RESEARCH ARTICLE

Network structure impacts global commodity
trade growth and resilience
Ali Kharrazi1,2*, Elena Rovenskaya1,3, Brian D. Fath1,4
1 Advanced Systems Analysis Program, International Institute for Applied Systems Analysis (IIASA),
Laxenburg, Austria, 2 Graduate School of Public Policy, University of Tokyo, Tokyo, Japan, 3 Faculty ofComputational Mathematics and Cybernetics, Lomonosov Moscow State University (MSU), Moscow, Russia,
4 Biology Department, Towson University, Towson, Maryland, United States of America

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OPEN ACCESS
Citation: Kharrazi A, Rovenskaya E, Fath BD (2017)
Network structure impacts global commodity trade
growth and resilience. PLoS ONE 12(2): e0171184.
doi:10.1371/journal.pone.0171184
Editor: Rodrigo Huerta-Quintanilla, CinvestavMerida, MEXICO
Received: November 21, 2016
Accepted: January 17, 2017
Published: February 16, 2017
Copyright: © 2017 Kharrazi et al. This is an open
access article distributed under the terms of theCreative Commons Attribution License, which
permits unrestricted use, distribution, andreproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data is available
from the United Nations Comtrade Database. Thereis no specific URL for this database. Please follow
instructions in our paper under section Data andMethodology to download the relevant data fromthe following link: http://comtrade.un.org/data/
Please follow instructions in our paper under
section Data and Methodology HERE IS THE
SECTION: 3.Methodology: Constructing the Global
Commodity Trade Network The data used for thisstudy are based on the United Nations Commodity
Trade Statistics Database (UN-Comtrade). These
data represent annual global trade detailed bycommodities and partner countries in nominal US* ali@pp.u-tokyo.ac.jp

Abstract
Global commodity trade networks are critical to our collective sustainable development.
Their increasing interconnectedness pose two practical questions: (i) Do the current network
configurations support their further growth? (ii) How resilient are these networks to economic shocks? We analyze the data of global commodity trade flows from 1996 to 2012 toevaluate the relationship between structural properties of the global commodity trade networks and (a) their dynamic growth, as well as (b) the resilience of their growth with respect
to the 2009 global economic shock. Specifically, we explore the role of network efficiency
and redundancy using the information theory-based network flow analysis. We find that,while network efficiency is positively correlated with growth, highly efficient systems appear
to be less resilient, losing more and gaining less growth following an economic shock. Whileall examined networks are rather redundant, we find that network redundancy does not hinder their growth. Moreover, systems exhibiting higher levels of redundancy lose less andgain more growth following an economic shock. We suggest that a strategy to support making global trade networks more efficient via, e.g., preferential trade agreements and higher
specialization, can promote their further growth; while a strategy to increase the global trade
networks’ redundancy via e.g., more abundant free-trade agreements, can improve theirresilience to global economic shocks.

Introduction
Global commodity trade is perhaps one of the most critical networks of our modern age.
Given the expansion of globalization over the last centuries, trade networks have grown enormously in size and complexity. As reflected in the 17th Sustainable Development Goal [1], toaccommodate the growing population and to overcome poverty, the sustainable growth anddevelopment of global trade networks is essential beyond the current state. In leveraging thecomparative advantage of production by various countries, flourishing trade is a fundamental
basis for economic growth. Furthermore, food [2] and energy [3] security also become critically dependent on the reliable functioning of commodity trade networks.

PLOS ONE | DOI:10.1371/journal.pone.0171184 February 16, 2017

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Network structure impacts global commodity trade growth and resilience

dollar values. Three main classification systems areprovided in the UN-Comtrade data and here we use
the Harmonized System (HS), introduced in 1988,
which is a detailed breakdown of products toindividual categories. Given this detailed productbreakdown, the HS classification is more suitablefor questions concerning tariffs, customs, andtrade quotas. We have considered the entire HS
1996 revision UN-Comtrade dataset for country-tocountry trade of commodity goods in 97
classifications (SI text, section 4). Each of theseclassifications represents particular commodities
traded globally through densely interconnected
network – as an example Fig 2 depicts the global of
trade of cotton in 2009. In total, we have analyzed
1,649 network–years (97 sectors from 1996 to2012). Given that these datasets are aninternational effort, their compilation takes time
and therefore the early years of 1992–1995 andmost recent years 2013–2014 of the HSclassifications were not considered, as the reported
countries are not complete and, therefore, datasets
might be distorted. For a full list of available years
please consult the UN-Comtrade website. In theinterest of repeating our results, the following
technical issues should be mentioned. Firstly, it is
not uncommon to see a directed trade flow
reported twice in the database with a large
difference between the two values, i.e., one by animporting country and one by an exporting
country. This difference may be due to the fact thatthe data reported by the importer is based on Cost,
Insurance, and Freight (CIF) accounting whereas
the data reported by the exporter is based on FreeOn-Board (FOB) accounting. With the assumptionthat imports are carefully scrutinized for taxing
purposes and therefore more accurate, we give
primacy to the importer’s reports when available.
Secondly, we considered only trade between
countries and territories in our compilation; datafrom areas not elsewhere specified (NES),
specialized categories, free zones, bunkers, anddata from the reporter to itself were notconsidered. Thirdly, the reported flows are basedon nominal US dollar values and therefore were
adjusted to real values using US dollar Commodity
Price Index (CPI) inflation data from the U.S.
Bureau of Labor Statistics. Fourthly, the data
sources reflect aggregate level trade flows ofcommodities and their immediate by-products, but
not on embodied commodities.
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declaredthat no competing interests exist.

Local or global shocks, such as economic and financial crises, political instability, and environmental disasters [4], can affect countries’ production or damage their exports and imports,
causing financial losses for an exporter and supply deficits for an importer. Through internationally spread supply chains, a cascade of secondary negative effects can propagate throughout the entire trade network. A prominent example is the 2009 global economic crisis, which
was reported to have led to long-term negative impacts on investments and trade needed forsustaining food and agricultural systems- especially in emerging and least developed countries[5].
Diversity in trade can help mitigate risks and enhance sustainability; however, overly connected commodity trade networks are prone to spreads of shocks. It is highly important, therefore, to explore which structures are favorable for limiting losses from shocks, while keeping
conditions for short- and long-term growth, as well as which precautionary ex-ante measures
can guide the commodity networks towards these structures.
Research leading to better management of highly interconnected global economic systems
while in their early stage [6] emphasize the importance of system-level network properties for
explaining these dynamics and informing grounded strategies and practices. The majority of
research on international trade networks has focused on binary relationships, i.e., un-weighted
flow links. Notably, an initial in-depth analysis of the world trade web (WTW) by [7,8] foundthat trade networks exhibit complex network properties of scale-free degree distribution, smallworld properties, strong disassortativity, and clustering. These studies characterize world tradeas an adjacency matrix and consider trade links as homogenous. However, this approach may
not be realistic as actual import and export flows are heterogeneous and asymmetric in theirintensity of connections. More recent studies examining world trade as weighted networks havefound these networks to exhibit different properties than when examined through a binary
approach. In particular, [9,10] have found that the WTW is only weakly disassortative and thatcountries characterized with relationships of higher trade intensity are more and not less clustered. However, these conclusions are difficult to translate into grounded practices and consequently new calls have emerged for network research approaches that can incorporate boththe micro perspective of agency behavior and the macro perspective of a network’s structuraldynamism [11]. Given the evolutionary history of survival of natural systems over centuries andmillennia [12], new insights arising from our understanding of the resilience of ecological networks can be useful to understand the resilience of trade networks.
In this avenue, the WTW has been examined through the lens of extinction analysis andfound to be resilient to random failures while fragile to targeted attacks and thus indicatingthat connectance, arising from globalization, has both positive and negative effects on network
resilience [13]. The WTW has also been examined through the lens of evolutionary theory
whereby it was found that decreases in the modularity of trade networks decreases their resilience to shocks [14]. In this paper, we examine the WTW through the ecological informationbased approach, developed initially for analyzing the resilience of food webs [15], and emphasize the importance of the ratio between the system redundancy and efficiency for resiliency inthe system. In this approach, a redundant network is one with a multiplicity of pathways todeliver flows, giving a higher probability of flow between any two randomly chosen nodes,
whereas an efficient network is one that is articulated in a manner that restricts and constrains
the flow choices. In this manuscript, for the first time, we demonstrate empirically how the system redundancy and efficiency are related to the ability of the global trade networks to ensure
their resilience of growth in the face of a financial crisis while sustaining both long- and shortterm growth.
We use the global commodity trade data from 1996 to 2012 to study both their long- andshort-term growth, as well as to examine their resilience of growth to the global economic

