The ECB’s enhanced effective exchange rates and harmonised competitiveness indicators : an updated weighting scheme including trade in services.
Date: 2024-11-07
Institutions: European Central Bank
Subjects: competitiveness, exchange rate, macroeconomics, trading operation, world economy

Martin Schmitz, Andreas Dietrich, 
Rémy Brisson 

Statistics Paper Series 

The ECB’s enhanced effective 
exchange rates and harmonised 
competitiveness indicators 

An updated weighting scheme including trade in 
services 

No 49 

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

 
 
 
 
Contents 

Abstract 

1 

2 

Introduction 

Methodology for calculating the ECB’s effective exchange rates 

2.1 

2.2 

2.3 

2.4 

The nominal effective exchange rate 

The real effective exchange rate and its deflators 

Trading partners and baskets of currencies 

Trade basis 

2.5  Weighting method 

2.6  Overview of the availability of the ECB’s EERs/HCIs 

3 

Bilateral trade in manufactured goods and services 

3.1 

Initial availability of reported data 

3.2  Extending the coverage with mirror data 

3.3  Addressing asymmetries with balanced values 

3.4  Completing the dataset with a gravity model 

3.5 

Final dataset 

3.6  Specific issues of trade in services data 

4 

Enhanced indicators for competitiveness analysis 

4.1 

Trade weights 

4.2  EERs and HCIs 

5 

Conclusions 

References 

Annex 

A: Summary trade flows data sources 

B: Overview of data availability – manufactured goods 

C: Overview of data availability – services 

D: Symmetry indices 

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Abstract 

The  nominal  effective  exchange  rate  (EER)  of  a  currency  is  an  index  of  the  trade-
weighted average of its bilateral exchange rates vis-à-vis the currencies of selected 
trading  partners,  while  the  real  EER  is  derived  by  adjusting  the  nominal  index  for 
relative  prices  or  costs.  The  nominal  EER  provides  a  summary  measure  of  a 
currency’s external value, while the real EER is the most commonly used indicator of 
the  international  price  and  cost  competitiveness  of  an  economy. Additionally,  for  all 
individual  euro  area  countries,  harmonised  competitiveness  indicators  (HCIs)  are 
published by the European Central Bank (ECB) based on the same methodology as 
the euro EERs. This paper describes how the calculation of the ECB’s EERs and HCIs 
has been enhanced to take into account in the underlying trade weights the evolution 
of  international  trade  linkages  and,  in  particular,  the  growing  importance  of  trade  in 
services.  The  paper  includes  an  in-depth  description  of  the  methodology  used  to 
calculate these enhanced EERs and HCIs. In particular, it presents how to overcome 
the challenges arising from the inclusion of services trade, foremost in terms of data 
availability,  with  imputation  and  estimation  techniques.  Importantly,  the  ECB’s  well-
established methodology – which in particular accounts for competition faced by euro 
area  exporters  in  third  markets  –  did  not  have  to  be  changed  with  the  inclusion  of 
services  trade.  Finally,  the  paper  provides  some  evidence  on  the  usefulness  of  the 
enhanced indicators for policymakers, economic analysts and the public at large. 

JEL classification: C82, F10, F17, F30, F31, F40.  

Keywords: competitiveness, effective exchange rate (EER), harmonised 
competitiveness indicator (HCI), nominal effective exchange rate (NEER), real 
effective exchange rate (REER), services trade, trade weights, gravity model. 

ECB Statistics Paper Series No 49 

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1 

Introduction 

Nominal effective exchange rates (NEERs) are indices based on the weighted average 
of  bilateral  nominal  exchange  rates  vis-à-vis  the  currencies  of  selected  trading 
partners, while real effective exchange rates (REERs) are derived by adjusting these 
nominal indices for relative price and cost developments between a given economy 
and its trading partners. Similarly to NEERs and REERs, harmonised competitiveness 
indicators (HCIs) are computed for individual euro area Member States.1 

NEERs provide a summary measure of a currency’s value, while REERs are the most 
commonly used indicator of a country’s international price and cost competitiveness 
and represent a key component of an economy’s external balance assessment. The 
importance of REERs is reflected, for instance, by their inclusion in the scoreboard of 
the European Commission’s macroeconomic imbalance procedure (MIP) and in the 
International Monetary Fund’s (IMF’s) external balance assessment (EBA). 

Since 2021 the EERs and HCIs published by the European Central Bank (ECB) have 
been compiled by the Deutsche Bundesbank in close cooperation with the ECB.2 The 
EERs and HCIs were originally solely based on manufacturing trade weights (Schmitz 
et  al.,  2012),  before  the  weighting  scheme  was  expanded  in  2020  to  also  include 
services trade (Fidora and Schmitz, 2020).3 While manufacturing trade still accounts 
for  the  largest  part  of  euro  area  trade,  trade  in  services  has  gained  importance  in 
recent decades: extra-euro area services trade relative to GDP has more than doubled 
since 2001 and accounted for around 30% of total extra-euro area trade at the end of 
2021  (Chart  1,  panels  a  and  b).  Having  said  this,  the  role  of  the  services  sector  in 
international trade is even larger in value added terms than suggested by gross trade 
data (Johnson and Noguera, 2012). The growth in services trade reflects the fact that 
a  wide  range  of  heterogeneous  services  are  nowadays  traded  internationally,  in 
particular those affected by new technologies such as ICT services or other business 
services such as consultancy, accounting and legal (Chart 2). Due to digitalisation and 
increased  virtual  proximity  between  countries,  such  services  can  increasingly  be 
delivered at distance without physical interaction between producers and customers 
(Hellmanzik and Schmitz, 2015) which also gives rise to trade in intermediate services 
is  affected  by  price 
(Baldwin,  2022).  Moreover,  euro  area  services 
competitiveness  developments 
to  manufactured  goods 
(Christodoulopoulou and Tkacevs, 2016). For all these reasons it is crucial to include 
services  trade  in  the  weighting  schemes  underlying  the  effective  exchange  rates 
(EERs).  

in  a  similar 

fashion 

trade 

1   While intra-euro area trade flows are not considered in the calculation of trade weights for euro EERs, 
they are used for HCIs, which are constructed from the perspective of individual euro area Member 
States. Hence, in the HCIs all other euro area countries are considered as trading partners. 

2   Since 2021 the Deutsche Bundesbank has been responsible for the production of daily, monthly and 
quarterly EERs/HCIs, the production of the underlying trade weights and user support. The ECB 
remains involved in the future development of EERs and HCIs, including methodological changes, 
publishes the data on its website and provides the underlying dataset on cost and price deflators. 
3   The ECB’s weighting scheme does not include trade in agricultural products, raw materials and energy 

products (see Section 2.4). 

ECB Statistics Paper Series No 49 

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Chart 1 
Increasing importance of euro area services trade 

a) In absolute terms… 

b) … and relative terms 

(services trade as a percentage of GDP, four-quarter moving sum) 

(services trade as a percentage of total trade, four-quarter moving sum) 

Source: ECB.  

Source: ECB. 

The  inclusion  of  services  is  particularly  important  for  countries  where  patterns  in 
services trade differ significantly from those observed in manufactured goods. Chart 3 
reveals  substantial  country  heterogeneity  across  the  euro  area  in  terms  of  trade 
composition.  Cyprus,  Ireland,  Luxembourg  and  Malta  exhibit  a  services  share 
exceeding 60% of total trade (when considering manufacturing plus services trade), 
while at the other end of the spectrum this share drops below 25% for countries like 
Belgium, Germany, Italy, Slovakia and Slovenia. 

Chart 2 
New technologies affect the composition of euro area 
services trade 

Chart 3 
Country heterogeneity in trade composition 

 (services exports as a percentage of GDP) 

 (shares as percentage of manufacturing plus services trade, exports and imports 
combined, 2019-21) 

Source: ECB.  

Source: ECB. 

ECB Statistics Paper Series No 49 

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222426283032343620012003200520072009201120132015201720192021Services share in exportsServices share in imports020406080100ServicesManufacturing345678920012003200520072009201120132015201720192021Services exports (% of GDP)Services imports (% of GDP)0.00.51.01.52.02.5TransportTravelInsuranceandfinancialICTOtherbusinessInt.propertyOther20092021 
 
 
 
 
 
 
Previous efforts to include services trade in the weighting scheme underlying  EERs 
were undertaken by different institutions. The Federal Reserve added services to its 
trade weights in 2019. However, this change was combined with a simplification of the 
methodological approach by switching to simple trade weights, i.e. moving away from 
the calculation of third-market effects (Beschwitz et al., 2019). Such a simplification 
implies a significant loss of information on the presence of global competition among 
the considered group of trading partners. Similarly, the Bank of England uses bilateral 
services trade data without accounting for third-market effects (Lynch and Whitaker, 
2004), while the IMF includes trade in services by using tourism flows for countries 
where these are sizeable (Bayoumi et al., 2005). As regards the euro area, Schmitz 
(2012) constructed experimental EERs/HCIs for the euro and 17 euro area Member 
States based on bilateral services trade with up to 36 partner countries, for the periods 
2004-06 and 2007-09. 

Insufficient data availability previously precluded the inclusion of services trade in the 
weighting scheme underlying the computation of the ECB’s official EERs. Significant 
progress in terms of data availability has been made in recent years, so that the data 
coverage now allows for the construction of enhanced EERs and HCIs based on both 
manufacturing and services trade, while remaining fully in line with the existing state-
of-the-art methodology (i.e. including third-market effects). 

This paper presents in Section 2 the methodology used for computing the enhanced 
EERs and HCIs including trade in services, while Section 3 focuses on the collection 
and processing of trade data, as this step brings particularly complex challenges when 
it comes to services. The paper details in Section 4 the main reasons why the new 
indicators  provide  an  enhanced  assessment  of  international  competitiveness 
developments across countries and time for policymakers, economic analysts and the 
public at large, before concluding in Section 5. 

ECB Statistics Paper Series No 49 

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2 

Methodology for calculating the ECB’s 
effective exchange rates 

This  section  describes  the  methodology  behind  the  EERs  provided  by  the  ECB, 
building on Buldorini et al. (2002) and Schmitz et al. (2012). HCIs for individual euro 
area  Member  States  are  constructed  using  the  same  method.  This  section  first 
presents the formula to compute NEERs, followed by the formula for REERs and a 
description  of  the  associated  deflators. The  different  groups  of  trading  partners  are 
then introduced, with an emphasis on the most recent changes made to the largest 
group of partner countries. The following two subsections deal with the methodology 
of  the  underlying  trade  weights  and  in  particular  the  inclusion  of  trade  in  services. 
Finally, an overview of the EERs and HCIs available at the ECB is provided.  

2.1 

The nominal effective exchange rate 

NEERs  are  calculated  as  the  geometric  weighted  average  of  a  basket  of  bilateral 
nominal exchange rates. In the case of the euro, this can be formalised in the following 
way:  

𝑁
𝑁𝐸𝐸𝑅𝑡 = ∏(𝑒𝑖,𝑒𝑢𝑟𝑜
𝑖=1

𝑡

𝑤𝑖
)

(1) 

where 𝑁 stands for the number of countries in the reference group of trading partners, 
𝑡
 is an index of the average exchange rate of the currency of partner country 𝑖 
𝑒𝑖,𝑒𝑢𝑟𝑜
vis-à-vis the euro in period 𝑡 (expressed in terms of foreign currency per euro) and 𝑤𝑖 
is the trade weight assigned to the trading partner 𝑖.  

It  is  important  to  note  that  the  underlying  trade  weights  are  computed  for  non-
overlapping three-year periods. This implies that in equation (1) above, a given trading 
partner does not carry a single trade weight, but differing weights for each of the non-
overlapping  three-year  periods  (1995-97,  1998-2000,  2001-03,  2004-06,  2007-09, 
2010-12,  2013-15,  2016-18  and  2019-21).  Hence,  the  following  process  is 
implemented to obtain the final indicators.  

• 

• 

• 

Computation  for  the  different  sets  of  weights:  Equation  (1)  is  first  applied 
separately for each set of trade weights (𝑤𝑖
Chain-linking: The different pieces are subsequently chain-linked at the end of 
each of the three-year periods, starting from the one based on the trade weights 
for 1995-97.  
Rebasing: Finally, the series is rebased to the first quarter of 1999 (i.e. Q1 1999 
= 100).  

1995−97, … , 𝑤𝑖

2019−21). 

ECB Statistics Paper Series No 49 

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2.2 

The real effective exchange rate and its deflators 

The calculation framework  for REERs is very similar to  that of NEERs described in 
Section  2.1. The  only  difference  lies  in  the  fact  that  the  bilateral  nominal  exchange 
rates are deflated using relative price or cost measures. It can therefore be formalised 
as follows, using the example of the euro:  

𝑁

𝑅𝐸𝐸𝑅𝑡 = ∏ (

𝑖=1

𝑡
𝑡
𝑑𝑒𝑢𝑟𝑜
𝑒𝑖,𝑒𝑢𝑟𝑜
𝑡
𝑑𝑖

𝑤𝑖

)

        (2) 

where 𝑁 stands for the number of countries in the reference group of trading partners, 
𝑡
 is an index of the average exchange rate of the currency of partner country 𝑖 
𝑒𝑖,𝑒𝑢𝑟𝑜
𝑡 are, respectively, the deflators for the euro 
vis-à-vis the euro in period 𝑡, 𝑑𝑒𝑢𝑟𝑜
area and partner country 𝑖, and 𝑤𝑖 is the trade weight assigned to the trading partner 𝑖.  

 and 𝑑𝑖

𝑡

The rest of the process (computation for the different sets of weights, chain-linking and 
rebasing) is the same as for NEERs. 

One can easily infer from equation (2) that the choice of deflators is crucial. The aim 
is  to select  an  indicator that  has  the  most  direct link  with  international  price  or cost 
competitiveness.  However, 
the  concept  of 
competitiveness  is  measured,  so  that  different  options  are  offered  to  economic 
analysts and policymakers. The deflators used to construct REERs are taken from the 
ECB’s deflator database as described in Schmitz et al. (2012).  

this  depends  ultimately  on  how 

• 

• 

Consumer price index (CPI): The most commonly used deflator is the  CPI. It 
has  the  advantage  of  timely,  monthly  and  broad  data  availability  as  well  as 
comparability (being defined in a similar manner by many countries, in particular 
among  industrial  economies).  On  the  downside,  however,  CPI  baskets  also 
include  non-tradable  goods  and  services,  while  excluding  capital  and 
international 
intermediate  goods.  This  can  hinder 
competitiveness,  particularly  if  there  are  significant  differences  in  productivity 
between tradable and non-tradable sectors. Moreover, consumer prices can be 
distorted due to taxes and subsidies.  

the  analysis  of 

Producer price index (PPI): The PPI, meanwhile, includes industrial products 
and intermediate goods that can be traded internationally, while excluding retail 
sales. Consequently, PPIs are viewed as a reasonable proxy for tradable goods 
prices.  However,  they  exclude  services  prices  and  their  composition  and 
compilation vary considerably across countries. 

