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Regional and Sectoral Structures and Their Dynamics of Chinese Economy: A Network Perspective from Multi-Regional Input-Output Tables

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  • Tao Wang
  • Shiying Xiao
  • Jun Yan
  • Panpan Zhang

Abstract

A multi-regional input-output table (MRIOT) containing the transactions among the region-sectors in an economy defines a weighted and directed network. Using network analysis tools, we analyze the regional and sectoral structure of the Chinese economy and their temporal dynamics from 2007 to 2012 via the MRIOTs of China. Global analyses are done with network topology measures. Growth-driving province-sector clusters are identified with community detection methods. Influential province-sectors are ranked by weighted PageRank scores. The results revealed a few interesting and telling insights. The level of inter-province-sector activities increased with the rapid growth of the national economy, but not as fast as that of intra-province economic activities. Regional community structures were deeply associated with geographical factors. The community heterogeneity across the regions was high and the regional fragmentation increased during the study period. Quantified metrics assessing the relative importance of the province-sectors in the national economy echo the national and regional economic development policies to a certain extent.

Suggested Citation

  • Tao Wang & Shiying Xiao & Jun Yan & Panpan Zhang, 2021. "Regional and Sectoral Structures and Their Dynamics of Chinese Economy: A Network Perspective from Multi-Regional Input-Output Tables," Papers 2102.12454, arXiv.org.
    • Handle: RePEc:arx:papers:2102.12454
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    References listed on IDEAS

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    1. João Amador & Sónia Cabral, 2017. "Networks of Value-added Trade," The World Economy, Wiley Blackwell, vol. 40(7), pages 1291-1313, July.
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    Cited by:

    1. Zhang, Panpan & Wang, Tiandong & Yan, Jun, 2022. "PageRank centrality and algorithms for weighted, directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    2. Xiao, Shiying & Yan, Jun & Zhang, Panpan, 2022. "Incorporating auxiliary information in betweenness measure for input–output networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    3. Li, Yongqing & Ma, Huimin & Xiong, Jie & Zhang, Jinlong & Ponnamma Divakaran, Pradeep Kumar, 2022. "Manufacturing structure, transformation path, and performance evolution: An industrial network perspective," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    4. Shen, Yi & Yang, Huang & Xie, Yuangcheng & Liu, Yang & Ren, Gang, 2023. "Adaptive robustness optimization against network cascading congestion induced by fluctuant load via a bilateral-adaptive strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).

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