Trends of the World Input and Output Network of Global Trade

Abstract

The international trade naturally maps onto a complex networks. Theoretical analysis of this network gives valuable insights about the global economic system. Although different economic data sets have been investigated from the network perspective, little attention has been paid to its dynamical behaviour. Here we take the World Input Output Data set, which has values of the annual transactions between 40 different countries of 35 different sectors for the period of 15 years, and infer the time interdependence between countries and sectors. As a measure of interdependence we use correlations between various time series of the network characteristics. First we form 15 primary networks for each year of the data we have, where nodes are countries and links are annual exports from one country to the other. Then we calculate the strengths (weighted degree) and PageRank of each country in each of the 15 networks for 15 different years. This leads to sets of time series and by calculating the correlations between these we form a secondary network where the links are the positive correlations between different countries or sectors. Furthermore, we also form a secondary network where the links are negative correlations in order to study the competition between countries and sectors. By analysing this secondary network we obtain a clearer picture of the mutual influences between countries. As one might expect, we find that political and geographical circumstances play an important role. However, the derived correlation network reveals surprising aspects which are hidden in the primary network. Sometimes countries which belong to the same community in the original network are found to be competitors in the secondary networks. E.g. Spain and Portugal are always in the same trade flow community, nevertheless secondary network analysis reveal that they exhibit contrary time evolution.

Community structure in the World Trade Network based on communicability distances

“…For instance, Japan and the USA have been classified by the methodology in different communities. Indeed, in the literature, empirical analyses show a negative correlation coefficient between normalized trade strengths of these countries (see, for example, Kozmetsky and Yue 2012;del Rio-Chanona et al 2017). Similar arguments can be extended also to other pairs of countries.…”

“…Similar arguments can be extended also to other pairs of countries. For instance, Germany is negatively correlated with the USA (see Kozmetsky and Yue 2012) and shows a high positive correlation with Belgium and France (see del Rio-Chanona et al 2017) that belong to the same community.…”

Carbon Communities and Hotspots for Carbon Emissions Reduction in China

“…Modularity Q measures the extent to which a network can be grouped into communities with distinct boundaries. It is commonly used to detect unfolding communities in many large networks for a number of different contexts [21][22][23][24].…”

Fragility of a multilayer network of intranational supply chains

“…The inclusion of interlayer flows or couplings is crucial as they have been shown to enhance the propagation of shocks (Vespignani 2010 ). Multilayer networks built using MRIO data have recently been used to analyze international economic flows among countries (Cerina et al 2015 ; Cingolani et al 2017 ; del Río-Chanona et al 2017 ; Maluck and Donner 2015 ; Piccardi et al 2017 ; Xing et al 2017 ). This is to our knowledge the first application of multilayer networks to study shock propagation within intranational MRIO data.…”