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Trade Dynamics in the Euro Area:
A Disaggregated Approach
Peter Wierts a, Henk van Kerkhoff a and Jakob de Haan a,b,c
a
b
De Nederlandsche Bank, the Netherlands
University of Groningen, the Netherlands
c
CESifo, Munich, Germany
Draft, October 2011 (work in progress)
Abstract
We investigate the contribution of export composition to trade imbalances in the euro area.
Results show that export composition matters. The elasticity of real exports with respect to
the real exchange rate decreases from -1 to -0.3 along the spectrum from low- to hightechnology export. Core EMU countries have a comparatively higher technology intensity of
exports and a lower response to the real exchange rate, while the opposite is the case in the
southern periphery. Results also underline the role of development of the domestic economy
in increasing technology intensity.
The views expressed are those of the authors only and do not necessarily reflect the views of
De Nederlandsche Bank.
1. Introduction
Although it is widely believed that unsustainable fiscal policies are the root cause of the
current debt crisis in the euro area, a case can be made that current account imbalances play
perhaps a more important role (Mansori, 2011). Although Greece and Portugal had high
budget deficits between 2000 and 2007 (average deficit of 5.4 and 3.7% of GDP,
respectively), Ireland and Spain had no deficits on average (average surplus of 0.3 and 1.5
percent of GDP, respectively). However, according to the figures provided by Mansori, these
four countries take the top positions in the ranking based on the average current account
deficit as a percentage of GDP, followed by Italy.1
No matter what the root cause of the crisis, the size and persistence of these current account
imbalances of countries in the European Economic and Monetary Union (EMU) warrant
further analysis. According to Berger and Nitsch (2010), the size and persistence of bilateral
current account imbalances have increased after the introduction of the euro. Countries with
less flexible labour and product markets exhibit systematically lower trade surpluses than
others. This finding suggests the need for more market flexibility, but Bednarek et al. (2010)
find that EMU has had no effect on labour market reform thereby rejecting the popular belief
that EMU will create greater labour market flexibility. This belief is based on the argument
that in the union, monetary policy is no longer available to individual countries to respond to
asymmetric shocks, which increases incentives to undertake structural reform.
The present paper focuses on the export performance of countries in the euro area. Exports
and imports contribute differently to trade imbalances and are driven by different variables.
Whereas imports are mainly demand driven, the ability to export is linked to the capability to
compete on European and world markets. Exports also determine the capacity to sustain
imports without running down foreign assets or increasing liabilities (for given factor
earnings). Moreover, with exports as a dependent variable, the role played by the real
exchange rate is highlighted relative to studies that investigate the size of bilateral trade (i.e.
measured as exports plus imports, divided by two). In the latter case, changes in the real
exchange rate will have offsetting effects (Flam and Nordström, 2003).
1
Ireland had a surplus on its trade account (see Table 1).
1
There is much diversity in export growth rates of countries in the euro area, both across
countries and over time. As will be discussed in more detail in section 2, previous research
points to high labour costs in explaining a lack of real exchange rate flexibility. Indeed,
Figure 1 suggests a negative relationship between unit labour costs and export growth.
However, it also becomes clear that the relationship is rather weak.
Figure 1. Export growth and growth of unit labour costs, 1996-2010
Unit Labour Costs versus Export Performance of Euro Area Countries
Annual growth for 1996-2010
20
15
Export-Performance
10
5
0
-5
-10
-15
-6
-4
-2
Euro Area Countries
0
2
Unit Labour Cost
4
6
8
Southern Periphery
Source: AMECO.
2
In this paper we investigate a complementary explanation related to the composition of
export. We start from a simple question. Which products and which trade partners explain
most of the export dynamics of each country? The hypothesis is that the type of products
exported and the growth performance of the trade partner play a role in explaining export
growth, a role that may be even more important than price competiveness. For example,
German export to China and other emerging markets grew strongly, but exports from several
other euro area countries, notably those in the Southern periphery, did not. Anecdotal
evidence suggests that the composition of exports may play a role here. In emerging
economies, demand for high-quality products from Germany – such as capital goods and cars
– was stronger than demand for low-quality products. Investigating disaggregated trade data
may help to shed light on such arguments.
Our main research question considers the response of different classes of exports to price and
income developments. The lower is the technology intensity of exports, the stronger could be
international competition, and the more sensitive exports may be to increases in the real
exchange rate.2 Moreover, rising incomes in emerging markets may give rise to stronger
demand for high-quality goods. We therefore run separate regressions for different export
categories, classified according to technology intensity. Our hypothesis is that in hightechnology intense countries, exports may respond less to real exchange rate appreciation and
more to increases in income abroad. Our dataset contains exports from euro area countries to
their top 20 trade partners. We use the OECD ISIC classification that distinguishes between
exports from high-technology, medium-high technology, medium-low technology and low
technology industries.
The remainder of this paper is organised at follows. Section 2 briefly reviews literature on
current account imbalances, adjustment capacity and the effect of EMU on trade. Section 3
takes a first look at our data. Section 4 presents results from our baseline regressions on
disaggregated export categories and (groups of) countries. Section 5 presents robustness
checks and section 6 offers our conclusions and discusses the policy implications of our
findings.
2
Moreover, higher technology intensity may also lead to higher profit margins, so that exchange rate changes
may – at least temporary – lead to lower profit margins instead of quantity adjustments.
3
2. Literature review
Imbalances
The emergence of persistent current account imbalances in the euro area is well documented.
Berger and Nitsch (2010) find that bilateral intra-euro area imbalances have become more
persistent after the introduction of the euro. Countries with high labour and product market
inflexibility and slow movements in the real exchange rate have larger and more persistent
deficits. Chen et al. (2010) add that growing current account deficits are also related to
asymmetric trade developments vis-à-vis the rest of the world, in particular China and Central
and Eastern Europe. Lane (2010) finds that the introduction of the euro did not immediately
induce a widening of imbalances. Instead, the dispersion of current account imbalances
increased markedly after 2003. The financing of current account deficits was facilitated by
increased financial integration following the introduction of the euro. Moreover, financial
innovations related to the credit and securitisation boom that started around 2003/4 also
contributed. In the same vein, Giavazzi and Spaventa (2010) find that widening imbalances
were not driven by a matching increase in the production capacity of traded goods and
services in ‘cohesion countries’ (Spain, Ireland, Greece and Portugal). Instead, capital inflows
were used to finance a boom in non-tradable consumption and residential construction.
Adjustment options therefore go beyond traditional channels of price and wage adjustment,
structural reform and fiscal consolidation, but also include a macro-prudential policy
perspective on aggregate credit growth in the financial system as a whole (Jaumotte and
Sodsriwiboon, 2010).
Adjustment capacity
The debate on adjustment capacity in EMU can also be seen from a somewhat longer
historical perspective. Debated questions are (i) whether the euro area resembles an optimal
currency area (OCA) and (ii) if not, how this would affect adjustment to asymmetric shocks.
At the start of EMU there was broad consensus among economists that it is not an optimum
currency area (OCA). For example, as early as 1993, Bayoumi and Eichengreen distinguished
between ‘core’ and ‘periphery’ countries. In their analysis of shocks and adjustment they find
that countries around Germany experience relatively small and highly correlated aggregate
supply disturbances. In the periphery, supply disturbances are larger and more idiosyncratic.
4
These authors therefore conclude that monetary union with the core countries would be less
problematic than for the larger group. After the start of EMU, Artis (2002) uses six criteria3 to
distinguish three clusters: ‘the core’ (Germany, France, Austria, Belgium, The Netherlands),
and the ‘northern’ (Ireland and Finland) and ‘southern’ (Italy, Spain, Portugal and Greece)
peripheries. We use this classification in our empirical analysis.
A controversial issue is how a ‘non-optimal’ currency area would function. EMU was argued
to lead to long and costly adjustment processes in case of asymmetric shocks, a lack of
flexible market adjustment and low labour market mobility. A more optimistic view is that
monetary union would increase trade intensity and business cycle synchronization. This
would fuel an endogenous process in the direction of an optimum currency area (Frenkel,
1997). The latter argument hinges on the trade effect of EMU, and on its subsequent effect on
business cycle synchronization. Initial studies on the trade effect indicated that currency
unions should as the EMU may lead to large increase in international trade (Rose, 2000).
Since then, estimates of the trade effect of EMU have become progressively smaller over time
(see, e.g., Micco et al., 2003, Baldwin, 2006). Berger and Nitsch (2008) even conclude that,
after controlling for the trend increase in trade integration, the euro’s impact on trade
disappears. Although there is a broad consensus in the literature that higher trade intensity
leads to more business cycle synchronization, Inklaar et al. (2008) find that the trade effect on
business cycle synchronization is smaller than previously thought.
A related question concerns the effect of EMU on structural reforms. With monetary policy
no longer available to respond to asymmetric shocks, the need for flexibility and incentives to
undertake structural reform may increase (Bean, 1998). Alesina et al. (2008) report that the
adoption of the euro has been associated with acceleration in the pace of structural reforms,
but did not lead to labour market reforms. Likewise, Bednarek et al. (2010) find that EMU
has had no effect on labour market reforms that enhance the capacity of an economy to adjust
to economic shocks and those that aim to raise long-run equilibrium output. Overall, the
available evidence therefore provides limited support at best for an endogenous process
towards an optimal currency, in line with the emergence of large and persistent imbalances.
3
These criteria, measured relative to Germany, are: (1) inflation; (2) business cycle cross-correlation; (3) labour
market performance; (4) real bilateral exchange rate volatility; (5) trade intensity and (6) monetary policy
5
Our paper in perspective
The two papers closest to our study are Chen et al. (2010) and Flam and Nordström (2003).
Chen et al. (2010) run separate export and import regressions. They find no evidence that
Germany benefits from higher export demand elasticities than other euro area countries.
When focusing specifically on export to China, they do find that the export demand
elasticities of Greece, Italy, and Spain vis-à-vis China are significantly lower than the euro
area average. Contrary to their approach, we start from the hypothesis that differences in
export elasticities should be related to the type of export goods and services (using
disaggregated data). We also focus on the response of export categories to the real exchange
rate (price elasticity) in addition to demand (or income) elasticities as in their approach.
Flam and Nordström (2003) show results of export regressions that are disaggregated
according to 8 SITC categories. They find mixed results for the euro effect across trade
categories. Contrary to our approach, they include 20 industrialised countries and do not
include emerging markets. Moreover, they do not classify export categories according to
technology intensity. Also, our focus is not on the trade effect of the euro but on adjustment
mechanisms, i.e. the price and income elasticities across export categories.
3. A first look at the data
Trade imbalances
Table 1 shows the development of trade balances of euro area countries since the start of
EMU. It becomes clear that the core and northern periphery countries generally have a
positive balance, whereas the southern periphery countries generally have a negative balance.
What is more, there is no clear tendency that these deficits reduce over time.
[Insert Table 1 here]
Partner countries
As indicated, our disaggregated approach concentrates on partner countries and export
composition. The literature on the trade effects of the euro illustrates the relevance of taking
into account the trend increase in trade over time (see section 2). A priori, we see no reason
correlation.
6
why this trend would only apply to the euro area. In investigating export dynamics, we
therefore correct for the trend increases and focus on the share of particular products in total
exports. Moreover, we investigate exports as a percentage of GDP, so that we do not lose
sight of the size of trade. Table 2 shows the destination of the exports of the various country
groups that we distinguish for particular years. Several conclusions can be drawn from this
table. First, column (3) of Table 1 shows that since 1990 the share of the core countries in the
exports of the southern periphery has declined. So, in contrast to popular belief, European
integration has not led to an increase in the share of the core and the northern periphery in the
exports of the southern periphery. In fact, over time the exports of countries in the southern
periphery became more oriented towards other countries in the southern periphery.
Interestingly, also for the countries in the core the share of exports to other countries in the
core has declined over time. Second, exports of the core countries are more oriented towards
emerging economies than exports of the southern periphery. In fact, the aggregate figures for
the southern periphery suggest a rosier picture than is the case for most countries in this
region, as exports of Italy have become more oriented towards emerging markets than exports
of the other countries in the southern periphery as will be discussed below. Third, when
exports are expressed as a share of GDP it become clear that the southern periphery is less
oriented towards exports than the core. In the first years of the present century, exports as
share of GDP increased in the core, while it was stagnant in the southern periphery.
[Insert Table 2 here]
Figure 2 shows more detailed information for each country. As said, apart from Italy, the
share of emerging countries in exports of the southern periphery has hardly increased over
time. In contrast, in several (but not all) countries in the core, emerging economies have
become an important export market. For Portugal and Greece, and to a lesser extent Spain,
the southern periphery has become more important over time for their exports. Even though
the core is still the most important destination for exports (except for Greece that exports a lot
to its neighbouring countries), the importance of exports to the core has declined in all
countries in the southern periphery.
[Insert Figure 2 here]
7
Composition of exports
Figure 3 shows the development of the composition of exports of manufactured goods, using
four different categories of manufactured goods, ranging from high to low technology goods
(see Annex 1 for details). Unfortunately, a similar decomposition for services could not be
made. Figure 3 shows that the share of high-tech manufactured goods in most core countries
is about 20 percent, while it is much lower in the southern periphery. Except for Greece, the
share of high-tech manufactured goods also has not increased in the southern periphery, in
contrast to the northern periphery.
[Insert Figure 3 here]
4. Baseline results
Baseline regression
We now investigate econometrically the role of the real exchange rate, the composition of
exports and export behaviour of country groups in the euro area. We start from a standard
gravity equation for exports, as estimated e.g. by Marquez (1990), Bayoumi (1999), Flam and
Nordström (2003) and Chen et al. (2010). The main innovative element is that we run
separate regressions for export classified according to technology intensity, as shown in the
previous section. We also run regressions for separate groups of countries, to see if results
match with the differences in export composition.
The baseline specification is:
log( Ecijt )  1 L log Ecijt    2 ( L) log( Rijt )   3 L log( PI jt )   4 L log( RI it )  c  cij  tt   cijt
Subscripts are for export category c of reporting country i to partner country j, at time t. L is
the lag operator. Ecijt is the volume of bilateral exports, of category c, from euro area country
i (the reporting country) to partner country j in year t. Rijt is the real bilateral exchange rate
between countries i and j at time t, PIj is real income of partner country j, RIi a measure of
income/development in reporting country i. c is a constant, cij a country pair fixed effect that
picks up the effect of all variables that are (near) constant between trade partners, such as
8
distance and culture. tt is a time effect (dummy for each year) to capture common shocks and
 cijt a residual.
The main variables of interest are the long run elasticities of real exports with respect to the
real exchange rate (expected coefficient is negative), real foreign income (expected
coefficient is positive) and a measure of domestic development (expected coefficient is
positive). The inclusion of the last variable is motivated by new trade theory that stresses
increases returns to scale (Bayoumi, 1999). The proxy that Bayoumi (1999) and Flam and
Nordström (2003) use is real income of the reporting country. We use real GNI/Capita at PPP
basis in order to focus on differences in development.4 Moreover, we instrument this variable,
or include it with a lag, to avoid possible reverse causality problems.
