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The similarity of global value chains: A network-based measure

Published online by Cambridge University Press:  08 June 2018

ZHEN ZHU ,
GREG MORRISON ,
MICHELANGELO PULIGA ,
ALESSANDRO CHESSA  and
MASSIMO RICCABONI
ZHEN ZHU
Affiliation:
IMT School for Advanced Studies Lucca, 55100 Lucca, Italy Department of International Business and Economics, University of Greenwich, SE10 9LS London, UK (e-mail: z.zhu@gre.ac.uk)
GREG MORRISON
Affiliation:
Department of Physics, University of Houston, Houston TX 77204, USA (e-mail: gcmorris@central.uh.edu)
MICHELANGELO PULIGA
Affiliation:
IMT School for Advanced Studies Lucca, Italy and Linkalab, Cagliari, Italy (e-mail: michelangelo.puliga@imtlucca.it, alessandro.chessa@imtlucca.it)
ALESSANDRO CHESSA
Affiliation:
IMT School for Advanced Studies Lucca, Italy and Linkalab, Cagliari, Italy (e-mail: michelangelo.puliga@imtlucca.it, alessandro.chessa@imtlucca.it)
MASSIMO RICCABONI
Affiliation:
IMT School for Advanced Studies Lucca, 55100 Lucca, Italy Department of Managerial Economics, Strategy and Innovation, KU Leuven, 3000 Leuven, Belgium (e-mail: massimo.riccaboni@imtlucca.it)
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Abstract

International trade has been increasingly organized in the form of global value chains (GVCs). In this paper, we provide a new method for comparing GVCs across countries and over time. First, we use the World Input–Output Database (WIOD) to construct both the upstream and the downstream global value networks. Second, we introduce a network-based measure of node similarity to compare the GVCs between any pair of countries for each sector and each year available in the WIOD. Our network-based similarity is a better measure for node comparison than the existing ones because it takes into account all the direct and indirect relationships between the country–sector pairs, is applicable to both directed and weighted networks with self-loops, and takes into account externally defined node attributes. As a result, our measure of similarity reveals the most intensive interactions among the GVCs across countries and over time. From 1995 to 2011, the average similarity between sectors and countries have clear increasing trends, which are temporarily interrupted by the recent economic crisis. This measure of the similarity of GVCs provides quantitative answers to important questions about dependency, sustainability, risk, and competition in the global production system.

Keywords

networksnode similarityinput–output analysisglobal value chainsvertical specializationinternational trade
Type
Research Article
Information
Network Science , Volume 6 , Issue 4 , December 2018 , pp. 607 - 632
Copyright
Copyright © Cambridge University Press 2018 

