the topology of the ITN. Combining these two complementary approaches is still an open problem. Here we review these approaches and emphasize the double role played by GDP in empirically determining both the existence and the volume of trade linkages. Moreover, we discuss a unified model that exploits these patterns and uses only the GDP as the relevant macroeconomic factor for reproducing both the topology and the link weights of the ITN.

Suggested Citation

  • Assaf Almog & Tiziano Squartini & Diego Garlaschelli, 2015. "The double role of GDP in shaping the structure of the International Trade Network," Papers 1512.02454, arXiv.org, revised Dec 2015.
  • Handle: RePEc:arx:papers:1512.02454
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    File URL: http://arxiv.org/pdf/1512.02454
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    Cited by:

    1. Tanya Ara'ujo & M. Ennes Ferreira, 2016. "The Topology of African Exports: emerging patterns on spanning trees," Papers 1604.03522, arXiv.org.
    2. Hao Liao & Xiao-Min Huang & Xing-Tong Wu & Ming-Kai Liu & Alexandre Vidmer & Ming-Yang Zhou & Yi-Cheng Zhang, 2018. "Enhancing Countries’ Fitness with Recommender Systems on the International Trade Network," Complexity, Hindawi, vol. 2018, pages 1-12, October.
    3. 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.
    4. 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.
    5. Hao Liao & Xiao-Min Huang & Xing-Tong Wu & Ming-Kai Liu & Alexandre Vidmer & Mingyang Zhou & Yi-Cheng Zhang, 2019. "Enhancing countries' fitness with recommender systems on the international trade network," Papers 1904.02412, arXiv.org.

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