Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-22T21:09:42.211Z Has data issue: false hasContentIssue false

Energy and resilience: The effects of endogenous interdependencies on trade network formation across space among major Japanese firms

Published online by Cambridge University Press:  25 January 2016

PETR MATOUS
Affiliation:
School of Engineering, Department of Civil Engineering, University of Tokyo, Tokyo, Japan and Complex Systems Research Group, Faculty of Engineering and IT, The University of Sydney, New South Wales 2006, Australia (e-mail: [email protected])
YASUYUKI TODO
Affiliation:
Graduate School of Economics, RIETI and Waseda University, 1-6-1 Nishi-Waseda, Shinjuku-ku, Tokyo 169-0051, Japan (e-mail: [email protected])
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The dynamic drivers of interfirm interactions across space have rarely been explored in the context of disaster recovery; therefore, the mechanism through which shocks propagate is unclear. This paper uses stochastic actor-oriented modeling to examine how trade networks among the 500 largest Japanese companies evolved during 2010 and 2011, i.e. before and after the Great East Japan Earthquake to identify sources of vulnerability in the system. In contrast to previous reports on broken supply chains, the network displayed only modest change even in the directly affected areas. Controlling for distance and for firm size, we find that when firms changed their partners, they preferred firms that were popular among other firms, that had partners in common with them and that also bought some products or services from them. These findings concur with a criticism that Japanese firms avoid external actors and exhibit inflexibility in reorganizing their networks in times of need, which contrasts with the non-cliquish network structures observed in high-performing economic sectors. The results also highlight the role of energy firms in disaster resilience. Unlike other large Japanese companies that cluster in major urban centers, energy firms are distributed across Japan. However, despite their peripheral physical locations, energy firms are centrally located in trade networks. Thus, while a disaster in any region may affect some energy firms and lead to large-scale temporary shocks, the entire network is unlikely to be disconnected by any region-specific disaster because of the spatial distribution of the topological network core formed by energy companies.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Cambridge University Press 2016

