Narrow Results

World Trade Web is the backbone of the global economy system. Identifying influential countries and regions in such a network and revealing their importance evolution over time are helpful for understanding global economic development. Here, we collect the worldwide trade data in commodities of 232 countries and regions from 1996 to 2015 from the UN Comtrade Database, based on which a series of weighted world trade networks are constructed. Since the networks are almost fully connected, most of the existing methods may fail in identifying the important nodes. To tackle this issue, we apply the generalized Degree, $H$-index and Coreness (DHC) theorem to the constructed networks and use weighted degree and coreness to quantify nodes’ importance, since they can make full use of the weight information to accurately evaluate nodes’ significance. Then, we analyze the rankings of countries and regions measured by various indicators, whose differences and advantages are also compared. We further present the evolution of countries’ significance over time, two typical groups of countries. The results show that the influence of a country or region has a strong correlation with its economic scale, but a relatively weak correlation with the diversity of its trade structure. Finally, based on the findings, we put forward corresponding strategies to enhance the trade influence for different types of countries.

Over the past four decades, the high-performing Asian economies (HPAE) have followed a development strategy based on the exposure of their local markets to the presence of foreign competition and on outward-oriented production. In contrast, Latin American (LATAM) economies began taking steps in this direction only in the late 1980s and early 1990s, but before this period they were more focused on the implementation of import substitution policies. These divergent paths have led to sharply different growth performances in the two regions. Yet, standard trade openness indicators fall short of portraying the peculiarity of the Asian experience, and of explaining why other emerging markets with similar characteristics have been less successful over the last 25 years. We offer an alternative perspective on this issue by exploiting recently developed indicators based on weighted network analysis. We study the evolution of the core–periphery structure of the World Trade Network (WTN) and, more specifically, the evolution of the HPAE and LATAM countries within this network. Using random walk betweenness centrality, we show that the HPAE countries are more integrated into the WTN and many of them, which were on the periphery in the 1980s, are now in the core of the network. In contrast, the LATAM economies have at best maintained their position over the 1980–2005 period, and in some cases have fallen in the ranking of centrality.

In recent years, World Trade Web (WTW), also known as international trade network (ITN) was expressed by different models, including binary undirected network (BUN), weighted undirected network (WUN), binary directed network (BDN) and weighted directed network (WDN), etc. Making use of the models, scholars have analyzed topological properties (e.g., node's degree and strength), clustering (e.g., clustering coefficient) and dynamics, etc. In this paper, we express the strength of trade relations of China and the United States (or other bilateral or multilateral trade) by number or weight of subgraphs or subdigraphs in WTW instead of node's degree and strength, we also propose patterns of subgraphs or subdigraphs including only 3 nodes C (China), A (the United States) and i (a 3rd country or district) in BUN, WUN, BDN and WDN. These indexes not only overcome the limitation of node's degree or strength in WTW, but also express bilateral or multilateral trade status. In WDN, we particularly propose 16 patterns of subdigraphs in place of the traditional patterns (e.g., export, import, import and export) of trade status of a country or district, which gives a better description of trade status of China and the United States.