Tradability of Output and the Current Account:
An empirical investigation for Europe
Roman Stöllingerǂ
Abstract
In this paper we put forward the hypothesis that increasing specialisation in the production
of non-tradable output has a negative impact on the current account balance. To test this
tradability hypothesis empirically we proceed in two steps. Firstly, we develop a tradability
index which captures specialisation patterns with regard to the tradability of output.
Secondly, we embed the tradability index into an empirical current account model for the
full sample of European countries. We find strong evidence for a positive relationship
between the current account balance and the tradability index in both in the short and in
the long run. The relationship is stronger for emerging economies in Europe than for
developed countries. This finding has an important policy implication: the anxieties about
‘de-industrialisation’ in many parts of Europe seem justified because the resulting loss of
export capacity increases the risk of external imbalances.
Keywords: current account, tradability index, tradable goods, structural change, value
added exports.
JEL Codes: F41, F32, F10, F14
Version: April 2016
Acknowledgement: Research for this paper was financed by the Jubilee Fund of Oesterreichische Nationalbank
(Project No. 15291). Financial support provided by Oesterreichische Nationalbank for this research is gratefully
acknowledged. The author would like to thank Vladimir Gligorov, Mario Holzner, Michael Landesmann, Leon
Podkaminer and Robert Stehrer for very insightful discussions and suggestions. Thanks also goes to the
participants at the 7th FIW Research Conference in Vienna, the Conference on Competitiveness, Capital Flows
and Structural Reforms in Brno and the 18th Göttinger Workshop Internationale Wirtschaftsbeziehungen for
helpful comments and suggestions. The author is particularly indebted to Alexandra Bykova for very valuable
research assistance.
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1
Tradability of Output and the Current Account:
An empirical investigation for Europe
1 Introduction
Current account imbalances in Europe are a recurring issue that attracts the concern of policy-
makers and the interests of academics alike (e.g. Gaulier and Vicard, 2012; Lane and Pels, 2012;
Ca’Zorzi et al., 2012; Blanchard and Giavazzi, 2002). Among the various potential determinants of the
current account this paper will focus on role of the production structure. We put forward the
hypothesis that the size of the more tradable sectors relative to the sectors producing less tradable
output is an important determinant of a country’s current account position. To test this “tradability
hypothesis” we develop an indicator that we label tradability index (TI)1. This index allows us to
examine the relationship between countries’ sectoral structure and the current account position in a
more systematic and comprehensive way. This is because the TI reflects an economy’s entire
economic structure and in particular how tradable the output that a country produces is. The first
components of the TI are the sector-specific ratios between the global value added exports (Johnson
and Noguera, 2012) and industry-specific global value added. These sector-specific ratios, the
tradability scores, are then weighted by the countries’ value added shares and summed up to arrive
at the TI. The tradability index can be interpreted as the expected export openness given a country’s
production structure. By construction, the TI does not contain a country’s own trade flows and
therefore eliminates an important source of endogeneity that plagues the use of conventional
openness measures in empirical work. We argue that the TI is sufficiently exogenous to be used
directly in a regression framework for testing the tradability hypothesis.
The tradability hypothesis is linked to the debate about the de-industrialisation in Europe. A shrinking
manufacturing sector (in relative terms but in several European countries also in absolute terms) is a
widespread phenomenon in many European countries. Although the structural shift out of the
manufacturing sector has been and continues to be quite uneven across Member States with the
result that manufacturing activities are increasingly concentrated in a ‘Central European
Manufacturing Core’ (Stöllinger and Stehrer, 2014; IMF, 2013), the decline of manufacturing is a
European-wide concern. One reason for this concern is that a relatively smaller manufacturing
sector, which is the main tradable goods producing sector in most European countries, may put
pressure on the current account. In addition, if some areas are exempted from the general trend, as
it is the case with the members of the Central European Manufacturing Core, the resulting structural
divergence will give rise to growing external imbalances within Europe. Countries with shrinking
manufacturing sectors can be expected to end up with current account deficits while countries with
more robust manufacturing sectors will tend to run current account surpluses.
1 Zeugner (2013) uses a measure of country-specific value added in trade to evaluate the tradability of sectors in the context
of calculating unit value costs.
2
The use of the TI for studying the relationship between the production structure and the current
account has three main advantages. First of all, instead of focusing only on the manufacturing sector
or any other sector that is suspected to be particularly important for the external balance, the TI is a
comprehensive measure which reflects a country’s entire economic structure. Therefore the TI is
capable of capturing basically all structural phenomena such as de-industrialisation (respectively the
lack of industrialisation) or booms in the construction sector (as analysed in Gehringer, 2015).
Second, the TI is a more direct way of testing the tradability hypothesis than the value added share of
manufacturing which commonly serves as measure for the tradable sector (for a recent example see
Ehmer, 2014). This is because the TI is constructed using a value added based measure for exports.
The value added exports represent the amount of value added produced by an industry in one
particular country but consumed in another. The ratio between value added exports and value added
is therefore the natural proxy for the tradability of output. The TI ensures that one actually captures
the effect of the production structure, while a the positive relationship between the share of
manufacturing and the current account position may also signal other features of the sector such as
high increasing returns to scale. The TI is also appropriate from a theoretical perspective because the
essential distinction in the context of the tradability-current-account-nexus is not between the
manufacturing sector and the services sector but between the tradable and the non-tradable sector.
Third, the TI avoids applying a dichotomous classification of sectors into tradable and non-tradable
sectors (as for example in De Gregorio et al., 1994) which requires the choice of a discretionary
threshold. In contrast, the TI incorporates the fact that in reality there is no strict distinction between
tradables and non-tradables but rather gradual differences in the tradability of output produced in
the various sectors of the economy.
We use the TI for testing econometrically the effect of the tradability of output on the current
account position in a truly European-wide context. Our sample comprises 46 European countries
which are observed over the period 1995-2014. This means that our sample goes well beyond the
euro area which has attracted a lot of interest in the recent current account literature (see e.g.
Blanchard and Giavazzi, 2002; Gaulier and Vicard, 2012). A major advantage of a European wide
sample is that it results in a sufficiently large number of observations for a cross-country analysis.
The contribution of this paper to the literature is twofold. Firstly, we develop the tradability index
which makes use of the World Input-Output Database (WIOD). Secondly, we establish this tradability
index as a new determinant of the current account in a European context.
The rest of the paper is structured as follows. Section 2 discusses some of the related literature.
Section 3 explains the construction of the tradability index and presents some descriptive results.
Section 4 discusses the empirical model and the data while section 5 contains the estimation results.
Section 6 concludes with some policy implications of our findings.
2 Related Literature
There is no shortage of explanations for the re-occurring current account imbalances. Authors have
related these imbalances to fiscal policy and budgetary discipline (e.g. Schnabl and Wollmershäuser,
2013), productivity shocks (Cova et al., 2009; Fournier and Koske, 2010; Coricelli and Wörgötter,
2012) and structural policies such as wage policies (Kerdrain et al., 2010). Other contributions
highlight the development of financial markets (Mendoza et al., 2009), the degree of financial
3
integration (Blanchard and Giavazzi, 2002) or the existence of safe assets available to people with
savings (Caballero et al. ,2008) as being decisive for current account imbalances.
Another potential explanation is the production structure and changes thereof. The role of an
economy’s economic structure on the current account is, however, far from being universally
accepted. A major reason for this is that, according to the absorption approach (Alexander, 1952),
the current account is ultimately determined by the difference between savings and investment (as
the mirror image of the capital account). The inter-temporal approach to the current account
(Obstfeld and Rogoff, 1996), an extension of the absorption approach, emphasises macroeconomic
fundamentals as the determinants of the current account. In this framework, current account deficits
and surpluses are basically the results of either shocks to the economy or differences in expected
growth rates which result in shifts of consumption between periods. This typically leaves little room
for trade policies, trade openness or specialisation patterns for influencing the current account. With
regard to economic structures, one important aspect in this strand of the literature is the distinction
between a tradable goods producing sector and a non-tradable goods producing sector. (see e.g.
Ostry and Reinhart, 1991; Obstfeld and Rogoff, 1996; Ferrero et al., 2010). This distinction is essential
with regards to the tradability hypothesis. The theoretical predictions regarding the relationship
between the relative development of the tradable and the non-tradable sector and the current
account balance are generally ambiguous though and depend inter alia on the type of the shock and
the elasticity of intertemporal substitution. The most relevant scenario for our empirical investigation
is a wealth-neutral shift in the economic structure from one sector to another where the sectors
differ with regard to their tradability. In the simple case of an endowment economy this can be
modelled as an increase in the supply of the non-tradable good by one unit and a simultaneous
decrease in the supply of the tradable good by one unit. This scenario differs from a positive
productivity shock which in an intertemporal current account model would induce a current account
deficit as consumers will want to consume a part of the additional wealth already now due to
consumption smoothing. This effect is switched off by simultaneously increasing the output of the
non-tradable sector and decreasing the output of the tradable sector. In this scenario, a wealth-
neutral shift towards the non-tradable sector leads to a current account deficit only if the elasticity of
substitution is sufficiently small. The reason is an income effect which arises from a decrease in the
price level (because the price of the non-tradable good declines as the sector expands assuming
constant consumption shares). As a result the effective (i.e. consumption based) interest rate
increases, because postponing consumption until later now not only benefits from the interest
payment but also from the fact that for the same income more of the non-tradable good can be
consumed2. In principle, this income effect tends to cause a current account surplus. But if the
elasticity of substitution is sufficiently small, a strong desire for consumption smoothing will make
households shift forward consumption again, causing, ceteris paribus, a current account deficit.
Hence, the tradability hypothesis put forward in this paper is in line with the standard intertemporal
model of the current account only if the intertemporal elasticity of substitution is sufficiently small.
More recently, researchers have proposed a series of current account models that explicitly
incorporate production and export specialisation. Jin (2012) develops a model which combines inter-
temporal trade with intra-temporal factor-proportions-based trade. This combination introduces a
composition effect into the inter-temporal current account model which arises in addition to (and
2 Given the Cobb-Douglas consumption function, there is no intratemporal substitution effect between the tradable and
the non-tradable good. For further discussion of these issues see for example Harms (2008).
4
works against) the traditional convergence effect3 in current account models. The composition effect
suggests that the current account balance depends (among other things) on the capital intensity of
the output produced in a country. Since in a Heckscher-Ohlin framework richer (more capital-
abundant) countries specialise in the production of capital-intensive goods, investment will be higher
in these countries and they tend to run current account deficits. As a result capital will flow from
capital-poor to capital-rich countries, a fact that is observable for example in US-China relations.
Nedoncelle work with is a trade-cost augmented partial equilibrium version of the model by
Jin (2012). He tests for a joint effect of capital intensity and trade costs on the current account. The
main finding is that a reduction in trade costs worsens the current account of countries specialised in
the production of capital-intensive goods. These results can be directly related to the tradability
hypothesis forwarded in this paper. The composition effect runs counter the tradability hypothesis if
the tradable sector is characterised by higher capital intensity than the services sector. In the
opposite case, the composition effect and the tradability hypothesis would alter the current account
balance in the same direction.
Baraterri (2014) extends the model by Obstfeld and Rogoff (2000) in which all goods are tradable but
the goods are differentiated by the level of associated trade costs. In this model factor endowments
drive specialisation and the resulting comparative advantages which in turn matter for the current
account. Assuming completely specialised countries this model predicts that countries specialising in
services (such as the US) tend to run current account deficits. The reason for this is faster trade
liberalisation in the manufacturing sector than in the services sector (interpreted as the sector that
produces less-tradable output).4 The main inter-temporal mechanism in this model is the
asymmetrical timing of trade policies which affects saving decisions. More precisely, countries that
specialise in the production of manufacturing goods (and hence importing services) will postpone
consumption of imported services in expectation of future reductions in trade costs of services. This
way, households can benefit from lower prices of services in the future.5 Empirically Baraterri (2014)
finds that countries with revealed comparative advantages in services tend to run current account
deficits for the period after 1995.
The empirical literature referring to the inter-temporal approach to the current account has followed
two directions. One direction that uses various estimation techniques to establish evidence in favour
of the baseline model and another direction that attempts to identify short, medium or long-run
determinants of the current account which emerge from a broader class of models (see Bussière et
al., 2006). This paper belongs to the second strand of the literature and as such follows the approach
of Debelle and Faruqee (1996) and Chinn and Prasad (2003). This has the advantage that also other
key determinants can be controlled for. For example, we will incorporate the convergence effect
which is one of the testable predictions that the inter-temporal current account model delivers. It
predicts that countries which are below their steady state equilibrium (e.g. poorer countries or
countries experiencing a positive productivity shock) will run current account deficits because the
rate of return on capital will be above the (world) interest rate (Obstfeld and Rogoff, 1995).
Particularly in a European context, where capital still flows in ‘the right direction’, the (relative)
3 The convergence effect relates to the theoretical prediction that capital flows towards countries the effective capital-
labour ratio is relatively lower. In other words, countries with lower GDP per capita will borrow internationally against future growth, part of which is already consumed today. Therefore low income countries are expected to run current account deficits.
4 The relationship between specialisation in services and current account deficits is found to be robust only after 1995.
