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Analysis of inter-country input-output table based on citation network: How to measure the competition and collaboration between industrial sectors on the global value chain

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  • Lizhi Xing

Abstract

The input-output table is comprehensive and detailed in describing the national economic system with complex economic relationships, which embodies information of supply and demand among industrial sectors. This paper aims to scale the degree of competition/collaboration on the global value chain from the perspective of econophysics. Global Industrial Strongest Relevant Network models were established by extracting the strongest and most immediate industrial relevance in the global economic system with inter-country input-output tables and then transformed into Global Industrial Resource Competition Network/Global Industrial Production Collaboration Network models embodying the competitive/collaborative relationships based on bibliographic coupling/co-citation approach. Three indicators well suited for these two kinds of weighted and non-directed networks with self-loops were introduced, including unit weight for competitive/collaborative power, disparity in the weight for competitive/collaborative amplitude and weighted clustering coefficient for competitive/collaborative intensity. Finally, these models and indicators were further applied to empirically analyze the function of sectors in the latest World Input-Output Database, to reveal inter-sector competitive/collaborative status during the economic globalization.

Suggested Citation

  • Lizhi Xing, 2017. "Analysis of inter-country input-output table based on citation network: How to measure the competition and collaboration between industrial sectors on the global value chain," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-29, September.
    • Handle: RePEc:plo:pone00:0184055
    DOI: 10.1371/journal.pone.0184055
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    References listed on IDEAS

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    1. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    2. Howard D. White & Belver C. Griffith, 1981. "Author cocitation: A literature measure of intellectual structure," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 32(3), pages 163-171, May.
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    Cited by:

    1. Xing, Lizhi & Guan, Jun & Wu, Shan, 2018. "Measuring the impact of final demand on global production system based on Markov process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 148-163.
    2. Tokito, Shohei, 2018. "Environmentally-Targeted Sectors and Linkages in the Global Supply-Chain Complexity of Transport Equipment," Ecological Economics, Elsevier, vol. 150(C), pages 177-183.
    3. 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.
    4. Xing, Lizhi & Guan, Jun & Dong, Xianlei & Wu, Shan, 2018. "Understanding the competitive advantage of TPP-related nations from an econophysics perspective: Influence caused by China and the United States," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 164-184.
    5. Xing, Lizhi & Wang, Dawei & Li, Yan & Guan, Jun & Dong, Xianlei, 2020. "Simulation analysis of the competitive status between China and Portuguese-speaking countries under the background of one belt and one road initiative," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    6. Barauskaite, Kristina & Nguyen, Anh D.M., 2021. "Global intersectoral production network and aggregate fluctuations," Economic Modelling, Elsevier, vol. 102(C).

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