European economic statistics : 2008 edition.
Date: 2008-08-06
Institutions: Eurostat
Subjects: EU statistics, economic analysis, economic statistics, statistical method

S t a t i s t i c a l   b o o k s

European Economic Statistics

2008 edition

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ISBN 978-92-79-08886-5
DOI 10.2785/10459
Cat. No. KS-30-08-410-EN-C

Theme: Economy and finance
Collection: Statistical books 

© European Communities, 2008
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EUROSTAT
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on Eurostat Internet site.

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Acknowledgements

This Statistical Book could only be published thanks to the assistance and support of the Eurostat Editorial Committee, 
the authors of editorial and methodological articles, and the following colleagues in Eurostat who contributed to the data 
analysis sections:

EUROSTAT, THE STATISTICAL OFFICE OF THE EUROPEAN COMMUNITIES

DIRECTORATE C: NATIONAL AND EUROPEAN ACCOUNTS

C1 National accounts – methodology and analysis: P. Konijn, L.Svennebye

C2 National accounts - production: L. Biedma, D. Leythienne, A. Krüger, J. Jalava

C3 Public finance: L. Frej-Ohlsson

C4 Balance of payments: M. Hult, M. Hussain, L. Pappalardo, G. Vergina, T. Petridou

C5 Validation of public accounts: P. Parlasca, J. Verrinder, M. Wozowczyk

DIRECTORATE D: ECONOMIC AND REGIONAL STATISTICS

D4 Price statistics: C. Wirtz, T. Mrlianova

DIRECTORATE F: SOCIAL STATISTICS AND INFORMATION SOCIETY

F2 Labour market statistics: A. de la Fuente, F Ramb

DIRECTORATE G: BUSINESS STATISTICS

G3 International trade statistics — production: G. Gambini

Eurostat would like to thank following institutions for their assistance:

DIRECTORATE-GENERAL FOR TRANSLATION OF THE EUROPEAN COMMISSION

OFFICE FOR OFFICIAL PUBLICATIONS OF THE EUROPEAN COMMUNITIES

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Table of contents

Foreword ............................. ....................... .. ... . .... .... .... .... .... .... .... .... .... .... .... ..... .... .... .... .... ... ..... ... ..... ... ..... ... ..... ... ..... ... ..... ... ..... ... ........ ........ ........ 7

page

1.  Editorial............................. ....................... .. ... . .... .... .... .... .... .... .... .... .... .... .... ..... .... .... .... .... ... ..... ... ..... ... ..... ... ..... ... ..... ... ..... ... ..... ... ........ ........ ..... 9

1.1  How macroeconomic statistics shape economic policy and vice-versa ........................................... .............. .. ....... .. ....... ... 11

1.2  Global economic statistics: The example of purchasing power parities ........................................... ................ ....... .. ....... .. 17

2.   Statistical Analysis.................................... .................. ..... ... ...... .. ...... .. ...... .. ...... .. ...... .. ........ . ....... . ....... ........ ........ ........ ........ ........ ........ ..... 23

2.1  Overview of statistical analysis ............................. .......................... ... ...... .. ...... .. ...... .. ...... .. ...... .. ...... .. . ....... . ........ ........ ........ ........ . 25

2.2  National accounts .................................. ..................... ... ...... .. ...... .. ...... .. ...... .. ...... .. ...... .. . ....... . ........ ........ ........ ........ ........ ........ ........ . 28

2.3  Public finances.................................... ................... ... ...... .. ...... .. ...... .. ...... .. ...... .. ...... .. . ....... . ........ ........ ........ ........ ........ ........ ........ ....... 48

2.4 

Inflation, interest rates and exchange rates ........................................... .............. ... ...... .. ...... .. ...... .. ...... .. ...... .. ....... . .. ..... .. ....... .. ..76

2.5  External dimension of the economy .................................... .................. ..... ... ...... .. ...... .. ...... .. ...... .. ...... .. ........ . ....... . ....... ........ ......87

2.6  Labour market .................................... .................. ..... ... ...... .. ...... .. ...... .. ...... .. ...... .. ........ . ....... . ....... ........ ........ ........ ........ ........ ........ .. 106

3.  Methodology .................................... .................. ..... ... ...... .. ...... .. ...... .. ...... .. ...... .. ........ . ....... . ....... ........ ........ ........ ........ ........ ........ ........ .....119

3.1  Updating statistical methodology .................................................... .... ...... .. ...... .. ...... .. ...... .. ...... .. ...... .. .. ..... ... ..... .... ... .. .. ..... .. .. .. 121

3.1.1  Update of the SNA 1993 and revision of ESA 95 ........................................... .............. ... ...... .. ...... .. ...... .. ...... .. ....... ... 122

3.1.2  The Balance of Payments and International Investment Manual (BPM6) ....................................... ................. 130

3.2  European sector accounts........................................... .............. ... ...... .. ...... .. ...... .. ...... .. ...... .. ....... ... ..... ... ..... ... ..... ... ..... .. ....... .. ..... . 135

3.3  Harmonised indices of consumer prices ........................................... .............. ... ...... .. ...... .. ...... .. ...... .. ...... .. ....... ... ...... .. ..... .. .... 144

4.  Statistical annex........................................... .. ............ ... ...... .. ...... .. ...... .. ...... .. ...... .. ....... ... ..... ... ..... ... ..... ... ..... ... ..... ... ..... ... ..... . .. ..... .. ....... . 157

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Foreword

It is a great pleasure for me to introduce a new Eurostat flagship publication “European Economic Statistics”. There are 
already a number of Eurostat publications illustrating different areas of the economy. However, a comprehensive Eurostat 
publication giving an overall picture of the structure and development of the European economy has not been available. 
This publication fills that need.

Although the major part of the publication is devoted to presenting and analysing the most recent statistics on the Euro-
pean economy, its novel editorial and methodological sections provide background on topical issues and on the data pre-
sented. Preparation of high quality and relevant statistics requires a close coordination between countries at international 
level and also a sensitivity to user needs, including the use of the data for decision making. These aspects are explored in 
the editorial section.

The publication covers the full range of Eurostat’s economic indicators, including national accounts, government finances, 
balance of payments, prices, monetary and financial accounts, foreign trade and the labour market.

I would like to congratulate members of the Eurostat editorial board and contributors for their valuable input. 

Years ago a statistician might have claimed that statistics dealt only with the processing of data. A statistician today will be 
more likely to say that statistics is concerned with both processing data and ensuring they are relevant for decision making 
in the face of uncertainty. We can read in The Adventures of Sherlock Holmes “Data! data! data!” …I can’t make bricks 
without clay.” In this sense, I do hope that this publication will serve as clay for building bricks; that it will be found as a 
useful tool to study the European economy, and provide a helpful overview for the generalist user.

Hervé Carré
Director-General, Eurostat

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Editorial

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Editorial

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1.1  How macroeconomic statistics shape economic policy and vice-versa

Marco Buti and João Nogueira Martins*
European Commission — Directorate-General for Economic and Financial Affairs

“Nothing is more important for [economic] policy than good statistics.” 
Alexandre Lamfalussy (1996) 1

Economic  accounting  and  macroeconomic  statistics  have  been  extremely  successful  fields  of  economics  and  statistics. 
Nowadays, it is inconceivable to reflect on economic issues without the concepts developed by statisticians — such as GDP 
or GNI, net borrowings, current account, price deflators, etc. — or discuss policy options without the respective data. It is 
no coincidence that economic theory and macroeconomic accounting have evolved in parallel for most of the last 80 years.2
The success and popularity of economic accounting also extends to the public at large. It is now all but impossible to find 
a copy of a quality newspaper in a democratic country that does not refer to GDP growth rates, external balances, govern-
ment deficit and debt, savings ratio, etc. 

Economic statistics as a key input into economic policy

In his presidential address to the Econometric Society, over 50 years ago, Kenneth J. Arrow described policymaking as 
having four parts: (i) an objective function; (ii) a range of policy instruments; (iii) a model of empirical relations between 
macroeconomic variables and (iv) computational methods.3 Macroeconomic statisticians have provided not only the meas-
urement of the variables that enter each of the four parts identified by Arrow, but most of the concepts that constitute the 
language of modern economists and policymakers.

The objective function of policymakers expresses the relative desirability of different possible macroeconomic outcomes. Its 
form depends on a number of considerations, some cultural and ideological, and evolves with time. While acknowledging 
that the objective function should represent the welfare of a representative economic agent, economists often model policy-
maker preferences with the help of simplified functions which depend on statistical concepts, such as projected real growth, 
the inflation rate, unemployment, the government deficit, external imbalances, the share of taxes paid and benefits received 
by social strata, and the distribution of income among factors or within categories of population. It is useful to keep in mind 
that these are only simplified descriptions of social welfare functions. Indeed, it is not possible to list all the arguments 
that enter into objective policymaker functions, let alone to present that function in an explicit way. If ever it was possible 
to make explicit the objective function of a policymaker (e.g. of a central bank or of a fiscal policymaker), and to have full 
knowledge of the relationship between policy instruments and variables in the objective function, then policymaking could 
be delegated to some computation device. This is also why the macroeconomic statistics that are required by policymakers 
are so extensive and ever changing. 

To optimise the social welfare function — in analytical models, the objective function is often presented as a loss function 
of a quadratic form to be minimised — the policymaker has a number of instruments at its disposal. It may increase or 
decrease total government expenditure, modify the structure of expenditure either by changes in its economic classifica-
tion (e.g. increasing investment and reducing social benefits) or in its functional classification (e.g. increasing education ex-
penditure and reducing expenditure on public order), or replace social contributions with indirect taxes, among many other 
policy alternatives. The implementation of these policies will, in practice, involve changes in tax codes or in the budget laws, 

* The views expressed in this article are those of the authors and are not attributable to the European Commission.

1 The precise quote is “Nothing is more important for monetary policy than good statistics”, since it appeared in the foreword of a booklet published by the European Monetary 

Institute  – the forerunner of the ECB — specifically devoted to statistics for monetary policy (The Statistical Requirements for Monetary Union, Frankfurt am Main, July 1996). We 
are confident that the Baron Lamfalussy would agree that his view on statistics extends to economic policy in general.

2 On the progress of economic accounts over the twentieth century and its cross-fertilisation with economy theory, see André Vanoli, Une Histoire de la Comptabilité Nationale, 

Paris: La Découverte, 2002. English version: A History of National Accounting, Amsterdam: IOS Press, 2005.

3 Kenneth Arrow, Statistics and Economic Policy, presidential address to the Econometric Society (Cleveland, 27 December 1956); reprinted in Econometrica, 25(4): 523-531.

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or some kind of administrative decision. However, as these examples illustrate, the whole reflection by the policymaker is 
based on concepts defined and measured by macroeconomic statistics.

For structural policies, the instrument may be more difficult to measure or to describe in economic accounting terms 
— consider for example, a change in the unemployment allowance eligibility rules, or in the rules on working hours, a 
liberalisation in the opening times of shops, reform of the financial supervisory framework, minimum wages for low-
productivity workers or setting some minimum income for people on low incomes. However, these measures are expected 
to have some direct or indirect impact on variables monitored by macroeconomic statisticians — such as the poverty rate, 
growth,  government  deficit,  unemployment,  or  private  consumption  —  which  appear  in  the  objective  function,  or  on 
intermediate targets.

The policymaker’s model, that is, the relations connecting instruments and variables that enter the objective function, and 
the  tradeoffs  or  synergies  between  those  variables,  depend  on  theoretical  conjectures.  Purely  theoretical  considerations 
are, nevertheless, of limited interest for policymakers if they are not tested against empirical evidence. The information ac-
cumulated over many years by statisticians and synthesised in long time series represent a laboratory for testing economic 
theories. Or, as Alan Greenspan puts it in his memoirs, “a forecast system is only as good as the accuracy of its historical 
data base from which future in a cycle can be projected”.4 This brings us to an important issue: a statistic in isolation is of 
limited value; the data accumulated over a number of periods is more valuable than the sum of its components in isolation. 
To know that the euro area external trade surplus amounted to EUR 6.1 bn in October 20075 would not be particularly use-
ful for policymaking if one did not have comparable data for several years. 

The computational methods can be seen as concrete forecasting procedures, like the forecasts published by Commission 
departments,6 or those underlying the government budget in each country. These computational methods try to anticipate 
the consequences of policy actions or market developments on the variables that enter the objective function. These fore-
casts will have macroeconomic statistics as their key input and will show results according to the same concepts used by 
the statisticians. It is useful to note that, on most occasions, forecasts are the real triggers of policy action. Given the lags 
in  policy  decisions  and  in  policy  transmission  mechanisms,  policymakers  cannot  wait  for  hard  data  before  reacting.  A 
concrete example should make this clear: forward-looking central banks usually do not increase (or reduce) interest rates 
because the latest readings of price indices show inflation above (or below) some target, but because the latest available in-
formation — including policy actions and soft data — have led them to change their inflation projections. This also shows 
that the arguments of the objective function to be optimised are not the data released by statisticians, but the projection for 
future developments in each of the macroeconomic variables.7 This is because policymaking is intended to influence events 
to come, while statistics are a description of past developments. The release of statistics is often more important to monitor 
the implementation of policies than to decide them in the first place. 

In democratic societies, policymaking requires another step, not explicitly identified in the Arrow’s typology, in connection 
with the accountability of economic authorities. The latter must explain to the populations and to their elected representa-
tives what the macroeconomic issues are and how policy initiatives tackle those issues. The public should be able to check 
the statistics to assess how effective policymakers have been — in controlling inflation, promoting growth, fighting unem-
ployment, reducing income inequalities, etc. — whether policies are decided by elected governments or by independent 
agencies (such as a central bank or a fiscal council). This is not only because of the need for transparency in a democratic so-
ciety, but for efficiency reasons. If policy decisions cannot be justified by reference to empirical evidence, the policy-maker 
will lack credibility. There are plenty of examples that show that decisions by credible institutions gain in efficiency. As a 
result, the statistical indicators must be able to summarise complex developments and issues in a small number of simple 
variables that can be understood not only by experts, but by the public at large.

The availability of statistics has helped to make modern-day politicians and policymakers more accountable for their ac-
tions than ever. However, the role of statistics in the accountability of policymakers raises the issue of the statistical literacy 

4 Alan Greenspan (2007), The Age of Turbulence: Adventures in a New World, New York, Penguin Press.

5 Eurostat News Release No 180/2007, 18 December 2007.

6 The Commission services’ forecasts appear four times a year: two rounds of full-fledged forecasts in spring and autumn — more than sixty variables for all EU Member States, 

candidate countries and for a few other major economies — and two rounds of interim forecasts for a reduced set of countries and variables. See e.g. Economic Forecasts — Autumn 
2007, European Economy, No 7, 2007.

7 For example, in the literature on inflation targeting, the intermediate variable for the central bank is expected inflation and not the official inflation series compiled by the statistical 

offices. 

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of the public at large.8 In societies of ever-increasing complexity, with a proliferation of information sources of unequal 
quality, effective communication of official economic statistics is a task that needs to increase in relevance and priority for 
statistical offices. This communication and education includes explaining concepts, solving misunderstandings and avoid-
ing pitfalls, as well as stating the limits of simple statistics in depicting complex issues, and making clear the margins of 
error of the published data.

Parallel evolution of economic policy and of economic accounting

To understand how the available statistics shape and influence economic policy, it is useful to consider how macroeconomic 
issues have influenced data requirements. The ‘birth certificate’ of modern macroeconomic statistics is dated 1934 when the 
first modern estimates of national income for the US for 1929-1932 were published by the US Department of Commerce in 
a report to the Senate.9 Modern macroeconomic statistics appeared as an autonomous branch of economics and statistics 
because of a concrete policy need. When the US Senate asked the Department of Commerce to prepare income statistics, 
the United States — and, in fact, most Western economies — were in the middle of the Great Depression. There was an ur-
gent need to provide policymakers with information about the state of the economy that was more consistent and complete 
than the fragmentary information available until then, so that they could better understand effective developments, and 
ponder and calibrate measures to be taken.

The same policymaking purpose was present on several occasions in the last eight decades when economic accounting 
made a number of leaps forward. During World War II, in the UK and in the US, planning needs were the motivation for 
the development of macroeconomic statistics. There was a need to grasp what the military effort would imply for private 
consumption, or in the Keynes’s words ‘how to pay for the war’.10 The link between wartime policymaking and the develop-
ment of economic accounting in the UK is well illustrated by the fact that the first set of modern accounts for the UK, by 
Richard Stone and James Meade, were published as an appendix to the white paper that accompanied the UK budget of 
April 1941.11 The need to know the economic consequences of demobilisation on employment and unemployment were also 
behind the compilation of input-output tables in the US shortly after.

At the end of the 1940s, the main macroeconomic policy issues were how to allocate resources in order to reconstruct 
the European economies. This was the context in which Stone helped to draft the UN report on the measurement of na-
tional income and the construction of social accounts,12 which led to the 1953 edition of the System of National Accounts 
(SNA).13  In  the  1950s  and  1960s,  when  Keynesianism  was  reaching  its  zenith,  the  policy  issues  were  the  government’s 
tax-and-spend role with the aim of reaching and keeping full employment over the business cycle. The accumulation of 
consistent statistical data contributed to the development of the first macroeconomic models, such as those pioneered by 
Jan Tinbergen and Lawrence Klein, for forecasting and policy assessment purposes. The modelling of economies was also 
an incentive to compile more complete and detailed statistics and longer time series. At the same time, financial issues 
were  becoming  more  and  more  relevant  to  the  understanding  of  economic  developments. This  explains  several  of  the 
improvements in the SNA of 1968, the development of national accounting systems in many countries, improvements in 
the measurement of non-market output, on the production of constant-price measures that would allow better compari-
son of economies across time and the production of accounts that distinguish financial and non-financial corporations. 
The evolution of the balance of payment manuals14 was also heavily influenced by changes in the international monetary 
system and by a number of currency crises. In Europe, the establishment of the customs union was fundamental to the 
development of external trade statistics. 

8

Some authors even question statistical literacy among economists and policymakers! See for example Vito Tanzi, ‘Fiscal Policy: When Theory Collides with Reality’, CEPS Working 
Document, No 246, June 2006; and Fritz Bos, ‘Use, Misuse and Proper Use of National Accounts Statistics’, Statistics Netherlands Occasional Paper, NA-096, 2007.

9 US Department of Commerce (1934), ‘National Income 1929 to 1932’, Senate Document 124, 73d Congress, 2d session, January 4 1934; summary published in Survey of Current 

Business, 14(2), February, 17-19 available for download at: http://library.bea.gov/u?/SCB,4219.

10 Keynes, J. M., How to Pay for the War: A Radical Plan for the Chancellor of the Exchequer, London: Macmillan, 1940.

11 

‘An Analysis of the Sources of War Finance and an Estimate of the National Income and Expenditure in 1938 and 1940’, 7 April 1941.