References

Acemoglu, D., Ozdaglar, A. & Tahbaz-Salehi, A. (2015). Networks, shocks, and systemic risk. NBER Working Paper, (20931), 1–36.Google Scholar
Albert, R., Jeong, H., & Barabási, A.-L. (2000). Error and attack tolerance of complex networks. Nature, 406 (6794), 378382.CrossRefGoogle ScholarPubMed
Amiti, M., & Cameron, L. (2007). Economic geography and wages. Review of Economics and Statistics, 89 (1), 1529.CrossRefGoogle Scholar
Anderson, J. E. (1979). A theoretical foundation for the gravity equation. The American Economic Review, 69 (1), 106116.Google Scholar
Antràs, P., & Chor, D. (2013). Organizing the global value chain. Econometrica, 81 (6), 21272204.Google Scholar
Antràs, P., Chor, D., Fally, T., & Hillberry, R. (2012). Measuring the upstreamness of production and trade flows. American Economic Review, 102 (3), 412416.Google Scholar
Aoyama, H., Fujiwara, Y., Ikeda, Y., Iyetomi, H., & Souma, W. (2010). Econophysics and companies: Statistical life and death in complex business networks. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Bailey, J. P., & Bakos, J. Y. (1997). An exploratory study of the emerging role of electronic intermediariations and policy. International Journal of Electronic Commerce, 19 (5), 406417.Google Scholar
Barthélemy, M. (2011). Spatial networks. Physics Reports, 499 (1–3), 1101.CrossRefGoogle Scholar
Bergstrand, J. H., & Egger, P. (2007). A knowledge-and-physical-capital model of international trade flows, foreign direct investment, and multinational enterprises. Journal of International Economics, 73 (2), 278308.Google Scholar
Cairncross, F. (2001). The death of distance 2.0. New York and London: Texere.Google Scholar
Canals, C., Gabaix, X., Vilarrubia, J. M., & Weinstein, D. E. (2007). Trade patterns, trade balances and idiosyncratic shocks. Banco de España Research Paper No. WP-0721.Google Scholar
Caplow, T., & Forman, R. (1950). Neighborhood interaction in a homogeneous community. American Sociological Review, 15 (3), 357366.Google Scholar
Carrasco, J. A., Hogan, B., Wellman, B., & Miller, E. J. (2008a). Agency in social activity interactions: The role of social networks in time and space. Tijdschrift voor Economische en Sociale Geografie, 99 (5), 562583.Google Scholar
Carrasco, J. A., & Miller, E. J. (2006). Exploring the propensity to perform social activities: A social network approach. Transportation, 33 (5), 463480.CrossRefGoogle Scholar
Carrasco, J.-A., & Miller, E. J. (2008). The social dimension in action: A multilevel, personal networks model of social activity frequency between individuals. Transportation Research Part A, 43 (1), 90104.Google Scholar
Carrasco, J. A., Miller, E. J., & Wellman, B. (2008b). How far and with whom do people socialize? Empirical evidence about distance between social network members. Transportation Research Record: Journal of the Transportation Research Board, 2076, 114122.Google Scholar
Carvalho, V. M., Nirei, M., & Saito, Y. U. (2014). Supply chain disruptions: Evidence from the great east Japan earthquake. RIETI Discussion Paper Series, (14-E-035), 1–14.Google Scholar
Center For Spatial Information Science. University of Tokyo http://newspat.csis.u-tokyo.ac.jp/geocode/.Google Scholar
Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94 (ArticleType: research-article / Issue Title: Supplement: Organizations and Institutions: Sociological and Economic Approaches to the Analysis of Social Structure / Full publication date: 1988 / Copyright © 1988 The University of Chicago Press), S95–S120.Google Scholar
Cooper, M. C., Lambert, D. M., & Pagh, J. D. (1997). Supply chain management: More than a new name for logistics. International Journal of Logistics Management, 8 (1), 114.Google Scholar
Costinot, A., Vogel, J., & Wang, S. (2013). An elementary theory of global supply chains. The Review of Economic Studies, 80 (1), 109144.Google Scholar
Daraganova, G., Pattison, P., Koskinen, J., Mitchell, B., Bill, A., Watts, M., & Baum, S. (2012). Networks and geography: Modelling community network structures as the outcome of both spatial and network processes. Social Networks, 34 (1), 617.Google Scholar
Dixit, A. K., & Grossman, G. M. (1982). Trade and protection with multistage production. The Review of Economic Studies, 49 (4), 583594.CrossRefGoogle Scholar
Duranton, G., & Overman, H. G. (2005). Testing for localization using micro-geographic data. The Review of Economic Studies, 72 (4), 10771106.CrossRefGoogle Scholar
Duranton, G., & Puga, D. (2004). Chapter 48 micro-foundations of urban agglomeration economies. In Henderson, J. V., & Jacques-François, T. (Eds.), Handbook of regional and urban economics. vol. 4, (pp. 20632117). New York, NY: Elsevier.Google Scholar
Ellram, L. M., & Cooper, M. C. (1990). Supply chain management, partnership, and the shipper—third party relationship. International Journal of Logistics Management, 1 (2), 110.Google Scholar
Friedman, T. L. (2005). The world is flat: A brief history of the twenty-first century. New York: Farrar, Straus and Giroux.Google Scholar
Fujita, M., & Thisse, J.-F. (2013). Economics of agglomeration: Cities, industrial location, and globalization. Cambridge: Cambridge University Press.Google Scholar
Gabaix, X. (2011). The granular origins of aggregate fluctuations. Econometrica, 79 (3), 733772.Google Scholar
Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453 (7196), 779782.CrossRefGoogle ScholarPubMed
Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91 (3), 481510.Google Scholar
Granovetter, M. (2005). Business groups and social organization. In Smelser, N. J. & Swedberg, R. (Eds.), The handbook of economic sociology. Princeton: Princeton University Press.Google Scholar
Greenbaum, S. D., & Greenbaum, P. E. (1985). The ecology of social networks in four urban neighborhoods. Social Networks, 7 (1), 4776.Google Scholar
Harel, D., & Koren, Y. (2002). A fast multi-scale method for drawing large graphs. Journal of Graph Algorithms and Applications, 6 (3), 179202.Google Scholar
Hipp, J. R., & Perrin, A. J. (2009). The simoultaneous effects of social distance and physical distance on the formation of neighborhood ties. City & Community, 8 (1), 525.Google Scholar
Hoover, E. M. Jr., (1937). Spatial price discrimination. The Review of Economic Studies, 4 (3), 182191.Google Scholar
