Volume 24 
Issue 3 Thematic Issue: The Characteristics 
and Role of Intangible Capital in Central-Eastern 
Europe, the Balkans and in the Mediterranean 
Article 3 
September 2022 
Intangibles and Participation in Global Value Chains in the EU: Intangibles and Participation in Global Value Chains in the EU: 
Evidence from the GLOBALINTO Input-Output Intangibles Evidence from the GLOBALINTO Input-Output Intangibles 
Database Database 
Petros Dimas 
National Technical University of Athens, Laboratory of Industrial & Energy Economics, Athens, Greece, 
petrdimas@chemeng.ntua.gr 
Dimitrios Stamopoulos 
National Technical University of Athens, Laboratory of Industrial & Energy Economics, Athens, Greece 
Aggelos Tsakanikas 
National Technical University of Athens, Laboratory of Industrial & Energy Economics, Athens, Greece 
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Recommended Citation Recommended Citation 
Dimas, P ., Stamopoulos, D., & Tsakanikas, A. (2022). Intangibles and Participation in Global Value Chains 
in the EU: Evidence from the GLOBALINTO Input-Output Intangibles Database. Economic and Business 
Review, 24(3), 152-160. https://doi.org/10.15458/2335-4216.1303 
This Original Article is brought to you for free and open access by Economic and Business Review. It has been 
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ORIGINAL ARTICLE
Intangibles and Participation in Global Value Chains
in the EU: Evidence from the GLOBALINTO
Input-Output Intangibles Database
Petros Dimas*, Dimitrios Stamopoulos, Aggelos Tsakanikas
National Technical University of Athens, Laboratory of Industrial & Energy Economics, Athens, Greece
Abstract
The scope of this paper is to provide empirical evidence regarding intangible inputs, global value chains (GVCs)
participationandtheirlinkagewithexportsintheEUandtheUK,utilizingdatafromWIODandthenewlyconstructed
GLOBALINTO Input-Output Intangibles Database for the period 2000e2014. GVC participation metrics are calculated
based on a production-based decomposition framework and include backward and forward participation indices.
Intangible inputs follow a breakdown by origin into domestic and imported intangible inputs. Our empirical results
suggest that GVC participation (both backward and forward) is a significant driver for exports and highlight the
importance of intangibles’ origin in the exporting activities of the EU economies, especially in the case of the non-Euro
Area economies.
Keywords: Global value chains, Input-Output analysis, Intangible capital, Exports, Competitiveness
JEL classification: F14, O30, O52
Introduction and theoretical background
G
lobal Value Chains (GVCs) have been placed
in the epicentre of economic research as the
global economy is rapidly moving towards regional
and international production and trading clusters
where countries no longer act as individual trade
partners but rather embed themselves in supply
networks that function as a unified entity in inter-
national markets. The European Union (EU) can be
consideredasapredominantexampleofthistypeof
cluster, with EU members constantly trading with
each other and forming the concept of ‘the most
regionalized region in the world’ (Daudin et al., 2011).
Accordingly, EU is no stranger to GVCs as well,
with Amador et al. (2015) stating that the EU's GVC
participation is currently overcoming the US and
Eastern Asian economies. Benkovskis and Kar-
adeloglou (2015) further elaborated on the matter,
highlighting its linkages with competitiveness and
growth for the EU economies. In the same vein,
participation in GVCs is further acknowledged as a
driverforgrowthin theglobaleconomyaswell (see
global studies by Pahl and Timmer (2019) and
Constantinescu et al. (2019)).
Another point of interest in recent literature
streams is the role of intangible assets in the pro-
ductivity puzzle. Intangibles-related research faces
two main issues that have yet to find a univocal
answer in academia, which involve i) the definition
of intangible assets, and ii) their quantification.
Corrado et al. (2009) provided the first formal defi-
nition, according to which intangibles can be
grouped into three major categories: a) computer-
ized information (including computer software and
related activities), b) innovative property (including
research and development output, entertainment,
design, and intellectual property), and c) economic
competencies (branding, marketing, training, and
organizational capital). According to their nature,
Received 15 March 2021; accepted 25 January 2022.
Available online 15 September 2022
* Corresponding author.
