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The hidden hyperbolic geometry of international trade: World Trade Atlas 1870-2013

Author

Listed:
  • Guillermo Garc'ia-P'erez
  • Mari'an Bogu~n'a
  • Antoine Allard
  • M. 'Angeles Serrano

Abstract

Here, we present the World Trade Atlas 1870-2013, a collection of annual world trade maps in which distance combines economic size and the different dimensions that affect international trade beyond mere geography. Trade distances, which are based on a gravity model predicting the existence of significant trade channels, are such that the closer countries are in trade space, the greater their chance of becoming connected. The atlas provides us with information regarding the long-term evolution of the international trade system and demonstrates that, in terms of trade, the world is not flat but hyperbolic, as a reflection of its complex architecture. The departure from flatness has been increasing since World War I, meaning that differences in trade distances are growing and trade networks are becoming more hierarchical. Smaller-scale economies are moving away from other countries except for the largest economies; meanwhile those large economies are increasing their chances of becoming connected worldwide. At the same time, Preferential Trade Agreements do not fit in perfectly with natural communities within the trade space and have not necessarily reduced internal trade barriers. We discuss an interpretation in terms of globalization, hierarchization, and localization; three simultaneous forces that shape the international trade system.

Suggested Citation

  • Guillermo Garc'ia-P'erez & Mari'an Bogu~n'a & Antoine Allard & M. 'Angeles Serrano, 2015. "The hidden hyperbolic geometry of international trade: World Trade Atlas 1870-2013," Papers 1512.02233, arXiv.org, revised May 2016.
  • Handle: RePEc:arx:papers:1512.02233
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    Citations

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    Cited by:

    1. Dai, Liang & Derudder, Ben & Liu, Xingjian, 2018. "Transport network backbone extraction: A comparison of techniques," Journal of Transport Geography, Elsevier, vol. 69(C), pages 271-281.
    2. Inna Čábelková & Luboš Smutka & Svitlana Rotterova & Olesya Zhytna & Vít Kluger & David Mareš, 2022. "The Sustainability of International Trade: The Impact of Ongoing Military Conflicts, Infrastructure, Common Language, and Economic Wellbeing in Post-Soviet Region," Sustainability, MDPI, vol. 14(17), pages 1-14, August.
    3. Béatrice Dedinger & Paul Girard, 2018. "RICardo World Trade Web, 1834-1938," SciencePo Working papers Main hal-03619636, HAL.
    4. Robert Jankowski & Antoine Allard & Marián Boguñá & M. Ángeles Serrano, 2023. "The D-Mercator method for the multidimensional hyperbolic embedding of real networks," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Junya Wang & Yi-Jiao Zhang & Cong Xu & Jiaze Li & Jiachen Sun & Jiarong Xie & Ling Feng & Tianshou Zhou & Yanqing Hu, 2024. "Reconstructing the evolution history of networked complex systems," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    6. repec:hal:spmain:info:hdl:2441/6h7io1v56e8k4qtht2cuvjcfa5 is not listed on IDEAS
    7. Béatrice Dedinger & Paul Girard, 2018. "RICardo World Trade Web, 1834-1938," Post-Print hal-03619636, HAL.
    8. Ortiz, Elisenda & Serrano, M. Ángeles, 2022. "Multiscale voter model on real networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    9. Pawanesh & Charu Sharma & Niteesh Sahni, 2024. "Explaining Indian Stock Market through Geometry of Scale free Networks," Papers 2404.04710, arXiv.org, revised Oct 2024.
    10. Zádor, Zsófia & Zhu, Zhen & Smith, Matthew & Gorgoni, Sara, 2022. "A weighted and normalized Gould–Fernandez brokerage measure," Greenwich Papers in Political Economy 37794, University of Greenwich, Greenwich Political Economy Research Centre.
    11. repec:spo:wpmain:info:hdl:2441/6h7io1v56e8k4qtht2cuvjcfa5 is not listed on IDEAS
    12. Antoine Allard & M Ángeles Serrano, 2020. "Navigable maps of structural brain networks across species," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-20, February.

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