•  GDP  deflator  (GDPD): The  GDPD  also  focuses  on  the production  side  of  the 
economy. It has the advantage of being comparable across countries and takes 
into account inputs into a firm’s production from other sectors, including services. 
GDPDs  are  considered  to  be  the  relative  price  measure  that  best  reflects  the 
“price of value added”, as they are the most direct summary measure of capital 
and  labour  costs.  On  the  negative  side,  they  include  non-tradable  goods  and 
suffer from possible distortions stemming from taxes and subsidies. Furthermore, 

ECB Statistics Paper Series No 49 

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GDPDs are published at a lower frequency (quarterly) compared with CPIs and 
PPIs. 

• 

Unit labour costs in the total economy (ULCT) and unit labour costs in the 
manufacturing sector (ULCM): Turning to cost measures, ULCM is often used 
as  a  proxy  for  unit  labour  costs  in  the  tradable  goods  sector.  This  popular 
measure of competitiveness may, however, be too narrow a concept as it only 
focuses on a certain sector of the economy. ULCT circumvents this problem but 
has  the  disadvantage  that  it  also  reflects  costs  in  non-tradable  goods  and 
services. In general, unit labour cost measures are rather volatile and sometimes 
subject  to  significant  data  revisions.  As  with  GDPDs,  unit  labour  costs  are 
published less frequently (quarterly) than CPIs and PPIs. In addition, they do not 
cover all costs incurred by firms (e.g. the cost of capital, distribution costs and 
taxes).  Moreover,  factor  substitution  may  affect  these  indicators  without 
necessarily resulting in a change in productivity. Finally, available cost measures 
are typically more affected by data quality issues than price measures.  

Among  the  deflators,  a  distinction  can  therefore  be  made  between  “broad”  and 
“narrow” concepts. Broad deflators capture price and cost developments in the whole 
economy  and  include  the  CPI,  GDPD  and  ULCT.4 As  regards  narrow  deflators,  the 
ECB’s database comprises the PPI and ULCM.5 These two deflators measure price 
and cost developments in the manufacturing sector.  

In the same vein, one could envisage the use of services-specific deflators such as 
the  services  component  of  the  CPI  and  unit  labour  costs  in  the  services  sector. 
Currently, however, these deflators are not available for a sufficiently large group of 
countries.  

2.3 

Trading partners and baskets of currencies 

The EERs of the ECB are currently computed against three different groups of trading 
partners from the perspective of the euro area: 

• 

• 

4  

a narrow group of 12 partner countries (EER-12), including Australia, Canada, 
Denmark,  Hong  Kong,  Japan,  South  Korea,  Norway,  Singapore,  Sweden, 
Switzerland, the United Kingdom and the United States; 

an extended group of 18 partner countries (EER-18), comprising the EER-12 
plus  China  and  the  five  non-euro  area  EU  Member  States  not  included  in  the 
EER-12 (Bulgaria, the Czech Republic, Hungary, Poland and Romania); 

In addition, the Deutsche Bundesbank uses deflators of total sales, which take into account the prices 
of imported goods, in addition to domestic value added, and therefore reflect price and cost 
developments to an even broader extent than GDP deflators (see Deutsche Bundesbank, 2016; 
Fischer et al., 2017; and Deutsche Bundesbank, 2023). 

5   Alternatively, one could consider export prices as a direct measure of prices in foreign markets. 

However, export price indices suffer from disadvantages in terms of methodology and data availability, 
which complicate their use for competitiveness analysis (see Schmitz et al., 2012). 

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• 

a broad group of 41 partner countries (EER-41), encompassing the EER-18 
plus Algeria, Argentina, Brazil, Chile, Colombia, Iceland, India, Indonesia, Israel, 
Malaysia, Mexico, Morocco, New Zealand, Peru, the Philippines, Russia, Saudi 
Arabia,  South Africa,  Taiwan,  Thailand,  Türkiye,  Ukraine  and  the  United Arab 
Emirates. 

In addition, the HCIs of individual euro area Member States also consider the 19 other 
euro area countries as trading partners.  

The  composition  of  the  broad  group  of  trading  partners  was  adjusted  in  July  2020, 
resulting in an expansion from 37 to 41 countries.  

• 

• 

Venezuela was excluded due to difficulties in measuring the exchange rate of the 
Venezuelan  bolívar and  Venezuelan  consumer prices.  Moreover,  its  trade  with 
the euro area has decreased dramatically in recent years.6 

Colombia, Peru, Saudi Arabia, Ukraine and the United Arab Emirates were added 
since, at the time of the analysis,  they met the requirements used by the ECB 
when assessing the addition of new countries to the list of partner countries (i.e. 
presence  in  the  largest  economies  not  yet  included  in  terms  of  GDP;  sizeable 
trade flows with the euro area; availability of reliable and timely data on exchange 
rates, bilateral trade flows, consumer prices and GDP, Chart 4).  

Chart 4 
Largest economies not included in the old broad group of trading partners 

 (GDP in 2019, current prices, USD billions) 

Sources: ECB and IMF. 

As further detailed in Section 2.6, it is important to note that nominal, CPI-deflated and 
GDP-deflated indices are available for all three groups of trading partners, while PPI-
deflated, ULCM-deflated and ULCT-deflated indicators cannot currently be computed 
against  the  broad  group  of  partner  countries  (EER-41)  due  to  data  availability 
constraints.  

6   The Bank for International Settlements (BIS) and the Federal Reserve followed the same approach in 

recent years and excluded Venezuela from the list of trading partners. 

ECB Statistics Paper Series No 49 

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0100200300400500600700800900Notincluded in the broad group of trading partnersIncluded in the broad group of trading partners 
 
2.4 

Trade basis  

their  purpose  of 
EERs  require  an  appropriate  weighting  scheme 
comprehensively summarising the developments of a currency. Hence, it is common 
practice  to  use  bilateral  trade  to  determine  the  weight  of  each  partner  country’s 
currency,  with  larger  weights  being  assigned  to  countries  accounting  for  a  higher 
proportion of an economy’s external trade.  

to  serve 

Trade flows can be broadly classified into three main categories: manufactured goods, 
commodities  and  services. As  outlined  by  Buldorini  et  al.  (2002)  and  Schmitz  et  al. 
(2012), the EERs and HCIs of the ECB were for years solely based on trade weights 
in  manufactured  goods,  as  defined  in  Sections  5  to  8  of  the  Standard  International 
Trade Classification (SITC).7 This choice reflects the exclusion of commodities such 
as agricultural products, raw materials and energy products in the weighting scheme. 
Indeed,  these  items  are  considered  homogeneous  goods  whose  prices  are 
determined  in  global  markets  without  being  influenced  by  the  competitiveness  of 
individual  countries.  Their  inclusion  would  distort  any  competitiveness  analysis,  as 
such goods are often heavily regulated or subsidised.  

The omission of services on the other hand was driven by data availability constraints. 
From a conceptual point of view, trade in services should be included, considering its 
increasing  share  and  role  in  global  market  competition  (Section  1).  Improved  data 
coverage  made  the  full  inclusion  of  services  finally  feasible  in  2020,  while  also 
remaining fully in line with the existing state-of-the-art ECB methodology (i.e. including 
third-market  effects).  Using  the  methods  shown  in  this  paper,  services  were 
incorporated in the weighting scheme of the ECB’s EERs in July 2020.  

The  ECB’s  enhanced  EERs  and  HCIs  reflect  this  important  adjustment  for  their 
broadest  indicators  (i.e.  nominal  and  deflated  by  CPI,  GDPD  and  ULCT),  while 
manufacturing-oriented indices (i.e. deflated by PPI and ULCM) continue to be based 
on  manufacturing  weights.  If  services-specific  deflators  are  used  in  the  future,  they 
could be similarly based on services-only weights. To summarise: 

Combined trade weights (covering manufacturing and services trade) are used for: 

• 

• 

nominal EERs/HCIs; 

EERs/HCIs deflated by CPI, GDPD and ULCT. 

Manufacturing trade weights are used for: 

• 

EERs/HCIs deflated by PPI and ULCM. 

7   These categories comprise chemicals and related products, manufactured goods, machinery and 

transport equipment, and miscellaneous manufactured articles. 

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2.5 

Weighting method 

The ECB updates the trade weights underlying the calculation of its EER indices every 
three years in order to capture medium-term changes in the pattern of euro area trade 
in  a  timely  fashion.  The  general  framework  for  the  calculation  follows  the  BIS 
methodology  presented  in  Turner  and  Van’t  dack  (1993),  which  was  subsequently 
adapted to the euro area by Buldorini et al. (2002) and Schmitz et al. (2012).  

For the sake of clarity, the euro area perspective is used throughout the description 
outlined below (i.e. the calculation is presented for the trade weights required for the 
EERs  of  the  euro),  but  the  same  approach  is  used  for all  countries  included  in  the 
EER-41 group of trading partners (i.e. for the trade weights required for their respective 
EERs) as well as for each euro area Member State (i.e. for the trade weights required 
for their respective HCIs). Moreover, the broad group of trading partners (EER-41) is 
used as a reference, since the weights of the two narrower groups of partner countries 
(EER-12 and EER-18) are simply obtained by rescaling the indicators generated for 
the broad group.  

The weighting scheme combines information on both imports and exports, for which 
separate weights are initially created, following different methods. The logic pertaining 
to this framework is detailed below and further illustrated by an example of a simplified 
basket of euro area trading partners consisting of only two countries, country A and 
country B, and the rest of the world. The example focuses on services trade, but the 
same approach is applied to obtain manufacturing trade weights. 

The import weights are calculated as simple shares  of euro area imports from the 
broad group of trading partners (i.e. not including the rest of the world). In the case of 
services, the import weight of each partner country i can be expressed as follows:  

𝑤𝑖

𝑚 𝑆𝑒𝑟 = 𝑚𝑖

𝑁
𝑆𝑒𝑟/ ∑ 𝑚𝑘
𝑘=1

𝑆𝑒𝑟,

𝑖 = 1, 2, … , 𝑁 

        (3) 

𝑆𝑒𝑟𝑣  and  𝑚𝑘

𝑆𝑒𝑟  denote  gross  services  import  flows  into  the  euro  area  from 
where  𝑚𝑖
country i and k respectively during the reference period and 𝑁 stands for the number 
of countries in the largest group of trading partners. The same approach is applied to 
obtain manufacturing import weights. In the example, this yields 1,120 / (1,120 + 480) 
= 70% for country A and 480 / (1,120 + 480) = 30% for country B.8 

The calculation of export weights is more complex, as they are “double-weighted” to 
account  for  third-market  effects.  This  implies  that  they  are  designed  to  capture  the 
competition faced by euro area exporters, in each foreign market, from both domestic 
producers and third countries. Put simply, when it comes to euro area exports, a global 
exporter  present  in  many  countries  is  particularly  relevant,  much  more  so  than  the 
simple export flows from the euro area to this country would imply.  

8   All values in this example are expressed in euro (unless otherwise mentioned). For ease of exposition, 

denomination in euro is not explicitly included in the example. 

ECB Statistics Paper Series No 49 

11 

 
 
 
 
 
 
 
 
Table 1 

Trade weights in practice 

Supply matrix (including rest of the world) 

Exports by country 
 to market → 

Euro 
area 

Country A 

Country B 

1,000 

800 

1,120 

480 

600 

400 

Other  
countries 

400 

100 

300 

Euro area 

Country A 

Country B 

Domestic 
production 

2,500 

1,200 

1,600 

EXCLUDED 

Import weights 

Share of total euro 
area imports 

1,120 /  
(1,120 + 480)  
= 70% 

480 /  
(1,120 + 480)  
= 30% 

EXCLUDED 

Simple export weights (including rest of the world) 

Share of total euro 
area exports 

1,000 /  
(1,000 + 600 + 
400)  
= 50% 

600 /  
(1,000 + 600 + 
400)  
= 30% 

400 /  
(1,000 + 600 + 
400) 
 = 20% 

Intensity of competition – supply structure matrix 

Supply share of 
country A in 
market h 
Supply share of 
country B in 
market h 

Euro area simple 
export weights 
adjusted to account 
for third-market 
effects 

Euro area trade 
weights  

1,200 /  
(1,200 + 800)  
= 60% 
800 /  
(1,200 + 800) 
= 40% 

400 /  
(400 + 1,600)  
= 20% 
1,600 /  
(400 + 1,600) 
= 80% 

100 /  
(100 + 300) 
= 25% 
300 /  
(100 + 300) 
= 75% 

Double export weights 

(0.5 x 0.6) 
+(0.3 x 0.2) 
+(0.2 x 0.25)  
= 41% 

(0.5 x 0.4) 
+(0.3 x 0.8) 
+(0.2 x 0.75)  
= 59% 

Trade weights 

((0.7 x 1,600) 
+ (0.41 x 
2,000)) / 
(1,600 + 
2,000) 
= 53.89% 

((0.3 x 1,600) 
+ (0.59 x 
2,000)) / 
(1,600 + 
2,000) 
= 46.11% 

Hence, one cannot apply the method used for imports and compute the export weights 
as simple shares of euro area exports (i.e. not including the rest of the world). In the 
example, this would result in country A having a much larger weight than country B: 
country A would have a weight of 1,000 / (1,000 + 600) = 62.5%, while country B would 

ECB Statistics Paper Series No 49 

12 

 
 
 
 
 
 
 
 
 
 
 
 
have a weight of 600 / (1,000 + 600) = 37.5%. This would not accurately represent the 
relevance of each country for euro area exports. 

Although a very large percentage (50%) of euro area total exports to the world go to 
country A, it can be seen from the supply structure matrix that in country A, only 60% 
(1,200) of the total supply of products is due to domestic production and 40% (800) is 
accounted for by imports from country B. Moreover, while the share of total euro area 
exports  (including  the  rest  of  the  world)  to  country  B  is  significantly  lower  (30%), 
country  B  has  a  much  larger  share  of  domestic  production  at  80%  (1,600).  Hence, 
country A  generates  relatively  weaker  competition  in  country  B  than  vice  versa.  In 
addition, country B creates much stronger competition for the euro area than country 
A  in  the  rest  of  the  world.  For  these  reasons,  the  weight  of  country  B  should  be 
relatively  larger  than  in  the  simple  export  weights,  which  is  reflected  in  the  double 
export weights.  

