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Chain Of Firms' Bankruptcy: A Macroscopic Study Of Link Effect In A Production Network

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  • YOSHI FUJIWARA

    (NiCT/ATR CIS Applied Network Science, Kyoto 619-0288, Japan)

Abstract

A link in a supplier–customer network is usually a creditor–debtor relationship. If a firm goes into a state of financial insolvency or bankruptcy, then firms on its upstream are affected secondarily along the links. By using 10-year data from recent data on bankruptcy in Japan, we show that this "link effect" is by no means negligible in a nationwide economy. While the total debt of bankruptcy typically amounts to as much as a few percent of GDP in Japan, nearly 20% of all bankruptcies are due to the link effect. Interestingly, we find that such a link effect becomes comparable with other causes of bankruptcy, including poor performance in business (namely solo failure) with respect to the number of events, as the bankruptcy is larger. This means that the link effect grows for larger bankruptcies. Because the supplier–customer network has a heavy tail in its degree distribution, the ripple effect due to the bankruptcy chain is considerable. Since every firm is embedded in a giant network of production, this study would suggest the importance of understanding the heterogeneity in the large-scale network of suppliers and customers on a nationwide scale.

Suggested Citation

  • Yoshi Fujiwara, 2008. "Chain Of Firms' Bankruptcy: A Macroscopic Study Of Link Effect In A Production Network," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(05), pages 703-717.
    • Handle: RePEc:wsi:acsxxx:v:11:y:2008:i:05:n:s0219525908001994
    DOI: 10.1142/S0219525908001994
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    Cited by:

    1. Bargigli, Leonardo & Gallegati, Mauro & Riccetti, Luca & Russo, Alberto, 2014. "Network analysis and calibration of the “leveraged network-based financial accelerator”," Journal of Economic Behavior & Organization, Elsevier, vol. 99(C), pages 109-125.
    2. Hou, Yunzhang & Wang, Xiaoling & Wu, Yenchun Jim & He, Peixu, 2018. "How does the trust affect the topology of supply chain network and its resilience? An agent-based approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 229-241.
    3. Sinha, Priyank & Kumar, Sameer & Prakash, Surya, 2020. "Measuring and mitigating the effects of cost disturbance propagation in multi-echelon apparel supply chains," European Journal of Operational Research, Elsevier, vol. 282(1), pages 148-160.
    4. Delli Gatti, Domenico & Gallegati, Mauro & Greenwald, Bruce & Russo, Alberto & Stiglitz, Joseph E., 2010. "The financial accelerator in an evolving credit network," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1627-1650, September.
    5. Michael I.C. Nwogugu, 2019. "Complex Systems, Multi-Sided Incentives and Risk Perception in Companies," Palgrave Macmillan Books, Palgrave Macmillan, number 978-1-137-44704-3.
    6. Li, Shouwei & Sui, Xin, 2016. "Contagion risk in endogenous financial networks," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 591-597.

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