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Exploring Causal Network Complexity in Industrial Linkages: A Comparative Study.

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  • 1Department of Mathematics and Statistics, College of Science, Wuhan University of Science and Technology, Wuhan 430065, China.

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Summary

This study compares industrial network structures in China and the US using partial cross mapping (PCM). China

Keywords:
PCMcausal networksindustry linkagenetwork resilience

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Area of Science:

  • Economics
  • Network Science
  • Industrial Organization

Background:

  • Industrial linkages are vital for economic growth and spatial organization.
  • Understanding inter-industry causality is key to analyzing economic systems.
  • Comparative studies of industrial networks offer insights into national economic resilience.

Purpose of the Study:

  • To analyze and compare the causal network complexity within the industrial ecosystems of China and the United States.
  • To investigate interdependencies and causal structures across key sectors using high-frequency economic data.
  • To evaluate the network topology and resilience of both economies.

Main Methods:

  • Utilized partial cross mapping (PCM) technique to capture dynamic interactions and inter-industry causality.
  • Analyzed high-frequency economic data from 32 Chinese industries and 11 United States industries (2015-2023).
  • Employed network analysis to compare topology and resilience.

Main Results:

  • The US exhibits a diversified, service-oriented structure with finance and technology sectors as key drivers.
  • China's network shows higher centrality in heavy industries and manufacturing, supporting export-led growth.
  • China's industrial structure demonstrates enhanced network stability and interconnectivity.

Conclusions:

  • China's industrial network offers greater stability and resilience due to robust inter-industry linkages.
  • The US network's limited nodal points may constrain its overall resilience.
  • Comparative analysis reveals distinct structural dynamics and resilience factors in major economies.