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Statistical mechanics of the international trade network

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  • Agata Fronczak
  • Piotr Fronczak

Abstract

Analyzing real data on international trade covering the time interval 1950-2000, we show that in each year over the analyzed period the network is a typical representative of the ensemble of maximally random weighted networks, whose directed connections (bilateral trade volumes) are only characterized by the product of the trading countries' GDPs. It means that time evolution of this network may be considered as a continuous sequence of equilibrium states, i.e. quasi-static process. This, in turn, allows one to apply the linear response theory to make (and also verify) simple predictions about the network. In particular, we show that bilateral trade fulfills fluctuation-response theorem, which states that the average relative change in import (export) between two countries is a sum of relative changes in their GDPs. Yearly changes in trade volumes prove that the theorem is valid.

Suggested Citation

  • Agata Fronczak & Piotr Fronczak, 2011. "Statistical mechanics of the international trade network," Papers 1104.2606, arXiv.org, revised May 2012.
    • Handle: RePEc:arx:papers:1104.2606
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    Cited by:

    1. Zhuo-Ming Ren & An Zeng & Yi-Cheng Zhang, 2020. "Bridging nestedness and economic complexity in multilayer world trade networks," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-8, December.
    2. Ren, Zhuo-Ming & Zhao, Li & Du, Wen-Li & Weng, Tong-Feng & Liu, Chuang & Kong, Yi-Xiu & Zhang, Yi-Cheng, 2024. "Tunable resource allocation dynamics for interpreting economic complexity," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    3. 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.
    4. Lizhi Xing & Shuo Jiang & Simeng Yin & Fangke Liu, 2024. "Substitution effect of Asian economies on China’s industrial and supply chains: from the perspective of global production network," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-27, December.
    5. Alexei Kireyev & Andrei Leonidov, 2018. "Network Effects of International Shocks and Spillovers," Networks and Spatial Economics, Springer, vol. 18(4), pages 805-836, December.
    6. Di Vece, Marzio & Garlaschelli, Diego & Squartini, Tiziano, 2023. "Reconciling econometrics with continuous maximum-entropy network models," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    7. Kireyev, Alexei & Leonidov, Andrei, 2021. "Twin trade shocks: Spillovers from US-China trade tensions," International Economics, Elsevier, vol. 167(C), pages 174-188.
    8. Yuke Li & Tianhao Wu & Nicholas Marshall & Stefan Steinerberger, 2016. "Extracting Geography from Trade Data," Papers 1607.05235, arXiv.org, revised Jul 2016.
    9. Li, Yuke & Wu, Tianhao & Marshall, Nicholas & Steinerberger, Stefan, 2017. "Extracting geography from trade data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 205-212.
    10. Marco Bardoscia & Paolo Barucca & Stefano Battiston & Fabio Caccioli & Giulio Cimini & Diego Garlaschelli & Fabio Saracco & Tiziano Squartini & Guido Caldarelli, 2021. "The Physics of Financial Networks," Papers 2103.05623, arXiv.org.
    11. Liu, Linqing & Shen, Mengyun & Sun, Da & Yan, Xiaofei & Hu, Shi, 2022. "Preferential attachment, R&D expenditure and the evolution of international trade networks from the perspective of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    12. Jeroen van Lidth de Jeude & Riccardo Di Clemente & Guido Caldarelli & Fabio Saracco & Tiziano Squartini, 2019. "Reconstructing Mesoscale Network Structures," Complexity, Hindawi, vol. 2019, pages 1-13, January.

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