5 This result depends on the assumption that the inter-temporal rate of substitution is large (greater than 1).
5
income level is an important control variable. Blanchard and Giavazzi (2002) suggests that
particularly in the EU current account positions are increasingly related to countries’ income per
capita, i.e. that the convergence effect gains importance. This convergence effect or stages of
development effect is also argued for by Debelle and Faruqee (1996). However, their cross-section
estimations for fails to find statistical evidence for the convergence effect in a sample of industrial
countries in the non-linear specification.6 Chinn and Prasad (2003) do find a convergence effect in
their full sample but not for the subsample of developing countries. They also test, for the effect of
trade openness on the current account but fail to find a significant effect in almost all specifications.
There in a large number of additional determinants of the current account that have been tested in
the literature and which we will also include in our empirical model such as the dependency ratio
(e.g. Chinn and Prasad, 2003; Ca’Zorzi et al., Lane and Pels, 2012) and the real exchange rate
(Brissimis et al., 2010) will be discussed in detail in the next section.
Ehmer (2014) and Gehringer (2015) are to recent examples of papers that directly test for the impact
of the particular sector shares on the current account. Based on a sample of euro area countries,
Ehmer (2014) reports a positive effect of the share of the manufacturing sector in GDP on the current
account. Gehringer (2015) finds a worsening effect of an expanding construction sector on the
current account for the peripheral crisis-stricken euro area. In contrast, she her results do not
suggest a significant relationship between the share of the manufacturing sector and the current
accounts in any subset of EU countries.
3 Tradability Index
A natural benchmark for the tradability of goods and services is how much they are actually traded
(De Gregorio et al., 1994)7. De Gregorio et al. (1994) consider a sector as tradable if more than 10
percent of total output is exported. We will depart from this approach by switching from a
dichotomous classification of sectors into either tradable or non-tradable to a continuous measure of
sectors’ tradability. This gradual approach gives due credit to the fact that basically all goods and
increasingly also services are potentially tradable though to a different extent. Hence, in our
empirical model we will replace the dual distinction between tradable sectors and non-tradable
sectors with a continuous ‘tradability score’ specific to each sector.
The tradability score can be obtained from industry level information on value added and either
gross exports or value added exports (Johnson and Noguera, 2012). Both measures of exports will be
used but our preferred metric of the tradability score is based on the value added exports (VAX)
concept. The methodological expositions will therefore outline the construction of the tradability
scores and the tradability index based on value added exports. The first step for the derivation of the
tradability index is therefore the calculation of the value added exports at the industry-country level.
For this we follow the trade in value added concept in Johnson and Noguera (2012) and the
6 The authors explain the lack of significance with potential multicollinearity between income per capita and the capital-
output ratio which is also included in the regression. 7 An alternative approach to capture the tradability of goods (or sectors) is to look at tariffs or trade barriers more
generally. The difficulty is that the magnitude of such trade barriers is hard to identify. While the trade costs for merchandise can be estimated with gravity models (see e.g. Anderson and Wincoop, 2004), this approach is harder to implement for services.
6
expositions in Stehrer (2012). Intuitively, the value added export of a particular industry and country
is the value added created by that country and industry but absorbed in other countries.8
The calculation of the value added exports will be based on the most detailed data that is available to
us, i.e. the input-output table of the World Input-Output Database (WIOD), which comprises 40
countries (plus the rest of the world) and 35 industries. Once the industry and country-specific VAX
have been retrieved they are summed up over all countries to obtain global VAX data. In a next step
we aggregate the VAX of the 35 industries to 14 broad sectors9. The reason for aggregating to 14
sectors is that for the countries in our sample it is hard to find more disaggregated production data.10
Hence the tradability scores are ultimately calculated for 14 broader sectors. Finally, the global VAX
as well as the global value added data are aggregated over the available time period, i.e. 1995-2011.
By summing up over time we make the implicit assumption that the tradability of output does not
change over time. Formally, we arrive at the global tradability score ( ) by calculating the ratio
between the global sector-level value added exports ( ) and the sector-level value added ( ).
where the subscript t indexes time and j indexes countries.
The resulting tradability scores for the 14 sectors are shown in Figure 1. The figure presents both the
tradability score based on value added exports (dark blue bars) and the tradability score based on
gross exports (light blue bars). Both rankings are very intuitive. In the tradability score based on value
added exports (TSvax) mining and manufacturing emerge as the sectors producing by far the most
tradable output with a tradability score of 0.51 and 0.41 respectively. They are followed by the
transport and communication sector and the agricultural sector. At the bottom of the ranking are the
services sectors health and public administration which are both characterised by a very low
tradability score amounting to 0.006 and 0.014 respectively.
8 See Appendix for the methodological details of calculating the value added exports.
9 For the list of the resulting 14 sectors and the corresponding NACE Rev.1 and NACE Rev.2 industry codes see Appendix.
10 Another factor influencing the choice of the 14 sectors is that the calculation of the tradability score requires data
based on both NACE Rev. 1 and NACE Rev. 2 sections. Since the sample period comprises years for which only either NACE Rev. 1 data or NACE Rev. 2 data are available, the only solution is to add up the broader aggregate. For example, while NACE Rev. 1 distinguishes between Agriculture (A) and Fisheries (B), NACE Rev. 2 does not. Hence, the project uses Agriculture and Fisheries as one sector. For details see Appendix.
7
Figure 1: Global tradability scores (TS) of sectors
Note: TSvax = tradability score based on value added exports. TSx = tradability score based on gross exports
Source: WIOD, author’s own calculations.
The tradability scores based on gross exports (TSx) are by definition higher. Nevertheless, the ranking
is very similar though with some important differences. The major difference is that mining and
manufacturing switch position compared to the value added exports based scores. The reason is that
manufacturing is characterised by intensive trade in intermediates which leads to double counting of
in gross export flows. This double counting is corrected for in the value added based approach.
Certainly, if one considers a good that is crossing borders (i.e. is exported) several times as being
more tradable than a good that crosses borders only once, then the gross exports based tradability
scores is preferable. However, we have a clear preference for the tradability scores based on value
added exports (TSvax). This is because we believe that (i) no exported good should enter export term
more than once (i.e. a good cannot be more than 100% traded) and (ii) only domestically value added
should enter export term which is also methodologically correct because they are related to value
added.
The gravity literature emphasises that a particular product may face higher trade costs (which
typically includes various types of costs such as tariffs, non-tariff measures and transportation costs)
in one country than in another. This implies that the tradability scores of sectors may differ across
countries. Despite some potential differences across countries, it seems very likely that if the output
of industry A (say the manufacturing industry) is more tradable than that of industry B (say the health
sector) then it seems acceptable to assume that this is also the case in other countries. To be sure,
we run Spearman rank correlation tests between the country level industry rankings by their
tradability score and the global industry ranking just discussed. This is done for all European
countries covered in the WIOD database (Table 1).
0.00
0.20
0.40
0.60
0.80
1.00
trad
abili
ty s
core
TSvax TSx
8
Table 1: Spearman's rank correlation coefficients of European country's tradability scores with the global tradability score (time invariant)
country Code ρ country code ρ
Austria AT 0.9560 ***
Italy IT 0.9780 ***
Belgium BE 0.9912 ***
Lithuania LT 0.7275 ***
Bulgaria BG 0.9209 ***
Luxembourg LU 0.8330 ***
Cyprus CY 0.9253 ***
Latvia LV 0.9165 ***
Czech Republic CZ 0.8198 ***
Malta MT 0.5341 **
Germany DE 0.9868 ***
Netherlands NL 0.9516 ***
Denmark DK 0.9868 ***
Poland PL 0.9473 ***
Spain ES 0.9560 ***
Portugal PT 0.9692 ***
Estonia EE 0.9341 ***
Romania RO 0.8945 ***
Finland FI 0.9429 ***
Russia RU 0.8857 ***
France FR 0.9736 ***
Slovakia SK 0.8681 ***
United Kingdom UK 0.9121 ***
Slovenia SI 0.9516 ***
Greece EL 0.9516 ***
Sweden SE 0.9516 ***
Hungary HU 0.9121 ***
Turkey TR 0.9077 ***
Ireland IE 0.6659 ***
Note: ρ is the spearman rank correlation coefficient, ***, **, and * indicate p-values being statistically significant at the 1%, 5% and 10%
level respectively.
Source: WIOD, author’s own calculations.
Not surprisingly we find a very strong rank correlation that exceeds 0.90 on average. The reason why
the rank correlation is not perfect is that the differences in the tradability score between the sectors
with very low scores are small. Hence, the ranking is not completely identical across countries. The
second reason is that that commodity-producing countries differ from the rest of the countries in the
sample insofar as the tradability score of their mining sector exceeds that of the manufacturing
sector whereas in all other countries the opposite is true.
Also worth mentioning is the case of Malta which has by far the lowest Spearman rank correlation.
The reason for this is not entirely clear but one reason may be that the availability and hence the
quality of the input-output data for Cyprus and Malta is not as good as for the other European
countries.11 The correlation coefficient is also below average in the case of Ireland which may be
related to the country’s role as a location for foreign headquarters. Given this information, we will
also perform the econometric analysis excluding Malta and Ireland.
The global tradability score is the first element in the calculation of the tradability index. To obtain
the tradability index the tradability scores are weighted with each country’s sectoral value added
shares, i.e. the sector-specific value added (
) over total value added (
). Note that the
value added shares vary over time and over countries. The tradability index is retrieved by summing
up the weigthed tradability scores over all industries i. Formally, the TI of country j in any year t is
calculated as:
11
See Timmer (2012) for details.
9
The advantage of the tradability index is that it reflects the entire composition of production of each
country. This makes it an interesting summary variable for an investigation of the nexus between the
tradability of output and the current account. Note that the tradability index, , is country and time
specific and that the variance over time is coming uniquely from the changes in the respective
country’s sector composition of value added. This is because we chose the tradability scores to be
time invariant.
Figure 2 presents the ranking of the 46 countries in the sample according to the value added exports
based tradability index. There is quite some variation in the index across countries ranging from
0.278 for Azerbaijan to a mere in 0.131 in Cyprus. Next to Azerbaijan mainly other oil and commodity
exporters are found at the top of the ranking. At the bottom of the ranking one finds a number of EU
countries which are Cyprus Greece, Luxembourg and France. Among the EU countries, Romania and
the Czech Republic have the highest tradability indices. The sample average of the tradability index is
0.176 which is about the value found for Lithuania. A country with a TI of 0.176 implies that, given its
production structure, this country is expected to export 17.6% of its value added to other countries.
The Appendix shows that the country ranking for the TI based on gross exports is similar though not
identical to the ranking in Figure 2. The Spearman rank correlation coefficient between the two
alternative TI measures, when based on countries’ year averages, is 0.91512.
Figure 2: Tradability index across countries, value added based (average 1995-2014)
Note: Tradability index based on value added exports.
Source: WIOD, author’s own calculations.
While we emphasised that the TI should be interpreted as the predicted openness of a country given
its production structure, we should equally stress that these predictions are in most cases nowhere
close to the respective country’s actual export openness. This is not surprising given that a country’s
trade openness does not only depend on its economic structure. Rather it depends for example also
strongly on country size as smaller countries tend to be more open economies. A major advantage of
the tradability index is that it is by construction independent of country size. The reason is that it is is
derived using a global sectoral measure for trade openness, i.e. the tradability scores. Moreover, the
tradability index also depends much less on trade policy than the actual trade openness. Indirectly,
12
The Spearman rank correlation between the yearly TI measures is similarly high amounting to 0.909.
sample mean TIvax
0.12
0.16
0.20
0.24
0.28
0.32
0.36
AZ
KZ
NO
R
U
BY
UA
R
O
TR
CZ
SK
IE
RS SI
B
G
HU
P
L M
D
AM
FI
D
E LT
MK
EE
H
R
SE
XK
A
T G
E IT
LV
CH
N
L B
E D
K
BA
U
K
MT
AL
PT ES
IS
M
E FR
LU
EL
C
Y
trad
abili
ty in
dex
(V
AX
bas
ed)
10
countries’ trade policies may show up though due to the resulting specialisation in production. But
the latter is exactly what we intend to capture with our tradability index. Importantly, the tradability
index of a country does not reflect its own exports which would be a problem for the econometric
model. The only thing the tradability index reflects is the share of global value added exports in
output. Hence, in contrast to export openness, which reflects many country characteristics such as
country size, trade policy and the success of the latter, the tradability index is to a large extent
purged from these influences and therefore basically reflects a country’s specialisation in the
production of tradable output.13 To demonstrate how different the measures actually are, Figure 3
shows the correlations between the tradability index and the export openness.
Figure 3: Tradability of Output and actual trade openness across countries, (average 1995-2014)
Note: Graph shows value added exports based tradability and export openness based on gross exports. Luxembourg is not shown for
reasons of scale but it is reflected in the fitted line.
Source: WIOD, World Bank (WDI), author’s own calculations.
There is basically no relationship between the actual openness of a country and its predicted openness given the production structure, i.e. the tradability index. Hence the two measures are very different and for our research question at hand, the tradability index is obviously the more informative indicator. Moreover, as just explained, it has in our view a more straightforward interpretation.
13
One may argue that the tradability index reflects changes in the global stance to trade policy because it includes the global value added exports to vale added ratio. However, since we take the average over time of this ratio, only a long term average of the world’s stance towards ‘globalisation’ all that is included in the tradability score.