As shown in the example, two types of data are required to compute the double export 
weights:  the  simple  export  weights  (including  the  rest  of  the  world)  and  the  supply 
structure  matrix.  The  export  weights  also  take  into  account  each  trading  partner’s 
bilateral exports to different foreign markets. In this way, the trade weights reflect the 
competition faced by euro area exporters in each given foreign market from exporters 
of the countries included in the group of trading partners. For this purpose, a distinction 
is made between 𝑁, the trading partners, and 𝑅, the group of countries referred to as 
the  rest  of  the  world  (whereby  𝐻 = 𝑁 + 𝑅,  with 𝐻  being  the  total  number  of  export 
markets in the world). In order to capture the effect of competition faced by euro area 
exporters  from  domestic  producers  in  the  economies  of  the  trading  partners,  the 
domestic supply in these countries is included in the calculation of export weights. It 
is assumed that the euro area and the 𝑁 competitor countries are the only suppliers 
of services in the 𝑅 countries. Consequently, the calculations neither include exports 
from the rest of the world to the 𝑁 trading partners nor the rest of the world’s domestic 
output. 

The simple export weights (including the rest of the world) are first calculated as the 
share of each market 𝑗 in total euro area exports to the world: 

𝑤(𝑠𝑖𝑚𝑝𝑙𝑒)𝑗

𝑥 𝑆𝑒𝑟 = 𝑥𝑗

𝐻
𝑆𝑒𝑟/ ∑ 𝑥𝑘
𝑘=1

𝑆𝑒𝑟,

𝑗 = 1, 2, … , 𝐻 

        (4) 

𝑆𝑒𝑟 and 𝑥𝑘

𝑆𝑒𝑟 denote the gross services export flows of the euro area to markets 
where 𝑥𝑗
𝑗 and 𝑘 respectively in the reference period, while 𝐻 represents the total number of 
export markets in the world.  

ECB Statistics Paper Series No 49 

13 

 
 
 
 
 
 
 
 
 
The simple export weights are subsequently adjusted to capture third-market effects, 
which yields the double export weights of each partner country 𝑖: 

𝐻

𝑤(𝑑𝑜𝑢𝑏𝑙𝑒)𝑖

𝑥 𝑆𝑒𝑟 = ∑(𝑆(𝑠ℎ𝑎𝑟𝑒)𝑖,𝑗

𝑆𝑒𝑟  𝑤(𝑠𝑖𝑚𝑝𝑙𝑒)𝑗

𝑥 𝑆𝑒𝑟)

𝑗=1

,

𝑖 = 1,2 … , 𝑁 

        (5) 

where 𝑆(𝑠ℎ𝑎𝑟𝑒)𝑖,𝑗

𝑆𝑒𝑟 is the share of country 𝑖 in the services supply of market 𝑗.  

The shares are obtained in the following way: 

𝑆(𝑠ℎ𝑎𝑟𝑒)𝑖,𝑗

𝑆𝑒𝑟 = 𝑆𝑖,𝑗

𝑁
𝑆𝑒𝑟
𝑆𝑒𝑟/ ∑ 𝑆𝑘,𝑗
𝑘=1

        (6) 

𝑆𝑒𝑟  (for  𝑖𝑗, 𝑖 = 1,2, … , 𝑁,  and  𝑗 = 1,2, . . , 𝐻)  denotes  the  gross  services 
where  𝑆𝑖,𝑗
𝑆𝑒𝑟 (for 𝑖 = 1,2, … , 𝑁) represents the 
export flows from country 𝑖 to market 𝑗 and 𝑆𝑖,𝑖
𝑆𝑒𝑟 serves as 
gross services output of country 𝑖 that is sold in its domestic market. 𝑆𝑖,𝑖
a proxy for the gross value of the domestically produced supply of services. For each 
country, it is obtained by adding services imports to the value added of the services 
sector  (using  data  from  the  United  Nations,  Eurostat  and  the  Organisation  for 
Economic Co-operation and Development (OECD), with the exception of Taiwan for 
which  the  national  statistical  institute  was  the  only  available  source),  and  then 
subtracting services exports. Imports of services are used as a proxy for both domestic 
inputs stemming from other sectors and imported inputs.  

According  to  Turner  and  Van’t  dack  (1993),  this  method  can  be  used  to  obtain  an 
estimate  of  the  gross  value  of  services  produced  and  sold  domestically  which  is 
comparable  with  international  trade  data  that  are  also  expressed  in  similar  (gross 
value)  terms.  The  approach  to  estimate  domestic  production,  although  well 
established in the literature, can in rare cases lead to negative domestic supply values, 
incompatible with the computation process for double export weights. For this reason, 
corrections were implemented in such cases, bringing the values to zero. As previously 
explained and illustrated in Table 1, the total supply (of each market 𝑗) presented in 
equation (6) (∑
) does not incorporate the rest of the world’s domestic output 

𝑁
𝑘=1

𝑆𝑒𝑟
𝑆𝑘,𝑗

and exports (countries outside the broad group of partner countries).  

The overall weight of each partner country 𝑖 in the broad group of trading partners is 
obtained as the weighted average of export and import weights, i.e.: 

𝑚𝑆𝑒𝑟

𝑤𝑖

𝑆𝑒𝑟 = (

𝑥 𝑆𝑒𝑟 + 𝑚𝑆𝑒𝑟) 𝑤𝑖

𝑚𝑆𝑒𝑟

𝑥 𝑆𝑒𝑟

+ (

𝑥 𝑆𝑒𝑟 + 𝑚𝑆𝑒𝑟) 𝑤(𝑑𝑜𝑢𝑏𝑙𝑒)𝑖 

𝑥𝑆𝑒𝑟

 ,

𝑖 = 1,2, … , 𝑁 

        (7) 

𝑚𝑆𝑒𝑟

𝑥𝑆𝑒𝑟

 and 𝑤(𝑑𝑜𝑢𝑏𝑙𝑒)𝑖 

 are partner country 𝑖’s import and export weights 
where 𝑤𝑖
respectively  and  𝑚𝑆𝑒𝑟  and  𝑥 𝑆𝑒𝑟  are  total  imports  and  exports  by  the  euro  area 
respectively.  

ECB Statistics Paper Series No 49 

14 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The trade weights synthesising manufactured goods and services trade information 
are derived by combining the two types of weights previously computed:  

𝑤𝑖

𝐶𝑜𝑚𝑏𝑖𝑛𝑒𝑑 = 

𝑥 𝑆𝑒𝑟 + 𝑚𝑆𝑒𝑟

𝑥 𝑆𝑒𝑟 + 𝑚𝑆𝑒𝑟 + 𝑥𝑀𝑎𝑛 + 𝑚𝑀𝑎𝑛) 𝑤𝑖
(

𝑥𝑀𝑎𝑛 + 𝑚𝑀𝑎𝑛

𝑆𝑒𝑟 + (

𝑥 𝑆𝑒𝑟 + 𝑚𝑆𝑒𝑟 + 𝑥𝑀𝑎𝑛 + 𝑚𝑀𝑎𝑛) 𝑤𝑖

,

𝑖 = 1,2, … , 𝑁 

𝑀𝑎𝑛  

        (8) 

The coefficients used to determine the weight of each trade basis in the final combined 
scheme are therefore determined in line with the structure of each analysed economy, 
i.e. reflecting if it is more manufacturing-oriented or services-oriented. In the case of 
euro area countries  (Table  2),  strong  heterogeneity  is  visible,  with  certain  countries 
such as Cyprus and Luxembourg showing services shares  well above average and 
consequently primarily affected by the enhanced weighting scheme. 

Table 2 
Share of services and manufacturing in combined trade flows (2019-21) 

(percentage of trade flows, imports and exports combined) 

Country 

Services 

Manufacturing 

Austria 

Belgium 

Cyprus 

Germany 

Estonia 

Spain 

Finland 

France 

Greece 

Croatia 

Ireland 

Italy 

Lithuania 

Luxembourg 

Latvia 

Malta 

Netherlands 

Portugal 

Slovenia 

Slovakia 

Euro area 

27.2 

24.5 

75.5 

23.6 

33.3 

27.9 

37.4 

33.2 

45.2 

33.5 

64.3 

20.7 

29.6 

85.2 

28.3 

65.7 

32.1 

28.8 

16.0 

12.5 

30.9 

72.8 

75.5 

24.6 

76.4 

66.7 

72.1 

62.6 

66.8 

54.8 

66.5 

35.7 

79.3 

70.4 

14.8 

71.7 

34.3 

67.9 

71.2 

84.0 

87.5 

69.1 

Source: ECB. 

ECB Statistics Paper Series No 49 

15 

 
 
 
 
 
 
Once the weighting scheme of the broad group of trading partners is produced, the 
trade  weights  of  the  two  smaller  groups  of  partner  countries  are  obtained  by 
proportionally rescaling the weights of the broad group under equations (7) and (8).  

For the narrow group of countries (EER-12): 

𝑤𝑖(𝑛𝑎𝑟𝑟𝑜𝑤) =

𝑤𝑖(𝑏𝑟𝑜𝑎𝑑)
12
𝑘=1

𝑤𝑖(𝑏𝑟𝑜𝑎𝑑)

 ,

∑

𝑖 = 1,2, … ,12 

For the extended group of countries (EER-18): 

𝑤𝑖(𝑒𝑥𝑡𝑒𝑛𝑑𝑒𝑑) =

𝑤𝑖(𝑏𝑟𝑜𝑎𝑑)
18
𝑘=1

𝑤𝑖(𝑏𝑟𝑜𝑎𝑑)

∑

 ,

𝑖 = 1,2, … ,18 

        (9) 

        (10) 

2.6 

Overview of the availability of the ECB’s EERs/HCIs  

The ECB publishes, in cooperation with the Deutsche Bundesbank, EERs for the euro 
and for the currencies of each of the countries present in the different groups of trading 
partners (e.g. the NEER of the US dollar against the EER-12, EER-18 and EER-41). 
The availability of EERs provided by the ECB is summarised in Table 3.  

Table 3 
Availability of effective exchange rates (EERs) 

Nominal 
(NEER) 

Real (REER) deflated by … 

CPI 1 

PPI 2 

GDPD 3 

ULCT 4 

ULCM 5 

TMS 6 

TMS 

MAN 7 

TMS 

TMS 

MAN 

Daily 

Monthly 

Monthly  Quarterly  Quarterly  Quarterly 

Weighting 
scheme 
Highest 
frequency 

Dissemination 

Every working 
day at 16:30 

First working day of 
month Mt+1 

First working day of quarter Qt+2 

Date of first observation 

EER-12 8 

EER-18 9 

21 Sep 1981 

Jan 1993 

Jan 1993  Q1 1995  Q1 1995  Q1 1995 

4 Jan 1993 

Jan 1993 

Jan 1995  Q1 1995  Q1 1995  Q1 1995 

EER-41 10 

4 Jan 1993 

Jan 1993 

Q1 1998 

Notes: Time series available for the euro and all non-euro area currencies in the group of trading partners.  1 Consumer price index. 
2 Producer  price  index.  3 Gross  domestic  product  deflator.  4 Unit  labour  costs  in  the  total  economy.  5 Unit  labour  costs  in  the 
manufacturing sector. 6 Combined manufactured products (SITC 5 to 8) and services (Total EBOPS Services). 7 Manufactured products 
(SITC 5 to 8). 8 Narrow group of trading partners Australia, Canada, Denmark, Hong Kong, Japan, Norway, Singapore, South Korea, 
Sweden, Switzerland, the United Kingdom and the United States. 9 Extended group of trading partners EER-12 plus Bulgaria, China, 
the Czech Republic, Hungary, Poland and Romania. 10 Broad group of trading partners EER-18 plus Algeria, Argentina, Brazil, Chile, 
Colombia, Iceland, India, Indonesia, Israel, Malaysia, Mexico, Morocco, New Zealand, Peru, the Philippines, Russia, Saudi Arabia, South 
Africa, Taiwan, Thailand, Türkiye, Ukraine and the United Arab Emirates. 

ECB Statistics Paper Series No 49 

16 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
In addition, the ECB provides EERs for the individual euro area Member States, called 
harmonised HCIs in this context. An overview  of the data availability is displayed in 
Table 4.  

Table 4 
Availability of harmonised competitiveness indicators (HCIs) 

Nominal 

Weighting scheme  

TMS 6 

Real deflated by … 

CPI 1 

TMS 

PPI 2 

GDPD 3 

ULCT 4 

ULCM 5 

MAN 7 

TMS 

TMS 

MAN 

Highest frequency 

Daily 

Monthly 

Monthly  Quarterly  Quarterly  Quarterly 

Dissemination 

Every 
working day 
at 16:30 

Fourth working day of 
month Mt+1 

Date of first observation 

Fourth working day of quarter Qt+2 

EA20 8 

Jan 1993 

Jan 1993 

Jan 1993  Q1 1995  Q1 1995  Q1 1996 

EER-12 9 

Jan 1993 

Jan 1993 

Jan 1994  Q1 1995  Q1 1995  Q1 1996 

EER-18 10 

Jan 1993 

Jan 1993 

Jan 1995  Q1 1995  Q1 1995  Q1 1996 

EER-41 11 

Jan 1993 

Jan 1993 

Q1 1998 

EA20 + EER-12 

Jan 1993 

Jan 1993 

Jan 1994  Q1 1995  Q1 1995  Q1 1996 

EA20 + EER-18 

Jan 1993 

Jan 1993 

Jan 1995  Q1 1995  Q1 1995  Q1 1996 

EA20 + EER-41 

Jan 1993 

Jan 1993 

Q1 1998 

Notes: Time series available for all 20 euro area legacy currencies. 1 Consumer price index. 2 Producer price index. 3 Gross domestic 
product  deflator.  4 Unit  labour  costs  in  the  total  economy.  5 Unit  labour  costs  in  the  manufacturing  sector  (currently  not  published). 
6 Combined  manufactured  products  (SITC  5  to  8)  and  services  (Total  EBOPS  Services).  7 Manufactured  products  (SITC  5  to  8). 
8 Former legacy currencies of the 20 euro area Member states. 9 Narrow group of trading partners Australia, Canada, Denmark, Hong 
Kong, Japan, Norway, Singapore, South Korea, Sweden, Switzerland, the United Kingdom and the United States.  10 Extended group 
of trading partners EER-12 plus Bulgaria, China, the Czech Republic, Hungary, Poland and Romania. 11 Broad group of trading partners 
EER-18  plus Algeria, Argentina,  Brazil,  Chile,  Colombia,  Iceland,  India,  Indonesia,  Israel, Malaysia,  Mexico, Morocco,  New Zealand, 
Peru, the Philippines, Russia, Saudi Arabia, South Africa, Taiwan, Thailand, Türkiye, Ukraine and the United Arab Emirates. 

