Firm-level propagation of shocks through supply-chain networks

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.

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.

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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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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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