12 United Nations, Measurement of National Income and the Construction of Social Accounts, Geneva: United Nations.

13 Organisation for European Economic Cooperation, A Standardised System of National Accounts, Paris: OEEC, 1952; and United Nations, A System of National Accounts and 

Supporting Tables, 1952.

14 The balance of payments manual was initially published in 1948, and then revised in 1950 (2nd edition), 1961 (3rd edition), 1977 (4th edition) and 1993 (5th edition). The 6th edition 

is expected to be completed by the IMF in 2008.

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The publication of ESA (1st and 2nd editions, 1970 and 1979 respectively)15 was directly related to the need for macroeco-
nomic coordination among the EU Member States. ESA expanded on previous manuals in areas like production and fi-
nance, distribution and redistribution of income and allowed more precise comparison of different economies. During the 
stagflation years of the 1970s, interest and methodological progress moved to the development of more accurate measures 
of  prices  and  of  inflation-adjusted  output.  More  recently,  technological  progress,  and  major  changes  in  the  quality  and 
price of some products and services, led to better measurements of deflators, including direct measurement of non-market 
output, hedonic pricing and chain-linking of constant-price series. The SNA of 199316 and the corresponding European 
version — ESA9517– can be seen as a reply to the changing structure of economies, given a stronger emphasis on financial 
issues, identifying new financial instruments, widening the concept of capital formation (e.g. expenditure in software) and 
opening the way to environmental accounts — an area that remains underdeveloped but that one may expect will increase 
in political relevance in coming years. 

Over the last 15 years, economic and monetary union has been a major catalyst of the increase in quantity and quality of 
macroeconomic statistics in the EU. Initially, attention focused on the variables that were relevant to the assessment of con-
vergence as defined in the Maastricht Treaty, in particular harmonised price indices and the government deficit and debt. 
Inflation and fiscal data were, and remain, crucial in the identification of countries eligible to join the monetary union. In 
both areas, prices and fiscal, there has been major progress in terms of quality, comparability and timeliness of the data.18

Since 1998, attention has focused on the availability and timeliness of several other indicators — notably quarterly national 
accounts; short-term public finance data; labour market statistics; short-term business statistics, including industrial pro-
duction, construction and retail trade; and external trade indicators — not only for the EU and the monetary union but 
for each country. This development came from the clear perception by both statisticians and policymakers that the euro 
area was more than the simple sum of its component countries and that a proper analysis of economic developments in the 
monetary union required a strategy that would go beyond the aggregation of statistics compiled by each country for its own 
use, with less than fully consistent definitions and published at different intervals. However, in spite of major advances in 
the last decade, macroeconomic statistics in the euro area are still somewhat behind those available for the US, notably in 
terms of timeliness and wealth of detail. Moreover, most statistics for the euro area are still the result of aggregating data 
from different countries, rather than a direct estimation of data for the area as a whole.

Challenges for the future

Macroeconomic statistics have long lead times between the decision to compile specific data and the effective use of those 
data by policymakers. Methodological issues need to be discussed; concepts harmonised among countries; the proper in-
frastructure to compile data needs to be put in place; the statistical quality of the first estimates needs to be tested; series 
for past years have to be estimated or retropolated; new concepts need to be explained to data users; users themselves also 
need time to become acquainted with a new series, understanding its attributes and links with other variables, and even 
to acquire trust in the new figures. As a result, statisticians need to anticipate policy needs; this can only be done if there is 
permanent interaction between data users and data producers.

A number of trends are emerging in the statistical requirements for the next decades. Economies are increasingly complex. 
Though statistics have the merit of summarising complex events in simple indicators, the complexity of real life will re-
quire the compilation of different statistics with similar purposes. This will allow different sets of data to be tested for their 
plausibility and accuracy and give users different perspectives on the same phenomenon. This refers to different inflation 
measures (for example by including the price of assets, imputed rentals for owner-occupied housing, or establishing price 
indices by social stratum), different growth measures (taking for example environmental issues and depletion of natural 
resources into account), or different concepts of government deficit (for example, calculated with alternative government 
delimitations, or on competing basis, such as accruals and cash) or of debt (for example including contingent commitments 
or implicit liabilities). In several areas, the competition between different indicators may also help to solve the trade-offs 
between the different dimensions of statistical quality, such as reliability, completeness, timeliness and comparability across 

15 Eurostat, European System of Integrated Economic Accounts, Luxembourg, 1970 (2nd edition 1979).

16 Eurostat-IMF-OECD-UN-World Bank, System of National Accounts 1993, Luxembourg, New York, Paris and Washington.

17 Official Journal of the European Communities, L 310, 30 November 1996.

18 On preparation for monetary union and the development of statistics, see Hans van Wijk, Bridging the Fault Lines — The Early Years of the CMFB, 2001; and Peter Bull, The 

Development of Statistics for the Economic and Monetary Union, Frankfurt: ECB, 2004. 

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time and space. However, this competition among different indicators for similar purposes is not without risks. Data us-
ers, both experts and the public at large, should be informed of the merits and drawbacks of each indicator — for example 
their respective margins of uncertainty — so that they learn how to compare pears with pears and apples with apples, when 
assessing the performance of different countries and regions. Moreover, communication with the public also needs to im-
prove where there are persistent gaps between the official measurements and popular perceptions.

The fast change in economies means that policy reactions should be quicker than they used to be to remain effective. Policy-
makers will need data that are published with a higher frequency and with shorter lags after the events they describe. More 
and more, statisticians are, and will be, asked to ‘nowcast’ a number of variables, blurring the boundaries between ex post 
hard data and ex ante forecasts. In a number of areas, data that used to be available annually are now compiled quarterly, 
and data that are available quarterly will have to be published monthly.19 While higher frequency data and short publication 
lags may contribute to improving policymaking by reducing the lag between the appearance of a macroeconomic issue and 
the policy response, there is also a need to ensure that high-frequency data properly identify new trends and distinguish 
these from the intrinsic volatility of variables, and that the reduction in compilation lags is not achieved at the expense of a 
deterioration in the quality of first outcomes. 

Globalisation, that is the progressive economic and financial integration of countries raises another major challenge for 
statisticians. There are several interrelated issues here. Notably, the increasing interdependency of economies is making 
statisticians’ work more difficult, as some data may become more demanding to compile in spite of technological progress 
in automated data collection. This is illustrated by the fact that the aggregate current account of the World does not bal-
ance out — a fact that is increasingly bothersome for global economic analyses. Another example relates to the vertical 
integration of production within large multinational firms established in several countries; given their strategies of transfer 
pricing, it is increasingly difficult to identify precisely the geographic distribution of the value added of a number of goods. 
Moreover, policymakers are interested, not only in what is happening in their own countries’ economies, but also in their 
partners’ and competitors’. International organisations have a major responsibility to harmonise concepts, and to make 
data published by different countries comparable so that statistics are a public good not only within each country, but at a 
more global scale.

The pervasive impact of financial markets on modern economies is obliging policymakers to improve their knowledge of 
the financial assets and liabilities of the economies, including those liabilities which are contingent. Movements in the price 
of financial assets (and non-financial assets, such as housing) are also acquiring particular relevance. The financial turmoil 
initiated in the summer 2007 with the crisis in the subprime segment of the mortgage market in the US shows that our 
knowledge of the financial exposure of economic agents is not good enough, and that statistics have been at a loss to follow 
financial innovation. 

In knowledge-based economies, the best assets of countries are no longer their natural resources, nor even their tangible 
infrastructure, but human capital and ability to change and innovate. Macroeconomic statisticians will have to consider the 
measurement of human capital and to widen the definition of investment to better measure spending with research and de-
velopment. The measurement of human capital should also give a new impetus to better statistics on income distribution.

Services now represent a very significant share of value added in all advanced economies. As a result, indices of services 
output are gradually becoming more relevant for policymakers than data on manufacturing. However, for most countries, 
short-term business statistics are still excessively focused on manufacturing, though the latter keeps losing relevance in the 
economic structure of modern countries.

The ageing of the population is among the most significant challenges for economic policy in the coming decades. To mini-
mise adverse consequences and promote favourable developments, policymakers need to react long in advance. Issues that 
were of secondary relevance in a stable demographic environment — such as pension spending and pension entitlements, 
healthcare costs, etc. — are attracting particular interest. In the EU, there have been discussions on these issues for some 
time. The regular publication of statistics on pension entitlements, which are currently under preparation, will be most 
welcomed by economists. Concerns on the quality of public finances are also requiring better statistics on the composition 
of government expenditure, better measurement of government output and other data that will allow us to assess value for 
money in government expenditure and the efficiency of tax systems.

19 For example, one of the recommendations of the so-called Allsopp review of 2004 in the UK was the compilation of monthly gross value added (Christopher Allsopp, Review of 

Statistics for Policy Making  – Final Report to the Chancellor of the Exchequer, the Governor of the Bank of England and the National Statistician, 2004).

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There  is  increasing  interest  in  the  environmental  consequences  of  public  policies.  Environment  issues  are  arguably  the 
major challenge for policymakers in the coming decades; better integration of macroeconomic and environment statistics 
would contribute substantially to sustainable economic development. Moreover, the link between the environment and the 
economy will be a crucial step in the direction of a better measurement of economic well-being.

The EU has pioneered the use of statistics in a number of high-profile administrative and political uses. Macroeconomic 
statistics have a crucial role in the selection of countries to join the euro area, in the mechanism of fiscal discipline of the EU 
— the Stability and Growth Pact20 –, in establishing the percentage of each national central bank in the ECB share capital 
and therefore in the distribution of monetary income, in deciding how much each country contributes to the EU budget and 
even in selecting the regions eligible to receive financing from structural funds. These administrative and political uses are 
themselves part of policymaking processes. Yet, the peculiarity is that there is more of a univocal cause-and-effect relation 
between statistics and policy decisions. Once the data are revealed by the statistical authorities, one already knows what 
the decision is going to be and what the consequences are; the leeway for policymakers, if any, is very limited. Although the 
administrative use of statistics in the EU has been successful — they have helped to increase predictability in a number of 
decisions and to base key choices on objective criteria, thus avoiding fruitless discussions — they have put a heavy burden 
on the statisticians’ shoulders. Moreover, experience has shown that a number of political incentives — both good and bad 
— appear when a statistical indicator becomes a target or a high-profile reference value. To remain successful in the future, 
statisticians should explicitly consider these political incentives when identifying their priorities, defining their conven-
tions and accounting rules and ultimately when compiling the data.

20 For the economic foundations and institutional procedure of the Stability and Growth Pact, see Anne Brunila, Marco Buti and Daniele Franco (eds.), The Stability and Growth Pact 
— The Architecture of Fiscal Policy in EMU, Basingstoke: Palgrave, 2001 and Marco Buti and Daniele Franco, Fiscal Policy in Economic and Monetary Union, Cheltenham: Edward 
Elgar, 2005 and references therein.

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1.2  Global economic statistics: The example of purchasing power parities

Lars Svennebye
Eurostat, National accounts: methodology and analysis

When preliminary results of the 2005 round of the International Comparison Programme (ICP) were released in December 
2007, these were the first new global estimates of the size of economies and of countries’ price levels for nearly two decades. The 
development of the ICP and the successful implementation of the 2005 round can serve as an example of large-scale interna-
tional statistical cooperation that goes beyond the mere harmonisation of existing national statistics. 

International statistical cooperation

National Statistical Institutes (NSIs) were initially established to serve the nation state, in response to a demand from gov-
ernments for data on their people and economies. Still today, the needs of domestic policymakers are, not surprisingly, an 
important guideline for NSIs in determining what statistics to produce and how to allocate resources.

However, policymaking is no longer the exclusive domain of the nation state. Increased interaction among nation states in 
fields like trade, investment and migration since the mid-20th century has created a number of global and regional political 
bodies, like the United Nations, the European Union, the World Bank and the International Monetary Fund. In an increas-
ingly globalised and interdependent world, policymakers can no longer restrict themselves to their own countries. This 
has given rise to substantial demand for statistics that allow reliable comparisons of economic and demographic indicators 
across countries.

To this day, the standardisation and harmonisation of statistics initially produced by National Statistical Institutes remains 
perhaps the most dominant aspect of international statistical cooperation. The aim of these standardisation and harmoni-
sation efforts is to allow international comparisons of national statistics. Typically, this form of cooperation involves har-
monising concepts and definitions, developing common classifications, and unifying the format of dissemination, leaving 
each country in charge of the actual production of the statistics within the mutually agreed methodological framework. 

Some fields of statistics, however, require not only a common conceptual framework, but also a high degree of centralised 
management and supervision. These are statistics that by their very nature are multilateral, that is, they are not or cannot 
be produced by each National Statistical Institute unilaterally. This may be so because the results for one country depend 
on the results for another, or because the predominant user community is located outside the nation state, for instance, in 
international organisations and agencies.

For the remainder of this article, a closer look will be taken at a very comprehensive and demanding statistical undertaking 
that serves as an example of global statistical cooperation in a field that cannot be appropriately surveyed within the context 
of national statistics. The International Comparison Programme (ICP) and its European counterpart, the Eurostat-OECD 
PPP Programme, are both aimed at producing price-level adjustment factors, or purchasing power parities (PPPs), applied 
primarily in international comparisons of national accounts (NA). The main task for the participating countries is to carry 
out price surveys for a regionally determined sample of consumer goods and services, and to provide the other input data 
required, notably expenditure weights, price data on non-market services and on investment goods. This requires highly 
harmonised methodologies and practices, and a substantial degree of overall coordination.

Special attention will be paid to the organisation of the recently completed 2005 round of the ICP, with some selected results 
of the exercise included for the sake of illustration. The Eurostat-OECD PPP Programme will also be discussed, focusing in 
particular on its internal structure and integration with the ICP.

The International Comparison Programme

The origins of international price and volume comparisons of GDP can be traced back to the experimental comparisons 
carried out by the Organisation for European Economic Cooperation (OEEC), the forerunner of the OECD, in the 1950s. 

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At first, this programme covered just five countries, but was later expanded to nine.

Building on the OEEC experience, the International Comparison Project (later to become the International Comparison 
Programme) was launched in the late 1960s. Begun as a research project, its goal was to create a framework for worldwide 
PPP-based comparisons to be carried out on a regular basis. The United Nations Statistics Division (UNSD) and the Univer-
sity of Pennsylvania shared responsibility for the project, with the latter taking the lead role. However, the first three rounds 
of the ICP (1970, 1973 and 1975) remained essentially experimental in character.

Following the 1975 round, four major developments occurred. First, the ICP became a regular part of the work programme 
of the UNSD, with the University of Pennsylvania advising on methodological issues. Second, Eurostat started to play an 
increasingly important role, organising the comparisons for the EU Member States and providing technical and financial 
assistance with regional comparisons in Africa. Third, the OECD became involved in the work. Fourth, and most signifi-
cantly, the ICP adopted a regionalised management structure.

Regionalisation placed a greater share of the work on the regional organisations of the United Nations, like the Economic 
Commission for Europe (UNECE) and similar agencies in other regions. This left UNSD at the centre to coordinate the 
regional comparisons and to ensure that they could be linked into a global comparison. Two rounds of the ICP were com-
pleted after regionalisation. By 1985, 64 countries took part worldwide.

The subsequent round of the ICP was planned for the reference year 1993. Although some regions did eventually publish 
their regional results, the 1993 round was widely regarded as a failure. Poor management and insufficient resources at all 
levels — central, regional and national — were identified as the principal reasons for this lack of success. The ICP was judged 
unable to meet user demands, especially with regard to reliability and timeliness of the results. Under these circumstances, 
the main recommendation for future rounds was that the ICP, to improve its credibility, needed to undergo thorough or-
ganisational reform. 

The 2005 ICP round

In 2002, after several years of preparation, the United Nations Statistical Commission (UNSC) finally decided to re-launch 
the ICP. Following the failure of the 1993 round and based on professional recommendations, a very explicit organisational 
framework was created. At central level, a corporate-style management structure was adopted, with an Executive Board 
in charge of strategic decisionmaking, a secretariat (“Global Office”) with several full-time employees responsible for day-
to-day coordination of the programme, both administrative and technical, and a Technical Advisory Group to advise on 
methodological and technical issues. The Executive Board included representatives of the World Bank, IMF, UNSD, Euro-
stat, and OECD, plus all the Regional Coordination Offices, while the Technical Advisory Group consisted of highly quali-
fied professionals in the field of PPPs and statistical methodology in general, appointed on individual merit. 

At regional level, Regional Coordination Offices were established with existing agencies. These were the African Develop-
ment Bank, the Asian Development Bank, the Interstate Statistical Committee of the Commonwealth of Independent States 
(CISSTAT) and the Federal State Statistical Service of the Russian Federation (ROSSTAT), the UN Economic Commission 
for Latin America and the Caribbean, Statistics Canada, and the UN Economic and Social Commission for Western Asia. 
The focal role of the Regional Coordinators was to coordinate and supervise the conduct of price surveys and the provision 
of other data in the participating countries, to lead the inter-country validation of the survey results, and to function as a 
link between the participating countries and the Global Office.

In addition, the ICP maintained close links with the Eurostat-OECD PPP Programme, which, while maintaining its status 
as a separate statistical undertaking, was simultaneously included as a region in the ICP. 

The new management structure brought about considerable improvements over the previous rounds. In the organisation of 
surveys, the participating countries were involved in the whole process of planning and executing surveys, and validating 
the regional results in close cooperation with their Regional Coordinator. Furthermore, at regional level, several training 
workshops were held, while the Global Office provided handbooks, manuals and standardised software to assist countries 
and Regional Coordinators in the collection and validation of data. The Technical Advisory Group provided useful input 
on methodology, contributing substantially to increasing the reliability of the results.

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The number of countries taking part in the exercise (146) was much higher than in any previous round. The lack of a sense 
of “ownership” of the ICP by the participating countries had previously been identified as a weakness, and the need for in-
creased capacity building and coordination with other parts of countries’ national statistical programmes were thus given 
considerable attention. In particular, close integration with National Accounts and with the Consumer Price Index (CPI) 
data collection was encouraged in order to improve the quality of results while simultaneously incorporating the ICP into 
the regular work of National Statistical Institutes.  

The countries participating in the Eurostat-OECD PPP Programme conducted their exercise independently, according 
to their established work plan. However, great efforts were made to ensure methodological consistency between the two 
programmes.

The Eurostat-OECD PPP Programme has a long history independent of the ICP. With advancing economic integration in 
Europe, the need gradually arose for reliable price and GDP volume measures for the member states of the European Com-
munities. Eurostat undertook its first comparison of price levels and National Accounts in the then nine EC Member States 
in 1975. Since then, the Eurostat programme has expanded to include, as of 2008, a total of 37 countries: the 27 EU Member 
States, three EU candidate countries, three Member States of the European Free Trade Association (EFTA), and four further 
countries in South-Eastern Europe that are not included in any of these categories. The OECD coordinates eight further 
countries, most of them non-European OECD Member States. The Eurostat group and the OECD group of countries adhere 
to the same methodological framework, and, with a few minor exceptions, to the same practices. 