Kim, K. A., & Nofsinger, J. R. (2005). Institutional herding, business groups, and economic regimes: Evidence from Japan. The Journal of Business, 78 (1), 213242.Google Scholar
Koskinen, J., Caimo, A., & Lomi, A. (2015). Simultaneous modeling of initial conditions and time heterogeneity in dynamic networks: An application to foreign direct investments. Network Science, 3 (1), 5877.Google Scholar
Koskinen, J., & Lomi, A. (2013). The local structure of globalisation—the network dynamics of foreign direct investments in the international electricity industry. Journal of Statistical Physics, 151 (3–4), 523548.CrossRefGoogle Scholar
Lincoln, J., & Gerlach, M. (2004). Japan's network economy: Structure, persistence, and change. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Lospinoso, J. A., & Snijders, T. A. B. (2011). Goodness of fit for social network dynamics. Sunbelt XXXI. St. Pete's beach, Florida.Google Scholar
Marshall, A. (1920). Principles of economics. London: Macmillan and Co, Ltd. Library of Economics and Liberty [Online]. Retrieved from http://www.econlib.org/library/Marshall/marP.html Google Scholar
Matous, P., & Todo, Y. (2015). Dissolve the keiretsu or die: A longitudinal study of disintermediation in the Japanese automobile industry. RIETI Discussion Paper Series, (15-E-039), 1–24.Google Scholar
Matous, P., Todo, Y., & Mojo, D. (2013). Boots are made for walking: Interactions across physical and social space in infrastructure-poor regions. Journal of Transport Geography, 31 (0), 226235.Google Scholar
Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business Logistics, 22 (2), 125.Google Scholar
Motohashi, K. (2008). Growing R&D collaboration of Japanese firms and policy implications for reforming the national innovation system. Asia Pacific Business Review, 14 (3), 339361.Google Scholar
Nakajima, K., Saito, Y., & Uesugi, I. (2012). Localization of interfirm transaction relationships and industry agglomeration. RIETI Discussion Paper Series. Tokyo: The Research Institute of Economy, Trade and Industry.Google Scholar
Nakajima, K., & Todo, Y. (2013). Determinants of transaction partners' quality: Evidence from the great east Japan earthquake. RIETI Discussion Paper Series, (13-J-024), 1–15.Google Scholar
Onnela, J.-P., Arbesman, S., González, M. C., Barabási, A.-L., & Christakis, N. A. (2011). Geographic constraints on social network groups. PLoS ONE, 6 (4), e16939.Google Scholar
Preciado, P., Snijders, T. A. B., Burk, W. J., Stattin, H., & Kerr, M. (2012). Does proximity matter? Distance dependence of adolescent friendships. Social Networks, 34 (1), 1831.Google ScholarPubMed
Puga, D. (2010). The magnitude and causes of agglomeration economies. Journal of Regional Science, 50 (1), 203219.Google Scholar
Ripley, R., Snijders, T. A. B., & Preciado, P. (2012). Manual for SIENA version 4. Oxford: University of Oxford.Google Scholar
Robins, G. (2013). Network governance of environmental systems: Structure, culture and effectiveness, day-to-day and in disasters. Paper presented at Xi'an INSNA conference. Xi'an.Google Scholar
Rosenthal, S. S., & Strange, W. C. (2001). The determinants of agglomeration. Journal of Urban Economics, 50 (2), 191229.Google Scholar
Sanyal, K. K. (1983). Vertical specialization in a ricardian model with a continuum of stages of production. Economica, 50 (197), 7178.Google Scholar
Sato, H., Hirata, N., Koketsu, K., Okaya, D., Abe, S., Kobayashi, R., . . . Harder, S. (2005). Earthquake source fault beneath Tokyo. Science, 309 (5733), 462464.Google Scholar
Sato, Y. (2012). The impact of the great east Japan earthquake on companies in the non-affected areas: Structure of the inter-company network of supply chains and its implication. RIETI Discussion Paper Series, 12-J-020.Google Scholar
Schaefer, D. R. (2012). Youth co-offending networks: An investigation of social and spatial effects. Social Networks, 34 (1), 141149.Google Scholar
Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 31 (1), 361395.Google Scholar
Snijders, T. A. B., Lomi, A., & Torló, V. J. (2013). A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice. Social Networks, 35 (2), 265276.CrossRefGoogle Scholar
Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32 (1), 4460.CrossRefGoogle Scholar
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010). Limits of predictability in human mobility. Science, 327 (5968), 10181021.Google Scholar
Steglich, C., Snijders, T. A. B., & Pearson, M. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40 (1), 329393.Google Scholar
Tallontire, A. (2000). Partnerships in fair trade: Reflections from a case study of Cafe direct. Development in Practice, 10 (2), 166177.Google Scholar
Todo, Y., Nakajima, K., & Matous, P. (2014). How do supply chain networks affect the resilience of firms to natural disasters? Evidence from the great east Japan earthquake. Journal of Regional Science, 55 (2), 209229.Google Scholar
Tokui, J., Arai, N., Kawasaki, K., Miyagawa, T., Fukao, K., Arai, S., . . . Noguchi, N. (2012). Higashi-nihon dai-shinsai no keizaiteki eikyo: kako no saigai tono hikaku, sapurai chen no sundan koka, denryoku seiyaku no eikyo (in Japanese). RIETI Policy Discussion Paper, No. 12-P-004.Google Scholar
Tokyo Shoko Research (2010). Kigyou Soukan Jouhou Tokyo Shoko Research. Tokyo Shoko Research, Tokyo.Google Scholar
van der Berg, P., Arentze, T., & Timmermans, H. (2010). A multilevel path analysis of contact frequency between social network members. Geographical Systems, 14 (2), 117.Google Scholar
Wellman, B., & Tindall, D. B. (1992). How telephone networks connect social networks. Progress in Communication Research, 13, 6394.Google Scholar
Wilkerson, G. K., Ramin, & Schmid, S. (2013). Urban mobility scaling: lessons from ‘Little Data’. ARXIV arXiv:1401.0207, 6.Google Scholar
Wilson, A. G. (1967). A statistical theory of spatial distribution models. Transportation Research, 1 (3), 253269.Google Scholar
Woo-Sung, J., Fengzhong, W., & Stanley, H. E. (2008). Gravity model in the Korean highway. EPL (Europhysics Letters), 81 (4), 48005.Google Scholar
Supplementary material: PDF

Matous and Todo supplementary material

Appendix

Download Matous and Todo supplementary material(PDF)
PDF 648.6 KB