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Hierarchicality of Trade Flow Networks Reveals Complexity of Products

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Listed:
  • Peiteng Shi
  • Jiang Zhang
  • Bo Yang
  • Jingfei Luo

Abstract

With globalization, countries are more connected than before by trading flows, which currently amount to at least 36 trillion dollars. Interestingly, approximately 30-60 percent of global exports consist of intermediate products. Therefore, the trade flow network of a particular product with high added values can be regarded as a value chain. The problem is weather we can discriminate between these products based on their unique flow network structure. This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent $\eta$ can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, the flow networks of products with higher added values and complexity, such as machinery&transport equipment with larger exponents, are highlighted. These higher values indicate that their trade flow networks are more hierarchical. As a result, without extra data such as global input-output table, we can identify the product categories with higher complexity and the relative importance of a country in the global value chain solely by the trading network.

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  • Peiteng Shi & Jiang Zhang & Bo Yang & Jingfei Luo, 2014. "Hierarchicality of Trade Flow Networks Reveals Complexity of Products," Papers 1401.3103, arXiv.org.
    • Handle: RePEc:arx:papers:1401.3103
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    References listed on IDEAS

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    Cited by:

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    3. Hao Xiao & Tianyang Sun & Bo Meng & Lihong Cheng, 2017. "Complex Network Analysis for Characterizing Global Value Chains in Equipment Manufacturing," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-22, January.
    4. Guo, Liangzhu & Lou, Xiaodan & Shi, Peiteng & Wang, Jun & Huang, Xiaohan & Zhang, Jiang, 2015. "Flow distances on open flow networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 235-248.
    5. Zongning Wu & Hongbo Cai & Ruining Zhao & Ying Fan & Zengru Di & Jiang Zhang, 2020. "A Topological Analysis of Trade Distance: Evidence from the Gravity Model and Complex Flow Networks," Sustainability, MDPI, vol. 12(9), pages 1-17, April.
    6. Yao, Can-Zhong & Lin, Ji-Nan & Liu, Xiao-Feng, 2016. "A study of hierarchical structure on South China industrial electricity-consumption correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 129-145.
    7. Picciolo, Francesco & Papandreou, Andreas & Hubacek, Klaus & Ruzzenenti, Franco, 2017. "How crude oil prices shape the global division of labor," Applied Energy, Elsevier, vol. 189(C), pages 753-761.
    8. Oliver Heidrich & Alistair C. Ford & Richard J. Dawson & David A. C. Manning & Eugene Mohareb & Marco Raugei & Joris Baars & Mohammad Ali Rajaeifar, 2022. "LAYERS: A Decision-Support Tool to Illustrate and Assess the Supply and Value Chain for the Energy Transition," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    9. Zhang, Xiaohang & Cui, Huiyuan & Zhu, Ji & Du, Yu & Wang, Qi & Shi, Wenhua, 2017. "Measuring the dissimilarity of multiplex networks: An empirical study of international trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 380-394.
    10. Iliopoulos, Panagiotis (Takis), 2022. "A quantitative analysis of governance structures in the world economy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    11. Zhen Zhu & Michelangelo Puliga & Federica Cerina & Alessandro Chessa & Massimo Riccaboni, 2015. "Global Value Trees," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-17, May.

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