E-mail addresses: petrdimas@chemeng.ntua.gr (P. Dimas), dstamopoulos@mail.ntua.gr (D. Stamopoulos), atsaka@central.ntua.gr (A. Tsakanikas).
https://doi.org/10.15458/2335-4216.1303
2335-4216/© 2022 School of Economics and Business University of Ljubljana. This is an open access article under the CC-BY-NC-ND license (http://creativecommons.
org/licenses/by-nc-nd/4.0/).

intangibles are pivotal in the production and diffu-
sion of innovation and an alternative characteriza-
tion for them is knowledge-based capital (Jona-
Lasinio et al., 2019). This definition provided a solid
theoretical foundation for empirical efforts towards
the quantification of intangibles and the construc-
tion of relevant empirical databases. The common
baseline among these efforts is the treatment of in-
tangibles as capital approximations and thus the
quantification procedures included the estimation
of capital formation from dedicated expenditure
data and specific components of fixed capital (see
for example INTAN-Invest by Corrado et al., 2018;
INNODRIVEbyPiekkola,2011)andthemostrecent
release of the EU-KLEMS database (Stehrer et al.,
2019). These databases provided the empirical data
to support several investigations regarding the ef-
fect of intangibles to growth, with Roth and Thum
(2013), Niebel et al. (2017) and Piekkola (2018)
providing evidence of strong and positive relation-
ship between intangible capital and productivity
growth. Intangibles are also connected with partic-
ipation in GVCs, as intangible intensive activities
appear to accumulate maximum shares in VA
appropriation along the production chain (OECD,
2013). In support of this claim, Jona-Lasinio et al.
(2019) provided evidence of the importance of
intangible capital as a driver for GVC participation.
As the main body of intangibles-related literature
focusesontheircontributiontoproductivitygrowth,
anothermainaspectregardingthescopeandnature
of intangibles is still absent from relevant research,
namely the origin dimension of intangible assets.
The recently developed GLOBALINTO Input-
Output Intangibles Database-GIOID (Dimas et al.,
2022) provides the proper framework and empirical
tools to explore this dimension, as it sheds light to
some missing dimensions that previous approaches
failedtoaddress,suchaswheretheintangibleassets
come from and who produces them. Under this
framework, the database investigates the origin
dimension regarding intangibles in the EU, intro-
ducing a novel approach based on inter-industry
trade of utilities and the treatment of intangibles as
intermediate inputs/producer's services. The novel
intangibles metrics are compatible with the tradi-
tional and advanced GVC participation indices and
can be co-integrated in studies that focus on GVC
trade in the EU and the investigation of the com-
bined effect of intangibles (domestic and imported)
and GVC participation (in all its aspects) to growth
and competitiveness via traditional growth ac-
counting exercises and novel empirical approaches.
This paper is embedded in the latter framework
and aims to provide quantitative insights regarding
GVC participation and intangible assets in the EU-
27 plus the United Kingdom,
1
and empirically
investigate their relationship and effects in
competitiveness for the EU members. Our main
research hypothesis indicates that both intangible
assets and GVC participation are major de-
terminants that drive the exporting performance of
the EU economies. We empirically investigate this
hypothesis through the calculation of the produc-
tion-based GVC indicators following the innovative
framework provided by Wang et al. (2017) and uti-
lizing data at the country level from the World
Input-Output Database (WIOD) (Timmer et al.,
2015) and the newly constructed GIOID (Dimas
et al., 2022; Tsakanikas et al., 2022) for the period
2000e2014. Furthermore, we test the calculated in-
dicatorsinsimplepanelregressionsasdeterminants
of exports for the EU. In this line, we develop two
separate sub-samples, distinguishing between Euro
Area (EA) and non-Euro Area economies, and
further introduce different intangible factors in the
specifications to investigate different aspects of in-
tangibles contribution to exports.
The structure of the remainder of this paper is as
follows: At the first stage, we provide a description
regarding the quantification of GVC participation
indicators based on IeO analysis and the produc-
tion-based decomposition approach applied in this
paper. The second stage of this paper presents a
short description of the GIOID, with details
regarding the methodological approach, the data,
and the key novelties that this database presents.
The third stage of this paper provides the method-
ology and the main indicators utilized in the
empirical part of this study and the fourth stage
provides descriptive statistics and the regression
results. The fifth and final part presents the con-
clusions of this study and discusses limitations and
future research.
