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World Input-Output Network.

Cerina F, Zhu Z, Chessa A, Riccaboni M - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Linkalab, Complex Systems Computational Laboratory, Cagliari, Italy; Department of Physics, Università degli Studi di Cagliari, Cagliari, Italy.

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.

No MeSH data available.


Related in: MedlinePlus

Clustering coefficient of the WION over time.Panel (A) shows the average weighted clustering coefficient of the WION over time. Panel (B) further decomposes the clustering coefficient into domestic clustering coefficient and foreign clustering coefficient. Clearly the behavior in Panel (A) is more explained by the foreign clustering coefficient.
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pone.0134025.g005: Clustering coefficient of the WION over time.Panel (A) shows the average weighted clustering coefficient of the WION over time. Panel (B) further decomposes the clustering coefficient into domestic clustering coefficient and foreign clustering coefficient. Clearly the behavior in Panel (A) is more explained by the foreign clustering coefficient.

Mentions: The hump-shaped behavior is also observed in the clustering coefficient. Panel (A) of Fig 5 shows that the average weighted clustering coefficient of the WION has been steadily increasing but was followed by a decline since 2007. Again, a possible explanation is that the booming economy before 2007 introduced more interactions between industries, hence higher clustering coefficient, and the financial crisis after 2007 stifled the excessive relationships. Panel (B) further decomposes the clustering coefficient into domestic clustering coefficient and foreign clustering coefficient. Clearly the behavior in Panel (A) is more explained by the foreign clustering coefficient, which implies that the increasingly integrated production chains tend to make the WION more clustered.


World Input-Output Network.

Cerina F, Zhu Z, Chessa A, Riccaboni M - PLoS ONE (2015)

Clustering coefficient of the WION over time.Panel (A) shows the average weighted clustering coefficient of the WION over time. Panel (B) further decomposes the clustering coefficient into domestic clustering coefficient and foreign clustering coefficient. Clearly the behavior in Panel (A) is more explained by the foreign clustering coefficient.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4519177&req=5

pone.0134025.g005: Clustering coefficient of the WION over time.Panel (A) shows the average weighted clustering coefficient of the WION over time. Panel (B) further decomposes the clustering coefficient into domestic clustering coefficient and foreign clustering coefficient. Clearly the behavior in Panel (A) is more explained by the foreign clustering coefficient.
Mentions: The hump-shaped behavior is also observed in the clustering coefficient. Panel (A) of Fig 5 shows that the average weighted clustering coefficient of the WION has been steadily increasing but was followed by a decline since 2007. Again, a possible explanation is that the booming economy before 2007 introduced more interactions between industries, hence higher clustering coefficient, and the financial crisis after 2007 stifled the excessive relationships. Panel (B) further decomposes the clustering coefficient into domestic clustering coefficient and foreign clustering coefficient. Clearly the behavior in Panel (A) is more explained by the foreign clustering coefficient, which implies that the increasingly integrated production chains tend to make the WION more clustered.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Linkalab, Complex Systems Computational Laboratory, Cagliari, Italy; Department of Physics, Università degli Studi di Cagliari, Cagliari, Italy.

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.

No MeSH data available.


Related in: MedlinePlus