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Firm-level propagation of shocks through supply-chain networks

Nature Sustainability volume 2, pages 841–847 (2019)Cite this article

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Abstract

Social and economic networks can be a channel of negative shocks and thus deteriorate resilience and sustainability in societies. This study focuses on supply chains, or supplier–customer networks of firms and examines how these supply chains enable production losses caused by natural disasters to propagate and persist in regions not directly affected by the disaster. We apply an agent-based model to the actual supply chains of nearly one million firms in Japan to estimate the direct and indirect effects of the 2011 Great East Japan earthquake. We then employ the same model to predict the effect of the Nankai Trough earthquake, a mega earthquake predicted to hit major industrial cities in Japan in the near future. We find that the indirect effects of the disasters on production due to propagation (10.6% of gross domestic product in the case of the Nankai earthquake) are substantially larger than their direct effects (0.5%). Our simulation analyses to compare the actual network with hypothetical networks suggest that these indirect effects are more prominent and persistent when supply chains are characterized by scale-free properties, difficulty in substitution among intermediate products, and complex cycles in networks.

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Fig. 1: Simulated dynamics of value added after the 2011 Japan earthquake.
Fig. 2: Geographic and dynamic propagation of the shock by the 2011 Japan earthquake.
Fig. 3: Simulated dynamics of value added after the predicted Nankai earthquake compared with the 2011 Japan earthquake.
Fig. 4: Simulated dynamics of value added using supply chains with different network structures.
Fig. 5: Simulated dynamics of value added using supply chains with different levels of substitution.

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Data availability

The data that support the findings of this study are licensed from TSR to RIETI where the authors conducted this study and are available from TSR for a fee (http://www.tsr-net.co.jp/). Because of the restriction in the licensing agreement between TSR and RIETI, the authors have no right to disclose the data publicly.

Code availability

The simulation code can be accessed at https://github.com/HiroyasuInoue/ProductionNetworkSimulator. The minimal input for the code is supplier–client relationships, firms’ initial productions and supplies to final consumers, and industrial sectors of firms. The test data for the minimal inputs are also provided.

References

  1. Barabási, A.-L. Network Science (Cambridge Univ. Press, 2016).

  2. Watts, D. & Strogatz, S. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998).

    Article  CAS  Google Scholar 

  3. Watts, D. Small Worlds: The Dynamics of Networks Between Order and Randomness (Princeton Univ. Press, 1999).

  4. Valente, T. W. Network Models of the Diffusion of Innovations (Hampton Press, 1995).

  5. Banerjee, A., Chandrasekhar, A. G., Duflo, E. & Jackson, M. O. The diffusion of microfinance. Science 341, 1236498 (2013).

    Article  Google Scholar 

  6. Kreindler, G. E. & Young, H. P. Rapid innovation diffusion in social networks. Proc. Natl Acad. Sci. USA 111, 10881–10888 (2014).

    Article  CAS  Google Scholar 

  7. Acemoglu, D., Akcigit, U. & Kerr, W. R. Innovation network. Proc. Natl Acad. Sci. USA 113, 11483–11488 (2016).

    Article  CAS  Google Scholar 

  8. Jackson, M. O. Social and Economic Networks (Princeton Univ. Press, 2010).

  9. Battiston, S., Puliga, M., Kaushik, R., Tasca, P. & Caldarelli, G. Debtrank: too central to fail? Financial networks, the fed and systemic risk. Sci. Rep. 2, 541 (2012).

    Article  CAS  Google Scholar 

  10. Thurner, S. & Poledna, S. Debtrank-transparency: controlling systemic risk in financial networks. Sci. Rep. 3, 1888 (2013).

    Article  Google Scholar 

  11. Huang, X., Vodenska, I., Havlin, S. & Stanley, H. E. Cascading failures in bi-partite graphs: model for systemic risk propagation. Sci. Rep. 3, 1219 (2013).

    Article  CAS  Google Scholar 

  12. Carvalho, V., Nirei, M., Saito, Y. & Tahbaz-Salehi, A. Supply Chain Disruptions: Evidence from The Great East Japan Earthquake Research Paper 17-5 (Columbia Business School, 2016).

