Networks of value added trade. No 1931 / July 2016.
Date: 2016-07-13
Institutions: European Central Bank
Subjects: added value, econometrics, goods and services, international trade, production policy, trading operation

Working Paper Series 

João Amador and 
Sónia Cabral 

Networks of value added trade 

No 1931 / July 2016 

Note: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). 
The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

TheCompetitivenessResearchNetworkCompNet 
 
 
 
Abstract

Global Value Chains (GVCs) became the paradigm for the production of most
goods and services around the world. Hence, interconnections among countries
can no longer be adequately assessed through standard bilateral gross trade ﬂows
and new methods of analysis are needed. In this paper, we compute measures
of network analysis and apply visualisation tools to value added trade ﬂows in
order to understand the nature and dynamics of GVCs. The paper uses data on the
bilateral foreign value added in exports for the period 1995-2011 and, in each year,
GVCs are represented as directed networks of nodes (countries) and edges (value
added ﬂows). The analysis is extended beyond total trade ﬂows to discuss the
distinct roles of goods and services in GVCs. Moreover, the differences between
Germany, the US, China and Russia as major suppliers of value added in GVCs
are also examined.

Keywords: International trade; Global value chains; Network analysis; Input-output tables;

JEL Codes: F12, F14, C67

ECB Working Paper 1931, July 20161Non-technical summary

In recent decades, the rise of Global Value Chains (GVCs) has dramatically changed the

organisation of world production of goods and services, making a deep and lasting impact

on international trade and investment patterns, as well as on labour and product markets.

As GVCs spread worldwide, the concept of “country of origin” becomes increasingly dif-

ﬁcult to apply, because a country may stand as a large exporter of a speciﬁc good without

adding much value to it. Hence, the analysis of gross trade ﬂows has to be complemented

with the analysis of trade in value added, tracking down the original source country of the

value added. Even if GVCs are a complex phenomenon, it is essential that policy-analysis

takes on board their impacts on the quantiﬁcation and interpretation of traditional trade and

competitiveness indicators and on the forecasting of macroeconomic developments.

The expansion of GVCs has also strongly increased the economic interdependence between

countries.

In this context, since exports increasingly embody a sizeable share of foreign

value added, important questions about the interconnections among countries arise, notably

in relation to the impact and propagation of economic shocks. For example, the signiﬁcant

role of speciﬁc countries in the functioning of GVCs poses questions regarding the resilience

of the world trade system if they are hit by large shocks. All these aspects have a bearing on

monetary policy decisions.

The measurement of trade in value added, breaking down gross trade ﬂows along sources and

destinations of value added, has beneﬁted from the recent availability of global input-output

(I-O) matrices. One of the simplest measures of participation in GVCs is the use of imported

inputs to produce goods that are afterwards exported, that is the “foreign value added content

of exports”. These foreign value added ﬂows can be interpreted as the ﬁnal result of complex

linkages established between ﬁrms in different sectors and countries over time.

In spite of the intense research over the last decades, the mapping and measurement of GVCs

is still incomplete and new research strands may bring further valuable results. Given the spe-

ciﬁc features of GVCs measured from global I-O matrices, network analysis is a useful tool

to examine the international ﬂows of value added and countries’ position within GVCs. This

type of analysis prompts the I-O relationship between any two countries not to be studied in

isolation, focusing instead on its structural dimension, that is, taking into account the effect

of all other participants in GVCs. In other words, network analysis assumes the interdepen-

dence of observations and explores the entire pattern of connections, instead of focusing on

the isolated characteristics of each individual element.

ECB Working Paper 1931, July 20162The main goal of this paper is to provide a general picture of the characteristics and dynamics

of GVCs from a complex network perspective, offering an economic interpretation of the

results whenever possible. The paper uses data on the bilateral foreign value added in exports

from the World Input-Output Database (WIOD) for the period 1995-2011. In each year, the

GVC is represented as a directed network of nodes (countries) and edges (value added ﬂows).

The analysis starts with the computation of several aggregate network metrics and the evolu-

tion of the key properties of the networks of foreign value added in total, goods and services

exports is discussed. Next, we analyse in more detail the network of total foreign value added

in exports and study the roles of goods and of services as both inputs and outputs in GVCs.

The main differences between Germany, the US, China and Russia as major suppliers of

foreign inputs embodied in the exports of other countries are also examined. Therefore, this

paper offers an analysis of GVCs that is complementary to that of other papers also produced

within the scope of the Competitiveness Research Network (CompNet) of the European Sys-

tem of Central Banks (ESCB), namely Amador et al. (2015), Benkovskis and Wörz (2015)

and Nagengast and Stehrer (2016).

We ﬁnd that large countries play a vital role and that the regional dimension of GVCs is still

dominant, though it is progressively giving way to a more global network. The network of

foreign value added in goods exports is denser than that of services exports, but some purely

services-based GVCs are already visible. At country-level, Germany and the US maintain a

robust participation in GVCs over the whole period, but with meaningful differences between

them. Germany mostly bases its role as a major supplier of goods value added to be used

in other countries’ exports of goods, while the US supplies more services inputs to services

exports.

In addition, Germany also has some relevance as a client of value added to be

embodied in German exports, while the US mostly acts as a supplier of value added to other

countries. The rising importance of China as a supplier of value added is a clear result of

the analysis. The emergence of China is mostly centred in the supply of goods inputs to the

exports of goods of other countries.

The correct understanding of GVCs is crucial to predict shifts in their future dynamics,

which, in turn, are important to forecast macroeconomic developments and to assess the

role, if any, that policy can play in shaping this phenomenon. There is still substantial work

to be done in the interplay between GVCs and network theory. A complex network approach

that takes due account of the full set of linkages among countries and their positions in GVCs

can contribute to a better assessment of how globalisation affects each national economy.

ECB Working Paper 1931, July 201631

Introduction

Over the last decades, international trade has grown strongly and its pattern has changed

signiﬁcantly. International production sharing has always been part of international trade

as countries import goods to be incorporated in their exports. However, the acceleration of

technological progress, the reduction of transport and communication costs and the removal

of political and economic barriers to trade greatly increased the opportunities for the inter-

national fragmentation of production, i.e., a paradigm where different countries specialise

in particular stages of the production chain (see Amador and Cabral (2016) for a review).

Such international fragmentation of production, which has led to the emergence of Global

Value Chains (GVCs), poses challenges to policy-makers and it has contributed to deepen

the structural interdependence of the world economy in the last decades. The organisation

of global production networks is very complex and involves ﬁrms in manufacturing, logis-

tics, transportation and other services, as well as customs agents and other public authorities

across different countries.

The empirical analysis of GVCs has been focusing on the computation of indicators that

break down gross trade ﬂows along sources and destinations of value added, taking advan-

tage of the recent availability of global input-output (I-O) matrices. One of the simplest

indicators of participation in GVCs is the foreign value added content of exports (FVAiX),

which measures the use of imported inputs to produce goods that are exported afterwards,

as deﬁned, for instance, in Foster-McGregor and Stehrer (2013) and Koopman et al. (2014).

This I-O based measure of fragmentation focuses on the (direct and indirect) import content

of exports, it captures cases where the production is carried out in at least two countries and

the products cross at least twice the international borders and was initially formulated by

Hummels et al. (2001), who labelled it “vertical specialisation”.

This paper studies GVCs from a complex network perspective. The ﬂows of value added

in a GVC tend to occur in a sequential way with ﬁrms incorporating foreign value added as

they embody intermediate goods in production that is subsequently exported for ﬁnal con-
sumption or integrated into other products or services.1 Therefore, the path taken by each

unit of value added in the world economy before it reaches the ﬁnal consumer may be ex-

tremely complex and long. In conceptual terms, this path could be identiﬁed stepwise in the

global I-O matrix, as implied in the notion of average propagation lengths of Dietzenbacher

et al. (2005). However, given the structure of the matrix, the number of iterations would be

1See Baldwin and Lopez-Gonzalez (2015) for an overview of global patterns of supply-chain trade.

ECB Working Paper 1931, July 20164huge and the resulting network virtually impossible to visualise. Instead, economic theory

has been focusing on the Leontief inverse matrix to capture the ﬁnal impact of this iterative

process. This is also the approach adopted in this paper: the network represents the ﬁnal

foreign value added ﬂows after all stages of production have propagated through the world

economy, and not individual ﬂows in successive stages of the production chain.

International value added ﬂows obtained from global I-O frameworks can be interpreted as

the ﬁnal outcome of complex linkages established between ﬁrms in different sectors and

countries over time. Since exports increasingly embody a sizeable share of foreign value

added, the interdependence between economies becomes even more relevant, notably in

terms of the impact and propagation of economic shocks and the related co-movement across

countries. In fact, the recent ﬁnancial and economic crisis showed that GVCs affect the mag-

nitude and international transmission of macroeconomic shocks. During this period, the col-

lapse in global trade was severe, synchronised across the world, and particularly pronounced

for trade in capital and intermediate goods. Several transmission mechanisms were at play

but GVCs appear to have had a central role in the transmission of what was initially a demand
shock in some markets affected by a sharp credit shortage.2 The signiﬁcant role of speciﬁc

countries in the functioning of GVCs poses also serious questions regarding the resilience

of the world trade system in case they are hit by large shocks. In this vein, Carvalho (2014)

discusses the extension of the analysis of production networks to an open economy set-up to

account for global supply chain networks.