As in the ICP, the main task facing the participating countries is the provision of national input data into the PPP calcula-
tion. Until 1999, Eurostat itself undertook the task of preparing, coordinating and validating the surveys, in cooperation 
with the countries. With the number of countries in the programme steadily increasing, this task became more and more 
complex, and for this reason, it was decided to reorganise the price surveys for consumer goods and services. These surveys 
are now carried out relatively autonomously in three geographically defined country groups. Each of these regional groups 
has a group leader country, which leads and supervises the price surveys among the countries in the group, and coordinates 
the intra-group work undertaken with that of the other groups, in order to maintain comparability at the European level 
and secure the reliability of the overall European results. 

An important advantage of this organisational model is that the group leaders are themselves participant countries in the 
process, and thus familiar with the challenges facing the NSIs in the process of providing the input data required. Another 
factor contributing to the quality of the survey results is that the regional sub-groups are in many ways more homogeneous 
with regard to consumption patterns than the entire group of 37 countries taken as a whole. In addition, group leaders have 
over time developed substantial knowledge of the product markets in “their” countries, knowledge that is of considerable 
value when it comes to establishing a comparable and representative product sample for each price survey. 

PPPs are of great operational importance for the EU. This is particularly so in the field of regional policies, where the regions 
eligible for support from the Structural Funds are determined by the regional GDP of each region, expressed in Purchasing 
Power Standard (PPS)21. Furthermore, PPPs are used in analyses of price convergence in Europe, and in a variety of other, 
sector-specific analyses. Since the results of the Eurostat PPP programme have immediate budgetary consequences for the 
EU, it was deemed necessary to formalise the procedures of the PPP production process and clarify the rights and obliga-
tions of all parties involved. For this reason, the methodology and procedures of the Eurostat PPP programme are laid 
down in a separate Regulation of the European Parliament and the Council.22

As stated above, the ICP is divided into six regions, each one responsible for the coordination of the price surveys conducted 
in the countries of the region, and for the provision of other input data. The Eurostat-OECD PPP Programme, while main-
taining its status as a separate entity, is also one of the six regions in the ICP. However, the participation of the Eurostat-
OECD region in the ICP does not affect the day-to-day operation of the Eurostat programme or cause more work for the 
countries. As pointed out in the previous sections, Eurostat and the OECD also plays an important role in the ICP in their 
capacity as members of the Executive Board, and by providing technical and practical support on a more informal basis, 
drawing on their experience from their own, long established PPP programme.

21 PPS is the technical term chosen by Eurostat for the common currency in which NA aggregates are expressed when they are adjusted for price level differences using PPPs. Thus, 

PPPs can be interpreted as the exchange rate of the PPS against the euro.

22 Regulation (EC) No 1445/2007 of the European Parliament and of the Council of 11 December 2007 establishing common rules for the provision of basic information on 

purchasing power parities and for their calculation and dissemination, OJ L 336, 20.12.2007.

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Spatial comparisons of National Accounts

The availability of PPPs is of the utmost importance in global comparisons of income and expenditure levels. While the 
main focus of national accounts (NA) has traditionally been to measure the development of economic aggregates over time, 
there is substantial and increasing demand for indicators of the relative size of economies, and of per capita estimates of 
gross domestic product (GDP) and various NA sub-aggregates, like actual individual consumption (AIC). In comparing 
these indicators across countries, that is, in a spatial dimension, several factors must be taken into account.

First and foremost, strict adherence to the common rules and standards of NA is required of all countries that take part in 
the comparison. Specifically, the United Nations System of National Accounts (SNA) lays down a conceptual framework 
that must be adhered to by all countries. The development and implementation of the SNA and its European counterpart, 
the European System of Accounts (ESA), are prime examples of international statistical cooperation that aims at harmonis-
ing existing practices and further developing statistics along a common methodological path.

Second, countries’ NA data needs to be expressed in a common currency. For example, it is fairly obvious that the GDP of 
the United States, expressed in US dollars, and the GDP of Germany, expressed in euros, are not directly comparable. In 
order to ensure formal comparability, one could choose to just convert the GDP figure for the United States into euros at the 
nominal exchange rate, and compare this figure with Germany’s.

However, the fact that countries have different price levels implies that a spatial comparison based on nominal exchange 
rates as outlined in the previous paragraph will systematically overestimate the GDP, or any other NA aggregate, of coun-
tries with high price levels, relative to countries with low price levels — and vice versa. This is because a country’s GDP 
expressed in national currency can be seen as the product of a volume component and a price component. If the volume 
component is of identical magnitude in countries A and B, while the price component is substantially higher in country B, 
the latter will appear to have a higher GDP than the former, even though the real volumes are identical. PPPs are applied in 
lieu of nominal exchange rates in order to achieve comparability of real volumes across countries. In other words, PPPs are 
indicators of relative price levels that enable us to express each country’s GDP in a common currency and at a common price 
level. The following section illustrates the importance of applying PPP-based figures in comparisons of countries’ GDP.

Selected ICP results

In the end, 146 countries participated in the 2005 round of the ICP, including the 45 countries in the Eurostat-OECD PPP 
Programme. This represents a remarkable increase on the ICP rounds of the 1980s and 1990s. The price and other input data 
were validated in each region, and regional PPPs calculated. These regional results were then linked via a group of countries 
(the “Ring countries”) in order to achieve a set of PPPs for all countries in the global comparison.

The preliminary, global results of the 2005 ICP round were published in December 2007. Table 2.2.1 summarises the results 
for selected economies. These results deviate substantially from estimates previously published by the World Bank and the 
International Monetary Fund. For example, the size of India’s economy appears to have been grossly overestimated in previ-
ous publications. This does not come as a big surprise, since the previous estimates were based on data that in many cases 
dated back to the 1980s. These estimates have long been acknowledged to be of questionable accuracy.

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Table 2.2.1:

ICP 2005 results for selected economies

GDP, bln

GDP per Capita

Price 
Level 
Index

GDP per Capita 
Indices (US=100)

Shares (WORLD=100)

Economy

EU27

United States

EA13

China

Japan

India

Russian Federation

Brazil

South Africa

Based on 
nominal 
US$
13694

12376

10042

2244

4549

779

763

882

242

Based on 
PPP

13018

12376

9260

5333

3870

2341

1698

1585

398

Based on 
nominal 
US$
27839

41674

31798

1721

35604

707

5328

4791

5162

Based on 
PPP

US=100

26465

41674

29323

4091

30290

2126

11861

8606

8477

105

100

108

42

118

33

45

56

61

Based on 
nominal 
US$
66.7

100.0

76.2

4.1

85.4

1.7

12.8

11.5

12.4

Based on 
PPP

63.4

100.0

70.3

9.8

72.7

5.1

28.5

20.7

20.3

GDP in 
nominal 
US$
31.1

27.9

22.8

5.1

10.3

1.8

1.7

2.0

0.5

GDP in 
PPP

Popu-
lation

23.9

22.5

17.0

9.7

7.0

4.3

3.1

2.9

0.7

8.0

4.8

5.2

21.3

2.1

18.0

2.3

3.0

0.8

Table 2.2.1 compares some major economies with respect to their GDP, GDP per capita, and comparative price levels, based 
on the 2005 ICP results. The table also provides a useful exercise for understanding the vital importance of using PPPs, and 
not nominal exchange rates, in international comparisons of this kind. For example, while the economy of the European 
Union (EU27) appears to be 6.1 times bigger than China’s at nominal exchange rates, it is only 2.4 times bigger when the 
price level difference between the two countries is taken into account: China’s price level is estimated at just 40 percent of 
Europe’s.

A similar effect can be seen for GDP per capita, a much-used indicator in international comparisons of the general popula-
tion’s material standard of living. The GDP per capita indices based on nominal exchange rates, containing both a price and 
a volume component, tend to exaggerate the differences between relatively rich and relatively poor countries and regions. 
Still, unsurprisingly, the volume of GDP per capita in countries like India, China, South Africa and Brazil remain very 
substantially below the level of the United States, the EU or Japan, even when PPPs are applied.

Summary

Involving the active participation of the National Statistical Offices of 146 countries, a number of regional development 
agencies and other international organisations, and with its explicit management structure, the ICP 2005 is certainly one of 
the most comprehensive and complex exercises in international statistical cooperation ever undertaken.

Until the 2005 results were released, the only PPP data available for countries outside the Eurostat-OECD region were es-
timates that, in some cases, were based on surveys carried out as much as twenty years earlier. Given the effort put into the 
re-launched ICP, both in terms of organisational and methodological development, there can be little doubt that the results 
of the 2005 ICP provide a major contribution to better, more reliable analyses of national accounts data across countries. 
It is obvious that without the extensive cooperation between the parties involved, this major step forward would not have 
come about.

Whereas the Eurostat-OECD PPP programme is well established, underpinned by EC legislation, and serves important 
operational needs of the European Commission, the 2005 round of the ICP was more of a pioneering effort. However, given 
the need for reliable economic indicators, especially in the major international and regional organisations, but also for re-
search purposes, it seems clear that the 2005 round has established a sound organisational basis for further ICP rounds in 
the future. From the user communities’ viewpoint, there is an obvious need to establish the ICP as a permanent statistical 
undertaking, with data collection and new results calculations taking place at regular intervals.

All this does not mean, of course, that there are no challenges ahead. One of these challenges concerns country coverage. 
Even though far more countries took part in 2005 ICP than in any previous round, truly international statistics must cover 

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all countries in order to be of full value to the user communities. Another, probably even more demanding, challenge con-
cerns the quality of countries’ national accounts data, which in many cases is acknowledged to be less than optimal. Since 
international comparisons of the kind undertaken by the ICP rest on two pillars, national accounts data and PPP data, ef-
forts must be made to improve the quality of both data sources. So important is the quality of national accounts data for the 
ICP that synergies for national accounts can be expected to result from a country’s participation in the ICP, provided that 
the organisational framework promotes it. Positive synergies could also be achieved in relation to the consumer price index 
(CPI), where a potential for increased coordinated data collection certainly exists with the ICP consumer goods surveys.  

The ICP 2005 provides an example of how statistics of vital importance to international economic comparisons come about 
through close international cooperation, in an interactive process with a multitude of parties involved. It seems clear from 
the experience of this and previous ICP rounds that these statistics cannot be produced at a sufficient level of quality unless 
careful attention is paid to the organisation and management of the exercise across countries. 

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2

2.1  Overview of statistical analysis

The following chapters provide a statistical analysis of the main aspects of economic data for European Union, EFTA and 
candidate countries, with an emphasis on data since the year 2002. They are based on the latest data which were available to 
Eurostat at end-April 2008. All data are published on the Eurostat public website23, and a selection of these data are repro-
duced in the statistical annex to this publication.

Unless otherwise stated, in the following chapters “EU” means the European Union 27, and “EA” or “euro area” mean the 
euro area 1324. These are the compositions which were present in the last year of available data (2007). The term “new Mem-
ber States” is sometimes used to denote those 12 countries which have joined the EU since 2004, with the term “EU15” used 
to denote those countries which were EU members by 2003.

The chapters should be seen as a coherent set of data which have many links with each other (commonly through the na-
tional accounts framework). This is particularly appropriate for users. There are other detailed aspects of the economy in 
Europe which are not covered specifically in this publication (for example agricultural and business statistics), however 
these aspects are included in the aggregate data presented. Short summary of the main results for each chapter follows.

National Accounts

This chapter covers a set of indicators derived from non-financial national accounts. National accounts are a powerful tool 
for studying many aspects of the economy. GDP in current prices gives an indication of the size of the economy and just five 
Member States account for three quarters of the EU Economy. GDP per capita expressed in Purchasing Power Standards 
allows us to make cross country comparisons of income levels, although with some caveats for specific cases. 

EU countries display very different income levels but a catching-up process has taken place in recent years and relatively 
poorer  Member  States  are  gradually  approaching  richer  ones. The  chapter  considers  in  which  industries  value  added  is 
generated and concludes that the EU is a service-based economy where more than 70% of total value added corresponds to 
service industries. The incomes generated by production are mainly spent on private consumption and it may be noted that 
half of EU investment is made on construction related fixed assets. 

By looking at how income is distributed between economic sectors, it is clear that the share of GDP devoted to compensa-
tion of employees is steadily declining and that the average compensation per employee in the EU is around 30 thousand 
euros. By analyzing specific sectors of the economy, at the EU level around one tenth of the disposable income of households 
is saved and the share of business profits of non-financial corporations is slightly below 40%. 

The EU had an average annual economic growth of 1.7% during the period 2000-2006, which translated, taking into ac-
count the increase in population, into 1.4% per capita volume growth. Around two thirds of this 1.4% per capita volume 
growth originated in increases in labour productivity while the other third was due to the increase in the share of employed 
persons in the total population. 

National accounts also provide information at the regional level. Member States are calculating a number of key variables 
in particular at the regional level NUTS-2, which subdivides the EU into 271 regional units. The divergences between GDP 
per inhabitant among the regions of the EU are still very high, but have been narrowing over recent years; at the level of 
Member States however this applies only to the EU15 countries, while regional discrepancies in new Member States are still 
widening.

Public Finance

There is a strong focus in the European Union on government finance statistics because of the constraints on governments’ 
fiscal policies in the Stability and Growth Pact (and more specifically the Excessive Deficit Procedure).

23 See the following link: http://epp.eurostat.ec.europa.eu/portal/page?_pageid=1090,30070682,1090_33076576&_dad=portal&_schema=PORTAL

24 Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Slovenia and Spain.

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Government expenditure has fallen as a proportion of the economy in the EU27 and EA13 in recent years, to 45.8% and 
46.3% of GDP respectively in 2007. This equated to EUR 11,400 per inhabitant of the EU. The average includes a wide di-
versity amongst countries, with some countries over 50% and others below 40%. The structure of government expenditure 
by function has remained relatively stable over the years, with by far the largest proportion (40%) being spent on social 
protection (social security benefits).

Government revenue has risen significantly in recent years to reach 44.9% of GDP in the EU27 and 45.6% in the EA13. Most 
government revenue derives from taxes and social contributions, and at European level there is a rather even split between 
indirect taxes, direct taxes and social security contributions (though this marks a wide diversity at country level). 

The  difference  between  government  revenue  and  expenditure  (the  government  net  position,  or  “public  balance”)  fell  in 
2007 to the lowest level in the EU since the 1970s, reaching 0.9% of GDP in the EU27 and 0.6% in the euro area. Eleven EU 
Member States recorded a government budgetary surplus. Government debt fell as a proportion of GDP to 58.7% and 66.3% 
at end-2007 for the EU27 and EA13 respectively.

Inflation, interest rates and exchange rates

Annual average consumer price inflation (as measured by the Harmonised Index of Consumer Prices, or HICP) was rela-
tively stable over the 2000-2007 period, however for the EU the figure for 2007 (2.3%) was the highest in this period. In the 
euro area inflation was more moderate in 2007 at 2.1%. The most notable price increases have occurred in food (including 
restaurants), education, alcohol and tobacco, and hotels. Downward effects on inflation were present from communications 
and clothing, amongst others.

In 2007 the highest annual inflation rates among the EU Member States were observed in countries that joined the EU 
in 2004 and 2007, with three countries experiencing inflation above 7.5%. By contrast some countries had inflation of 
only 1.6%.

Interest rates generally fell over the period 2000-2005, before rising in recent years in the EA13 and other EU countries. This 
was reflected in both longer term and shorter term interest rates where in 2007 the average 10-year EU government bond 
yield was at 4.58%, and three-month money market rates in the EA13 were 4.28 % on average.

In terms of exchange rates, the euro has appreciated strongly against the US Dollar (by 44.9%) and Japanese Yen (by 
36.6%) since 2002. By contrast there have only been relatively minor fluctuations between the euro and other European 
currencies.

External dimension of the economy

The European Union plays a major role in the world trade: it accounts for about one fifth of the international trade in goods 
and for about one forth of the global trade in services. Over recent years, despite its role of biggest world exporter, the EU 
showed increasing deficits for trade in goods, while it was a net creditor in the exchange of services. The greater relative 
size of commodity trade moved also the EU current account towards negative balances in recent years, as the other two 
components, income and current transfers, have more or less balanced. However, in relative terms (as a share of GDP) the 
EU current account deficit in 2007 was rather small, namely -0.6% of GDP.

Among the major product categories, the EU had in 2007 a surplus in ‘chemicals’ and ‘machinery and vehicles’, while showed 
deficits in all the other sectors and in particular for energy products; the category ‘petroleum and petroleum products’ was 
alone responsible for about one fifth of the total EU imports. In trade in services, in 2007 the EU recorded surpluses in most 
of the categories, and especially in financial services, transportation and ‘other business services’, the biggest deficit was 
registered for the travel sector. Both for goods and services, USA was the most important trading partner for the EU. 

The relation of the EU with other economies can be also measured by the flow of foreign direct investments (FDI) made 
in and received from other countries. These investments have been growing over the last years and EU outflows have been 
considerably greater than investments received from abroad, making the European Union a net investor. The main EU 
partners for these flows are North America, other non-EU European countries and South and Central America, which 

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Statistical analysis

2

together hosted more than three quarters of the EU outward FDI stocks at end-2006. Another way to measure the interna-
tionalisation of EU economy is to consider the affiliates abroad controlled by EU businesses. Even if statistical data are still 
partial for this phenomenon, the results confirm that EU partners’ share are similar to those for EU direct investments, but 
with a greater role of Asia, in particular, as regards the increasing number of persons employed as a result of the outsourc-
ing of production.

Labour market

This chapter describes the present situation and the changing patterns in the European labour market. The recent evolution 
of employment growth is positive and in line with the cycle of economic expansion. In 2007 employment grew by 1.6% in 
the EU27 and by 1.8% in the EA13, leaving the employment rates at 65.4% and 65.7% respectively. A sustained rise in the 
total actual hours worked reflects that the labour supply is maintained by an increase in the number of jobholders rather 
than by changes in the average hours per person. 

Employment is moving from agriculture and manufacturing to services and construction. Employment growth in recent 
years was stimulated by higher participation of women in the labour market and, to a lower extent, of older workers too. In 
some countries immigrant workers also contributed significantly. 

More part-time jobs and hours worked in part-time jobs also go in parallel with the increased involvement of women in 
labour market. More fixed-term contracts and a widening distribution of usual weekly hours of work per person point to 
more flexible work time arrangements, improvement of work organisation and better reconciliation of work and life. 

The unemployment rate fell in 2007 in the EU27 to 7.1%, a fall from 8.9% in 2005. There was fall in unemployment in 2007 
in every Member State, and also for unemployment of younger persons and the long-term unemployed.