1 Input-Output frameworks of analysis for
GVC participation
1.1 Methodological overview regarding Inter-
Country Input-Output tables
The methodological basis of the present paper
originates from Leontief's (1936) IeO framework,
1
For abbreviation purposes, we will refer to EU-27 and the UK as EU-28 in the following sections of this paper.
ECONOMIC AND BUSINESS REVIEW 2022;24:152e160 153

which has since been a predominant tool for the
quantitative analysis of production in-
terdependencies and the modelling of economic
systems. IeO models generally treat an economy as
a set of interconnected sub-components, each
requiring inputs of goods and services from the
other components and producing goods and ser-
vices that are then consumed by the other compo-
nents for production or final use. These
interdependencies are depicted in IeO tables and
the framework relies on three main assumptions
about the nature and structure of the economic
system depicted in the table. Namely the system is
completely internal (all output is eventually usede
consumed),with noeffects ofproductionscaling,and
finally, that every industry corresponds to a single
product and vice versa (i.e., no substitute products
exist).
While the IeO tables are usually compiled by
statistical institutions at the national level (the eco-
nomic system is the economy and the components
are the different national industries and final uses)
based on its supply and use tables, there exist dif-
ferences in currency, accounting practices, trade
balances and industry classifications among
different countries that often pose difficulties in
drawing meaningful comparisons. To overcome
such issues, Inter-Country Input-Output Tables
(ICIOTs) have been developed, with the most
prominent ones being the World Input-Output
Database (Timmer et al., 2015) and OECD's Inter-
Country Input-Output (ICIO) Tables.
Assuming a hypothetical inter-national economy
with N countries, with K industries each, an ICIOT
follows the structure presented below in Table 1.
In Table 1, X is the global matrix of intermediate
consumption, with each sub-matrix X
i;j
being a K K sized block containing the flows of intermediate
goods and services from the sectors of country C
i
to
the sectors of country C
j
for intermediate con-
sumption. F
i;j
is a K D matrix regarding the con-
sumptionoftheDfinalusersfromtheproductionof
country C
i
to country C
j
, VA
i
is a 1 K vector of the
value added per sector of production from country
C
i
, and Y
i
;I
i
are K 1 and 1 K vectors containing
thetotal gross output andthetotal requirementsfor
inputs per sector and country pair. Following the
core assumptions of the framework, total output
must equal total input: Y
i
¼ I
i
0
. The global co-
efficients of production can then be estimated
2
as
A ¼ XY
0 1
,resultingina NK NK matrix.Following
these formulations, total output of the economy can
be written as Y ¼ AI
0
þ F,orasY¼ð I
id
 AÞ
 1
I,
with ðI
id
 AÞ
 1
¼ L being the familiar Leontief in-
verse matrixfor theglobaleconomyand I
id
an NK NK identity matrix.
Since the seminal work of Hummels et al. (2001),
the quantification of GVC participation has been
based on various metrics that derive from ICIOTs,
such as exports of intermediates and various value-
added (VA) decomposition approached developed
by different research groups at both industry and
country levels. The foundations for GVC research
were set through the construction of global ICIOTs
such as the aforementioned WIOD (Timmer et al.,
2015) and OECD's ICIOTs. Koopman et al. (2014)
provided a formal framework for the decomposition
of gross exports into two major components that
defined the GVC empirical research; domestic value
added(DVA)and foreign value added(FVA)embodied
in gross exports. This framework paved the way for
the development of backward (focused on FVA) and
forward (focused on DVA) participation indices that
wereutilizedwidelyinempiricalgrowthresearchby
both academia and economic research institutions
(see for example Amador et al., 2015; Benkovskis &
Karadeloglou, 2015; Constantinescu et al., 2019;
Tsakanikas et al., 2022; World Bank, 2020).
Table 1. Input-Output table framework for an international economy of N partner countries with K industries per country.
Intermediate Consumption Final Uses Total Output
Country C
1
C
2
[…] C
N
C
1
[…] C
N
Intermediates Supply C
1
X
1;1
X
1;2
[…] X
1;N
F
1;1
[…] F
1;N
Y
1
C
2
X
2;1
X
2;2
[…] X
2;N
F
2;1
[…] F
2;N
[…]
[…][ …][ …][ …][ …][ …][ …][ …][ …]
C
N
X
N;1
X
N;2
[…] X
N;N
F
N;1
[…] F
N;N
Y
N
Value Added VA
1
[…][ …] VA
N
Total Input I
1
I
2
I
N
2
In the context of the present study the apostrophe (A
0
) denotes the transpose matrix of A and the hat accent (
b
A) the diagonal matrix of line/column
vector A.
