Abstract
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.
This paper is an extended version of Mizuno et al. (2014).
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Notes
- 1.
For example, the U.S. Federal Reserve chairman Ben Bernanke stated in the aftermath of the earthquake: “U.S. economic growth so far this year looks to have been somewhat slower than expected. Aggregate output increased at only 1.8 % at an annual rate in the first quarter, and supplychain disruptions associated with the earthquake and tsunami in Japan are hampering economic activity this quarter.” (Speech at the International Monetary Conference, Atlanta, Georgia, U.S. on June 7, 2011).
- 2.
The study of networks as phenomena that deserve analysis goes back to the small-world network model by Watts (Watts and Strogatz 1998) and has gained popularity in a variety of scientific disciplines including statistical physics, computer science, biology, and sociology. The methodology developed in those disciplines has been introduced into economics only relatively recently (Jackson 2010; Goyal 2012). However, it has produced important contributions on bank-firm relationships (Souma 2003), cross shareholdings (Garlaschelli et al. 2005), supply chains (Atalay et al. 2011; Saito et al. 2007; Ohnishi et al. 2010; Takayasu et al. 2008; Fujiwara and Aoyama 2010; Watanabe et al. 2012), systemic risks in financial markets (Battiston et al. 2007; Acemoglu et al. 2013a), and international trade (Garlaschelli and Loffredo 2005; Garlaschelli and Loffredo 2004; Di Giovanni and Levchenko 2010).
- 3.
The number of firms in the augmented lists is 552,145 for 2008, 541,816 for 2009, 518,565 for 2010, 520,087 for 2011, and 525,836 for 2012.
- 4.
More specifically, we pick 134,067 firms that are on the augmented customer/supplier lists for each year in 2008–2011 and whose sales data are available for 1980–2009. We will focus on the same set of firms in the analysis in Section 2.5.
- 5.
We eliminate growth rate correlations among firms as follows. For a particular firm, we randomly pick 2 years, swap the growth rates for the 2 years, and repeat this for other pairs of years. We do the same for all other firms until we have completely eliminated any correlation between the growth rates for any pair of firms.
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Acknowledgments
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.
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Mizuno, T., Souma, W., Watanabe, T. (2015). Buyer-Supplier Networks and Aggregate Volatility. In: Watanabe, T., Uesugi, I., Ono, A. (eds) The Economics of Interfirm Networks. Advances in Japanese Business and Economics, vol 4. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55390-8_2
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