Data and estimation method
The dependent variable captures exports from each euro area country to at least its top 20
trade partners. This leads to the inclusion of 44 partner countries in total. Our sample period
is 1988-2009. Export data are in current dollars. We first revert them to euros, and then
deflate with the export deflator of each country.5 Our preferred way of calculating bilateral
real exchange rates would be to use export prices or ULC. Like other authors, we end up
using CPI due to data availability. Finally, GDP data are deflated by the GDP deflator.
Our specification contains a lagged dependent variable, while the time dimension is short
relative to the large number of country-pair groups. The Arellano-Bond estimator was
developed for small-T large-N panels that include endogenous variables; hence this is our
preferred estimation method. In addition, the robustness of results across estimation methods
is shown.6
4
Regressions with real domestic income give similar patterns in the results. Moreover, excluding this variable
has a minor effect on the other coefficients of interest.
5
Several studies mention that deflators for bilateral exports (in dollars) do not exist. They use therefore deflators
at country level. It seems that different solutions are implemented. Chen et al. (2010, p. 46) use the reporters
country’s export deflators. Micco et al. (2003, p. 327) deflate exports for all countries by US CPI.
6 We also show result from the method proposed by Bruno (2005) that corrects the bias on the lagged
dependent variable (i.e. Least Squares Dummy Variables Corrected, or LSDVC). This estimator does not allow
for instruments, so that we lag reporter income with a lag; moreover it is intended for panels with a small number
of individuals. For comparison, the baseline regression also shows results estimated with LSDV (least squares
dummy variables), so that the effects from the dynamic bias can be seen. Finally, we also show results from a
fixed effects regression where reporter income is instrumented.
9
Results
Table 3 shows results for our baseline regression for all euro area countries following a
general to specific strategy. We start from including three lags for all variables, and remove
those that are statistically insignificant. The lagged dependent variable is highly statistically
significant. Excluding it leads to autocorrelation in the residuals.7 Moreover, two lags of the
real exchange rate need to be included, and one lag of real partner income.
Our interest is in the long-run elasticities. Table 4 therefore reports them directly, based on
the results of Table 3. The long-run coefficients are calculated by removing the time
dimension, adding up the different lags, and dividing them by 1 minus the coefficient of the
lagged dependent variable. Coefficients have the expected sign, and are mostly highly
significant. A 1% appreciation in the real exchange rate leads to a 0.6-0.8% decrease in real
exports. This finding is in line with the results of Marquez (1990, p. 74), who finds
elasticities between -0.5 and -1.1. It is also close to the result reported by Flam and
Nordström (2003) who find a coefficient of approximately 1. The elasticity of real exports
with respect to real partner income is about -1. This is slightly slower than results reported in
other studies which find coefficients just above 1 (Flam and Nordstrom, 2003) or elasticities
between 1 and 2 (Marquez, 1990). Our results for reporter capita per GNI/capita also show
elasticities around 1 for most estimation methods. If we use real reporter income instead, we
find elasticities in the range of 0.5-0.7. This finding of a higher elasticity for partner income
than for own income is in line with other studies (Bayoumi, 1999; Marquez, 1990)
[Insert Tables 3 and 4 here]
We now move to the regressions for the composition of exports. To save space, Table 5 only
reports the long-term elasticities (calculated as in Table 4). Results are always highly
significant, unless stated otherwise in the table. In line with our hypothesis, we find that price
elasticities differ considerably among export categories. Moving from low-technology to
high-technology industries decreases the elasticity from -0.9 to -0.3. This is in line with the
idea that price competition is stronger in low-technology industries.
7
Of the studies mentioned, only Marquez (1990) includes a lagged dependent variable, and finds it to be
10
Moving to the demand side – i.e. the effect of real partner income – we find mixed results.
Exports from low-technology industries indeed show the lowest response to partner income.
At the same time, demand elasticities decrease when we move from medium-low to hightechnology industries. In a related approach, Chen et al. (2010, p. 15) do not find that
Germany (which has a relatively high share of high technology export) benefits from higher
export demand elasticities than other euro area countries.
Perhaps the most surprising results concern the role of own income. Moving along the
spectrum from low to high-technology exports, the coefficient increases strongly, and
becomes highly statistically significant.8 This suggests that structural development of the
domestic economy coincides with higher technology intensity of exports, presumably through
development of human and physical capital. As a consequence, it lessens the impact of real
exchange rate movements on exports.
As a first sensitivity test, Table 6 excludes The Netherlands and Belgium from the sample.
Due to their geographical location, these countries have a large and growing share of reexports, of over 50% of total exports for The Netherlands. Re-exports are generally of a high
quality, but production costs incurred in the re-exporting cover only a very small proportion
of total costs. Prices are for 90% determined by import prices (Mellens et al., 2007, p. 24).
These effects may potentially influence our results. Indeed, the results in Table 6 show that
dropping these countries increases the difference in responses to the real exchange. The
elasticity of real export to the real exchange rate now decreases from -1 for low technology
industries to -0.3 for high technology industries.