154 ECONOMIC AND BUSINESS REVIEW 2022;24:152e160

These indicators were exports-driven and calcu-
lated based on a country's/sector's gross exports. In
this study, we adopt an alternative approach to-
wards the quantification of GVC participation, that
is production-based and relies on the decomposi-
tion of value-added into domestic and foreign
components, as introduced by Wang et al. (2017).
Our indicators are constructed utilizing available
data from WIOD for the period 2000e2014. The
detailed procedure towards the construction of the
production driven backward and forward GVC
participation indices is described in the following
section.
1.2 Tracing of value added in inter-country trade
following a production-based approach
In an ICIOT, the flows of goods and services be-
tween the different sectors and countries are
immediately identifiable and can be traced along
the production chains. However, information about
the traded value added that is incorporated in those
flows requires further elaboration. The first step is
to estimate the share of value added embodied in
the total output of each sector and country:
VA
to
¼VAY
0 1
ð1Þ
Multiplying VA
to
by the global Leontief matrix
and the vector of gross final uses per industry and
country of origin, the global matrix of value-added
traded by partner industryecountry pair can be
formed:
VA
oc; oi; pc; pi
tr
¼
d
VA
to
L
c
F
to
ð2Þ
This matrix can then be further decomposed to
form the country-industry network of value-added
flows by partner of destination or partner of origin
and by type of use (for intermediate or final uses).
The present paper follows the decomposition
method described by Wang et al. (2017), which uti-
lizes the domestic and foreign submatrices of pro-
duction coefficients and final demand due to
bilateral trade from the ICIOTs to separate VA that
is consumed in its country of origin from VA that is
exported and then consumed abroad or is further
embodied in other partner countries exports:
VA
oc; oi; pc; pi
tr
¼ VA
cons
d/d
þVA
cons
d/f
þVA
exp=imp
d/f
ð3Þ
where VA
cons
d/d
is the VA that is consumed in the
country in which it firstly originates, VA
cons
d/f
is the
VA that is produced, then embodied in the final
goods exports of a countryesector pair and then
imported and consumed in another countryesector
pair, and VA
exp=imp
d/d
is the VA that is embodied in the
intermediate exports or imports a countryesector
pair. This last form of VA can then be either used
in the partner country for production or be further
re-exported.
Todevelop themeasuresofparticipationinGVCs
at the country level, we adopt the net trade concept
(Wang et al., 2017) with the necessary modifications
to account for the higher aggregations. The share of
a country's total VA that consists of domestic VA
that originates through downstream activities (‘for-
ward’ participation in GVCs) can be written
3
as
VA
oc
GVC; ds
¼
X
j¼K
j¼1
X
i¼N
i¼1
VA
exp=imp
d/f
X
j¼K
j¼1
X
i¼N
i¼1
VA
oc; oi; pc; pi
tr
ð4Þ
while the share of foreign VA imported through
intermediates in its final uses (‘backward’ participa-
tion in GVCs) is given at the country level by:
VA
pc
GVC; us
¼
X
i¼K
i¼1
X
j¼N
j¼1
VA
exp=imp
d/f
X
i¼K
i¼1
X
j¼N
j¼1
VA
oc; oi; pc; pi
tr
ð5Þ
2 Intangibles inter-country trade: the
GLOBALINTO Input-Output intangibles
database
Most of the available intangible capital databases
provide insightful information regarding invest-
ment in intangible capital and a breakdown among
different categories of intangible assets in line with
Corrado et al. (2009) definition. However, some
important questions still remain unanswered, such
as Where do these investments go? and Who produces
these intangible assets and where do the intangible assets
come from?
The GLOBALINTO IeO Intangibles Database
provides the empirical insights to tackle these gaps
in intangibles quantification literature, by treating
intangiblesasintermediate inputs in theproduction
process of each industry/country. The database
provided intangibles-related data for 56 2-digit
NACE REV. 2 industries from all EU-27 countries
including the UK, for the period 2000e2014. The
framework of this novel database is constructed
based on theCorradoet al.(2009) approach andkey
elements of the INNODRIVE methodology (Piek-
kola, 2011), and identifies certain knowledge-
3
(i;jÞ along with ðN;KÞ denote the summation direction (e.g., row wise for all countries first and column wise for all industries second, etc.).
ECONOMIC AND BUSINESS REVIEW 2022;24:152e160 155

intensive service sectors in the economy as in-
tangibles producing sectors. The treatment of in-
tangibles as inputs and more accurately as
producer's services enables the quantification of
trade-in-intangibles between industries and coun-
tries and goes beyond the level of established da-
tabases through the introduction of the origin
dimension, as the user can distinguish between the
intangibles that are produced domestically, and the
intangibles purchased from abroad via imports.
The database is constructed drawing raw IeO
data from the 2016 release of WIOD, which covers
the inter-industry trade between 56 2-digit NACE
Rev.2economicsectorsfrom43countries(including
all EU-28 members) and an estimate for the rest of
the world. In the framework of the database, 4
knowledge-intensive services sectors are identified
as intangibles producers, namely:
 J62-J63: Computer programming, consultancy,
and related activities; Information service
activities.
 M72: Scientific Research and Development
(R&D).
 M73: Advertising and market research.
 N sector: Administrative and support service
activities.
Under this scope, intangibles are quantified as
producer services in the inter-industry and inter-
country trade, a novel dimension that enables the
study of trade-in-intangibles in the international
markets and the globalized economy. The method-
ological framework regarding the identification of
intangibles is embedded in the basic principles of
IeO analysis and is centered around the in-
tangibles-producing sectors. As a result, this
approach can be further expanded into different
ICIOTs that provide appropriate data regarding the
inter-industry and inter-country trade.
The intangibles-related data in GIOID are further
consolidated with export and competitiveness sta-
tistics calculated from the WIOD and R&D expen-
diture data from Eurostat. Furthermore, the
database includes information about patent appli-
cations to the European Patent Office (EPO) per
industry, at the NACE Rev.2 level, which derive
from Eurostat.
A detailed description regarding the construction
and the key elements of GIOID can be found in
Dimas et al. (2022) and its conceptual and the
methodological underpinnings are thoroughly dis-
cussed in Tsakanikas et al. (2022).
3 Empirical methodology and model
specifications
To empirically assess the contribution of GVC
participation and intangibles in the competitiveness
of the EU,
4
we formulate two different sub-samples
of the EA
5
and non-EA economies,
6
based on the
assumption that a common currency enhances
traditionaltradeandGVC-tradeactivities.Weselect
a relevant export performance indicator from
GIOID that is the share of gross exports to total
output per country, as a proxy for competitiveness:
GX
Yc;t
¼
GX
c;t
Y
c;t
ð6Þ
where GX
c;t
and Y
c;t
represent the gross exports and
total output of country c at time t respectively.
To account for the effect of intangibles, we intro-
duce the origin dimension as a key novelty in the
intangibles-related studies, utilizing two separate
indicators from GIOID, as described in the
following equations:
dIntan
c;t
¼
dom:intangibleinputs
c;t
I
c;t
ð7Þ
iIntan
c;t
¼
imp:intangibleinputs
c;t
I
c;t
ð8Þ
where dIntan
c;t
stands for the share of domestically
produced intangibles inputs in c at time t to its total
intermediate consumption of utilities (I
c;t
)a n d
iIntan
c;t
stands for the share of imported intangibles
inputs.
Previous empirical investigations regarding in-
tangibles were directed towards exploring the
overall effect of investment in intangible capital
rather than trying to distinguish between the
domestically purchased and imported assets. In
order to provide an element of comparison and
highlightthekeynoveltiesoftheintangibleinputs’
indicators incorporated in GIOID, we also intro-
duced an aggregate intangibles indicator in our
specification, which does not account for in-
tangibles origin, as described in equation (9):
4
Cyprus, Luxembourg, and Malta are excluded from the study, due to the nature of their economies and their special characteristics regarding inter-
national trade and domestic economic activities.
5
Euro Area members include Austria, Belgium, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Netherlands, Portugal,
Slovakia, Slovenia, and Spain.
6
Non-Euro Area economies include Bulgaria, Croatia, Czech Republic, Denmark, Hungary, Poland, Romania, Sweden, and the United Kingdom.
156 ECONOMIC AND BUSINESS REVIEW 2022;24:152e160

tIntan
c;t
¼
totalintangibleinputs
c;t
I
c;t
ð9Þ
The GVC participation indices utilized in the
empirical analysis are the ‘backward’ and ‘forward’
participation in GVCs as described in detail in the
previous section and are depicted in equation
(10).
gvc
b
c;t
¼VA
pc
GVC; us
gvc
fc;t
¼VA
oc
GVC; ds
ð10Þ
We model the aforementioned variables into
four separate specifications to study the effects of
intangible inputs and GVC participation to exports
for the EA and non-EA EU countries in the period
2000e2014. The specifications are presented in the
following equations:
i: GX Y
c;t
¼a
o
þa
1
gvc b
c;t
þa
2
gvc f
c;t
þa
3
tIntan
c;t
þl
t
þ3
c;t
ii: GX Y
c;t
¼b
o
þb
1
gvc b
c;t
þb
2
gvc f
c;t
þb
3
dIntan
c;t
þb
4
iIntan
c;t
þl
t
þ3
c;t
iii: GX Y
c;t
¼g
o
þg
1
gvc b
c;t
þg
2
gvc f
c;t
þg
3
dIntan
c;t
þl
t
þ3
c;t
iv: GX Y
c;t
¼d
o
þd
1
gvc b
c;t
þd
2
gvc f
c;t
þd
3
iIntan
c;t
þl
t
þ3
c;t
Our specifications follow a reverse approach.
At first, we introduce a model with GVC participa-
tion indices and total intangible inputs to account
for the effects of GVC participation and intangibles
in exports. Subsequently, we distinguish between
domestic and imported intangibles to identify to
which type (or both) of intangibles the previously
identified effect is attributed to. Furthermore, we
provide specifications that account for the in-
tangiblesorigindimensionseparatelytocorroborate
our results. The econometric approach includes
simple Fixed Effects (FE)
7
panel regressions to ac-
count for the country-specific effects with time
dummies to account for unobserved time effects via
l
t
, while e
c;t
represents the error term. We further
turn to Driscoll and Kraay (1998) robust standard
errors to account for heteroskedasticity, serial
autocorrelation and contemporaneous autocorrela-
tion (cross sectional dependence) present in the
results.
8
4 Results and discussion
The main descriptive statistics regarding the
sampleoftheEU-28economiesutilizedinthisstudy
is presented in Table 2.
Table 3 presents the correlation matrix of the
variables included in this study.
The backward and forward participation indices
of the EU-28 economies at the start (2000) and end
(2014) of the study's timeframe are reflected on the
following scatterplots in Fig. 1. First, there is a
notable upwards shift in the participation intensity
for almost all countries, both via downstream and
upstream production activities, as can be observed
fromtherelativepositionsofthecountriesalongthe
(x; y) axis. Even economies that were initially less
involved in the backward participation in GVCs
(notably Bulgaria, Greece, Portugal) have been
increasing their shares of imported inputs used for
final production purposes. Second, there has been a
gradual and proportional increase in both forward
and backwards participation, which is evident by
theclusteringoftheobservationsalongthediagonal
linein2014comparedto2000.Thispatterndepictsa
bidirectional GVC deepening for most of the Euro-
pean economies and relates with a positive rela-
tionship between forward and backward
participation.Thisfindingisfurthercorroboratedby
the correlation statistics of Table 3, where forward
andbackwardparticipationinGVCsarefoundtobe
positively correlated (Pearson stat. value of 0.776).
Furthermore, we document apparent clusters of
countries with geographical proximity and eco-
nomic conditions. For example, the Southern Euro-
pean economies (Greece, Portugal, Spain, Italy)
move closer to each other and settle in the more
forward-intensive part of GVC participation, while
some central European countries (Hungary, Czech
Republic, Slovakia, Slovenia) also tend to increase
Table 2. Summary descriptive statistics for the variables of the adjusted
EU-28 sample for the period 2000e2014.
Obs. Mean Standard
Dev.
Min Max
GX_Y 375 0.227 0.085 0.054 0.514
gvc_b 375 0.196 0.059 0.103 0.372
gvc_f 375 0.180 0.059 0.059 0.351
dIntan 375 0.062 0.025 0.018 0.146
iIntan 375 0.016 0.029 0.002 0.218
tIntan 375 0.078 0.039 0.020 0.253
7
Random Effects and Pooled OLS were also tested, and FE were selected based on the results of the Hausman specification test (Hausman, 1978).