The main goal of this paper is to provide a general picture of the nature and dynamics of

GVCs from a complex network perspective, offering an economic interpretation of the re-

sults whenever possible. We base on the World Input-Output Database (WIOD) for the

period 1995-2011 and examine the characteristics of the international ﬂows of value added

both analytically and graphically using tools of network analysis. The paper goes beyond

total trade in order to asses the speciﬁc role played by goods and services as both inputs and

outputs. Given the speciﬁc features of GVCs obtained from global I-O matrices, network

analysis proves to be a useful tool to examine international ﬂows of value added and coun-

tries’ position within such chains. This type of analysis makes it possible to focuses on the

structural dimension of an I-O relation between any two countries, i.e., taking into account

the effect of all other participants in GVCs instead of taking it in isolation.

2Baldwin (2009) provides a useful discussion on the several causes of the great trade collapse, as well as on its consequences and

prospects for the future of the global economy.

ECB Working Paper 1931, July 20165The paper is organised as follows. Section 2 discusses relevant literature on the network

analysis of international trade ﬂows. Section 3 brieﬂy presents the methodology used to

decompose value added in trade, the deﬁnition of the networks of foreign value added in

exports and the database used. In section 4, the evolution of the networks of foreign value

added in exports is examined through the computation of aggregate network metrics and us-

ing network visualisation tools, with a focus on the differences between goods and services.

Finally, section 5 presents some concluding remarks.

2 Related literature on international trade networks

In order to study interconnections between agents, economic research has been making pro-

gressive use of network analysis tools. The appeal of network analysis to study economic re-

lations comes from the ability to identify the full structure of interactions. Networks assume

the interdependence of observations and explore the entire pattern of connections, instead of

focusing on the isolated characteristics of each individual element. The research on networks

has suggested several measures to examine analytically the large-scale statistical properties
of graphs and summarise the main characteristics of a network as a whole.3 Additionally,

the visualisation of the network structure, using graphs that contain the architecture of nodes

linked by edges, is a useful tool to facilitate the interpretation of complex inter-linkages,

allowing also for the study of the properties of individual nodes within the network.

Economic research based on social network analysis already covers a wide set of issues, thus
bridging the two disciplines.4 In the area of econophysics, a number of articles have focused

on the empirical analysis of international trade interactions from the perspective of complex

networks. In the so-called World Trade Web (WTW) or International Trade Network (ITN),

each country is a node and the bilateral trade ﬂow between two countries deﬁnes an edge

between them. Several aspects of the structural and topological properties of the WTW in its

undirected/directed and binary/weighted forms are studied by Serrano and Boguñá (2003),

Garlaschelli and Loffredo (2004a, 2005), Serrano et al. (2007), Kali and Reyes (2007), Bhat-

tacharya et al. (2007), Fagiolo et al. (2009, 2010), Reyes et al. (2010) and Fan et al. (2014),

among others. The binary WTW was found to display a scale-free degree distribution, where

some countries, often called hubs, present many more connections than others. This network

3There is a vast interdisciplinary literature on the different measures and statistics for characterising network structures, which we do
not attempt to survey here. The textbooks by Wasserman and Faust (1994) and Newman (2010) provide an extensive review of the essential
methods used for the network analysis.

4See Jackson (2014) for a discussion on how networks can help to model and understand economic behaviours.

ECB Working Paper 1931, July 20166is also characterised by a relatively high level of clustering (i.e., high probability that two

trade partners of a country are themselves connected) and by a disassortative mixing (i.e.,

countries with many trade partners tend to be linked with countries with few partners), sug-

gesting a hierarchical structure with strong heterogeneity among countries.

The conventional empirical international trade literature has also applied network metrics to

examine the characteristics of world trade. The evolution of the binary WTW over time is

studied by De Benedictis and Tajoli (2011). They ﬁnd an increase of trade integration at the

world level, but with a high level of heterogeneity among countries, and a signiﬁcant role for

trade policy in shaping the network. In the same vein, De Benedictis et al. (2014) describe

the topology of the WTW in its binary and weighted versions by calculating and discussing

a number of network statistics. Other authors have used network analysis to examine how

trade in speciﬁc sectors has evolved over time. Akerman and Seim (2014) study the global

arms trade network from 1950 to 2007 and conclude that it became more dense, clustered

and decentralised over time. Amighini and Gorgoni (2014) use network analysis to study the

trade in auto parts and components and ﬁnd that the rise of emerging economies as suppliers

led to a structural change in the international organisation of auto production.

Some recent papers study GVCs from a complex network perspective. Cerina et al. (2015)

examine the total world input-output network (WION) as a directed and weighted network of

country-sector pairs and compute several local and global network metrics over time. They

ﬁnd a strong rise in cross-country connectivity over time, as countries increasingly partici-

pate in GVCs. Using community detection techniques, they detect an important European

community led by Germany and document also the rising importance of China. Zhu et al.

(2015) produce a detailed topological view of industry-level GVCs as global value trees for

a large set of pairs country-sector and compute a measure of industry importance based on

them. In a different vein, Ferrarini (2013) uses international trade data on products classiﬁed

as parts and components to quantify vertical trade among countries. The author uses network

visualisation tools to map the resulting global network of vertical trade, highlighting the rise

of China and the importance of the automotive and electronics sectors in GVCs.

As in this paper, Cerina et al. (2015) and Zhu et al. (2015) use the World Input-Output

Database (WIOD) for the period 1995-2011, but the networks analysed are distinct. Firstly,

the measures of participation in GVCs considered are different: they examine the total value

added in ﬁnal demand and our paper studies the foreign value added in exports. Secondly,

they aim at the entire set of country-sector linkages, i.e., the nodes are the individual sectors

ECB Working Paper 1931, July 20167in each country, while we focus on the geographical linkages. Finally, they examine the

entire set of value added ﬂows among each country-sector pair, including ﬂows of domestic

value added in ﬁnal demand. Instead, as we use the foreign value added in exports, all value

added ﬂows within a given country are eliminated from the analysis.

3 Methodology and data

3.1 Foreign value added in exports

This section brieﬂy reviews the methodology underlying the computation of the measure

used to asses the participation in GVCs - the foreign value added content in a country’s gross

exports (FVAiX). The concept of trade in value added links with the fact that both domestic

and foreign value added are combined to produce exports, which may be latter embodied in

other products or consumed as ﬁnal goods and services. Nowadays, imports of intermediate

products to be embodied in exports are a very important part of the production process, thus

gross exports tend to be much larger than their domestic value added component. In addition,

the domestic value added included in exports can circulate in the global economy embodied

in intermediate products used along the production chain and, in this process, part of it can

even return to the domestic economy.

In this context, the measurement of trade in value added implies allocating the value added

along the GVC to each producer, thus requiring world I-O tables with information on all

bilateral ﬂows of intermediate and ﬁnal goods and services. The availability of global I-O

matrices, where country-sector pairs of inputs are disentangled along country-sector pairs of

outputs, has led to several methodological contributions on metrics of trade in value added,

i.e., new proxies of participation in GVCs. Several recent articles generalise the concept

of “vertical specialisation” of Hummels et al. (2001) and capture different dimensions of

international ﬂows of value added. The initial contributions were those of Daudin et al.

(2011), Johnson and Noguera (2012) and Koopman et al. (2014). The FVAiX is part of this

last generation of indicators and it can be computed at a detailed breakdown level.

Next, we follow closely Amador et al. (2015) for a simple presentation of the FVAiX. The

most intuitive way to introduce this indicator is to start by deﬁning the domestic value added

in exports (DVAiX).

ECB Working Paper 1931, July 20168The global Leontief inverse matrix is denoted as L = (I − A)−1, with dimension NC × NC,

where N stands for the number of sectors and C for the number of countries, and where

I is the identity matrix and A is the NC × NC global I-O matrix. The Leontief inverse

matrix is the sum of a converging inﬁnite geometric series with common ratio A, that is,
[I − A]−1 = (cid:2)I + A + A2 + A3 + · · · + Ax(cid:3), when x → ∞. The elements of the Leontief inverse
matrix are often termed as output multipliers, as they capture the total impact of a change in

ﬁnal demand, taking into account both the direct as well as all indirect rounds of consecutive

effects arising from the interdependence of sectors in production.

The vector of value added coefﬁcients, i.e., value added created per unit of gross output
in country r, is denoted by vr. This 1 × NC vector contains the value added coefﬁcients

for country r and zeros otherwise. Further, exports of country r are written in the vector
er, which is of dimension NC × 1 and reports the exports as positive elements and zeros

otherwise.

The DVAiXr takes the on-diagonal block in the Leontief inverse for country r, pre-multiplies

by the value added coefﬁcients in each sector and post-multiplies by the values of exports,

that is:

DVAiXr = vrLrrer

(1)

The FVAiXsr provides the value added directly and indirectly created in the country from

which intermediates are imported (source country s) for production of exports of country

r and is calculated in a similar way. It implies pre-multiplying the Leontief inverse by the

vector containing the value added coefﬁcients for country s and zeros otherwise, denoted as
vs, and post-multiplying by the vector of exports of country r. In other words, the FVAiXsr

basically takes the off-diagonal blocks of the global Leontief inverse for country r, pre-

multiplies by country s value added coefﬁcients and post-multiplies by the vector of country

r’ exports. Formally, this is written as:

FVAiXsr = vsLsrer

(2)

Summing up over all partner countries, the total foreign value added embodied in exports of

country r is obtained as:

FVAiXr = ∑

s,s(cid:54)=r

vsLsrer

(3)

ECB Working Paper 1931, July 20169Adding the domestic and the foreign value added in exports, as presented in equations 1 and

3, provides the value of total exports of country r in gross terms:

Xr = DVAiXr + FVAiXr

(4)

All value added decompositions computed in this paper were made using the R package

decompr (Quast and Kummritz, 2015). The analysis is based on the World Input-Output

Database (WIOD), which links national supply and use tables with bilateral trade data in

goods and services to produce a global I-O table. This database covers 27 European countries

and 13 other major world economies and comprises 35 industries, corresponding to a broad

NACE classiﬁcation. The description of the sectors and countries available in the WIOD is

included in Appendix A. The sample period starts in 1995 and ends in 2011. Timmer et al.