  13. Barrot, J. & Sauvagnat, J. Input specificity and the propagation of idiosyncratic shocks in production networks. Q. J. Econ. 131, 1543–1592 (2016).

    Article  Google Scholar 

  14. Tierney, K. Business impacts of the northridge earthquake. J. Conting. Crisis Man. 5, 87–97 (1997).

    Article  Google Scholar 

  15. Pelling, M., Özerdem, A. & Barakat, S. The macro-economic impact of disasters. Prog. Dev. Stud. 2, 283–305 (2002).

    Article  Google Scholar 

  16. Acemoglu, D., Carvalho, V., Ozdaglar, A. & Alireza, T. The network origins of aggregate fluctuations. Econometrica 80, 1977–2016 (2012).

    Article  Google Scholar 

  17. Acemoglu, D., Akcigit, U. & Kerr, W. Networks and the macroeconomy: an empirical exploration. NBER Macroecon. Annu. 30, 273–335 (2016).

    Article  Google Scholar 

  18. Bak, P., Chen, K., Scheinkman, J. & Woodford, M. Aggregate fluctuations from independent sectoral shocks: self-organized criticality in a model of production and inventory dynamics. Ric. Econ. 47, 3–30 (1993).

    Article  Google Scholar 

  19. Delli Gatti, D. et al. A new approach to business fluctuations: heterogeneous interacting agents, scaling laws and financial fragility. J. Econ. Behav. Organ. 56, 489–512 (2005).

    Article  Google Scholar 

  20. Lee, K.-M. & Goh, K.-I. Strength of weak layers in cascading failures on multiplex networks: case of the international trade network. Sci. Rep. 6, 26346 (2016).

    Article  CAS  Google Scholar 

  21. Burt, R. Structural holes and good ideas. Am. J. Sociol. 110, 349–399 (2004).

    Article  Google Scholar 

  22. Centola, D. The spread of behavior in an online social network experiment. Science 329, 1194–1197 (2010).

    Article  CAS  Google Scholar 

  23. Newman, M. Networks: An Introduction (Oxford Univ. Press, 2010).

  24. Watts, D. J. A simple model of global cascades on random networks. Proc. Natl Acad. Sci. USA 99, 5766–5771 (2002).

    Article  CAS  Google Scholar 

  25. Fujiwara, Y. & Aoyama, H. Large-scale structure of a nation-wide production network. Eur. Phys. J. B 77, 565–580 (2010).

    Article  CAS  Google Scholar 

  26. Saito, Y. U., Watanabe, T. & Iwamura, M. Do larger firms have more interfirm relationships? Physica A 383, 158–163 (2007).

    Article  Google Scholar 

  27. Haimes, Y. & Jiang, P. Leontief-based model of risk in complex interconnected infrastructures. J. Infrastruct. Syst. 7, 1–12 (2001).

    Article  Google Scholar 

  28. Santos, J. & Haimes, Y. Modeling the demand reduction input-output (i-o) inoperability due to terrorism of interconnected infrastructures. Risk Anal. 24, 1437–1451 (2004).

    Article  Google Scholar 

  29. Okuyama, Y., Hewings, G. J. & Sonis, M. in Modeling Spatial and Economic Impacts of Disasters (eds Okuyama, Y. & Chang, S. E.) 77–101 (Springer, 2004).

  30. Rose, A. & Liao, S. Modeling regional economic resilience to disasters: a computable general equilibrium analysis of water service disruptions. J. Reg. Sci. 45, 75–112 (2005).

    Article  Google Scholar 

  31. Hallegatte, S. An adaptive regional input-output model and its application to the assessment of the economic cost of Katrina. Risk Anal. 28, 779–799 (2008).

    Article  Google Scholar 

  32. Hallegatte, S. Modeling the Roles of Heterogeneity, Substitution, and Inventories in the Assessment of Natural Disaster Economic Costs (The World Bank, 2012).

  33. Henriet, F., Hallegatte, S. & Tabourier, L. Firm-network characteristics and economic robustness to natural disasters. J. Econ. Dyn. Control 36, 150–167 (2012).

    Article  Google Scholar 

  34. Inoue, H. & Todo, Y. Propagation of negative shocks through firm networks: evidence from simulation on comprehensive supply-chain data. PLoS ONE 14, e0213648 (2019).