AL AM
AT AZ
BA
BE
BG
BY
CH
CY
CZ
DE
DK
EE
EL
ES
FI
FRGE
HR
HU
IE
IS
IT
KZ
LT
LV
MD
ME
MK
MT
NL
NO
PL
PT
RO
RS
RU
SE
SI
SK
TR
UA
UK
XK
0.2
.4.6
.8
expo
rt o
pen
ne
ss
.12 .16 .2 .24 .28tradability index
11
4 Empirical Model and Data
4.1 Empirical Model
In the discussion of the tradability index we emphasised that it was unaffected by each country’s
own exports and to a large extent also to the country’s trade policy. This is a great advantage for the
econometric analysis because this strongly reduces the reverse causality running from the current
account to the economic structure and hence to the tradability account. For this reason we choose a
simple econometric approach that consists of using the tradability index directly as an explanatory
variable in our current account model.
In addition to the tradability index as our main variable of interest the empirical model encompasses
a large number of control variables which have been identified by the literature as determinants of
the current account. In our choice of control variables we draw heavily on the contributions by
Debelle and Faruqee (1996), Chinn and Prasad (2003) and Ca’Zorzi et al. (2012).
The general econometric approach is then to regress the current account balance expressed in per
cent of GDP, , on the tradability index (TI) and a set of control variables. Our first approach is to
investigate the long run effects of the tradability of output on the current account. To this end we
follow Debelle and Faruqee (1996) by taking the average of all variables over the sample period and
run a pure cross-country regression of the form:
(1)
where is a vector of control variables, is the error term and j is the country index. The tradability
hypothesis predicts to be positive.
In addition we also exploit the panel structure of our data which has the advantage that a part of the
potential omitted variable bias can be eliminated by including country and time effects. Since both
the current account series and the tradability index series are for most countries integrated of order
one and we are unable to detect a co-integration relationship, we estimate the panel in first
differences. These results should therefore be interpreted as short term effects. The regression
equation thus becomes
(2)
where the subscript indicates the time index and and denote country and time effects
respectively.
We will present and discuss the cross-section model and the panel model based on estimates using
the value added exports based tradability index. As a robustness check some results for the gross
export based TI along with the traditional approach of using the share of manufacturing as the proxy
for the tradability of output will be shown. Furthermore, we will equally run the models for several
subsets of countries.
12
4.2 Data
Our analysis covers basically the whole of Europe resulting in a sample of 46 countries14. Hence, the
countries are a mix of developed and emerging countries as well as economies in transition. Since
there is a debate to what extent transition has already been accomplished (see e.g. Shleifer and
Treisman, 2014) in a number of former Socialist countries, these countries will, together with Turkey
and the countries of former Yugoslavia, be referred to as ‘emerging Europe’. The sample period
generally stretches from 1995 to 2014 though the fact that the sample comprises the whole of
Europe implies that the sample will be slightly unbalanced as for countries that have gained
independence more recently, such as Montenegro, data is not available back until 1995.15
The primary sources for the current account and the sector-level value added data are the
wiiw Annual Database (wiiw ADB) and Eurostat. An additional data source is the OECD SNA database.
For the countries covered by neither of these databases we turn to the IMF International Financial
Statistics (IMF IFS) and the IMF World Economic Outlook Database (IMF WEO). For the sector-level
value added data an additional data source is the United Nations SNA database. Taken together this
yields in a highly balanced panel. Several countries are covered by two or more databases and the
data series are not always entirely identical.16 As a general rule we use the data source with the most
recent methodology. Apart from this, we use the wiiw ADB as our preferential source of data. If a
country is not covered in the wiiw ADB, data from Eurostat is used. The next choice is the OECD SNA,
followed by the IMF databases and the WDI. The details concerning data sources and data availability
for both the current account data and the TI are found in the Appendix.
As explained in the previous section, the calculation of the tradability index based on value added
exports also requires global input-output data which we get from the World Input-Output Database
(WIOD).
The current account developments of European countries are very heterogeneous. There are a
number of countries which run persistent current account surpluses during the entire sample period
(e.g. Germany, the Netherlands, Sweden) or permanent deficits such as several of the Balkan
countries. Over time, there are a number of countries which by inspection would fit the tradability
hypothesis of this paper. For example, Germany, with its strong manufacturing sector, has been
running a permanent and increasing current account surplus while in France and Britain – both
countries which are reported to have experienced a de-industrialisation – the deficits are persistent
and on the rise.17 Another interesting feature of the data is that the current account positions of
many deficit countries have declined considerable and in some cases abruptly after 2008. A further
observation is that the current account balances appear to be more volatile in commodity exporting
countries (e.g. Ukraine and Russia) than those of the other European countries. We mention this
because due to its high tradability score, the mining sector is strongly influencing the tradability
index of commodity exporters.
Figure 4 gives a first indication of the relationship between the tradability of output and the current
account position in Europe. Clearly a positive relationship is emerging between the two variables
14
Exceptions are Lichtenstein, Monaco, San Marino and the Vatican. See Appendix for the list of countries. 15
See Appendix for details on data availability. 16
Some discrepancies between data sources are explained by reporting in different SNA series (e.g. ESA 1995 vs. ESA 2010) or reporting according to different generations of the Balance of Payment Manuals.
17 For country specific developments of the current account, the reader is referred to the Appendix.
13
(panel a). This positive relationship also prevails when Azerbaijan and Montenegro, two potential
outliers, are excluded from the sample (panel b). This relationship is what we are going to investigate
econometrically, taking into account the effects of other determinants of the current account.
Figure 4: Current account positions and the tradability of output, 1995-2014
(a) Full sample (b) excluding AZ and ME
Note: Tradability index based on value added exports. Right panel excludes Azerbaijan (AZ) and Montenegro (ME).
Source: WIOD, wiiw ADB, Eurostat, IMF IFS, IMF WEO, OECD SNA database, World Bank (WDI), author’s own calculations.
In the following these control variables will be discussed briefly.
Relative per capita income. Stages of development models predict that poorer countries will borrow
from abroad to finance their catch-up process, i.e. they are borrowing against an expected higher
income stream in the future. This implies that poorer countries tend to run current account deficits.
In an inter-temporal framework this is captured by the convergence effect.18 The proxy used will be
GDP per capita expressed relative to the average of the sample (rel gdpcap). GDP per capita will also
enter in quadratic form (rel gdpcap sq) in order to capture the possibility that this income effect
levels off as countries grow richer and approach the average level of GDP per capita. The theoretical
predictions of stages of developments models let us expect a positive sign for the income level and a
negative sign for the quadratic term as the convergence effect typically levels off as a country grows
richer. The data sources are wiiw ADB and Eurostat as well as the United Nations Commission for
Europe (UNECE) for Armenia, Azerbaijan, Georgia and Moldova.
Real GDP growth. Related to the convergence effect it is expected that countries with higher growth
rates (gdp growth) tend to run current account deficits. The results on the GDP growth variable in the
literature concerning GDP growth are mixed though. Nevertheless we are bound to expect a negative
sign as growing incomes will be, at least partly, partly spent on foreign goods leading to an increase
in imports and a deterioration of the current account. The data for real GDP growth is taken from
wiiw ADB, Eurostat, national sources and the World Bank’s WDI database.
18
This requires considering poorer (and less capital-abundant) countries as countries that are further away from their steady state which further implies that they also grow faster.
AL
AL
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AMAMAM
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BABA
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NONONONO NO
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NONO
PL
PLPLPL
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PL
PLPLPLPL
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RURU
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RU
RURURU
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RURURURURU
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RUSESESESESESESESESESESESESESE
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SISISISISISI
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-.1
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cu
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.12 .16 .2 .24 .28 .32 .36tradability index
AL
AL
AL
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AL
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NO
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NO
NO
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NONO
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UK
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-.25
-.2
-.15
-.1
-.05
0
.05
.1.1
5.2
.25
cu
rren
t acco
unt in
% o
f G
DP
.12 .16 .2 .24tradability index
14
Dependency ratios. The age structure of the population may affect the current account through the
savings rate. Old and young people do not earn income and therefore cannot save.19 Therefore,
countries with higher dependency ratios can be expected to save less and we expect a negative sign
for the impact of the dependency ratio on the current account position. Following Blanchard and
Giavazzi (2002) the overall dependency ratio, comprising old and young people, is defined as the
ratio of population to the labour force. The dependency ratios (dep ratio) are taken from the World
Bank’s World Development Indicators.
Financial depth. Domestic credit (dom cred) provided by private banks relative to GDP is one of the
proxies for financial depth as suggested by Rajan and Zingales (1998). We will fall back on this
indicator because private credit booms are often associated with housing bubbles or other bubbles in
non-tradable sectors. The presumption here is that in periods of rapidly expanding domestic credit,
funds are predominantly channelled into non-tradables. This will drive up prices of non-tradables,
leading to an appreciation of the real exchange rate and consequently to a worsening of the current
account balance. At the same time, there is also the view that financial deepening may increase the
savings rate (e.g. Chinn and Prasad, 2003) which tends to improve the current account. The effect of
domestic credit on the current account is therefore ambiguous.
Government balance. In the absence of full Ricardian equivalence, public expenditure and taxation
can be expected to affect the current account balance. In case of a budget deficit (surplus), the
government uses up (adds to) domestic savings, which tends to worsen the current account balance
(see for example Debelle and Faruqee, 1996). This is the famous twin-deficit hypothesis. In
overlapping generation models, a positive correlation between the government balance and the
current account arises because budget deficits lead to a shift of income forward from future to
present generations. The government balance (gov bal) enters our empirical model expressed in per
cent of GDP. As with our main variables, the data for the government balance comes from various
sources, mainly the wiiw ADB and Eurostat supplemented with data from national sources in the case
of Armenia, Azerbaijan, Moldova and Georgia and in the latter case also from the CIS database.
Net foreign asset position. The net foreign asset (NFA) position is tightly connected to the current
account position through the fact that the current account balance equals the trade account plus the
(positive or negative) return in the stock of NFA. Therefore the NFA is an important initial condition
for future current account balances because countries with large negative NFAs have to pay interest
(or dividends) on the assets owned by foreigners which contributes negatively to the current account
balance. This suggests a positive relationship between the current account position and the NFA
position. However, from an inter-temporal perspective, the current account should be balanced in
the steady state, implying that there is no relationship between the current account and the NFA
position. However, if countries grow over time, it is possible that current account deficits are
persistent and equal to the nominal growth rate times the NFA position. The NFA position will also
enter the empirical model in relative terms, i.e. expressed as a percentage of GDP (nfa) and we
expect a positive sign20. We use the net international investment positions (IIP) as reported by the
IMF IFS. For the more distant years for which IIP are not reported we used the estimates from the
19
They need to draw down on their wealth or rely on their parents respectively. 20
Ca’Zorzi et al. (2012) argue that the expected effect is ambiguous because in the intertemporal approach to the current account heavily indebted countries (i.e. those with already high NFAs) should not be able to continuously run current account deficits.
15
External Wealth of Nations database assembled by Lane and Milesi-Ferretti and described in Lane
and Milesi-Ferretti (2007).
Euro area membership. Given the strong interest in current account imbalances in the euro area, the
empirical model will include a dummy variable for the members of the euro area (EA MS). This
dummy variable is expected to be positive as it reflects monetary and financial integration.
Oil exporter. This is a dummy variable taking the value one for oil exporting countries and zero
otherwise. We define as oil exporters countries with more than 20% export revenues stemming from
oil exports.21 This threshold defines Azerbaijan, Kazakhstan, Norway and Russia as oil exporters. With
the inclusion of a dummy for oil exporters we attempt to exclude the possibility that we are only able
to detect a positive relationship between the current account balance and the tradability index
because of the high export proceeds of oil exporting countries.
Table 2 provides an overview over the dependent variable and the explanatory variables. Two things
are worth mentioning here. First, Europe on average has run a current account deficit over the
sample period (though this is an unweighted average) and the variability of the current account
position is relatively high with deficits and surpluses reaching 50% (Montenegro in 2008) and 35%
(Azerbaijan in 2008) of GDP respectively. In contrast, the variability of the value added based TI is
relatively low, also compared to the two alternative structural indicators which are the gross exports
based TI and the value added share in manufacturing.
Table 2: Summary statistics
Variable explanantion Obs. Mean Std. Dev. Min Max
Dependent variable:
ca current account balance in % of GDP 904 -0.0265 0.0807 -0.4975 0.3548
Main explanatory variable:
TIvax tradability index, value added exports based 890 0.1751 0.0279 0.1234 0.3566
TIx tradability index, gross exports based 890 0.2310 0.0483 0.1157 0.3802
shmanuf share of sector value added in total value added
890 0.1655 0.0563 0.0424 0.3312
control variables:
rel gdpcap GDP per capita at purchasing power parity (PPP) relative to sample average
893 1.0000 0.6438 0.1103 3.3841
rel gdpcap sq. squares relative GDP per capita at PPP. 893 1.4140 1.7215 0.0122 11.4524
gdp growth real growth rate of GDP 908 0.0334 0.0547 -0.1510 0.8602
gov bal government balance in % of GDP 886 -0.0231 0.0409 -0.3230 0.1870
nfa net foreign asset position in % of GDP 860 -0.3280 0.7111 -6.6215 1.4866
dep ratio dependency ratio 910 2.1742 0.4648 1.6700 6.4600
dom cred domestic credit in % of GDP 854 0.8547 0.6648 -0.0600 3.8000
21
We rely on the export of crude oil (HS Code 2709). The data is obtained from UN Comtrade database accessed via the World Bank’s WITS download tool.