(2015) describe in detail the contents of this database and illustrate its potential to examine

different aspects of the international fragmentation of production.

3.2 Deﬁning the networks

The construction of a network requires the identiﬁcation of a set of nodes or vertices and a

criterion for the interactions between them, which deﬁne the edges. In this paper, the nodes

are the 40 individual countries that are present in the WIOD (N = 40). The criterion for the

existence of an edge is set to reﬂect the importance of a source country s as a supplier of

value added for the production of the exports of country r. For the purpose of deﬁning what

is an important supplier of value added in exports, a threshold is set to obtain an incomplete

adjacency matrix. The choice of the threshold is made in such a way that the resulting net-

work is simple enough to interpret and visualise, while capturing the relevant interrelations

between nodes, i.e., the main features of GVCs that are active around the world. The foreign

value added threshold was set at 1 percent of total gross exports of the user country, resulting
in a coverage of more than 65 percent of total foreign value added ﬂows in every year.5

The existence of a clear interpretation for the orientation of the edge, i.e., directed from a

country whose value added share in another country’s exports is larger than the threshold,

5Alternative threshold percentages were tested and the main features of the networks remained qualitatively unchanged for similar
values. Signiﬁcant differences are only detected with threshold values above 4 percent of total gross exports of the user country, where less
than 30 percent of total foreign value added ﬂows are considered. All detailed results are available from the authors upon request.

ECB Working Paper 1931, July 201610makes this network directed. More precisely:

−→asr =



1

0

i f

FVAiXsr
Xr

otherwise

> 0.01 for each country s (cid:54)= r = 1, 2 . . . N

(5)

where AM = [asr] is the N × N connectivity or adjacency matrix.

The analysis in this paper disregards the strength of the edges identiﬁed, i.e., the values of the

foreign value added shares in exports. Hence, we will only use the binary information con-

tained in the data (unweighted network) and focus on the extensive margin of value added

trade among countries. The option to perform a binary analysis based on two main argu-

ments. Firstly, as far as we know, this is the ﬁrst application of network analysis to foreign

value added in exports at the country level, thus a binary analysis seemed the natural way

to start. Secondly, a major research question in this paper is the identiﬁcation of the main

users and suppliers of foreign value added in exports. Setting the threshold, as in equation

5, focuses on the importance of the phenomenon relatively to the size of the user country.

Hence, small countries do not necessarily disappear from the analysis. Even if the level of

their trade in value added is small compared to the world total, their imports of value added

can represent a large share of gross exports in case they are deeply engaged in GVCs. A

weighted network analysis of foreign value added in exports, using the levels of value added

ﬂows between countries as weights, constitutes a distinct and complementary work that can

answer different economic questions.

A very simple but powerful notion in network analysis is the degree of a node. This is simply

the number of connections or edges that it has with all other nodes. If the network is directed,

every node has two different degrees: outdegree and indegree. The outdegree is the number

of outgoing edges and the indegree is the number of incoming edges, that is:

dout
s =

−→asr

N
∑
r=1

and

din
s =

−→ars

N
∑
r=1

Therefore, the edges pointing towards a country identify its main suppliers and, conversely,

the edges originating from a country reveal its importance as supplier in GVCs.

The analysis in this paper is extended beyond the total value added trade network with a

view to discussing the distinct roles of goods and services in GVCs. Although it would

be possible to examine the interrelations within all 35 sectors available in the WIOD, we

ECB Working Paper 1931, July 201611focus the analysis on these two broad sectors as they tend to reﬂect major technological

differences. We start by deﬁning the networks of total foreign value added in exports of

goods and of services. For each broad sector (goods or services), the edges in the network

are set by pairs of countries where the supplier’s total value added share in the user country’s

exports of the selected sector is above the threshold. Hence, the reading of the sectoral

networks must always take into account that the importance of foreign suppliers of value

added is set relative to the user countries’ gross exports of goods or of services. In the same

way, we deﬁne the individual networks that result from considering the foreign value added

that is originated in the each of these two broad sectors and is embodied in either exports of

goods or of services.

4 What can we learn from the networks of value added trade?

4.1 Aggregate network metrics

The research on complex networks has developed a rich set of quantitative metrics aimed at

describing their main structural characteristics. Tracking such aggregate metrics over time

can also shed light on the dynamics of the patterns of network formation. Figure 1 displays

some of these macro measures for the networks of total foreign value added embodied in

total, goods and services exports over time. Complementarily, Appendix B reports the values

of these measures for all networks discussed along the paper. Overall, the results of the

aggregate metrics are broadly similar for the cases of total and goods exports, while the

measures computed for the network of total foreign value added in services exports show a

distinct behaviour.

A very simple aggregate metric is the average degree of the network, which measures its

average connectivity (panel a). From 1995 to 2011, there was an increase in the average de-

gree, meaning that, on average, each country has a larger number of client/supplier relations.

Therefore, over this period, the GVC network became more complex and strongly connected,

as trade in intermediates among countries intensiﬁed. This result is much stronger for total

trade and goods than for services networks.

The geodesic distance is the length of the shortest path between two nodes and the average

geodesic distance or characteristic path length is simply its average over all nodes. It is a

measure of how close nodes are to each other in a network and could be seen as a measure

of economic integration. The average geodesic distances depicted in panel b) have relatively

ECB Working Paper 1931, July 201612low values, similar to those of comparable random networks. In addition, a decreasing trend

is visible for total and goods networks, in spite of a slight upturn during the global crisis,

meaning that countries are becoming more integrated.

The prime node-speciﬁc network metrics are the centrality measures that aim at identifying

the most important nodes in a network. Several deﬁnitions of centrality exist in the liter-

ature in line with the distinct meanings of importance of a node. As discussed in Jackson

(2008), node centrality measures can be broadly categorised into four groups: degree (how

connected a node is); closeness (how easily a node can reach other nodes); betweenness

(how important a node is for connecting other nodes); and neighbours’ characteristics (how

important a node’s neighbours are). In this latter class of centrality measures, the central-

ity of a node is recursively related to the centralities of the nodes it is connected to, i.e., a

node’s importance depends on how important its neighbours are. This category includes the

measure of eigenvector centrality used herein, among others.

Centralisation is an aggregate metric that characterises how a network is centred around one

or a few important nodes by examining the differences in centrality between the most central

node in a network and all others. Higher levels of centralisation indicate a more concentrated

network structure, dominated by one or a few very central nodes.

In this sense, a very

centralised network is less resilient to shocks because it can fail if such important nodes are

not present. The centralisation measures of the three value added networks are very high

but decreased over time, specially for total trade and goods networks (panel c). This means

that node eigenvector centrality scores have become relatively closer over time.Therefore,

while a set of major economies maintained their core positions in the network over the entire

period, their inﬂuence has declined as other countries became also relevant players in GVCs.

With directed networks, it is also relevant to examine the extent to which ties are recipro-

cated, as a predominance of asymmetric relations points to a hierarchical structure. Panel d)

of Figure 1 presents the measure of reciprocity of Garlaschelli and Loffredo (2004b), which

computes the proportion of edges that are reciprocated, while accounting for the density of

the network. Hence, this measure is appropriate to examine the evolution of reciprocity of a

network with time-varying density. The services network shows an antireciprocal behaviour

over the whole period, as the probability of occurring mutual links is smaller than in a ran-

dom network. Total trade and goods networks are reciprocal networks in most periods but

the values obtained are very low. However, for these two networks, there is some increase

in the proportion of mutual connections in the last years. This increase in the percentage

ECB Working Paper 1931, July 201613of two-way value added ﬂows within the network suggests some deepening of GVCs with

some countries acting both as important clients and suppliers of each other.

The pattern of connectivity among nodes of varying degrees also affects the interaction dy-

namics of the network. If the high-degree nodes in a network tend to be connected with other

high-degree nodes, then the network is said to be assortative or to show assortative mixing.

On the contrary, the network is said to be disassortative if the nodes with many connections

tend to be attached to other nodes with few connections. Degree assortativity is a network-

level measure which quantiﬁes the tendency of nodes to link with nodes with similar degrees,

i.e., it refers to the correlation between the degrees of adjacent nodes. Starting from the work

of Serrano and Boguñá (2003), most empirical studies on international trade networks have

found that they are characterised by a disassortative mixing, as highly connected countries

tend to connect to poorly connected ones. The results obtained for the value added networks

included in panel e) also show such a disassortative pattern. This feature reﬂects the exis-

tence of a few big and central countries that act as hubs and its economic interpretation can

be linked with the discussion on core-periphery relationships.

Another important feature of networks is how tightly clustered they are. There is a variety

of concepts that measure how cohesive or closely knit a network is. The global clustering

coefﬁcient or weak transitivity is deﬁned as the probability of two nodes being connected

if they share a mutual neighbour and gives an overall indication of clustering in the whole

network. For total trade and goods value added networks, there was a slight increase in the

clustering coefﬁcient until 2008 and some decline afterwards in the former (panel f). How-

ever, the clustering values are much higher than those corresponding to a random network

of the same size, which suggests the presence of a hierarchical structure and a tendency of
countries to group together around some inﬂuential players.6 The values of the global clus-

tering coefﬁcient are always smaller in the value added network of services and its path is

more stable over time.

In summary, the analysis of these aggregate metrics shows that the networks of foreign value

added in exports are very centralised and asymmetric networks, where a few large economies

act as hubs, thus, creating their areas of inﬂuence. Overall, these results are in line with those

obtained from studies of the WTW in its binary form, which was found to have small-world

properties (high clustering coefﬁcient and low average geodesic distance) and a hierarchical

6We choose to represent the global clustering coefﬁcient over time in panel f) of Figure 1 because it is less sensitive to the inclusion of
low degree nodes than the average local clustering coefﬁcient. In our case, the average local clustering coefﬁcient is higher than the global
clustering coefﬁcient in all networks considered and both are higher than the clustering coefﬁcient of an equivalent random network.

