The evolution of the world trade web: a weighted-network analysis

This paper empirically studies the statistical properties of the world trade web (WTW) and its evolution over time using a weighted network approach. Previous works have characterized the WTW as a binary network, where countries play the role of nodes and a link is in place between any two countries if there exists a sufficiently large amount of trade between them. On the contrary, we exploit the heterogeneity of trade relationships and weight each existing link by some measure of the actual amount of trade carried through that link. Our results indicate that the WTW is a strongly symmetric network, where the majority of trade relationships (and their intensities) are reciprocated. We also find that: (i) the majority of countries hold many weak trade relationships and coexist with a few countries holding less but more intense export relationships; (ii) countries that hold more intense trade relationships preferably trade with poorly-connected countries, but are typically more clustered; (iii) rich countries tend to form more intense trade links and to be more clustered. Furthermore, all structural properties of the WTW display a remarkable stationarity across years. From a methodological point of view, our paper suggests that a weighted network approach is able to provide more precise conclusions than a binary analysis. Many implications that are indeed valid in binary networks are reversed in our weighted analysis. Finally, we show that all our main results are robust to alternative weighting procedures.

[1]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[2]  Giorgio Fagiolo Directed or Undirected? A New Index to Check for Directionality of Relations in Socio-Economic Networks , 2006 .

[3]  T. Schøtt Models of dyadic and individual components of a social relation: Applications to international trade , 1986 .

[4]  Mark E. J. Newman A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[5]  Sangmoon Kim,et al.  A Longitudinal Analysis of Globalization and Regionalization in International Trade: A Social Network Approach , 2002 .

[6]  Duncan J. Watts,et al.  Six Degrees: The Science of a Connected Age , 2003 .

[7]  K. Kaski,et al.  Intensity and coherence of motifs in weighted complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  John Scott Social Network Analysis , 1988 .

[9]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Ronald L. Breiger,et al.  Structures of Economic Interdependence among Nations , 1982 .

[11]  Matthew C. Mahutga The Persistence of Structural Inequality?: A Network Analysis of International Trade, 1965-2000 , 2006 .

[12]  D. Garlaschelli,et al.  Structure and evolution of the world trade network , 2005, physics/0502066.

[13]  Mikko Kivelä,et al.  Generalizations of the clustering coefficient to weighted complex networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Guanrong Chen,et al.  Complexity and synchronization of the World trade Web , 2003 .

[15]  John F. Padgett,et al.  Robust Action and the Rise of the Medici, 1400-1434 , 1993, American Journal of Sociology.

[16]  San Luis Obispo,et al.  The Linchpins of a Modern Economy , 2006 .

[17]  Carson C. Chow,et al.  Small Worlds , 2000 .

[18]  G. Fagiolo Clustering in complex directed networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Luciano Rossoni,et al.  Models and methods in social network analysis , 2006 .

[20]  Mark S. Granovetter Getting a Job: A Study of Contacts and Careers , 1974 .

[21]  E. Kick,et al.  Structural Position in the World System and Economic Growth, 1955-1970: A Multiple-Network Analysis of Transnational Interactions , 1979, American Journal of Sociology.

[22]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[23]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[24]  Bethany S. Dohleman Exploratory social network analysis with Pajek , 2006 .

[25]  Javier Reyes,et al.  The architecture of globalization: a network approach to international economic integration , 2006 .

[26]  Diego Garlaschelli,et al.  Fitness-dependent topological properties of the world trade web. , 2004, Physical review letters.

[27]  J. Steen,et al.  Measuring Globalisation: An Evolutionary Economic Approach to Tracking the Evolution of International Trade , 2006 .

[28]  Diego Garlaschelli,et al.  Patterns of link reciprocity in directed networks. , 2004, Physical review letters.

[29]  A RAPOPORT,et al.  A study of a large sociogram. , 2007 .

[30]  Albert-László Barabási,et al.  Evolution of Networks: From Biological Nets to the Internet and WWW , 2004 .

[31]  Alessandro Vespignani,et al.  Weighted evolving networks: coupling topology and weight dynamics. , 2004, Physical review letters.

[32]  K. Gleditsch,et al.  Expanded Trade and GDP Data , 2002 .

[33]  Marián Boguñá,et al.  Topology of the world trade web. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  Vladimir Batagelj,et al.  Exploratory Social Network Analysis with Pajek , 2005 .

[35]  Alessandro Vespignani,et al.  Evolution and structure of the Internet , 2004 .

[36]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .