Patterns of net trade in value added and factors

Abstract

Based on recent approaches to measuring the factor content of trade when intermediates are traded we decompose value added trade and its components (capital and labor, as well as their subcomponents ICT and Non-ICT capital and educational attainment categories) distinguishing between various categories of domestic value added content of exports and imports. We add to the literature by simultaneously considering both exports and imports which allows one to focus on the patterns and dynamics of net value added trade and its components rather than vertical specialization patterns based on exports. Here we show that a country's trade balance in value added equals its trade balance in gross trade, which however does not hold in bilateral relationships or for factors of production. Empirically we present results of an application of the proposed decomposition method based on the recently compiled World Input-Output Database (WIOD) covering 40 countries and 35 industries over the period 1995-2006. We show that the domestic value added content of exports and the foreign value added content of bilateral trade dominates, but that the foreign or multilateral part is increasing over time. As an extension we consider country's performance with respect to the value of trade in particular production factors. Patterns of trade in net value added closely resemble net trade flows but there are distinct patterns when looking at individual factors. We show that the US is still a net exporter of high-educated labor.

Full text

Patterns of net trade in value added and factors
Neil Foster?, Robert Stehrer?, and Gaaitzen de Vries∗
?The Vienna Institute for International
Economic Studies (wiiw)
Rahlgasse 3, A-1060 Vienna, Austria.
∗University of Groningen (RUG)
9700 AB Groningen, The Netherlands.
Corresponding author: Robert.Stehrer@wiiw.ac.at
Draft version:
June 6, 2011
This paper was written within the 7th EU-framework project ’WIOD: World Input-Output Database:
Construction and Applications’ (www.wiod.org) under Theme 8: Socio-Economic Sciences and Hu-
manities, Grant agreement no. 225 281.

Abstract
Based on recent approaches to measuring the factor content of trade when intermediates are traded we
decompose value added trade and its components (capital and labor, as well as their subcomponents ICT
and Non-ICT capital and educational attainment categories) distinguishing between various categories
of domestic value added content of exports and imports. We add to the literature by simultaneously con-
sidering both exports and imports which allows one to focus on the patterns and dynamics of net value
added trade and its components rather than vertical specialization patterns based on exports. Here we
show that a country’s trade balance in value added equals its trade balance in gross trade, which however
does not hold in bilateral relationships or for factors of production. Empirically we present results of an
application of the proposed decomposition method based on the recently compiled World Input-Output
Database (WIOD) covering 40 countries and 35 industries over the period 1995-2006. We show that the
domestic value added content of exports and the foreign value added content of bilateral trade dominates,
but that the foreign or multilateral part is increasing over time. As an extension we consider country’s
performance with respect to the value of trade in particular production factors. Patterns of trade in net
value added closely resemble net trade flows but there are distinct patterns when looking at individual
factors. We show that the US is still a net exporter of high-educated labor.
Keywords: factor content of trade; trade integration; net value added trade; vertical specialization
JEL-classification: F1; F15; F19;

Contents
1 Introduction 1
2 Some stylized facts about trade in intermediates and final goods 3
2.1 Specialization structures in intermediates trade . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Industry specific patterns of intermediates trade . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Two-way trade in intermediates and final goods . . . . . . . . . . . . . . . . . . . . . . 9
3 Patterns of net trade in value added and factors 9
3.1 Measuring net trade in value added . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Trade balance in value added and gross trade . . . . . . . . . . . . . . . . . . . . . . . 13
3.3 Trade in value added components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4 The world input-output database (WIOD): short description 15
5 Net trade in value added - Selected results 17
5.1 Nettradeinvalueadded .................................. 17
5.2 Net trade in value added by use category . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5.3 Nettradeinfactors ..................................... 23
5.3.1 Trade in capital and labor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.3.2 Trade in ICT and Non-ICT capital . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.3.3 Trade in labor by educational attainment categories . . . . . . . . . . . . . . . . 24
6 Conclusions 26
References 28
3

PATTER NS OF NET TRA DE IN VALUE ADDED AND FACTORS1
1 Introduction
Trade in value added has become an increasingly debated topic due to the rapid integration of production
processes and the further inclusion of countries in this process. Though this process has been ongoing
for quite some time there have been rapid integration processes in the world economy taking place over
the last decade or so. In the 1990s this was the creation of the North American Free Trade Agreement
(NAFTA) concerning the US, Canada and Mexico and the integration of formerly communist countries
with Western EU countries which started after the transformational recession in these countries and led
to the accession of some countries into the European Union in 2004. Further, large developing countries
such as Brazil, Russia, India and China (and Indonesia and South Africa to a lesser extent) - termed the
BRIC, BRIIC, or BRICS countries - became important players on world markets at least in particular
industries. This implied an increase in overall trade flows in the world economy with increasing shares of
imports and exports between these newly integrating countries and the developed world. This integration
of trade flows in the world economy was further accompanied by increasing foreign direct investment
activities. One particular feature of this integration process was also the integration of production struc-
tures in the sense that firms offshore activities to other countries to exploit cost advantages in particular
stages of production. This integration of production processes has been theoretically analyzed under
different headings including ’fragmentation’, ’slicing up the value chain’, ’outsourcing’ and ’offshoring’
or the ’great unbundling’ and recent contributions emphasizing ’trade in tasks’.
From an empirical point of view there is still the challenge to properly measure this ongoing integra-
tion of production processes. The literature ranges from particular case studies for products like the Bar-
bie doll (Tempest, 1996), the iPod (Linden et al., 2009; Varian, 2007), computers (Kraemer and Dedrick,
2002), or the Nokia N95 (Ali-Yrkko¨
o, 2010) or more complex products like cars (Baldwin, 2009) or
airplanes (Grossman and Rossi-Hansberg, 2008), to studies of trade patterns in particular products such
as ’parts and components’ and overall trade in intermediates versus trade in final goods (Miroudot et al.,
2009; Stehrer et al., 2011) and a number of studies focusing on the magnitude and changes of ’vertical
specialization’ patterns. In the European context the changes in the international structure of production
are discussed from a multi-disciplinary point of view in Faust et al. (2004). This book also provides a
1We would like to thank Wolfgang Koller for useful suggestions. Participants at the WIOD consortium meeting in May
25-27, 2011, Seville, Spain provided useful comments.
1

number of case studies at the level of industries (the automobile industry, the electronics industry, and the
apparel industry). Other recent studies focus on measuring trade in value added between countries thus
trying to measure how much of value added created in the production process in one country is exported
thus ’netting out’ the value already embodied in imported products and the extent of ’vertical specializa-
tion’ or ’vertical integration’ (Hummels et al., 2001; Daudin et al., 2009; Johnson and Noguera, 2009;
Koopman et al., 2010), with an overview of these approaches provided by Meng and Yamano (2010);
see also Meng et al. (2011) for a decomposition of vertical specialization measures. Related to these
are papers on the measurement of trade in value added, examples including Escaith (2008); Maurer and
Degain (2010); Timmer et al. (2011). Further there are a number of papers with a focus on the Asian
production and trade network (recent examples include Meng and Inomata, 2009; Hiratsuka and Uchida,
2010; Yamano et al., 2010).
In the international trade literature this issue has to some extent been addressed over a number of
years with work measuring the factor content of trade flows. The seminal contribution in this respect was
that of Vanek (Vanek, 1968) and the so called Heckscher-Ohlin-Vanek model; for a recent overview see
(Baldwin, 2008). In this model the perspective switches from that on trade in goods to trade in factors
of production embodied in the goods traded. Empirically, this goes back even earlier to the important
contribution of Leontief (Leontief, 1953) which triggered a number of subsequent studies focusing on the
’Leontief paradox’. Only recently have there been successful attempts to solve this ’paradox’ by allowing
for (Hicks neutral) technology differences across countries (Trefler, 1993). One particular concern in
these contributions was to properly account for trade in intermediate products, an issue which has been
the focus of some recent contributions including those of Davis and Weinstein (2001), Reimer (2006),
and Trefler and Zhu (2010), though this issue was considered earlier by Deardorff (1982) and Staiger
(1986).
The starting point of this paper are these recent papers accounting for intermediates trade and in
particular the contribution of Trefler and Zhu (2010) where a Vanek-consistent measure of the factor
content of trade is proposed. Based on this approach we introduce an alternative approach to decompose
trade flows in value added and its components such as ICT and Non-ICT capital and labor differentiated
by skills and relate these to recent approaches of measuring vertical specialization patterns (Hummels
et al., 2001; Daudin et al., 2009; Johnson and Noguera, 2009; Koopman et al., 2010). Our approach can
be aligned with the measures of vertical specialization proposed in these studies which will be discussed
below. We add to this literature by simultaneously looking at both exports and imports of value added
2

thus focussing on net trade in value added rather than exports or imports of value added separately. The
proposed framework also allows to show that a country’s net export in value added equals its net exports
in gross trade which aligns this approach to national accounting. We differentiate between domestic and
foreign components in value added exports and imports. The data allow us to further break down the
figures of (net) trade in value added in to the components of value added. Particularly, we split value
added (in value terms) into capital and labor income, and these two into ICT and Non-ICT capital and
high, medium and low educated (by ISCED categories) labor income, respectively. The paper thus tries
to link the literature on trade in value added and vertical specialization and on the factor content of trade
by applying a decomposition approach.2
The paper proceeds as follows. In Section 2 we summarize some important points regarding the
structure of trade in intermediates arguing that it is important to incorporate both exports and imports of
intermediates trade simultaneously. In Section 3 we introduce our method of decomposing trade in value
added. Section 4 provides a short overview of the recently compiled WIOD database that we use. Based
on this we present selected results in Section 5. Section 6 concludes and points towards further avenues
of research.
2 Some stylized facts about trade in intermediates and final goods
Before presenting our approach and the results concerning the patterns of trade in value added and factors
let us shortly summarize a few results on the relative importance and patterns of trade in intermediates
which are the vehicle for international supply chains. We present only a short overview of some important
stylized facts with respect to trade in intermediates as compared to trade in final products, however with
an emphasis on the former category (see also Chen et al., 2005; Miroudot et al., 2009). This is based
on detailed trade data as outlined below. A more detailed analysis for the EU countries can be found
in Stehrer et al. (2010). The figures presented here are based on the data used for the construction of
the WIOD database. We emphasize this as the notion of ”supply chains” - as often emphasized in case
studies as mentioned above - is misleading when taking into account the fact that intermediate inputs (or
components) are also themselves produced by various other inputs (intermediates and primary). Thus,
though the notion of a supply chain might be relevant for particular products it does not properly account
for the integrated nature of the whole production process (which might be better described as ”supply
2In future research this decomposition can be continued further as will be outlined in the conclusions.
3

loops” or the old notion of ”roundaboutness” as discussed by B¨
ohm-Bawerk (1888) for example.3In the
literature other notions are also used such as ’modular production networks’ (see e.g. Faust et al., 2004).
For a discussion of supply chains and its conceptualizations see MacKechnie (2008) who proposes a
discussion in terms of hierarchy, networks and markets. In essence, we point towards the fact that
countries are both exporters and importers of intermediates even in narrowly defined industries which
has to be taken into account when measuring trade in value added.
For the sake of figuring out the value added content of a country’s exports and imports one has
to notice that also intermediates exports or imports embodies value added which has to be taken into
account properly.
2.1 Specialization structures in intermediates trade
When differentiating trade into end use categories it turns out that on average roughly 50 percent of trade
is traded intermediates whereas the remaining part is either for final consumption or gross fixed capital
formation. Here one has to note that the category ’intermediates’ is rather broad including raw materials
and agricultural goods, and in particular one has to mention that it is much broader than trade in parts
and components which is often considered in the literature. Again these patterns are relatively stable
over time as can be seen in Figure 1. More precisely, the shares of intermediate imports in total imports
range from a little more than 43 percent in Cyprus and Russia to more than 70 percent in Bulgaria,
India, Indonesia and Korea. Higher shares are mostly found for emerging and transition economies.
These patterns are relatively stable over time as can be seen in Figure 2 which plots the shares in 1995
against the shares in 2006. As one can see most countries are close to the 45 degree line thus pointing
towards relative constancy of these shares over time. The most significant shift occurred in Bulgaria
which saw a decrease in this share from about 80 percent in 1995 to about 60 percent in 2006. With
respect to exports we find an even broader range from about 20 percent in Cyprus to almost 90 percent in
Russia. Other countries with relatively low shares of intermediate exports in total exports are Denmark,
China and Ireland with around 40 percent. Intermediate exports play a dominant role in Australia, Brazil
and the Slovak Republic. These patterns raise the question of whether one can find a specialization
structure in terms of intermediates trade, i.e. whether some countries are specialized in the production of
intermediates whereas others in the production and thus exports of final products. Typically one would
argue that advanced countries produce complex intermediates which are then assembled in less developed
3One should note however that the focus in this contribution was different; see also (Samuelson, 1966) for a critical assess-
ment.
4

AUS
AUT BEL BGR
BRA
CAN
CHN
CYP
CZE
DEU
DNK
ESP
EST
FIN
FRA
GBR
GRC
HUN
IDN
IND
IRL
ITA
JPN
KOR
LTU
LUX
LVA
MEX
MLT
NLD
POL
PRT
ROM
RUS
SVK
SVN
SWE
TUR
TWN
USA
40.0
50.0
60.0
70.0
80.0
2006
40.0 50.0 60.0 70.0 80.0
1995
Figure 1: Share of intermediates in total imports, 1995 and 2006
AUS
AUT
BEL
BGR BRACAN
CHN
CYP
CZE
DEU
DNK
ESP
EST
FIN
FRA
GBR
GRC HUN
IDN
IND
IRL ITA JPN KOR
LTU
LUX
LVA
MEX
MLT
NLD
POL
PRT
ROM
RUS
SVK
SVN SWE
TUR
TWN
USA
20.0
40.0
60.0
80.0
100.0
2006
20.0 40.0 60.0 80.0 100.0
1995
Figure 2: Share of intermediates in total exports, 1995 and 2006
5

countries, a pattern which is driven by relative factor endowments. However, this has to be seen more
carefully as intermediates can also be simple products (in particular, raw materials) which go into the
production process of more complex goods which would reverse the patterns above. To show this in detail
we use a commonly used measure of revealed comparative advantages (RCA).4Results are presented in
Figure 3 where a positive number would indicate that the country is specialized in intermediates In this
AUS
AUT
BEL
BGR
BRA
CAN
CHN
CYP
CZE
DEU
DNK
ESP
EST
FIN
FRA
GBR
GRC
HUN
IDN
IND
IRL
ITA JPN
KOR
LTU
LUX
LVA
MEX
MLT
NLD
POL
PRT
ROM
RUS
SVK
SVN
SWE
TUR
TWN USA
−2
−1
0
1
2
3
2005 RCA
−2 −1 0 1 2 3
1995 RCA
Figure 3: Specialization measure, 1995 and 2005
case, Russia turns out to be highly specialized in intermediates mostly due to exports of raw materials
such as oil and gas. Similarly, Australia shows a comparative advantage in intermediates due to exports
of agricultural products and mining products. On the other side of the spectrum China, India, Korea,
and Turkey show negative numbers for both years indicating that these countries are larger importers of
intermediates pointing towards the importance of processing trade. Again these patterns are relatively
stable over time. Figure 4 shows the RCA measure for those countries within the range (−1,1) for both
years considered. Countries in the first and third quadrant maintain their relative position, i.e. having a
comparative advantage or disadvantage in intermediates trade respectively. Only a few countries shifted
from a negative value to a slightly positive one, these countries being Greece, Bulgaria, Ireland and
4The measure applied here is
RCAr
k= ln Xr
k/Pj,j6=kXr
j
Pp,p6=cXp
j/Pp,j;p6=c,j6=kXp
j
−ln Mr
k/Pj,j6=kMr
j
Pp,p6=cMp
j/Pp,j;p6=c,j6=kMp
j
where Xdenotes exports, Mis imports, rdenote country and kis for the category under consideration. See e.g. Vollrath
(1991) for an overview of such measures. Results do not depend on the exact measure used.
6

AUT
BEL
BGR
BRA
CAN
CZE
DEU
DNK
ESP
EST
FIN
FRA
GBR
GRC
HUN
IDN
IRL
ITA
JPN
KOR
LTU
LUX
LVA
MEX
MLT
NLD
POL
PRT ROM SVK
SVN
SWE
TWN
USA
−1
−.5
0
.5
1
2005 RCA
−1 −.5 0 .5 1
1995 RCA
Figure 4: Specialization measure, 1995 and 2005, for selected countries
Taiwan. Some countries (Austria, Czech Republic, Slovak Republic and Brazil) shifted in the opposite
direction.5
2.2 Industry specific patterns of intermediates trade
A further question is whether there are specific industries which are more likely to have a high share of
imports or exports in intermediates which is likely the case for industries in need of raw materials for
example or products with complex production structures. This would imply that a countries’ exports
and imports depends on its industrial specialization rather than its role as a producer of intermediates
or final products. Figures 5 and 6 present box plots for 2006 for each industry NACE 1-37 for imports
and exports, respectively. First, there seems to be a positive correlation across countries, i.e. there are
particular industries which are more prone to intermediates trade than others irrespective of the coun-
try. However, there are some notable outliers (i.e. the countries outside the whiskers of the box plots
which are labeled). These have to be studied in more detail and might reflect within industry patterns
of specialization. At the lower end industries such as 16 (tobacco products), 18 (Wearing apparel), 05
(fish and fishing products), 15 (food products and beverages), and 19 (leather and leather products) show
little trade in intermediates. Amongst the industries with the highest shares are mining industries, basic
5To study these patterns and their changes over time in more detail and to trace them back to potential explanatory factors
such as endowment structures, industry patterns, technology, etc. is a matter for future research. Further one has to take into
account relative price movements over time (in particular for raw materials).
7

RUS
IND
GRC
TUR
CHN
IND
IDN
MEX
AUS
TWN
CHN
IDN
TUR
IND
SVK
RUS
KOR
TWN
IRL
CZE
CYP
IND
CHN
IDN
RUS
GRC
MLT
USA
JPN
NLD
GBR
BEL
IND
CHN
USA
ROM
ROM
LTU
RUS
LTUDNK
0 20 40 60 80 100
Share of intermediate imports
16180519153633303529343222011731242328252126022014371011121327
Figure 5: Share of intermediates imports in total imports by product category, 2006
RUS
BRA
FIN
EST
GRC
KOR
BGR
ROM
BRA
IDN
FIN
KOR
RUS
AUS
ROM
MLT
LUX
CYP
IRL
LUX
LUX
IRL
CZE
POL
CYP
MLT
CHN
DNK
LUX
MEX
TUR
CYP
ROM
NLD
TUR
ITA
BEL
DNK
CHN
IRL
KOR
ITA
BEL
GBR
RUS
MLT
CAN
IND
CHN
USA
SVNNLDTWN
0 20 40 60 80 100
Share of intermediate exports
16180515193633303432352922170131242328252126022037141011121327
Figure 6: Share of intermediates exports in total exports by product category, 2006
8

metals (27) and secondary raw materials (37) having shares of around 100 percent.6
2.3 Two-way trade in intermediates and final goods
Further, the rank correlation of exports and imports shares of intermediates is again very high. This
points towards the fact that there might exist a lot of two-way trade also intermediates. To study this
we use a measure of intra-industry trade, the ’generalised Grubel-Lloyd index but broken down by end
use categories.7On average there are few differences with respect to two way trade between countries
across end-use categories. The simple arithmetic mean over countries is 0.54 for consumption goods and
about 0.5 for intermediates. The index for capital goods is even higher with an average of about 0.6, with
a similar pattern found when looking at the median. As expected, the ratios tend to be lower for less
developed economies. At the industry level the index tends to be even higher. Thus there is a substantial
amount of two-way trade going on in all end use categories.
3 Patterns of net trade in value added and factors
In this section we introduce our approach to the decomposition of trade flows in value added exports and
imports and consequently net trade. The same approach is also used to further to split up these flows
into value added components, i.e. the value of labor and capital traded which can be further split up by
various categories as outlined below. There is already a wide literature on the measurement of vertical
specialization, value added chains and trade in value added (see e.g. Hummels et al., 2001; Johnson and
Noguera, 2009; Daudin et al., 2009; Koopman et al., 2010; Timmer et al., 2011).
Often this literature focuses on measuring the vertical integration of production processes focusing
on exports and thus leaving out the aspect that all countries are also important importers of intermediates
and the existence of two-way trade in intermediates as outlined above.8On the other hand, the literature
6To some extent these patterns reflect the correspondence applied between HS 6-digit codes and the end use categories
applied to the data. One point of concern is that this classification applied (though we tried to improve on the commonly
applied HS 6-digit to BEC correspondence) is still unsatisfactory. In particular, HS 6-digit product descriptions might provide
too little information on the actual use of the product. Furthermore, here a more differentiated view of intermediates trade (like
primary, processed, parts and components, etc.) would be necessary.
7This index is given by
CGLIr
k=2 min{Xr
k, M r
k}
PcXc
k+PcMc
k−

PcXc
k−PcMc
k


This measure was proposed in Greenaway et al. (1994) and is based on Grubel and Lloyd (1975).
8The literature focuses on the ’import content of exports’; using supply-driven IO models allows one to also calculate the
’export content of imports’ (see for example Meng and Yamano, 2010).
9

focusing on the effects of outsourcing on labor markets (employment and wages) and other variables
like productivity often focus on the import side only. In this paper we therefore aim at including both
sides of trade to measure the extent of exports, imports and net trade in value added and its relative
importance across countries’ trading patterns. The WIOD database (see below) further allows us to
follow the respective trends over time and to further decompose value added flows into its components.
Another strand of literature which is related to the issue of trade in value added and vertical special-
ization focuses on trade in factors and is often motivated by the Heckscher-Ohlin-Vanek theorem with
the further complication when trade in intermediates has to be accounted for (see Deardorff (1982) and
Staiger (1986) for early contributions and Reimer (2006) and Trefler and Zhu (2010) for more recent
ones). The approach suggested here is motivated by a recent paper on trade in factors, Trefler and Zhu
(2010), which focuses on the correct (or ’Vanek consistent’ way) of calculating the factor content of trade
with trade in intermediates. We apply a similar method of calculating the factor content with two mod-
ifications. First, we apply this approach using value added shares in gross output and capital and labor
income shares in gross output rather than physical input coefficients which most of the papers focusing
on trade in factors is based. In essence, we therefore not only allow for cross-country and cross-industry
differences in direct and indirect input coefficients but also for differences in factor rewards.9Second, we
decompose the resulting measure into several categories which are outlined below in detail. In particular,
this latter aspect links this paper to other approaches of measuring vertical integration and trade in value
added.
3.1 Measuring net trade in value added
The starting point for the analysis are indicators of the share of value added in gross output denoted
by vector v, the Leontief inverse of the global input-output matrix, L= (I−A)−1with Adenoting
the coefficients matrix, and the flows of exports and imports of goods between countries denoted by t.
For simplicity we first discuss our approach for the case of three countries without an industry dimen-
sion. Further, we discuss net trade in value added from the viewpoint of country 1 without any loss in
generality. In this special case the vector of value added coefficients becomes v0= (v1, v2, v3), the
Leontief-inverse is of dimension 3×3and the trade vector is written as t= (x1∗,−x21,−x31 )where
x1∗=Pp,p6=1 x1pdenotes exports of country 1 to all countries and xr1denotes exports of country rto 1,
i.e. imports of country 1. These imports are included in negative terms which results in net trade of value
9This can later be decomposed into the effects of changes in productivity, factor rewards and trade patterns by splitting ratios
over gross output into factor rewards and physical input coefficients, i.e. to disentangle quantity and factor price effects.
10

added for country 1, i.e. tV=v0Lt. For the decomposition procedure however we need the individual
entries of the matrix capturing exports and imports of country 1 which is achieved by a diagonalization
of the value added coefficients and trade vector which results in the following exposition:
T1
V=





v10 0
0v20
0 0 v3










l11 l12 l13
l21 l22 l23
l31 l32 l33










x1∗0 0
0−x21 0
0 0 −x31





=





v1l11x1∗−v1l12 x21 −v1l13x31
v2l21x1∗−v2l22 x21 −v2l23x31
v3l31x1∗−v3l32 x21 −v3l33x31





The first matrix contains the value added coefficients of the three countries, the second matrix denotes the
elements of the Leontief inverse from the global input-output matrix and the last matrix contains exports
of country 1 and imports of country 1 from the other countries which are included as negative values.
Summing up this matrix over rows and columns therefore gives a measure of net trade of value added
for country 1. One should note however that this also includes indirect flows of value added and imports
and it is therefore advisable to discuss the entries in these matrix separately. This will also document the
decomposition of value added exports and imports in its various forms.
•Exports: The first column in matrix T1
Vdescribes value added exports of country 1.
∗Domestic value added content of exports: The first entry, v1l11x1∗, denotes total direct and
indirect value added exports of country 1 to all other countries.
∗Foreign value added content of exports: The production of these exports also requires inputs
from other countries. For production of these inputs - used to produce exports of country 1 -
value added in the other countries is created. This is captured by the remaining terms in the
first column by partner country, i.e. Pp,p6=1 vplp1x1∗. Note, that this is added to value added
exports of country 1, though value added is created in the other countries.
•Imports: The other columns capture the value added content of country 1’s imports.
∗Foreign value added content of bilateral imports: The exports of country 2 to country 1
embody value added from the second country. Thus the second term in the second column
captures country 1’s value added imports from country 2. Similarly, the third entry in the
11

third column captures the value added imports from country 3. Generally, the elements of
the diagonal in the import block contain bilateral value added imports, −Pp6=1 vplppxp1.
∗Foreign multilateral value added content of imports: Country 2’s exports to country 1 also
require inputs from other countries. Thus, for example the entry in row 3 of column 2 cap-
tures the value added imports of country 1 from country 3 which are embodied in imports
from country 2. An analogous interpretation holds for the entry in row 2 of column 3. Thus,
the total amount of these imports is given by −Pp,q,p6=q;p,q6=1 vqlqp xp1.
∗Re-Imports: Exports of country 2 to country 1 can also require inputs from country 1 itself.
Therefore, the first entry in column 2 captures value added imports of country 1 embodied in
imports from country 2; analogously for the third term in the first row. Total re-imports of
value added are therefore −Pp6=1 v1l1pxp1.
Analogous interpretations would also hold for countries 2 and 3 and generally for Ncountries. To
disentangle these five components of net value added trade for country 1 it is convenient to rewrite the
sum of the equation in the following way:
tr
V=X
s,s6=r
vrlrr xrs
|{z }
Domestic
+X
s,s6=rX
p,p6=r
vplprxr s
| {z }
Foreign
| {z }
Value added content of exports
−X
p6=r
vplppxpr
| {z }
Bilateral
+X
p,q,p6=q;p,q6=r
vqlqpxpr
| {z }
Re-imports
+X
r6=p
vrlrpxpr
| {z }
Multilateral

| {z }
Value added content of imports
(1)
There is a close relationship of this measure to others on vertical specialization already existing in the
literature. Koopman et al. (2010) sorts out the measures as supposed by Hummels et al. (2001), Johnson
and Noguera (2009) and Daudin et al. (2009) and provided an explicit derivation of the VS1 measure as
supposed by Hummels et al. (2001). Relying on these results we can interpret the five terms in the above
equation accordingly: The first term is country 1’s domestic value added in direct exports, the second is
the ’true’ VS11measure capturing the import content of exports (see Hummels et al., 2001; Koopman
et al., 2010), the third term are country 1’s direct imports of value added or the other countries’ direct
exports of value added to country 1 (where each import of country 1 is valued with the trading partner’s
value added coefficients), the fourth term is the VS1∗1measure capturing the re-imported value added of
exports (see Daudin et al., 2009; Koopman et al., 2010) and the last term are country 1’s indirect value
added imports through third countries which is therefore the sum of VS1pmeasures (see Hummels et al.,
2001) where this was derived as value added exports through third countries (see also Koopman et al.,
12

2010, where this was derived explitely).
Extending the above framework to many sectors requires only some slight modifications in the di-
mensionality of the matrices involved. Let Ndenote the number of countries and Gthe number of
industries. Tr
V=ˆv0Lˆ
trvis now a NG ×1matrix, the Leontief inverse Lis of dimension NG ×NG
and tris of dimension NG ×1; with sector specific information on exports (to all countries) and sector
specific information of imports from individual countries. Calculations can then be performed in exactly
the same way as indicated above with additionally summing up over industries.10 To derive country spe-
cific results one first has to add up block-wise. Thus the algebra has to be rewritten in the following way
with R=I⊗ιand S=R0denoting summation matrices where Iis the identity matrix of dimension
N×Nand ιdenoting a vector of ones of dimension G×1;⊗denotes the Kronecker symbol. Matrix
Ris therefore of dimension NG ×N. Pre- and post-multiplying the industry specific matrix Tr
Vwhich
is of dimension NG ×NG by Sand Rrespectively, results in a matrix of dimension N×Nwhich has
the same interpretation as above (having however incorporated industry-specific interrelations).
3.2 Trade balance in value added and gross trade
Following this approach allows us to show the relationship between a country’s trade balance in gross
and value added trade. This is important to look at in detail as in many instances case studies show that a
country is running a trade deficit in gross terms of a particular product whereas when taking account of
intermediates trade, or considering trade in value added, the trade deficit in value added term is lower or
even turns into a trade surplus (see e.g. Linden et al., 2009; Xing and Detert, 2010).
Based on the framework introduced above it can easily be shown that a country’s net trade in value
added equals net trade from gross exports and imports (see also Johnson and Noguera, 2009, where this
is shown in a 2×2example) From an intuitive point it is clear that total exports in value added of a
country must be imported in another country (as all exports of goods must be imported somewhere else).
As trade in goods is traced back to primary factor inputs and rewards and the coefficients of direct and
indirect value added creation in a closed system is equal to one the trade deficit of a country equals the
deficit measured in value added. Thus, this equality is a consequence of national accounting identities
in a closed system of world trade. Further, as we view trade deficits from the viewpoint of individual
countries we consider exports and imports as a form of final (exogenous) demand.
From an algebraic point of view this can be shown relatively straightforward. The vector of value
10This will further allow us to provide industry or industry-group specific results.
13

added, which we will denote by w, can be expressed in the following way from which value added
coefficients can easily be derived.
w=q−ˆqA0ι
ˆq−1w=ˆq−1q−ˆq−1ˆqA0ι
v=ι−A0ι
v0=ι0−ι0A
=ι0I−A
Inserting into our equation for measuring net trade in value added we get
tnet
V=v0I−A−1t=ι0I−AI−A−1t=ι0t=tnet
i.e. net trade in value added equals net trade in goods and services. Similarly one can show (by using trade
vectors consisting of the export cell or the import cells) that the ratio of value added exports (imports)
to gross exports (imports) equals one. The reason for this result is that in this framework all goods
(intermediates and final goods) are produced by capital and labor as the only two primary factors which
capture all the value added.
Thus one has carefully to consider the results that a country’s trade deficit in value added might be
lower than in terms of gross trade when considering bilateral flows. This might be the case in a bilateral
relationship though it is not true when taking trade with all countries into account.11
3.3 Trade in value added components
Instead of doing the analysis with the vector of value added coefficients vwe can now exploit the fact
that value added is a composite of income of various factors. Thus given data at hand one might split up
each element of the value added coefficients vector into subcomponents like labor and capital, i.e. vr
i=
Pfvr
i,f where fdenotes the factors considered. The data set at hand which are described below in more
detail allows us to distinguish first between labor and capital income. The former can be split into three
categories by educational attainment levels according to ISCED classification (high, medium, and low
educational attainment) and the latter into ICT and Non-ICT capital. This means that we can differentiate
11This is in more detail considered in Foster and Stehrer (2011).
14

trade in value added into trade in capital and labor and the respective categories. These individual factors
of value added trade then sum nicely up to the aggregate as described above. Importantly, this allows
then to consider in which factors a country is running a trade deficit or surplus. As we will see a country
which is running a trade deficit can nonetheless be a net exporter of a particular factor like high-educated
labor.
Summarizing, this approach of measuring net trade in value added is consistent with measures of
net trade in gross terms, incorporates other measures as suggested in the recent literature and allows
for a decomposition of value added trade along various dimensions which we document in subsequent
sections.
4 The world input-output database (WIOD): short description
The data used for the analysis is taken from ’The World Input-Output Database’ (WIOD) as available
in January 2011.12 In this section we provide a short description of the data to be used and how these
have been constructed; more detailed information can be obtained from papers mentioned below. The
WIOD data are the outcome of an effort undertaken to bring together information from national accounts
statistics, supply and use tables, trade in goods and services data and corresponding data on factors of
production (ICT and Non-ICT capital, labor by educational attainment categories) for 40 countries over
the period 1995-2006. A detailed description of datasources can be found in Erumban et al. (2010) on
national accounts data and the supply and use tables, Francois and Pindyuk (2010) on services trade and
P¨
oschl and Stehrer (2010) on goods trade.
National accounts data have been collected for all countries over the period 1995-2006 which served
as benchmark values. Existing supply and use tables have then been adjusted to these national accounts
data with some of the tables being estimated for years for which these were not available. Some countries
only provide input-output tables which have been transformed back into supply and use tables. Through
this process all tables have been standardized over years and across countries with respect to product and
industry codings. These tables contain information on supply and use of 59 products in 35 industries to-
gether with the information on final use and value added. Accompanying this information corresponding
trade data were collected at the same level of disaggregation at the product level. With respect to goods
trade which are taken from UN COMTRADE data at the HS 6-digit level this is rather straightforward
as there exists a correspondence from HS-6 to the product level in the supply and use tables (CPA).
12See www.wiod.org.
15

However services trade is only available from balance-of-payment statistics providing information on a
detailed basis only in BoP categories. Using a rough correspondence these were merged to the product
level data provided in the supply and use tables. Additionally, the trade data are split up into use cate-
gories fitting the needs of supply and use tables, i.e. intermediates, consumption and gross fixed capital
formation. Goods trade has been split up by applying a categorization of products into intermediates,
final consumer goods and gross fixed capital goods. The correspondence used for this was created by
beginning with the usually used BEC classification (provided by UN) but adapting the classification to
the specific needs (see P¨
oschl and Stehrer, 2010). In particular, the correspondence between HS6-digit
and BEC categories has been revised and in a number of cases we use weights for particular products to
distinguish between intermediates and the other categories. For services trade, however, there is no such
information available. Therefore, we used data from existing input-output and supply and use data and
applied average shares across countries. Relying on these underlying data we started from the import
vector provided in the supply tables. Import values for each country and product are split up first into the
three use categories. Second, within each use category a proportionality assumption is applied to split up
the imports for each use category across the relevant dimensions. For example, imports of intermediates
are split across using industries according to the shares resulting from the original use table. Similarly,
imports for final consumption are split up into final demand categories. Investment are allocated only to
gross fixed capital formation (i.e. not considering changes in inventories and valuables). This resulted in
an import use table for each country. Finally each cell of the import use table was again split up by coun-
try of origin resulting in 39+1 (including the rest of world) import use tables for each country. Merging
these tables together provides a full set of inter-country supply and use tables. Finally, an international
input-output table was constructed by applying the transformations of model D as described in the Eu-
rostat manual (Eurostat, 2008). This results in a world input-output database for 40 countries and 35
industries, i.e. the intermediates demand block is of dimension 1400 ×1400, plus the additional rows on
value added and columns on final demand categories. The rest of the world is not explicitly modeled in
this case but appears only in the import columns (imports from rest of the world by product) and export
column (exports to rest of the world). In the application below an assumption on the structure of input
coefficients is necessary which will be outlined below.
Corresponding data at the industry level allow splitting up value added into capital and labor income.
Furthermore, capital income can be split up into ICT and Non-ICT income, and labor income into income
of low, medium and high educated workers. These additional data for the factor incomes corresponds
16

in construction to the method applied in the EU KLEMS database (Timmer et al., 2007) and efforts
undertaken in the World KLEMS project.13 In the results section we present figures for two groups
of countries, EU-15 and EU-12 plus Turkey, and 13 individual countries which might be grouped into
NAFTA (Canada, USA and Mexico), Asian countries (Japan, Korea and Taiwan), and the BRIICs (Brazil,
Russia, India, Indonesia and China) to which we also add Australia. One should note however that all
calculations are performed at the level of individual countries and industries thus taking account of all
information available. Finally, the database also includes imports from rest of the world and exports to
rest of the world.14 To take account of trade with these countries one would have to construct such an
entity. For the purpose of this paper we can do this by adding additional blocks (rows and columns) in
the coefficient matrix. In this paper we present results when assuming that this rest of the world has the
same structure as Brazil. Qualitatively the results do not depend on this assumption.
5 Net trade in value added - Selected results
In this section we present selected results on the patterns of value added by applying equation (3.1).
For this we proceed in a series of steps: First, we present the magnitudes of exports and imports of
value added comparing them to trade in goods and services and the net trade of value added for the 40
countries. Trade in value added is then differentiated by factors. For this we report results for the years
1995, 2000 and 2005.
5.1 Net trade in value added
Table 1 reports the figures for exports and imports of goods and services in value added terms in billions
of US dollars. As we have shown above exports and imports correspond to the measure of value added
trade and therefore net trade also equals net value added trade. First, for all countries the magnitude of
exports and imports increased quite strongly, particularly for countries in the EU-12 where figures are
three times higher in 2005 compared to 1995 and in China where the flows increased by a factor of five.
The last three columns present net trade figures where a negative sign implies that this country is a net
importer and a positive sign that it is a net exporter. One can clearly see the rising trade deficit of the
US which amounted to about 700 billion US dollars in 2005 and the trade surplus of China. Net trade
13Some of these data are still preliminary and will be replaced later by improved information. Furthermore, for a number of
countries and factors we had to impute values from other countries which again is a source of a potential imprecision.
14In the construction process of the WIOD intercountry tables exports to rest of the world also serves as a balancing item.
17

for the European countries has been or has slipped into the negative for all years and country groups.
The EU-15 in particular turned a trade surplus of around 65 billion US dollars into a trade deficit of
similar magnitude. The Asian countries, Japan, Korea and Taiwan, and the other countries all show
a trade surplus at least in 2005 with the only exception being Australia. Table 2 provides the shares
Table 1 Trade in goods and services and trade in value added, in bn US-$
(Value added) Exports (Value added) Imports Net trade
Reporter 1995 2000 2005 1995 2000 2005 1995 2000 2005
EU-15 2241.7 2486.0 4070.3 -2177.0 -2541.6 -4137.8 64.8 -55.6 -67.4
EU-12 173.2 225.3 499.9 -176.0 -241.8 -521.0 -2.9 -16.4 -21.1
TUR 26.9 38.7 78.9 -37.0 -57.2 -113.3 -10.1 -18.5 -34.4
CAN 210.2 315.1 408.2 -193.6 -277.8 -372.3 16.6 37.3 35.9
USA 717.5 958.6 1149.1 -833.5 -1324.5 -1829.7 -116.0 -365.9 -680.6
MEX 78.3 166.9 214.0 -70.5 -174.0 -217.4 7.8 -7.1 -3.4
JPN 480.9 512.7 652.5 -389.8 -426.6 -565.1 91.0 86.1 87.4
KOR 134.5 175.3 277.0 -128.1 -148.9 -236.6 6.4 26.4 40.4
TWN 126.5 161.4 218.2 -117.7 -148.0 -194.0 8.8 13.4 24.2
AUS 71.0 88.3 145.9 -69.9 -83.2 -148.6 1.1 5.0 -2.7
BRA 51.2 59.0 123.5 -58.7 -64.5 -87.1 -7.5 -5.5 36.4
CHN 168.0 279.6 836.6 -128.2 -216.5 -612.2 39.8 63.0 224.5
IDN 53.2 62.1 96.4 -51.7 -47.4 -78.3 1.5 14.7 18.2
IND 38.9 62.0 144.1 -34.6 -51.1 -135.2 4.3 11.0 8.9
RUS 82.1 103.9 237.3 -58.7 -47.0 -119.5 23.4 56.9 117.8
ZROW 600.4 970.0 1594.1 -729.6 -814.7 -1377.8 -129.2 155.3 216.2
Source: WIOD database, Version January 2011; author’s calculations
according to our decomposition into the five components with respect to domestic and foreign contents
of value added trade. As countries become more and more integrated in to international production
processes one would expect that the share of the foreign value added content of exports would be rising
over time. Further, smaller countries would be expected to show higher values. In fact, this share was
rising for almost all countries as reported in Table 2, the only exceptions being Canada, Australia and
Indonesia. There have been particularly strong increases for EU-12, Turkey and Taiwan. For China the
share increased from 12.4 to 16.8 percent and for Japan from 5.8 to 10 percent. Less strong increases
are found for other developed countries including the US (8.1 to 10.3) and the EU-15 (19.5 to 22.5) for
example. In magnitudes, the shares are particularly high for Taiwan (40 percent) and the EU-12 (33
percent). Analogously, we would also expect that the share of foreign imports of value added would rise
as the imports from other countries increasingly embody value added from third countries. Again this is
18

what actually happened for all countries with the exception of Indonesia for which it was however more
or less stable. The strongest increase from 10.5 to 15.3 took place in Mexico. Generally, however the
changes are less strong compared to the exports. Finally, the shares of re-imports are fairly small but are
increasing in most cases over time. Only the US shows significant magnitudes which probably results
from trade with Mexico.
Table 2 Decomposition of total trade (in %)
Foreign VA content Foreign VA content
of exports Re-Imports of VA of multilateral imports
Reporter 1995 2000 2005 1995 2000 2005 1995 2000 2005
EU-15 19.5 22.1 22.5 1.7 1.7 1.9 14.9 17.2 18.3
EU-12 23.2 30.1 32.9 0.3 0.3 0.4 16.4 19.4 20.8
TUR 9.6 13.6 16.0 0.1 0.1 0.2 14.8 18.6 18.5
CAN 24.0 25.5 21.7 1.2 1.8 2.0 9.9 10.3 12.3
USA 8.1 8.5 10.3 6.5 8.4 6.0 9.1 9.8 11.6
MEX 22.4 27.1 24.3 0.4 0.6 0.7 10.5 11.6 15.3
JPN 5.8 7.2 10.0 1.8 1.9 1.9 9.3 10.6 11.3
KOR 18.3 19.7 21.5 0.4 0.6 0.9 9.8 10.0 12.0
TWN 31.2 34.2 40.7 0.2 0.5 0.7 10.1 11.2 13.4
AUS 8.3 7.4 7.0 0.3 0.4 0.6 11.4 12.1 13.8
BRA 4.3 6.8 6.8 0.1 0.1 0.2 10.8 12.7 13.7
CHN 12.4 12.0 16.8 0.6 1.2 2.4 13.0 14.6 15.3
IDN 13.3 12.0 10.5 0.2 0.3 0.4 11.8 10.3 11.6
IND 4.8 4.7 6.7 0.1 0.1 0.2 10.8 11.0 12.2
RUS 4.8 5.9 5.0 0.8 0.8 1.1 12.9 15.1 16.9
ZROW 5.3 7.1 7.1 0.1 0.1 0.1 13.1 11.8 12.3
Source: WIOD database, Version January 2011; author’s calculations
5.2 Net trade in value added by use category
The trade figures can be split into trade in final goods (consumption and investment goods) and interme-
diates. When focusing on final goods trade only one circumvents the problem with ’double-counting’
but one misses the fact that trade in intermediates also carries value added (or rewards of primary factors
capital and labor). In our approach we can split trade by these use categories easily and thus take account
of the relative importance of these with respect to trade in value added. One should note that exports,
imports and net trade sum up to the total trade figures as shown in Table 1 and that (net) trade by use
categories in value added terms again equals (net) trade in gross terms. Let us highlight some important
19

Table 3 Trade in goods and services and trade in value added by use category, in bn US-$
(Value added) Exports (Value added) Imports Net trade
Reporter 1995 2000 2005 1995 2000 2005 1995 2000 2005
Final goods trade
EU-15 1056.5 1203.9 1952.1 -891.2 -1048.2 -1704.5 165.3 155.7 247.5
EU-12 89.0 114.4 240.9 -68.4 -89.2 -181.6 20.6 25.2 59.3
TUR 15.9 22.4 42.7 -12.8 -21.3 -37.0 3.1 1.1 5.7
CAN 87.8 123.0 149.5 -85.2 -119.7 -167.1 2.6 3.3 -17.6
USA 276.3 377.5 446.8 -361.9 -597.3 -753.6 -85.6 -219.7 -306.8
MEX 39.7 95.7 107.8 -19.7 -51.4 -67.9 20.0 44.3 39.9
JPN 217.4 247.3 293.5 -155.9 -168.3 -212.3 61.5 79.0 81.3
KOR 60.8 84.1 112.5 -41.3 -34.9 -56.0 19.5 49.2 56.5
TWN 67.5 79.2 74.9 -40.7 -53.2 -56.6 26.7 26.0 18.3
AUS 22.6 27.4 34.1 -27.4 -39.6 -74.3 -4.8 -12.1 -40.2
BRA 21.6 23.4 46.5 -24.2 -22.3 -26.3 -2.6 1.1 20.2
CHN 114.5 180.3 461.3 -35.2 -54.5 -120.7 79.3 125.9 340.5
IDN 23.3 28.4 32.4 -18.9 -17.5 -21.1 4.5 10.9 11.3
IND 21.9 33.8 88.9 -8.8 -10.3 -41.9 13.1 23.5 47.0
RUS 23.0 37.4 65.3 -34.1 -22.4 -65.9 -11.1 15.0 -0.6
ZROW 208.1 296.1 456.7 -520.3 -624.3 -1019.1 -312.2 -328.2 -562.4
Intermediate goods trade
EU-15 1185.2 1282.1 2118.3 -1285.8 -1493.4 -2433.2 -100.5 -211.3 -315.0
EU-12 84.1 110.9 259.0 -107.6 -152.6 -339.4 -23.5 -41.7 -80.4
TUR 11.0 16.3 36.2 -24.2 -35.9 -76.3 -13.2 -19.6 -40.1
CAN 122.4 192.1 258.6 -108.3 -158.1 -205.2 14.1 33.9 53.5
USA 441.2 581.1 702.3 -471.6 -727.3 -1076.1 -30.5 -146.2 -373.8
MEX 38.6 71.2 106.2 -50.8 -122.6 -149.5 -12.2 -51.4 -43.4
JPN 263.5 265.4 358.9 -234.0 -258.3 -352.8 29.5 7.2 6.1
KOR 73.7 91.2 164.5 -86.9 -114.0 -180.6 -13.2 -22.8 -16.1
TWN 59.0 82.2 143.2 -77.0 -94.8 -137.4 -17.9 -12.7 5.9
AUS 48.4 60.9 111.8 -42.5 -43.7 -74.3 6.0 17.2 37.5
BRA 29.6 35.7 76.9 -34.5 -42.2 -60.8 -4.9 -6.5 16.2
CHN 53.5 99.2 375.3 -93.0 -162.1 -491.4 -39.6 -62.8 -116.1
IDN 29.8 33.6 64.0 -32.8 -29.9 -57.2 -3.0 3.8 6.8
IND 17.0 28.3 55.2 -25.8 -40.8 -93.3 -8.8 -12.5 -38.1
RUS 59.1 66.5 172.0 -24.6 -24.6 -53.6 34.5 41.9 118.4
ZROW 392.3 673.9 1137.4 -209.3 -190.4 -358.7 183.0 483.5 778.6
Source: WIOD database, Version January 2011; author’s calculations
20

aspects from the results in Table 3. Most countries are net exporters of final goods, the most important
exceptions being the US and Australia as well as the rest of world which is a major importer of final
goods. Most of these countries tend to be net importers of intermediates, now also including the US
and China though not Japan and Taiwan. The most important net exporters are Australia, Russia and
the rest of the world (mainly driven by raw materials trade including oil). However, there are also some
interesting patterns and changes over time. In most cases the value of intermediates exports and imports
are quite close to each other. However, there are interesting exceptions to this: For example, US exports
in intermediates are much higher than US exports in final goods. Similarly, US imports of intermediates
are (in absolute terms) also higher but have been growing even faster than US exports. As a consequence,
US net trade in final goods is similar to that in intermediates, but the latter has been growing by a factor
of around ten while the former has grown by a factor of only around four. Thus it is not the case that
the huge US trade deficit was mainly caused by the fact that it imported more and more final goods
(which more or less doubled) but mainly due the fact that its imports of intermediates have been growing
relatively fast (by a factor of 2.5). On the other hand, China shows much lower exports in intermediates
as compared to final goods but with a strong increase between 2000 and 2006. China’s imports of inter-
mediates are (again in absolute terms) higher than its imports in final goods and the former have been
growing faster than the latter. China therefore shows a trade deficit in intermediates and a huge surplus
in final goods. This can be compared to the US which is running trade deficit of similar magnitudes in
both categories. xxx Note however, that this description so far would be also seen from usual statistics
in gross trade (as outlined above). Table 4 therefore again shows the decomposition into the categories
with respect to domestic and foreign contents. First, the shares by use category are not too different
for the individual countries though there are some notable exceptions such as India. The shares of the
foreign multilateral content of VA imports however tend to be lower for the more advanced countries.
The reason for this is that these countries’ shares in bilateral content of VA imports of intermediates are
high because of imports of raw materials (also from the rest of the world). Importantly however, in most
cases these shares are increasing over time both for final and intermediates goods trade. The latter in
particular implies that the production of intermediates goods trade has also become more integrated over
time.
21

Table 4 Decomposition of trade by use category (in %)
Foreign VA content Foreign VA content
of exports Re-Imports of VA of multilateral imports
Reporter 1995 2000 2005 1995 2000 2005 1995 2000 2005
Final goods trade
EU-15 19.9 22.5 22.9 1.9 1.8 2.0 16.1 19.1 20.4
EU-12 23.2 30.6 32.5 0.3 0.3 0.4 17.9 20.8 22.5
TUR 9.8 13.7 16.0 0.1 0.1 0.2 17.1 23.1 23.3
CAN 29.2 32.0 27.4 1.2 1.7 1.9 10.7 11.3 13.6
USA 9.1 9.0 11.3 7.5 9.1 6.5 10.5 11.4 13.7
MEX 24.8 30.1 27.6 0.4 0.6 0.6 10.6 11.9 15.8
JPN 5.7 7.1 9.4 2.0 2.3 2.3 10.5 12.2 13.3
KOR 17.6 19.8 21.4 0.5 0.7 0.9 10.4 10.8 12.8
TWN 29.7 32.4 36.3 0.2 0.5 0.7 9.9 10.9 13.7
AUS 8.3 8.1 8.9 0.3 0.4 0.5 11.6 12.8 15.1
BRA 4.0 7.4 7.0 0.2 0.1 0.2 11.4 13.5 14.9
CHN 13.1 12.6 16.5 0.5 1.2 2.5 14.3 15.4 16.0
IDN 16.2 15.7 14.2 0.1 0.3 0.3 12.9 10.9 12.9
IND 4.5 4.5 7.0 0.1 0.1 0.2 11.6 13.6 14.2
RUS 4.1 4.9 5.9 0.6 0.7 1.0 13.5 16.0 16.8
ZROW 5.4 8.4 7.8 0.1 0.1 0.1 13.6 13.3 13.4
Intermediate goods trade
EU-15 19.0 21.7 22.2 1.6 1.5 1.9 14.1 15.8 16.8
EU-12 23.2 29.5 33.2 0.4 0.4 0.4 15.5 18.5 19.8
TUR 9.4 13.5 16.0 0.1 0.1 0.2 13.6 15.9 16.1
CAN 20.4 21.4 18.4 1.3 1.9 2.1 9.2 9.5 11.2
USA 7.6 8.1 9.7 5.7 7.8 5.6 8.0 8.4 10.2
MEX 19.9 23.0 20.9 0.4 0.7 0.7 10.5 11.5 15.1
JPN 6.0 7.3 10.5 1.6 1.6 1.6 8.5 9.6 10.2
KOR 18.9 19.6 21.6 0.4 0.6 0.9 9.5 9.7 11.8
TWN 33.0 35.9 43.0 0.2 0.5 0.7 10.1 11.3 13.3
AUS 8.4 7.1 6.4 0.3 0.4 0.6 11.3 11.4 12.6
BRA 4.5 6.4 6.6 0.1 0.1 0.2 10.3 12.3 13.2
CHN 11.0 11.0 17.1 0.6 1.2 2.4 12.6 14.3 15.1
IDN 11.0 8.9 8.5 0.2 0.3 0.5 11.3 10.0 11.2
IND 5.2 4.9 6.2 0.1 0.1 0.2 10.5 10.4 11.3
RUS 5.1 6.4 4.7 1.0 0.9 1.3 12.1 14.2 17.0
ZROW 5.3 6.6 6.9 0.1 0.0 0.1 11.8 7.1 9.4
Source: WIOD database, Version January 2011; author’s calculations
22

5.3 Net trade in factors
Trade in value added is itself composed of trade in capital and labor as outlined above. From a theoretical
perspective the HOV results suggest that countries being abundant with labor (capital) would be net
exporters of labor (capital) services at least in productivity adjusted terms. As in this paper we focus
on trade in value terms (rather than in physical units) this picture becomes distorted as we allow for
differences in factor rewards (i.e. no factor-price equalization). However, the picture is already distorted
when allowing for trade in intermediates or ’trade in tasks’ (see Baldwin and Robert-Nicoud, 2010).
5.3.1 Trade in capital and labor
Table 5 presents the results when using capital and labor coefficients instead of value added coefficients.
As the former two sum up to the latter trade flows in capital and labor must also sum up to flows in value
added terms. A number of countries are both net exporters of labor and capital (including China, India,
Indonesia, Russia, Japan, Korea and Taiwan). Conversely, the US and Turkey have been net importers of
both labor and capital over the whole period. There are also a few countries where the signs are different.
The EU-15, for example, was a net exporter of labor but a net importer of capital. And the EU-12,
Canada, Mexico (in the latter years) and Australia have been net exporters of capital and net importers
of labor. Some countries, like Brazil, show a mixed pattern. Table 5 again includes the five components
of value added trade with respect to the domestic and foreign content which is presented in Table 6 (we
leave out the share of re-imports as this is rather small). In magnitude these are quite close to the ones
for total trade and also the patterns across countries are quite similar. Again, the share of foreign value
added content of exports tend to be larger than the share of indirect imports. There is however no clear
evidence of whether these shares are higher or lower for capital or labor across countries.
5.3.2 Trade in ICT and Non-ICT capital
Now we move on to split up the capital component into ICT and Non-ICT capital15 and later on also
labor into its subcomponents. Table 7 presents the results for the two capital categories. First, one has to
note that trade in ICT capital has a much lower share than trade in Non-ICT capital. Again, in most cases
surplus countries are net exporters of both types of capital. However, some interesting changes over time
can be observed. The EU-15 is a net importer of ICT capital, though the trade deficit diminishes whereas
the US which has been a net exporter of ICT capital becomes a net importer in 2005 which is similarly
15The ICT share in capital income was imputed for some countries and thus results are preliminary.
23