ECB Statistics Paper Series No 49 

17 

 
 
 
 
 
 
 
3 

Bilateral trade in manufactured goods 
and services 

The methodology behind the weighting scheme used for the ECB’s EERs and HCIs 
(as detailed in Sections 2.4 and 2.5) relies on obtaining the required trade data. The 
scope of the initial data collection covers bilateral trade flows between all countries 
included in the broad group of trading partners (EER-41) plus the individual euro area 
Member States, thus representing 61 countries. The trade flows consist of imports and 
exports in both manufacturing goods and services. In addition to bilateral trade flows, 
time series on total imports and exports are needed. 

The methods used to overcome data availability problems and to obtain the full matrix 
of  bilateral  flows  are  presented  in  detail  in  this  section.  Starting  from  the  reported 
figures, mirror data and balancing techniques are used to increase data coverage and 
reduce trade asymmetries. Subsequently, an econometric approach is employed via 
a  gravity  model  to  fill  any  remaining  data  gaps.  Finally,  additional  corrections  are 
applied to ensure consistency between the total trade flows of each country and the 
(partly estimated) bilateral trade flows.  

This enhanced methodology, implemented for the first time in 2020, not only allows for 
the inclusion of trade in services in the weighting, but also offers improvements to the 
data collection for manufacturing trade. 

3.1 

Initial availability of reported data 

In order to cover the data needs as much as possible with actual (i.e. non-estimated) 
data and thus limit the use of estimation techniques, different trade data sources are 
combined. As  shown  in  the  summary  table  available  in Annex A,  data  on  trade  in 
manufactured goods were collected from the United Nations Comtrade Database and 
Eurostat.  In  line  with  the  previous  ECB  methodology,  manufacturing  is  classified 
according  to  Sections  5  to  8  of  the  SITC.  The  data  from  the  two  sources  were 
combined  in  a  single  dataset,  giving  priority  to  Eurostat  if  data  were  available  from 
both  sources.  In  practice,  such  cases  correspond  to  the  Member  States  of  the 
European Union for which there are no major deviations across the two datasets.9  

International trade flows in manufactured goods are characterised by very high data 
availability. For the more recent three-year periods (since 2004-06), the reported data 
cover more than 95% of the required data, while earlier periods show only moderately 
lower data availability (Table 5).  

9   Differences may arise from vintage issues or if different trade concepts are applied (in some cases the 

community concept is reported for Eurostat and the national concept for UN Comtrade). 

ECB Statistics Paper Series No 49 

18 

 
Table 5 
Data availability after download – manufacturing data 

 (percentage of available observations) 

MANUFACTURING 

1995-
97 

1998-
2000 

2001-
03 

2004-
06 

2007-
09 

2010-
12 

2013-
15 

2016-
18 

2019-
21 

Full sample 

Stage 1:  
Reported 
values 

81.0 

88.6 

92.7 

95.0 

95.7 

96.3 

96.6 

96.5 

95.9 

Euro area 

82.4  

89.7  

94.6  

95.9  

96.4  

97.2  

97.9  

98.2 

99.0 

Note: 100% coverage is defined as full coverage of bilateral flows plus total flows vis-à-vis the rest of the world. 

Euro area Member States report nearly all required data on manufacturing trade flows 
for the  most  recent  three-year periods. As  shown  in Annex  B.1,  only  two  euro area 
countries exhibit data availability lower than 98% for the period 2019-21 (Cyprus and 
Malta  with  90%  and  96%  respectively).  Excluding  the  strong  downward  bias 
introduced  by  Luxembourg  in  the  period  1995-98,  the  picture  remains  fairly  stable 
across periods. As illustrated in Annex B.2, the situation for countries included in the 
broad  group  of  trading  partners  (EER-41)  is  very  similar,  also  for  recently  added 
countries  (with  the  noticeable  exceptions  of  the  United Arab  Emirates  in  the  period 
1995-97 and Algeria in the period 2019-21). 

The statistics on international trade in services were collected from three institutions 
(as detailed in the summary table available in Annex A): the United Nations, Eurostat 
and the OECD. Depending on availability, data corresponding to both the Extended 
Balance of Payments Services classification 2002 (EBOPS 2002) and the Extended 
Balance of Payments Services classification 2010 (EBOPS 2010) or only one of the 
two classifications were extracted (always for the item total EBOPS services).10  

Services span a wide range of economic activities, are very heterogeneous and also 
exhibit intangible characteristics. They are therefore inherently more difficult to define 
and measure than goods. In this context, the coverage of trade in services statistics is 
predictably  less  complete. The  data  availability  of  bilateral  services  trade  flows  has 
nevertheless substantially improved in recent years, so that the reported values now 
cover  about  55%  of  the  required  observations  across  the  full  sample  of  countries, 
encompassing all trading partners of the EER-41 group plus the individual euro area 
Member States (Table 6).  

10  

Ideally, complete time series information based on a unique classification should be used throughout, 
but this proved impossible for data availability reasons. 

ECB Statistics Paper Series No 49 

19 

  
 
 
 
Table 6 
Data availability after download – services data 

 (percentage of available observations) 

SERVICES 

1995-
97 

1998-
2000 

2001-
03 

2004-
06 

2007-
09 

2010-
12 

2013-
15 

2016-
18 

2019-
21 

Full sample 

12.8 

21.8 

37.3 

50.7 

55.7 

59.9 

55.2 

54.9 

54.9 

24.8  

40.7  

70.4  

88.3  

90.8  

97.0  

96.0  

95.4 

96.5 

Euro area 

Stage 1:  
Reported 
values 

Note: 100% coverage is defined as full coverage of bilateral flows plus total flows vis-à-vis the rest of the world. 

For euro area Member States, the availability of trade in services data is much higher, 
reaching  more  than  95%  since  2010  and  closely  matching  the  data  availability  of 
manufactured  goods. As  shown  in Annex  C.1,  only  three  countries  presented  data 
availability lower than 90% during the period 2019-21 (Germany, Malta and Spain, with 
78%, 81% and  89%  respectively).  For the  countries  included  in  the  broad  group  of 
trading partners, the picture is less satisfactory (Annex C.2), with many countries not 
reporting any bilateral flows. Hence, additional methods were used to fill any remaining 
data gaps (as described in the following subsections).  

3.2 

Extending the coverage with mirror data 

As  shown  in  Section  3.1,  the  required  trade  data  cannot  be  solely  obtained  via  the 
collection of reported figures. Considering the nature of trade statistics, the incomplete 
original  building  block  of  reported  data  can  be  extended  considerably  by  means  of 
mirror  data  techniques.  Conceptually,  a  trade  flow  is  always  characterised  by  its 
symmetrical nature. An export of country A to country B is an import of country B from 
country A. The same flow or economic phenomenon is in principle reported twice, from 
the perspective of country A and from the perspective of country B. This is commonly 
referred as mirror statistics.  

Mirror data were employed as an initial step to complete the matrix of bilateral flows: 
in case a country does not report bilateral data vis-à-vis a certain partner country, the 
gap is filled by mirror data reported by the partner country, if available. As the coverage 
of reported values is already remarkably high for manufacturing trade flows, the impact 
of mirror data is mostly visible for earlier periods. For example, the data availability for 
the period 1995-97 was expanded from 81.0% to 87.5% using mirror data (Table 7). 

ECB Statistics Paper Series No 49 

20 

  
 
 
Table 7 
Data availability after download and use of mirror data techniques – manufacturing 
data 

(percentage of available observations) 

MANUFACTURING 

1995-
97 

1998-
2000 

2001-
03 

2004-
06 

2007-
09 

2010-
12 

2013-
15 

2016-
18 

2019-
21 

81.0 

88.6 

92.7 

95.0 

95.7 

96.3 

96.6 

96.5 

95.9 

87.5 

93.1 

96.4 

97.4 

97.9 

98.3 

98.5 

98.7 

98.8 

Stage 1:  
Reported 
values 
Stage 2:  
Mirror / 
balanced 
values 

Note: 100% coverage is defined as full coverage of bilateral flows plus total flows vis-à-vis the rest of the world. 

The impact of mirror data techniques is greater when it comes to trade in services. 
Data availability for the most recent periods increases to around 80%, while coverage 
for earlier periods substantially improves as well (Table 8). The two periods with data 
availability  below  50%  (1995-97  and  1998-2000)  are  therefore  the  ones  primarily 
affected by the use of estimation techniques (see Section 3.4). 

Table 8 
Data availability after download and use of mirror data techniques – services data 

(percentage of available observations) 

1995-
97 

1998-
2000 

2001-
03 

2004-
06 

2007-
09 

2010-
12 

2013-
15 

2016-
18 

2019-
21 

SERVICES 

12.8 

21.8 

37.3 

50.7 

55.7 

59.9 

55.2 

54.9 

54.9 

22.1 

37.5 

58.2 

73.8 

78.6 

83.4 

79.5 

79.1 

79.3 

Stage 1:  
Reported 
values 
Stage 2:  
Mirror / 
balanced 
values 

Note: 100% coverage is defined as full coverage of bilateral flows and total flows vis-à-vis the rest of the world. 

3.3 

Addressing asymmetries with balanced values 

The mirror data framework described in Section 3.2 needs to be further adjusted to 
tackle  a  well-known  problem  in  trade  data:  bilateral  trade  asymmetries.  Such  a 
phenomenon occurs when for a given flow the value reported by a country does not 
match the mirror data reported by the partner country. Considering the prominence of 
this issue, a balancing process based on a symmetry index was developed, derived 
partly from the approach used in the Balanced Trade in Services (BaTIS) dataset of 
the OECD and World Trade Organization (WTO), as outlined by Fortanier et al. (2017). 

In cases where a country reports bilateral trade data vis-à-vis a certain partner country 
and  the  corresponding  mirror  data  are  also  available  (as  reported  by  the  partner 
country),  a  weighted  average  between  the  two  values  is  computed.  The  weight 

ECB Statistics Paper Series No 49 

21 

  
  
attributed  to  each  value  is  defined  by  the  reporting  “reliability”  of  each  country.  In 
general terms, a reporter is assumed to be more reliable if a larger share of its reported 
observations  corresponds  closely  (i.e.  is  “symmetrical”)  to  the  data  reported  by  the 
trading partners.  

More  specifically,  a  trade  flow  is  considered  symmetrical  if  the  absolute  difference 
between  the  two  values  (from  the  reporter  and  from  the  partner  country)  is  smaller 
than 30% of their sum (Table 9). As an example, if the sum of the two values reported 
for a given bilateral flow equals €200 million, the reporting of the flow will be considered 
symmetrical only if the absolute difference between the two values is lower than €60 
million (e.g. €90 million from the reporter and €110 million from the partner results in 
an absolute difference of €20 million).  

The  “30%  criteria”  to  assess  the  symmetry  may  be  perceived  as  relatively 
accommodating,  but  it  takes  into  account  that  a  certain  degree  of  asymmetry  is 
inherently  present  in  all  trade  flows.  It  is  moreover  strict  enough  to  meet  the 
fundamental objective of detecting and highlighting cases where countries consistently 
report figures that are drastically misaligned with the mirror data across many trading 
partners. To account for reporting changes and potential evolutions in compilation and 
estimation  methods,  a  time-varying  approach  was  selected,  with  symmetry  indices 
being computed for each year and used accordingly in the balancing process.  

An  alternative  approach,  used  by  Fortanier  et  al.  (2017),  consists  of  basing  the 
symmetry  indices  on  the  share  of  symmetrical  bilateral  trade  (analysed  in  levels), 
instead of the pure percentage of the reported flows meeting the symmetry criterion 
(regardless of the level of the flows). This strategy has the advantage of giving more 
importance to bilateral trade flows representing a larger share of the total trade of a 
country.  However,  it  has  the  disadvantage  of  introducing  a  negative  bias  towards 
reporters that trade extensively with countries that report lower-quality data.  

ECB Statistics Paper Series No 49 

22 

Table 9 
Criterion of symmetrical reporting and symmetry index 

Formula 

Criterion of 
symmetrical 
reporting 

𝑟𝑒𝑝 − 𝑥𝑖,𝑗

|𝑥𝑖,𝑗

𝑚𝑖𝑟|   <   (𝑥𝑖,𝑗

𝑟𝑒𝑝 + 𝑥𝑖,𝑗

𝑚𝑖𝑟) ∗  30% 

𝑟𝑒𝑝
Where 𝑥𝑖,𝑗
country 𝑖, and 𝑥𝑖,𝑗
by country 𝑗 

 is the gross export flows of country 𝑖 to country 𝑗, as reported by 

𝑚𝑖𝑟 represents the same flow but from the mirror data reported 

|𝑚𝑖,𝑗

𝑟𝑒𝑝 − 𝑥𝑖,𝑗

𝑚𝑖𝑟|   <   (𝑚𝑖,𝑗

𝑟𝑒𝑝 + 𝑥𝑖,𝑗

𝑚𝑖𝑟) ∗  30% 

𝑟𝑒𝑝 is the gross import flows of country 𝑖 from country 𝑗, as reported 

𝑚𝑖𝑟 represents the same flow but from the mirror data 

Where 𝑚𝑖,𝑗
by country 𝑖, and 𝑥𝑖,𝑗
reported by country 𝑗  

Symmetry index 

 𝑆𝐼𝑖,𝑡  =

𝑠𝑦𝑚𝑚𝑒𝑡𝑟𝑖𝑐𝑎𝑙

𝐹𝑙𝑜𝑤𝑠𝑖,𝑡 

𝐹𝑙𝑜𝑤𝑠𝑖,𝑡

𝑡𝑜𝑡𝑎𝑙

Where: 
𝑆𝐼𝑖,𝑡 stands for the symmetry index of country 𝑖 in year 𝑡 

𝑠𝑦𝑚𝑚𝑒𝑡𝑟𝑖𝑐𝑎𝑙

𝐹𝑙𝑜𝑤𝑠𝑖,𝑡 
 is the number of flows (imports and exports) reported by 
country 𝑖 in year 𝑡 for which the mirror data are available and the reporting is 
considered symmetrical 
𝐹𝑙𝑜𝑤𝑠𝑖,𝑡
by country 𝑖 in year 𝑡 for which the corresponding mirror data are available  

𝑡𝑜𝑡𝑎𝑙 represents the total number of flows (imports and exports) reported 

Notes: The trade flows between all 61 trading countries are considered (EER-41 countries + euro area Member States). For each country, 
the symmetry index is computed separately for manufacturing and services. If a country does not report bilateral trade data, no symmetry 
index can be computed. Hence, mirror data, if available, are used as a default in these cases. 

In concrete terms, the method outlined in Table 9 implies that if country A reports in 
year t for a given trade flow a value of €100 million, while the mirror data of partner 
country B show a value of €200 million, a weighted average between the two figures 
will  be  computed.  Assuming  that  the  reported  data  of  country  A  are  generally 
considered rather symmetrical, with a symmetry index of 0.9 in year t, and that country 
B  shows  a  symmetry  index  of  only  0.4  in  year  t,  the  final  value  of  the  flow  will  be 
(€100 million  *  0.9  +  €200 million  *  0.4)  /  (0.9  + 0.4) = €130.8  million. This  value is 
hence  closer  to  the  one  reported  by  country A,  in  line  with  the  assessed  reporting 
reliability of the two countries.  