    Article  CAS  Google Scholar 

  35. Beroza, G. C. How many great earthquakes should we expect? Proc. Natl Acad. Sci. USA 109, 651–652 (2012).

    Article  CAS  Google Scholar 

  36. Milly, P. C. D., Wetherald, R. T., Dunne, K. & Delworth, T. L. Increasing risk of great floods in a changing climate. Nature 415, 514–517 (2002).

    Article  CAS  Google Scholar 

  37. Fujimoto, T. Supply Chain Competitiveness and Robustness: A Lesson from the 2011 Tohoku Earthquake and Supply Chain ‘Virtual Dualization’ (Manufacturing Management Research Center, 2011).

  38. Cohen, R., Havlin, S. & Ben-Avraham, D. in Handbook of Graphs and Networks: From the Genome to the Internet (eds Bornholdt, S. & Schuster, H. G.) 85–110 (Wiley‐VCH, 2003).

  39. Valente, T. W. Social Networks and Health: Models, Methods, and Applications (Oxford Univ. Press, 2010).

  40. Valente, T. W. Putting the network in network interventions. Proc. Natl Acad. Sci. USA 114, 9500–9501 (2017).

    Article  CAS  Google Scholar 

  41. Aoki, M. Information, Incentives and Bargaining in the Japanese Economy: A Microtheory of the Japanese Economy (Cambridge Univ. Press, 1988).

  42. Alshamsi, A., Pinheiro, F. L. & Hidalgo, C. A. Optimal diversification strategies in the networks of related products and of related research areas. Nat. Commun. 9, 1328 (2018).

    Article  Google Scholar 

  43. The 2011 Updated Input–output Table (METI, 2011).

  44. Broder, A. et al. Graph structure in the web. Comput. Netw. 33, 309–320 (2000).

    Article  Google Scholar 

  45. White Paper on Disaster Management (Cabinet Office in Japan, 2012).

  46. White Paper on International Economy and Trade (METI, 2011).

  47. White Paper on Disaster Management (Cabinet Office in Japan, 2014).

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Acknowledgements

This research was conducted as part of a project entitled ‘Large-scale Simulation and Analysis of Economic Network for Macro Prudential Policy,’ undertaken at the Research Institute of Economy, Trade, and Industry. This research was also supported by MEXT as Exploratory Challenges on Post-K computer (Studies of Multi-level Spatiotemporal Simulation of Socioeconomic Phenomena). The authors thank H. Aoyama, N. Ito, Y. Fujiwara, other members of Exploratory Challenges on Post-K computer, S. Hallegatte, M. Yano and seminar participants at RIETI and the World Bank for helpful comments. The authors are also grateful for the financial support of JSPS Kakenhi Grant Nos. 15K01217, 18K04615, 18H03642 and 25101003. The opinions expressed and arguments employed herein do not necessarily reflect those of RIETI, University of Hyogo, Waseda University, or any institution with which the authors are affiliated.

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Authors and Affiliations

  1. Graduate School of Simulation Studies, University of Hyogo, Kobe, Japan

    Hiroyasu Inoue

  2. Graduate School of Economics, Waseda University, Tokyo, Japan

    Yasuyuki Todo

  3. Research Institute of Economy, Trade and Industry, Tokyo, Japan

    Yasuyuki Todo

Authors
  1. Hiroyasu Inoue
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  2. Yasuyuki Todo
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Contributions

H.I. and Y.T. designed the research. H.I. performed the research and analysed the data. H.I. and Y.T. wrote the paper.

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Correspondence to Hiroyasu Inoue.

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Supplementary Information

Supplementary methods, Supplementary Figs. 1–6, Supplementary Videos 1 and 2, Supplementary refs. 1–14.

Supplementary Video 1

The video shows the simulated geographic propagation of negative shocks directly caused by the 2011 Japan earthquake through supply chains for 100 days after the earthquake.

Supplementary Video 2

The video shows the simulated geographic propagation of negative shocks directly caused by the Nankai earthquake through supply chains for 100 days after the earthquake.

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Inoue, H., Todo, Y. Firm-level propagation of shocks through supply-chain networks. Nat Sustain 2, 841–847 (2019). https://doi.org/10.1038/s41893-019-0351-x

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