16
We close the discussion of the data with the variance decomposition of the data (Table 3).
Table 3: Decomposition of variance into between and within components
variable between within between (BSS) within (WSS) total (TSS) BSS TSS
variability sum of squares share
ca 0.0624 0.0553 3.2569 2.6221 5.8790 0.5540 0.4460
TIvax 0.0256 0.0116 0.5789 0.1134 0.6923 0.8363 0.1637
TIx 0.0436 0.0225 1.6489 0.4268 2.0758 0.7944 0.2056
shmanuf 0.0518 0.0236 2.3492 0.4718 2.8210 0.8327 0.1673
gov bal 0.0285 0.0300 0.7250 0.7569 1.4819 0.4892 0.5108
nfa 0.6493 0.4370 278.9750 155.4459 434.4210 0.6422 0.3578
dep ratio 0.6011 0.0965 188.3063 8.0446 196.3509 0.9590 0.0410
dom cred 0.5740 0.3471 279.6073 97.3508 376.9581 0.7417 0.2583
gdp growth 0.0212 0.0518 0.4033 2.3151 2.7184 0.1484 0.8516
rel gdpcap 0.6621 0.1093 359.6310 10.1102 369.7411 0.9727 0.0273
What is remarkable is that while the variance of the current account position is almost evenly split
between the between and the within dimension, the variability of almost all explanatory variables is
to a very large extent due to the between variability. This is particularly true for our main
independent variable, the value added exports based TI, for which the between component accounts
for more than 80%. The only exception among the explanatory variables to the predominance of the
between components is the growth rate of GDP whose variability is to 85% explained by the within
component.
5 Results
We report several sets of results. A first set of results (6.1) tries to capture the long run relationship
and is based on the sample average for each country. This leaves us with a sample of 46
observations. The second set (6.2) exploits the panel dimension. The relationship is estimated in first
differences using annual data and the results are to be interpreted as the short term relationship. In
addition, we report some panel results for sub-samples of countries (6.3). Finally, we compare the
results for the value added based TI with those using the gross exports based TI and the share of
manufacturing in value added as the main explanatory variable respectively.
5.1 Cross-section Results
The results for the model in equation (1) are shown in Table 4. We interpret these as the long term
relationship between the explanatory factors and the current account over the entire sample period
which stretches over 20 years.
Specification (1) starts off with a univariate regression of the tradability index (VAX based) on the
current account position. This yields a coefficient of 0.61 which is, however, only significant at the
10% level. In terms of magnitude, this result suggests that a 1 percentage point increase in the
tradability of output (say from 0.18 to 0.19) improves the current account balance in percent of GDP
by 0.61 percentage points. This may be regarded as a relatively large effect but it should be
17
considered that a one percentage point change in the tradability index is actually associated with a
relatively large change in the production structure. Assume that the tradability index increases from
0.18 – which is approximately the sample average – to 0.19 due to shift of production towards
manufacturing. We can now ask by how much the manufacturing sector must expand in order to
arrive at this 1 percentage point change in the TI. The tradability score of the manufacturing sector is
about 0.4. If we assume that the initial share of the manufacturing sector in GDP was 20% and that
resources were shifted out of a sector with an tradability score of about 0.18 (i.e. agriculture), the
share of the manufacturing sector needs to increases by approximately 5 percentage points
(≈(0.40-0.19) x ((0.20+0.25)/2), i.e. to 25% of GDP, in order to accomplish a 1 percentage point
increase in the tradability index. In any case, the positive coefficient of the tradability index is a first
indication that the tradability hypothesis holds.
Table 4: Long term cross-section regression
Dependent variable: Current Account Position in % of GDP
(1) (2) (3) (4) (5) (6)
TIvax 0.6124* 1.4642*** 0.8844*** 0.7380*** 0.5481** 0.5447**
(0.3302) (0.2370) (0.1962) (0.1662) (0.2157) (0.2105)
gdp growth -0.9815*** -0.8700*** -0.8759*** -0.9244*** -0.9503***
(0.3310) (0.2418) (0.2069) (0.2267) (0.2148)
rel gdpcap 0.0648*** 0.0558*** 0.0497*** 0.0494** 0.0533**
(0.0216) (0.0153) (0.0174) (0.0196) (0.0215)
rel gdpcap sq. 0.0027 -0.0023 -0.0020 -0.0017 -0.0015
(0.0055) (0.0037) (0.0038) (0.0045) (0.0044)
govbal 0.1756 0.2806* 0.1854 0.1210
(0.1657) (0.1472) (0.1583) (0.1785)
nfa 0.0488*** 0.0527*** 0.0532*** 0.0535***
(0.0085) (0.0082) (0.0088) (0.0084)
dep ratio -0.0135*** -0.0133*** -0.0126***
(0.0038) (0.0037) (0.0036)
domcred -0.0033 -0.0061 -0.0061
(0.0084) (0.0095) (0.0096)
oil 0.0269 0.0285
(0.0216) (0.0215)
EA MS -0.0123
(0.0145)
Observations 46 46 46 46 46 46
R-squared 0.0623 0.7592 0.8957 0.9124 0.9177 0.9198
R-squared adj. 0.0409 0.736 0.880 0.893 0.897 0.897
F-test 3.441 42.15 102.7 106.8 90.80 76.64
Note: TIvax = tradability index based on value added exports. EA MS=euro area members. Robust standard errors in parentheses. ***, **, and * indicate statistical significant at the 1%, 5% and 10% level respectively. All regressions are based on actual sample averages used in the regression. All regressions include a constant.
In specification (2) the growth rate and GDP per capita are added as control variables. Note first, that
this addition improves greatly the explanatory power of the regression with the (adjusted) coefficient
of determination going up to 0.76. Specification (2) shows that by controlling for the growth rate and
GDP per capita, both the size and the magnitude of the tradability index goes up considerably. The
coefficient of the growth rate itself is negative (-0.98) confirming the hypothesis that higher growth
18
rates tend to worsen the current account balance because of increased import demand. This may be
due to both increased imports of consumption goods (as household shift forward consumption with
expected higher incomes in future) and of investment goods (needed to sustain the growth process).
The fact that the inclusion of the growth rate and the GDP per capita improves the significance of the
tradability index is very reassuring. It is reassuring because these two determinants should capture
the wealth effect described above so that the tradability index really picks up the structural effect,
i.e. the impact of pure changes in the composition of output on the current account. While the
coefficient of the growth rate is statistically significant at the 1% level in our model, it does not
belong to the most likely determinants of the current account according to the Bayesian model
selection approach of Ca’Zorzi et al. (2012).
Specification (2) also adds the relative GDP per capita, including a quadratic term. The estimated
coefficient is highly significant and has the expected positive sign. This is as expected by the stages of
development model (e.g. Calderon et al., 2002) as it suggests that richer countries are closer to their
steady state, more capital intensive and are therefore providers of capital to poorer countries. This
result is in line with those of Debelle and Faruqee (1996) and Chinn and Prasad (2003) who also find a
positive effect of relative income in their cross-section results. In terms of magnitude of the
coefficients, the 0.065 we find are in a similar range as those in Chinn and Prasad (2003) who find a
coefficient of 0.11 in their full sample. In general, the positive coefficient of the GDP per capita
variable that we obtain confirms the conclusion in Blanchard and Giavazzi (2002) and Lane and Pels
(2012) that in Europe capital continues to flow from rich to poor countries (though their sample is
limited to the euro area and the EU plus EFTA respectively). What we cannot establish, however, is
that this positive effect of income per capita diminishes as countries grow richer because the
quadratic term turns out to be not statistically significant.
For the government balance (specification 3) we do not obtain a significant coefficient reflecting the
outcome in Debelle and Faruqee (1996) for their global sample. Hence, there is no evidence for the
twin deficit hypothesis in a European context in the long run. On the contrary, Chinn and Prasad
(2003) do find that government deficits go together with current account deficits in both their full
sample results and the results for industrial countries. The government balance is also one of the
explanatory variables that are quite commonly included the models resulting from the selection
criteria in Ca’Zorzi et al. (2012).
Another important control variable is the net foreign asset position whose estimated coefficient is
highly statistically significant and also quite large in magnitude amounting to 0.05. This implies that
countries with positive NFA positions tend to have higher current account surpluses respectively
more moderate deficits. The rationale behind this is that countries with accumulated positive foreign
assets earn interest on these assets. This result would also suggest that it is quite possible that
countries are able to persistently run (more or less moderate) current account deficits. This is also
what can be observed in the country-specific time series of the current account balance as there is
quite a large number of countries that run either persistent surpluses or persistent deficits.
The next controls we add are the dependency ratio and the domestic credit (specification 4). For the
former we find a negative coefficient (-0.014) that is significant at the 1 percent level. This reflects
the fact that children and retired people do not save – or at least have lower savings rates than the
active part of the population. In terms of magnitudes, however, the effect is not very large. In fact it
19
is much smaller than those found for example by Ca’Zorzi et al. (2012) who distinguish between the
dependency ratio of old people and that of young people.
Less relevant according to our cross-section model is the amount of domestic credit. This mirrors the
result in Chinn and Prasad (2003) for their industrial countries sample though they find an effect in
their analysis for their full sample of countries.
Finally, we include dummy variables for oil exporting countries and the members of the the euro
area. While the euro area dummy turns out to have little impact on the results, but effect of the oil
exporter dummy is noteworthy. While not statistically significant itself, the oil exporter dummy
reduces the size of the coefficient of the TI considerably – from 0.74 in specification (4) to 0.55 in
specification (5). This is not entirely surprising given that a high endowment with oil resources is an
important structural feature of an economy. This effect is so to speak “deducted” from the structural
effect that the tradability index is capturing. What is important to see, is that the tradability index is
also capable of explaining some of the variation in the current account apart from the particularities
of oil exporting countries. In other words, we can basically rule out that the positive relationship
between the tradability index and the current account is an artefact caused by oil exporters.
To summarise, we find that – in line with the tradability hypothesis – the tradability index emerges as
a determinant of the current account which is a result that holds throughout all specifications.
5.2 Panel Results
We now turn to the short term model of the current account in equation (2) which exploits the panel
structure of our data. The results are summarised in Table 5.
We start with “pooled” estimations, i.e. the panel is estimated without country effects. Time fixed
effects, however, are included (specifications 1 & 2). One difference to the long term model is that
we omit the growth rate as explanatory variable because the model is estimated in differences and
so the changes in the income per capita already captures the potential impact of economic growth
on the current account.
The most interesting result for us is of course the positive and statistically significant coefficient of
the tradability index. In the model including control variables, this coefficient amounts to 3.06 which
is more than 3 times larger than what was found for the cross-section regression. The reason for this
huge coefficient is that the economic structure is a slow moving variable so that short term changes
tend to be very small. Therefore a one unit change in the TI, which corresponds to a one percentage
point change, would be an extremely large movement.
The result concerning both the statistical significance and the large magnitude of the TI coefficient
holds throughout specifications with different estimation methods. These include random effects,
fixed effects and generalized method of moments (GMM) using the Arellano-Bond estimator
(Arellano and Bond, 1991). The GMM approach has the advantage that it allows controlling for
dependencies between the current and lagged values of the dependent variable which may be
relevant in the case of current account data as well.
For deciding the appropriate estimator we perform a Hausman test which does not reject the use of
the random effects model at conventional levels of significance. However, with a p-value of 0.13 the
20
pass of the test for consistency is not overwhelming and so we also report the fixed effects model
and the GMM specification in Table 5.
Table 5: Panel regression in first differences
Dependent variable: ΔCurrent Account Position in % of GDP
(1) (2) (3) (4) (5) (6) (7) (8)
(POOLED) (POOLED+) (RE) (RE+) (FE) (FE+) (XTA) (XTA+)
ΔTIvax 2.8548*** 2.9541*** 2.8548*** 2.9541*** 2.8910*** 2.9329*** 3.0567*** 3.1521***
(0.4532) (0.5299) (0.6678) (0.7880) (0.6590) (0.7650) (0.2548) (0.2908)
Δrel gdpcap -0.1512 -0.1512 -0.2350 -0.1564
(0.0992) (0.1213) (0.1422) (0.1157)
Δrel gdpcap sq 0.0340 0.0340 0.0509 0.0354
(0.0263) (0.0329) (0.0349) (0.0316)
Δgov bal -0.0183 -0.0183 -0.0071 -0.0323
(0.0918) (0.0898) (0.0880) (0.0681)
Δnfa (t-1) -0.0184*** -0.0184*** -0.0179*** -0.0232***
(0.0059) (0.0059) (0.0058) (0.0071)
Δdep ratio 0.0263 0.0263 0.0419 0.0464
(0.0286) (0.0312) (0.0423) (0.0418)
Δdom cred -0.0156** -0.0156** -0.0157** -0.0158*
(0.0064) (0.0075) (0.0077) (0.0084)
Δca (t-1) -0.0482 -0.0528
(0.0335) (0.0370)
country fixed effects no no no no yes yes yes yes
time fixed effects yes yes yes yes yes yes yes yes
Observations 841 711 841 711 841 711 753 653
R-squared 0.2481 0.2833 0.2500 0.2787
R-squared adj. 0.231 0.258 0.233 0.253
rho 0 0 0.1213 0.0298
F test 6.238 6.776 7.382 26.14
Number of id 46 46 46 46 46 46
Note: POOLED= pooled panel regression; RE= random effects regression; FE=fixed effects regression; XTA=Arellano-Bond fixed effects estimator. TIvax = tradability index based on value added exports. Robust standard errors in parentheses. ***, **, and * indicate statistical significant at the 1%, 5% and 10% level respectively. All regressions include a constant and time fixed effects.