As detailed in Annex D.1, the manufacturing symmetry indices are relatively high and 
stable over time. Taking the average over time, they vary between 54% (Hong Kong) 
and 96% (Germany and Italy), with the median reaching 85%. Overall, the symmetry 
indices of the euro area Member States are comparatively high, in particular when it 
comes to the largest economies (96%, 92%, 96% and 95% for Germany, France, Italy 
and Spain respectively). Two notable exceptions are Cyprus (62%) and Malta (66%), 
which have some of the lowest scores in the sample.  

The  analysis  of  services  symmetry  indices  (Annex  D.2)  is  somewhat  hindered  by 
relatively  lower  data  availability.  The  averages  across  years  range  between  23% 
(Chile) and 91% (Germany), with a median level of 67%. This pronounced difference 
with  manufacturing  symmetry  indices  is  nevertheless  expected  considering  the 
inherent  difficulties  of  measuring  trade  in  services  statistics  and  ultimately  explains 

ECB Statistics Paper Series No 49 

23 

 
 
why a balancing process is needed. Importantly, large services exporters such as the 
United States, Japan, South Korea and the United Kingdom (i.e. the countries most 
affected  by  the  inclusion  of  services  in  the  weighting  scheme)  seem  on  average  to 
report more consistent data, as shown in higher symmetry indices. 

3.4 

Completing the dataset with a gravity model 

As previously indicated, the use of mirror data techniques allows almost full coverage 
to  be  achieved  for  trade  in  manufactured  goods.  Resorting  to  advanced  estimation 
techniques  was  therefore  not  essential  as  long  as  the  focus  was  solely  put  on 
manufacturing  data.  Indeed,  the  very  few  remaining  gaps  in  the  time  series  could 
easily be filled by using leads and lags (i.e. the observations available directly after or 
before  the  missing  periods).  However,  the  inclusion  of  trade  in  services  in  the 
weighting  scheme  created  the  need  to  update  this  methodology,  as  a  significant 
number of required trade flows were still missing after using mirror data techniques. 
This became even more necessary with the incorporation of additional countries into 
the broad group of trading partners in July 2020. 

To complete the coverage of bilateral flows, the missing data points were estimated 
by means of an econometric gravity model. Such models represent a well-established 
methodology  in  economics, used  in  particular  to  explain  trade  patterns. The  gravity 
model was first introduced by Tinbergen (1962) to explain bilateral trade flows by the 
size of the economies and the distance between the countries. It has since become 
the workhorse model for bilateral trade (Head and Mayer, 2014). In essence, it states 
that bilateral trade between two countries is proportional to the size of their economies, 
as  measured by  their  respective  GDP,  and  inversely  proportional  to  the  trade costs 
between  them  (captured  in  particular  by  geographical  distance).  Certain  additional 
factors, such as a common language or common legal systems, enter the equation as 
factors easing bilateral trade costs. The specifications defined in this paper are largely 
consistent  with  those  used  for  the  OECD-WTO  BaTIS  dataset  by  Fortanier  et  al. 
(2017).  

As  an  initial  step,  a  regression  model  was  defined  to  estimate  the  determinants  of 
imports  of  manufactured  goods.  The  selected  gravity 
bilateral  exports  and 
specification involved two time-varying independent variables: GDP of the reporter and 
GDP  of  the  partner  country  (obtained  from  the  IMF  World  Economic  Outlook 
database), in log terms based on current US dollar values. In addition, the following 
time-invariant regressors were included in the equation (from the GeoDist database of 
the Centre d’Études Prospectives et d’Informations Internationales (CEPII)):11  

• 
• 
• 
• 
• 
• 

log of population-weighted distance in kilometres (pop-wt); 
contiguity (contig); 
time zone difference (tdiff); 
common language (comlang_ethno); 
common legal system (comleg_posttrans); 
common colony (comcol). 

11   See Mayer and Zignago (2011). 

ECB Statistics Paper Series No 49 

24 

 
Moreover, a dummy variable was included for joint EU membership. 

The regression results, as shown in Table 10, confirm the very high predictive power 
of the model, in line with the solid conceptual framework behind the use of the gravity 
equation in international trade. All the variables included in the specification generally 
show a very high degree of statistical significance and plausible coefficients.  

Table 10 
Results of the regressions (exports and imports of manufactured goods) 

GDP reporter 

GDP partner 

Weighted distance 

Contiguity 

Time zone difference 

Common language 

Common legal system 

Common colony 

EU partners 

Constant 

Reporter FE 

Partner FE 

Year FE 

Observations 

R2 

Adjusted R2 

Residual std. error (df = 94951) 

Dependent variable: 

log(manufacturing trade flows) 

Exports 

Imports 

0.631*** (0.015) 

0.874*** (0.015) 

0.874*** (0.015) 

0.631*** (0.015) 

-1.250*** (0.009) 

-1.250*** (0.009) 

0.406*** (0.019) 

0.019*** (0.002) 

0.449*** (0.013) 

0.174*** (0.007) 

0.598*** (0.025) 

0.443*** (0.015) 

0.406*** (0.019) 

0.019*** (0.002) 

0.449*** (0.013) 

0.174*** (0.007) 

0.598*** (0.025) 

0.443*** (0.015) 

20.615*** (0.129) 

20.615*** (0.129) 

Yes 

Yes 

Yes 

95,107 

0.871 

0.871 

0.953 

Yes 

Yes 

Yes 

95,107 

0.871 

0.871 

0.953 

F statistic (df = 155; 94951) 

4,142.511*** 

4,142.511*** 

Note: *p<0.1; **p<0.05; ***p<0.01. 

As a second step, the predicted values were used to generate the full matrix of bilateral 
manufacturing trade flows.  

•  Purely estimated values: For cases in which bilateral trade flows were fully 
missing from the dataset (after the implementation of mirror data techniques), 
the predicted values were copied as such, after checking their plausibility.  

•  Chain-linked  estimates:  For  cases  in  which  flows  were  incomplete  (with 
certain bilateral observations being available but showing some gaps across 
time), the growth rate of the predicted values was used to link the estimates 
and the actual (non-estimated) values, without creating breaks in the final time 
series. 

This  process  represents  a  strong  refinement  compared  with  the  method  used 
previously to fill the gaps in the time series for manufacturing trade, which was based 
on a pure backward and/or forward extension of the available values. 

ECB Statistics Paper Series No 49 

25 

 
 
 
Subsequently, the same approach was applied to services trade, with the exception 
that manufacturing trade was also included as an additional independent variable in 
the regression models. As presented in Table 11, the regression results closely match 
those  of  manufacturing  flows,  in  line  with  the  literature  on  gravity  models  for 
international services (Head and Mayer, 2014). The high statistical significance of the 
additional  regressor  confirms  the  intuition  of  the  value  added  from  including 
manufacturing data in the prediction of services trade flows (corroborated by extensive 
robustness tests performed using multiple specifications). 

Table 11 
Results of the regressions (exports and imports of services)  

GDP reporter 

GDP partner 

Manufacturing trade 

Weighted distance 

Contiguity 

Time zone difference 

Common language 

Dependent variable: 
log(services trade flows) 

Exports 

Imports 

0.325*** (0.020) 

0.553*** (0.020) 

0.553*** (0.020) 

0.325*** (0.020) 

0.460*** (0.004) 

0.460*** (0.004) 

-0.666*** (0.013) 

-0.666*** (0.013) 

0.212*** (0.022) 

0.212*** (0.022) 

-0.010*** (0.003) 

-0.010*** (0.003) 

0.127*** (0.017) 

0.127*** (0.017) 

Common legal system 

0.164*** (0.009) 

0.164*** (0.009) 

Common colony 

-0.354*** (0.034) 

-0.354*** (0.034) 

EU partners 

Constant 

Reporter FE 

Partner FE 

Year FE 

Observations 

R2 

Adjusted R2 

Residual std. error (df = 64258) 

0.0002     (0.021) 

0.0002     (0.021) 

10.762*** (0.209) 

10.762*** (0.209) 

Yes 

Yes 

Yes 

64,415 

0.868 

0.868 

0.951 

Yes 

Yes 

Yes 

64,415 

0.868 

0.868 

0.951 

F statistic (df = 156; 64258) 

2,709.970*** 

2,709.970*** 

Note: *p<0.1; **p<0.05; ***p<0.01. 

3.5 

Final dataset 

A  final  correction  is  needed  to  preserve  the  internal  consistency  of  the  dataset.  In 
exceptional cases, it may indeed occur that the reported total imports or exports (vis-
à-vis all countries in the world) are lower than the sum of final bilateral flows, which 
should conceptually not be the case. In such instances, the totals were recomputed 
from  the  aggregation  of  bilateral  trade.  This  method  was  also  applied  in  case  of 
missing total series. 

ECB Statistics Paper Series No 49 

26 

 
 
 
At  this  stage,  the  two  datasets  containing  the  required  manufacturing  and  services 
trade data are fully complete (Table 12). In terms of data hierarchy, balanced values 
represent the best-case scenario and are always used when available. When they are 
missing but either reported values or mirror values are present, the latter are selected. 
In the rest of the cases, chain-linked estimates are prioritised over purely estimated 
values  since  the  former  are  deemed  more  reliable  thanks  to  their  link  to  existing 
values. The purely estimated values, solely based on the gravity model, only account 
for 0.2% of the final manufacturing dataset and 12.6% of the final services dataset. 

Table 12 
Structure of final trade datasets 

(percentage of the final datasets, covering 100% of the trade data requirements) 

1995-
97 

1998-
2000 

2001-
03 

2004-
06 

2007-
09 

2010-
12 

2013-
15 

2016-
18 

2019-
21 

MANUFACTURING 

7.3 

5.2 

4.5 

3.4 

3.2 

3.0 

2.7 

3.1 

3.8 

6.6 

4.5 

3.7 

2.4 

2.2 

2.0 

1.8 

2.2 

2.9 

72.8 

82.5 

87.4 

90.9 

91.8 

92.6 

93.2 

92.7 

91.4 

12.3 

6.8 

3.4 

2.4 

1.9 

1.5 

1.4 

1.2 

1.0 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.9 

0.9 

0.8 

0.7 

0.7 

0.7 

0.7 

0.8 

0.8 

SERVICES 

10.3 

16.9 

22.3 

24.4 

24.2 

24.8 

25.5 

25.3 

25.4 

9.3 

15.6 

20.9 

23.1 

22.9 

23.5 

24.3 

24.2 

24.4 

2.0 

4.6 

14.8 

25.9 

31.1 

34.7 

29.2 

29.1 

29.0 

65.3 

49.9 

29.2 

13.6 

8.8 

4.0 

7.9 

8.3 

8.1 

12.6 

12.6 

12.6 

12.6 

12.6 

12.6 

12.6 

12.6 

12.6 

0.6 

0.4 

0.3 

0.4 

0.4 

0.4 

0.4 

0.5 

0.5 

Reported 
values 
only 
Mirror 
values 
only 
Balanced 
values 
Chain-
linked 
estimates 
Purely 
estimated 
values 
Correction 
of totals 

Reported 
values 
only 
Mirror 
values 
only 
Balanced 
values 
Chain-
linked 
estimates 
Purely 
estimated 
values 
Correction 
of totals 

Note: 100% coverage is defined as full coverage of bilateral flows plus total flows vis-à-vis the rest of the world. 

ECB Statistics Paper Series No 49 

27 

  
 
 
3.6 

Specific issues of trade in services data 

Trade in services data are affected by a number of data issues that need to be taken 
into account when constructing a dataset of bilateral flows.  

One  of  these  issues  relates  to  the  presence  of  non-plausible  zeros  in  the  reported 
observations. Considering that “only” 61 countries in total are considered as part of 
the computation of the enhanced EERs/HCIs, the world’s smallest economies are de 
facto  excluded.  Hence,  one  should  not  expect  any  (or  too  many)  true  “zero”  total 
services trade flows between two countries in the sample. In practice, however, some 
zeros are reported, which can be deemed implausible taking the size of the economies 
into account (e.g. flows between Portugal and Belgium or between Finland and New 
Zealand). Consequently, zero flows have been removed from the initial dataset and 
treated in the same way as missing observations.  

Additionally,  bilateral  total  EBOPS  services  trade  data  can  be  affected  by  negative 
values. This is noticeable in the data from certain reporters, for example France and 
Germany.  Conceptually,  these  negative  values  can  stem  from  two  sub-items  of  the 
EBOPS classification: 

• 

EBOPS 2002: insurance services (“S253“) and other business services 
(“S268”, due to the lower item “merchanting”); 

• 

EBOPS 2010: insurance and pension services (“SF“). 

As  the  inclusion  of  negative  values  is  not  compatible  with  the  computation  of  trade 
weights or trade shares, these values are removed from the dataset and treated as 
missing, similarly to the “zero” values. From a conceptual point of view, a preferable 
option would be to re-aggregate total EBOPS services trade data from the lower items, 
excluding the “problematic” sub-items in case of negative values. However, such an 
approach proved impossible due to insufficient data availability for the various EBOPS 
sub-items  across  the  country  sample.  In  addition,  certain  inconsistencies  between 
total EBOPS services and the different categories further prevent re-aggregation from 
the sub-items. 

ECB Statistics Paper Series No 49 

28 

4 

Enhanced indicators for 
competitiveness analysis  

4.1 

Trade weights 

The inclusion of services in the weighting scheme underlying the computation of the 
ECB’s EERs not only corresponds to a conceptual refinement but has a practical and 
sizeable  impact  on  the  indicators.  This  can  be  directly  perceived  by  comparing  the 
manufacturing, services and combined trade weights of the euro area’s largest trading 
partners (Chart 5). 

Chart 5 
Trade weights in the euro EER-41: manufacturing, services and combined 

(percentages) 

Source: ECB. 
Notes: Trade weights are averages over the period 2019-21. “Central and eastern European EU Member States” comprises Bulgaria, 
the  Czech  Republic,  Hungary,  Poland  and  Romania;  “other  advanced  European  economies”  comprises  Denmark,  Iceland,  Norway, 
Sweden and Switzerland; “other advanced non-European economies” comprises Australia, Canada, Hong Kong SAR, Israel, Japan, 
New Zealand, Singapore, South Korea and Taiwan; and “other emerging market economies” comprises Algeria, Argentina, Brazil, Chile, 
Colombia, India, Indonesia, Malaysia, Mexico, Morocco, Philippines, Peru, Russia, Saudi Arabia, South Africa, Thailand, Türkiye, Ukraine 
and the United Arab Emirates. 

