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. 2017 Sep 5;12(9):e0184055. doi: 10.1371/journal.pone.0184055. eCollection 2017.

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.

MeSH terms

  • Bibliographies as Topic
  • Cluster Analysis
  • Cooperative Behavior*
  • Industry*
  • Internationality*
  • Models, Theoretical

Grants and funding

The Author acknowledges support from Beijing Municipal Social Science Foundation Youth Program 2017 "Construction of 'the Belt and the Road' and Global Cooperation on Production Capacity" and Research Base of Beijing Modern Manufacturing Development Tender Project 2017.