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Statistical analysis

2.2  National accounts

2.2.1  

Introduction

This chapter covers a set of indicators derived from non-financial national accounts. National accounts are a powerful tool 
for studying many aspects of the economy. GDP in current prices gives an indication of the size of the economy. It is worth 
noting that only five Member States account for three quarters of the EU Economy. GDP per capita expressed in Purchasing 
Power Standards allows making cross country comparisons of income levels, although with some caveats for specific cases. 
EU countries display very different income levels but some sort of catching-up process has taken place in recent years and 
relatively poorer Member States are gradually approaching richer ones. The chapter shows in which industries value added 
is generated and concludes that the EU is a service-based economy where more than 70% of total value added corresponds 
to service industries. Analyzing how GDP is used one can find out that is mainly spent on private consumption and that 
half of the EU investment is made on construction related fixed assets. By looking at how income is distributed between 
economic sectors it can be onserved that the share of GDP devoted to compensation of employees is steadily declining and 
that the average compensation per employee in the EU is around 30 thousand Euros. The EU had an average annual eco-
nomic growth of 1.7% during the period 2000-2006, which translated, taking into account the increase in population, into 
1.4% per capita volume growth. Around two thirds of this 1.4% per capita volume growth originated in increases in labour 
productivity while the other third was due to the increase in the share of employed persons in the total population. The next 
step will be to analyze specific sectors of the economy to find out that around one tenth of the disposable income of house-
holds is saved and that the share of business profit of non-financial corporations is slightly below 40%. National accounts 
also provide information at the regional level. Member States are calculating a number of key variables in particular at the 
regional level NUTS-2, which subdivides the EU into 271 regional units. One can observe that the divergences between 
GDP per inhabitant among the regions of the EU are still very high, but have been narrowing over recent years; at the level 
of Member States however this applies only to the EU15 countries, while regional discrepancies in new Member States are 
still widening.

2.2.2   Nominal GDP and GDP per capita

The European Union (EU) economic data is the result of aggregating the data for the individual economies of 27 Member 
States. The 27 Member States are fairly heterogeneous in terms of size, income levels, economic structure, and recent eco-
nomic performance. Table 2.2.1 below provides an overview of the relative size of the economy of the 27 Member States in 
2000 and 2007 based on GDP measured at current prices and current exchange rates. Member States are sorted in descend-
ing order according to their share in EU27 GDP in 2007. They have been classified into three groups. A first group of five 
large Member States accounted for almost three quarters of the EU27 economy in 2007 (72.5%). A second group of eleven 
medium-sized Member States accounted for almost one quarter (23.7%). Lastly, a group of eleven small Member States 
represented less than 5% (3.7%) of the EU economy. 

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Table 2.2.1

Member States’ relative economic size

Statistical analysis

2

Large Member States,  
more than 5% 

Medium-sized Member States, 
between 1% and 5% 

Small Member States,  
less than 1%

Country

Germany

United Kingdom

France

Italy

Spain

Subtotal

Netherlands

Sweden

Belgium

Poland

Austria

Greece

Denmark

Ireland

Finland

Portugal

Czech Republic

Subtotal

Romania

Hungary

Slovakia

Luxembourg

Slovenia

Bulgaria

Lithuania

Latvia

Cyprus

Estonia

Malta

Subtotal

Share in EU27 GDP, 2000

Share in EU27 GDP, 2007

22.5

17.1

15.7

13.0

6.9

75.2

4.6

2.9

2.7

2.0

2.3

1.5

1.9

1.1

1.4

1.3

0.7

22.5

0.44

0.57

0.24

0.24

0.23

0.15

0.13

0.09

0.11

0.07

0.05

2.3

19.7

16.5

15.2

12.5

8.6

72.5

4.6

2.7

2.7

2.5

2.2

1.9

1.9

1.5

1.5

1.3

1.0

23.7

0.99

0.82

0.45

0.29

0.27

0.24

0.23

0.16

0.13

0.13

0.04

3.7

The table provide some interesting facts. For example, the Member State with the largest economy (Germany) is more or less 
the same size as the combined economies of the twenty smaller Member States. It therefore follows that the main features 
of the EU economy will chiefly result from developments in a few Member States.

Table 2.2.1 provides also the same weights for the year 2000. Comparing 2007 with 2000, it is clear that all large Member 
States with the exception of Spain have lost relative weight, and that all medium-sized and small Member States have main-
tained, and in most cases increased their weights, (the exceptions being Austria, Sweden and Malta). 

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Statistical analysis

In Table 2.2.1 GDP is measured at current prices and market exchange rates. If one wants to obtain a proper measure of 
relative income levels (see Box 2.2.1) in different countries, one should use another indicator: GDP per capita expressed 
in Purchasing Power Standards. This indicator is the result of combining four elements. First, GDP is measured at cur-
rent prices and exchange rates. Second, in order to allow per capita comparisons GDP levels are divided by population. 
Third, GDP per capita in Euros is converted into an artificial currency using Purchasing Power Parity (PPP) exchange 
rates. The reason is that the same amount of euros can buy a different amount of goods and services in different countries 
due to the existence of differences in price levels, especially for non-tradable items, such as haircuts, health and educa-
tion. Finally the amounts expressed in PPP are scaled to euros, so that the aggregate for the EU as a whole is the same 
whether expressed in Euros or PPS.

Figure 2.2.1: GDP per capita in Purchasing Power Standards, EU27=100

300

280

260

240

220

200

180

160

140

120

100

80

60

40

20

0

LU NL AT DK IE

SE BE DE FI UK IT

FR EA
13

EU
27

ES CY GR MT SI PT CZ HU SK PL EE LT LV BG RO

IS NO CH

2000

2006

Figure 2.2.1 shows the results, indexed to EU=100, for the years 2000 and 2006. In 2006, six countries had an index of over 
20% above the EU average and eleven countries were more than 20% under the EU average. Although PPS are intended 
only for spatial comparisons and not for temporal ones, comparing the relative positions in 2000 and 2006 provides some 
interesting insights. It can be clearly seen that in a majority of cases (thirteen out of fifteen) countries that were below the 
EU average in 2000 improved their relative position in 2006. Consequently, the opposite is also true; ten out of the twelve 
countries that were above 100 in 2000 saw their relative position worsen. This provides some evidence of a convergence 
process within the EU in the period 2000-2006. The GDP per capita of countries that were relatively poorer in 2000 grew 
faster than the GDP per capita of countries that were relatively richer in 2000.

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Statistical analysis

2

BOX 2.2.1: GDP SHORTCOMINGS FOR MEASURING INCOME LEvELS IN LUXEMBOURG AND 
IRELAND

GDP is the standard measure for international comparisons of income levels. There are many reasons for that. It is very 
timely, closely harmonised across countries and widely known by users. Nonetheless, in certain cases it may give a 
misleading picture of relative income levels and other alternatives in the framework of National Accounts may be pref-
erable. Such as, for example, Gross National Income (GNI) which is the measure used to calculate a major part of the 
contribution of EU Member States to the EU budget. The difference between GDP and GNI is mainly primary incomes 
with the rest of the world (GNI = GDP + primary incomes with the rest of the world). Primary incomes comprise com-
pensation of employees and property income. In most EU countries the magnitude of the balance is relatively small, 
and therefore GDP is very similar to GNI. Indeed for the EU as a whole GDP and GNI are almost the same amounts. 
Nonetheless there are two countries, Luxembourg and Ireland, for which the difference is significant. In the case of Lux-
embourg the difference is partly due to the large daily influx of commuter workers coming from France, Belgium and 
Germany. What they produce is taken into account in Luxembourg’s GDP, but the salaries are not included in its GNI. In 
the case of Ireland, the difference is due to the major presence of foreign multinational corporations. Their profits are 
included in Ireland’s GDP, but the dividends repatriated by the multinationals are not included in GNI.

The GNI of Ireland and Luxembourg in PPP shows that both countries are comparatively less rich than is indicated by 
their GDP. Luxembourg would be 2.3 times richer than the EU average, instead of 2.8 times, and Ireland would be 25% 
richer than the EU average instead of 45%.

2.2.3   The production side

This section analyses in which industries the gross value added was generated. Gross value added is the difference between 
output and intermediate consumption. It should not be confused with production (output). To calculate the gross value 
added of an industry one computes its total production and subtract the value of goods and services consumed or used as 
inputs in the production. For the EU, around 50% of output is used as intermediate consumption. Therefore Gross Value 
Added for the EU27 represents around 50% of the total production of goods and services.

Gross value added is also different from GDP. This is due to the fact that output is valued at basic prices25 and intermediate 
consumption is valued at purchasers’ prices. To obtain GDP at market prices it is necessary to adjust gross value added by 
adding taxes and subtracting subsidies on products. For the EU, taxes less subsidies on products represent around 11% of 
GDP. As the information regarding taxes less subsidies is only available for the total economy and not by industry, it is not 
possible to calculate the GDP of specific industries. That is why total gross value added is used to analyse the importance of 
different industries, and not GDP. In the interest of readability the economy has been broken down into six industries.

25 Because of transport costs, trade margins and taxes less subsidies on products, the producer and the user of a given product usually perceive its value differently. In order to keep as 
close as possible to the views of the transactors, the system records all uses at purchaser’s prices, which include transport costs, trade margins and taxes less subsidies on products, 
while output is recorded at basic prices, which exclude these elements.

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Statistical analysis

Figure 2.2.2:  EU Gross value Added by industry, % total Gross value Added

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

22.1

22.2

22.5

22.8

22.8

22.8

22.6

22.4

26.0

26.3

26.7

27.2

27.4

27.7

28.0

28.2

21.6

5.6

22.3

2.4

2000

21.8

5.7

21.7

2.4

2001

21.7

5.7

21.1

2.2

2002

21.6

5.8

20.5

2.2

2003

21.5

5.9

20.3

2.2

2004

21.4

6.0

20.2

1.9

2005

21.2

6.2

20.2

1.8

2006

21.1

6.3

20.2

1.9

2007

Agriculture, hunting, forestry and ﬁshing

Total industry (excluding construction)

Trade, transport and communication services

Financial services and business activities

Construction

Other services

In 2007, the three services industries were the major contributors to the EU27 gross value added, namely Financial services 
and business activities (28.2%), Other services, which includes public administration and defence, education, health, etc, 
(22.4%) and Trade, transport and communication services (21.1%). These three combined represent more than 70% of total 
gross value added. Industry(excluding construction) generated 20.2% of the total gross value added , Construction 6.3% and, 
lastly, Agriculture, hunting, forestry and fishing 1.9%.

Analysing the period 2000-2007 one can see that the weight of industries have remained relatively stable in general, but Fig-
ure 2.2.2 reveals some clear patterns. There was a steady growth of Financial services and business activities which increased 
its weight by 2.2 points during the period. There was a steady decline of Agriculture, hunting forestry and fishing. Industry 
(excluding construction) decreased at the beginning of the period (2000-2003) but remained stable thereafter (2004-2007) 
and Construction increased towards the end of the period (2003-2007).

Figure 2.2.3 shows the weights of industries for individual countries for 2006. Some figures can be highlighted. Countries 
with relatively large shares for Agriculture, hunting forestry and fishing (e.g. Romania (8.8%), Bulgaria (8.5%), Lithuania 
(5.2%) and Poland (4.3%)), are, as shown in Figure 2.2.1, relatively poorer compared to other EU countries. This is explained 
by the low productivity of the agricultural industry relative to other industries. Unlike Agriculture, hunting forestry and 
fishing, there seems not to be a direct relationship between the weight of Industry (excluding construction) and income levels. 
One can find countries with relatively high shares of industry, like the Czech Republic (31.7%), Slovak Republic (28.6%) and 
Romania (27.5%) with income levels below the EU average but also countries like Germany (25.4%) and Ireland (25.0%) with 
income levels above the EU average. For Construction, it is worth noting the high share for Spain (12.2%), which is more 
than double that of the EU (6.2%), and also for Ireland (9.9%). This is not a new phenomenon, as both Spain and Ireland 
displayed higher shares in 2000 but at that time they were closer to the EU average. The three Baltic States account for the 
largest share of Trade, transport and communication services: Latvia (34.2%), Lithuania (31.1%) and Estonia (29.6%) plus 
Greece (30.1%). The importance of the financial services industry for Luxembourg can be easily confirmed, as Financial 
services and business activities make up almost half of its total value added. Denmark (27.0%), Malta (26.7%) and Sweden 
(26.5%) show the highest weights for Other services.

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Figure 2.2.3:  Gross value Added by industry, % total Gross value Added, 2006.

Statistical analysis

2

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

EU
27

EA
13

BE BG CZ DK DE EE

IE GR ES FR IT CY LV LT LU HU MT NL AT PL PT RO SI

SK FI

SE UK

IS NO CH

Agriculture, hunting, forestry and ﬁshing

Total industry (excluding construction)

Trade, transport and communication services

Financial services and business activities

Construction

Other services

2.2.4  The expenditure side

This section focuses on the main expenditure components of GDP. Private final consumption is by far the largest category 
and includes the expenditure made by households and non-profit institutions serving households (NPISH26). Government 
final consumption comprises the value of goods and services produced by general government itself, other than own ac-
count capital formation and sales, and purchases by general government of goods and services that are supplied to house-
holds (see Box 2.2.2). Gross capital formation consists of Gross fixed capital formation, which measures resident producers’ 
acquisitions, less disposals, of fixed assets plus certain additions to the value of non-produced assets, and Changes in inven-
tories, which measures the value of the entries into inventories less the value of withdrawals and the value of any recurrent 
losses of goods held in inventories. Finally, the External balance represents the difference between exports and imports of 
goods and services.27

Figure 3.2.4 shows the respective weights of each expenditure component in GDP for the years 2000-2007 for the EU. As 
stated earlier, Private final consumption is by far the most important component representing a little under 60% of GDP 
throughout the period. Its weight shows a slight downward trend during the period analysed. Both Government final con-
sumption and Gross capital formation represent around 20% of GDP each. However, while no clear trend can be discerned 
for Government final consumption, the weight of Gross capital formation decreased between 2000-2003 and increased there-
after, reaching a higher weight in 2007 (21.8%) than at the beginning of the period (21.3%).

26 NPISHs are private, non-market producers which are separate legal entities. Their principal resources, apart from those derived from occasional sales, are derived from voluntary 
contributions in cash or in kind from households, from payments made by general governments and from property income. Examples of NPISHs are churches, trade unions and 
political parties.

27 Exports and imports of goods and services are analyzed in detail in chapter 2.5

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Statistical analysis

Figure 2.2.4:  EU expenditure components, % of GDP

0.2

21.3

0.8

20.5

1.5

19.6

1.2

19.6

1.1

20.0

0.6

20.3

0.2

21.1

0.5

21.8

19.8

20.0

20.4

20.8

20.7

20.9

20.8

20.5

58.6

58.7

58.5

58.4

58.2

58.3

57.8

57.2

100%

80%

60%

40%

20%

0%

2000

2001

2002

2003

2004

2005

2006

2007

Private Final Consumption

Government Final Consumption

Gross Capital Formation

External balance of goods and services

BOX 2.2.2: PRIvATE FINAL CONSUMPTION vERSUS ACTUAL INDIvIDUAL CONSUMPTION AND
CROSS-COUNTRy COMPARISONS

There are some caveats to be taken into account before making cross-country comparisons of Private final consump-
tion. A part of Government final consumption is made up of purchases by general government of goods and services 
produced by market producers that are supplied to households; this is called Government individual consumption. Im-
agine that we are comparing two countries, one in which the education is paid for directly by households and another 
in  which  the  government  finances  education  and  households  do  not  make  any  direct  payment,  although  they  do 
indirectly finance for education through the tax system. To make a proper comparison of consumption by households 
between both countries this difference has to be taken into account. It is recommended to use the variable Actual in-
dividual consumption, which is the sum of Private final consumption and Government individual consumption. As stated 
earlier, for a proper cross-country comparison of levels the unit chosen should be amounts converted into PPS. The 
following table illustrates this situation by comparing the three different consumption items for Austria and Sweden 
for the year 2006 in data expressed in PPS per capita:

Private final consumption
Government individual consumption
Actual individual consumption

Austria
16 600
3 300
19 900

Sweden
13 900
5 600
19 500

This partly explains why Private final consumption in the United States accounts for a much bigger share of GDP (around 
70%) than in the EU27 (around 60%), since in the United States some services which are financed by the government 
sector in the EU are paid directly by households. However, not all of the difference can be attributed to this factor.

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2

At the level of individual Member States, there are sizeable differences in the weight of expenditure components. Luxem-
bourg (36%) and Ireland (45%) show the lowest weights for Private final consumption. Part of the explanation is that for 
theses two countries GDP is not the most appropriate measure of their income levels (see Box 2.2.1). Also both countries 
have substantial positive external balances. This is partly explained, in the case of Luxembourg, by the significant purchases 
of fuel, cigarettes and alcohol by non-residents (which is counted as Exports of services) and by the strong export intensity 
of foreign multinationals located in Ireland. Sweden and the Netherlands (both on 47.4%) also have relatively lower shares, 
which are offset by a relatively higher share of Government final consumption. At the other end of the scale, Greece (71.0%), 
Bulgaria (70.4%) and Romania (68.9%) have the highest shares which are partly offset by relatively lower Government final 
consumption shares. Looking at Gross capital formation, Latvia (39.7%) and Estonia (38.2%) display the highest shares as 
might be expected of very fast growing economies, while Germany (17.8%) and the United Kingdom (18.0%) have the low-
est shares.

Figure 2.2.5:

Expenditure Components, % of GDP, 2006.

100%

80%

60%

40%

20%

0%

EU
27

EA
13

BE

CZ DK DE

IE

FR IT

CY

ES

EE

GR

-20%

BG

LU HU

MT

NL AT PL

SI

SK

FI

SE

UK

NO CH

PT

RO

IS

LT

LV

Private Final Consumption

Government Final Consumption

Gross Capital Formation

External balance of goods and services

As other chapters will deal in more detail with some expenditure components (Chapter 2.4 for Private final consumption, 
Chapter 2.3 for Government final consumption and Chapter 2.5 for exports and imports of goods and services), the focus of 
this chapter is limited to Gross fixed capital formation.

Figure 2.2.6 shows the breakdown by fixed asset28 type for the EU for the years 2000 and 2007. In 2007, more than half 
of Gross fixed capital formation was devoted to construction related fixed assets, either to Other buildings and structures
(28.6%) or Dwellings (25.7%). Other machinery and equipment represented around one quarter, while Intangible fixed assets
and Transport equipment were around 10% of total Gross fixed capital formation. When compared with the year 2000, it can 
be observed that the weight of Other machinery and equipment fell by five percentage points while the weights of Dwellings

28 Fixed assets are tangible or intangible assets produced as outputs from processes of production that are themselves used repeatedly, or continuously, in processes of production for 

more than one year.

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Statistical analysis

and Other building and structures increased by 2.4 and 1.9 percentage points respectively. The share of Intangible fixed as-
sets also increased marginally, by 0.8 percentage points, while for the other two assets, Transport equipment and Cultivated 
assets the respective shares in the total remained virtually unchanged.