The most striking difference is visible for China, which is the largest trading partner of 
the  euro  area  in  terms  of  manufactured  goods  (21%),  whereas  it  is  only  the  fourth 
largest partner for services (6%). By contrast, the United States and United Kingdom 
are by far the largest euro area trading partners for services, with much higher shares 
than for manufacturing (22% vs 13% for the United States, 16% vs 8% for the United 
Kingdom).  For  the  United  States,  this  phenomenon  is  partly  explained  by  its  high 
relevance as a trading partner of the euro area when it comes to telecommunications, 
computer and information services as well as other business services (according to 
euro  area  balance  of  payments  data).  The  services  weight  of  the  “other  advanced 
European economies” group is also much higher than for manufacturing (driven partly 
by Switzerland),  while it is considerably lower for central and eastern European EU 
Member States (such as the Czech Republic and Poland), whose trade linkages with 
the  euro  area  are  to  a  large  extent  shaped  by  their  integration  in  European 
manufacturing value chains. 

ECB Statistics Paper Series No 49 

29 

0510152025United StatesChinaUnited KingdomCentral and easternEuropean EUMember Statesother advancedEuropean economiesother advanced non-European economiesother emergingmarket economiesManufacturingServicesCombined 
 
These  patterns  have  consequences  for  the  combined  weights:  compared  with  the 
manufacturing weights (the unique trade basis of the previous ECB methodology), the 
United  States  surpasses  China  as  the  euro  area’s  largest  trading  partner,  while 
Switzerland overtakes Poland as the fourth largest trading partner. 

Chart 6 presents longer time trends for the combined weights of the largest countries 
and  country  groups  since  1995.  It  shows  that  the  shares  of  the  United  States,  the 
United Kingdom and other advanced economies (European and non-European) have 
decreased over time. Over the past decade, however, the decrease in importance of 
the United States, in particular, and non-European advanced economies has tailed off 
and even partly reversed. The trade weight of China has increased strongly over time, 
with  yet  another  rise  in  the  latest  three-year  period,  while  that  of  other  emerging 
economies, which became increasingly important for euro area trade during the 2000s, 
has declined over the past decade. Central and eastern European EU Member States 
have consistently gained in importance owing to their further integration into European 
value chains. 

Chart 6 
The evolution of combined trade weights in the euro EER-41 over time 

a) Advanced economies 

(percentages) 

b) Other countries 

(percentages) 

Source: ECB. 

Source: ECB. 

These  broad  shifts  in  trading  partner  importance  reflect  –  sometimes  diverging  –
developments in manufacturing (Chart 7) and services trade (Chart 8). Whereas the 
services weights of the United States and other advanced economies declined from 
the late 1990s, their importance increased over the past decade; by contrast, those of 
the  United  Kingdom  decreased  further.  In  manufacturing  trade,  developments  have 
been similar for the United States – albeit from a lower initial trade weight – while the 
United  Kingdom  and  other  advanced  economies  have  steadily  lost  importance  as 
trading partners. At the same time, the rise in the trade shares of China and central 

ECB Statistics Paper Series No 49 

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0510152025ChinaOther emerging market economiesCentral and eastern European EU Member States0510152025United StatesUnited KingdomOther advanced European economiesOther advanced non-European economies 
 
 
 
 
 
 
 
and  eastern  European  EU  Member  States  was  more  contained  in  services  than  in 
manufacturing, while the share of other emerging economies evolved in a comparable 
fashion in manufacturing and services trade. 

Chart 7 
Evolution of trade weights of the EER-41 group of 
trading partners – manufacturing 

Chart 8 
Evolution of trade weights of the EER-41 group of 
trading partners – services 

 (percentages) 

 (percentages) 

Source: ECB. 

Source: ECB. 

As  illustrated  in  Section  2.5,  third-market  effects  can  have  a  significant  impact  on 
export weights and thus on the final overall weights. Considering the importance of 
this type of competition, a simplification of the methodology by excluding third-market 
effects would imply a much lower information content of the overall trade weights. The 
most noticeable pattern in this regard relates to China when it comes to manufacturing 
trade (Chart 9), as the double export weight of China (20%) is double the simple export 
weight (10%). This reflects China’s role as the leading global exporter of goods, which 
also  implies  that  it  is  an  important  competitor  in  third  markets.  The  manufacturing 
weights  of  other  advanced  non-European  economies  also  gain  from  third-market 
effects,  in  particular  Japan,  Hong  Kong  and  South  Korea,  as  they  are  important 
competitors for the euro area in third markets. By contrast, the manufacturing weights 
of  central  and  eastern European  EU Member States  are  negatively  affected  by  the 
inclusion of third-market effects. The role this region plays in Europe’s highly integrated 
value chains implies that it is more important for the euro area in terms of imports than 
exports,  both  on  the  basis  of  direct  exports  and  even  more  so  when  third-market 
competition is considered (Fidora and Schmitz, 2020).  

ECB Statistics Paper Series No 49 

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051015202530United StatesChinaUnited KingdomOther advanced European economiesOther advanced economiesOther emerging market economiesCentral and eastern European EU Member States0510152025United StatesChinaUnited KingdomOther advanced European economiesOther advanced economiesOther emerging market economiesCentral and eastern European EU Member States 
 
 
 
 
 
 
Chart 9 
The importance of third-market effects in the manufacturing trade weights of the euro 
EER-41 

(percentages) 

Source: ECB. 
Note: Trade weights refer to the period 2019-21.  

Interestingly,  third-market  effects  are  also  important  for  services  trade  weights 
(Chart 10), although the patterns differ partly from those observed for manufacturing. 
Other  advanced  non-European  economies  and  other  emerging  market  economies 
gain  in  importance  when  including  third-market  effects  by  displaying  the  largest 
positive discrepancy between simple and double export weights. 

Chart 10 
The importance of third-market effects in the services trade weights of the euro 
EER-41 

(percentages) 

Source: ECB. 
Note: Trade weights refer to the period 2019-21.  

ECB Statistics Paper Series No 49 

32 

0510152025United StatesChinaUnited KingdomOther advancedEuropeaneconomiesOther advancednon-EuropeaneconomiesOther emergingmarket economiesCentral and easternEuropean EUMember StatesTotal weightImport weightSimple export weightDouble export weight051015202530United StatesChinaUnited KingdomOther advancedEuropeaneconomiesOther advancednon-EuropeaneconomiesOther emergingmarket economiesCentral and easternEuropean EUMember StatesTotal weightImport weightSimple export weightDouble export weight 
  
 
 
  
 
4.2 

EERs and HCIs 

Since the inception of the euro in 1999, there has been close co-movement for the 
broad euro NEER-41, no matter if based on  manufacturing, services or (enhanced) 
combined  weights  (Chart  11).  However,  swings  in  the  services-based  indicators 
appear to be slightly more pronounced, especially in the euro appreciation episodes 
of  2008  and  2019,  implying  that  the  enhanced  indicators  exhibit  somewhat  larger 
fluctuations  compared  with  the  purely  manufacturing-based  indicators  of  the  old 
methodology. 

Chart 11 
Nominal EERs of the euro 

(Q1 1999 = 100)  

Sources: ECB and own calculations. 
Notes: Based on the broad group of trading partners (EER-41). A downward movement reflects a depreciation of the euro, while an 
upward movement indicates an appreciation. 

Chart  12  shows  a  similar  picture  for  real  euro  EERs  deflated  by  consumer  prices, 
however  with  a  higher  divergence  between  manufacturing-based  and  enhanced 
indicators,  especially  in  2021.  Between  January  1999  and  December  2023,  the 
enhanced  euro  REER-41  indicated  a  somewhat  smaller  real  depreciation  (6.7%) 
compared with the manufacturing-based indicator (7.1%).  

ECB Statistics Paper Series No 49 

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80901001101201301999200120032005200720092011201320152017201920212023Basedon manufacturing weightsBasedon services weightsBasedon combined weights 
Chart 12 
Real EERs of the euro deflated by consumer prices 

 (Q1 1999 = 100)  

Sources: ECB and own calculations. 
Notes: Based on the broad group of trading partners (EER-41). A downward movement reflects a depreciation of the euro, while an 
upward movement indicates an appreciation. 

Chart  13  reveals  a  comparable  pattern  for  the  GDP-deflated  REER-41.  For  the 
nominal, CPI-deflated and GDP-deflated indicators, the close co-movement between 
the  different indicators  until 2007  is  very  striking,  before  larger  divergences  appear. 
This  suggests  that  since  then  differences  in  the  trade  weights  among  the  various 
indicators have been more strongly correlated with differences in bilateral exchange 
rate developments. 

Chart 13 
Real EERs of the euro (GDP-deflated) 

(Q1 1999 = 100) 

Sources: ECB and own calculations. 
Notes: Based on the broad group of trading partners (EER-41). A downward movement reflects a depreciation of the euro, while an 
upward movement indicates an appreciation. 

ECB Statistics Paper Series No 49 

34 

808590951001051101151999200120032005200720092011201320152017201920212023Basedon manufacturing weightsBasedon services weightsBasedon combined weights75808590951001051101999200120032005200720092011201320152017201920212023Basedon manufacturing weightsBasedon services weightsBasedon combined weights 
  
 
 
For  euro  area  countries,  especially  those  with  a  high  share  of  services  trade,  the 
analysis  of  competitiveness  differs  quite  substantially  between  the  manufacturing-
based  and  the  enhanced  indicators.  Chart 14  presents  such  evidence  for Greece’s 
CPI-deflated broad HCI comprising the EER-41 trading partners and the other 19 euro 
area  countries.  Importantly,  the  real  appreciation  in  Greece  (i.e.  the  loss  of  price 
competitiveness)  between  2000  and  2010  was  more  pronounced  in  the  enhanced 
indicator  (28%)  than  in  the  manufacturing  indicator  based  on  the  previous 
methodology (22%). Such differences demonstrate that the methodology underlying 
the  enhanced  indicators  matters  quantitatively  and  offers  a  more  complete 
assessment  of  international  competitiveness  and  external  balances  with  important 
implications for policymakers.  

Chart 14 
HCIs of Greece deflated by consumer prices 

 (Q1 1999 = 100)  

Sources: ECB and own calculations. 
Notes: Based on the broad group of trading partners (EER-41 + 20 euro area countries). A downward movement reflects a 
depreciation of the exchange rate, while an upward movement indicates an appreciation. 

ECB Statistics Paper Series No 49 

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808590951001051101151201999200120032005200720092011201320152017201920212023Basedon manufacturing weightsBasedon services weightsBasedon combined weights 
5 

Conclusions 

This  paper  provides  a  detailed  description  of  enhancements  to  the  trade  weighting 
scheme  underlying  the  EER  and  HCI  indices  provided  by  the  ECB.  Since  services 
account for an increasing share of international trade and thus play a growing role in 
assessing competitiveness, it is critical to take them into account when constructing 
trade weights for the EERs and HCIs.  

This paper illustrates the impact of services on trade weights as well as on EER and 
HCI  indices.  Moreover,  it  shows  the  challenges  associated  with  the  inclusion  of 
services trade, foremost in dealing with constraints in terms of data availability. While 
incomplete data coverage impeded the inclusion of services in the past, improved data 
coverage  for  the  most  recent  periods  associated  with  imputation  and  estimation 
techniques has enabled the inclusion of services trade. Importantly, the ECB’s well-
established methodology – which in particular accounts for competition faced by euro 
area  exporters  in  third  markets  –  did  not  have  to  be  changed  with  the  inclusion  of 
services trade. 

In  addition,  this  paper  provides  an  up-to-date  general  overview  of  the  ECB’s 
methodology  for  calculating  EER  and  HCI  indices,  as  previously  introduced  by 
Buldorini et al. (2002) and Schmitz et al. (2012). Consequently, the paper may serve 
as  a  reference  guide  for  analysts  and  researchers  working  with  the  EER  and  HCI 
indices provided by the ECB. 

Looking ahead, it is important that the ECB’s methodology remains state-of-the art to 
provide an encompassing and up-to-date measurement of EERs and HCIs. This may 
imply  changes  to  trading  partner  groups  (as  regularly  considered  with  the  triennial 
update  of  the  trade  weights)  as  well  as  improvements  to  the  data  used  for  trade 
weights and deflators. Moreover, potential methodological improvements need to be 
evaluated  against  their  costs  and  merits.  In  recent  years,  discussions  on  potential 
extensions of the ECB’s indicators mainly related to the impact of global value chains 
in the measurement of competitiveness (Gunnella, Fidora and Schmitz, 2017).  

ECB Statistics Paper Series No 49 

36 

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ECB Statistics Paper Series No 49 

38 

Annex 

A: Summary trade flows data sources 

Table A 

SOURCE 

DATABASE 

ITEM 

MANUFACTURING 

United Nations 

Comtrade Database 

Sections 5 to 8 of the Standard 
International Trade Classification (SITC) 

Eurostat 

EU trade since 1988 by SITC 

Sections 5 to 8 of the Standard 
International Trade Classification (SITC) 

United Nations 

Comtrade Database 

Total EBOPS Services (“200”) / EBOPS 
2002 

SERVICES 

Eurostat 

Eurostat 

Eurostat 

OECD 

OECD 

International trade in services (from 
1985 to 2003) (bop_its_deth) 

Total EBOPS Services (“200”) / EBOPS 
2002 

International trade in services 
(since 2004) (bop_its_det) 

Total EBOPS Services (“200”) / EBOPS 
2002 

International trade in services 
(since 2010) (bop_its6_det) 

Total EBOPS Services (“S”) / EBOPS 
2010  

EBOPS 2002 - Balanced 
International Trade in Services 
(1995-2012) 

Total EBOPS Services (“200”) / EBOPS 
2002 / reported values 

EBOPS 2010 - Trade in services 
by partner economy 

Total EBOPS Services (“S”) / EBOPS 
2010 

ECB Statistics Paper Series No 49 

39 

 
 
B: Overview of data availability – manufactured goods 

B.1 Euro area countries  
Percentage of the required data that is available (directly reported) 

B.2 Non-euro area countries  
Percentage of the required data that is available (directly reported) 

ECB Statistics Paper Series No 49 

40 

 
 
 
C: Overview of data availability – services 

C.1 Euro area countries 
Percentage of the required data that is available (directly reported) 

C.2 Non-euro area countries 
Percentage of the required data that is available (directly reported) 

ECB Statistics Paper Series No 49 

41 

 
 
 
D: Symmetry indices  

D.1 Manufactured goods 

REPORTER  AVG  1995  1996  1997  1998  1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016  2017  2018  2019  2020  2021 