8
Heteroskedasticity was detected via the implementation of the modified Wald statistic for group heteroskedasticity (Greene, 2000), serial autocorre-
lation was detected via Wooldridge's (2010) test and contemporaneous autocorrelation/cross section dependence via Pesaran's (2007) diagnostic test.
ECONOMIC AND BUSINESS REVIEW 2022;24:152e160 157

their participation in GVCs (in this case, both for-
ward and backward) and move closer together.
Another observation that can be drawn relates to
the differences in GVC participation between our
two separate sub-groups, the EA (shown in bold
font in the graphs) and non-EA economies (rest of
the sample of the EU-28). There has been a signifi-
cant shift towards backward participation for EA
economies, regardless of their generally perceived
statuses as either ‘mainly exporting’ or ‘mainly
importing’ countries. This showcases the gradual
increase of share the foreign VA content of the
goods and services that are consumed in final uses,
as the EU becomes more strongly interconnected in
terms of trade for production purposes and is
gradually deepening its bidirectional participation
in GVCs.
Following the descriptive analysis, the main
empirical results of the model specifications are
presented in Table 4.
Table 3. Correlation matrix for the variables of the adjusted EU-28
sample for the period 2000e2014.
GX_Y gvc_b gvc_f dIntan iIntan tIntan
GX_Y 1.000
gvc_b 0.665*** 1.000
gvc_f 0.793*** 0.776*** 1.000
dIntan  0.080  0.463***  0.191*** 1.000
iIntan 0.540*** 0.434*** 0.470***  0.028 1.000
tIntan 0.357*** 0.019 0.229*** 0.650*** 0.742*** 1.000
Note. Pearson correlation statistics reported. *Significant at 10%
level. **Significant at 5% level. ***Significant at 1% level.
Fig. 1. Backward and forward participation in GVCs for all EU-28 counties in 2000 and 2014. Note. EA economies are presented in bold font and
green markers. Source: Authors' calculations based on WIOD.
Table 4. Fixed effects panel regression results with Driscoll and Kraay (1998) robust standard errors.
Euro-Area Rest of the EU-28
GX_Y (i) (ii) (iii) (iv) (i) (ii) (iii) (iv)
gvc_b 0.37*** 0.33** 0.42** 0.28** 0.36*** 0.29*** 0.36*** 0.28***
(0.09) (0.11) (0.15) (0.10) (0.07) (0.08) (0.08) (0.08)
gvc_f 1.12*** 1.13*** 1.14*** 1.15*** 1.03*** 1.04*** 1.03*** 1.04***
(0.09) (0.08) (0.07) (0.08) (0.04) (0.04) (0.04) (0.04)
tIntan 0.34*** 0.16
(0.11) (0.12)
dIntan 0.25* 0.06 0.07 0.06
(0.13) (0.12) (0.11) (0.12)
iIntan 0.37*** 0.30** 1.43** 1.42**
(0.12) (0.12) (0.54) (0.53)
constant  0.07**  0.06*  0.06  0.03  0.06***  0.05***  0.05***  0.04**
(0.03) (0.03) (0.03) (0.03) (0.01) (0.01) (0.01) (0.01)
Time dummies✓✓✓✓✓✓✓✓
Observations 240 240 240 240 135 135 135 135
within R
2
0.79 0.80 0.78 0.79 0.96 0.96 0.95 0.96
No. of groups 16 16 16 16 9999
Note. Driscoll and Kraay (1998) robuststandard errors in parentheses.*Significant at 10% level.**Significant at 5% level.***Significant
at 1% level.
158 ECONOMIC AND BUSINESS REVIEW 2022;24:152e160

The empirical results indicate that GVC partici-
patione both backward and forwarde is vital for
theexportingactivitiesoftheEU-28memberswitha
positive and statistically significant impact on ex-
ports in all specifications and sub-samples.
Focusing on intangibles, we observe a positive
andstatisticallysignificanteffectonexportsforEA.
Delving more into the matter through the break-
down of intangibles per origin to domestic and
imported,weobserveastrongereffectforthelatter
with domestic intangibles being a driver for ex-
ports only in the presence of imported intangibles.