When running a joint F-test for statistical significance of the country fixed effects we find that the
country fixed effects can as well be omitted. Hence, it is equally appropriate to interpret the results
of the “pooled” model without country fixed effects but including time effects. Likewise, in the
random effects model the variance due to the country dimension is 0 indicating the appropriateness
of the pooled model as well. In addition to the econometric arguments in favour of a pooled panel
regression approach Chinn and Prasad (2003) as well as Ca’Zorzi et al. (2012) also support the pooled
specification because the explanatory variables capture more of the cross-country variation in the
data much of which would potentially be captured by the country effects. Given that the majority of
the variability in our data comes from the between dimension (i.e. cross-section) we are also
sympathetic with this argument. In the end, however, the choice of estimator has no effect on our
results as there are only very minor differences in the coefficients.
Sticking to specification (2) we find that changes in the TI are a significant determinant of the current
account also in the short term. The estimated coefficient of 2.95 appears to be very large but also
21
here it has to be borne in mind that shifts in the composition of output from year to year are rather
small so that a 1 percentage point change in the TI must be considered to be very large.
In general, however, the fit of our short term model for the current account seems to be less good
than the long term version. Apart from the tradability index, only changes in the domestic credit and
the net foreign asset position are suggested to affect the current account. Regarding the latter, the
negative coefficient of the net foreign asset position is a bit of a puzzle. Since switches from being a
current account surplus country to being a deficit country or vice verse are relatively rare in our data,
one would rather expect that changes in the foreign asset position of the previous year should be
positively associated with changes in the current account balance. So at this stage we are unable to
offer a plausible explanation for this result.
In contrast, the negative sign obtained for the provision of domestic credit is as expected. It reflects
the supposition that times of rapid expansion of domestic credit are associated with booms in the
housing sector, the financial sector or other non-tradable sectors which will drive up the real
exchange rate and thus lead to a deterioration of the current account.
Qualitatively, all results are confirmed by the Arellano-Bond estimator (Arellano and Bond, 1991).
The GMM approach allows controlling for the correlation of the current account balances of a
country over time. When performing the estimation in first differences, however, this is less of an
issue as indicated by the coefficient of the lagged current account position which is not statistically
significant.
Overall we find a very robust relationship between the tradability of an economy’s output and the
current account balance also for the sort run although in general our short term model of the current
account seems to be less convincing.
5.3 Results by country groups
We no want to find out whether our results hold for individual groups of countries. Therefore we re-
estimate our model separately for developed and emerging European countries. Another sub-group
will be the EU respectively the euro area members. A final group we test are the Central, Eastern and
South Eastern European (CESEE) countries which comprise basically the Balkan countries, the former
Communist countries and Turkey. This sensitivity analysis is done for both the long term and the
short term relationship where for the latter we report the results of the pooled model.
As shown in Table 6. the results – and in particular the result regarding the tradability index – also
hold for most sub-samples. The only exceptions are the euro area members if taken by themselves
and the emerging markets. To some extent this lack of statistical significance may also be due to the
fact that the number of observations gets relatively small if we only take sub-samples of countries.
What is worth mentioning, however, is that a part of the impact of the TI on the current account is
indeed due to oil resources. This is less of an issue for the developed countries in Europe but it
matters for Emerging Europe. In fact, if oil is controlled for, the impact of the tradability index on the
current account remains positive but it is not statistically significant anymore. As mentioned before,
however, we believe that oil the production of oil is also a part of the composition of output that we
want to capture with our tradability index.
22
Table 6: Long term cross-section regression, various subsamples
Dependent variable: Current Account Position in % of GDP
(1) (1') (2) (2') (3) (3') (4) (5) (6) (7) (8)
(full sample) (developed) (emerging) (EU) (EA) (CESEE) (ex AZ & ME) (ex IE<&MT)
TIvax 0.7380*** 0.5481** 0.4910* 0.7640** 0.6784* 0.3521 0.6117** 0.3199 0.7491*** 0.7834*** 0.7922***
(0.1662) (0.2157) (0.2606) (0.2795) (0.3263) (0.3881) (0.2652) (0.3153) (0.1729) (0.2259) (0.1854)
gdp growth -0.8759*** -0.9244*** -0.8484** -1.0141*** -0.9302 -1.1571** -0.6315** -0.4372 -0.9259*** -0.8091*** -0.8378***
(0.2069) (0.2267) (0.3095) (0.2848) (0.5591) (0.4263) (0.3006) (0.5045) (0.2763) (0.2315) (0.2293)
rel gdpcap 0.0497*** 0.0494** 0.0652** 0.0701*** 0.0933 0.0351 0.1015*** 0.1124* 0.1063** 0.0507*** 0.0473**
(0.0174) (0.0196) (0.0230) (0.0199) (0.2326) (0.1527) (0.0304) (0.0525) (0.0447) (0.0174) (0.0181)
rel gdpcap sq. -0.0020 -0.0017 -0.0055 -0.0068 -0.0197 0.0304 -0.0140* -0.0164 -0.0597 -0.0018 -0.0017
(0.0038) (0.0045) (0.0052) (0.0048) (0.2455) (0.1560) (0.0072) (0.0121) (0.0370) (0.0038) (0.0038)
gov bal 0.2806* 0.1854 0.4258*** 0.6447*** 0.1434 -0.1662 0.6051*** 0.3054* 0.1716 0.3252** 0.2472
(0.1472) (0.1583) (0.1464) (0.1682) (0.5592) (0.3948) (0.1955) (0.1661) (0.4453) (0.1554) (0.1518)
nfa (t-1) 0.0527*** 0.0532*** 0.0386*** 0.0362*** 0.0661*** 0.0656*** 0.0279* 0.0582*** 0.0670*** 0.0426*** 0.0533***
(0.0082) (0.0088) (0.0044) (0.0036) (0.0117) (0.0079) (0.0161) (0.0164) (0.0079) (0.0059) (0.0081)
dep ratio -0.0135*** -0.0133*** -0.0088*** -0.0081** -0.0285 -0.0149 0.0037 -0.0623 -0.0132*** -0.0115*** -0.0122***
(0.0038) (0.0037) (0.0026) (0.0029) (0.0409) (0.0384) (0.0290) (0.0382) (0.0035) (0.0032) (0.0037)
dom cred -0.0033 -0.0061 -0.0039 -0.0038 -0.0143 -0.0315 -0.0106 -0.0131 0.0092 -0.0018 0.0027
(0.0084) (0.0095) (0.0129) (0.0118) (0.0699) (0.0453) (0.0092) (0.0091) (0.0598) (0.0089) (0.0094)
oil 0.0269 -0.0589** 0.0525**
Observations 46 46 27 27 19 19 28 19 27 44 43
R-squared 0.9124 0.9177 0.9314 0.9438 0.9075 0.9446 0.8913 0.9549 0.8910 0.8908 0.9161
R-squared-adj. 0.893 0.897 0.901 0.914 0.834 0.889 0.845 0.919 0.843 0.866 0.896
F-test 106.8 90.80 129.2 . 48.20 588.0 150.4 342.6 100.7 112.8 128.1
Note: TIvax = tradability index based on value added exports. Robust standard errors in parentheses. ***, **, and * indicate statistical significant at the 1%, 5% and 10% level respectively. All regressions are based on actual sample averages used in the regression. All regressions include a constant.
23
I few neglect the oil issue, the results by country groups suggest that the tradability of output is more
important for the external balance for emerging countries than for developed countries. The reason
for this may be that components of the current account which are not (or only indirectly) linked to
the tradability of output such as payments of factor incomes play a larger role in developed
countries. Another reason may be that industrial countries only require a smaller industrial base
which is increasingly interlinked with and performs an important carrier function for services by
absorbing a large amount of services which are exported only indirectly via manufactures.
A large coefficient for the TI is found for the CESEE countries. For this country group the oil issue also
plays a role as with Russia, Azerbaijan and Kazakhstan it includes three oil exports. Another
contributing factor to this result for the CESEE region are the countries of the Southern Balkan which
tend to run large current account deficits while having great difficulties with building up
manufacturing capacities.
Interestingly, the government balance turns out to be statistically significant for the industrialised
European countries and the EU member states. Hence, for these countries, the twin deficit
hypothesis seems to have some relevance. Most other control variables are very robust across the
various sub-samples. This is particularly true for the net foreign assets and the relative GDP per
capita and the growth rate.
To conclude, we take a look at specifications (7) and (8) which relate to the full sample but excludes
outliers regarding the (descriptive) current account-tradability nexus (Azerbaijan and Montenegro)
and the countries which are outliers regarding the tradability scores of sectors (Ireland, Lithuania,
Malta) respectively. The particularities of these countries do not seem to drive the main results.
Next we turn to the short term regressions distinguishing between the same sub-groups (Table 7). By
and large, the short term analysis by country group confirms the result from the full sample. The
tradability index is again positive and statistically significant in all subsamples except for the EU. The
pattern that the tradability of output is more important for Emerging Europe than for the developed
countries also holds true in the short term. A new finding is that the growth effect, i.e. changes in the
GDP per capita, is relevant for the current account mainly for the developed countries in Europe.
Moreover, the negative effect of the expansion of domestic credit on the current account balance
which is found in the full sample is mainly due to the experiences in the CESEE region. Finally, it is
worth mentioning that in the short run model at least the magnitude of the coefficient to the TI is
sensitive to the outliers in the sample, i.e. Azerbaijan and Montenegro (specification 7).
24
Table 7: Panel regression (pooled) in first differences, various subsamples
Dependent variable: ΔCurrent Account Position in % of GDP
(1) (2) (3) (4) (5) (6) (7) (8)
(full sample) (developed) (emerging) (EU) (EA) (CESEE) (ex AZ ME) (ex IE LT MT)
ΔTivax 2.9541*** 1.0635* 3.5405*** 0.6242 1.2452* 3.2916*** 1.7055*** 3.1450***
(0.5299) (0.5886) (0.5836) (0.4940) (0.6380) (0.5594) (0.3848) (0.5324)
Δrel gdpcap -0.1512 -0.4422*** 0.2194 -0.4078*** -0.2788*** -0.0063 -0.2325*** -0.1227
(0.0992) (0.1062) (0.3073) (0.0906) (0.1027) (0.2578) (0.0890) (0.0987)
Δrel gdpcap sq. 0.0340 0.1147*** -0.1150 0.0977*** 0.0548** -0.0275 0.0619*** 0.0260
(0.0263) (0.0278) (0.2054) (0.0238) (0.0236) (0.1417) (0.0232) (0.0261)
Δgov bal -0.0183 0.0417 -0.1052 -0.0517 -0.0894* -0.1336 0.0430 -0.0249
(0.0918) (0.1038) (0.1461) (0.0546) (0.0495) (0.0922) (0.0869) (0.1234)
Δnfa (t-1) -0.0184*** -0.0215*** -0.0215 -0.0092 0.0047 -0.0329** -0.0218*** -0.0186***
(0.0059) (0.0077) (0.0132) (0.0108) (0.0108) (0.0143) (0.0060) (0.0057)
Δdep ratio 0.0263 -0.0187 0.1513* 0.0461 -0.0611 0.0384 0.0029 0.0353
(0.0286) (0.0240) (0.0862) (0.0461) (0.0705) (0.0324) (0.0264) (0.0301)
Δdom cred -0.0156** -0.0173*** -0.0176 -0.0082 -0.0019 -0.0507** -0.0138** -0.0210*
(0.0064) (0.0053) (0.0189) (0.0052) (0.0079) (0.0219) (0.0060) (0.0110)
Observations 711 415 296 444 187 424 690 674
R-squared 0.2833 0.2508 0.3906 0.2016 0.2825 0.3661 0.2065 0.3111
R-squared-adj. 0.258 0.205 0.337 0.156 0.196 0.328 0.178 0.286
F-test 6.776 4.581 4.337 3.689 . 4.938 6.627 7.065
Note: TIvax = tradability index based on value added exports. Robust standard errors in parentheses. ***, **, and * indicate statistical significant at the 1%, 5% and 10% level respectively. All regressions include a constant and time fixed effects.
25
5.4 Comparing structural indicators
Another interesting comparison is between the one between the various measures of tradability of
output and their effects on the current account. For this purpose we compare the long run effects on
the current account of the value added exports based tradability index (TIvax) with that of the gross
exports based TI (TIx)and also the simple share of manufacturing in value added.
Table 8 demonstrates that all three structural indicators deliver similar qualitative results. Also, the
control variables show very robust patterns across the three structural indicators.
