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Unfolding the complexity of the global value chain: Strengths and entropy in the single-layer, multiplex, and multi-layer international trade networks

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Listed:
  • Luiz G. A. Alves
  • Giuseppe Mangioni
  • Francisco A. Rodrigues
  • Pietro Panzarasa
  • Yamir Moreno

Abstract

The worldwide trade network has been widely studied through different data sets and network representations with a view to better understanding interactions among countries and products. Here we investigate international trade through the lenses of the single-layer, multiplex, and multi-layer networks. We discuss differences among the three network frameworks in terms of their relative advantages in capturing salient topological features of trade. We draw on the World Input-Output Database to build the three networks. We then uncover sources of heterogeneity in the way strength is allocated among countries and transactions by computing the strength distribution and entropy in each network. Additionally, we trace how entropy evolved, and show how the observed peaks can be associated with the onset of the global economic downturn. Findings suggest how more complex representations of trade, such as the multi-layer network, enable us to disambiguate the distinct roles of intra- and cross-industry transactions in driving the evolution of entropy at a more aggregate level. We discuss our results and the implications of our comparative analysis of networks for research on international trade and other empirical domains across the natural and social sciences.

Suggested Citation

  • Luiz G. A. Alves & Giuseppe Mangioni & Francisco A. Rodrigues & Pietro Panzarasa & Yamir Moreno, 2018. "Unfolding the complexity of the global value chain: Strengths and entropy in the single-layer, multiplex, and multi-layer international trade networks," Papers 1809.07407, arXiv.org, revised Dec 2018.
  • Handle: RePEc:arx:papers:1809.07407
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    Cited by:

    1. Ying Zhou & Sajid Anwar, 2022. "Immigrant Diversity, Institutional Quality, and GVC Position," Sustainability, MDPI, vol. 14(4), pages 1-19, February.
    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).
    3. Maulana, Ardian & Hokky, Situngkir, 2024. "Exploring The Spatial Structure of Interregional Supply Chain: A Multilayer Network Approach," MPRA Paper 121129, University Library of Munich, Germany.
    4. Georgios Angelidis & Nikos C. Varsakelis, 2023. "Economic Shock Transmission through Global Value Chains: An Assessment using Network Analysis," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 29(3), pages 111-128, August.
    5. Georgios Angelidis & Charalambos Bratsas & Georgios Makris & Evangelos Ioannidis & Nikos C. Varsakelis & Ioannis E. Antoniou, 2021. "Global Value Chains of COVID-19 Materials: A Weighted Directed Network Analysis," Mathematics, MDPI, vol. 9(24), pages 1-19, December.
    6. Guan, Jun & Li, Yafei & Xing, Lizhi & Li, Yan & Liang, Guoqiang, 2020. "Closeness centrality for similarity-weight network and its application to measuring industrial sectors’ position on the Global Value Chain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    7. Felipe Chávez-Bustamante & Elliott Mardones-Arias & Julio Rojas-Mora & Jaime Tijmes-Ihl, 2023. "A Forgotten Effects Approach to the Analysis of Complex Economic Systems: Identifying Indirect Effects on Trade Networks," Mathematics, MDPI, vol. 11(3), pages 1-20, January.

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