ECB Working Paper 1931, July 201614structure with a disassortative pattern.7 Over time, value added trade networks became more

complex and intensely connected and their hierarchical structure has been somewhat mod-

erated with the entrance of new players and the establishment of new value added linkages

among them.

Figure 1: Aggregate network metrics over time

(a) Average degree

(b) Average geodesic distance

(c) Centralisation of eigenvector centrality

(d) Reciprocity correlation

(e) Degree assortativity

(f) Global clustering coefﬁcient

Notes: Network metrics were computed using the R packages statnet (Handcock et al., 2003) and igraph (Csardi and Nepusz, 2006). With
the exception of the reciprocity correlation coefﬁcient, all aggregate network measures were computed ignoring the directionality of the
edges.

4.2 The network of total foreign value added trade

After describing the main topological features of the networks of foreign value added in

exports in the previous section, the next sections will graphically represent those networks

and calculate standard local network measures to detect and analyse the major users and

suppliers of foreign value added over time. In fact, one of the primary methods of network

analysis is the graphic visualisation of its structure, focusing on the characteristics of individ-

ual nodes within the network. Figure 2 displays the network representations of total foreign

value added in total gross exports in 1995 and 2011.

7Small-world networks, according to Watts and Strogatz (1998), are a class of networks that are highly clustered, like regular networks,
and have small characteristic path lengths, like random graphs. For a discussion of the small-world properties of a network, see Humphries
and Gurney (2008) and Telesford et al. (2011).

8.012.016.00.04.0199519971999200120032005200720092011TotalGoods Services1.81.92.02.11.51.61.7199519971999200120032005200720092011TotalGoods Services0.650.700.750.550.60199519971999200120032005200720092011TotalGoods Services0.020.060.100.14-0.10-0.06-0.02199519971999200120032005200720092011TotalGoods Services-0.2-0.10.0-0.5-0.4-0.3199519971999200120032005200720092011TotalGoods Services0.30.40.50.60.00.10.2199519971999200120032005200720092011TotalGoods ServicesECB Working Paper 1931, July 201615Each country is represented by a circle or a square, with arrows pointing from supplier to

receiver of value added. Given the deﬁnition presented in equation 5, the scale of an economy

interacts with its integration in GVCs to establish its importance within the network. In this

setup, a force-directed layout algorithm is typically used to determine the location of the

nodes in the network visualisation. All network graphs in this paper are based on the Harel-

Koren fast multi-scale algorithm (Harel and Koren, 2002) and are drawn with the use of
NodeXL (Hansen et al., 2010).8 In all network graphs, the size of each node is proportional

to its total degree (sum of indegree and outdegree) and the color of the node is mapped to its

indegree, with darker shades indicating higher values. In addition, the shape of the vertices

is related to its coreness. A k-core is a maximal subset of nodes such that each is connected

to at least k others in the subset, that is, every node in the sub-graph has at least degree k.

The core of maximum order is also called the main core. The nodes that belong to the main

core are shaped as a square in the ﬁgures.

In general, larger countries tend to have bigger nodes and to locate in the centre of the

network, mostly because they are important suppliers of value added. Smaller economies

tend to locate in the outer layers of the network. These countries are usually placed in

intermediate stages of the GVC and act as clients of other countries either at the beginning

of the chain (e.g. focused on R&D and engineering or raw materials) or at the ﬁnal stages (as

assemblers). In addition, some small countries have the darkest nodes in the graph as they

use value added from several sources, signalling also a strong integration in the network.

The increase in the density of the network from 1995 to 2011, due to a larger number of

edges linking the 40 countries in the database, stands out in Figure 2. The ﬂows of for-

eign value added embodied in gross exports became larger, increasing the number of cases

where the threshold is surpassed and the respective edges are represented. The position of

the nodes takes into consideration their relative importance in the network. In 1995, the

countries standing in the main core are the large European countries, like Germany, France

and the UK, as well as the US. Secondary relations are seen in Asia, centred in Japan as a

supplier and linking countries like China, Korea and Taiwan. Other secondary edges locate

in Central and Eastern Europe, with Russia supplying value added to several other countries

in the region.

8Other layout algorithms were tried, like the Fruchterman-Reingold force-directed algorithm (Fruchterman and Reingold, 1991), with-

out substantial differences in the visualisation of the networks.

ECB Working Paper 1931, July 201616Figure 2: Network graphs of total foreign value added in exports - 1995 and 2011

(a) 1995

(b) 2011

Notes: The networks are directed and the arrows that represent the edges point towards countries whose exports embody more than 1
percent of value added from the source country. The size of each node is proportional to its total degree (sum of indegree and outdegree)
and the color of the node is mapped to its indegree, with darker shades indicating higher values. The nodes that belong to the k-core of
maximum order are shaped as a square. The network graphs are based on the Harel-Koren fast multi-scale algorithm and are drawn with
the use of NodeXL (see Hansen et al. (2010) for details).

ECB Working Paper 1931, July 201617In 2011, the network is denser than in 1995 and China joins the inner core, while Germany

and the US maintain their central positions. A more subtle difference between these two

central countries, Germany and the US, is visible when the shade of the nodes is consid-

ered. Even if their nodes are almost of the same size (i.e., similar total degree), the node

of Germany is darker than that of the US in both periods. This means that the role of the

US is mostly that of a supplier of foreign value added to other countries, while Germany

also has some relevance as a client of value added to be embodied in German exports (i.e.,

higher indegree). Finally, Russia became a member of the main core and gained importance

in the network as a supplier of other countries. This is evident from its squared, bigger but

still light-shaded node in 2011, which mostly reﬂects Russia’s role as a major exporter of

energy products. These facts are in line with the conclusions of other authors that highlight

the progressive transformation of GVCs, evolving from a regional dimension into a truly

global network, i.e., the emergence of the so-called “factory world” (see Los et al. (2015) for

a discussion).

4.2.1 Degree distribution

The examination of the degree distribution provides additional insights about the structure of

a network. In contrast to a random network, in most real world networks the large majority

of nodes has a relatively small degree, while a few nodes have very large degrees. Hence,

the degrees of the nodes in most networks are highly right-skewed and their distribution has

a long right tail of values that are much higher than the mean. Figure 3 shows the outdegree

and indegree marginal cumulative distributions of the networks of total foreign value added

in exports for 1995 and 2011. The cumulative outdegree (indegree) distribution Pk gives
the percentage of nodes that have an outdegree (indegree) larger than or equal to k, i.e, the

probability that the outdegree (indegree) of a node is greater or equal than k.

The visual inspection of these distributions conﬁrms that they are markedly right-skewed

and became more polarised over time, i.e., with more density concentrating around extreme

values as the complexity of the network increases. The marginal outdegree distribution is

particularly right-skewed as large countries tend to dominate the supply of foreign value

added into other countries’ exports. For instance, in 1995, 45 percent of the nodes had a null

outdegree but that percentage declined to 22.5 percent in 2011, pointing to a deepening of

GVCs over time. Another signal of the increase in the complexity of the network is the rise

in the percentage of nodes with more outdegrees. In 1995, 17.5 percent of the nodes had

ECB Working Paper 1931, July 201618outdegree ≥ 8 and in 2011 that percentage increased to 22.5 percent. In addition, in 2011, 10

percent of the nodes (i.e., 4 countries) had an outdegree ≥ 25. The countries that emerged as

the largest input suppliers to the world economy were China, the US, Germany and Russia,

with at least 25 other countries using their value added in the production of exports in a

percentage larger than 1.

The same broad features are visible from the marginal indegree distributions, though not so

markedly, as the distributions are less right-skewed than in the outdegree case. That is, the

asymmetry among countries in terms of the number of different sources of foreign inputs

embodied in exports is smaller than in the supply side where a small number of countries

stand as major suppliers in GVCs. The fact that GVCs became more complex over time is

also visible in the increase in the percentage of countries whose exports incorporate foreign

value added from many sources. In 1995, 12.5 percent of the nodes had an indegree ≥ 8 and

that percentage increased to 37.5 in 2011.

Figure 3: Outdegree and indegree marginal cumulative distributions - 1995 and 2011

(a) Outdegree

(b) Indegree

Notes: The x-axis gives the outdegree (indegree) of each country in a log scale. The y-axis, also in log scale, gives the probability of
ﬁnding a country with outdegree (indegree) ≥ x, that is, the empirical cumulative distribution Px.

4.2.2 Degree centrality

Centrality indicators are used to determine how important nodes are in a network. Node de-

gree, which is also designated as degree centrality, is the simplest form of centrality. Despite

its simplicity, the indegree and outdegree of the various countries in these networks allow

for the identiﬁcation of the most important users and suppliers of foreign value added in ex-

ports in each period. Recall that the international ﬂows of value added were computed using

the Leontief inverse matrix and, hence, reﬂect the ﬁnal static equilibrium after all stages of

production have propagated through the world economy.

ECB Working Paper 1931, July 201619Figure 4 displays the evolution of indegree and outdegree centralities for the main countries

in the GVC network from 1995 to 2011 and underlines some of the major features observ-

able in the networks of value added trade described above. The values of the indegree and

outdegree centralities of each country in 2011 are presented in Appendix C.