PLOS ONE | DOI:10.1371/journal.pone.0171184 February 16, 20172 / 13

Network structure impacts global commodity trade growth and resilienceshock of 2009. Using global commodity trade data from UN Comtrade (http://comtrade.un.
org/db/), an import-export database of the United Nations Statistics Division, in conjunction
with complex network analysis, we highlight how systems-level network properties can reveal
the success of explicit strategies which trade associations, e.g., national and international
chambers of commerce and specialized commodity associations, can apply to promote growthand enhance the resilience of commodity trade growth. The 2009 global shock was chosen as it
is viewed as the biggest economic crisis experienced in our modern global era and also becauseof the higher availability and quality of commodity trade data surrounding this year. While
our results are derived from the 2009 global economic crisis, we anticipate that this approach
may be applicable to other shocks and stresses in trade networks and also has a potential tocontribute to emerging empirical research on resilience in other fields.

Theoretical framework: Measuring network properties of efficiency
and redundancy
The ecological information-based approach is a framework for identifying holistic properties
based on an analysis of the network flows of material, energy, or information. While thisapproach has been developed in the ecological discipline [16,17], given the significant topological and dynamic similarities in all flow systems [18,19], it has been utilized in urban management [20,21] and more recently for research focusing on economic systems [19,22,23]. In thispaper, we present a novel application for analyzing the growth and resilience of growth inglobal commodity trade networks. The underlying assumption within this approach is thatgrowth and development are two fundamentally distinct processes of systems. While growthreflects the extensive property, e.g., the size of the system as total system throughput (TST),development on the other hand implies organizational and intensive properties of a system. In
this approach the development of systems is based on two dialectically related system-level variables of network efficiency and redundancy.
While systems in the long-term exhibit a potential to increase their efficiency at the expenseof redundancy [23,24], the relative dominance of these two system variables varies based on
the system’s environmental constraints and decisions of its agents. The efficiency of a system
reflects the degree of articulation or constraints to network flows. Efficiency tends to increase
naturally in systems where agents select preferential interactions with other agents using a
combination of competition and cooperation to develop pathways with higher intensity andspecialization of resource flows. In global trade systems, preferential interactions are largely
determined by locational proximity, cultural links, and strategic partnerships (supported forexample by free-trade agreements). In the network approach employed here [25,26], the average mutual information is used to define the network efficiency of a system as follows:
Efficiency ¼

X Tij
T::
i;j

log

Tij T::
Ti: T:jðEq: 1Þ

Here, Tij is a flow from agent i to agent j, Ti. = ∑jTij is the total flow leaving agent i, T.j = ∑iTijis the total flow entering agent j and the sum of all flows in the system, T.. = ∑ijTij, is known as
the Total System Throughput (TST).
The redundancy of a system reflects the degree of freedom or overhead in the network
flows. Redundancy is exhibited as the diversity of pathways and is critical for a system’s capacity for innovation and ability to adapt to changing environmental conditions arising fromshocks and disturbances. In global trade systems, the ability to choose from different agents ofsupply and demand is central to free market principles and enables maneuverability in supply.
In the ecological information-based approach, conditional entropy is used to define the

PLOS ONE | DOI:10.1371/journal.pone.0171184 February 16, 20173 / 13

Network structure impacts global commodity trade growth and resilience

network redundancy [25] of a system as follows:
Redundancy ¼

X Tij
i;j

T::

log

T2ij
Ti: T:jðEq: 2Þ

Intuitively, systems need to balance these opposing properties. For example, HS40, the‘Rubber and articles thereof’ commodity network is highly redundant (16 year average efficiency of 0.6622 bits and redundancy of 5.6326 bits); while HS01, the ‘Live animals’ commodity network is highly efficient (16 year average efficiency of 1.7726 bits and redundancy of
2.9934 bits). Overly redundant networks may be stagnant and lack the efficiency to grow,while overly efficient networks may be brittle and prone to collapse when subjected to stress.
To help determine a balance between constraints imposed by efficiency and the flexibility provided by redundancy, the relative order α in the system is introduced as:
a ¼ Efficiency=ðEfficiency þ RedundancyÞ; where 0  a  1

