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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

Regional clustering coefficient and intra-region foreign share of the foreign intermediate transactions over time.Panel (A) shows the average weighted clustering coefficient over time for three regions, EU27 (i.e., “Euro-Zone” and “Non-Euro EU” in S1 Table), NAFTA (see S1 Table), and East Asia (see S1 Table). EU27 and NAFTA have higher coefficients than East Asia and EU27 has an increasing trend over time, which coincides with the detection of the growing European community led by Germany. Panel (B) considers the intra-region foreign share out of the total foreign intermediate transactions for the same three regions. EU27 relies on the intra-region foreign trade the most and East Asia the least. Moreover, while the intra-region share in the other two regions is roughly stable, it has a clear decreasing trend in EU27.
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pone.0134025.g011: Regional clustering coefficient and intra-region foreign share of the foreign intermediate transactions over time.Panel (A) shows the average weighted clustering coefficient over time for three regions, EU27 (i.e., “Euro-Zone” and “Non-Euro EU” in S1 Table), NAFTA (see S1 Table), and East Asia (see S1 Table). EU27 and NAFTA have higher coefficients than East Asia and EU27 has an increasing trend over time, which coincides with the detection of the growing European community led by Germany. Panel (B) considers the intra-region foreign share out of the total foreign intermediate transactions for the same three regions. EU27 relies on the intra-region foreign trade the most and East Asia the least. Moreover, while the intra-region share in the other two regions is roughly stable, it has a clear decreasing trend in EU27.

Mentions: Some global-level indicators can also shed light on the regional dynamics of the WION. For instance, Panel (A) in Fig 11 shows the average weighted clustering coefficient over time for three regions, EU27 (i.e., “Euro-Zone” and “Non-Euro EU” in S1 Table), NAFTA (see S1 Table), and East Asia (see S1 Table). EU27 and NAFTA have higher coefficients than East Asia and EU27 has an increasing trend over time, which coincides with the detection of the growing European community led by Germany. On the other hand, Panel (B) in Fig 11 considers the intra-region foreign share out of the total foreign intermediate transactions for the same three regions. EU27 relies on the intra-region foreign trade the most and East Asia the least. Moreover, while the intra-region share in the other two regions is roughly stable, it has a clear decreasing trend in EU27, which at first glance seems to be at odds with the detection of the growing European community led by Germany. One explanation for the contradiction is that, although the intra-region share in an absolute sense has been decreasing in EU27, relative to other regions, EU27 has been restructured in such a way that it has developed much stronger internal relationships than the external ones. Like what Zhu, Cerina, Chessa, Caldarelli, and Riccaboni [30] find in the international trade network, the peripheral countries have strong incentive to connect with Germany since it has the most external connections outside Europe. As a result, centering on Germany, the European community structure has become more hierarchical and significant over time even if the overall intra-region share of foreign intermediate transactions has been declining. Therefore, some aspects of the regional dynamics can be captured by using the global-level indicators along with the predetermined regions. However, the community detection techniques are indispensable for understanding the regional dynamics since the predetermined regions are often unavailable in practice and the global-level indicators may be oversimplified and misleading.


World Input-Output Network.

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

Regional clustering coefficient and intra-region foreign share of the foreign intermediate transactions over time.Panel (A) shows the average weighted clustering coefficient over time for three regions, EU27 (i.e., “Euro-Zone” and “Non-Euro EU” in S1 Table), NAFTA (see S1 Table), and East Asia (see S1 Table). EU27 and NAFTA have higher coefficients than East Asia and EU27 has an increasing trend over time, which coincides with the detection of the growing European community led by Germany. Panel (B) considers the intra-region foreign share out of the total foreign intermediate transactions for the same three regions. EU27 relies on the intra-region foreign trade the most and East Asia the least. Moreover, while the intra-region share in the other two regions is roughly stable, it has a clear decreasing trend in EU27.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0134025.g011: Regional clustering coefficient and intra-region foreign share of the foreign intermediate transactions over time.Panel (A) shows the average weighted clustering coefficient over time for three regions, EU27 (i.e., “Euro-Zone” and “Non-Euro EU” in S1 Table), NAFTA (see S1 Table), and East Asia (see S1 Table). EU27 and NAFTA have higher coefficients than East Asia and EU27 has an increasing trend over time, which coincides with the detection of the growing European community led by Germany. Panel (B) considers the intra-region foreign share out of the total foreign intermediate transactions for the same three regions. EU27 relies on the intra-region foreign trade the most and East Asia the least. Moreover, while the intra-region share in the other two regions is roughly stable, it has a clear decreasing trend in EU27.
Mentions: Some global-level indicators can also shed light on the regional dynamics of the WION. For instance, Panel (A) in Fig 11 shows the average weighted clustering coefficient over time for three regions, EU27 (i.e., “Euro-Zone” and “Non-Euro EU” in S1 Table), NAFTA (see S1 Table), and East Asia (see S1 Table). EU27 and NAFTA have higher coefficients than East Asia and EU27 has an increasing trend over time, which coincides with the detection of the growing European community led by Germany. On the other hand, Panel (B) in Fig 11 considers the intra-region foreign share out of the total foreign intermediate transactions for the same three regions. EU27 relies on the intra-region foreign trade the most and East Asia the least. Moreover, while the intra-region share in the other two regions is roughly stable, it has a clear decreasing trend in EU27, which at first glance seems to be at odds with the detection of the growing European community led by Germany. One explanation for the contradiction is that, although the intra-region share in an absolute sense has been decreasing in EU27, relative to other regions, EU27 has been restructured in such a way that it has developed much stronger internal relationships than the external ones. Like what Zhu, Cerina, Chessa, Caldarelli, and Riccaboni [30] find in the international trade network, the peripheral countries have strong incentive to connect with Germany since it has the most external connections outside Europe. As a result, centering on Germany, the European community structure has become more hierarchical and significant over time even if the overall intra-region share of foreign intermediate transactions has been declining. Therefore, some aspects of the regional dynamics can be captured by using the global-level indicators along with the predetermined regions. However, the community detection techniques are indispensable for understanding the regional dynamics since the predetermined regions are often unavailable in practice and the global-level indicators may be oversimplified and misleading.

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