Trends of the world input and output network of global trade
File(s)
Published version
Author(s)
Jensen, HJ
del Rio-Chanona, RM
Grujic, J
Type
Journal Article
Abstract
The international trade naturally maps onto a complex networks. T
heoretical analysis
of this network gives valuable insights about the global economic sys
tem. Although
different economic data sets have been investigated from the net
work perspective,
little attention has been paid to its dynamical behaviour. Here we tak
e the World
Input Output Data set, which has values of the annual transactio
ns between 40
different countries of 35 different sectors for the period of 15 y
ears, and infer the time
interdependence between countries and sectors. As a measure o
f interdependence we
use correlations between various time series of the network chara
cteristics. First we
form 15 primary networks for each year of the data we have, wher
e 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 ea
ch of the 15 networks
for 15 different years. This leads to sets of time series and by calcu
lating the
correlations between these we form a secondary network where t
he links are the
positive correlations between different countries or sectors. Fu
rthermore, we also form
a secondary network where the links are negative correlations in or
der to study the
competition between countries and sectors. By analysing this seco
ndary network we
obtain a clearer picture of the mutual influences between countrie
s. As one might
expect, we find that political and geographical circumstances play
an important role.
However, the derived correlation network reveals surprising aspe
cts which are hidden
in the primary network. Sometimes countries which belong to the sam
e 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, nev
ertheless
secondary network analysis reveal that they exhibit contrary tim
e evolution.
heoretical analysis
of this network gives valuable insights about the global economic sys
tem. Although
different economic data sets have been investigated from the net
work perspective,
little attention has been paid to its dynamical behaviour. Here we tak
e the World
Input Output Data set, which has values of the annual transactio
ns between 40
different countries of 35 different sectors for the period of 15 y
ears, and infer the time
interdependence between countries and sectors. As a measure o
f interdependence we
use correlations between various time series of the network chara
cteristics. First we
form 15 primary networks for each year of the data we have, wher
e 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 ea
ch of the 15 networks
for 15 different years. This leads to sets of time series and by calcu
lating the
correlations between these we form a secondary network where t
he links are the
positive correlations between different countries or sectors. Fu
rthermore, we also form
a secondary network where the links are negative correlations in or
der to study the
competition between countries and sectors. By analysing this seco
ndary network we
obtain a clearer picture of the mutual influences between countrie
s. As one might
expect, we find that political and geographical circumstances play
an important role.
However, the derived correlation network reveals surprising aspe
cts which are hidden
in the primary network. Sometimes countries which belong to the sam
e 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, nev
ertheless
secondary network analysis reveal that they exhibit contrary tim
e evolution.
Date Issued
2017-01-26
Date Acceptance
2017-01-17
Citation
PLOS One, 2017, 12 (1)
ISSN
1932-6203
Publisher
Public Library of Science
Journal / Book Title
PLOS One
Volume
12
Issue
1
Copyright Statement
© 2017 del Rı´o-Chanona et al. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
ECONOMIC COMPLEXITY
WEB
General Science & Technology
MD Multidisciplinary
Publication Status
Published
Article Number
e0170817