ðEq: 3Þ

The ratio α is a convenient way to express the degree of which property dominates the system at a given time. Departing from the relative order and invoking the Boltzmann measure[27] of its disorder, the level of a system’s Theoretical Resilience can be expressed as [22]:Theoretical Resilience ¼

a log ðaÞðEq: 4Þ

From Eq 4, a maximum value for Theoretical Resilience is derived as 1/e  0.3704 (independent of the logarithm’s base). Empirical investigations have determined that natural networks, e.g., ecosystems and food webs, lie in close proximity to this maximum [25], while
economic systems indicate higher levels of redundancy [23]. The maximum resilience value
however, should be seen as a theoretical benchmark; optimal (minimal) vulnerability of real
heterogeneous systems under various environmental conditions may be different from thisvalue.
Theoretical Resilience (Eq 4) signifies the balance between efficiency and redundancy asa single metric and therefore is useful in exploring and comparing the configurations of heterogeneous networks. However, the analytical implications of the variable are limited andshould be approached with caution. Firstly, the variable does not differentiate among shocks
against to which the network system might be judged to be resilient. Secondly, despite arguments of bio-mimicry, derived from evolutionary observations [23], it may not be possible
or desirable to prescribe an optimal level of Theoretical Resilience to socio-economic networks. Thirdly, changes to the levels of Theoretical Resilience are difficult to translate into
actions, strategies, and practices by policy- and decision- makers at either the local or systems level.

2.1 Growth and resilience of growth to economic shocks
In this study, we examine the network configurations of global commodity trade sectors
between the years of 1996–2012 using the ecological information-based approach. Our basic
network diagnostics indicate that the 17-year averages of the global commodity networks,
while over-redundant, lie rather close (mean of 0.2778 and standard deviation of 0.0424) to the
Theoretical Resilience maximum (Fig 1A). In the commodity trade networks examined in thisstudy, we generally observe that networks with higher efficiency tend to exhibit lower redundancy. Furthermore, it is enlightening to realize that in redundancy-dominated systems close
to the Theoretical Resilience maximum, the value of Theoretical Resilience is defined mostly
by efficiency, while redundancy plays a negligible role (see Fig A in S1 File).

PLOS ONE | DOI:10.1371/journal.pone.0171184 February 16, 20174 / 13

Network structure impacts global commodity trade growth and resilienceFig 1. A) Average values of the relative order α and Theoretical Resilience for 97 commodity trade networks
1996–2012. B) Trend of global commodity imports T.j and exports Ti. between 1996 and 2012.
doi:10.1371/journal.pone.0171184.g001

In consideration of the above, we use the ecological information-based approach to examine empirically how the underlying network configurations affected the growth of trade andthe resilience of growth in the face of global economic shocks. Here, we consider 2009 as an
economic shock year based on the IMF criteria for a global recession [28], i.e., a decline inannual real world GDP per-capita (purchasing power parity weighted). In line with the IMF
criteria, the global commodity trade data also indicate a significant drop in global imports andexports in 2009 (Fig 1B).

Methodology: Constructing the global commodity trade network
The data used for this study are based on the United Nations Commodity Trade Statistics
Database (UN-Comtrade). These data represent annual global trade detailed by commodities
and partner countries in nominal US dollar values. Three main classification systems are provided in the UN-Comtrade data and here we use the Harmonized System (HS), introduced in
1988, which is a detailed breakdown of products to individual categories. Given this detailed
product breakdown, the HS classification is more suitable for questions concerning tariffs, customs, and trade quotas.
We have considered the entire HS 1996 revision UN-Comtrade dataset for country-tocountry trade of commodity goods in 97 classifications (see Table A in S2 File). Each of theseclassifications represents particular commodities traded globally through a densely interconnected network–as an example Fig 2 depicts the global trade of cotton in 2009. In total, we
have analyzed 1,649 network–years (97 sectors from 1996 to 2012). Given that these datasets
are an international effort, their compilation takes time and therefore the early years of 1992–
1995 and most recent years 2013–2014 of the HS classifications were not considered, as thereported countries are not complete and therefore the datasets might be distorted. For a fulllist of available years please consult the UN-Comtrade website.
In the interest of repeating our results, the following technical issues should be mentioned.
Firstly, it is not uncommon to see a directed trade flow reported twice in the database with alarge difference between the two values, i.e., one by an importing country and one by an exporting country. This difference may be due to the fact that the data reported by the importer isbased on Cost, Insurance, and Freight (CIF) accounting whereas the data reported by the