Table 8: Cross section regressions, different tradability measures
Dependent variable: Current Account Position in % of GDP
TIvax TIx Manufacturing share
(1) (2) (3) (1’) (2’) (3’) (1’) (2’) (3’)
TIvax 0.6124* 0.7380*** 0.5481**
(0.3302) (0.1662) (0.2157)
TIx 0.4080* 0.2506** 0.2355**
(0.2343) (0.1098) (0.0951)
sh manuf 0.2038 0.0735 0.1954**
(0.2092) (0.0802) (0.0841)
gdp growth -0.8759*** -0.9244*** -0.5831** -0.8295*** -0.4634 -0.7312***
(0.2069) (0.2267) (0.2236) (0.2210) (0.2816) (0.2280)
rel gdpcap 0.0497*** 0.0494** 0.0462** 0.0380* 0.0613** 0.0336
(0.0174) (0.0196) (0.0223) (0.0219) (0.0265) (0.0243)
rel gdpcap sq. -0.0020 -0.0017 -0.0016 0.0017 -0.0064 0.0028
(0.0038) (0.0045) (0.0052) (0.0051) (0.0061) (0.0057)
gov bal 0.2806* 0.1854 0.4203*** 0.1870 0.4626*** 0.1992
(0.1472) (0.1583) (0.1366) (0.1509) (0.1459) (0.1490)
nfa 0.0527*** 0.0532*** 0.0545*** 0.0525*** 0.0584*** 0.0527***
(0.0082) (0.0088) (0.0085) (0.0087) (0.0093) (0.0088)
dep ratio -0.0135*** -0.0133*** -0.0157*** -0.0126*** -0.0191*** -0.0128***
(0.0038) (0.0037) (0.0042) (0.0036) (0.0048) (0.0036)
dom cred -0.0033 -0.0061 -0.0055 -0.0038 -0.0148 -0.0031
(0.0084) (0.0095) (0.0100) (0.0102) (0.0106) (0.0107)
oil 0.0269 0.0469*** 0.0625***
(0.0216) (0.0140) (0.0121)
Observations 46 46 46 46 46 46 46 46 46
R-squared 0.0623 0.9124 0.9177 0.0803 0.8959 0.9185 0.0283 0.8827 0.9176
R-squared adj. 0.0409 0.893 0.897 0.0594 0.873 0.898 0.00622 0.857 0.897
F-test 3.441 106.8 90.80 3.033 103.0 89.94 0.950 83.94 86.13
Note: TIvax = tradability index based on value added exports. Tx= tradability index based on gross exports. shmanuf = value added share of manufacturing. Robust standard errors in parentheses. ***, **, and * indicate statistical significant at the 1%, 5% and 10% level respectively. All regressions are based on actual sample averages used in the regression. All regressions include a constant.
When comparing the magnitude of the coefficients of the tradability indices on the one hand and the
share of manufacturing on the other hand one has to bear in mind, that the variation in the former
tends to be smaller because it reflects the whole structure of the economy. As mentioned before a
one percentage point increase in the manufacturing share would trigger a much smaller change in
the TI.
26
Another interesting aspect is the fact that dummy variable for oil exporters is not significant in the
specification with the value added exports based TI. One interpretation for this result is that the
latter captures better the impact of structural features on the current account (such as countries
being strong oil exports) than the other two indicators. Certainly, one could also make the argument
that it is preferable is isolate the particularities of oil production. In any case, what Table 8 shows is
that the positive association between the tradability of output does not depend on the particular
choice of the index, whether it is value added exports or gross exports based. The use of the share of
the manufacturing sector in GDP also yields similar results but we would strongly argue that the
tradability index we have developed is a much more comprehensive and hence more informative
indicator for the tradability-current account nexus.
6 Conclusions
We claim to have identified a new important empirical determinant for the current account. This
determinant is the tradability index which summarises the tradability of the output a country
produces. Importantly, this indicator is not constructed using the respective country’s exports.
Rather it is based on global value added exports to value added ratios weighted by the respective
country’s sectoral structure. As such the tradability index is the predicted export openness given a
country’s economic structure.
We find robust evidence for the tradability hypothesis in a sample of 46 European countries over the
period 1995-2014 in both the short and the long run. The result holds irrespective of whether a
cross-section (based on averages) or a panel is estimated. The results are robust across various
European country groups such as Emerging Europe, the European industrialised countries or the
CESEE regions.
This result is highly relevant for economic policy and the debate about external imbalances and the
phenomenon of de-industrialisation in Europe. The fact that, on the other hand the tradability of
output is a key determinant of the current account and on the one hand many European countries
are experiencing a structural shift towards services and hence relatively less tradable output
increases the risk of external imbalances. At least this is true for countries whose main tradable
sector is manufacturing. Though rather intuitive, this structural problem of increasingly service-
oriented economies is much too often neglected. One reason is that in the orthodox view the current
account is ultimately determined by purely macroeconomic factors, i.e. by the saving decisions by
households that are optimising their consumption path over time. In contrast, our empirical results
suggest that the tradability of output is an important structural feature of the economy which is
highly relevant for the current account balance and external stability.
For the EU our results imply that the increasing diverging structural developments, in particular with
regards to manufacturing activities, in the Central European Manufacturing Core (Stehrer and
Stöllinger, 2014; IMF, 2013) and the rest of the EU member states must be expected to foster
external imbalances within the Union. This calls for a solution which à la long can only consist in a
comprehensive reform of the current fiscal framework, either by significantly strengthening the
cohesion efforts or by introducing an internal transfer mechanism of some sort.
27
Our results also indicate that the current account is a complicated matter co-determined by a
plethora of factors which are often tightly intertwined. For example, Rodrik (2012) suggests that the
manufacturing sector unconditionally serves as an accelerator for economic development. Economic
development implies higher growth. According to this manufacturing convergence hypothesis we
should observe that countries which shift towards manufacturing grow faster. Faster growth,
however, tends to create current account deficits. In contrast, our tradability hypothesis suggests
that a move towards tradable activities such as manufacturing should improve the current account
position. This is why in the empirical application we made a great effort to control for a wide range of
factors that are equally relevant for the current account position.
We see this analysis as the beginning of more intensive research in this area. The natural extension of
this paper is to take the test of the tradability-current account nexus to the global level. While there
will be serious data constraints, for OECD countries, several South and South East Asian countries as
well as South American countries the required data would be available. A main topic that we have
not taken on board in this paper is the role of the real exchange rate. Since trade theories suggest
that relative prices determine specialisation pattern, the exchange rate could be responsible for a
part of the effect of our tradability index on the current account. Therefore one further route will be
to integrate the real exchange rate in the current account model. Another interesting exercise would
be an analysis of the tradability-current account nexus for selected countries for which longer time
series are available (such as France or Hungary). This would make it possible to explore the dynamics
of the relationship between the current account and the tradability index.
28
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30
Appendix 1: List of countries
Abbreviation Country Country Category
AL Albania Emerging
AM Armenia Emerging
AT Austria Developed
AZ Azerbaijan Emerging
BY Belarus Emerging
BE Belgium Developed
BA Bosnia and Herzegovina Emerging
BG Bulgaria Emerging
HR Croatia Emerging
CY Cyprus Emerging
CZ Czech Republic Developed
DK Denmark Developed
EE Estonia Developed
FI Finland Developed
FR France Developed
GE Georgia Emerging
DE Germany Developed
EL Greece Developed
HU Hungary Developed
IS Iceland Developed
IE Ireland Developed
IT Italy Developed
KZ Kazakhstan Emerging
LV Latvia Developed
LT Lithuania Emerging
LU Luxembourg Developed
MK Macedonia Emerging
MT Malta Emerging
MD Moldova Emerging
ME Montenegro Emerging
NL Netherlands Developed
NO Norway Developed
PL Poland Emerging
PT Portugal Developed
RO Romania Emerging
RU Russia Emerging
RS Serbia Emerging
SK Slovakia Developed
SI Slovenia Developed
ES Spain Developed
SE Sweden Developed
CH Switzerland Developed
TR Turkey Emerging
UA Ukraine Emerging
UK United Kingdom Developed
XK Kosovo Emerging
Note: The distinction between ‘Developed’ and ‘Emerging’ mirrors the categorisation of European countries as ‘Advanced’ and
‘Emerging and Developing’ by the IMF as of April 2014.
31
Appendix 2: List of sectors for the calculation of the tradability index
Number Sector NACE Rev. 1 NACE Rev. 2
1 Agriculture, hunting and forestry + Fishing A+B A
2 Mining and quarrying C B
3 Manufacturing D C
4 Electricity, gas and water supply E D+E
5 Construction F F
6 Wholesale, retail trade, repair of motor vehicles etc. G G
7 Hotels and restaurants H I
8 Transport, storage + Communication I H + J
9 Financial intermediation J K
10 Real estate, renting and business activities K L+M+N
11 Public administration, defence, compuls.soc.security L O
12 Education M P
13 Health and social work N Q
14 Other community, social and personal services +
Private households with employed persons O+P R+S+T
32
Appendix 3: Methodology for calculating value added exports and the tradability scores
Deriving the tradability score requires the calculation of the value added exports (VAX) at the
industry-country level. This appendix illustrates the basic input-output methodology to calculate the
VAX, including a 3-country, 2-sector example.
Following the trade in value added concept in Johnson and Noguera (2012) and the expositions in
Stehrer (2012) we require three components in order to calculate the value added exports. For any
country r, these components are the value added requirements per unit of gross output, ; the
Leontief inverse of the global input-output matrix, ; and the final consumption vector, . Both
vectors as well as the Leontief inverse have an industry dimension . The industry index is omitted in
order to facilitate the exposition.
Country r’s value added coefficients are defined as
. The value added coefficients
are arranged in a diagonal matrix of dimension 1435 x 1 (40 countries x 35 industries). This matrix
contains the value added coefficients of country r for all industries along the diagonals. The
remaining entries of the matrix are zero because the interest here is with the value added created in
country r.
The second element is the Leontief inverse of the global input-output matrix, where
denotes the coefficient matrix. In the WIOT the coefficient matrix (and hence the Leontief matrix) is
of dimension 1435 × 1435 which contains the technological input coefficients of country r in the
diagonal elements and the technological input coefficients of country r’s imports (from a column
perspective) and exports (from a row perspective) in the off-diagonal elements.
The final building block is the global final consumption vector. This vector is also industry specific and
if of dimension 1435 × 1. Most importantly, for our purposes, final consumption must be split into
separate blocks indication the origin of the consumed goods though within the elements in the
column vector. As usual, each row is associated with one source of the final demand. The full
consumption vector, , in the 3-country one sector case has the form
where the subscript J indicates that the vector comprises the consumption of all countries . The
typical element of this vector contains the final consumption from all possible sources. For example,
the element captures the value of final goods that country 3 demands from country r. Since the
idea of value added exports is that it comprises only value added that is created in one country but
absorbed in another, the final demand from country r itself needs to be eliminated for the calculation
of country r’s VAX. Therefore we will work with an adjusted final demand vector,
in which
country r’s final demand (i.e. the first column in the above matrix) is set to zero. Country r’s value
added exports can then be calculated as
(1)
where are the sector specific value added exports of country r to all partner countries.
33
To illustrate this, we illustrate the matrices in the three countries–two sector case, where country r
acts as the model country and we label the industries with m (for manufacturing) and s (for services).
Equation (1) then has the following form:
The coefficients in the Leontief matrix represent the total direct and indirect input requirements of
any country in order to produce one dollar worth of output for final demand. For example, the
coefficient indicates the input requirement of country r’s services sector from country r’s
manufacturing sector for producing one unit of output. Likewise the coefficient indicates
country r’s input requirement in the manufacturing sector supplied by country 3’s manufacturing
sector.
The resulting elements in this example,
and
are the total value added exports of
country r’s manufacturing respectively services sector to all other sectors of all partner countries.
The VAX are not only calculated for country r but for all 40 countries plus the rest of the world.
Hence, the final step needed to arrive at the global industry-level VAX is to sum up the VAX of all
countries for each individual sector i. Dividing the global industry-specific VA by the corresponding
industry-specific value added yields the tradability score by sector.
34
Appendix 4: Tradability index across countries, gross exports based (average 1995-2014)
Figure A.1: Tradability index across countries, based on gross exports (average 1995-2014)
Note: Tradability index based on gross exports.