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References

Acemoglu, D., Carvalho, V. M., Ozdaglar, A. & Tahbaz-Salehi, A. (2012). The network origins of aggregate fluctuations. Econometrica, 80 (5), 1977–2016.Google Scholar
Albert, R. & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74 (1), 47–97.Google Scholar
Amador, J., & Cabral, S. (2014). Global value chains: A survey of drivers and measures. Journal of Economic Surveys, 30 (2), 278–301.Google Scholar
Baldwin, R. & Lopez-Gonzalez, J. (2014). Supply-chain trade: A portrait of global patterns and several testable hypotheses. The World Economy, 38 (11), 1682–1721.Google Scholar
Baldwin, R., & Venables, A. J. (2013). Spiders and snakes: Offshoring and agglomeration in the global economy. Journal of International Economics, 90 (2), 245–254.Google Scholar
Blöchl, F., Theis, F. J., Vega-Redondo, F., & Fisher, E. O. (2011). Vertex centralities in input-output networks reveal the structure of modern economies. Physical Review E, 83 (4), 046127.Google Scholar
Blondel, V. D., Gajardo, A., Heymans, M., Senellart, P., & Van Dooren, P. (2004). A measure of similarity between graph vertices: Applications to synonym extraction and web searching. SIAM Review, 46 (4), 647–666.Google Scholar
Caldarelli, G., Chessa, A., Pammolli, F., Gabrielli, A., & Puliga, M. (2013). Reconstructing a credit network. Nature Physics, 9 (3), 125–126.Google Scholar
Cerina, F., Zhu, Z., Chessa, A., & Riccaboni, M. (2015). World input-output network. Plos One, 10 (7), e0134025.Google Scholar
Costinot, A., Vogel, J., & Wang, S. (2013). An elementary theory of global supply chains. The Review of Economic Studies, 80 (1), 109–144.Google Scholar
De Backer, K., & Miroudot, S. (2013). Mapping global value chains. Paris, France: OECD Publishing.Google Scholar
Dean, J. M., Fung, K.-C., & Wang, Z. (2011). Measuring vertical specialization: The case of china. Review of International Economics, 19 (4), 609–625.Google Scholar
Elliott, M., Golub, B., & Jackson, M. O. (2014). Financial networks and contagion. The American Economic Review, 104 (10), 3115–3153.Google Scholar
Everett, M. G., & Borgatti, S. (1988). Calculating role similarities: An algorithm that helps determine the orbits of a graph. Social Networks, 10 (1), 77–91.Google Scholar
Fagiolo, G., Reyes, J., & Schiavo, S. (2009). World-trade web: Topological properties, dynamics, and evolution. Physical Review E, 79 (3), 036115.Google Scholar
Feenstra, R. C., & Hanson, G. H. (1999). The impact of outsourcing and high-technology capital on wages: Estimates for the united states, 1979–1990. Quarterly Journal of Economics, 114 (3), 907–940.Google Scholar
Ferrantino, M. J. & Taglioni, D. (2014). Global value chains in the current trade slowdown. Economic premise, 137. Washington, DC: World Bank Group.Google Scholar
Ferrarini, B. (2011). Mapping vertical trade. Asian Development Bank Economics Working Paper Series, 263. http://hdl.handle.net/11540/2032.Google Scholar
Finger, M. J., & Kreinin, M. E. (1979). A measure of ‘export similarity’ and its possible uses. The Economic Journal, 89 (356), 905–912.Google Scholar
Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99 (12), 7821–7826.Google Scholar
Grossman, G. M. & Rossi-Hansberg, E. (2008). Trading tasks: A simple theory of offshoring. American Economic Review, 98 (5), 1978–1997.Google Scholar
Guimerá, R., & Amaral, L. A. N. (2005). Cartography of complex networks: Modules and universal roles. Journal of Statistical Mechanics, 2005 (02), P02001.Google Scholar
Hummels, D., Ishii, J., & Yi, K.-M. (2001). The nature and growth of vertical specialization in world trade. Journal of International Economics, 54 (1), 75–96.Google Scholar
Ioffe, S. (2010). Improved consistent sampling, weighted minhash and L1 sketching. In Proceedings of the IEEE 10th International Conference on Data Mining, p. 246.Google Scholar
Jeh, G., & Widom, J. (2002). Simrank: A measure of structural-context similarity. In Proceedings of the 8th ACM SIGKDD Knowledge Discovery and Data Mining, pp. 538–543.Google Scholar
Johnson, R. C. (2014). Five facts about value-added exports and implications for macroeconomics and trade research. The Journal of Economic Perspectives, 28 (2), 119–142.Google Scholar
Johnson, R. C., & Noguera, G. (2012). Accounting for intermediates: Production sharing and trade in value added. Journal of International Economics, 86 (2), 224–236.Google Scholar