PLOS ONE | DOI:10.1371/journal.pone.0171184 February 16, 2017

5 / 13

Network structure impacts global commodity trade growth and resilienceFig 2. Global trade of cotton (HS 52) in 2009; only 49 countries with trade above $ 100 million USD are depicted. Names ofcountries are represented by the ISO 3166 standard three letter code.
doi:10.1371/journal.pone.0171184.g002

exporter is based on Free-On-Board (FOB) accounting. With the assumption that imports arecarefully scrutinized for taxing purposes and therefore more accurate, we give primacy to theimporter’s reports when available. Secondly, we considered only trade between countries andterritories in our compilation; data from areas not elsewhere specified (NES), specialized categories, free zones, bunkers, and data from the reporter to itself were not considered. Thirdly, thereported flows are based on nominal US dollar values and therefore were adjusted to real values
using US dollar Commodity Price Index (CPI) inflation data from the U.S. Bureau of Labor Statistics. Fourthly, the data sources reflect aggregate level trade flows of commodities and their
immediate by-products, but not on embodied commodities.

PLOS ONE | DOI:10.1371/journal.pone.0171184 February 16, 20176 / 13

Network structure impacts global commodity trade growth and resilience

Results and discussions
4.1 What network properties are conducive to commodity trade growth?
The differences in the growth of commodity trade may be explained by examining their underlying network configurations. We examine correlations between the long- and short-term sectorial TST growth rates and corresponding commodity trade network configurations using two
key network indicators of efficiency (Eq 1) and redundancy (Eq 2). For the long-term, we use
the average TST growth rates over the 1997–2012 period and average network configurations
over the same period. For the short-term, we use 3-, 5-, and 7-year moving averages of both TST
growth rates and network configurations. The TST growth rate (gr) for sector (s) at time (t) isdefined as: grts ¼ ðTSTts TSTts 1 Þ=TSTts 1 and the τ-year moving average (MAτ) TST growthrate for sector (s) at time (t) is defined as: MAst;t ¼ ðgrts t þ grts tþ1 þ :: þ grts Þ=t ðt ¼ 1; 3; 5; 7Þ.
The 3-sigma rule [29] was applied to remove outliers in the correlation data. We use Spearman’srank correlation coefficient to detect any statistically significant monotonic relationshipbetween the two examined variables and Pearson’s correlation coefficient to detect any linear
relationship.
In examining long- and short-term growth rates and efficiency, the Spearman’s rank andPearson’s correlations reveal a strong positive correlation. The Spearman’s rank and Pearson’s
correlations reveal no correlation between long-term growth rates and redundancy, while
revealing a strong positive correlation between short-term growth rates and redundancy(Table 1 and Fig 3).
Additionally, we use multiple regression analysis to examine if the TST growth rates (bothlong- and short-term) can be explained better if both independent variables of efficiency andredundancy are taken into consideration simultaneously. These investigations reveal no additional insights regarding the relation between the long-term average and short-term moving
average (3, 5, and 7 years) sectorial TST growth rates and corresponding network properties of
efficiency and redundancy (Table 2).
These results indicate 1) a significant positive correlation between the network efficiency
and both long- and short-term commodity trade growth, 2) no significant correlation between
redundancy and long-term growth, and 3) a significant positive correlation between redundancy and short-term commodity trade growth. The role of redundancy here may be attributed to the fact that commodity trade networks are over-redundant. Therefore, networks withhigher efficiency tend to achieve higher levels of growth, while, despite possible contrary
assumptions, higher network redundancy in the short-term does not hinder growth rates.