AE 

AR 

AT 

AU 

BE 

BG 

BR 

CA 

CH 

CL 

CN 

CO 

CY 

CZ 

DE 

DK 

DZ 

EE 

ES 

FI 

FR 

GB 

GR 

HK 

HR 

HU 

ID 

IE 

IL 

IN 

IS 

IT 

JP 

KR 

LT 

LU 

LV 

MA 

MT 

MX 

MY 

NL 

NO 

NZ 

PE 

PH 

PL 
PT 

RO 

RU 

SA 

SE 

SG 

SI 

SK 

TH 

TR 

TW 

UA 

US 

ZA 

0.71  NA  NA  NA  NA  0.72  0.73  0.75  0.79  0.73  0.66  0.62  0.63  0.67  0.75  0.75  0.74  0.75  0.70  0.76  0.72  0.75  0.77  0.65  0.65  0.59  0.69  0.73 

0.83  0.92  0.90  0.86  0.81  0.83  0.80  0.82  0.87  0.90  0.89  0.89  0.92  0.91  0.88  0.89  0.86  0.87  0.85  0.85  0.80  0.82  0.84  0.82  0.66  0.58  0.65  0.64 

0.90  0.93  0.93  0.95  0.94  0.93  0.93  0.94  0.94  0.90  0.89  0.86  0.92  0.88  0.88  0.86  0.86  0.88  0.88  0.88  0.87  0.86  0.90  0.90  0.88  0.90  0.91  0.88 

0.87  0.93  0.90  0.90  0.90  0.90  0.85  0.84  0.86  0.86  0.84  0.84  0.89  0.82  0.83  0.85  0.87  0.84  0.86  0.85  0.89  0.87  0.92  0.86  0.89  0.84  0.88  0.85 

0.93  0.96  0.93  0.94  0.94  0.92  0.94  0.93  0.91  0.91  0.91  0.90  0.93  0.92  0.92  0.93  0.92  0.94  0.94  0.91  0.94  0.96  0.92  0.92  0.93  0.92  0.91  0.91 

0.87  NA  0.89  0.91  0.90  0.84  0.87  0.83  0.82  0.85  0.82  0.78  0.83  0.91  0.89  0.89  0.90  0.89  0.85  0.85  0.85  0.86  0.87  0.92  0.91  0.88  0.94  0.89 

0.89  0.93  0.91  0.95  0.93  0.96  0.95  0.88  0.88  0.87  0.89  0.90  0.88  0.90  0.87  0.89  0.87  0.91  0.92  0.87  0.86  0.86  0.86  0.83  0.84  0.87  0.88  0.89 

0.80  0.89  0.84  0.81  0.78  0.75  0.72  0.73  0.79  0.81  0.83  0.82  0.84  0.82  0.80  0.83  0.79  0.76  0.83  0.80  0.86  0.83  0.81  0.81  0.86  0.74  0.74  0.78 

0.86  0.89  0.91  0.90  0.88  0.94  0.90  0.91  0.85  0.94  0.90  0.92  0.92  0.85  0.89  0.88  0.82  0.83  0.80  0.79  0.81  0.80  0.83  0.82  0.82  0.81  0.80  0.79 

0.88  0.89  0.84  0.86  0.85  0.86  0.84  0.86  0.92  0.86  0.90  0.90  0.87  0.88  0.87  0.87  0.91  0.91  0.87  0.87  0.88  0.86  0.87  0.92  0.92  0.92  0.91  0.91 

0.77  0.62  0.68  0.69  0.71  0.67  0.68  0.71  0.71  0.76  0.78  0.82  0.81  0.84  0.82  0.85  0.82  0.81  0.82  0.80  0.82  0.82  0.80  0.82  0.79  0.79  0.80  0.77 

0.84  0.92  0.89  0.86  0.85  0.84  0.86  0.90  0.88  0.90  0.80  0.80  0.80  0.92  0.86  0.88  0.85  0.81  0.88  0.84  0.81  0.76  0.87  0.77  0.81  0.78  0.84  0.80 

0.62  0.83  0.71  0.73  0.76  0.68  0.67  0.64  0.63  0.71  0.77  0.63  0.53  0.65  0.55  0.52  0.55  0.57  0.41  0.41  0.53  0.58  0.49  0.68  0.63  0.53  0.68  0.59 

0.88  0.88  0.90  0.91  0.88  0.80  0.79  0.76  0.78  0.80  0.89  0.91  0.94  0.90  0.95  0.93  0.91  0.93  0.92  0.95  0.89  0.91  0.93  0.91  0.89  0.86  0.85  0.84 

0.96  0.95  0.97  0.98  0.98  0.97  0.97  0.97  0.97  0.97  0.95  0.95  0.95  0.96  0.92  0.97  0.97  0.97  0.96  0.95  0.97  0.95  0.95  0.98  0.97  0.96  0.95  0.94 

0.93  0.98  0.95  0.92  0.95  0.94  0.94  0.93  0.97  0.94  0.95  0.93  0.95  0.94  0.96  0.88  0.93  0.95  0.95  0.95  0.96  0.96  0.94  0.84  0.89  0.88  0.86  0.85 

0.80  0.75  0.77  0.68  0.76  0.88  0.81  0.89  0.73  0.88  0.84  0.80  0.80  0.84  0.80  0.78  0.77  0.78  0.79  0.87  0.84  0.78  0.83  0.79  NA  NA  NA  NA 

0.83  0.93  0.83  0.88  0.85  0.80  0.82  0.81  0.83  0.81  0.89  0.85  0.83  0.87  0.84  0.86  0.76  0.76  0.75  0.73  0.78  0.81  0.82  0.90  0.85  0.80  0.86  0.85 

0.95  0.96  0.95  0.93  0.96  0.97  0.95  0.97  0.95  0.97  0.95  0.93  0.93  0.93  0.94  0.97  0.95  0.96  0.97  0.96  0.93  0.94  0.94  0.97  0.95  0.93  0.95  0.92 

0.88  0.91  0.94  0.92  0.94  0.91  0.92  0.84  0.84  0.84  0.85  0.87  0.87  0.88  0.83  0.83  0.85  0.86  0.89  0.87  0.86  0.91  0.90  0.91  0.93  0.90  0.90  0.92 

0.92  0.91  0.90  0.90  0.90  0.92  0.89  0.91  0.90  0.91  0.93  0.90  0.96  0.94  0.94  0.93  0.95  0.93  0.92  0.91  0.92  0.88  0.92  0.92  0.91  0.92  0.94  0.89 

0.94  0.94  0.92  0.94  0.95  0.93  0.93  0.93  0.93  0.95  0.94  0.92  0.90  0.97  0.94  0.96  0.95  0.95  0.96  0.93  0.92  0.93  0.91  0.93  0.92  0.94  0.93  0.95 

0.89  0.88  0.86  0.90  0.92  0.95  0.91  0.91  0.86  0.84  0.86  0.88  0.87  0.89  0.89  0.89  0.89  0.87  0.91  0.89  0.90  0.90  0.92  0.89  0.92  0.86  0.87  0.88 

0.54  0.62  0.57  0.58  0.58  0.58  0.57  0.53  0.54  0.54  0.56  0.56  0.54  0.54  0.55  0.55  0.54  0.51  0.52  0.51  0.50  0.51  0.51  0.54  0.52  0.52  0.53  0.53 

0.85  0.90  0.94  0.88  0.87  0.86  0.89  0.88  0.81  0.79  0.79  0.74  0.80  0.75  0.79  0.81  0.81  0.78  0.78  0.81  0.95  0.93  0.90  0.90  0.85  0.89  0.91  0.89 

0.84  0.89  0.84  0.84  0.88  0.86  0.76  0.77  0.79  0.78  0.83  0.83  0.84  0.87  0.90  0.84  0.89  0.88  0.86  0.88  0.83  0.82  0.86  0.82  0.85  0.86  0.84  0.83 

0.81  0.86  0.85  0.78  0.60  0.77  0.83  0.81  0.81  0.80  0.78  0.79  0.83  0.80  0.80  0.85  0.86  0.81  0.79  0.82  0.83  0.83  0.82  0.86  0.79  0.79  0.81  0.81 

0.82  0.93  0.92  0.92  0.90  0.86  0.87  0.87  0.92  0.93  0.82  0.83  0.82  0.80  0.79  0.84  0.76  0.81  0.77  0.80  0.79  0.79  0.77  0.66  0.71  0.75  0.75  0.69 

0.86  0.91  0.90  0.90  0.90  0.92  0.89  0.89  0.91  0.93  0.89  0.89  0.88  0.79  0.87  0.86  0.84  0.87  0.88  0.82  0.78  0.81  0.83  0.80  0.85  0.87  0.77  0.81 

0.90  0.91  0.89  0.91  0.92  0.85  0.87  0.89  0.87  0.92  0.94  0.95  0.98  0.92  0.90  0.88  0.87  0.87  0.89  0.88  0.89  0.87  0.90  0.92  0.90  0.91  0.91  0.91 

0.79  0.93  0.88  0.81  0.93  0.88  0.89  0.75  0.79  0.86  0.87  0.77  0.81  0.76  0.79  0.82  0.79  0.82  0.76  0.72  0.67  0.72  0.66  0.67  0.72  0.73  0.75  0.69 

0.96  0.96  0.97  0.96  0.96  0.97  0.96  0.95  0.98  0.97  0.95  0.96  0.97  0.97  0.97  0.98  0.98  0.98  0.97  0.97  0.98  0.98  0.95  0.97  0.96  0.95  0.93  0.93 

0.85  0.91  0.89  0.88  0.87  0.84  0.82  0.80  0.78  0.82  0.88  0.87  0.86  0.86  0.86  0.84  0.84  0.84  0.84  0.83  0.85  0.83  0.85  0.81  0.84  0.85  0.85  0.85 

0.87  0.93  0.90  0.88  0.91  0.87  0.86  0.85  0.85  0.87  0.88  0.89  0.85  0.89  0.90  0.89  0.88  0.82  0.85  0.88  0.86  0.86  0.90  0.89  0.87  0.83  0.87  0.86 

0.85  0.88  0.90  0.88  0.84  0.79  0.80  0.82  0.74  0.78  0.92  0.93  0.85  0.90  0.89  0.92  0.87  0.83  0.84  0.75  0.81  0.81  0.84  0.86  0.86  0.83  0.87  0.83 

0.81  NA  NA  NA  NA  0.90  0.80  0.81  0.85  0.84  0.82  0.86  0.78  0.79  0.89  0.82  0.78  0.83  0.77  0.80  0.77  0.79  0.77  0.77  0.80  0.83  0.82  0.78 

0.86  0.78  0.86  0.92  0.94  0.91  0.92  0.94  0.91  0.97  0.92  0.90  0.78  0.90  0.79  0.83  0.79  0.77  0.78  0.78  0.79  0.82  0.81  0.88  0.82  0.84  0.87  0.92 

0.77  0.80  0.79  0.75  0.87  0.89  0.85  0.84  0.83  0.78  0.82  0.80  0.81  0.72  0.79  0.75  0.78  0.74  0.73  0.75  0.74  0.68  0.70  0.63  0.71  0.71  0.74  0.74 

0.66  0.77  0.88  0.78  0.81  0.69  0.74  0.79  0.72  0.80  0.77  0.74  0.73  0.74  0.69  0.64  0.62  0.58  0.59  0.51  0.65  0.46  0.50  0.51  0.57  0.55  0.50  0.48 

0.65  0.71  0.76  0.78  0.73  0.66  0.68  0.66  0.67  0.61  0.58  0.60  0.70  0.63  0.60  0.61  0.62  0.63  0.65  0.68  0.65  0.66  0.68  0.65  0.68  0.64  0.57  0.55 

0.80  0.89  0.81  0.81  0.79  0.81  0.78  0.75  0.77  0.76  0.77  0.79  0.78  0.83  0.78  0.76  0.78  0.81  0.79  0.81  0.82  0.81  0.77  0.82  0.82  0.83  0.83  0.77 

0.88  0.88  0.90  0.92  0.95  0.92  0.92  0.92  0.91  0.93  0.90  0.90  0.88  0.87  0.88  0.88  0.88  0.87  0.87  0.84  0.83  0.90  0.88  0.85  0.84  0.79  0.78  0.83 

0.71  0.75  0.79  0.81  0.71  0.75  0.67  0.68  0.72  0.69  0.72  0.77  0.80  0.72  0.72  0.73  0.71  0.69  0.71  0.68  0.74  0.73  0.68  0.65  0.62  0.60  0.67  0.62 

0.89  0.92  0.92  0.86  0.88  0.91  0.87  0.91  0.91  0.90  0.92  0.88  0.89  0.86  0.88  0.86  0.92  0.83  0.93  0.93  0.88  0.92  0.87  0.86  0.91  0.84  0.85  0.79 

0.89  0.88  0.88  0.85  0.82  0.89  0.90  0.89  0.88  0.89  0.93  0.92  0.89  0.94  0.91  0.89  0.90  0.89  0.93  0.89  0.86  0.90  0.90  0.85  0.88  0.88  0.84  0.85 

0.76  0.71  0.89  0.86  0.82  0.86  0.81  0.77  0.79  0.81  0.70  0.75  0.74  0.76  0.71  0.70  0.66  0.60  0.77  0.71  0.73  0.77  0.81  0.77  0.78  0.81  0.79  0.75 

0.87  0.87  0.88  0.88  0.82  0.84  0.81  0.79  0.81  0.79  0.87  0.90  0.93  0.90  0.94  0.95  0.91  0.88  0.86  0.88  0.85  0.86  0.88  0.92  0.87  0.91  0.88  0.87 
0.86  0.95  0.93  0.96  0.94  0.92  0.95  0.93  0.92  0.88  0.86  0.82  0.81  0.83  0.86  0.85  0.85  0.84  0.84  0.77  0.85  0.83  0.80  0.82  0.81  0.79  0.80  0.85 

0.85  0.90  0.94  0.90  0.92  0.89  0.88  0.88  0.88  0.83  0.81  0.82  0.72  0.87  0.85  0.85  0.85  0.84  0.85  0.88  0.85  0.81  0.83  0.81  0.84  0.84  0.85  0.80 

0.65  NA  0.54  0.61  0.70  0.67  0.56  0.60  0.60  0.68  0.64  0.62  0.67  0.65  0.65  0.60  0.60  0.73  0.65  0.70  0.71  0.69  0.71  0.68  0.73  0.64  0.64  0.67 

0.78  0.79  0.85  NA  0.83  0.80  0.81  0.81  0.71  0.86  0.80  0.84  0.78  0.77  0.81  0.80  0.80  0.78  0.80  0.73  0.76  0.76  0.78  0.76  0.73  0.76  0.77  0.69 

0.94  0.91  0.93  0.92  0.93  0.96  0.94  0.96  0.94  0.95  0.97  0.94  0.95  0.95  0.97  0.94  0.93  0.94  0.96  0.94  0.96  0.93  0.94  0.96  0.96  0.95  0.90  0.90 