The most important suppliers of value added throughout the entire period are the US and

Germany, countries whose value added is regularly used in the exports of more than 30 other

countries. Panel a) of Figure 4 also shows a sharp rise in the outdegree of China since the

beginning of the 2000s, accelerating after 2003 and standing as the most important supplier

in 2011. In this year, 35 other countries are identiﬁed as using Chinese value added in their

exports above the deﬁned threshold. Moreover, the role of Russia as a supplier of value

added in world GVCs has also slowly increased since the mid 2000s. Panel b) focuses

on other relevant economies, which seem to have lost some of their relative importance as

suppliers in the network. The reduction in the outdegree of Japan is clear, while the UK

shows an upturn in the latest years of the period, though not compensating the decline that

took place after the beginning of the 2000s.

A complementary analysis bases on the identiﬁcation of the countries that import value added

from many sources to embody in their exports. Panel c) of Figure 4 identiﬁes smaller Euro-

pean countries that stand as the most important receivers of foreign value added in the GVC

network, i.e., countries with high indegree. The Czech Republic, Hungary and Slovakia pro-

gressively increased their importance as clients of foreign value added to be incorporated in

exports, which conﬁrms their important role in the intermediate stages of European produc-

tion chains. Although less markedly, the same evolution is visible for Belgium.

Figure 4: Main suppliers and users of foreign value added in exports over time

(a) Outdegree centrality

(b) Outdegree centrality

(c) Indegree centrality

Notes: The outdegree centrality of a country reﬂects its relevance as a supplier of foreign value added in exports, while the indegree
centrality signals its importance as a user of foreign value added in exports.

2025303540051015199519971999200120032005200720092011DEUUSARUSCHN2025303540051015199519971999200120032005200720092011GBRFRAITAJPN81012141602468199519971999200120032005200720092011CZEHUNSVKBELECB Working Paper 1931, July 2016204.3 The networks of goods and services foreign value added trade

One important dimension of the networks of foreign value added in exports relates to the

role of different sectors in the organisation of GVCs. Technology, as deﬁned by the structure

of I-O tables, imposes a relation between speciﬁc sectors, thereby affecting the linkages in

production networks. In addition, choices of ﬁrms regarding the international organisation

of the production process also crucially shape the linkages between countries that act as sup-

pliers and users of different types of value added. To study the roles of goods and of services

as both inputs and outputs on foreign value added trade networks, this section graphs these

networks and computes simple local network metrics to identify and examine the main users

and suppliers of foreign value added of goods and of services embodied in exports of goods

and of services. The values of the indegree and outdegree centralities of each country in 2011

for all of these networks are included in Appendix C and support several of the inferences

made in this section.

4.3.1 Goods and services as outputs

Figure 5 represents the networks of total foreign value added in exports of goods and of

services in 2011. It is clear from the visualisation of these networks that GVCs are more

developed and integrated in goods than in services, i.e., there are more edges among countries

in the former case. In line with the analysis of section 4.1, the representation of the goods

exports network in 2011 strongly resembles that of foreign value added in total exports. In

both networks, China, the US and Germany belong to the main core and stand as the 3 top

suppliers of foreign value added, respectively. This inference is also supported by the fact

that the order of the main k-core is very similar in both networks.

In addition, German

exports of goods also use foreign value added from more sources than those of the other two

main suppliers. The main users of foreign value added in goods exports are again the Czech

Republic, Hungary and Slovakia.

As for the services exports network in 2011, the US stands out as the main supplier of foreign

value added to be embodied in the services exports of other countries, followed by Germany

and then China. Moreover, these three countries are less important as users of foreign value

added in their services exports than in their goods exports.

In 2011, the country whose

services exports use value added from more sources is Denmark, with an indegree of 11.

ECB Working Paper 1931, July 201621Figure 5: Network graphs of total foreign value added in exports of goods and of services

(a) Exports of goods 2011

(b) Exports of services 2011

Notes: The networks are directed and the arrows that represent the edges point towards countries whose exports embody more than 1
percent of value added from the source country. The size of each node is proportional to its total degree (sum of indegree and outdegree)
and the color of the node is mapped to its indegree, with darker shades indicating higher values. The nodes that belong to the k-core of
maximum order are shaped as a square. The network graphs are based on the Harel-Koren fast multi-scale algorithm and are drawn with
the use of NodeXL (see Hansen et al. (2010) for details).

ECB Working Paper 1931, July 201622Overall, the analysis so far conveys the message that GVCs presently play a stronger role

in goods than in services. As previously mentioned, the distinct shapes of the goods and

services networks depicted in Figure 5 reﬂect not only the technological differences implicit

in the global I-O matrix but also the differences in the organisation of GVCs. Therefore, it

could be argued that the liberalisation of services trade and the increased demand for ser-

vices around the world will drive the expansion of GVCs towards more foreign value added

of services being embodied in exports of goods or of services. In order to shed some light

on these issues, the roles of goods and of services both as inputs and as outputs in GVCs are

explicitly considered in the next subsection.

4.3.2 Goods and services as inputs and outputs

The four panels in Figure 6 present the combined roles of goods and of services as both

inputs and outputs on value added trade networks in 2011. The comparison of panels a)

and b) makes it clear that foreign value added of goods is mostly used in GVCs that lead to

exports of goods. The network of foreign value added of goods used in exports of services

is the least dense of the four networks considered: its average degree is less than half of the

other networks considered and around a ﬁfth of the value obtained for the network of goods

foreign value added in goods exports. This is not surprising as classical GVCs relate to trade

of parts and components to be embodied in different stages of the manufacturing process,

while goods tend to be embodied in services typically as energy sources.

The comparison of panels c) and d) reveals that foreign inputs of services are embodied both

in exports of goods and of services. This result is in line with other studies that highlight the

importance of services in GVCs (see, for instance, Amiti and Wei (2005) for a description of

the main world trends in outsourcing of services and Francois et al. (2015) for an analysis of

the value added trade linkages between services and goods). In fact, the efﬁcient operation of

GVCs involves signiﬁcant inputs of services, like logistics, transportation and other business

services, and depends on the availability of the adequate services at low cost.

A complementary reading of panels a) and c) states that foreign value added in goods exports

comes both from goods and from services inputs, while foreign value added embodied in

services exports originates mostly from services inputs (comparison of panels b) and d)). In

recent decades, the sharp progress in information and communication technologies and the

strong fall in telecommunication costs have enhanced the development of GVCs within the

ECB Working Paper 1931, July 201623services sector. The network displayed in panel d) conﬁrms the existence of these purely

services-based GVCs.

It is interesting to assess whether countries that stand as the main users of foreign value added

are the same in the four panels of Figure 6. There are important differences in countries’

indegrees that relate with their distinct roles in GVCs (see Appendix C). In 2011, the main

users of goods foreign value added embodied in exports of goods are Hungary and Slovakia

with indegrees of 8. These two Central-Eastern European countries are engaged in classical

goods GVCs that have been documented in the literature. In this respect, Kaminski and Ng

(2005) offer a detailed analysis of the integration of Central European countries in global,

mostly EU-based, networks of production and distribution. In addition, goods exports of

Belgium, Taiwan and Malta incorporate goods inputs from 7 other countries. In the sparser

network of foreign goods inputs to services exports, the largest indegrees are just 4 for Malta

and 3 for Denmark.

Regarding services inputs to goods exports, Belgium and Malta include value added from

6 other countries in their exports, while the number for Finland, Luxembourg, Ireland and

Hungary is equal to 5. Finally, in the network of services foreign value added in services

exports, the two main users in 2011 are Luxembourg and Denmark, embodying foreign in-

puts from many more sources than the other countries (indegrees of 9 and 8, respectively).

Luxembourg and Denmark participate in services GVCs mostly through the use of foreign

ﬁnancial services and transport and other business activities on the production of their sig-

niﬁcant exports of services. The next group of countries presents indegrees equal to 4 and is

composed by the Netherlands, Belgium, Ireland, Estonia and Hungary.

The main four suppliers of foreign value added in the networks of Figure 6 are Germany,

the US, China and Russia, with the exception of the UK, which is the third most important

supplier of services inputs to exports of services in 2011 (panel d). Even if the countries that

act as hubs in these GVCs are the same, their ranking is not the same in the four networks.

The main suppliers of goods value added to exports of goods are China and Germany, with

outdegrees of 31 and 28, respectively, in 2011. The number of countries whose goods exports

use goods value added from Russia and the US is much smaller (19 and 17, respectively).

Regarding the sparser network of goods inputs to services exports, the maximum outdegrees

are, as expected, much lower. The main suppliers are Russia and Germany with outdegrees

of 10, while China and the US have outdegrees of 4 and 3, respectively.

ECB Working Paper 1931, July 201624Figure 6: Network graphs of goods and services foreign value added in goods and services exports in 2011

(a) Goods inputs to goods exports

(b) Goods inputs to services exports

(c) Services inputs to goods exports

(d) Services inputs to services exports

Notes: The networks are directed and the arrows that represent the edges point towards countries whose exports embody more than 1
percent of value added from the source country. The size of each node is proportional to its total degree (sum of indegree and outdegree)
and the color of the node is mapped to its indegree, with darker shades indicating higher values. The nodes that belong to the k-core of
maximum order are shaped as a square. The network graphs are based on the Harel-Koren fast multi-scale algorithm and are drawn with
the use of NodeXL (see Hansen et al. (2010) for details).

ECB Working Paper 1931, July 201625The two main suppliers of services foreign value added are the same in the case of exports of

goods and of services: the US and Germany. However, there is a substantial difference in the

relative magnitude of their outdegrees in the two networks depicted in panels c) and d). In

the services inputs to goods exports network, the two countries have similar outdegrees (24

for the US and 23 for Germany), while, in the services inputs to services exports, the outde-

gree of the US doubles that of Germany (24 and 12, respectively). These differences point

to distinct roles played by two of the largest economies on today’s geographically dispersed

production and the next subsection tries to examine them in more detail.

