Buyer-Supplier Networks and Aggregate Volatility

  • Takayuki MizunoEmail author
  • Wataru Souma
  • Tsutomu Watanabe
Part of the Advances in Japanese Business and Economics book series (AJBE, volume 4)


This chapter investigates the structure and evolution of customer–supplier networks in Japan using a unique dataset that contains information on customer and supplier linkages for over 500,000 incorporated non-financial firms for the 5 years from 2008 to 2012. We find, first, that the number of customer links is unequal across firms: the customer link distribution has a power-law tail with an exponent of unity (i.e., it follows Zipf’s law). We interpret this as implying that competition among firms to acquire new customers yields winners that attract a large number of customers, as well as losers that end up with fewer customers. We also show that the shortest path length for any pair of firms is, on average, 4.3 links. Second, we find that link switching is relatively rare. Our estimates indicate that 92 % of customer links and 93 % of supplier links survive each year. Third and finally, we find that firm growth rates tend to be more highly correlated the closer two firms are to each other in a customer–supplier network (i.e., the smaller is the shortest path length for the two firms). This suggests that a non-negligible portion of firm growth fluctuations stem from the propagation of microeconomic shocks—shocks that affect a specific firm—through the customer–supplier chains.


Buyer-supplier network Aggregate volatility Input–output analysis Power-law distribution PageRank 



We would like to thank Vasco Carvalho, Hiro Ishise, Makoto Nirei, Tack Yun, and partcipants at European Conference on Complex Systems held in Vienna on September 12–16, 2011 for helpful comments and suggestions, and Masahiro Miyatani for his careful explanation of the TDB data. This research forms part of a project on “Designing Interfirm Networks to Achieve Sustainable Economic Growth” carried out at Hitotsubashi University with financial support from the Ministry of Education, Culture, Sports, Science, and Technology. The dataset we use in this paper is compiled jointly by Teikoku Databank, Ltd. and the HIT-TDB project of Hitotsubashi University.