Table 1. Spearman’s rank and Pearson’s correlations between long-term growth rates (16-year average, number of points 95) and short-term growth rates (moving average (MA) year, number of points
MA(7): 947, MA(5): 1132, MA(3): 1322) and corresponding network properties of efficiency andredundancy.
Spearman’s Coeff. (P-value)

Pearson’s Coeff. (P-value)

Long-term TST growth rate & Efficiency

Pairs of Correlated Variables

0.2189 (0.0330)

0.2818 (0.0057)

TST growth rate MA(7) & Efficiency

0.1930 (0.0000)

0.2252 (0.0000)

TST growth rate MA(5) & Efficiency

0.1231 (0.0000)

0.1319 (0.0000)

TST growth rate MA(3) & Efficiency

0.0643 (0.0194)

0.0803 (0.0035)

Long-term TST growth rate & Redundancy

0.0925 (0.3726)

0.0424 (0.6832)

TST growth rate MA(7) & Redundancy

0.1202 (0.0002)

0.0645 (0.0471)

TST growth rate MA(5) & Redundancy

0.0969 (0.0011)

0.0835 (0.0049)

TST growth rate MA(3) & Redundancy

0.0918 (0.0008)

0.0773 (0.0049)

doi:10.1371/journal.pone.0171184.t001

PLOS ONE | DOI:10.1371/journal.pone.0171184 February 16, 20177 / 13

Network structure impacts global commodity trade growth and resilienceFig 3. Long-term average and short-term moving average (3, 5, and 7 years) sectorial TST growth rates and corresponding network properties of
efficiency and redundancy and significance of the paired Pearson’s correlations. Number of points, 16-year: 95, MA(7): 947, MA(5): 1132, MA(3):1322.
doi:10.1371/journal.pone.0171184.g003

4.2 How do network properties affect the resilience of growth toeconomic shocks?
The configurations of global commodity trade networks may significantly affect their vulnerability to loss of growth following an economic shock. Considering the 2009 global economic
shock year, we define here the Vulnerability Index (VI) of a commodity trade network as thedifference between its 3-year average pre-shock and post-shock growth rates. We further
dichotomize networks with a positive VI value as ‘gainers’ and those with a negative value as‘losers’.
To ensure statistical quality and minimize the influence of random variables, the VI isbased on the difference between the 3-year average pre-shock and post-shock growth rates (gr)of the commodity sector (s) as:
VI s ¼ ðgrÞ2010;2011;2012

ðgrÞ2006;2007;2008

ðEq: 5ÞTable 2. Multiple regressions between long-term and short-term moving average sectorial TST
growth rates (dependent variables) and network properties of efficiency and redundancy (independent variables). Y = a + a0Efficiency + a1Redundancy. Number of points for long-term average: 97, MA(7):970, MA(5): 1164, MA(3): 1358.
Dependent variable—Y

a0 (p-value)

a1 (p-value)

Long-term TST growth rate

0.0263 (0.0205)

0.0039 (0.4846)

TST growth rate MA(7)0.0369 (0.0000)

0.0040 (0.1500)

TST growth rate MA(5)0.0360 (0.0000)

0.0057 (0.0704)

TST growth rate MA(3)0.0331 (0.0000)

0.0047 (0.1894)

doi:10.1371/journal.pone.0171184.t002

PLOS ONE | DOI:10.1371/journal.pone.0171184 February 16, 20178 / 13

Network structure impacts global commodity trade growth and resilience

Table 3. Point-biserial correlations between VI and pre-shock 3-year averages of efficiency andredundancy.
Pairs of Correlated Variables

Point-biserial Coeff. (r)P-value

Efficiency vs. VI−0.28360.0049

Redundancy vs. VI−0.0105

0.9186

doi:10.1371/journal.pone.0171184.t003

and the dichotomized VI of commodity sector (s) as:
VI s ¼ f0 gainer0 if VI s  0 & 0 loser 0 if VI s < 0g