Figure 2.2.6:  EU, breakdown of Gross Fixed Formation by six fixed asset types

2000

2007

Intangible fixed assets 
9.6%

Cultivated assets
0.3%

Other machinery 
and equipment 
30.4%

Intangible fixed assets 
10.4%

Cultivated assets
0.2%

Other machinery 
and equipment 
25.4%

Other buildings 
and structures 
26.7% 

Other buildings 
and structures 
28.6% 

Dwellings 
23.3%

Dwellings 
25.7%

Transport 
equipment 
9.7% 

Transport 
equipment 
9.7% 

2.2.5  The income side

The income side shows how GDP is distributed among different participants in the production process. The relevant com-
ponents are: Compensation of employees, which is the total remuneration, in cash or in kind, payable by an employer to 
an employee; Gross operating surplus and mixed income, which is the surplus (or deficit) on production activities before 
account has been taken of the interest, rents or charges paid or received for the use of assets; plus the remuneration for the 
work carried out by the owner (or by members of his/her family) of an unincorporated enterprise29; Taxes on production and 
imports less subsidies, which consist of compulsory (in the case of taxes) unrequited payments to or from general govern-
ment or institutions of the EU, in respect of the production or import of goods and services, the employment of labour, and 
the ownership or use of land, buildings or other assets used in production taxes on production and imports less subsidies. 
Compensation of employees can be broken down into Wages and salaries and Employers’ social contributions. 

Figure 2.2.7 illustrates a well known stylised fact which has been discussed at length in the economic literature: namely the 
steady decline of Compensation of employees as a share of GDP. The decrease is explained by the Wages and salaries compo-
nent, as the weight of Employer’s social contributions did not change throughout the period. The decrease has been matched 
by a parallel increase in the share of Gross operating surplus and mixed income, while the share of Taxes on production and 
imports less subsidies has remained stable. The decline in the share of Compensation of employees contributes to explaining 
the decline in the share of Private final consumption commented in section 2.2.2. The evolution of Private final consump-
tion is mainly driven by the change in Household disposable income, of which Wages and salaries are the most important 
component.

29 This is referred to as ’mixed income’ since it cannot be distinguished from the entrepreneurial profit of the owner.

36

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Figure 2.2.7:  EU income components, change in their share of GDP (share in GDP for 2007 in the labels)

Statistical analysis

2

2.0

1.5

1.0

0.5

0

-0.5

-1.0

-1.5

-2.0

2000

2001

2002

2003

2004

2005

2006

2007

Compensation of employees (48.4% in 2007)

Wages and salaries (38.3% in 2007)

Employer's social contributions (10.1% in 2007)

Gross operating surplus and mixed income (39.3% in 2007)

Taxes on production and imports less subsidies (12.4% in 2007)

By dividing Compensation of employees by the number of employees30 one obtains the average compensation per employee. 
In 2006, the EU average compensation per employee was 30.7 thousand euros. The highest values were recorded in relatively 
richer countries: Luxembourg (51.1), Denmark (44.2), Belgium (44.1) and Ireland (42.7), while the lowest were found in the 
relatively poorer countries: Bulgaria (3.1), Romania (6.1), Latvia (7.4) and Lithuania (8.1).

Figure 2.2.8:  Compensation per employee in euros, 2006

60.000

50.000

40.000

30.000

20.000

10.000

0

LU DK BE

IE UK FR SE

FI AT NL EA13 DE

IT EU27 ES GR CY SI

PT MT HU CZ EE PL SK LT

LV RO BG

NO

30 See chapter 2.6 for more detailed information on employment figures.

  European Economic Statistics

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2

Statistical analysis

2.2.6  Economic growth

So far the analysis was restricted to figures in current prices or in Purchasing Power Standards, but in order to analyse 
the behavior of economies over time one needs to use volume changes which exclude price movements (see Box 2.2.3). As 
seen before, GDP per capita in PPS is a measure for calculating relative income levels but it is not the most suitable indica-
tor available for observing the increase over time of the economic well-being of a country. The GDP per capita in PPS of 
a country is affected by many factors: exchange rates movements, evolution of domestic prices and population changes in 
that country, plus the same factors in all the other countries included in the comparison. To gauge the economic growth of 
a country the most used indicator is the volume change of GDP. 

The EU and the euro area experienced healthy economic growth in 2007 confirming the upturn observed in 2006. The EU 
grew by 2.9% and the euro area by 2.6% in 2007, after 3.1% and 2.8% in 2006. These growth rates are substantially higher 
than those achieved in the 2001-2005 period. Slovakia (10.4%) and Latvia (10.3%) were the countries that grew faster in 
2007. Lithuania (8.8%), Estonia (7.1%), the Czech Republic and Poland (6.5%), Bulgaria (6.2%) and Slovenia (6.1%) grew 
above  6%.  Hungary  (1.3%)  and  Italy  (1.5%)  grew  more  slowly.  Denmark  (1.8%),  Portugal  (1.9%)  and  France  (1.9%)  saw 
growth below 2%.

Figure 2.2.9:

volume GDP growth in 2007, percentage change on previous year

SK

LV

LT

EE

PL
CZ

BG

SI
RO

IE
LU*

FI
CY

GR

MT
ES

NL

AT

UK

EU
BE

SE
EA
DE

PT
FR
DK

IT
HU

0.0

2.0

CH

NO

IS

4.0

6.0

8.0

10.0

12.0

38

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Statistical analysis

2

The volume change of GDP is a rough indication for the short term evolution of living standards. But over longer periods 
the increase in volume GDP does not necessary translate into an improvement in living standards: changes in population 
should be taken into consideration. For short term economic analysis (quarter-on-quarter or year-on-year developments), 
population changes are relatively small and therefore the volume change of GDP is a very good approximation of the in-
crease in living standards.

BOX 2.2.3: vOLUME MEASURES AND THE CALCULATION OF AGGREGATES AND 
CONTRIBUTIONS TO GDP GROWTH

volume measures have traditionally been expressed in constant prices of a base year (commonly moved ahead every 
five years). With a view to producing more accurate measures of volume growth, the price base is now updated every 
year, giving data in previous year’s prices, which - together with data expressed at current prices - allow the calculation 
of  volume  growth  rates.  Multiplying  successive  growth  rates  starting  from  a  reference  year  level  provides  a  chain-
linked volume time series.

Chain-linked volume of year t = Chain-linked volume of year t-1 x (Previous year prices of year t / Current prices of year t – 1) 

A  fundamental  feature  of  chain-linking  is  the  loss  of  additivity  for  all  years  except  the  reference  year  and  the  year 
directly following. Consequently, it is not simply a matter of adding up chain-linked data to obtain aggregates, such 
as GDP growth of Baltic States or the growth rate of industry plus construction, like it was done with constant prices. 
Custom aggregations should be obtained by summing up the components of the desired aggregate at previous year’s 
prices and current prices and subsequently chain link the series. Not all Member states provide data at previous year’s 
prices, but these can be easily reconstructed from the available data at current prices and chain-linked volume series 
by using the following reformulation of the above equation:

Previous year prices of year t = Chain-linked volume of year t x (Current prices of year t – 1/ Chain-linked volume of year t-1)

The lack of addtivity also prevents a direct use of chain linked data for the calculation of contributions to GDP growth of 
individual variables, like Gross fixed capital formation (GFCF). It would be necessary to combine data at previous year’s 
prices and current prices. For example, for calculation the contribution of GFCF to GDP growth the following expres-
sion should be used:

(GFCF at previous year’s prices for year t – GFCF at current prices for year t-1) / GDP at current prices for year t-1

Figure 2.2.10 shows the average annual growth of volume GDP per capita and average population changes for the period 
2000-2006.

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2

Statistical analysis

Figure 2.2.10: Average annual growth of volume GDP for the period 2000-2006

9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

-1.0

-2.0

LV EE LT RO IE

SK BG GR HU CZ LU SI PL ES CY FI

SE UK EU27 BE AT DK FR EA13 NL MT IT PT DE

IS NO CH

GDP per person

Population

As it can be seen, the EU experienced an average annual growth rate of volume GDP of 1.7% during the period 2000-2006 
while the average annual growth rate of volume GDP per capita was 1.4%. The lower GDP per capita growth was due to 
a 0.3% average annual increase in population. Most of the new 10 Member States are at the top of the table, as they have 
experienced the highest GDP volume growth. As in many cases their population has decreased, GDP per capita growth has 
outstripped GDP growth in these countries. Most of the old 15 Member States are at the bottom of the table and, with the 
exception of Germany, GDP growth per capita has been lower than GDP growth, as the population has increased. Germany 
and Italy provide good examples of the shortcomings of focusing on GDP growth rate only. While Italy has experienced a 
slightly higher GDP growth rate, because Germany’s population has remained unchanged while Italy’s has increased, the 
growth in volume GDP per capita in Germany has been double that of Italy. 

The following section will focus on the sources of the change in GDP volume per capita. This can be due to changes in 
the amount of labour input or to changes in labour productivity. We will measure labour input as the number of persons 
employed31 and labour productivity as GDP per person employed. During the past 200 years advanced economies’ higher 
living standards have mainly been the result of increased labour productivity. This also holds true for the foreseeable future, 
since employment cannot for prolonged periods be the engine of growth due to the existence of an upper limit to the labour 
input as defined by the maximum population. Labour productivity on the other hand can grow without bounds. 

In equation 2.2.1 GDP in volume per person is decomposed into its components:

GDP
population

=

GDP
employment

×

employment
population

(2.2.1)

31 Hours worked instead of persons employed can be considered a better variable to perform this analysis, but insufficient data coverage does not allow this calculation for many 

countries.

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Statistical analysis

2

In Figure 2.2.11 GDP in volume per person growth is thus further decomposed into the contributions of the growth of 
labour productivity per person employed and the growth of persons employed divided by the population for the period 
2000-2006. It can be seen that more than two thirds of EU27 and EA13 growth stemmed from higher labour productivity. 

In all EU member states – except Cyprus, Italy, Luxembourg and Spain – economic growth per person stemmed principally 
from labour productivity growth. The most impressive growth rates were displayed by the Baltic States and Poland (+5.5% 
to +5.8%). Slovakia’s labour productivity increased annually by 4.0%. Hungary, the Czech Republic, Bulgaria and Slovenia 
increased their labour productivity per person employed by 2.9 to 3.4 per cent per annum. The EU27 average growth rate 
was 1.0 per cent per year. The only country that experienced a negative labour productivity growth was Italy (-0.3%). 

Latvia (+2.4%), Bulgaria (+2.4%), Estonia (+1.8%), Luxembourg (+1.7), Spain (+1.5%) and Lithuania (+1.3%) increased their 
employment ratios the most. One per cent increases were also seen in Ireland and Greece. The EU27 average growth rate 
was 0.4 per cent per year. The countries that experienced decreases in their employment ratios were Germany (-0.1%), Por-
tugal (-0.2%) and Poland (-2.2%).

Figure 2.2.11: Average annual growth of GDP per capita for the period 2000-2006

10.0

8.0

6.0

4.0

2.0

0.0

-2.0

-4.0

LV EE LH BG SK HU CZ GR SI

PL

IE

LU FI

SE UK ES EU
27

CY DK BE AT FR NL EA
13

DE MT

IT

PT

IS NR CH

Labour productivity per person employed

Persons employed / Population

Employment and labour productivity for PL estimated. RO omitted.

2.2.7  Sector accounts

For about ten years, the annual sector accounts of the Member States of the European Union have been collected according 
to one common methodology described in the European System of Accounts 1995 (ESA 95)32.

Since 2006, the non-financial annual sector accounts of the euro area and of the European Union have been published 
by  Eurostat,  together  with  the  sector  accounts  of  most  Member  States.  Since  June  2007,  quarterly  series  have  also  been 
released, for the euro area and the European Union but with no national breakdown. All these data are available, together 
with methodological information in English, French and German, at the following website: http://ec.europa.eu/eurostat/
sectoraccounts. A synthesis of the methods used to compile European sector accounts on the basis of Member States data is 
also provided in the methodological part (chapter 3.2) of this publication.

Annual sector accounts represent a wealth of information that allows analysing the economic behaviour of each sector in 
the economy, mainly: non-financial corporations, financial corporations, general government and households. The trans-
actions of the economy as a whole vis-à-vis third countries are recorded in the accounts of the “rest of the world”.

32 For more details, see http://forum.europa.eu.int/irc/dsis/nfaccount/info/data/esa95/en/titelen.htm).

  European Economic Statistics

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2

Statistical analysis

The behaviour of households and non-financial corporations is particularly relevant for economic analysis. Households 
are generally the main source of national saving which itself finances investment in the national economy or abroad. Non-
financial corporations are the main driver of investment in productive assets which determines long-term growth to some 
extent. Considered together, household saving and business investment generally explain the main developments in the 
lending capacity or borrowing needs of an economy. 

In addition, sector accounts also give valuable information for analysing the profitability of firms. One possible indicator for 
this purpose is the profit share defined as the portion of value added that remunerates capital. This profit share is the com-
plement of wages costs that remunerate labour, plus net taxes on production that (partially) finances government services.

In the first sub-section, the saving rate of households is commented whereas the last sub-section focuses on the investment 
rate and profit share of non-financial corporations.

Household saving rate

The households sector covers individuals or groups of individuals as consumers and as entrepreneurs provided, in the latter 
case that their activities as market producers are not carried out by separate entities. In the following, this sector has been 
merged with the small sector of non profit institutions serving households (e.g. associations, charities etc…).

In national accounts terms, the gross household saving rate is defined as gross saving divided by gross disposable income. 
The latter has been adjusted to take into account the net increase/decrease in the equity of households in pension fund 
reserves.

The household saving rate is provided gross, which means before deducting the normal wear and tear of fixed assets, mainly 
dwellings in this case.

The figure below displays in descending order the saving rates of households as measured in 2006 for all Member States, for 
which data were available, the EA13 and the EU. 

Figure 2.2.12:  (Gross) household saving rates in the EU (%, 2006 data if available)

18%

16%

14%

12%

10%

8%

6%

4%

2%

0%

-2%

y
n
a
m
r
e
G

i

a
n
e
v
o
S

l

l

y
a
t
I

e
c
n
a
r
F

a
i
r
t
s
u
A

3
1
a
e
r
a
o
r
u
E

i

m
u
g
e
B

l

7
2
U
E

s
d
n
a
l
r
e
h
t
e
N

)
5
0
0
2
(
y
r
a
g
n
u
H

d
n
a
e
r
I

l

i

n
a
p
S

n
e
d
e
w
S

d
n
a
o
P

l

a

i

k
a
v
o
S

l

)
5
0
0
2
(

l

a
g
u
t
r
o
P

euro area countries

d
n
a
n
F

l

i

i

m
o
d
g
n
K
d
e
t
i
n
U

c

i
l

b
u
p
e
R
h
c
e
z
C

e
c
e
e
r
G

k
r
a
m
n
e
D

i

a
n
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t
s
E

i

a
n
a
u
h
t
i
L

)
5
0
0
2
(
a
v
t
a
L

i

42

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Statistical analysis

2

As shown in the figure above, the household saving rate in 2006 was more than 2 percentage points higher in the euro area 
(13.6 %) than in the EU (11.3 %). This gap is mainly explained by the low saving rates of Denmark (4.0 %), UK (5.0 %) and 
Poland (6.6 %).

In the euro area, saving rates are generally high and homogeneous. Only Finland and Greece have a low saving rate whereas 
the three largest economies of the euro area (Germany, Italy and France) rank in the first positions.

Member States that are not part of the euro area, Baltic countries in particular, have the lowest household saving rates (1.1% 
for Latvia, 0.7% for Lithuania, and even -0.7% for Estonia33).

Table 2.2.2:   Changes in the gross household saving rates between 2000 and 2006

(Percentage points)

EU27

-0.4%

EA13

0.2%

BE

-2.9%

LT

-6.6%

LU

:

HU *

-2.9%

BG

:

MT

:

* change calculated from 2000 to 2005

CZ

DK

-3.6%

-0.9%

DE

1.1%

EE

-4.8%

NL

0.4%

AT

PL

PT *

1.3%

-4.2%

-1.0%

IE

:

RO

:

EL

ES

FR

IT

-1.4%

-0.7%

0.4%

0.7%

Cy

:

Lv *

-1.8%

SI

SK

FI

SE

UK

1.5%

-4.6%

-1.9%

3.0%

-0.1%

When analysing changes over the 2000-2006 period of time, as provided in the above table, only one significant increase 
can be observed, that with respect to Sweden (+ 3.0%).

On the contrary, several important decreases are recorded in particular for “new” Member-States like Poland (-4.2%), Slo-
vakia (-4.6%), the Czech Republic (-3.6%) and Baltic countries (-6.6% for Latvia and -4.8% for Estonia). In these countries, 
households have increased their final consumption at a higher pace than their disposable income. 

Among EU15 Member States, only Belgium (-2.9 %) and Finland (-1.9 %) show such important decreases in their household 
saving rates.

Non-financial corporations

The  non-financial  corporations  sector  covers  enterprises  whose  principal  activity  is  the  production  of  goods  and  non-
financial services to be sold on the market.

In order to analyse the propensity of this sector to invest (in buildings, machinery etc…) and to therefore contribute to the 
long-term growth of the economy, the investment rate can be used as an indicator. It is defined as gross investment (fixed 
capital formation) divided by gross value added. By gross, we mean that the amount of fixed assets used up during the year 
as a result of normal wear and tear is not deducted.

Business investment rate

In Figure 2.2.13, the investment rate of non-financial corporations is displayed for all available Member States, EA13 and 
the EU. 

33 A negative saving rate means that the household sector as a whole has to borrow to finance part of its current expenditures.

  European Economic Statistics

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2

Statistical analysis

Figure 2.2.13:

(Gross) investment rates of non-financial corporations in the EU (%, 2006 data if available)

40%

35%

30%

25%

20%

15%

10%

5%

0%

i

n
a
p
S

i

a
n
o
t
s
E

a

i

k
a
v
o
S

l

)
5
0
0
2
(
a
v
t
a
L

i

i

a
n
e
v
o
S

l

a
i
r
t
s
u
A

k
r
a
m
n
e
D

e
c
e
e
r
G

)
5
0
0
2
(

l

a
g
u
t
r
o
P

)
5
0
0
2
(
y
r
a
g
n
u
H

c

i
l

b
u
p
e
R
h
c
e
z
C

l

y
a
t
I

d
n
a
o
P

l

7
2
U
E

i

a
n
a
u
h
t
i
L

n
e
d
e
w
S

3
1
a
e
r
a
o
r
u
E

euro area countries

i

m
u
g
e
B

l

e
c
n
a
r
F

y
n
a
m
r
e
G

d
n
a
n
F

l

i

d
n
a
e
r
I

l

s
d
n
a
l
r
e
h
t
e
N

i

m
o
d
g
n
K
d
e
t
i
n
U

The business investment rate in the EU is same as in the euro area (22.1%). Among non euro area countries, the Member-
States that have joined the EU in 2004 generally have high investment rates. This is the case in particular for Slovakia (38.7 
%), Latvia (37.1 %), Estonia (33.3 %) and Slovenia (29.8%).

On the contrary, only a few EU15 Member States still have high investment rates, notably Spain (35.3%) and, to a lesser 
extent, Greece (28.9 %), Portugal (28.2 %) and Austria (27.2 %).

