Networks of Value Added Trade

Amador, João; Cabral, Sónia

doi:10.2139/ssrn.2805373

Cited by 12 publications

(14 citation statements)

References 27 publications

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“…Finally, the availability of global input-output matrices has paved the way to several methodological contributions on measures of trade in value added, or proxies of GVC participation (see [21] for a review). In view of diagonal and off-diagonal blocks of our Global Value-Added and Global Final Demand Matrices, the proposed metrics below provide some new insights, or novel proxies for the analysis of global production chains:…”

Section: Theorem 3 (I) the Global Final Demand Matrix M Can Be Comput...mentioning

confidence: 99%

On the Structure of the World Economy: An Absorbing Markov Chain Approach

Kostoska

Stojkoski

Kocarev

2020

Entropy

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The expansion of global production networks has raised many important questions about the interdependence among countries and how future changes in the world economy are likely to affect the countries' positioning in global value chains. We are approaching the structure and lengths of value chains from a completely different perspective than has been available so far. By assigning a random endogenous variable to a network linkage representing the number of intermediate sales/purchases before absorption (final use or value added), the discrete-time absorbing Markov chains proposed here shed new light on the world input/output networks. The variance of this variable can help assess the risk when shaping the chain length and optimize the level of production. Contrary to what might be expected simply on the basis of comparative advantage, the results reveal that both the input and output chains exhibit the same quasi-stationary product distribution. Put differently, the expected proportion of time spent in a state before absorption is invariant to changes of the network type. Finally, the several global metrics proposed here, including the probability distribution of global value added/final output, provide guidance for policy makers when estimating the resilience of world trading system and forecasting the macroeconomic developments.

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Section: Theorem 3 (I) the Global Final Demand Matrix M Can Be Comput...mentioning

confidence: 99%

On the Structure of the World Economy: An Absorbing Markov Chain Approach

Kostoska

Stojkoski

Kocarev

2020

Entropy

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“…More recently, network theory has been applied to disaggregate trade and I-O matrices to investigate GVCs [39,43]. Markov chain theory has been previously applied to disaggregate trade to investigate the allometric scaling of networks and the structure of GVCs [4,50]. Compared to the methods aimed at directly assessing the GVCs by measuring the traded value added in I-O matrices, our approach is different for the following reasons: 1) we do not measure just the share of incorporated value in exports/imports between pairs of countries, i.e.…”

Section: Interpreting the Cycling Indexmentioning

confidence: 99%

“…(5) from 1960 to 2011. From lighter to darker color: Γ (2) (orange line), Γ(3) (red line), Γ(4) (purple line), and Γ (∞) (black line).…”

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confidence: 99%

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How crude oil prices shape the global division of labor

et al. 2017

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“…In contrast, network analysis tools enable not only the measures at the region-sector level such as centrality, but also the natural investigation on local clustering, community detection, and backbone extraction as well as global network features such as assortativity and clustering coefficient (Leonidov and Serebryannikova, 2019;Xu and Liang, 2019). With a sequence of input-output tables over different years, the dynamic changes of network features can be investigated, which are of great value in structural and regional analyses (Cerina et al, 2015;del Río-Chanona et al, 2017;Amador and Cabral, 2017). For the Chinese MRIOTs, in part due to their limited availability, no network analysis has been done to study the regional and sectoral structure of the Chinese economy.…”

Section: Introductionmentioning

confidence: 99%

Regional and Sectoral Structures and Their Dynamics of Chinese Economy: A Network Perspective from Multi-Regional Input-Output Tables

Wang,

Xiao,

Yan

et al. 2021

Preprint

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A multi-regional input-output table (MRIOT) containing the transactions among the regionsectors in an economy defines a weighted and directed network. Using network analysis tools, we analyze the regional and sectoral structure of the Chinese economy and their temporal dynamics from 2007 to 2012 via the MRIOTs of China. Global analyses are done with network topology measures. Growth-driving province-sector clusters are identified with community detection methods. Influential province-sectors are ranked by weighted PageRank scores. The results revealed a few interesting and telling insights. The level of inter-province-sector activities increased with the rapid growth of the national economy, but not as fast as that of intra-province economic activities. Regional community structures were deeply associated with geographical factors. The community heterogeneity across the regions was high and the regional fragmentation increased during the study period. Quantified metrics assessing the relative importance of the province-sectors in the national economy echo the national and regional economic development policies to a certain extent.

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Networks of Value Added Trade

Cited by 12 publications

References 27 publications

On the Structure of the World Economy: An Absorbing Markov Chain Approach

On the Structure of the World Economy: An Absorbing Markov Chain Approach

How crude oil prices shape the global division of labor

Regional and Sectoral Structures and Their Dynamics of Chinese Economy: A Network Perspective from Multi-Regional Input-Output Tables

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