World Input-Output Network

Abstract

Production systems, traditionally analyzed as almost independent national systems, are increasingly connected on a global scale. Only recently becoming available, the World Input-Output Database (WIOD) is one of the first efforts to construct the global multi-regional input-output (GMRIO) tables. By viewing the world input-output system as an interdependent network where the nodes are the individual industries in different economies and the edges are the monetary goods flows between industries, we analyze respectively the global, regional, and local network properties of the so-called world input-output network (WION) and document its evolution over time. At global level, we find that the industries are highly but asymmetrically connected, which implies that micro shocks can lead to macro fluctuations. At regional level, we find that the world production is still operated nationally or at most regionally as the communities detected are either individual economies or geographically well defined regions. Finally, at local level, for each industry we compare the network-based measures with the traditional methods of backward linkages. We find that the network-based measures such as PageRank centrality and community coreness measure can give valuable insights into identifying the key industries.

Network analysis of inter-sectoral relationships and key sectors in the Greek economy

“…The node degrees of the Greek economy network follow an asymmetric, left-skewed distribution (Fig. 2a), which is consistent with findings corresponding to other economies (Acemoglu et al 2012;Cerina et al 2015). This type of network can be regarded as highly connected and different from most of the other real (social, technological, information) networks, which are characterized by scale-free characteristics with negative exponential and power-law (very long right-tailed) degree distributions (Newman 2003;Boccaletti et al 2006;Newman et al 2006;Caldarelli 2007;Gabaix 2009;Sinha et al 2010;Katerelos et al 2013).…”

Complex Network Analysis for Characterizing Global Value Chains in Equipment Manufacturing

“…Over all, similar to Cerina et al (2015) [8], the GVC networks in the European community was led by Germany. To something different, other countries in Asia-Pacific region made up the Asia-Pacific community and Japan, the United States and China became the core of the Asia Pacific community successively through the sample interval.…”

“…The complex network approach has gained increased attention from a growing number of researchers interested in examining the structural and dynamical properties involving networks in a wide variety of disciplines, such as nonlinear analysis [1–5], social network analysis in sociology [6], biology [7], economics [8], and many more. Moreover, a key question in network science concerns the topological measures utilized to define the properties of the network connecting the agents, and in what way these properties influence the behaviour of the agents as well as the evolution of the system analysed [9].…”

A Study on Strategic Industry Selection by Analysis of Inter-industry Network: The Case of Korea

“…The IO tables have been mainly used to analyze the economic structure, but recently it has been used variously in the area of predictions as well as analysis of the ripple effect and the establishment of government's economic policies [2][4] [10]. Especially, as the network analysis skill has improved, there has been attempts to study the effect of the inter-industry network structure on the overall fluctuation of the economy or the dynamics of the economic shocks spreading to the network [2][4] [5][11] [12]. One of the important goals of these studies is to establish a specific strategy by understanding the diffusion dynamics of the economic shock, or to find industries that play an important role in the network [2][4] [8][11] [13].…”

“…That is, economic shocks to central industries are more strongly propagating than shocks applied to other industries. F. Cerina et al [12] reported that network-based measures such as community coreness and PageRank centrality are more helpful in identifying key industries than traditional inter-industry analysis methods such as backward linkages. F. Blochl et al pointed out the limitations of traditional centralities and suggested the random walking centrality and the counting betweenness [11].…”