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Bankruptcy propagation on a customer-supplier network: An empirical analysis in Japan

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  • ARATA Yoshiyuki
Abstract
Since firms are interrelated via customer-supplier relationships, the bankruptcy of a firm may lead to the bankruptcy of its suppliers. Due to this contagion effect, one bankruptcy may trigger many subsequent bankruptcies of direct and indirect and have a nonnegligible impact on an aggregate economy. This paper empirically analyzes this bankruptcy propagation on a customer-supplier network by using a comprehensive dataset consisting of more than one million firms and their customer-supplier relationships, and bankruptcy records over April 2013 to February 2017 in Japan. We find that the contagion effect is significant at the firm-level; for example, if 50% customers of a firm go bankrupt, the firm's bankruptcy probability approximately triples. However, it does not immediately imply that there is a substantial risk at the aggregate level that a nonnegligible fraction of firms are forced into bankruptcies by the contagion effect. In fact, by simulating our model, we find that the reach of bankruptcy propagation is very limited in most cases and it is highly unlikely that bankruptcy spread extensively on the network. This is because of the structure of the customer-supplier network. The network structure contributes to absorbing bankruptcy shocks by an aggregate economy rather than spreads bankruptcy on the network.

Suggested Citation

  • ARATA Yoshiyuki, 2018. "Bankruptcy propagation on a customer-supplier network: An empirical analysis in Japan," Discussion papers 18040, Research Institute of Economy, Trade and Industry (RIETI).
  • Handle: RePEc:eti:dpaper:18040
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    References listed on IDEAS

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    1. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    2. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    3. Bauer, Julian & Agarwal, Vineet, 2014. "Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 432-442.
    4. Markus K. Brunnermeier, 2009. "Deciphering the Liquidity and Credit Crunch 2007-2008," Journal of Economic Perspectives, American Economic Association, vol. 23(1), pages 77-100, Winter.
    5. Xavier Gabaix, 2011. "The Granular Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 79(3), pages 733-772, May.
    6. Sanjiv R. Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2007. "Common Failings: How Corporate Defaults Are Correlated," Journal of Finance, American Finance Association, vol. 62(1), pages 93-117, February.
    7. Darrell Duffie & Andreas Eckner & Guillaume Horel & Leandro Saita, 2009. "Frailty Correlated Default," Journal of Finance, American Finance Association, vol. 64(5), pages 2089-2123, October.
    8. Frederic Boissay & Reint Gropp, 2013. "Payment Defaults and Interfirm Liquidity Provision," Review of Finance, European Finance Association, vol. 17(6), pages 1853-1894.
    9. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    10. Philippe Jorion & Gaiyan Zhang, 2009. "Credit Contagion from Counterparty Risk," Journal of Finance, American Finance Association, vol. 64(5), pages 2053-2087, October.
    11. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    12. Duan, Jin-Chuan & Sun, Jie & Wang, Tao, 2012. "Multiperiod corporate default prediction—A forward intensity approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 191-209.
    13. Tor Jacobson & Erik Schedvin, 2015. "Trade Credit and the Propagation of Corporate Failure: An Empirical Analysis," Econometrica, Econometric Society, vol. 83(4), pages 1315-1371, July.
    14. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    15. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    16. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
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