Table 2.2.3: 

Changes in the business investment rates between 2000 and 2006
(in percentage points)

EU27

EA13

BE

-0.6% -0.6%

-2.1%

LT

0.1%

LU

:

HU *

-3.9%

BG

:

MT

:

* change calculated from 2000 to 2005

CZ

-7.4%

DK

1.5%

DE

EE

-3.0%

1.9%

NL

AT

PL

PT *

-2.8%

-2.9% -12.9% -4.8%

IE

:

RO

:

EL

ES

FR

IT

5.5%

4.8%

-0.2%

1.0%

SI

SK

FI

SE

-1.8%

6.4%

-1.9%

-1.7%

Lv *

1.2%

Cy

:

UK

:

When turning to the dynamics of investment rates, as provided in table 2.2.3, it can be observed that the same decrease was 
recorded in the euro area and in the EU (-0.6 %). At the country level, a fall is observed for Poland (-12.9 %) and in the Czech 
Republic (-7.4 %). Several countries record a more limited, but still sizeable, decrease: Portugal (-4.8%), Hungary (-3.9%), 
Germany (-3.0 %), Austria (- 2.9 %), the Netherlands (-2.8%) and Belgium (-2.1 %). An important surge in investment rates 
is observed in three countries only, namely: Slovakia (6.4 %), Greece (5.5%) and Spain (+4.8 %).

Business profit share

Another important variable derived from the sector accounts is the profit share of the non-financial corporations measured 
as their gross operating surplus divided by gross value added. This indicator measures the portion of value added that re-
munerates the capital. When put in relation to investment rates, it helps us to understand whether the investment behaviour 
of firms is linked to their current/past profitability.

44

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Figure 2.2.14:  (Gross) profit shares of non-financial corporations in the EU (%, 2006 data, if available)

Statistical analysis

2

60%

50%

40%

30%

20%

10%

0%

e
c
e
e
r
G

i

a
n
a
u
h
t
i
L

a

i

k
a
v
o
S

l

d
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a
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I

l

i

a
n
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t
s
E

)
5
0
0
2
(
a
v
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L

i

d
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a
o
P

l

c

i
l

b
u
p
e
R
h
c
e
z
C

d
n
a
n
F

l

i

i

m
u
g
e
B

l

l

y
a
t
I

a
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r
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s
u
A

y
n
a
m
r
e
G

s
d
n
a
l
r
e
h
t
e
N

)
5
0
0
2
(
y
r
a
g
n
u
H

euro area countries

7
2
U
E

i

n
a
p
S

k
r
a
m
n
e
D

3
1
a
e
r
a
o
r
u
E

n
e
d
e
w
S

)
5
0
0
2
(

l

a
g
u
t
r
o
P

i

m
o
d
g
n
K
d
e
t
i
n
U

i

a
n
e
v
o
S

l

e
c
n
a
r
F

The  profit  share  of  non-financial  corporations  is  one  percentage  point  higher  in  the  euro  area  (39.1  %)  than  in  the  EU 
(38.1 %). Low rates are observed for France (30.7%), Slovenia (32.1%) and Sweden (33,3%). At the other extreme, the highest 
profit shares can be observed for Greece (56.3%), Lithuania (53.4%), Slovakia (53.2%) and Ireland (52.0%).

Table 2.2.4: 

Changes in the profit share of non-financial corporations between 2000 and 2006
(Percentage points)

EU27

1.3%

EA13

1.4%

BE

4.2%

LT

1.7%

LU

:

HU *

-0.2%

BG

:

MT

:

* change calculated from 2000 to 2005

CZ

DK

DE

EE

0.5%

-2.1%

4.9%

3.5%

NL

1.3%

AT

PL

PT *

2.8%

10.2% -0.8%

IE

:

RO

:

EL

ES

FR

IT

-2.2%

0.8%

-0.6%

-3.7%

SI

SK

FI

SE

3.2%

4.7%

-0.4%

3.0%

Lv *

-0.2%

Cy

:

UK

:

Profit shares have increased by 1.3% in the EU and by 1.4% in the euro area between 2000 and 2006. The most noticeable 
increases concern Poland (+10.2 %), Germany (+4.9 %), Slovakia (+4.7 %) and Belgium (+4.2 %). Some sizeable decreases 
can be observed in Italy (-3.7 %), Greece (-2.2 %) and Denmark (-2.1%).

2.2.8  Regional GDP

Map 2.2.1 provides an overview of the regional distribution of per capita GDP for the year 2005 (as a percentage of the aver-
age for EU of 22 400 expressed in PPS) for the European Union. It ranges from 24% of the EU average (PPS 5 430) per capita 
in north-east Romania to 303% (PPS 67 798) per capita in the UK capital region of Inner London. The difference between 
the two ends of the range is therefore 12.5 to 1. Luxemburg, at 264% (59 202 PPS) and Brussels at 241% (53 876 PPS) follow in 
second and third places, and Hamburg at 202% (45 271 PPS) and Vienna at 178% (39 774 PPS) take fourth and fifth places. 

The most prosperous regions are situated in southern Germany, in the south of the UK, in northern Italy and in Belgium, 
Luxembourg, the Netherlands, Ireland and Scandinavia. The capital regions of Madrid, Paris and Prague also fall into this 
category. Most of the economically weaker regions are in the southern and south-western periphery of the EU, in eastern 
Germany and the new Member States.

  European Economic Statistics

45

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2

Map 2.2.1

Statistical analysis

GDP per inhabitant, in PPS,
by NUTS 2 regions, 2005

In percentage of EU-27=100

<= 50

50 - <= 75

75 - <= 90

90 - <= 110

110 - <= 125

> 125

Data not available

Data source: Eurostat
© EuroGeographics Association, for the administrative boundaries
Cartography: Eurostat - GISCO, 05/2008

0

600 km

Guadeloupe (FR)

Martinique (FR)

0

25

0

20

Guyane (FR)

Réunion (FR)

0

100

0

20

Açores (PT)

Madeira (PT)

0

100

0

20

Canarias (ES)

Malta

0 100

0

10

Ísland

0 100

46

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Statistical analysis

2

Prague (Czech Republic), the region with the highest GDP per inhabitant in the new Member States, has already risen to 
twelfth place with 160% of the EU average (PPS 35 901), and Bratislavský kraj (Slovakia) with 148% (PPS 33 124) has reached 
eighteenth place out of the 271 level-two regions of the EU27. However, these two regions are exceptions in the new Mem-
ber States, as the next ones are lagging far behind: Közép-Magyarország (Hungary) at 105% (23 489 PPS) in 111th place, 
Zahodna Slovenija (Slovenia) also at 105% (23 453 PPS) in 112th place and Cyprus at 93% (20 753 PPS) in 157th place. With 
the exception of two other regions (Mazowieckie in Poland and Malta), all the remaining regions of the new Member States 
have a GDP per inhabitant of less than 75% of the EU27 average.

If the 271 regions are divided into classes according to their GDP (in PPS) per inhabitant, the following picture emerges: 
in 2005, GDP in 69 regions was less than 75% of the EU average. These 69 regions account for 24.9% of the population, of 
which three quarters are in the new Member States, and one quarter in EU15 countries. 12.1% of the population live in re-
gions whose per capita GDP is less than 50% of the EU average; all of these regions are situated in the new Member States.

At the upper end of the spectrum, 43 regions have a per capita GDP of more than 125% of the EU average. 21.7% of the 
population live in these regions. A total of 53.4% of the population, i.e. the majority, live in regions with a per capita GDP 
between 75% and 125% of the EU average. 

A comparison of the ranges between 2000 and 2005 shows that the gap between the most and the least prosperous regions 
of the EU is beginning to narrow. While the difference between the two ends of the range was 15.8 to 1 in 2000, it decreased 
to 12.5 to 1 for the year 2005.

There are also substantial regional differences within countries themselves. In 2005, the highest per capita GDP was more 
than twice the lowest in 12 of the 21 countries with more than one NUTS-2 region. This group includes 5 of the 7 new 
Member States but only 7 of the 14 EU15 Member States. 

The largest regional differences are in the United Kingdom and Slovakia, where there is a factor of 3.9 and 3.4 respectively 
between the two extreme values. The lowest values can be found in Ireland and Sweden, with a corresponding factor of 1.5 
and 1.6 respectively. Moderate regional disparities in per capita GDP (i.e. factors of less than 2 between the highest value 
and the lowest) are found only in the EU15 Member States and in Bulgaria and Slovenia. 

In all the new Member States and Croatia, and in a number of the EU15 Member States, a substantial share of economic 
activity is concentrated in the capital regions. As a result, in 17 of the 21 countries included here in which there is more than 
one NUTS 2 region, the capital regions are also the regions with the highest GDP per inhabitant. For example, Map 2.2.1 
clearly shows the prominent position of the regions of Brussels, Prague, Sofia, Madrid, Paris, Lisbon, as well as Budapest, 
Bratislava, London, Warsaw and Bucharest. 

A comparison of the ranges between 2000 and  2005,  however, shows  that developments in the EU15 were significantly 
different to those in the new Member States. Whilst the ranges between the regional extremes in the new Member States 
tended to increase, they decreased in most of the EU15 countries.

  European Economic Statistics

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2

Statistical analysis

2.3 

Public finances

Governments play an important role in economies, through their activities in providing public services and in re-distrib-
uting income. The way in which they finance themselves (taxation, borrowing) and the size, pattern and function of their 
expenditure  have  major  impacts  on  other  economic  actors.  In  Europe  there  is  particular  interest  in  government  fiscal 
policy, owing to the Excessive Deficit Procedure (EDP – see box 2.3.5) which limits government deficits and debts, and a 
debate on the sustainability and quality of public finances. All of these aspects are monitored within the framework of the 
Stability and Growth Pact.

The following analysis examines the finances of EU governments over recent years. These data do not include public corpo-
rations which sell their products on a market (for example, most Post Offices); see Box 2.3.1 for further details.

BOX 2.3.1. DEFINITION OF THE GENERAL GOvERNMENT SECTOR

In the European System of Accounts (ESA95; paragraph 2.68) the sector “general government” has been defined as 
containing “all institutional units which are other non-market producers whose output is intended for individual 
and collective consumption, and mainly financed by compulsory payments made by units belonging to other sec-
tors, and/or all institutional units principally engaged in the redistribution of national income and wealth”. 

The main functions of general government units are therefore:

–   to organize or redirect flows of money, goods and services or other assets among corporations, among house-
holds or between corporations and households for the purpose of social justice, increased efficiency or other aims 
legitimated by the citizens (redistribution of national income and wealth), for example corporate income tax paid 
by companies used for financing unemployment benefits, or social contributions of employees paid for financing 
pension systems;

– 

to produce goods and services to satisfy households´ needs (e.g. state health care) or simultaneously meet needs 
of the whole community (e.g. defence, public order and safety).

By convention, the general government sector includes all the public corporations that are not able to cover at least 
50% of their costs by sales, and are thus considered non-market producers. 

Government expenditure 

a. 

general trends and structure

EU total government expenditure (as defined in box 2.3.2) reached 45.8% of GDP in 2007. This was the lowest level in the 
2002-2007 period (see Figure 2.3.1). The downward trend started in the EU in 2003, with a brief interruption between 2004 
and 2005 when total expenditure increased very slightly; euro area expenditure, on the other hand, decreased continuously 
from 2003 (by 1.8 percentage points of GDP to 2007).

48

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Figure 2.3.1: Total general government expenditure in European Union and euro area in years 2002-2007

Statistical analysis

2

P
D
G

f

o
%

48.5

48.0

47.5

47.0

46.5

46.0

45.5

2002

2003

2004

eu27

2005

ea13

2006

2007

A  major  proportion  of  government  expenditure  (42%  of  the  EU  total)  in  2007  was  for  the  purpose  of  redistribution  of 
income in the form of social transfers in cash and in kind. A further 37% was spent on government production of goods 
and services: compensation of employees (23%) and intermediate consumption (14%). The share of interest on borrowing 
and rents paid by government was 6% of the total, whereas public investment spending (acquisitions less disposals of fixed 
assets; gross of consumption of fixed capital) took another 5.5%. The remainder was for other current transfers (just below 
5%) and other components such as subsidies, capital transfers and taxes paid (5.5%). 

In the euro area the share of social transfers in total expenditure was around 4 percentage points larger than in the EU (see 
Figure 2.3.2). 

Figure 2.3.2: Composition of total government expenditure in EU27 and euro area in 2007

ea13

eu27

0%

20%

40%

60%

80%

100%

Social transfers

Compensation of employees

Intermediate consumption

Property income paid (incl. interest)

Public investment

Other current transfers

Others

  European Economic Statistics

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2

Statistical analysis

As mentioned above, the main purpose of EU governments’ spending is social protection (40% of total government ex-
penditure), followed by general public services and health (14% each), education (11%) and economic affairs (8%). For public 
order and safety, governments spend around 4% of their total expenditure, and the shares of other government functions 
are as follows:

–  defence 3.4%,

–  housing and community amenities 2.2%,

–  recreation, culture and religion 2.2%,

–  environmental protection 1.4%.

BOX 2.3.2. GOvERNMENT REvENUE AND EXPENDITURE

To ensure consistency between a national accounts logic expressed in the sequence of accounts (production, genera-
tion, distribution, redistribution and use of income, accumulation and financing) and a government budget perspec-
tive  (government  spending  and  receipts),  two  additional  concepts  are  defined  in  ESA95  with  reference  to  national 
accounts categories:

government revenue as a sum of:

government expenditure as a sum of:

–  sales consisting of: market output, output for own 
final use, payments for the other non-market out-
put,

– 

taxes on production and imports,

–  other subsidies on production, receivable,

–  property income,

–  current taxes on income and wealth, etc,

–  social contributions,

–  other current transfers,

–  capital transfers;

– 

intermediate consumption, 

–  gross capital formation, 

–  compensation of employees, 

–  other taxes on production,

–  subsidies, payable,

–  property income (including interest), payable,

–  current taxes on income, wealth, etc.,

–  social benefits other than social transfers in kind,

–  social transfers in kind related to expenditure on prod-
ucts supplied to households via market producers,

–  other current transfers, payable,

–  adjustment for the change in net equity of households 

in pension funds reserves,

–  capital transfers, payable,

–  acquisitions  less  disposals  of  non-financial  non-pro-

duced assets.

By convention the internal transactions inside the general government sector, i.e. between different sub-sectors or 
between different general government units belonging to the same sub-sector, related to property income, other cur-
rent transfers and capital transfers are excluded from government revenue and expenditure.

50

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Statistical analysis

2

BOX 2.3.3: CLASSIFICATION OF FUNCTIONS OF GOvERNMENT (COFOG)

COFOG was developed in its current version in 1999 by the Organization for Economic Cooperation and Development 
(OECD) and published by the United Nations Statistical Division as a standard classifying the purposes of government 
activities. The classification has three levels of detail:

–  divisions: describing the broad objectives of government,

–  groups and classes: defining the means by which these broad objectives are achieved.

Government broad objective (division) 

Sub-items

General public services 

Defence

Public order and safety 

Economic affaires

Environmental protection

Housing and community amenities 

Health

Recreation, culture and religion 

Education

Social protection 

Executive  and  legislative  organs,  financial  and  fiscal 
affairs,  external  affairs  foreign  economic  aid,  basic  re-
search,  R&D  related  to  general  public  services,  public 
debt services, transfers of a general character between 
different levels of government

Military and civil defence, foreign military aid, R&D re-
lated to defence

Police, fire-protection services, law courts, prisons, R&D 
related to public order and safety

General economic, labour and commercial affairs, agri-
culture, forestry, fishing and hunting, fuel and energy, 
mining,  manufacturing  and  construction,  transport, 
communication, other industries, related R&D

Waste and water waste management, pollution abate-
ment,  protection  of  biodiversity  and  landscape,  re-
lated R&D

Housing  development,  community  development,  wa-
ter supply, street lighting, R&D related

Medical products, appliances and equipment, outpa-
tient,  hospital  and  public  health  service,  R&D  related 
to health

Recreational and sporting, cultural services, broadcast-
ing  and  publishing  services,  religious  and  other  com-
munity services, R&D

Pre-primary, primary, secondary and tertiary education, 
post-secondary non-tertiary education, education non-
definable by level, subsidiary services to education, R&D

Sickness  and  disability,  old  age,  survivors,  family  and 
children,  unemployment,  housing,  R&D,  social  exclu-
sion, nec.

  European Economic Statistics

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2

Statistical analysis

Figure 2.3.3:

EU and euro area government expenditure by COFOG functions

ea13

eu27*

0%

20%

40%

60%

Social protection

Education

Others

General public services

Economic affairs

100%

80%

Health

Public order and safety

Source: Eurostat. Data for EU27 for 2004 and EA13 for 2005.

b. 

inter-country comparisons

Nine Member States recorded total general government expenditure over the EU27 average in 2007, as a share of the econ-
omy. The highest level, at 52.6% of GDP, was in Sweden and France, followed by Hungary and Denmark where total ex-
penditure also exceeded 50% of GDP. The lowest general government expenditures in 2007 were in the Slovak Republic, 
Luxembourg, Romania, Bulgaria, Spain, Lithuania, Latvia and Ireland – all below 40% of GDP, and in particular in Estonia, 
where it was below 35%. 

Compared to the situation in 2002, most Member States were able to stabilize or decrease the level of their government ex-
penditure. The highest decrease, by 8 percentage points, was achieved by the Slovak Republic, whilst in Denmark, Sweden, 
Germany, the Czech Republic, Slovenia and Luxembourg the fall was close to or above 4 percentage points. Only in Italy, 
Lithuania, Portugal, the United Kingdom, Latvia, Ireland and Cyprus did total government expenditures increase between 
2002 and 2007, in all cases by less than 4 percentage points of GDP. 

Total government expenditure in Norway in 2007 was 40.9% of GDP, lower than the 2002 level by 6 percentage points. In 
Iceland, it was 40.5% of GDP in 2006 (the latest year available), as compared to 44.2% of GDP in 2002.

52

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Figure 2.3.4:  Government expenditure in % of GDP in 2007 and its change since 2002

Statistical analysis

2

7
0
0
2
n

i

P
D
G

f

o
%
s
a
E
T

sk

se

dk

de

si
cz

lu

bg

ro

fr

at

ea13

hu

fi

be

nl

eu27

mt

es

gr

pl

ee

it

pt

uk

lv

ie

it

-8

-6

-4

-2

0

2

change in pp between 2002 and 2007

Source: Eurostat. 

55

50

45

cy

40

35

30

4

Government expenditure per inhabitant was above 11,400 euro on average in the EU in 2007 and differed significantly 
between Member States. In Luxembourg, in 2007, the government spent almost 29,000 euro per inhabitant 34, the highest 
value in the EU, whereas for Bulgaria the equivalent value was below 1,500 euro. Government spending per inhabitant was 
below 10,000 euro in all the Member States that have joined the EU since 1 May 2004 (Central and Eastern European coun-
tries, Malta and Cyprus) as well as in Spain, Greece and Portugal.