  1. Acemoglu, D., Carvalho, V. M, Ozdaglar, A., & Tahbaz-Salehi, A. (2012). The network origins of aggregate fluctuations. Econometrica, 80(5), 1977–2016.Google Scholar
  2. Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2013a). Systemic risk and stability in financial networks. No. w18727. National Bureau of Economic Research.Google Scholar
  3. Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2013b). The network origins of large economic downturns. No. w19230. National Bureau of Economic Research.Google Scholar
  4. Atalay, E., Hortacsu, A., Roberts, J., & Syverson, C. (2011). Network structure of production. Proceedings of the National Academy of Sciences, 108(13), 5199–5202.CrossRefGoogle Scholar
  5. Axtell, R. L. (2001). Zipf distribution of U.S. firm sizes. Science, 293(5536), 1818–1820.CrossRefGoogle Scholar
  6. Battiston, S., Delli Gatti, D., Gallegati, M., Greenwald, B., & Stiglitz, J. E. (2007). Credit chains and bankruptcy propagation in production networks. Journal of Economic Dynamics and Control, 31(6), 2061–2084.CrossRefGoogle Scholar
  7. Bonanno, G., Caldarelli, G., Lillo, F., Micciche, S., Vanndewalle, N., & Mantegna, R. N. (2004). Networks of equities in financial markets. The European Physical Journal B, 38, 363–371.CrossRefGoogle Scholar
  8. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks, 33(1–7), 107–117.Google Scholar
  9. Carvalho, V. M. (October 2010). Aggregate fluctuations and the network structure of intersectoral trade. CREI and University of Pompeu Fabra.Google Scholar
  10. Carvalho, V. M. (December 2012). A survey paper on recent developments of input–output analysis, Report for the European Commission under the CRISIS Consortium Agreement.Google Scholar
  11. Chaney, T. (2008). Distorted gravity: The intensive and extensive margins of international trade. American Economic Review, 98(4), 1707–1721.CrossRefGoogle Scholar
  12. Di Giovanni, J., & Levchenko, A. A. (2010). Putting the parts together: Trade, vertical linkages, and business cycle comovement. American Economic Journal: Macroeconomics, 2(2), 95–124.Google Scholar
  13. Dupor, B. (1999). Aggregation and irrelevance in multi-sector models. Journal of Monetary Economics, 43(2), 391–409.CrossRefGoogle Scholar
  14. Foerster, A. T., Sarte, P. D. G., & Watson, M. W. (2011). Sectoral versus aggregate shocks: A structural analysis of industrial production. Journal of Political Economy, 119, 1–38.CrossRefGoogle Scholar
  15. Fujiwara, Y., & Aoyama, H. (2010). Large-scale structure of a nation-wide production network. The European Physical Journal B, 77, 565–580.CrossRefGoogle Scholar
  16. Gabaix, X. (2011). The granular origins of aggregate fluctuations. Econometrica, 79(3), 733–772.CrossRefGoogle Scholar
  17. Garlaschelli, D., & Loffredo, M. I. (2004). Fitness-dependent topological properties of the world trade web. Physical Review Letters, 93(18), 18870–1.CrossRefGoogle Scholar
  18. Garlaschelli, D., & Loffredo, M. I. (2005). Structure and evolution of the world trade network. Physica A: Statistical Mechanics and its Applications, 355(1), 138–144.CrossRefGoogle Scholar
  19. Garlaschelli, D., Battiston, S., Castri, M., Servedio, V. D. P., & Caldarelli, G. (2005). The scale-free topology of market investments. Physica A, 350, 491–499.CrossRefGoogle Scholar
  20. Gourio, F., & Rudanko, L. (July 2011). Customer capital. National Bureau of Economic Research Working Paper, No. 17191.Google Scholar
  21. Goyal, S. (2012). Connections: An introduction to the economics of networks. Princeton University Press.Google Scholar
  22. Ibuki, T., Suzuki, S., & Inoue, J.-I. (2013). Cluster analysis and Gaussian mixture estimation of correlated time-series by means of multi-dimensional scaling. In F. Abergel et al. (Eds.), Econophysics of systemic risk and network dynamics (pp. 239-259). New Economic Windows, Springer.Google Scholar
  23. Jackson, M. O. (2010). Social and economic networks. Princeton University Press.Google Scholar
  24. Kelly, B., Lustig, H., & Van Nieuwerburgh, S. (February 2013). Firm volatility in granular networks. Chicago Booth Research Paper 12-56.Google Scholar
  25. Leontief, W. (1936). Quantitative input and output relations in the economic system of the United States. Review of Economics and Statistics, 18(3), 105–125.CrossRefGoogle Scholar
  26. Luttmer, E. G. J. (October 2006). Consumer search and firm growth. Federal Reserve Bank of Minneapolis Working Paper, No. 645.Google Scholar
  27. Markose, S., Giansante, S., Shaghaghi, A. R. (2012). 'Too interconnected to fail` financial network of US CDS market: Topological fragility and systemic risk. Journal of Economic Behavior and Organization, 83(3), 627–646.CrossRefGoogle Scholar
  28. Mizuno, T., Souma, W., & Watanabe, T. (2014). The structure and evolution of buyersupplier networks. PLoS ONE, 9(7): e10071–2. doi:10.1371/journal.pone.0100712.CrossRefGoogle Scholar
  29. Ohnishi, T., Takayasu, H., & Takayasu, M. (2010). Network motifs in inter-firm network. Journal of Economic Interaction and Coordination, 5, 171–180.CrossRefGoogle Scholar
  30. Plerou, V., Gopikrishnan, P., Rosenow, B., Amaral, L. A. N., Guhr, T., & Stanley, H. E. (2002). Random matrix approach to cross correlations in financial data. Physical Review E, 65, 06612–6.CrossRefGoogle Scholar
  31. Riccaboni, M., & Schiavo, S. (2010). Structure and growth of weighted networks. New Journal of Physics, 12(2), 02300–3.CrossRefGoogle Scholar
  32. Saito, Y. U, Watanabe, T., & Iwamura, M. (2007). Do larger firms have more interfirm relationships? Physica A, 383, 158–163.Google Scholar
  33. Souma, W., Fujiwara, Y., & Aoyama, H. (2003). Complex networks and economics. Physica A, 324, 396–401.CrossRefGoogle Scholar
  34. Stella, A. (September 2013). Firm dynamics and the origins of aggregate fluctuations. Available at SSRN 2222746.Google Scholar
  35. Takayasu, M., Sameshima, S., Ohnishi, T., Ikeda, Y., Takayasu, H., & Watanabe, K. (2008). Massive economics data analysis by econophysics method-the case of companies' network structure. Annual Report of the Earth Simulator Center, April 2007–March 2008, 263–268.Google Scholar
  36. Watanabe, H., Takayasu, H., & Takayasu, M. (2012). Biased diffusion on the Japanese inter-firm trading network: Estimation of sales from the network structure. New Journal of Physics, 14(4), 04303–4.CrossRefGoogle Scholar
  37. Watanabe, H., Takayasu, H., & Takayasu, M. (2013). Relations between allometric scalings and fluctuations in complex systems: The case of Japanese firms. Physica A, 392, 741–756.CrossRefGoogle Scholar
  38. Watts, D. J, & Strogatz, S. H. (1998). Collective dynamics of 'small-world` networks. Nature, 393, 440–442.CrossRefGoogle Scholar

Copyright information

© Springer Japan 2015

Authors and Affiliations

  • Takayuki Mizuno
    • 1
    • 2
    • 3
    • 4
    • 5
    Email author
  • Wataru Souma
    • 6
  • Tsutomu Watanabe
    • 4
    • 5
  1. 1.National Institute of InformaticsChiyoda-kuJapan
  2. 2.Department of InformaticsThe Graduate University for Advanced StudiesChiyoda-kuJapan
  3. 3.PRESTOJapan Science and Technology AgencyChiyoda-kuJapan
  4. 4.Graduate School of EconomicsThe University of TokyoBunkyo-kuJapan
  5. 5.The Canon Institute for Global StudiesChiyoda-kuJapan
  6. 6.College of Science and TechnologyNihon UniversityFunabashiJapan

Personalised recommendations