[Insert Tables 5 and 6 here]
Table 7 shows the results for different country groups in the euro area. We expect a stronger
response to the real exchange rate in the southern periphery, since the technology intensity of
export is generally lower (see section 3). Indeed, we find that real exports in core EMU
statistically significant.
8 We find a similar pattern when we use real own income (instrumented) instead. In this case, the variable is
always statistically significant, negative for low-technology industries, and positive and strongly increasing when
11
countries are less sensitive to real exchange rate movements than countries from the southern
periphery (elasticity of -0.46 versus -0.70). This provides additional insight in the causes of
persistent current account imbalances. The contribution of real exchange rate appreciations to
persistent current account imbalances is well known. Our results suggest that differences in
export composition may have contributed as well.
[Insert Table 7 here]
5. Robustness checks (still to be done)
We could add additional control variables:
1. Price effects from 3rd countries and competition from China
See previous note for how price competition from 3rd countries has been measured. In
addition, there is interest in the effect of increasing exports from China and other emerging
markets.
2. Institutional dummies
Additional to country pair dummies: EMU dummy for bilateral pairs of 11 Euro area
countries (1999-now) and Greece (2001-now);
Possibly: dummy for individual countries joining the EU after start sample period (this
dummy would capture country pairs within the EU; note however that it would show strong
overlap with the EMU dummy): Greece-1981; Portugal, Spain-both 1986; Austria, Finland,
Sweden-1995; Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland,
Slovakia, Slovenia-all 2004; Bulgaria, Romania-2007.
6. Conclusion and policy implications
Countries in the southern periphery of the euro area (Greece, Italy, Portugal and Spain) have
large and persistent current account imbalances. An important policy issue is how these
imbalances can be redressed. Whereas policymakers so far have focused on the lack of real
exchange rate flexibility, we examine the impact of the composition of exports and, related to
we move towards high-technology industries.
12
that, the destination of the exports. Our analysis shows that in contrast to popular belief,
European integration has not led to an increase in the share of the core and the northern
periphery in the exports of the southern periphery. In fact, over time the exports of countries
in the southern periphery became more oriented towards other countries in the southern
periphery. Exports of the core countries are more oriented towards emerging economies than
exports of the southern periphery. No doubt, this reflects the composition of exports. Whereas
the share of high-tech manufactured goods in most core countries is about 20 percent, it is
much lower in the southern periphery and generally has also not increased since the start of
EMU. This is the bad news coming from our analysis for the southern periphery. Policies
aiming at increasing the share of high-technology exports take time and will not have much
impact in the short run. The goods news coming from our analysis for the southern periphery
is that the elasticity of real exports with respect to the real exchange rate decreases from -1 to
-0.3 along the spectrum from low-technology to high-technology export. This implies that if
countries in the southern periphery are able to reduce their real exchange rate, i.e. increase
their price competitiveness, they will be able to substantially increase their exports, thereby
redressing their current account imbalances.
13
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Appendix Data Sources
The numerators for Table 1 were extracted in September 2011 from Eurostat. Link to the
table:
http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=tet00002
The nominal GDP denominators were taken from Thomson-Datastream.
The variables for Figure 1 were taken from the spring-2011 release of the AMECO-database
of the European Commission.
The data for Table 2 and Figure 2 were taken from the OECD ITCS-database in December
2010. Our selection of partner-countries is based on a top-20 ranking of export destinations
for the 12 euro area member countries for each of the years: 1980-2009. We studied the 1digit SITC-2 classification. We used this older classification for availability of longer timeseries. In this paper we only used the export totals rather than the 1-digit subtotals. The
nominal GDP denominators were taken from Thomson-Datastream.
For Figure 3 and the estimation of our regression-models we used the OECD STAN Bilateral
Trade database. Our decision to use this source rather than the mentioned ITCS-data was
based on the availability in this source of an export quality indication, which proved more
successful in modelling than the 1-digit SITC-2 classification. In addition to this the ISIC-3
classification that forms the basis for the export quality indicators, captures more of the
technological developments than the older SITC-2. Drawbacks of this choice are: shorter
time-series and the loss of some of the partner-countries from our top-20 based list.
In addition to the data shown we used extra variables for our regression models:
-
-
Deflators of the exports of goods for the 12 euro area member countries from the
AMECO-database (spring-2011 release).
CPI-series for all 12 euro area and partner-countries from the IMF IFS-database. For
some countries (China, Belarus, United Arab Emirates and Ukraine) supplemented
from Thomson-Datastream.
Real-GDP series for all 12 euro-area and partner-countries from the IMF WEOdatabase.
GNI per capita series in PPP terms for all 12 euro area and partner-countries from the
World Bank Global Development Indicators.
US-Dollar exchange rates for all 12 euro area and partner-countries from the IMF IFSdatabase.
Fixed exchange rates for the euro versus the old national currencies for the 12 euro
area member states from the ECB.
15
Table 1.