ðEq: 6Þ

Based on the above definitions, we explore how network configurations are related to whether
a particular commodity network appears as a ‘gainer’ or ‘loser’ after the shock. The 3-sigma rule
was applied to remove two outlier commodity sectors, i.e., HS1 and HS99 sector (see Table A in
S2 File). For this purpose, we use the point-biserial correlation [30] to test for a relationshipbetween a dichotomous VI variable and the pre-shock 3-year average network configurations.
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
The point-biserial coefficient is calculated as: r ¼ ðVI S1 VI S0 Þ pð1 pÞ=SVIs , where, VI S0
is the mean of VIs when VIs < 0, VI S1 is the mean of VIs when VIs  0, SVIs is the standard deviation of VIs, and p is the proportion of values where VIs  0.
This result of this analysis, as depicted in Table 3, reveals a significant negative correlation
between the dichotomized VI and the pre-shock 3-year average efficiency and no significant
correlation with the pre-shock average redundancy.
In order to further explore the relation between the impact of the shock and network configurations, we examine Spearman’s rank and Pearson’s correlations between the VI and thepre-shock 3-year average of efficiency and redundancy. We find a significant negative correlation between the VI and average efficiency and no significant correlation between VI and average redundancy (Fig 4A and 4D and Table 4). These results warrant a further examination of
the networks categorized as ‘loser’ and ‘gainer’ and their separate relationship with the preshock network configurations. For ‘loser’ networks we find that their VI is negatively correlated with the average 3-year pre-shock average of efficiency and positively correlated with the
3-year pre-shock average of redundancy (Fig 4B and 4E and Table 4). For ‘gainer’ networks,
the VI is not found to be correlated with the pre-shock 3-year average of redundancy an efficiency (Fig 4C and 4F and Table 4).
We use the Chow test [31] to confirm that the network configurations of commodity trade
networks are indeed qualitatively different between ‘loser’ and ‘gainer’ networks. The Chow
ðS

ðS

þS

ÞÞ=ðkÞ

VI s 0
VI s <0
VI S
VI 0 þSVI s <0 Þ=ðNVI s 0 þNVI s <0

test is defined as: ðS s2kÞwhere, SVIS are the sum of squared residuals fromthe combined groups, SVIs 0 is the sum of the squared residuals from the ‘gainer’ group, and
SVIs <0 are the sum of the squared residuals from the ‘loser’ group. NVIs 0 and NVIs <0 are thenumber of observations in each group and k is the total number of parameters. The Chow test
statistics follows the F-distribution with k and NVIs 0 þ NVIs <0 2k degrees of freedom.
Results of the Chow test statistic indicate that a structural break indeed exists and confirms
the hypotheses that the independent variables of efficiency and redundancy maintain different
impacts on the dependent variable of VI [Efficiency: F = 33.8928 and Redundancy: F =41.0834, where F-critical (3, 91) ~ P (0.05) = 2.7047]. Therefore, networks identified as ‘gainer’or ‘loser’ are impacted differently from their pre-shock efficiency and redundancy levels.
These results indicate 1) commodity networks with higher levels of efficiency before an economic shock are more likely to be losers of growth following an economic shock and 2) these
networks lose less growth if they had maintained lower levels of efficiency and consequently

PLOS ONE | DOI:10.1371/journal.pone.0171184 February 16, 20179 / 13

Network structure impacts global commodity trade growth and resilienceFig 4. VI values and corresponding 3-year pre-shock averages of efficiency and redundancy and significance of the paired Pearson’s correlations.
Total VI values (A, D), negative VI values (B, E), and positive VI values (C, F).
doi:10.1371/journal.pone.0171184.g004

higher levels of redundancy. Therefore, economic shocks impact networks differently basedon their configurations; higher efficiency makes commodity networks less resilient in growthfollowing an economic shock.

Summary and conclusion
Evolutionary and ecological traits may advance our ability to situate the concept of resilience
to our social, environmental, and economic activities and increase our capacity for sustainable
Table 4. Spearman’s rank and Pearson’s correlations between VI and pre-shock 3-year averages of
efficiency and redundancy. Number of points, VIs: 95, VIs < 0: 49, VIs > 0: 46.
Pairs of Correlated Variables

Spearman’s Coeff. (P-value)

Pearson’s Coeff. (P-value)

Efficiency vs. VIs−0.3791 (0.0002)−0.3625 (0.0003)

Efficiency vs. VIs < 0−0.3892 (0.0057)−0.4127 (0.0032)

Efficiency vs. VIs > 00.1011 (0.5039)

0.0881 (0.5603)

Redundancy vs. VIs

0.0929 (0.3703)

0.1028 (0.3216)s

Redundancy vs. VI < 00.3502 (0.0136)

0.3976 (0.0047)

