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Business cycles’ correlation and systemic risk of the Japanese supplier-customer network

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  • Hazem Krichene
  • Abhijit Chakraborty
  • Hiroyasu Inoue
  • Yoshi Fujiwara

Abstract

This work aims to study and explain the business cycle correlations of the Japanese production network. We consider the supplier-customer network, which is a directed network representing the trading links between Japanese firms (links from suppliers to customers). The community structure of this network is determined by applying the Infomap algorithm. Each community is defined by its GDP and its associated business cycle. Business cycle correlations between communities are estimated based on copula theory. Then, based on firms’ attributes and network topology, these correlations are explained through linear econometric models. The results show strong evidence of business cycle correlations in the Japanese production network. A significant systemic risk is found for high negative or positive shocks. These correlations are explained mainly by the sector and by geographic similarities. Moreover, our results highlight the higher vulnerability of small communities and small firms, which is explained by the disassortative mixing of the production network.

Suggested Citation

  • Hazem Krichene & Abhijit Chakraborty & Hiroyasu Inoue & Yoshi Fujiwara, 2017. "Business cycles’ correlation and systemic risk of the Japanese supplier-customer network," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-22, October.
  • Handle: RePEc:plo:pone00:0186467
    DOI: 10.1371/journal.pone.0186467
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    Cited by:

    1. Abhijit Chakraborty & Yuichi Kichikawa & Takashi Iino & Hiroshi Iyetomi & Hiroyasu Inoue & Yoshi Fujiwara & Hideaki Aoyama, 2018. "Hierarchical communities in the walnut structure of the Japanese production network," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-25, August.
    2. Chakraborty, Abhijit & Krichene, Hazem & Inoue, Hiroyasu & Fujiwara, Yoshi, 2019. "Characterization of the community structure in a large-scale production network in Japan," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 210-221.
    3. Abhijit Chakraborty & Hiroyasu Inoue & Yoshi Fujiwara, 2020. "Economic complexity of prefectures in Japan," Papers 2002.05785, arXiv.org, revised Aug 2020.
    4. Hazem KRICHENE & ARATA Yoshiyuki & Abhijit CHAKRABORTY & FUJIWARA Yoshi & INOUE Hiroyasu, 2018. "How Firms Choose their Partners in the Japanese Supplier-Customer Network? An application of the exponential random graph model," Discussion papers 18011, Research Institute of Economy, Trade and Industry (RIETI).

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