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Bridging nestedness and economic complexity in multilayer world trade networks

Author

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  • Zhuo-Ming Ren

    (Hangzhou Normal University)

  • An Zeng

    (Beijing Normal University
    University of Fribourg)

  • Yi-Cheng Zhang

    (Hangzhou Normal University
    University of Fribourg)

Abstract

Understanding the complexity of international trading is critical for a variety of issues ranging from quantifying the competitiveness of individual nations to forecasting the collective evolution of the world economy. Despite the significant progress made in this direction, the international trading system is mainly modeled with a single network in the previous works such as the monopartite product space network and the bipartite country-product network to capture economic complexity. In order to better capture the more detailed dynamics, we characterize the international trading system with a multilayer network with each layer representing the transnational trading relations of a product. This framework immediately reveals the nested structure in each layer and accordingly allows us to develop an alternative measure of the complexity of products. The metric provides a ranking of products’ complexity more consistent with common understanding. The nested structure of a network layer seems to correlate with the asymmetric export relations resulted from the technology barriers, and the evolution of product complexity indicates that the growth of product nestedness is faster than the relevance decay. Finally, we remark a comparison of trade competitive by nestedness between China and the United States to explore the evolution of the economy industries, and the aggregated nestedness index can predict a nation’s future economic growth.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:palcom:v:7:y:2020:i:1:d:10.1057_s41599-020-00651-3
    DOI: 10.1057/s41599-020-00651-3
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    References listed on IDEAS

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    Cited by:

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    2. Bartesaghi, Paolo & Clemente, Gian Paolo & Grassi, Rosanna, 2023. "Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).

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