4.3.3 Comparing Germany, the US, China and Russia as suppliers of value added

A complementary analysis to that performed with Figure 6 is to adopt the perspective of the

four main suppliers of foreign value added in exports and assess the relevance of each of

the goods and services I-O relationships for their outdegrees, i.e., which supply linkages are

dominant in terms of their role as hubs on GVCs between 1995 and 2011. Considering each

of the four detailed goods and services networks of Figure 6, the evolution of the values of

the outdegree centralities of Germany, the US, China and Russia over time is presented in
Figure 7.9

The outdegree centralities of Germany from 1995 to 2011 in the four detailed networks are

displayed in panel a) of Figure 7. The ﬁrst point to notice is the absence of a major trend

in the relative importance of each network, which suggests that Germany established its role

in GVCs before 1995. This role is mostly based on the supply of goods value added to be

used in other countries’ exports of goods. The supply of services inputs to be incorporated

in exports of goods of other countries is also relevant.

For the US, there is also no major trend in the relative importance of either goods or ser-

vices as inputs embodied in exports of goods or of services by other countries, signalling

a mature GVC participation. Nevertheless, there are some interesting differences relatively

to Germany. Although there is a relevant role for goods value added as an input of other

countries’ exports of goods, the US supplies more services value added for services exports.

9 For each country, the sum of the outdegrees obtained in each of the four panels of Figure 6, considering goods and services both as
inputs and as outputs, does not match the outdegrees of the total network represented in Figure 2. On the one hand, it is possible that a
supplier identiﬁed in Figure 2 provides both goods and services value added to be embodied in another country’s exports of goods and/or
services above the threshold. In the partial networks, these edges are shown separately and each outdegree is counted, while in the total
network of Figure 2 they correspond to just one outdegree. On the other hand, in the partial networks the set of inputs is restricted to goods
or to services, making it harder that their isolated value added represents a share of gross exports (of goods and/or services) of the user
country above the deﬁned threshold.

ECB Working Paper 1931, July 201626Such key role of US services inputs in services exports of other countries points to a type of

participation in GVCs that is distinct from that of Germany.

The path of China’s outdegree centralities along goods and services dimensions is described

in panel c) of Figure 7. As expected, the striking element is the sharp increase in the number

of outgoing edges after the beginning of the 2000s. The emergence of China as a major

world supplier of value added is mostly centred in goods inputs to goods exports. However,

in the latest years, there was also some increase in the number of countries using Chinese

services value added in their exports of both goods and services.

As for the case of Russia, there is no clear trend in the relative importance of each network

in this period but there is some volatility in the values of the outdegree centralities across

time. This volatility reﬂects the changes in the price of energy goods, which constitute an

important element of Russian value added used in other countries’ exports.

In any case,

Russia’s role as world supplier of value added mostly relates with goods inputs to goods

exports.

Figure 7: Main four suppliers of foreign value added in exports over time - goods and services

(a) Germany

(b) United States

(c) China

(d) Russia

Notes: In each panel, the values of outdegree centralities of the country are those obtained in each of the four networks of subsection
4.3.2, considering goods and services both as inputs and as outputs.

405060708090010203040199519971999200120032005200720092011Goods inputs to goods exportsGoods inputs to services exportsServices inputs to goods exportsServices inputs to services exports405060708090010203040199519971999200120032005200720092011Goods inputs to goods exportsGoods inputs to services exportsServices inputs to goods exportsServices inputs to services exports405060708090010203040199519971999200120032005200720092011Goods inputs to goods exportsGoods inputs to services exportsServices inputs to goods exportsServices inputs to services exports405060708090010203040199519971999200120032005200720092011Goods inputs to goods exportsGoods inputs to services exportsServices inputs to goods exportsServices inputs to services exportsECB Working Paper 1931, July 2016275 Final remarks

Global Value Chains (GVCs) have deeply changed the paradigm of world production and

cannot be perfectly understood under the classical concept of comparative advantages ap-

plied to broad sectors and countries. Instead, GVCs are mostly about combining value added

from different sources. Their effects span over multiple dimensions, namely trade ﬂows, pro-

ductivity and labour market developments. GVCs also have signiﬁcant policy implications,

changing the way policy-makers interpret trade policies, exchange rate ﬂuctuations and ex-

ternal competitiveness. The correct understanding of the nature and dynamics of GVCs is

crucial to reap the beneﬁts from international trade and to assess the role, if any, that eco-

nomic policy can play in shaping their evolution.

The expansion of GVCs requires new tools for evaluating the linkages among countries,

which can no longer be adequately appraised by bilateral gross trade ﬂows. This paper

makes use of standard tools of network analysis to examine the evolution of value added

trade linkages between countries in the period 1995-2011. More speciﬁcally, we focus on

the concept of foreign value added in exports and the GVC is represented as a directed

network of nodes (countries) and edges (value added ﬂows between them). Initially, several

aggregate network metrics are computed and the evolution of the key structural properties

of the networks of foreign value added in total, goods and services exports are discussed.

The analysis continues by examining in more detail the network of total foreign value added

in exports and is extended afterwards to study the roles of goods and of services as both

inputs and outputs in GVCs. The main differences between Germany, the US, China and

Russia as major suppliers of foreign inputs embodied in the exports of other countries are

also examined.

As the empirical research on the international fragmentation of production expands, the anal-

ysis of the networks of foreign value added in exports stands as an important complementary

tool. Therefore, this paper offers an analysis of GVCs that integrates with that of other papers

also produced within the scope of the Competitiveness Research Network (CompNet) of the

European System of Central Banks (ESCB), namely Amador et al. (2015), Benkovskis and

Wörz (2015) and Nagengast and Stehrer (2016).

Not surprisingly, several results of our network analysis of foreign value added in exports

conﬁrm the ﬁndings of previous studies. The evolution of the value added networks over

time is consistent with the growing fragmentation of production and deepening of GVCs.

ECB Working Paper 1931, July 201628Larger countries play a vital role and the regional dimension of GVCs is still dominant,

though it is progressively giving way to a more global network. The network of foreign

value added in goods exports is denser than that of services exports. We also ﬁnd evidence

of the important role of services inputs for exports of goods and some purely services-based

GVCs are visible.

At country-level, Germany and the US maintain a robust participation in GVCs over the

whole period, but with meaningful differences between them. Germany is a major supplier

of goods inputs to be used in other countries’ exports of goods, while the US supplies more

services value added to services exports. In addition, Germany also has some relevance as

a client of value added to be embodied in German exports, while the US acts mostly as a

supplier of value added to other countries. The rising importance of China as a supplier of

value added is impressive and chieﬂy reﬂects the supply of foreign inputs of goods. Russia

is an important supplier of goods inputs to goods exports, mainly due to its role as major

source country for energy products.

The analysis of aggregate network metrics reveals that GVCs are very centralised and asym-

metric networks, with a few large economies acting as hubs. These networks are also charac-

terised by small-world properties, showing a hierarchical structure with a disassortative pat-

tern. Over time, with the integration of new countries in GVCs, the networks of value added

trade became denser, more complex and intensely connected. However, all in all, there is still

room to expand and deepen the networks of value added trade in the global economy, both

through the stronger integration of peripheral economies and the development of linkages in

the services sector.

In spite of intense research over the last decades, the mapping and measurement of GVCs

is still incomplete and the use of tools of network analysis may bring valuable results. In

fact, the relevance of network analysis to understand the structure and organisation of world

production is large and the existing research is still in its infancy. A complex network ap-

proach that takes into account the full set of connections among countries and their positions

in GVCs can contribute to a better assessment of how globalisation affects each national

economy and of which policies are appropriate in that environment.

Network analysis and its metrics can help to capture the heterogeneity of the ﬁrms, sectors

and/or countries participating in GVCs, accounting for their direct and indirect linkages, and

to explore the complexity of the whole structure of interactions. As discussed in Carvalho

(2014), a network perspective of general production linkages offers important insights on

ECB Working Paper 1931, July 201629the propagation of shocks and on the origins of aggregate ﬂuctuations. An extension of this

literature that takes account of value added trade ﬂows can be extremely useful to examine

the international transmission of shocks and the synchronisation of business cycles across

countries. The development of models of global supply chains that incorporate the rich set

of measures in network theory is a promising avenue for future research.

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ECB Working Paper 1931, July 201633Appendices

A Sectoral and geographical breakdown of the database

Sectoral breakdown in the World Input-Output Database (WIOD) (35 sectors)

ISIC rev.3 code

Industry name

AtB
C
15t16
17t18
19
20
21t22
23
24
25
26
27t28
29
30t33
34t35
36t37
E
F
50
51
52
H
60
61
62
63
64
J
70
71t74
L
M
N
O
P

Agriculture, hunting, forestry and ﬁshing
Mining and quarrying
Food, beverages and tobacco
Textiles and textile products
Leather, leather products and footwear
Wood and products of wood and cork
Pulp, paper, printing and publishing
Coke, reﬁned petroleum and nuclear fuel
Chemicals and chemical products
Rubber and plastics
Other non-metallic mineral
Basic metals and fabricated metal
Machinery, not elsewhere classiﬁed
Electrical and optical equipment
Transport equipment
Manufacturing, not elsewhere classiﬁed; recycling
Electricity, gas and water supply
Construction
Sale and repair of motor vehicles and motorcycles; retail sale of fuel
Wholesale trade, except of motor vehicles and motorcycles
Retail trade and repair, except of motor vehicles and motorcycles
Hotels and restaurants
Inland transport
Water transport
Air transport
Other supporting transport activities
Post and telecommunications
Financial intermediation
Real estate activities
Renting of machinery & equipment and other business activities
Public administration and defence; compulsory social security
Education
Health and social work
Other community, social and personal services
Private households with employed persons

Notes: Throughout this paper, the goods aggregate includes ISIC rev. 3 industry codes from AtB to F and the services aggregate includes
ISIC rev. 3 codes between 50 and P.

