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Google matrix analysis of the multiproduct world trade network

Author

Listed:
  • Leonardo Ermann

    (TANDAR - Departamento de Fisica Teorica, GIyAm Comission Nacional de Energia Atomica - Argentina)

  • Dima Shepelyansky

    (ICQ - Cohérence Quantique (LPT) - LPT - Laboratoire de Physique Théorique - IRSAMC - Institut de Recherche sur les Systèmes Atomiques et Moléculaires Complexes - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique)

Abstract

Using the United Nations COMTRADE database we construct the Google matrix G of multiproduct world trade between the UN countries and analyze the properties of trade flows on this network for years 1962 - 2010. This construction, based on Markov chains, treats all countries on equal democratic grounds independently of their richness and at the same time it considers the contributions of trade products proportionally to their trade volume. We consider the trade with 61 products for up to 227 countries. The obtained results show that the trade contribution of products is asymmetric: some of them are export oriented while others are import oriented even if the ranking by their trade volume is symmetric in respect to export and import after averaging over all world countries. The construction of the Google matrix allows to investigate the sensitivity of trade balance in respect to price variations of products, e.g. petroleum and gas, taking into account the world connectivity of trade links. The trade balance based on PageRank and CheiRank probabilities highlights the leading role of China and other BRICS countries in the world trade in recent years. We also show that the eigenstates of G with large eigenvalues select specific trade communities.

Suggested Citation

  • Leonardo Ermann & Dima Shepelyansky, 2015. "Google matrix analysis of the multiproduct world trade network," Post-Print hal-01104619, HAL.
    • Handle: RePEc:hal:journl:hal-01104619
    DOI: 10.1140/epjb/e2015-60047-0
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    Citations

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    Cited by:

    1. Yue Fu & Long Xue & Yixin Yan & Yao Pan & Xiaofang Wu & Ying Shao, 2021. "Energy Network Embodied in Trade along the Belt and Road: Spatiotemporal Evolution and Influencing Factors," Sustainability, MDPI, vol. 13(19), pages 1-29, September.
    2. C'elestin Coquid'e & Jos'e Lages & Dima L. Shepelyansky, 2020. "Crisis contagion in the world trade network," Papers 2002.07100, arXiv.org.
    3. Denis Demidov & Klaus M. Frahm & Dima L. Shepelyansky, 2019. "What is the central bank of Wikipedia?," Papers 1902.07920, arXiv.org.
    4. V. Kandiah & H. Escaith & D. L. Shepelyansky, 2015. "Contagion effects in the world network of economic activities," Papers 1507.03278, arXiv.org.
    5. Vivek Kandiah & Hubert Escaith & Dima L. Shepelyansky, 2015. "Google matrix of the world network of economic activities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(7), pages 1-20, July.
    6. Qing Guan & Haizhong An & Xiaoqing Hao & Xiaoliang Jia, 2016. "The Impact of Countries’ Roles on the International Photovoltaic Trade Pattern: The Complex Networks Analysis," Sustainability, MDPI, vol. 8(4), pages 1-16, March.
    7. Célestin Coquidé & José Lages & Dima Shepelyansky, 2020. "Interdependence of sectors of economic activities for world countries from the reduced Google matrix analysis of WTO data," Post-Print hal-02132487, HAL.
    8. Célestin Coquidé & José Lages & Leonardo Ermann & Dima Shepelyansky, 2022. "COVID-19 impact on the international trade," Post-Print hal-03536528, HAL.
    9. Demidov, Denis & Frahm, Klaus M. & Shepelyansky, Dima L., 2020. "What is the central bank of Wikipedia?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).

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