Table 5 Net trade in capital and labor (total trade), in bn US-$
Net exports capital Net exports labor
Reporter 1995 2000 2005 1995 2000 2005
EU-15 -35.41 -77.64 -187.21 100.16 22.06 119.77
EU-12 4.29 3.09 6.69 -7.16 -19.53 -27.77
TUR -3.09 -7.93 -13.00 -6.97 -10.61 -21.38
CAN 18.72 43.31 43.04 -2.08 -6.06 -7.15
USA -64.47 -197.24 -367.60 -51.55 -168.64 -313.04
MEX 4.88 0.85 11.96 2.94 -7.96 -15.40
JPN 39.24 44.15 40.04 51.78 41.99 47.31
KOR -4.54 10.33 6.91 10.94 16.05 33.49
TWN 8.51 9.80 15.18 0.31 3.55 8.99
AUS 3.53 7.28 11.53 -2.39 -2.24 -14.27
BRA -3.85 -4.86 7.91 -3.61 -0.60 28.45
CHN 11.47 11.90 103.45 28.31 51.12 121.02
IDN 0.42 3.63 8.24 1.07 11.04 9.91
IND 0.78 0.50 3.85 3.54 10.45 5.07
RUS 14.30 26.97 76.05 9.10 29.95 41.74
ZROW 5.20 125.85 232.97 -134.38 29.44 -16.73
Source: WIOD database, Version January 2011; author’s calculations
also the case for Canada and Mexico. The Asian countries (Japan, Korea and Taiwan) are however
improving their position and becoming net exporters of ICT capital. Finally, China is also improving its
position in this category.
In the case of Non-ICT capital there seems to be a tendency for each country (group) to maintain
its position. Only a few countries succeeded in becoming net exporters of Non-ICT capital (Korea,
Brazil, Indonesia, and India). The other countries did not switch their position as either net exporters or
net-importers with the tendency that the trade deficit/surplus increased in each case.
5.3.3 Trade in labor by educational attainment categories
Finally, we present in Table 8 the results when splitting up trade flows in labor terms into the components
high-educated and medium and low educated taken together. The first interesting point here is that the
EU-15 is a net importer of high-educated labor (and increasingly so) whereas the US is maintaining its
position in being a net exporter of high-educated labor though again with the trade surplus declining
over time. The Asian countries (Japan, Korea and Taiwan) are net exporters of high-educated labor with
24

Table 6 Decomposition of trade in factors (total trade), in %
Capital Labor
Foreign VA content Foreign VA content Foreign VA content Foreign VA content
of exports of imports (multilateral) of exports of imports (multilateral)
Reporter 1995 2000 2005 1995 2000 2005 1995 2000 2005 1995 2000 2005
EU-15 20.9 23.3 25.0 16.9 18.8 20.5 18.7 21.5 21.1 16.6 18.8 20.0
EU-12 21.4 26.9 30.5 17.3 20.4 22.1 24.3 32.1 34.7 16.5 19.3 20.6
TUR 9.2 13.9 14.6 15.3 17.9 18.2 9.8 13.5 17.1 14.7 19.2 19.0
CAN 20.4 20.0 18.9 11.8 13.5 15.4 26.6 29.9 24.1 10.7 11.4 13.5
USA 9.0 10.5 12.5 14.6 16.8 16.9 7.7 7.5 9.1 16.2 19.0 18.2
MEX 20.2 24.6 20.4 11.7 14.0 17.4 23.7 28.5 27.6 10.5 11.4 15.0
JPN 5.8 7.1 10.6 10.8 12.1 12.6 5.8 7.3 9.5 11.2 12.7 13.7
KOR 21.8 18.5 23.1 10.2 10.1 12.7 16.7 20.5 20.4 10.1 10.8 13.1
TWN 30.1 32.7 40.1 10.7 11.8 14.4 32.1 35.2 41.2 10.1 11.5 13.9
AUS 7.4 6.4 6.0 11.4 12.5 15.1 9.1 8.3 7.9 11.8 12.4 13.9
BRA 4.6 7.3 8.0 10.7 12.1 13.0 4.1 6.6 6.0 11.0 13.3 14.7
CHN 13.2 13.4 16.4 14.1 15.3 17.9 12.0 11.2 17.0 13.4 16.1 17.5
IDN 13.8 13.0 10.5 11.9 10.4 11.4 13.0 11.5 10.4 12.1 10.8 12.6
IND 4.9 4.8 6.0 10.3 10.0 11.9 4.7 4.6 7.3 11.3 11.9 12.8
RUS 3.9 5.2 3.9 14.3 16.6 20.0 5.4 6.4 6.2 13.3 15.4 16.9
ZROW 5.0 6.3 7.1 14.5 12.1 14.2 5.6 7.8 7.1 12.5 11.8 11.4
Source: WIOD database, Version January 2011; author’s calculations
25

Table 7 Net trade in ICT and Non-ICT capital (total trade), in bn US-$
ICT capital Non-ICT capital
Reporter 1995 2000 2005 1995 2000 2005
EU-15 -16.24 -3.73 -6.27 -19.16 -73.90 -180.94
EU-12 1.65 -0.31 -0.19 2.64 3.40 6.88
TUR 0.20 -0.78 -1.18 -3.29 -7.15 -11.81
CAN -0.94 -0.12 -1.04 19.67 43.43 44.08
USA 2.76 5.62 -3.21 -67.22 -202.86 -364.39
MEX 2.19 -2.07 -1.18 2.69 2.92 13.14
JPN -0.30 6.34 4.84 39.54 37.81 35.20
KOR -2.41 0.14 1.33 -2.13 10.19 5.57
TWN -0.21 0.38 1.04 8.72 9.42 14.14
AUS -0.83 -0.57 -0.80 4.36 7.85 12.33
BRA 0.30 -0.79 1.30 -4.14 -4.06 6.61
CHN 4.44 0.72 10.14 7.03 11.18 93.31
IDN 1.34 0.10 0.14 -0.91 3.53 8.09
IND 0.78 0.66 2.60 -0.00 -0.15 1.25
RUS 4.08 2.53 6.21 10.22 24.43 69.83
ZROW 3.20 -8.12 -13.73 2.01 133.97 246.71
Source: WIOD database, Version January 2011; author’s calculations
increasing trade surpluses. Finally, China which has been a net importer of high-educated labor became
a net exporter in 2005 (with a very small trade surplus however). Nonetheless, China is also maintaining
its position as being an important net exporter of low-educated labor where it had a huge trade surplus.
Most of the other emerging economies also show large trade surpluses in medium and low-educated
labor. With respect to the advanced countries, the US is - as expected - a huge net importer of medium
and low-educated labor, whereas the EU-15 are net exporters of this category and also show a rising trade
surplus. With respect to high educated labor, the US is still a net exporter but the surplus diminishes over
time. The EU-15 is showing a trade deficit with respect to trade in high educated labor, which is also
deteriorating.
6 Conclusions
In this paper we introduce and apply a method for measuring trade in value added and its subcomponents
based on recent approaches as applied in the literature on measuring the factor content of trade when
taking into account trade in intermediates. This allows to take account of a country being an exporter and
26

Table 8 Net trade in labor by educational categories, in bn US-$
High educated Medium + low educated
Reporter 1995 2000 2005 1995 2000 2005
EU-15 -45.98 -55.58 -72.04 146.14 77.64 191.81
EU-12 -3.05 -7.51 -10.76 -4.11 -12.02 -17.01
TUR -1.97 -3.90 -6.85 -5.00 -6.71 -14.53
CAN -10.09 -16.01 -22.90 8.01 9.94 15.75
USA 73.17 86.94 53.09 -124.72 -255.58 -366.13
MEX -4.92 -17.52 -15.00 7.86 9.56 -0.40
JPN 22.67 29.06 37.59 29.12 12.93 9.72
KOR 9.72 16.29 35.92 1.21 -0.24 -2.43
TWN 1.11 2.26 6.14 -0.80 1.29 2.84
AUS -2.01 -1.81 -7.39 -0.38 -0.44 -6.88
BRA -1.76 -3.48 4.18 -1.86 2.87 24.27
CHN -3.96 -7.61 0.42 32.26 58.72 120.60
IDN -1.85 -1.08 -0.20 2.92 12.11 10.11
IND -0.78 -0.74 0.39 4.32 11.19 4.68
RUS 0.25 2.87 10.64 8.85 27.08 31.10
ZROW -30.56 -22.19 -13.23 -103.82 51.63 -3.50
Source: WIOD database, Version January 2011; author’s calculations
importer of intermediates simultaneously and the fact of considerable two-way trade in intermediates.
The supposed framework allows to split trade in value added into various forms of domestic and foreign
content of exports and imports which also links to recent measures of vertical specialization in production
networks. Based on this approach we are also able to show in a straightforward manner that a country’s
trade balance in gross terms equals its trade balance in value added terms which links it to national
accounting identities. To our knowledge this has not yet been shown in a general way. Finally, the link
of net gross and value added trade allows to analyze in which factors - as components of value added
trade - a country is a net exporter or net importer. Thus, this shifts the focus of trade in goods (maybe
differentiated by industries or types of products, e.g. by technology content) to net trade in factors.
27

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