0.75  0.83  0.79  0.81  0.84  0.80  0.76  0.77  0.82  NA  0.77  0.79  0.77  0.77  0.83  0.75  0.78  0.75  0.77  0.80  0.76  0.73  0.68  0.69  0.65  0.67  0.65  0.53 

0.90  0.90  0.85  0.91  0.89  0.92  0.89  0.89  0.88  0.84  0.89  0.88  0.92  0.87  0.94  0.93  0.91  0.92  0.93  0.93  0.97  0.92  0.92  0.90  0.88  0.89  0.87  0.85 

0.85  0.91  0.87  0.90  0.88  0.91  0.84  0.78  0.79  0.79  0.85  0.92  0.85  0.89  0.88  0.87  0.88  0.85  0.88  0.87  0.84  0.83  0.84  0.79  0.81  0.79  0.80  0.80 

0.86  0.87  0.88  0.94  0.89  0.89  0.88  0.84  0.85  0.81  0.82  0.87  0.83  0.87  0.89  0.87  0.83  0.85  0.85  0.85  0.83  0.87  0.85  0.88  0.84  0.85  0.83  0.85 

0.88  0.91  0.94  0.92  0.93  0.91  0.87  0.90  0.90  0.91  0.92  0.86  0.89  0.88  0.84  0.87  0.84  0.85  0.86  0.88  0.89  0.84  0.86  0.87  0.87  0.85  0.86  0.87 

0.83  0.90  0.89  0.88  0.85  0.79  0.80  0.78  0.81  0.82  0.82  0.83  0.84  0.83  0.80  0.85  0.84  0.83  0.81  0.84  0.80  0.84  0.85  0.83  0.82  0.82  0.83  0.81 

0.80  NA  0.75  0.81  0.84  0.82  0.83  0.72  0.78  0.82  0.81  0.90  0.88  0.91  0.87  0.80  0.85  0.86  0.75  0.74  0.69  0.73  0.78  0.78  0.70  0.83  0.76  0.81 

0.89  0.86  0.89  0.88  0.87  0.85  0.88  0.88  0.86  0.87  0.87  0.92  0.91  0.91  0.92  0.92  0.89  0.87  0.92  0.92  0.92  0.88  0.88  0.88  0.91  0.86  0.91  0.86 

0.85  0.88  0.90  0.86  0.82  0.90  0.81  0.83  0.83  0.85  0.88  0.86  0.89  0.86  0.85  0.88  0.88  0.84  0.86  0.82  0.89  0.85  0.84  0.84  0.81  0.81  0.76  0.76 

ECB Statistics Paper Series No 49 

42 

 
 
 
 
D.2 Services 

REPORTER  AVG  1995  1996  1997  1998  1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016  2017  2018  2019  2020  2021 

AE 

AR 

AT 

AU 

BE 

BG 

BR 

CA 

CH 

CL 

CN 

CO 

CY 

CZ 

DE 

DK 

DZ 

EE 

ES 

FI 

FR 

GB 

GR 

HK 

HR 

HU 

ID 

IE 

IL 

IN 

IS 

IT 

JP 

KR 

LT 

LU 

LV 

MA 

MT 

MX 

MY 

NL 

NO 

NZ 

PE 

PH 

PL 

PT 

RO 

RU 

SA 

SE 

SG 

SI 

SK 

TH 

TR 

TW 

UA 

US 

ZA 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.77  0.75  0.50  0.50  0.75  0.69  0.64  0.63  0.75  0.76  0.74  0.75  0.80  0.81  0.83  0.83  0.85  0.84  0.84  0.82  0.82  0.90  0.87  0.88  0.80  0.81  0.78  0.75 

0.78  0.85  0.85  0.89  0.89  0.83  0.70  0.78  0.90  0.93  0.79  0.81  0.89  0.71  0.73  0.73  0.71  0.68  0.63  0.70  0.75  0.69  0.77  0.80  0.82  0.88  0.70  0.54 

0.72  0.80  0.75  0.75  0.75  0.75  0.73  0.71  0.65  0.58  0.62  0.66  0.61  0.71  0.66  0.68  0.70  0.73  0.74  0.71  0.72  0.79  0.71  0.77  0.80  0.80  0.75  0.74 

0.66  NA  NA  NA  NA  NA  NA  NA  NA  NA  0.54  0.68  0.66  0.83  0.74  0.74  0.72  0.65  0.67  0.72  0.67  0.67  0.64  0.66  0.60  0.67  0.55  0.55 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.65  0.68  0.57  0.62  0.65  0.60  0.72  0.67  0.66  0.69  0.72  0.70  0.71  0.74  0.64  0.76  0.61  0.68  0.62  0.61  0.58  0.57  0.57  0.63  0.56  0.62  0.60  0.71 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.23  NA  NA  NA  NA  NA  NA  NA  NA  0.33  0.00  0.12  0.25  0.12  0.28  0.53  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.81  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  0.92  0.83  0.88  0.75  0.69  NA  NA  NA  NA  NA  NA  NA  NA  NA 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.35  NA  NA  NA  NA  NA  NA  NA  0.36  0.36  0.39  0.34  0.41  0.37  0.30  0.30  0.37  0.27  0.29  0.31  0.31  0.35  0.34  0.37  0.40  0.45  0.38  0.41 

0.72  NA  NA  NA  NA  NA  0.44  0.76  0.71  0.77  0.81  0.79  0.75  0.75  0.67  0.73  0.71  0.75  0.76  0.75  0.70  0.74  0.69  0.68  0.71  0.74  0.74  0.71 

0.91  1.00  1.00  1.00  1.00  0.86  0.78  0.82  0.84  0.90  0.87  0.86  0.84  0.90  0.90  0.88  0.91  0.90  0.93  0.90  0.94  0.94  0.94  0.92  0.98  0.96  0.87  0.83 

0.71  NA  NA  NA  NA  0.71  0.62  0.61  0.72  0.74  0.78  0.76  0.66  0.69  0.64  0.64  0.61  0.69  0.68  0.73  0.74  0.70  0.73  0.74  0.76  0.80  0.75  0.75 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.70  NA  NA  NA  NA  NA  NA  NA  NA  0.58  0.65  0.76  0.74  0.61  0.72  0.82  0.77  0.72  0.62  0.68  0.69  0.68  0.63  0.80  0.64  0.74  0.77  0.70 

0.65  0.00  0.25  0.25  0.50  0.58  0.57  0.62  0.69  0.74  0.73  0.73  0.75  0.79  0.69  0.70  0.66  0.66  0.66  0.73  0.75  0.72  0.77  0.76  0.82  0.79  0.76  0.78 

0.74  0.50  0.50  0.50  0.50  0.83  0.79  0.68  0.69  0.76  0.76  0.72  0.78  0.65  0.73  0.74  0.76  0.72  0.77  0.83  0.82  0.83  0.88  0.81  0.85  0.79  0.79  0.88 

0.84  0.83  0.88  0.75  0.75  0.88  0.75  0.80  0.83  0.81  0.83  0.81  0.80  0.76  0.69  0.82  0.79  0.88  0.92  0.91  0.89  0.91  0.91  0.83  0.89  0.94  0.87  0.85 

0.70  1.00  0.75  0.75  0.88  0.73  0.62  0.65  0.62  0.62  0.66  0.65  0.75  0.73  0.65  0.67  0.67  0.69  0.65  0.66  0.67  0.68  0.73  0.74  0.70  0.70  0.66  0.62 

0.67  0.25  0.25  NA  NA  0.75  0.50  0.50  0.50  0.72  0.79  0.76  0.83  0.74  0.74  0.84  0.60  0.64  0.61  0.73  0.70  0.69  0.79  0.75  0.77  0.79  0.76  0.71 

0.59  NA  NA  NA  NA  NA  0.57  0.64  0.54  0.54  0.53  0.52  0.58  0.56  0.55  0.60  0.58  0.66  0.67  0.62  0.64  0.65  0.65  0.58  0.58  0.60  0.46  NA 

0.67  NA  NA  NA  NA  0.36  0.47  0.55  0.44  0.57  0.62  0.65  0.44  0.67  0.67  0.74  0.76  0.68  0.71  0.82  0.72  0.79  0.81  0.80  0.78  0.76  0.79  0.85 

0.64  NA  NA  NA  NA  0.25  0.14  0.34  0.35  0.38  0.63  0.77  0.81  0.81  0.74  0.72  0.64  0.66  0.69  0.76  0.72  0.71  0.80  0.81  0.75  0.76  0.76  0.78 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.60  NA  NA  NA  NA  1.00  0.50  0.00  0.65  0.65  0.63  0.57  0.52  0.57  0.55  0.55  0.66  0.61  0.69  0.62  0.66  0.61  0.66  0.67  0.56  0.61  0.57  0.61 

0.25  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  0.31  0.22  0.23  NA  NA  NA  NA  NA  NA  NA  NA  NA 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.65  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  0.58  0.43  0.52  0.58  0.72  0.61  0.68  0.76  0.69  0.65  0.72  0.69  0.79 

0.75  0.17  0.38  0.38  0.50  0.75  0.92  0.78  0.81  0.72  0.82  0.83  0.79  0.75  0.74  0.83  0.78  0.73  0.81  0.87  0.83  0.90  0.89  0.80  0.87  0.89  0.87  0.90 

0.74  0.83  0.59  0.64  0.71  0.73  0.70  0.67  0.75  0.75  0.81  0.81  0.76  0.79  0.63  0.57  0.65  0.69  0.68  NA  0.78  0.78  0.79  0.82  0.85  0.88  0.76  0.74 

0.81  NA  NA  NA  0.50  0.75  0.75  1.00  1.00  1.00  1.00  0.62  0.60  0.49  0.48  0.57  0.56  0.54  0.83  1.00  1.00  1.00  1.00  1.00  1.00  1.00  1.00  0.75 

0.68  NA  NA  NA  NA  NA  NA  NA  NA  0.56  0.57  0.60  0.72  0.67  0.62  0.68  0.58  0.60  0.69  0.72  0.65  0.67  0.75  0.75  0.75  0.78  0.73  0.75 

0.55  1.00  0.50  0.50  0.50  0.40  0.40  0.56  0.54  0.50  0.56  0.51  0.55  0.50  0.56  0.52  0.53  0.51  0.50  0.48  0.55  0.56  0.55  0.61  0.62  0.64  0.65  0.64 

0.63  NA  NA  NA  NA  NA  0.56  0.50  0.70  0.71  0.45  0.65  0.76  0.68  0.71  0.68  0.62  0.65  0.49  0.62  0.62  0.76  0.64  0.58  0.64  0.60  0.65  0.69 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.46  NA  NA  NA  NA  NA  NA  NA  NA  NA  0.55  0.68  0.69  0.61  0.49  0.45  0.39  0.53  0.48  0.41  0.32  NA  NA  0.40  0.36  0.35  0.29  0.37 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.33  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  0.33  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.78  1.00  0.50  0.62  0.75  0.75  0.79  0.85  0.77  0.77  0.74  0.80  0.78  0.79  0.76  0.76  0.76  0.74  0.74  0.80  0.83  0.87  0.85  0.91  0.83  0.84  0.74  0.81 

0.55  NA  NA  NA  NA  0.33  0.50  0.50  0.69  0.60  0.50  0.54  0.46  0.47  0.41  0.44  1.00  0.62  0.66  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.66  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  0.66  0.67  0.62  0.59  0.78  0.71  0.69  0.55  0.66  0.74  0.67  0.60  0.67  0.67  0.64  0.61 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.77  NA  NA  NA  NA  NA  NA  NA  NA  NA  0.65  0.73  0.75  0.71  0.69  0.66  0.71  0.71  0.75  0.82  0.81  0.90  0.85  0.83  0.83  0.81  0.83  0.79 

0.70  0.50  0.00  0.00  0.00  0.75  0.75  0.71  0.86  0.84  0.86  0.83  0.86  0.87  0.76  0.82  0.75  0.74  0.74  0.79  0.81  0.81  0.82  0.84  0.79  0.77  0.71  0.79 

0.71  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  0.62  0.69  0.66  0.65  0.70  0.51  0.62  0.65  0.75  0.80  0.75  0.76  0.74  0.79  0.81  0.75  0.79 

0.65  NA  NA  NA  NA  NA  NA  NA  0.62  0.70  0.62  0.63  0.65  0.67  0.64  0.65  0.71  0.66  0.64  0.67  0.61  0.61  0.66  0.61  0.60  0.67  0.69  0.65 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.76  NA  NA  NA  NA  0.88  0.87  0.84  0.79  0.71  0.61  0.73  0.75  0.66  0.71  0.70  0.69  0.76  0.72  0.72  0.81  0.84  0.82  0.84  0.76  0.78  0.74  0.76 

0.62  NA  NA  NA  NA  NA  0.43  0.43  0.43  0.50  0.64  0.56  0.61  0.67  0.60  0.70  0.69  0.81  0.69  0.65  0.71  0.75  NA  NA  NA  NA  NA  NA 

0.68  NA  NA  NA  NA  0.00  0.20  NA  0.52  0.57  0.65  0.74  0.75  0.77  0.75  0.67  0.78  0.76  0.74  0.87  0.83  0.78  0.81  0.84  0.76  0.72  0.79  0.76 

0.61  NA  NA  NA  NA  0.00  0.63  0.60  0.56  0.59  0.53  0.54  0.51  0.67  0.67  0.65  0.44  0.41  0.59  0.65  0.69  0.71  0.74  0.75  0.77  0.78  0.77  0.76 

0.52  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  0.25  0.33  0.75  0.62  0.50  0.67  0.50  0.50  0.67  0.40 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

0.30  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  0.17  0.17  0.17  0.33  0.33  0.33  0.50  0.17  0.50 

0.73  0.83  0.78  0.72  0.70  0.79  0.81  0.81  0.81  0.78  0.75  0.78  0.73  0.70  0.70  0.72  0.79  0.76  0.66  0.74  0.62  0.77  0.79  0.72  0.74  0.67  0.51  0.46 

NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 

ECB Statistics Paper Series No 49 

43 

 
Acknowledgements 

We would like to thank Michael Fidora and Christoph Fischer for their helpful feedback. Moreover, we thank Sirko 
Leonhardt for the lively methodological discussions as well as his tremendous support in calculating the figures. 
Rémy Brisson worked on this paper while employed by the ECB and has in the meantime left the ECB.  

Martin Schmitz 
European Central Bank, Frankfurt am Main, Germany; email: Martin.Schmitz@ecb.europa.eu 

Andreas Dietrich 
Deutsche Bundesbank, Frankfurt am Main, Germany; email: Andreas.Dietrich@bundesbank.de 

Rémy Brisson 
Formerly European Central Bank; email: remy.brisson@protonmail.com 

© European Central Bank, 2024 

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ISBN 978-92-899-6790-7 ISSN 2314-9248  doi:10.2866/341775  QB-BF-24-003-EN-N