Source: WIOD, author’s own calculations.
sample mean TIx
0.12
0.16
0.20
0.24
0.28
0.32
0.36 B
Y A
Z C
Z R
O
IE
UA
R
U
SI
FI
SK
KZ
HU
TR
D
E R
S N
O
SE
MK
LT
P
L A
T B
G
CH
M
D
EE
AM
IT
H
R
BE
MT
DK
LV
ES
P
T G
E X
K
NL
UK
B
A
FR
IS
LU
ME EL
A
L C
Y
trad
abili
ty in
dex
(gr
oss
exp
ort
s b
ased
)
35
Appendix 5: Data and data availability: Current Account and TI, 1995-2014
Current Account: data sources and data availability
reporter country data source data availability switch BMP5 to BMP6
AL Albania wiiwADB 1995-2014 2013
AM Armenia IMF WEO/IMF IFS 1995-2014 2005
AT Austria EurostatBOP 1995-2014 1995
AZ Azerbaijan IMF WEO/IMF IFS 1995-2014 2005
BA Bosnia and Herzegovina wiiwADB 1998-2014 2007
BE Belgium EurostatBOP 1995-2014 2003
BG Bulgaria wiiwADB 1995-2014 2007
BY Belarus IMF WEO/IMF IFS
wiiw ADB 1995-1999
2000-2014 2000
CH Switzerland OECD 2005-2014 2000
CY Cyprus EurostatBOP 1995-2014 2008
CZ Czech Republic wiiwADB 1995-2014 1995
DE Germany EurostatBOP 1995-2014 1995
DK Denmark EurostatBOP 1995-2014 2005
EE Estonia wiiwADB 1995-2014 1995
EL Greece EurostatBOP 1995-2014 2004
ES Spain EurostatBOP 1995-2014 1995
FI Finland EurostatBOP 1995-2014 1995
FR France EurostatBOP 1995-2014 1999
GE Georgia IMF WEO
IMF WEO/IMF IFS 1995-1996 1997-2014
2005
HR Croatia wiiwADB 1995-2014 2000
HU Hungary wiiwADB 1995-2014 1995
IE Ireland EurostatBOP 1995-1997 1998-2014
1998
IS Iceland EurostatBOP 1995-2014 1995
IT Italy EurostatBOP 1995-2014 1995
KZ Kazakhstan IMF WEO wiiwADB
1995-1999 2000-2014
2005
LT Lithuania wiiwADB 1995-2014 2004
LU Luxembourg EurostatBOP 1995-2014 2000
LV Latvia wiiwADB 1995-2014 2000
MD Moldova IMF WEO/IMF IFS 1995-2014 2005
ME Montenegro wiiwADB 2001-2014 2013
MK Macedonia wiiwADB 1995-2014 1998
MT Malta EurostatBOP 1995-2014 2013
NL Netherlands EurostatBOP 1995-2014 2004
NO Norway EurostatBOP 1995-2014 2012
PL Poland wiiwADB 1995-2014 2004
PT Portugal EurostatBOP 1995-2014 1996
RO Romania wiiwADB 1995-2014 1999
RS Serbia wiiwADB 1997-2014 2007
RU Russia wiiwADB 1995-2014 2000
SE Sweden EurostatBOP 1995-2014 1995
SI Slovenia wiiwADB 1995-2014 1995
SK Slovakia wiiwADB 1995-2014 2008
TR Turkey wiiwADB 1995-2014 2010
UA Ukraine wiiwADB 1995-2014 2005
UK United Kingdom EurostatBOP 1995-2014 1997
XK Kosovo IMFWEO wiiw ADB
2000-2003 2004-2014
2004
36
Tradability index: data sources and data availability
reporter country data source years industry structure
AL Albania UN
wiiwADB 1995-2013
NACE Rev.1 1995-2007 NACE Rev.2 2008-2014
AM Armenia UN 1995-2013 NACE Rev.1 1995-2008 NACE Rev.2 2009-2014
AT Austria Eurostat 1995-2013 NACE Rev. 2 1995-2014; ESA2010
AZ Azerbaijan UN 1995-2013 NACE Rev.1 1995-2004 NACE Rev.2 2005-2013
BA Bosnia and Herzegovina UN
wiiwADB wiiwADB
1999-2014 NACE Rev.1 1995-2007 NACE Rev.1 2008-2004 NACE Rev.2 2005-2014
BE Belgium Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
BG Bulgaria UN
wiiwADB wiiwADB
1995-2013
NACE Rev.1 1995 NACE Rev.1 1996-1999 NACE Rev.2 2000-2013 ESA 2010
BY Belarus UN
wiiwADB 1995-2014
NACE Rev.1 1995-1999 NACE Rev.1 2000-2014
CH Switzerland UN
Eurostat 1995-2013
NACE Rev.1 1995-1997 NACE Rev.2 1997-2013 ECA 2010
CY Cyprus Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
CZ Czech Republic wiiwADB 1995-2014 NACE Rev. 2 1995-2014; ESA2010
DE Germany Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
DK Denmark Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
EE Estonia wiiwADB 1995-2014 NACE Rev. 2 1995-2014 ESA 1995: 1995-1999 ESA2010: 2000-2014
EL Greece Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
ES Spain Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
FI Finland Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
FR France Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
GE Georgia UN 1995-2014 NACE Rev.1 1995-2014
HR Croatia wiiwADB 1995-2013 NACE Rev. 2 1995-2014; ESA2010
HU Hungary wiiwADB 1995-2014 NACE Rev. 2 1995-2014; ESA2010
IE Ireland Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
IS Iceland Eurostat 1997-2013 NACE Rev. 2 1997-2013; ESA2010
IT Italy Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
KZ Kazakhstan
UN UN
wiiwADB wiiwADB
1995-2014
NACE Rev. 1 1995-1997 NACE Rev. 1 1998 NACE Rev. 1 1998-2005 NACE Rev. 2 2006-2014
LT Lithuania wiiwADB 1995-2014 NACE Rev. 2 1995-2014; ESA2010
LU Luxembourg Eurostat 1995-2014 NACE Rev. 2 1995-2014 ESA1995: 1995-1999 ESA2010: 2000-2014
LV Latvia wiiwADB 1995-2014 NACE Rev. 2 1995-2014; ESA2010
MD Moldova UN 1995-2014 NACE Rev.1 1995-2014
ME Montenegro wiiwADB 2000-2014 NACE Rev. 1: 2000-2009 NACE Rev. 2: 2010-2014
MK Macedonia wiiwADB 1997-2013 NACE Rev. 1: 1997-1999 NACE Rev. 2: 2000-2013
MT Malta Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
NL Netherlands Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
NO Norway Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
PL Poland Eurostat wiiwADB
1995-2014 NACE Rev. 2 1995-2014 ESA1995: 1995-2001 ESA2010: 2002-2014
PT Portugal Eurostat 1995-2013 NACE Rev. 2 1995-2013; ESA2010
RO Romania wiiwADB 1995-2014 NACE Rev. 2 1995-2014; ESA2010
RS Serbia wiiwADB 1995-2014 NACE Rev. 2 1995-2014; ESA2010
RU Russia UN
wiiwADB 1995-2014
NACE Rev.1 1995-2001 NACE Rev.1 2002-2014
SE Sweden Eurostat 1995-2014 NACE Rev. 2 1995-2014; ESA2010
SI Slovenia wiiwADB 1995-2014 NACE Rev. 2 1995-2014; ESA2010
SK Slovakia wiiwADB 1995-2014 NACE Rev. 2 1995-2014; ESA2010
TR Turkey UN
wiiwADB 1995-2014
NACE Rev.1 1995-1997 NACE Rev.1 1998-2014
UA Ukraine UN
wiiwADB wiiwADB
1995-2014 NACE Rev.1 1995-1999 NACE Rev.1 2000 NACE Rev.2 2001-2014
UK United Kingdom Eurostat 1995-2014 NACE Rev. 2 1995-2014 ESA1995: 1995-1997 ESA2010: 1998-2014
XK Kosovo wiiw ADB 2006-2014 NACE Rev. 1 2006-2007 NACE Rev. 2 2008-2014
37
Current Account in % of GDP series
reporter 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
AL 1.459 -1.888 -15.168 -2.533 -4.026 -4.694 -6.961 -9.437 -6.924 -5.783 -8.978 -6.572 -10.529 -15.696 -15.351 -11.321 -13.219 -10.203 -10.896 -12.864
AM -17.147 -20.176 -20.484 -21.995 -16.940 -15.778 -10.442 -6.221 -6.181 -2.204 -2.529 -2.396 -7.357 -14.225 -16.484 -13.622 -10.440 -9.962 -7.595 -7.292
AT -2.746 -2.777 -2.563 -1.870 -2.266 -0.707 -0.798 2.106 1.554 2.083 2.268 3.311 3.824 4.518 2.613 2.867 1.639 1.490 1.949 1.971
AZ -16.576 -29.310 -23.108 -31.881 -13.091 -3.182 -0.906 -12.330 -27.774 -29.823 1.263 17.633 27.256 35.475 22.974 28.425 26.450 21.799 16.633 13.769
BA -7.406 -9.438 -7.107 -12.855 -17.521 -19.106 -16.121 -16.614 -7.639 -9.079 -13.784 -6.410 -6.040 -9.475 -8.850 -5.494 -7.819
BE 5.310 4.916 5.426 5.080 4.966 3.937 3.317 4.369 3.528 3.269 2.084 1.920 1.950 -0.998 -1.077 1.764 -1.073 -0.054 -0.222 -0.216
BG -1.372 1.600 9.315 -0.218 -4.629 -5.324 -5.395 -2.309 -5.189 -6.211 -11.273 -16.991 -23.680 -21.882 -8.336 -0.875 0.916 -0.260 1.826 1.159
BY -13.543 -3.558 -6.094 -6.678 -1.596 -4.020 -4.372 -2.248 -2.588 -5.159 1.519 -3.758 -6.662 -8.212 -12.508 -15.071 -9.531 -2.939 -10.458 -6.853
CH 6.010 6.479 8.602 8.524 10.030 12.375 8.568 8.950 13.249 15.213 14.024 14.949 10.759 2.959 7.994 14.816 7.646 10.320 11.148 8.823
CY -2.096 -4.735 -4.379 2.828 -1.623 -4.977 -3.043 -3.480 -2.080 -4.585 -5.353 -6.233 -10.685 -15.647 -7.743 -10.655 -3.955 -5.629 -4.451 -4.565
CZ -2.272 -6.106 -5.821 -1.923 -2.272 -4.442 -4.859 -5.116 -5.716 -4.238 -0.949 -2.138 -4.309 -1.896 -2.314 -3.650 -2.119 -1.567 -0.529 0.619
DE -1.190 -0.658 -0.510 -0.714 -1.419 -1.746 -0.363 1.888 1.406 4.441 4.613 5.680 6.748 5.595 5.736 5.624 6.087 6.801 6.372 7.275
DK 0.674 1.424 0.599 -0.839 1.870 1.372 3.048 2.418 3.362 2.935 4.258 3.153 1.384 2.668 3.321 5.728 5.742 5.689 7.132 7.719
EE -3.939 -8.126 -11.120 -8.636 -4.287 -5.409 -7.107 -11.137 -12.922 -12.008 -8.742 -14.981 -15.003 -8.706 2.545 1.807 1.335 -2.434 -0.104 1.028
EL -2.085 -3.129 -3.349 -2.552 -3.431 -7.426 -6.955 -6.241 -6.297 -7.734 -8.877 -11.596 -15.228 -15.125 -12.367 -11.435 -10.006 -3.833 -2.044 -2.124
ES -0.932 -0.727 -0.722 -1.670 -3.298 -4.400 -4.391 -3.738 -3.883 -5.586 -7.494 -8.990 -9.648 -9.251 -4.281 -3.921 -3.180 -0.231 1.509 0.983
FI 3.987 3.780 5.095 5.151 5.174 7.548 8.060 8.224 4.642 5.800 3.000 3.800 3.800 2.200 1.900 1.243 -1.779 -1.934 -1.654 -0.941
FR 0.676 1.271 2.674 2.688 3.385 1.182 1.507 1.174 0.867 0.440 -0.018 0.036 -0.299 -0.953 -0.831 -0.836 -1.032 -1.194 -0.806 -0.926
GE -18.292 -12.574 -14.383 -7.616 -7.078 -5.785 -6.152 -6.392 -9.581 -6.945 -10.838 -15.369 -19.573 -21.964 -10.581 -10.276 -12.748 -11.679 -5.742 -10.521
HR -6.439 -4.597 -10.467 -5.364 -6.369 -2.189 -2.918 -7.061 -5.898 -4.050 -5.182 -6.503 -7.145 -8.783 -5.100 -1.085 -0.706 -0.048 1.017 0.846
HU -3.395 -3.719 -4.054 -7.155 -7.889 -8.488 -5.842 -6.339 -8.013 -8.563 -6.999 -7.038 -7.105 -7.088 -0.805 0.279 0.749 1.770 3.986 2.260
IE 2.473 2.715 2.336 0.779 0.244 0.616 0.176 0.247 0.492 -0.098 -3.303 -4.924 -6.071 -5.777 -4.142 -0.755 -1.155 -1.537 3.096 3.614