ECB Working Paper 1931, July 201634Geographical breakdown in the World Input-Output Database (WIOD) (40 countries)

ISO alpha-3 codes Country names

AUS
AUT
BEL
BGR
BRA
CAN
CHN
CYP
CZE
DEU
DNK
ESP
EST
FIN
FRA
GBR
GRC
HUN
IND
IDN
IRL
ITA
JPN
KOR
LTU
LUX
LVA
MEX
MLT
NLD
POL
PRT
ROM
RUS
SVK
SVN
SWE
TUR
TWN
USA

Australia
Austria
Belgium
Bulgaria
Brazil
Canada
China
Cyprus
Czech Republic
Germany
Denmark
Spain
Estonia
Finland
France
United Kingdom
Greece
Hungary
India
Indonesia
Ireland
Italy
Japan
South Korea
Lithuania
Luxembourg
Latvia
Mexico
Malta
The Netherlands
Poland
Portugal
Romania
Russia
Slovak Republic
Slovenia
Sweden
Turkey
Taiwan
United States

ECB Working Paper 1931, July 201635B Aggregate network metrics from 1995 to 2011

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Network of total foreign value added in exports (T)

Number of non-isolated nodes

39

39

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

Number of unique edges

184

194

204

207

210

216

221

210

218

240

240

259

264

261

240

237

258

Average degree

Average geodesic distance

9.2

1.8

9.7 10.2 10.4 10.5 10.8 11.1 10.5 10.9 12.0 12.0 13.0 13.2 13.1 12.0 11.9 12.9

1.8

1.8

1.8

1.8

1.8

1.7

1.8

1.8

1.7

1.7

1.7

1.7

1.7

1.7

1.8

1.7

Centralisation of eigenvector centrality 0.71 0.68 0.68 0.67 0.67 0.67 0.66 0.67 0.67 0.66 0.66 0.65 0.65 0.65 0.64 0.65 0.62

Reciprocity correlation

Degree assortativity

0.06 0.07 0.03 0.03 0.02 0.02 0.00 0.01 0.00 -0.01 0.01 0.00 0.00 0.02 0.03 0.04 0.02

-0.37 -0.38 -0.39 -0.39 -0.41 -0.39 -0.36

-0.4

-0.4

-0.4 -0.41 -0.39 -0.36 -0.38 -0.32 -0.36 -0.36

Global clustering coefﬁcient

0.41 0.39 0.40 0.40 0.39 0.41 0.40 0.39 0.40 0.42 0.42 0.44 0.48 0.46 0.44 0.43 0.44

Order of the main k-core

8

8

7

8

8

8

8

8

8

8

9

10

10

10

9

9

10

Network of total foreign value added in goods exports (G)

Number of non-isolated nodes

40

39

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

Number of unique edges

190

198

214

220

215

231

237

231

238

268

269

290

295

295

265

260

291

Average degree

Average geodesic distance

9.5

1.8

9.9 10.7 11.0 10.8 11.6 11.9 11.6 11.9 13.4 13.5 14.5 14.8 14.8 13.3 13.0 14.6

1.8

1.8

1.8

1.8

1.7

1.7

1.7

1.7

1.7

1.7

1.7

1.7

1.7

1.7

1.7

1.7

Centralisation of eigenvector centrality 0.70 0.70 0.68 0.67 0.67 0.66 0.64 0.65 0.65 0.64 0.65 0.64 0.63 0.63 0.62 0.63 0.61

Reciprocity correlation

Degree assortativity

0.10 0.09 0.06 0.05 0.03 0.04 0.02 0.02 0.03 0.04 0.04 0.01 0.00 0.00 0.04 0.04 0.02

-0.38 -0.36 -0.39 -0.38 -0.39 -0.38 -0.38 -0.38 -0.39 -0.36 -0.38 -0.34 -0.34 -0.35 -0.34 -0.36 -0.37

Global clustering coefﬁcient

0.39 0.40 0.39 0.40 0.40 0.43 0.41 0.40 0.41 0.45 0.45 0.49 0.50 0.50 0.47 0.47 0.50

Order of the main k-core

8

8

8

8

8

8

8

7

8

9

10

11

10

11

10

10

11

Network of total foreign value added in services exports (S)

Number of non-isolated nodes

38

38

37

37

38

37

37

37

38

38

39

39

39

39

39

39

40

Number of unique edges

120

123

126

132

136

141

136

128

124

130

134

137

149

146

135

130

142

Average degree

Average geodesic distance

6.0

2.0

6.2

2.0

6.3

1.9

6.6

1.9

6.8

1.9

7.1

1.9

6.8

1.9

6.4

1.9

6.2

1.9

6.5

2.0

6.7

2.0

6.9

2.0

7.5

1.9

7.3

1.9

6.8

2.0

6.5

1.9

7.1

2.0

Centralisation of eigenvector centrality 0.71 0.72 0.71 0.70 0.70 0.70 0.71 0.72 0.73 0.69 0.71 0.70 0.68 0.67 0.69 0.72 0.70

Reciprocity correlation

-0.03 -0.01 -0.05 -0.04 -0.06 -0.07 -0.06 -0.04 -0.05 -0.06 -0.06 -0.03 -0.03 -0.03 -0.01 -0.04 -0.07

Degree assortativity

-0.44 -0.42

-0.4 -0.39 -0.36 -0.41 -0.38 -0.41 -0.43 -0.43 -0.41 -0.42

-0.4 -0.42 -0.34 -0.42 -0.41

Global clustering coefﬁcient

0.25 0.28 0.28 0.29 0.30 0.33 0.31 0.29 0.27 0.28 0.29 0.30 0.32 0.26 0.30 0.27 0.29

Order of the main k-core

5

6

5

6

6

6

5

6

6

5

5

5

6

5

6

5

5

Notes: Network metrics were computed using the R packages statnet (Handcock et al., 2003) and igraph (Csardi and Nepusz, 2006). With
the exception of the reciprocity correlation coefﬁcient, all aggregate network measures were computed ignoring the directionality of the
edges.

ECB Working Paper 1931, July 2016361995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Network of goods foreign value added in goods exports (GG)

Number of non-isolated nodes

38

38

39

39

39

39

38

38

39

39

39

39

39

39

39

39

40

Number of unique edges

117

121

133

135

132

143

142

132

133

148

143

161

159

156

136

148

162

Average degree

Average geodesic distance

5.9

2.1

6.1

2.1

6.7

2.1

6.8

2.1

6.6

2.1

7.2

2.0

7.1

2.0

6.6

2.1

6.7

2.1

7.4

2.0

7.2

2.1

8.1

2.0

8.0

1.9

7.8

1.9

6.8

2.0

7.4

1.9

8.1

1.9

Centralisation of eigenvector centrality 0.76 0.77 0.75 0.75 0.76 0.75 0.75 0.76 0.76 0.73 0.73 0.71 0.71 0.73 0.70 0.74 0.70

Reciprocity correlation

Degree assortativity

0.05 0.08 0.09 0.07 0.07 0.05 0.02 0.04 0.04 0.04 0.07 0.05 0.04 0.03 0.02 0.00 0.02

-0.4

-0.3

-0.4

-0.4

-0.3

-0.4

-0.3

-0.3

-0.4

-0.3

-0.3

-0.4

-0.4

-0.3

-0.4

-0.3

-0.4

Global clustering coefﬁcient

0.28 0.29 0.31 0.31 0.32 0.33 0.32 0.31 0.29 0.30 0.30 0.34 0.33 0.32 0.30 0.33 0.33

Order of the main k-core

5

5

5

5

5

5

5

5

5

6

6

7

6

6

5

6

6

Network of goods foreign value added in services exports (GS)

Number of non-isolated nodes

Number of unique edges

Average degree

Average geodesic distance

28

28

1.4

3.1

27

28

1.4

2.4

28

33

1.7

2.9

28

31

1.6

3.1

28

27

1.4

4.1

28

27

1.4

3.5

28

24

1.2

2.7

22

19

1.0

2.4

27

23

1.2

2.4

26

26

1.3

2.5

26

25

1.3

2.1

30

32

1.6

3.7

31

32

1.6

3.4

30

31

1.6

3.8

29

25

1.3

2.8

29

26

1.3

2.2

32

32

1.6

2.8

Centralisation of eigenvector centrality 0.91 0.90 0.89 0.90 0.92 0.91 0.93 0.93 0.93 0.91 0.91 0.89 0.88 0.86 0.90 0.88 0.85

Reciprocity correlation

-0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.01 -0.01 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02

Degree assortativity

-0.4

-0.4

-0.4

-0.4

-0.4

-0.4

-0.3

-0.4

-0.4

-0.4

-0.4

-0.5

-0.4

-0.5

-0.5

-0.5

-0.5

Global clustering coefﬁcient

0.08 0.07 0.05 0.03 0.04 0.00 0.05 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Order of the main k-core

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

Network of services foreign value added in goods exports (SG)

Number of non-isolated nodes

Number of unique edges

Average degree

Average geodesic distance

35

78

3.9

2.3

35

77

3.9

2.2

35

84

4.2

2.1

36

87

4.4

2.1

35

83

4.2

2.1

38

92

4.6

2.2

38

98

4.9

2.1

38

96

4.8

2.0

36

96

4.8

2.0

37

99

5.0

2.0

37

95

4.8

2.1

37

97

4.9

2.1

37

101

5.1

2.1

37

99

5.0

2.1

36

89

4.5

2.2

38

89

4.5

2.2

38

93

4.7

2.2

Centralisation of eigenvector centrality 0.78 0.78 0.78 0.78 0.78 0.76 0.76 0.75 0.76 0.77 0.77 0.76 0.76 0.75 0.77 0.76 0.76