Redundancy vs. VIs > 0−0.1064 (0.4816)−0.0881 (0.5606)

doi:10.1371/journal.pone.0171184.t004

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Network structure impacts global commodity trade growth and resiliencedevelopment. This requires an assessment of the usefulness of the concept of resiliencethrough empirically testable theory. In this manuscript, we demonstrate how the ecologicalinformation-based approach can reveal network properties that significantly affect growth andthe resilience of growth to shocks. Similar to natural ecological systems, commodity trade networks exhibit higher growth with higher levels of efficiency and greater resilience of growth toshocks with higher levels of redundancy. This confirms a fundamental tradeoff between efficiency and growth, on one hand, and redundancy and resilience of growth on the other. Whilein this paper, we discuss network configurations favorable to growth and resilience of growth
based on correlations, given more detailed data availability, e.g., monthly trade data, questionsof causality can also be addressed in future research.
The properties of efficiency and redundancy are system-level structural configurations
which emerge from the metabolic flows of the network. Network flows quantify exchanges
between compartments which only implicitly reflect the various social and economic behavioral drivers of these compartments at the agency level. Therefore, it may be challenging tomodify a network’s structure through behavioral incentives at the agency level. However, totranslate our findings to grounded applications one must not focus on either agency or structure but accept their mutual dependency. While taking into consideration the challenges ofthis duality [32], we see a central role for trade associations in increasing awareness and promoting grounded practices and strategies based on the network properties of efficiency andredundancy for achieving growth and resilience of growth to economics shocks.
Global commodity trade networks constitute complex interactions between private andpublic organizations, including governments, manufacturers, consumers, shippers, insurancecompanies, and logistic service providers. A multi-stakeholder initiative including these actors
is best achieved through trade associations, e.g., national and international chambers of commerce and specialized commodity sector associations. Individual economic agents operatingin the same market establish trade associations to represent and provide services to them at asystems-level which is of collective benefit. These trade associations’ raison d’etre is to formulate strategies and practices that support what economists call ‘the public goods’, i.e., challenges
that improve collective well-being but unlikely to be undertaken by individual economic
agents. The basic functions of trade associations include collective bargaining, development ofstandard practices, dissemination of best-practices, and market influence.
In this vein, trade associations can increase redundancy by promoting and developing contingency plans for the diversification of trade among suppliers and consumers or conversely
increase efficiency by promoting vertical integration and focused supply chain networks.
Redundancies can also be enhanced through the diversification of production among morecountries and lead to higher export and import flows. While such strategies are well known in
the operations management literature [33], the impacts of the implementation of these strategies, their trade-offs, and associated costs have been difficult to measure. The ecological information-based approach provides objective measurements which can help evaluate the cost,benefit, and appropriate balance of available strategies. To improve the precision of thesestrategies, more research is required in quantifying the impact of economic shocks on trade
growth. This may be achieved by examining past experiences to economic shocks and byincreasing future visibility through dataset at higher temporal granularities, e.g., quarterly,
monthly, and real-time data.
Given the highly interconnected nature of today’s social, economic, and environmental networks, our sustainable development is made more vulnerable to globally adverted shocks.
Therefore, it is critical to advance our empirical understanding of the effect of network structure on growth and resilience of growth. The ecological information-based approach offers animportant empirical method for evaluating network resilience. In this paper, we applied thisPLOS ONE | DOI:10.1371/journal.pone.0171184 February 16, 201711 / 13

Network structure impacts global commodity trade growth and resilience

approach to commodity trade networks and empirically tested how the network properties ofredundancy and efficiency can be developed into grounded strategies for growth and resilienceof growth to economic shocks. Resiliency of growth, however, is not necessarily a sustainable
solution; it is an assessment of the given economic paradigm, not a statement of the desired,
and the merits of continued trade growth should be evaluated based on the three economic,
environmental, and social pillars of sustainable development. For future research we suggest
an examination of the overlaps and tradeoffs of redundancy and efficiency with other network
properties found in ecological systems, e.g. modularity and tightness of feedback loops [34],relevant to resilience. We also hope the work presented in this paper will encourage further
research in examining other critical networks relevant to sustainable development, e.g., thewater, food, and energy nexus, and in advancing the ecological information-based approachby rendering it more communicable to policy makers and making it more reliable as a methodological tool.

Supporting information
S1 File. Theoretical Resilience as a function of efficiency and redundancy.
(DOCX)
S2 File. Table of Harmonized System commodity classifications.
(DOCX)

Author Contributions
Conceptualization: ER BDF AK.
Data curation: AK.
Formal analysis: AK ER.
Investigation: ER BDF AK.
Methodology: ER BDF AK.
Software: AK.
Supervision: ER.
Validation: AK ER.
Visualization: AK.
Writing – original draft: AK.
Writing – review & editing: ER BDF AK.

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