Of the EEA countries, Norway recorded government expenditure per inhabitant in 2007 of above 24,600 euro, and in Ice-
land it amounted to 17,700 euro in 2006.

34 This figure for Luxembourg (and also the equivalent figure for revenue below) is inflated by the fact that around 45% of Luxembourgish labour force are non-residents.

  European Economic Statistics

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2

Statistical analysis

Figure 2.3.5: Government expenditure in euro per inhabitant in 2007. 

35000

30000

25000

20000

o
r
u
e

15000

10000

5000

0

LU DK SE

FI AT NL FR

IE

BE UK DE EA13 IT EU27 ES GR CY

SI

PT MT CZ HU EE

SK PL

LV

LT RO BG

Source: Eurostat. 

An analysis of the composition of total expenditure in individual Member States in 2007, gives rise to the following obser-
vations35:

– Belgium,  the  Czech  Republic,  Germany,  Greece,  France,  Italy,  Luxembourg,  the  Netherlands,  Austria,  Portugal  and 
the Slovak Republic spent at least two fifths of their total government expenditure on redistributive transactions (social 
transfers), with Germany the highest at over 56%, whereas in Cyprus, Latvia, Romania and Lithuania the share of social 
transfers was below 30%;

– the share of compensation of government employees was the greatest in Cyprus and Denmark (33%), and also exceeded 
30% in Latvia and Malta. However, in the Czech Republic, Austria, the Slovak Republic, Luxembourg and Germany it 
was below 20%;

– intermediate consumption (purchases of non-capital goods and services) was a relatively less important form of gov-
ernment spending in Belgium, Germany, France, Austria and Portugal, with a share of total expenditure below 10%, 
whereas in Bulgaria its share in 2007 was over 20%; 

– interest payments (making up most of the component ‘property income, payable’) had a relatively high share of total 
government expenditure in countries with a high level of government debt, such as Italy and Greece (both around 10%) 
and Belgium (close to 8%), and a very low share in Member States with a low level of government debt: in particular the 
Baltic States and Luxembourg (with shares not exceeding 2%),

– the EU Member States dedicating the greatest share of government spending to investment were Romania, Latvia and 
Lithuania (all around 15%). The share of investment was below the EU weighted average (5.5%) in eight Member States: 
Belgium, Denmark, Germany, Italy, Portugal, the Slovak Republic, Finland and the United Kingdom, but only in Aus-
tria was the share in 2007 below 3%,

35 The United Kingdom and Greece are excluded from the analysis of shares of social transfers and intermediate consumption in total government expenditure. For these countries, 
social transfers are underestimated and intermediate consumption is overestimated due to the statistical treatment of social transfers in kind related to expenditure on products 
supplied to households via market producers.

54

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Statistical analysis

2

– the share of other current transfers was relatively high in Latvia and Cyprus (above 10%), whereas the share of govern-
ment spending on subsidies (amounting to over 7%) and on capital transfers (over 4%) explains the high expenditure 
component ‘others’ (see Figure 6) in Austria.

Figure 2.3.6:  Main components of government expenditure in 2007

100%

e
r
u
t
i
d
n
e
p
x
e

l

a
t
o
t

f

o
%

80%

60%

40%

20%

0%

BE

BG CZ DK DE

EE

IE GR

ES

FR

IT

CY

LV

LT

LU HU MT NL AT

PL

PT

RO SI

SK

FI

SE UK

Social transfers

Compensation of employees

Intermediate consumption

Public investment

Other current transfers

Others

Property income paid 
(incl. interest)

Source: Eurostat. 

In Norway, the share of social transfers in total government expenditure in 2007 was 35%, compensation of employees 
amounted to 30% and intermediate consumption to 15%, followed by investment (7.4%), other current transfers (5%), sub-
sidies (4.6%) and interest paid (3%). In Switzerland (2005 data) the importance of social transfers was similar to Norway 
(36%); however, the shares of compensation of employees, intermediate consumption and public investment were lower (23, 
11 and 6.2%, respectively), whereas the share of subsidies was almost 7 percentage points higher (11.5%). In Iceland, com-
pensation of employees was the dominant type of government expenditure (over 38% of total) in 2006. 

There  are  also  some  interesting  differences  across  countries  in  the  purposes  for  which  government  spending  was  used. 
In most Member States, expenditure on social protection is dominant, with a share in total government expenditure in 
some cases of over 40% of GDP (in Denmark, Greece, France, Luxembourg, Austria, Finland and Sweden, with the largest 
share in Germany: almost 47%). However, in the Baltic States, Ireland, the Czech Republic and Romania, the share of this 
expenditure is below 30%. Cyprus is the only Member State that dedicates a comparable share of its total government ex-
penditure to social protection and general public services: 24% and 23% respectively. Spending on general public services is 
also clearly the next most important function in Belgium, Greece, Italy, Hungary, the Netherlands and the Slovak Republic, 
which is mainly explained by the fact that, in Belgium, Hungary, Greece and Italy, over 40% of this expenditure is made in 
respect of interest payments on debt. By contrast, in Estonia and Romania the respective share of general public services is 
below 10%. 

Ireland spent a higher proportion of its government expenditure on health (23%), followed by Czech Republic, Portugal, 
Romania and the United Kingdom (all with shares exceeding 15%). By contrast, Latvia and Cyprus dedicated just under 8% 
of total government expenditure to health. 

  European Economic Statistics

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2

Statistical analysis

Estonia spent over 18% of its government expenditure on education. Shares over 15% were also observed for Cyprus, Latvia, 
Lithuania and Portugal, whereas for Greece it was just 5.5% - the lowest level in the EU; in Italy and Germany the propor-
tion was close to 9%.

Economic affairs is also a relatively important function in many countries. Its share in total government expenditure was 
over 15% in the Czech Republic, Latvia and Romania. The EU Member States with the lowest shares – below 7% – were 
France, the United Kingdom and Denmark. 

The share of expenditure on public order and safety ranged from 1.9% in Denmark to 7.5% in Bulgaria. Amongst other pur-
poses of government spending, defence is quite important in the United Kingdom, Greece and Cyprus (making up over 5% 
of the total), with housing and community amenities relatively important in Romania and Cyprus, and recreation, culture 
and religion representing over 7% of government expenditure in Estonia. Malta is the EU Member State that dedicates the 
largest share of its government expenditure (close to 4%) to environmental protection.

In Norway, the main purpose of government expenditure is also social protection (over 38% of the total in 2006), followed 
by health (17%), education (13.5%) and general public services (almost 11%).

Figure 2.3.7: Government expenditure in EU Member States by COFOG functions (2005 and 2006 data36).

100%

80%

60%

40%

20%

0%

BE** BG**** CZ DK DE

EE

IE GR

ES

FR**

IT

CY

LV

LT

LU HU** MT NL** AT

PL

PT

RO** SI** SK** FI

SE** UK**

Social protection
Social protection

Economic affairs
Economic affairs

General public services
General public services

Public order and safety
Public order and safety

Health
Health

Others

Education
Education

Source: Eurostat. Data for the Slovak Republic are provisional.

36 Where available, data for 2006 are presented. However for some Member States (marked with “*”) the figures shown relate to 2005 - and for BG (marked with “**”) there is a 

2004-breakdown.

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Statistical analysis

2

Government revenue37

a. 

general trends and structure

Total revenue of general government in the EU amounted to 44.9% of GDP in 2007, the same level as in 2006. It was pre-
ceded by a decrease between 2003 and 2004, and then by a rise of 1 percentage point of GDP between 2004 and 2006. The 
same trend can be observed for total general government revenue for the euro area, but at a level around one percentage 
point of GDP higher.

The evolution of total revenue in the EU and the euro area, as presented in Figure 2.3.8 can be explained by the behaviour 
of its main components over this period: taxes and social contributions (see Figure 2.3.13).

Figure 2.3.8:  Total general government revenue in European Union and euro area in 2002-2007

P
D
G

f

o
%

46.0

45.5

45.0

44.5

44.0

43.5

43.0

Source: Eurostat

2002

2003

2004

eu27

2005

ea13

2006

2007

EU governments collect most of their revenue (around 60% on average) in the form of taxes, and a further 30% as social 
contributions. The share of revenue from sales of products and services by government is around 5%, whereas around 2% of 
revenue comes from rents and interest received (property income) and another 2% from current and capital transfers.

Figure 2.3.9: Composition of total revenue in European Union and euro area in 2007

ea13

eu27

0%

20%

40%

60%

80%

100%

% of total revenue

Government sales

Taxes

Social contributions

Property income

Others

Source: Eurostat

37 For formal definition of government revenue and expenditure see box 2.3.2

  European Economic Statistics

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2

Statistical analysis

b. 

Inter-country comparison

Figure 2.3.10 presents total government revenue as a % of GDP in particular Member States, as recorded in 2007, and its 
change in percentage points of GDP compared to 2002. It groups Member States into four categories:

– countries where total revenue of general government as a % of GDP is higher than the EU average and where total rev-

enue has fallen since 2002: Austria, Belgium and Finland;

– countries that recorded total government revenue in 2007 above the EU27 average and at a higher level than in 2002: 
Sweden (with the highest revenue as a % of GDP in the whole EU at 56% of GDP), Denmark, France, Italy, the Neth-
erlands and Cyprus, the latter with the highest increase in government revenue of more than 10 pp between 2002 and 
2007;

– countries with total revenue as a % of GDP lower than the EU average and where it has fallen since 2002: Germany, 

Slovenia, Luxembourg, the Slovak Republic and Romania;

– the remaining countries with total revenue as a % of GDP below the EU average in 2007 but at a higher level than in 

2002.

The lowest level of total revenue, below 35% of GDP, was recorded in 2007 by Lithuania, Romania and the Slovak Republic.

In Iceland, total government revenue amounted to 45.7% in 2006, an increase of 4 percentage points since 2002, whereas 
in 2007 Norway recorded a higher level of government revenue than any EU Member State, at 58.3% of GDP, which is 2 
percentage points higher than in 2002.

Figure 2.3.10: Government revenue in % of GDP in 2007 and its change since 2002

se

dk

fr

ea13

eu27

it
nl

hu

pt

ﬁ

be

de

at

si

7
0
0
2
n

i

P
D
G

f

o
%
s
a
E
T

lu

uk

gr

bg

es

mt

cz

pl

ee

lt

lv

ie

ro

sk

cy

60

55

50

45

40

35

-4

-2

0

2

6
4
change in pp between 2002 and 2007

8

10

30

12

Source: Eurostat. 

58

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Statistical analysis

2

Looking at the relationship between the value of total revenue and the country’s total population, using total government rev-
enue in euro per inhabitant as an indicator, it is clear that all the Member States that joined the EU since 1 May 2004 collect 
less revenue per inhabitant than the EU average (11,200 euro), with Spain, Portugal and Greece also below the EU average. By 
contrast, in Luxembourg, Denmark and Sweden, government revenue per inhabitant was above 20,000 euro in 2007. 

In Norway, government revenue in euro per inhabitant was above 35,100 euro, more than three times the EU average in 
2007, whereas in Iceland in 2006 it was just below 20,000 euro (having fallen by around 1,000 since 2005). 

Figure 2.3.11:  Government revenue per inhabitant in 2007.

35000

30000

25000

20000

o
r
u
e

15000

10000

5000

0

LU DK SE

FI NL

IE AT BE

FR UK DE EA13 IT EU27 ES CY GR

SI

PT MT CZ HU EE

SK

LV PL

LT RO BG

Source: Eurostat. 

As mentioned above, 90% of EU government revenue is collected in the form of taxes and social contributions. Only in 
Bulgaria, Greece, Latvia, the Slovak Republic and Finland did the overall share of other types of revenue exceed 15% of the 
total in 2007. All these Member States except Finland relied more heavily than other EU Member States on transfers from 
other sectors of the economy – Bulgaria and the Slovak Republic collect over 7% of their total revenue from other current 
transfers, whereas in Greece other capital transfers were above 6%. In Bulgaria, Latvia and Finland, government sales were 
higher than the EU average by more than three percentage points. Property income was also relatively important in Finland 
with a share of close to 8%; in the Netherlands government collected 5% of its total revenue from property income.

  European Economic Statistics

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Statistical analysis

Figure 2.3.12: Main components of government revenue in 2007

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

e
u
n
e
v
e
r

t
n
e
m
n
r
e
v
o
g

l

a
t
o
t

f

o
%
n

i

BE

BG CZ DK DE

EE

IE GR

ES

FR

IT

CY

LV

LT

LU HU MT NL AT

PL

PT

RO SI

SK

FI

SE UK

Taxes

Social contributions

Government sales

Property income

Others

Source: Eurostat

In Norway, most of government revenue also comes from taxes and social contributions: current taxes on income, wealth, 
etc. (over 37% of the total in 2007), taxes on production and imports (21%), social contributions (over 15%). However, what 
distinguishes  this  country  from  EU  Member  States  is  that  over  20%  of  government  revenue  is  collected  in  the  form  of 
property income (interest, rents received), largely relating to its “Government Pension Fund – Global” (oil fund). Iceland 
relies to the same extent on current taxes on income and wealth and taxes on production and imports (representing 40% in 
2006), whereas it collects less social contributions (around 7%). Its government sales are above 7% and property income is 
around 5%. In Switzerland, the most important revenues are current taxes on income, wealth, etc (the share in government 
revenue was around 43% in 2005), followed by taxes on production and imports and social contributions (each above 20%) 
and government sales with a share of just over 10%.

Taxes and social contributions

a. 

general trends in EU and euro area

The evolution of the main tax revenue components over the period 2002-2007 is presented in the figure 2.3.13. Between 
2002 and 2003 taxes on income and wealth began to fall, offset by increases in revenues from social contributions and capi-
tal transfers38. In 2004 the fall in current taxes on income and wealth continued, accompanied by a fall in social contribu-
tions (falling overall by 0.5 percentage point of GDP until 2007). In 2005 current taxes on income and wealth, etc. started to 
rise (by more than 1 percentage point to 2007), accompanied by a slow increase in taxes on production and imports between 
2004 and 2006. 

In 2007, the share of social contributions, taxes on production and imports and current taxes on income and wealth, etc. 
as revenue of general government (not including EU institutions) in terms of GDP amounted to 13.6, 13.5 and 13.4% in EU 
and to 15.2, 13.5 and 12.5% in euro area, respectively. The value of capital taxes levied at irregular and infrequent intervals 

38 Capital taxes and other capital transfers are not presented in figure 2.3.13, since in general their value is very small (below 1% of GDP).

60

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Statistical analysis

2

on the value of assets and net worth, such as inheritance taxes, was 0.25% of GDP in the EU and 0.27% in the euro area. The 
remainder of this section will concentrate on 2006 data, as the detailed breakdown of taxes and data on tax revenue of EU 
institutions is only available for 2006. 

Figure 2.3.13: Taxes and social contributions in European Union and euro area – evolution over 2002-2007

16.0

15.5

15.0

14.5

14.0

13.5

13.0

12.5

12.0

11.5

11.0

P
D
G

f

o
%

2002

2003

2004

2005

2006

2007

eu27_taxes on production and imports

eu27_current taxes on income and wealth, etc

eu27_social contributions

ea13_taxes on production and imports

ea13_current taxes on income and wealth, etc

ea13_social contributions

Source: Eurostat. The data do not allow for taxes on productions and imports collected for EU institutions that in 2006 for EU27 accounted for 0.3% of GDP.

Looking at the evolution of taxation by specific economic functions (labour, consumption and capital) in the EU, it can 
be noted that taxes on labour decreased by half a percentage point of GDP between 2003 and 2004 due to a fall in social 
contributions and personal income tax, and then remained stable at around 19.5% of GDP. Taxes on capital increased by 
1.3 percentage points of GDP since 2003 to reach 9.4% of GDP in 2006 (mostly due to an increase of 0.8 percentage point in 
corporate income tax), whereas taxes on consumption remained stable over the whole period 2002-2006, at 11.1% of GDP.

Data for the euro area generally followed the same pattern; however, taxation on labour is around 1 percentage point higher 
and taxation on consumption and capital is slightly lower (by 0.2-0.4 percentage point) than in the EU. 

  European Economic Statistics

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Statistical analysis

Figure 2.3.14: Taxation by economic structures – evolution in EU and euro area over 2002-2006

P
D
G

f

o
%

22.0

20.0

18.0

16.0

14.0

12.0

10.0

8.0

6.0

2002

2003

eu27_cons

eu27_lab

2004

ea15_cons

ea15_lab

2005

2006

ea15_cap

eu27_cap

Source: Taxation trends in the EU. Data for the EU Member States and Norway; 2008 edition

Figure 2.3.15:

Implicit tax rates in EU and euro area – evolution over 2002-2006

%

40

35

30

25

20

15

2002

2003

eu27_cons

eu27_lab

2004

ea15_cons

ea15_lab

2005

2006

ea15_cap

eu27_cap

Source: Taxation trends in the EU. Data for the EU Member States and Norway; 2008 edition

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2

Implicit tax rates show the relationship between taxes on specific economic functions and the size of their potential tax 
bases. In 2006, taxes on labour accounted in EU for 36.7% of the labour tax base (compensation of employees increased 
by payroll taxes and taxes on wage bill) and had remained fairly stable since 2002, with a small decrease (of 0.5 percentage 
point) between 2003 and 2004 and an equivalent increase between 2005 and 2006. Consumption taxes in EU are close to 
20% of final consumption expenditure of resident households over the whole period. There has been a significant increase 
in the implicit tax rate on capital since 2003, up 4.4 percentage points to 33.3 % in 2006.

It is also interesting to examine the relationship between the implicit tax rates (ITRs) on capital and consumption. Whereas 
capital was taxed slightly less than consumption as a % of GDP, by taking into account the value of the potential tax bases it 
can be seen that taxation of capital is more than 10 percentage points higher than that of consumption, and this difference 
is increasing over time. 

b. 

structure of taxation in EU and euro area in 2006

Looking at a more detailed breakdown of taxes, social contributions show up as the most significant source of tax revenue 
in EU, with a share of over 33%, followed by taxes on income (over 30%) and VAT and other taxes on products on produc-
tion (close to 17% each). 

Figure 2.3.16: Main components of tax revenue in EU and euro area in 2006. 

ea13

eu27

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% of total tax revenues

Social contributions

Taxes on income

Value added type taxes (VAT)

Other taxes on products and production

Others

Source: Eurostat. Component ‘Others’ includes: all current taxes on income and wealth, etc, but taxes on income and capital taxes reduced by amounts assessed but 
unlikely to be collected, where applicable.

The economic function bringing in most tax revenue (at close to 50%) is labour. Taxes on consumption account for almost 
28% of total taxation, whereas taxes on capital make up the remainder (over 23%).