Reciprocity correlation

-0.03 -0.05 -0.03 -0.06 -0.06 -0.06 -0.07 -0.07 -0.04 -0.05 -0.04 -0.04 -0.05 -0.07 -0.04 -0.06 -0.06

Degree assortativity

-0.5

-0.5

-0.5

-0.5

-0.4

-0.5

-0.5

-0.5

-0.5

-0.4

-0.4

-0.4

-0.4

-0.4

-0.4

-0.5

-0.5

Global clustering coefﬁcient

0.20 0.18 0.24 0.24 0.25 0.22 0.22 0.23 0.23 0.25 0.25 0.26 0.26 0.27 0.29 0.22 0.23

Order of the main k-core

4

4

5

5

4

4

4

5

5

5

5

5

5

5

5

4

4

Network of services foreign value added in services exports (SS)

Number of non-isolated nodes

Number of unique edges

Average degree

Average geodesic distance

34

75

3.8

2.4

35

82

4.1

2.2

35

82

4.1

2.3

36

86

4.3

2.3

34

81

4.1

2.2

34

83

4.2

2.3

33

79

4.0

2.3

33

76

3.8

2.2

32

68

3.4

2.4

34

78

3.9

2.4

33

76

3.8

2.4

34

79

4.0

2.4

35

82

4.1

2.5

36

81

4.1

2.5

35

77

3.9

2.4

35

71

3.6

2.4

35

73

3.7

2.4

Centralisation of eigenvector centrality 0.77 0.76 0.78 0.78 0.78 0.78 0.78 0.78 0.76 0.75 0.76 0.74 0.73 0.74 0.77 0.82 0.81

Reciprocity correlation

-0.02 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.02 -0.01 -0.03 -0.02 0.00 -0.03 -0.03 -0.02 -0.05 -0.05

Degree assortativity

-0.3

-0.5

-0.4

-0.4

-0.4

-0.4

-0.3

-0.4

-0.4

-0.4

-0.4

-0.4

-0.3

-0.4

-0.3

-0.4

-0.4

Global clustering coefﬁcient

0.23 0.20 0.22 0.23 0.22 0.25 0.29 0.22 0.20 0.21 0.24 0.23 0.27 0.24 0.24 0.23 0.24

Order of the main k-core

5

5

5

4

4

4

5

5

4

4

4

4

5

4

4

5

5

Notes: Network metrics were computed using the R packages statnet (Handcock et al., 2003) and igraph (Csardi and Nepusz, 2006). With
the exception of the reciprocity correlation coefﬁcient, all aggregate network measures were computed ignoring the directionality of the
edges.

ECB Working Paper 1931, July 201637C Outdegree and indegree centralities in 2011

Outdegree centrality

Indegree centrality

T

G

S GG GS

SG SS

T

G

S GG GS

SG SS

AUS

AUT

BEL

Australia

Austria

Belgium

BGR

Bulgaria

BRA Brazil

CAN Canada

CHN China

CYP

CZE

Cyprus

Czech Republic

4

3

4

0

3

4

4

4

3

0

4

3

0

1

3

0

1

2

4

2

1

0

0

1

35

38

22

31

0

3

0

3

0

1

0

2

0

0

0

0

0

0

4

0

0

0

0

1

0

1

0

10

0

1

0

0

2

0

0

1

7

0

1

2

8

9

6

2

2

6

7

2

8

11

9

2

2

6

9

14

14

DEU Germany

31

31

25

28

10

23

12

DNK Denmark

ESP

EST

FIN

Spain

Estonia

Finland

FRA

France

1

6

1

2

19

GBR United Kingdom 20

GRC Greece

HUN Hungary

IND

IDN

IRL

ITA

JPN

India

Indonesia

Ireland

Italy

Japan

KOR

South Korea

LTU

Lithuania

LUX Luxembourg

LVA

Latvia

MEX Mexico

MLT Malta

1

0

2

1

0

18

8

7

1

0

0

1

0

1

9

1

2

22

24

1

2

2

1

0

22

10

7

1

0

0

1

0

NLD The Netherlands

11

15

POL

PRT

Poland

Portugal

ROM Romania

6

0

1

7

0

1

0

3

0

1

4

11

0

0

1

1

0

4

3

2

0

0

1

0

0

4

1

0

1

1

1

0

1

7

6

1

0

2

0

0

14

4

5

1

0

0

1

0

3

5

0

1

0

1

0

0

0

2

0

0

0

0

0

1

1

0

0

0

0

0

0

0

0

0

0

0

1

0

1

4

4

0

0

0

0

0

3

2

0

0

0

0

0

0

4

0

0

0

Russia

25

27

14

19

10

12

RUS

SVK

SVN

Slovak Republic

Slovenia

SWE

Sweden

TUR

Turkey

TWN Taiwan

1

0

3

2

1

2

0

4

2

1

0

0

3

3

0

0

0

3

1

0

0

0

1

1

0

0

0

0

0

0

3

USA

United States

33

36

30

17

24

24

0

2

0

1

1

8

0

0

0

0

0

2

1

0

0

0

0

0

0

4

0

0

0

5

0

0

1

1

0

6

8

7

8

9

9

5

4

9

8

8

8

9

9

7

5

13

14

2

2

7

7

3

7

4

9

6

6

9

8

7

8

5

0

2

2

7

8

3

7

5

8

10

6

10

8

8

9

9

0

11

12

7

8

6

8

3

8

9

8

8

4

2

3

7

5

0

1

1

4

3

3

11

3

8

6

2

1

4

7

1

2

4

3

2

3

3

9

3

1

8

5

4

4

2

0

5

5

3

1

3

0

1

4

7

5

1

2

4

4

6

4

4

5

5

4

5

4

2

8

1

2

4

3

2

5

3

5

4

4

7

5

4

3

4

0

8

5

5

3

7

3

1

1

1

2

0

1

0

1

1

0

3

0

1

1

0

0

0

2

1

1

0

0

0

1

1

0

1

1

4

0

2

1

1

0

1

1

0

0

1

0

0

2

6

3

0

1

1

1

3

1

2

3

4

5

2

2

1

5

0

2

5

2

0

3

1

5

2

2

6

4

3

2

2

0

4

2

3

0

3

0

2

2

4

1

0

1

0

3

2

1

8

1

4

1

0

1

3

4

0

0

4

0

1

2

0

9

1

1

3

4

2

2

0

0

1

1

1

1

2

0

Notes: The outdegree centrality of a country reﬂects its relevance as a supplier of foreign value added in exports, while the indegree
centrality signals its importance as a user of foreign value added in exports. The columns correspond to the several networks of foreign
value added in exports included in the paper, namely T stands for the network of total foreign value added in total exports (Figure 2 panel
(b)); G and S stand for the networks of total foreign value added in exports of goods and of services, respectively (Figure 5); GG, GS SG
and SS stand for the networks of foreign value added of goods in exports of goods, foreign value added of goods in exports of services,
foreign value added of services in exports of goods, and foreign value added of services in exports of services, respectively (Figure 6). For
each country, the sum of the outdegree (indegree) centrality values of the detailed networks do not add-up to the outdegrees (indegrees) of
the total network (see footnote 9 in the main text.).

ECB Working Paper 1931, July 201638Competitiveness Research Network 

This paper presents research conducted within the Competitiveness Research Network (CompNet). The network is composed of 
economists from the European System of Central Banks (ESCB) - i.e. the 29 national central banks of the European Union (EU) and the 
European Central Bank – a number of international organisations (World Bank, OECD, EU Commission) universities and think-tanks, as 
well as a number of non-European Central Banks (Argentina and Peru) and organisations (US International Trade Commission). The 
objective of CompNet is to develop a more consistent analytical framework for assessing competitiveness, one which allows for a better 
correspondence between determinants and outcomes. The research is carried out in three workstreams: 1) Aggregate Measures of 
Competitiveness; 2) Firm Level; 3) Global Value Chains CompNet is chaired by Filippo di Mauro (ECB). Workstream 1 is headed by 
Pavlos Karadeloglou (ECB) and Konstantins Benkovskis (Bank of Latvia); workstream 2 by Antoine Berthou (Banque de France) and 
Paloma Lopez-Garcia (ECB); workstream 3 by João Amador (Banco de Portugal) and Frauke Skudelny (ECB). Monika Herb (ECB) is 
responsible for the CompNet Secretariat. The refereeing process of CompNet papers is coordinated by a team composed of Filippo di 
Mauro (ECB), Konstantins Benkovskis (Bank of Latvia), João Amador (Banco de Portugal), Vincent Vicard (Banque de France) and 
Martina Lawless (Central Bank of Ireland). The paper is released in order to make the research of CompNet generally available, in 
preliminary form, to encourage comments and suggestions prior to final publication. The views expressed in the paper are the ones of 
the author(s) and do not necessarily reflect those of the ECB, the ESCB, and of other organisations associated with the Network. 

Acknowledgements 

The authors thank an anonymous referee for helpful comments and suggestions. The opinions expressed in the paper are those of the 
authors and do not necessarily coincide with those of Banco de Portugal or the Eurosystem. The usual disclaimers apply. 

João Amador 

Banco de Portugal and Nova School of Business and Economics; email: jamador@bportugal.pt 

Sónia Cabral 

Banco de Portugal; email: scabral@bportugal.pt 

© European Central Bank, 2016 

Postal address 
Telephone 
Website 

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www.ecb.europa.eu 

All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library at 
or from RePEc: Research Papers in Economics.  

Information on all of the papers published in the ECB Working Paper Series can be found on the ECB’s website. 

ISSN 
ISBN 
DOI 
EU catalogue No 

1725-2806 (online) 
978-92-899-2179-4 
10.2866/63044 
QB-AR-16-048-EN-N