IS 0.187 -2.502 -2.364 -7.258 -7.186 -10.310 -4.644 1.187 -4.919 -9.877 -16.013 -23.237 -13.708 -25.946 -10.251 -6.679 -5.401 -4.063 5.730 3.322
IT 2.100 2.900 2.600 1.700 0.800 -0.300 0.100 -0.298 -0.642 -0.385 -0.946 -1.562 -1.452 -2.866 -1.939 -3.476 -3.079 -0.430 0.891 1.910
KZ -1.281 -3.570 -3.606 -5.532 -1.034 2.003 -6.273 -4.158 -0.884 0.777 -1.814 -2.469 -7.985 4.684 -3.574 0.936 5.090 0.490 0.352 2.635
LT -9.167 -8.618 -9.697 -11.548 -10.959 -5.905 -4.668 -5.089 -6.696 -7.600 -7.238 -10.514 -15.047 -13.346 2.074 -0.330 -3.867 -1.179 1.542 3.580
LU 11.471 11.075 10.669 9.401 8.312 12.537 8.401 9.468 6.562 11.923 11.099 10.021 9.843 7.669 7.340 6.785 6.183 6.061 5.677 5.497
LV -0.331 -4.693 -5.308 -8.994 -8.626 -4.714 -7.486 -6.466 -7.744 -12.205 -11.743 -20.722 -20.721 -12.361 8.099 2.363 -2.844 -3.271 -2.385 -1.980
MD -5.872 -11.324 -14.257 -19.723 -5.792 -7.467 -1.808 -1.191 -6.569 -1.775 -7.557 -11.339 -15.249 -16.112 -8.214 -7.524 -12.139 -8.738 -6.358 -7.088
ME -15.373 -12.864 -6.761 -7.175 -16.635 -31.342 -39.497 -49.755 -27.852 -22.726 -17.560 -18.471 -14.472 -15.205
MK -6.709 -7.705 -7.676 -7.822 -1.785 -2.732 -6.345 -9.483 -3.871 -7.904 -2.433 -0.427 -6.910 -12.732 -6.754 -2.025 -2.509 -3.164 -1.646 -0.806
MT -9.827 -10.675 -5.193 -5.436 -3.077 -12.128 -3.633 2.280 -2.878 -3.704 -6.515 -6.585 -1.916 -1.052 -6.578 -4.658 -2.441 1.322 3.607 2.977
NL 5.917 4.923 6.175 3.032 3.634 1.907 2.442 2.487 5.217 6.763 6.091 7.897 5.989 4.068 5.806 7.351 9.111 10.789 10.982 10.571
NO 3.453 6.701 6.183 0.637 6.023 15.196 15.031 12.493 12.111 12.447 15.984 16.795 13.689 17.217 12.865 11.959 13.335 12.426 10.218 9.709
PL 0.599 -2.041 -3.609 -3.983 -7.357 -5.999 -3.127 -2.819 -2.537 -5.458 -2.628 -4.021 -6.352 -6.711 -4.018 -5.387 -5.168 -3.714 -1.274 -2.020
PT -0.133 -4.388 -6.110 -7.594 -8.854 -10.802 -10.435 -8.487 -7.166 -8.329 -9.882 -10.673 -9.739 -12.127 -10.422 -10.148 -6.002 -2.000 1.400 0.549
RO -4.712 -6.911 -5.852 -6.904 -4.085 -3.744 -5.536 -3.387 -5.831 -8.340 -8.627 -10.357 -13.828 -11.806 -4.853 -5.072 -4.947 -4.789 -1.069 -0.451
RS -4.583 -2.469 -2.367 -0.626 2.043 -3.924 -7.189 -13.122 -8.425 -9.642 -18.585 -21.143 -6.628 -6.842 -10.938 -11.588 -6.124 -5.957
RU 2.220 2.768 -0.020 0.081 12.567 17.474 10.455 7.961 7.697 9.908 11.044 9.322 5.552 6.247 4.113 4.422 5.101 3.309 1.558 2.879
SE 3.178 3.332 3.904 3.640 3.936 3.953 4.732 4.482 6.595 6.294 6.467 8.260 8.863 8.596 5.885 6.003 6.885 6.627 6.707 5.724
SI -0.443 0.064 0.109 -0.669 -3.235 -2.782 0.039 0.870 -0.810 -2.690 -1.792 -1.831 -4.128 -5.317 -0.561 -0.119 0.190 2.584 5.634 6.989
SK 2.560 -9.082 -8.245 -8.695 -4.718 -3.406 -8.167 -7.766 -5.812 -7.653 -8.331 -7.688 -5.192 -6.473 -3.458 -4.724 -4.964 0.944 1.959 0.132
TR -1.379 -1.340 -1.386 0.746 -0.415 -3.719 1.961 -0.298 -2.484 -3.609 -4.451 -6.068 -5.830 -5.435 -1.975 -6.200 -9.697 -6.149 -7.881 -5.852
UA -3.113 -2.659 -2.662 -3.094 5.250 4.737 3.567 7.219 5.559 10.279 2.839 -1.447 -3.530 -6.794 -1.428 -2.136 -6.043 -7.851 -8.770 -3.487
UK -1.147 -0.744 0.146 -0.091 -2.523 -2.243 -2.031 -2.069 -1.694 -1.834 -1.249 -2.301 -2.509 -3.615 -3.036 -2.809 -1.694 -3.298 -4.492 -5.108
XK -20.158 -5.837 -6.688 -8.074 -7.152 -8.240 -7.245 -6.185 -11.871 -9.193 -11.715 -13.677 -7.514 -6.371 -7.849
38
Tradability index series
reporter 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
AL 0.185 0.164 0.159 0.161 0.159 0.156 0.152 0.148 0.146 0.148 0.148 0.149 0.150 0.143 0.142 0.149 0.154 0.157 0.160 0.163
AM 0.214 0.210 0.205 0.200 0.200 0.187 0.187 0.180 0.176 0.179 0.172 0.157 0.150 0.144 0.149 0.158 0.167 0.166 0.166 0.166
AT 0.167 0.166 0.168 0.167 0.168 0.169 0.170 0.169 0.167 0.167 0.168 0.169 0.170 0.168 0.163 0.164 0.165 0.165 0.164 0.163
AZ 0.225 0.219 0.214 0.188 0.215 0.258 0.270 0.267 0.261 0.262 0.314 0.341 0.357 0.349 0.311 0.322 0.326 0.305 0.287 0.270
BA
0.159 0.154 0.155 0.154 0.157 0.159 0.160 0.161 0.163 0.161 0.157 0.160 0.161 0.160 0.162 0.160
BE 0.170 0.169 0.171 0.170 0.167 0.167 0.166 0.164 0.162 0.162 0.161 0.159 0.159 0.155 0.149 0.151 0.149 0.148 0.147 0.146
BG 0.199 0.201 0.202 0.191 0.182 0.170 0.175 0.175 0.178 0.176 0.176 0.179 0.178 0.173 0.171 0.171 0.179 0.180 0.176 BY 0.217 0.223 0.225 0.221 0.219 0.210 0.203 0.204 0.202 0.207 0.208 0.204 0.203 0.208 0.200 0.196 0.216 0.210 0.195 0.194
CH 0.166 0.165 0.168 0.167 0.166 0.165 0.166 0.166 0.165 0.165 0.166 0.168 0.170 0.170 0.165 0.165 0.165 0.163 0.163 CY 0.142 0.141 0.140 0.140 0.139 0.138 0.137 0.135 0.132 0.131 0.129 0.126 0.125 0.123 0.124 0.126 0.125 0.125 0.127 0.128
CZ 0.192 0.193 0.196 0.194 0.193 0.196 0.197 0.191 0.189 0.193 0.193 0.196 0.196 0.193 0.186 0.188 0.192 0.192 0.192 0.196
DE 0.176 0.173 0.175 0.176 0.175 0.177 0.177 0.176 0.176 0.177 0.177 0.180 0.181 0.179 0.170 0.176 0.178 0.177 0.176 0.176
DK 0.161 0.161 0.163 0.160 0.161 0.167 0.165 0.164 0.161 0.162 0.164 0.165 0.164 0.162 0.152 0.155 0.157 0.158 0.157 0.156
EE 0.178 0.178 0.181 0.175 0.173 0.177 0.179 0.178 0.177 0.174 0.171 0.169 0.167 0.164 0.161 0.170 0.173 0.172 0.171 0.171
EL 0.153 0.151 0.146 0.145 0.146 0.147 0.145 0.145 0.141 0.139 0.141 0.136 0.139 0.140 0.134 0.135 0.137 0.138 0.140 0.139
ES 0.160 0.160 0.162 0.161 0.159 0.159 0.158 0.156 0.155 0.153 0.151 0.150 0.149 0.147 0.142 0.144 0.146 0.146 0.147 0.147
FI 0.185 0.181 0.183 0.187 0.187 0.191 0.190 0.188 0.184 0.182 0.180 0.181 0.183 0.178 0.163 0.165 0.163 0.156 0.156 0.156
FR 0.155 0.154 0.154 0.155 0.154 0.155 0.153 0.151 0.149 0.148 0.146 0.144 0.144 0.142 0.138 0.138 0.138 0.138 0.138 0.137
GE 0.188 0.194 0.183 0.176 0.177 0.176 0.175 0.175 0.176 0.168 0.166 0.159 0.150 0.144 0.142 0.148 0.151 0.150 0.154 0.154
HR 0.188 0.177 0.177 0.173 0.172 0.177 0.177 0.173 0.171 0.173 0.169 0.169 0.167 0.165 0.164 0.166 0.168 0.170 0.167 HU 0.177 0.179 0.184 0.185 0.183 0.181 0.180 0.176 0.175 0.177 0.177 0.180 0.180 0.177 0.174 0.179 0.182 0.183 0.183 0.184
IE 0.184 0.183 0.189 0.193 0.195 0.193 0.198 0.202 0.190 0.182 0.176 0.171 0.171 0.170 0.181 0.184 0.189 0.183 0.181 0.179
IS
0.164 0.160 0.155 0.153 0.158 0.152 0.147 0.146 0.138 0.139 0.137 0.146 0.152 0.159 0.159 0.154 0.153 IT 0.176 0.175 0.174 0.175 0.173 0.172 0.170 0.169 0.166 0.165 0.163 0.163 0.164 0.162 0.155 0.157 0.158 0.157 0.157 0.158
KZ 0.238 0.236 0.201 0.200 0.215 0.233 0.226 0.222 0.222 0.223 0.225 0.219 0.214 0.231 0.224 0.236 0.235 0.232 0.224 0.222
LT 0.175 0.174 0.172 0.168 0.166 0.176 0.178 0.176 0.177 0.180 0.181 0.177 0.170 0.168 0.168 0.178 0.183 0.185 0.181 0.179
LU 0.158 0.155 0.156 0.156 0.154 0.153 0.151 0.149 0.149 0.148 0.146 0.145 0.148 0.144 0.135 0.138 0.137 0.136 0.135 0.133
LV 0.181 0.182 0.185 0.172 0.165 0.166 0.170 0.170 0.166 0.165 0.162 0.155 0.149 0.146 0.152 0.164 0.161 0.161 0.159 0.157
MD 0.205 0.198 0.189 0.180 0.180 0.186 0.188 0.180 0.182 0.180 0.177 0.172 0.167 0.165 0.160 0.164 0.168 0.165 0.168 0.167
ME
0.162 0.170 0.166 0.160 0.157 0.152 0.152 0.143 0.140 0.136 0.139 0.138 0.134 0.136 0.137
MK
0.181 0.179 0.177 0.154 0.151 0.149 0.149 0.145 0.152 0.152 0.154 0.154 0.151 0.157 0.163 0.158 0.158 MT 0.173 0.170 0.170 0.171 0.171 0.176 0.163 0.160 0.159 0.154 0.152 0.153 0.153 0.154 0.147 0.147 0.147 0.146 0.140 0.138
NL 0.169 0.170 0.168 0.167 0.164 0.166 0.166 0.161 0.160 0.160 0.161 0.162 0.162 0.162 0.153 0.154 0.156 0.157 0.157 0.154
NO 0.199 0.210 0.211 0.191 0.202 0.236 0.228 0.216 0.215 0.223 0.235 0.242 0.231 0.242 0.214 0.218 0.227 0.225 0.220 0.214
PL 0.190 0.185 0.184 0.180 0.178 0.177 0.172 0.171 0.175 0.183 0.180 0.181 0.180 0.179 0.176 0.175 0.178 0.179 0.177 0.178
PT 0.165 0.166 0.165 0.163 0.161 0.157 0.156 0.153 0.151 0.149 0.147 0.148 0.149 0.147 0.143 0.146 0.146 0.148 0.148 RO 0.215 0.219 0.218 0.209 0.201 0.200 0.206 0.203 0.195 0.199 0.196 0.195 0.188 0.183 0.184 0.194 0.196 0.191 0.193 0.189
RS 0.200 0.192 0.186 0.192 0.194 0.202 0.200 0.187 0.181 0.176 0.176 0.175 0.173 0.173 0.173 0.175 0.177 0.180 0.184 0.181
RU 0.211 0.217 0.214 0.215 0.222 0.223 0.212 0.201 0.198 0.212 0.220 0.218 0.213 0.208 0.196 0.200 0.206 0.192 0.191 0.190
SE 0.176 0.173 0.175 0.176 0.175 0.177 0.174 0.171 0.170 0.170 0.170 0.170 0.170 0.167 0.159 0.166 0.165 0.161 0.159 0.157
SI 0.189 0.188 0.189 0.190 0.188 0.186 0.186 0.185 0.185 0.184 0.182 0.181 0.181 0.176 0.168 0.171 0.174 0.176 0.179 0.181
SK 0.199 0.194 0.186 0.188 0.183 0.189 0.193 0.184 0.187 0.189 0.189 0.187 0.187 0.183 0.168 0.176 0.178 0.178 0.176 0.179
TR 0.202 0.197 0.196 0.206 0.198 0.196 0.194 0.193 0.194 0.193 0.193 0.192 0.190 0.189 0.185 0.187 0.190 0.187 0.186 0.187
UA 0.228 0.219 0.213 0.218 0.229 0.215 0.205 0.205 0.203 0.200 0.204 0.203 0.202 0.202 0.191 0.193 0.196 0.192 0.188 0.189
UK 0.177 0.178 0.174 0.170 0.167 0.167 0.163 0.158 0.155 0.153 0.152 0.151 0.149 0.150 0.146 0.147 0.148 0.147 0.149 0.147
XK
0.144 0.142 0.169 0.170 0.171 0.164 0.165 0.167 0.164
39