Trade Imbalance for Euro-Area Countries and Regions
External trade by declaring country; Trade balance as a percentage of GDP
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Core:
Germany *
France
Austria
Belgium
Luxembourg
Netherlands
Total Core
3.2
0.7
-2.5
5.6
-14.2
3.0
2.9
-0.9
-2.4
4.7
-14.3
3.9
4.5
-0.4
-2.0
5.0
-12.7
5.5
6.2
0.2
0.2
7.0
-10.7
5.6
6.0
-0.4
-0.9
6.6
-9.8
5.8
7.1
-0.9
-0.5
5.9
-11.1
6.2
6.9
-1.9
-0.7
4.2
-8.2
6.7
6.9
-2.0
-0.1
3.8
-8.6
6.9
8.0
-2.8
0.2
4.2
-10.1
7.4
7.2
-3.5
-0.7
1.1
-10.7
6.5
5.8
-2.9
-1.6
3.8
-6.4
6.9
6.1
-3.3
-1.7
4.7
-7.9
7.2
2.2
1.5
2.6
3.8
3.5
3.8
3.3
3.2
3.5
2.6
2.4
2.4
Northern Periphery:
Ireland
Finland
25.4
7.8
27.2
9.5
30.8
8.5
29.1
8.0
24.5
6.5
23.1
5.3
20.3
3.4
16.0
3.8
14.5
3.4
15.8
1.7
23.9
0.8
27.8
0.5
Total Northern Periphery
15.3
17.4
18.7
18.1
15.3
14.1
12.0
10.1
9.1
8.6
11.9
13.1
-12.2
1.2
-14.5
-5.0
-13.3
0.2
-17.3
-7.0
-12.8
0.7
-16.4
-6.2
-10.8
0.6
-14.3
-5.7
-9.5
0.1
-16.1
-5.9
-10.4
-0.1
-16.3
-7.2
-13.2
-0.7
-15.3
-8.5
-12.9
-1.4
-16.2
-9.3
-12.8
-0.6
-17.7
-9.4
-14.7
-0.8
-19.0
-8.7
-11.6
-0.4
-14.9
-4.5
-11.7
-1.8
-13.7
-4.9
-2.4
-4.0
-3.4
-3.1
-3.5
-4.2
-5.1
-5.9
-5.7
-5.8
-3.6
-4.3
Southern Periphery:
Portugal
Italy
Greece
Spain
Total Southern Periphery
* Including former GDR from 1991.
16
Table 2 Exports of Goods by and between Euro-Area Regions
As a Percentage of Total Trade
Northern
Periphery
Southern
Periphery
41.8
1.1
11.9
24.5
0.6
4.5
40.4
0.7
6.0
22.6
5.0
17.6
100.0
46.6
13.2
10.7
100.0
40.6
1.2
14.5
As a Percentage of GDP
Northern
Periphery
Southern
Periphery
9.5
0.2
2.7
7.5
0.2
1.4
5.7
0.1
0.8
20.1
4.1
28.7
100.0
5.2
1.1
4.0
22.8
14.2
4.0
3.2
30.5
2.8
0.6
4.1
14.1
29.2
0.6
7.1
43.8
1.0
11.4
10.2
0.3
3.6
7.9
0.1
1.9
5.9
0.1
1.5
26.5
4.1
13.1
100.0
46.4
8.7
8.1
100.0
25.0
4.0
14.8
100.0
6.7
1.0
3.3
25.1
12.5
2.3
2.2
26.9
3.4
0.5
2.0
13.5
34.1
1.5
14.3
27.9
0.5
7.5
36.6
1.2
13.4
10.8
0.5
4.5
15.6
0.3
4.2
7.4
0.2
2.7
27.5
8.6
14.0
100.0
45.4
6.5
12.3
100.0
23.7
7.0
18.1
100.0
8.7
2.7
4.4
31.6
25.4
3.6
6.9
55.9
4.8
1.4
3.7
20.3
33.9
1.4
12.7
26.7
0.4
7.8
32.2
0.9
13.6
13.2
0.6
5.0
11.2
0.2
3.3
6.4
0.2
2.7
22.6
13.9
15.4
100.0
39.7
12.0
13.3
100.0
19.0
10.8
23.5
100.0
8.8
5.4
6.0
38.9
16.6
5.0
5.6
41.8
3.8
2.2
4.7
20.1
Core
Core
1980
Core
Northern Periphery
Southern Periphery
Rest Industrialised World
Emerging M arkets
Rest of the World
World
1990
Core
Northern Periphery
Southern Periphery
Rest Industrialised World
Emerging M arkets
Rest of the World
World
2000
Core
Northern Periphery
Southern Periphery
Rest Industrialised World
Emerging M arkets
Rest of the World
World
2008 *
Core
Northern Periphery
Southern Periphery
Rest Industrialised World
Emerging M arkets
Rest of the World
World
Core: Austria, Belgium, France, Germany, Luxembourg, Netherlands.
Northern Periphery: Finland, Ireland. Southern Periphery: Greece, Italy, Portugal, Spain.
Rest Industrialised World: Australia, Canada, Denmark, Japan, Korea, New Zealand, Norway,
Sweden, Switzerland, UK, US.
