Topology of the world trade web

Abstract

Economy, and consequently trade, is a fundamental part of human social organization which, until now, has not been studied within the network modeling framework. Here we present the first, to the best of our knowledge, empirical characterization of the world trade web, that is, the network built upon the trade relationships between different countries in the world. This network displays the typical properties of complex networks, namely, scale-free degree distribution, the small-world property, a high clustering coefficient, and, in addition, degree-degree correlation between different vertices. All these properties make the world trade web a complex network, which is far from being well described through a classical random network description. DOI: 10.1103/PhysRevE.68.015101 PACS number͑s͒: 89.75.Hc, 87.23.Ge, 05.70.Ln, 89.65.Gh The world is facing a challenging era. Social, political, and economic arrangements initiated after the end of the Second World War are now culminating in the recognition of globalization, a process which has been accelerated by the new technological advances. When applied to the international economic order, globalization involves control of capital flow and liberalization of trade. As a consequence, economies around the world are becoming more and more interrelated, in other words, the world is becoming a global village ͓1,2͔. In this scenario, trade plays a central role as one of the most important interaction channels between countries ͓3͔. The relevance of the international trade system goes beyond the fundamental exchange of goods and services. For instance, it can also be the channel for crises spreading ͓4͔. A good example is found in the recent Asiatic crisis, which shows how economic perturbations originating in a country can somehow propagate elsewhere in the world ͓5,6͔. Thus, it seems natural to analyze the world trade system at a global level, every country being important, regardless of its size or wealth. Despite the extremely complex nature of the problem, relevant structural information can be extracted from modeling the system as a network, where countries are represented as vertices and trade channels as links between these vertices. In this way, the global trade system can be examined under a topological point of view. This analysis will reveal complex properties that cannot be explained by the classical random graph theory, first studied by Erdös and Rényi ͓7͔.Complex networks have been the subject of an intense research activity over the last years ͓8,9͔. Examples range from metabolic networks, where cell functionality is sustained by the network structure, to technological webs, where topology determines the system's ability to transmit information ͓10-13͔. The term complex network typically refers to networks showing the following properties: ͑i͒ scale-free ͑SF͒ degree distribution, P(k)ϳk Ϫ␥ with 2Ͻ␥ р3, where the degree k is defined as the number of edges emanating from a vertex, ͑ii͒ the small-world property ͓14͔, which states that the average path length...

International trade and financial integration: a weighted network analysis

“…With respect to the distribution of the degree k (x) Garlaschelli & Loffredo (2004) claim that it is not a power law and therefore the WTW is not a scale-free network. On the other hand, they agree with Serrano & Boguñá (2003) in finding both disassortativity and that the clustering coefficient is decreasing in k, suggesting the presence of a hierarchical structure. Serrano et al (2006) build a weighted directed network of trade imbalances with links directed from net importers to net exporters.…”

“…A number of recent papers has studied the topological properties of the International Trade Network (ITN), but has only relied on binary (unweighted) analysis (Serrano & Boguñá 2003, Garlaschelli & Loffredo 2004. This means that a link between any two countries in the network is either in place or not depending on whether the trade flow is greater than some given threshold.…”

“…Within these works a few have used the world trade network or web (WTW) as a relevant example. Serrano & Boguñá (2003) study import and export flows for 179 counties in 2000 using a binary directed network (BDN) approach. Empirical analysis confirms the existence of a scale-free degree distribution implying high heterogeneity among actors.…”

Prediction in complex systems: The case of the international trade network

“…[6] the proximity φ αβ of products α and β was introduced as the symmetrized probability that a country exports product α, given that it exports product β. (2) where C i is the set of products exported by country i, P(α ∈ C i |β ∈ C i ) is the probability of exporting product α given that β is being exported, and φ αβ is the proximity of α and β. The proximity Φ iα of product α to country i is defined as the average proximity of products in i's export basket to product α…”

“…The complex network approach was used in Ref. [2] and showed that the international trade exhibits common features with the World Wide Web network. It has been shown that many features of the international trade can be explained using models based on the gravity equation [3][4][5].…”