Kitsak, M., Riccaboni, M., Havlin, S., Pammolli, F., & Stanley, H. E. (2010). Scale-free models for the structure of business firm networks. Physical Review E, 81 (3), 036117.Google Scholar
Koopman, R., Wang, Z., & Wei, S.-J. (2014). Tracing value-added and double counting in gross exports. The American Economic Review, 104 (2), 459–494.Google Scholar
Kose, M. A., & Yi, K.-M. (2001). International trade and business cycles: Is vertical specialization the missing link? The American Economic Review, 91 (2), 371–375.Google Scholar
Leicht, E. A., Holme, P., & Newman, M. E. J. (2006). Vertex similarity in networks. Physical Review E, 73 (2), 026120.Google Scholar
Leontief, W. W. (1936). Quantitative input and output relations in the economic systems of the united states. The Review of Economic Statistics, 18 (3), 105–125.Google Scholar
Lerner, J. (2005). Role assignments. In Brandes, U. & Erlebach, T. (Eds.), Network analysis. Lecture Notes in Computer Science (vol. 3418, pp. 216–252). Berlin, Heidelberg: Springer.Google Scholar
Lloyd, P. J. (2004). Measures of similarity and matching in international trade. In Kreinin, M. E. & Plummer, M. G. (Eds.), Empirical methods in international trade (pp. 21–34). Cheltenham, UK: Edward Elgar.Google Scholar
Los, B., Timmer, M. P., & Vries, G. J. (2014). How global are global value chains? A new approach to measure international fragmentation. Journal of Regional Science, 55 (1), 66–92.Google Scholar
Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A survey. Physica A, 390 (6), 1150–1170.Google Scholar
Lü, L., Jin, C. H., & Zhou, T. (2009). Similarity index based on local paths for link prediction of complex networks. Physical Review E, 80 (4), 046122.Google Scholar
Martens, P., Caselli, M., De Lombaerde, P., Figge, L., & Scholte, J. A. (2015). New directions in globalization indices. Globalizations, 12 (2), 217–228.Google Scholar
Miller, R. E., & Blair, P. D. (2009). Input-output analysis: Foundations and extensions. Cambridge, UK: Cambridge University Press.Google Scholar
Morrison, G., & Mahadevan, L. (2012). Discovering communities through friendship. PLoS One, 7 (7), e38704.Google Scholar
Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45 (2), 167–256.Google Scholar
Pammolli, F., & Riccaboni, M. (2002). Technological regimes and the growth of networks: An empirical analysis. Small Business Economics, 19 (3), 205–215.Google Scholar
Piccardi, C., Riccaboni, M., Tajoli, L., & Zhu, Z. (2018). Random walks on the world input-output network. Journal of Complex Networks, 6 (2), 187–205.Google Scholar
Riccaboni, M., & Schiavo, S. (2010). Structure and growth of weighted networks. New Journal of Physics, 12 (2), 023003.Google Scholar
Riccaboni, M., Rossi, A., & Schiavo, S. (2013). Global networks of trade and bits. Journal of Economic Interaction and Coordination, 8 (1), 33–56.Google Scholar
Richeardt, J., & White, D. R. (2007). Role models for complex networks. European Physical Journal B, 60 (2), 217–224.Google Scholar
Schweitzer, F., Fagiolo, G., Sornette, D., Vega-Redondo, F., Vespignani, A., & White, D. R. (2009). Economic networks: The new challenges. Science, 325 (5939), 422–425.Google Scholar
Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R., & de Vries, G. J. (2015). An illustrated user guide to the world input-output database: The case of global automotive production. Review of International Economics, 23 (3), 575–605.Google Scholar
Timmer, M. P., Erumban, A. A., Los, B., Stehrer, R. & de Vries, G. J. (2014). Slicing up global value chains. The Journal of Economic Perspectives, 28 (2), 99–118.Google Scholar
Tukker, A., & Dietzenbacher, E. (2013). Global multiregional input–output frameworks: An introduction and outlook. Economic Systems Research, 25 (1), 1–19.Google Scholar
Xing, Y. (2014). China's high-tech exports: The myth and reality. Asian Economic Papers, 13 (1), 109–123.Google Scholar
Zhou, H. (2003). Distance, dissimilarity index, and network community structure. Physical Review E, 67 (6), 061901.Google Scholar
Zhou, T., Lü, L., & Zhang, Y. C. (2009). Predicting missing links via local information. European Physical Journal B, 71 (4), 623–630.Google Scholar
Zhu, Z., Cerina, F., Chessa, A., Caldarelli, G., & Riccaboni, M. (2014). The rise of china in the international trade network: A community core detection approach. Plos one, 9 (8), e105496.Google Scholar
Zhu, Z., Puliga, M., Cerina, F., Chessa, A., & Riccaboni, M. (2015). Global value trees. Plos One, 10 (5), e0126699.Google Scholar