Emerging M arkets: Brazil, China, Czech Rep.**, Hong Kong, Hungary, India, Poland,
Russian Federation ***, Slovak Rep.**, Slovenia.
* 2008 is used because of missing trade-data for Spain in later years.
** Before 2003: Former Czechslovakia. *** Before 2002: Former USSR.
17
Table 3. Total exports, all euro area countries
GMM
Stata command:
Lagged dep. var.
Log RER
Log RER L1
Log RER L2
Log partner real income
Log partner real income L1
Log Reporter (GNI/Capita, PPP)
xtabond
0.68
(0.056)***
-0.64
(0.065)***
0.30
(0.050)***
0.10
(0.051)**
2.13
(0.17)***
-1.81
(0.22)***
-0.15
(0.16)
Log Reporter (GNI/Capita, PPP) L1
Time dummies
Pairwise fixed effects
Number of observations
Number of groups
Number of instruments
R-squared (overall)
Standard errors
Yes
Yes
7768
440
101
n.a.
robust
Dependent variable: log of real exports
Fixed effects Fixed effects Fixed effects, IV
Corrected
xtlsdvc
xtreg
xtivreg
0.79
0.69
0.69
(0.012)***
(0.019)***
(0.0073)***
-0.56
-0.59
-0.59
(0.19)***
(0.042)***
(0.027)***
0.31
0.25
0.25
(0.19)***
(0.047)***
(0.036)***
0.13
0.10
0.11
(0.097)
(0.041)**
(0.023)***
1.97
2.08
2.08
(0.35)***
(0.13)***
(0.085)***
-1.75
-1.76
-1.76
(0.42)***
(-13.10)***
(0.081)***
0.40
(0.045)***
0.22
0.36
(0.12)*
(0.064)***
Yes
Yes
Yes
Yes
Yes
Yes
8208
8208
8208
440
440
440
n.a.
bootstrapped
0.92
robust
0.91
conventional
Tabel 4. Long-run coefficients
GMM
Dependent variable: log of real export
Fixed effects Fixed effects Fixed effects, IV
Corrected
(LSDV)
(LSDVIV)
(LSDVC)
-0.59
-0.76
-0.75
1.01
1.02
1.02
1.04
1.17
1.30
-0.76
1.01
-0.48
(insignificant)
Notes: the estimation Arellano-Bond dynamic panel-data estimation. GMM type instruments: L(2/3).log(export
volume); L(2/3)log reporter income. Standard errors are adjusted for clustering on panel groups.
Log RER
Log partner real income
Log Reporter (GNI/Capita, PPP)
18
Table 5. Long-run coefficients by exports categories.
Total
Dependent variable: log of export
LowMedium-low
Medium-high
technology
technology
technology
industries
industries
industries
-0.93
-0.89
-0.80
0.91
1.59
1.23
Hightechnology
industries
-0.29
1.19
-0.76
Log RER
1.01
Log partner real
income
-0.48
-0.61
-0.17
1.23
2.25
Log Reporter
(insignificant)
(insignificant)
(insignificant)
(GNI/Capita,
PPP)
Number of
7768
7768
7759
7766
7731
observations
Number of groups
440
440
440
440
440
Number of
101
101
101
101
instruments
Notes: the estimation Arellano-Bond dynamic panel-data estimation. GMM type instruments: L(2/3).log(export
volume); L(2/3)log reporter income. Standard errors are adjusted for clustering on panel groups.
Table 6. Exports categories, excluding NL and BE, long-run coefficients
Total export
Dependent variable: log of export of
Low-technology Medium-low
Mediumindustries
technology
high
industries
technology
industries
-1.00
-0.88
-0.83
0.97
1.53
1.31
Hightechnology
industries
-0.84
-0.30
Log RER
1.10
1.18
Log partner
real income
0.19
-0.08
0.28
1.54
2.86
Log Reporter
(insignificant)
(insignificant)
(GNI/Capita, (insignificant)
PPP)
Number of
6968
6968
6959
6966
6931
observations
Number of
396
396
396
396
396
groups
Number of
101
101
101
101
101
instruments
Notes: the estimation Arellano-Bond dynamic panel-data estimation. GMM type instruments: L(2/3).log(export
volume); L(2/3)log reporter income. Standard errors are adjusted for clustering on panel groups.
19
Table 7. Total exports, country groups, long run coefficients
Dependent variable: log of export of
Log RER
All euro area
Core
Southern
periphery
Northern
periphery
-0.76
-0.46
-0.70
-1.03
1.01
1.16
1.37
1.22
Log partner
real income
-0.48
1.03
1.09
2.77
Log Reporter
(GNI/Capita, (insignificant)
PPP)
Number of
7768
2972
3196
800
observations
Number of
440
176
176
44
groups
Number of
101
98
101
63
instruments
Notes: the estimation Arellano-Bond dynamic panel-data estimation. GMM type instruments: L(2/3).log(export
volume); L(2/3)log reporter income. Standard errors are adjusted for clustering on panel groups.
