In this section
@article{Jensen:2017:10.1371/journal.pone.0170817,
author = {Jensen, HJ and del, Rio-Chanona RM and Grujic, J},
doi = {10.1371/journal.pone.0170817},
journal = {PLOS One},
title = {Trends of the world input and output network of global trade},
url = {http://dx.doi.org/10.1371/journal.pone.0170817},
volume = {12},
year = {2017}
}
TY - JOUR
AB - The international trade naturally maps onto a complex networks. Theoretical analysisof this network gives valuable insights about the global economic system. Althoughdifferent economic data sets have been investigated from the network perspective,little attention has been paid to its dynamical behaviour. Here we take the WorldInput Output Data set, which has values of the annual transactions between 40different countries of 35 different sectors for the period of 15 years, and infer the timeinterdependence between countries and sectors. As a measure of interdependence weuse correlations between various time series of the network characteristics. First weform 15 primary networks for each year of the data we have, where nodes are countriesand links are annual exports from one country to the other. Thenwe calculate thestrengths (weighted degree) and PageRank of each country in each of the 15 networksfor 15 different years. This leads to sets of time series and by calculating thecorrelations between these we form a secondary network where the links are thepositive correlations between different countries or sectors. Furthermore, we also forma secondary network where the links are negative correlations in order to study thecompetition between countries and sectors. By analysing this secondary network weobtain a clearer picture of the mutual influences between countries. As one mightexpect, we find that political and geographical circumstances playan important role.However, the derived correlation network reveals surprising aspects which are hiddenin the primary network. Sometimes countries which belong to the same community inthe original network are found to be competitors in the secondarynetworks. E.g.Spain and Portugal are always in the same trade flow community, neverthelesssecondary network analysis reveal that they exhibit contrary time evolution.
AU - Jensen,HJ
AU - del,Rio-Chanona RM
AU - Grujic,J
DO - 10.1371/journal.pone.0170817
PY - 2017///
SN - 1932-6203
TI - Trends of the world input and output network of global trade
T2 - PLOS One
UR - http://dx.doi.org/10.1371/journal.pone.0170817
UR - http://hdl.handle.net/10044/1/44096
VL - 12
ER -