This finding is even more acute in the case of the
rest of the EU-28 members that do not share a
common currency. For the non-EA countries, in-
tangibles appear to not affect exports in a statisti-
cally significant manner. However, when
accounting for the intangibles’ origin, we identify
thatthe insignificanceoftotal intangiblesis related
with the domestic intangibles as the imported
share appears to be a statistically significant driver
for exports in the respective sub-sample of
economies.
5 Conclusions
This paper provides insights regarding intangible
inputs and GVC participation (backward and for-
ward) in the EU utilizing available data from WIOD
and the novel GLOBALINTO IeO Intangibles
Database for the period 2000e2014. Furthermore,
we investigate the effect of GVC participation and
intangible inputs in the exporting activities of the
EU countries via simple panel regressions in order
to highlight different dimensions related with in-
tangibles that were previously unexplored in rele-
vant literature.
In the timeframe of this study (2000e2014), we
observedanincrease inboth forwardandbackward
participation intensity for almost all countries, with
further shifts towards incorporating more foreign
value added in their consumption and exports of
goods and services, especially for the EA countries.
Furthermore, a clustering pattern emerges for
economies with relatively comparable production
characteristics and overall macroeconomic condi-
tions, most notably in the case of the Southern Eu-
ropean and most Central European economies.
The key empirical findings regarding the contri-
butionofintangibleinputsandGVCparticipationin
exports are summed below:
i. GVC participatione both backward and for-
ward e is a driver for the exporting perfor-
mance of the EU members.
ii. Intangible inputs appear to be a driver for
exports for the EA economies.
iii. Intangibles' origin does matter for the non-EA
economies, as imported intangibles appear to
be a significant driver for exports, whereas
domestically produced intangibles are not
relatedwithexportsinastatisticallysignificant
manner.
The empirical evidence of this study highlights
theimportanceoftheorigindimensioninintangible
assets,asimportedintangiblesappeartobeadriver
for exports for all EU-28 economies. Especially in
the case of the non-EA economies, imported in-
tangibles outperform domestic intangibles in terms
of contribution to exports. This dimension was
previously unexplored in the intangibles-related
research and constitutes a key novelty of the GIOID
compared to the existing databases. Furthermore,
the producer services approach on intangible assets
adopted by GIOID provides a framework for the
quantification and study of trade-in intangibles be-
tween industries and across countries, a dimension
that the empirical evidence of this study highlights
as a significant factor that defines the exporting ac-
tivities of the EU-28 economies.
TheresultsofthisstudyareinlinewithTsakanikas
etal.(2020)whereintangiblesandGVCparticipation
(via a different approximation) appear to be drivers
forproductivityperformanceintheEUatthecountry
level. Furthermore, the significance of the imported
intangible inputs in the EU's competitiveness is also
corroboratedinTsakanikasetal.(2022)attheindustry
level and with a special focus on manufacturing ac-
tivitiesintheEU.Thecollectiveevidencethatderives
from research based on GIOID suggests that in-
tangibles shouldbe placed in theepicenter of future
industrial policy frameworks in the EU, with special
focus on trade-in intangibles and the intangible
transactions between different EU members. Future
policy frameworks should aim to establish and
properlysafeguardtheintangiblestradeintheEU,as
itappearstobeacrucialbeneficialfactorfortheEU's
economiesanditsoverallcompetitiveness.
As stated above, the focus of this paper is to
explore the relationship among export activity,
different types of GVC participation (forward and
backward) and intangible assets. However, our
approach remains agnostic to the actual ownership
of these assets, an inherited shortcoming that stems
from the nature of IeO models, which, in multire-
gional settingse as in the case of WIODe can only
provide information regarding the location of pro-
duction. Despite this fact, this novel dataset pro-
vides fertile ground for future research that should
ECONOMIC AND BUSINESS REVIEW 2022;24:152e160 159

aim to distinguish among different types of in-
tangibles and provide empirical evidence both per
origin as well as per type of an intangible asset. We
further encourage future endeavors to focus into
industry and country-specific case studies and pro-
vide novel insights that correspond to regional and
national determinants that shape export
performance.
Acknowledgement
This paper draws data from the GLOBALINTO
Input-Output Intangibles database, which was
developed under the H2020-EU.3.6.1.1. GLOBAL-
INTO project (GLOBALINTO: Capturing the value
of Intangible Assets in Micro Data to Promote the
EU'sGrowthandCompetitiveness,contractnumber
822259).
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