20
Figure 2
Figure 2A Exports of Goods From Euro-Area Countries by Destination: Core Countries
As a Percentage of Total Exports of Goods
Austria
As a Percentage of GDP
100%
50
80%
40
60%
30
40%
20
20%
10
0%
0
80
85
90
95
00
05
France
100%
80
85
90
95
00
05
80
85
90
95
00
05
80
85
90
95
00
05
80
85
90
95
00
05
100
80
60
40
50
200
100%
0%
80% 80
85
90
95
00
05
40
60%
30
40%
20
20%
10
0%
0
80
85
90
95
00
05
Germany
100%
50
80%
40
60%
30
40%
20
20%
10
0%
0
80
85
90
95
00
05
Belgium-Luxembourg
100%
100
80%
80
60%
60
40%
40
20%
20
0%
0
80
85
90
95
00
05
80
85
90
95
00
05
80
85
90
95
00
05
Netherlands
100%
100
80%
80
60%
60
40%
40
20%
20
0%
0
80
85
90
95
00
Emerging M arkets
Core
Northern Periphery
Southern Periphery
Other Industrialised Countries
Rest of the World
05
Source: OECD ITCS-Database.
21
Figure 2B Exports of Goods From Euro-Area Countries by Destination: Peripheral Countries
As a Percentage of Total Exports of Goods
Finland
As a Percentage of GDP
100%
100
80%
80
60%
60
40%
40
20%
20
0%
0
80
85
90
95
00
05
80
85
90
95
00
05
Ireland
100%
100
80%
80
60%
60
40%
40
20%
20
0%
0
80
85
90
95
00
05
80
85
90
95
00
05
80
85
90
95
00
05
80
85
90
95
00
05
80
85
90
95
00
05
80
85
90
95
00
05
Greece
100%
50
80%
40
60%
30
40%
20
20%
10
0%
0
80
85
90
95
00
05
Italy
100%
50
80%
40
60%
30
40%
20
20%
10
0%
0
80
85
90
95
00
05
Portugal
100%
50
80%
40
60%
30
40%
20
20%
10
0%
0
80
85
90
95
00
05
Spain
100%
50
80%
40
60%
30
40%
20
20%
10
0
0%
80
85
90
95
00
Emerging M arkets
Core
Northern Periphery
Southern Periphery
Other Industrialised Countries
Rest of the World
05
22
Figure 3
Figure 3A Exports of Goods From Euro-Area Countries by Quality: Core Countries
As a Percentage of Total Exports of Goods
Austria
As a Percentage of GDP
100%
50
80%
40
60%
30
40%
20
20%
10
0%
0
88
90
92
94
96
98
00
02
04
06
08
88
90
92
94
96
98
00
02
04
06
08
88
90
92
94
96
98
00
02
04
06
08
88
90
92
94
96
98
00
02
04
06
08
France
100%
50
80%
40
60%
30
40%
20
20%
10
0%
0
88
90
92
94
96
98
00
02
04
06
08
Germany
100%
50
80%
40
60%
30
40%
20
20%
10
0%
0
88
90
92
94
96
98
00
02
04
06
08
Belgium-Luxembourg
100%
100
80%
80
60%
60
40%
40
20%
20
0%
0
88
90
92
94
96
98
00
02
04
06
08
88
90
92
94
96
98
00
02
04
06
08
88
90
92
94
96
98
00
02
04
06
08
Netherlands
100%
100
80%
80
60%
60
40%
40
20%
20
0
0%
88
90
92
94
96
98
00
02
04
06
08
Non-manufactured Goods
Low Technology M anufactures
M edium-low Technology M anufactures
M edium-high Technology M anufactures
High Technology M anufactures
Source: OECD STAN Bilateral Trade Database.
23
Figure 3B Exports of Goods From Euro-Area Countries by Quality: Peripheral Countries
As a Percentage of Total Exports of Goods
Finland
As a Percentage of GDP
100%
100
80%
80
60%
60
40%
40
20%
20
0%
0
88
90
92
94
96
98
00
02
04
06
08
88
90
92
94
96
98
00
02
04
06
08
88
90
92
94
96
98
00
02
04
06
08
Ireland
100%
100
80%
80
60%
60
40%
40
20%
20
0%
0
88
90
92
94
96
98
00
02
04
06
08
Greece
100%
50
80%
40
60%
30
40%
20
20%
10
0%
0
88
90
92
94
96
98
00
02
04
06
08
88
90
92
94
96
98
00
02
04
06
08
88
90
92
94
96
98
00
02
04
06
08
88
90
92
94
96
98
00
02
04
06
08
88
90
92
94
96
98
00
02
04
06
08
Italy
100%
50
80%
40
60%
30
40%
20
20%
10
0%
0
88
90
92
94
96
98
00
02
04
06
08
Portugal
100%
50
80%
40
60%
30
40%
20
20%
10
0%
0
88
90
92
94
96
98
00
02
04
06
08
Spain
100%
50
80%
40
60%
30
40%
20
20%
10
0
0%
88
90
92
94
96
98
00
02
04
06
08
Non-manufactured Goods
Low Technology M anufactures
M edium-low Technology M anufactures
M edium-high Technology M anufactures
High Technology M anufactures
24