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Closeness centrality for similarity-weight network and its application to measuring industrial sectors’ position on the Global Value Chain

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  • Guan, Jun
  • Li, Yafei
  • Xing, Lizhi
  • Li, Yan
  • Liang, Guoqiang

Abstract

This paper focuses on measuring the industrial sector’s position on the Global Value Chain (GVC), as reinforcements to the present studies on international trade. We firstly reconsidered the length-related and position-related measures in literatures about vertical specialization from the perspective of bibliometrics and econophysics. Secondly, the inter-country and inter-sector propagating process of intermediate goods was redefined, resulting in the concept of Strongest Relevance Path Length (SRPL) based on Revised Floyd–Warshall Algorithm (RFWA). Thirdly, enlightened by closeness centrality, we introduced two new SRPL-based indices to measure the Interdependence from a given sector to all its upstream and downstream sectors, and proposed Relative Upstreamness Index (RUI) to measure the relative position of the industrial sector. Finally, these indices were applied to the empirical analysis of the global economic system by physical statistics.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119318679
    DOI: 10.1016/j.physa.2019.123337
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    References listed on IDEAS

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    7. Xing, Lizhi & Dong, Xianlei & Guan, Jun & Qiao, Xiaoyong, 2019. "Betweenness centrality for similarity-weight network and its application to measuring industrial sectors’ pivotability on the global value chain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 19-36.
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