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Statistical analysis

Figure 2.3.17: Composition of tax burden by economic functions in EU and euro area in 2006.

ea15

eu27

l

a
t
o
t

f

o
%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Source: Taxation trends in the EU. Data for the EU Member States and Norway; 2008 edition

Labour

Consumption

Capital

c. 

inter-country comparison

The share of social contributions is above the EU weighted average (33.5% of total tax revenue) in 14 Member States, com-
ing close to 40% in Greece and France and higher in Czech Republic (which, at 44.8%, has the highest share in the whole 
EU), Germany and the Slovak Republic. In Denmark, which finances its social benefits mainly from taxes on income, social 
contributions make up just under 4%. The second lowest share of social contributions, at over 18%, was recorded by Ireland. 
Cyprus, Malta and the United Kingdom have shares below 22%. 

Figure 2.3.18: Main types of taxes in EU Member States in 2006.

e
u
n
e
v
e
r

x
a
t

l

a
t
o
t

f

o
%

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

BE BG CZ DK DE

EE

IE GR

ES

FR

IT CY

LV

LT

LU HU MT NL AT

PL

PT RO

SI

SK

FI

SE UK

Social contributions

Taxes on income

Value added type taxes (VAT)

Other taxes on products and production

Others

Source: Eurostat

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2

Taxes on income are the most important source of tax revenue in Denmark, with a share close to 58% of the total. In Fin-
land, Sweden, the United Kingdom and Ireland the respective share was over 38% and in Belgium and Luxembourg over 
34%. In the Slovak Republic, Bulgaria and Romania this type of tax was relatively less important, generating no more than 
20% of total tax revenues in 2006.

Value added tax was very important in the structure of taxation in Bulgaria (over 36% of the total in 2006). In the Baltic 
States, Romania, Cyprus and Slovak Republic its share was above 25%, whereas Italy, Belgium and Luxembourg raised less 
than or close to 15% of their tax revenues from VAT.

Looking at the components of other taxes on products and production, a relatively high level of taxes and duties on imports 
was reported by Bulgaria, Estonia, Ireland and Luxembourg (close to or over 2% of GDP). Domestic excise, consumption 
and sales taxes, stamp taxes and taxes on capital and financial transactions generate revenue amounting to 5% of GDP or 
more in Italy, Spain, Denmark, Hungary, Malta and Portugal. Italy, France and Sweden also have relatively high revenues 
(above 3% of GDP) from taxation of land and buildings used for production (especially France), of total wage bills and pay-
roll taxes (especially Sweden) and from other taxes paid by enterprises as a result of engaging in production, where the taxes 
are independent of the quantity or value of goods and services produced or sold. 

The United Kingdom and Denmark collect revenues of over 1% of GDP from other current taxes, especially from current 
taxes on capital (e.g. property taxes on buildings periodically paid by individuals) that are not seen at all in the tax systems 
of Estonia, Ireland, Malta and Bulgaria (in 2006). 

Capital taxes levied at irregular and infrequent intervals are usually considered as inheritance taxes or gift taxes, rather 
than as taxes levied directly on the value of assets owned or net worth (so-called “wealth taxes”). Only in Belgium was the 
value of capital tax revenues in 2006 above 0.6% of GDP, although Estonia is the only Member State that does not collect 
this type of tax revenue at all.

Consumption is the economic function on which most tax revenues in Bulgaria (over 50%), Cyprus, Malta and Romania 
(over 40%) and Ireland (almost 38%) are levied, whereas in all the other Member States labour is the most common basis 
for taxation; only in the Slovak Republic and Poland did the difference between shares of both functions not exceed 2 per-
centage points in 2006. In general, taxation on capital is the lowest in all countries; only in Spain, Italy, Luxembourg and 
the United Kingdom did taxation on capital raise more tax revenues than taxation of consumption, and in the latter two 
Member States its share of the total tax burden was over 30% in 2006.

  European Economic Statistics

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Statistical analysis

Figure 2.3.19: Taxes by economic functions in EU Member States in 2006

100%

n
e
d
r
u
b
x
a
t

l

a
t
o
t

f

o
%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

BE

BG CZ DK DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU HU MT NL AT

PL

PT RO*

SI

SK

FI

SE UK

Labour

Consumption

Capital

Source: Taxation trends in the EU. Data for the EU Member States and Norway; 2008 edition

Figures 2.3.20-2.3.22 present the implicit tax rates on consumption, labour and capital (where available) for individual EU 
Member States. The Member State that raises the most taxes on domestic final consumption of its households is Denmark 
(34% in 2006), whereas the respective ratio in Spain and Lithuania is less than half of this (16.4 and 16.7%, respectively).

Figure 2.3.20:  Implicit tax rate on consumption in EU Member States in 2006 

35

30

25

20

%

15

10

5

0

DK

SE

FI

IE NL

BG HU LU

SI

EE

BE CZ

PT

AT CY

PL

SK

FR

LV MT UK DE RO EL

IT

LT

ES

Source: Taxation trends in the EU. Data for the EU Member States and Norway; 2008 edition

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2

Taxes on labour in relation to the compensation of employees are the highest in Sweden (44.5%), with the lowest implicit tax 
rates on labour recorded in Malta (21.5%), Cyprus, Ireland and the United Kingdom (close to or just above 25%)

Figure 2.3.21:  Implicit tax rate on labour in EU Member States in 2006

%

45

40

35

30

25

20

15

10

5

0

SE

IT

BE

FR

FI

AT CZ DE HU EL

SI DK

PL

LT

EE

LV NL

ES

BG SK

LU RO* PT UK

IE

CY MT

Source: Taxation trends in the EU. Data for the EU Member States and Norway; 2008 edition. For Romania 2005 data.

Amongst the Member States for which data on ITR on capital are available, the highest levels were recorded in Ireland, France, 
Denmark and the United Kingdom (close to or over 40%), whereas in Estonia and Latvia its level was four times lower.

  European Economic Statistics

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Statistical analysis

Figure 2.3.22:  Implicit tax rate on capital in EU Member States in 2006

%

45

40

35

30

25

20

15

10

5

0

IEIE

FRFR

DKDK

UKUK

ESES

CYCY

ITIT

BEBE

PT*PT*

CZCZ

FIFI

DEDE

ATAT

PL*PL*

NLNL

SKSK

LTLT

LV*LV*

EEEE

Source: Taxation trends in the EU. Data for the EU Member States and Norway; 2008 edition. For Portugal, Poland and Latvia 2005 data.

BOX 2.3.4. TAXATION

Total tax revenue is an aggregate comprising:

– taxes on production and imports, such as value added tax, import duties, excise duties and consumption taxes, 

stamp taxes, payroll taxes, taxes on pollution, and others,

– current taxes on income, wealth, etc, such as corporate and personal income taxes, taxes on holding gains, pay-
ments for households for licence to own or use car, hunt or fish, current taxes on capital that are paid periodically, 
and others,

– capital taxes, such as inheritance taxes, death duties and taxes on gifts and capital levies that are occasional or 

exceptional,

– actual social contributions paid on a compulsory or voluntary basis by employers or employees or the self- or 
non-employed to insure against social risks (sickness, invalidity, disability, old age, survivors, family, maternity),

– implicit social contributions payable under unfunded social insurance schemes (in which employers pay social 
benefits to their employees, ex-employees or their dependants out of their own resources without creating special 
reserve for the purpose).

reduced by the amount of taxes and social contributions assessed unlikely to be collected, where applicable.

The  ESA95  category  “taxes  on  production  and  imports”  is  also  known  under  the  economic  term  “indirect  taxes”, 
whereas “taxes on income, wealth, etc” and “capital taxes” are defined as “direct taxes”.

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Statistical analysis

2

An alternative classification of taxes may be made according to their economic function. Since this split does not cor-
respond fully with the ESA95 breakdown of taxes, it is undertaken specifically for each Member States in the annual 
exercise by the European Commission (DG TAXUD) and Member States cooperating in the Working Group Structures of 
Taxation, and the results are published in the report “Taxation trends in the European Union. Data for the EU Member 
States and Norway” that is the source for the data presented and the methodological information below.

Breakdown of taxes by economic functions is as follows:

– taxes on consumption, i.e. levied on transactions between final consumers and producers and on the final con-
sumption goods, such as vAT, taxes and duties on imports excl. vAT, stamp taxes, taxes on financial and capital 
transactions, taxes on international transactions, on pollution, under-compensation of vAT, poll and expenditure 
taxes, payments by households for licences,

– taxes on labour – on employed labour, i.e. taxes directly linked to wages and mostly withheld at source, paid by 
employees and employers, including compulsory social contributions and on non-employed labour income, i.e. all 
taxes and compulsory social contributions raised on transfer income of non-employed persons, where these could 
be identified (e.g. unemployment, health care benefits),

– taxes on capital – defined as taxes on capital and business income that economic agents earn or receive from 
domestic resources or from abroad (e.g. corporate income tax, tax on income and social contributions of self-em-
ployed, taxes on holding gains) and taxes on capital stock that include the wealth tax (paid periodically on the 
ownership and use of land or buildings by owners, and current taxes on net wealth and on other assets, such as 
jewellery and other external signs of wealth), capital taxes, real estate tax, taxes on use of fixed assets, professional 
and business licences and some taxes on products.

Implicit tax rates are special tax indicators defined separately for each economic function, measuring the actual or 
effective tax burden levied on different types of economic income or activities that could potentially be taxed. They are 
computed as the ratio of total tax revenues of the specific economic category (consumption, labour, capital) to a proxy 
of the potential tax base defined using the production and income accounts of national accounts.

Implicit tax rate on: 

Definition

consumption 

all  taxes  on  consumption  divided  by  final  consumption  expenditure  of  house-
holds on the economic territory;

labour

capital 

direct  taxes,  indirect  taxes  and  compulsory  actual  social  contributions  paid  by 
employees and employers on labour employed divided by compensation of em-
ployees increased by wage bill and payroll taxes;

ratio between revenue from all capital taxes, and all (in principle) potentially tax-
able capital and business income in the economy, such as net operating surplus 
of corporations and non-profit institutions, imputed rents of private households, 
net mixed income by self-employed, net interest, rents and dividends, insurance 
property income.

Government deficit and debt

After analysing the financial position of governments in the European Union and the euro area (Figure 2.3.23) over the last 
six years, the following conclusions can be drawn:

– the government balance (the difference between total government expenditure and revenue) of the EU and euro area 
has been in deficit over the whole period; nevertheless, since 2003, when the EU and euro area deficit was over the 
Maastricht reference value of 3% of GDP, the EU deficit has decreased by around 2.2 percentage points and stayed 
below 3% in all years;

– government debt shows a decreasing trend since 2005, falling below the Maastricht reference value of 60% of GDP in the 

EU in 2007, but still above it (at 66.4%) in the euro area.

  European Economic Statistics

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Statistical analysis

Figure 2.3.23:  Evolution of EU27 and EA13 public balance (scale inverted) and debt over years 2002-2007

P
D
G

f

o
%
n

i

t
i
c
fi
e
d

-13

-12

-11

-10

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

Source: Eurostat

P
D
G

f

o
%
n

i

t
b
e
d

70

68

66

64

62

60

58

2002

2003

2004

2005

2006

2007

government deficit/surplus EA13

government deficit/surplus EU27

government debt EU27

government debt EA13

Looking  at  the  budgetary  position  of  individual  countries  in  2007,  11  out  of  27  Member  States  recorded  a  government 
surplus, which for Finland reached 5.3% of GDP; there were also significant government surpluses in Denmark, Sweden, 
Bulgaria,  Cyprus,  Luxembourg  and  Estonia.  Of  the  16  countries  recording  a  deficit,  the  worst  performing  country  was 
Hungary (-5.5%), which was the only Member State that exceeded the Maastricht deficit threshold of 3% of GDP in 2007. 

Compared to the situation in 2002, all but four Member States improved their government budgetary position as a percent-
age of GDP. Cyprus achieved the biggest improvement, moving from a deficit of 4.4 % of GDP in 2002 to a surplus of 3.3% 
of GDP in 2007, mostly by increasing its tax revenues, especially from taxes on production and imports in this period. It 
was followed by the Slovak Republic and the Czech Republic, both with deficit reductions above 5 percentage points; in the 
Slovak Republic these reductions resulted from expenditure cuts significantly exceeding reductions in revenues and, in the 
Czech Republic, from a mixture of expenditure cuts and revenue rises. Austria returned in 2007 to its deficit level of the 
year 2002 and the government deficit rose between 2002 and 2007 in the United Kingdom, Romania and Belgium, albeit by 
one percentage point or less. 

In Norway, the government balance stood at a surplus of 19% of GDP in 2006, which was 10 percentage points higher than 
the balance in 2002.

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2

BOX 2.3.5. THE EXCESSIvE DEFICIT PROCEDURE (EDP)

The fiscal framework of the European Monetary Union (the Protocol on the Excessive Deficit Procedure annexed to the 
Maastricht Treaty) requires from euro area member and candidate countries a soundness of public finances, defined 
on the basis of the following criteria:

– negative public balance (deficit) not exceeding 3% of GDP,

– public debt not exceeding 60% of GDP. 

For sake of comparability between Member States these criteria are measured based on (though not fully identical to) 
two economic categories from the national accounts framework:

– net lending(+)/ net borrowing (-) of general government,

– liabilities of general government, respectively.

In the framework of the EDP, all Member States are requested to report their data to Eurostat before 1 April and 1 
October each year. Following the assessment, Eurostat shall, within three weeks after the deadline, provide the actual 
government deficit and debt data through publication. 

The respective definitions are presented in the following table:

National accounts (ESA95)

Excessive deficit procedure (EDP)

Net lending (+)/ net borrowing (-)

= net acquisition of financial assets less net incurrence 
of liabilities or

=  gross  saving  (defined  as  gross  disposable  income 
less  final  consumption  expenditure)  corrected  by  net 
capital transfers and gross acquisitions less disposals of 
non-financial assets, or

= total revenue less total expenditure

Government surplus / deficit (net lending/ borrow-
ing under EDP)

= net lending (+)/ net borrowing (-)of General Govern-
ment (as defined in ESA95), plus net streams of interest 
payments resulting from swaps arrangements and for-
ward rate agreements

Liabilities

six categories of liabilities:

– currency and deposits,

Government  consolidated  gross  debt  (“Maastricht 
debt”)

sum of government liabilities as defined in ESA95 in: 

– currency and deposits, 

– securities other than shares,

– securities other than shares, excluding financial de-

– loans,

– shares and other equity,

– insurance technical reserves,

– other accounts, payable.

rivatives and 

– loans

outstanding at the end of the year, measured at nomi-
nal value and consolidated.

  European Economic Statistics

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Statistical analysis

Figure 2.3.24:  Government surplus (+) / deficit (-) in EU Member States in 2007 and 2002

6

4

2

0

-2

-4

-6

-8

-10

P
D
G

f

o
%
n

i

)

+

(

l

s
u
p
r
u
s
/

)
-
(
t
i
c
fi
e
d
t
n
e
m
n
r
e
v
o
g

FI DK SE BG CY LU EE

ES NL

IE DE

LV

SI

BE AT

EA
13

EU
27

LT CZ MT

IT

PL

SK RO PT

FR GR UK HU

Source: Eurostat. 

2007

2002

Figure 2.3.25:  Primary balance and interest paid in Member States in 2007

6

4

2

0

-2

-4

-6

FI DK

SE BG CY LU EE

ES NL

IE DE

LV

SI

BE AT

EA
13

EU
27

LT CZ MT

IT

PL

SK RO PT

FR GR UK HU

primary balance

interest paid

Source: Eurostat

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Statistical analysis

2

The latest (2007) results can be broken down into two elements:

– primary government deficit/surplus, 

– interest payable.

All Member States except Lithuania, Czech Republic, Slovak Republic, Romania, the United Kingdom and Hungary were 
able to cover all their government expenditure except interest on public debt from their revenues. In Bulgaria, Denmark, 
Spain, Cyprus, Finland and Sweden, the primary surplus was close to or above 4% of GDP, whereas the primary deficit in 
Hungary and Romania exceeded 1% of GDP. 

For the assessment of the long-term sustainability of public finances, it is essential to measure the financial commitments 
the country will have to face in the future. Whilst this is largely determined by expected future cash flows, the starting point 
for governments is their accumulated commitments from the past, measured by convention as gross general government 
consolidated debt (“Maastricht debt”). In 2007, the highest levels of government debt in relation to GDP were recorded 
by Italy (104% of GDP), Greece (95%) and Belgium (85%). Hungary, Germany, France, Portugal and Malta also recorded 
government debt over 60% of GDP. Cyprus and Austria were just below the Maastricht debt threshold and above the EU 
weighted average debt (58.7% of GDP). Other Member States (Bulgaria, Czech Republic, Denmark, Baltic States, Ireland, 
Slovenia, Slovak Republic, Romania and Luxembourg) had much lower government debt to GDP ratios, close to or below 
30%. In Estonia, Luxembourg and Romania, government debt was even below 10% of GDP.

Looking at the changes in government debt in relation to GDP between the years 2002 and 2007, 18 Member States were able 
to decrease or stabilize their debt levels, even though debt in absolute terms increased in almost all countries (the exceptions 
were Bulgaria, Denmark, Sweden and Spain). The most spectacular decrease was achieved by Bulgaria which, mostly due to 
a high increase in nominal GDP over the period, reduced its debt by 35 percentage points (pp) of GDP. Falls of over 10 per-
centage points in government debt were seen in Denmark (22pp), Belgium (19pp), Spain (16pp), the Slovak Republic (14pp), 
Sweden (13pp) and Romania (12pp). Rises of 5 percentage points or more in the ratio of government debt as a proportion of 
GDP were observed in Germany, France, United Kingdom, Portugal and Hungary; on the other hand, smaller increases (no 
more than 3pp) were recorded in Poland, Malta, Luxembourg and Czech Republic.

In Norway, government debt at end-2006 was at 48.9%, almost 13 percentage points higher than in 2002, in spite of running 
large surpluses in all years. This situation is explained by operations of the “Pension fund global” (oil fund) in short-term 
loans.

While the government deficit/surplus in countries normally explains most of the change in government debt, there are also 
other contributing factors. The difference between the change in government debt and the government deficit/surplus for a 
given period is called the “stock-flow adjustment”. The stock-flow adjustment is made up of 15 different elements incorpo-
rating the main groups: “net acquisition of financial assets” including financial transactions which are not contributing to 
the deficit but only to the change in debt, “net incurrence of liabilities in financial derivatives and other liabilities”, which 
are those liabilities excluded from the Maastricht debt, and a third group relating to effects of face valuation, appreciation/
depreciation of foreign currency debt, other changes in volume (such as reclassification of units outside or inside govern-
ment, etc) and statistical discrepancies, reflecting differences arising from the diversity of data sources39.

39 Eurostat publishes a twice yearly note on the stock-flow adjustment in government accounts in the context of the latest reporting of data in the framework of the excessive deficit 

procedure.

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Statistical analysis

Figure 2.3.26:  Public debt at end-2007 and end-2002

120

100

80

60

40

20

0

P
D
G

f

o
%
n

i

IT GR BE EA13 HU DE

FR

PT MT CY AT EU27 NL PL UK SE

ES

FI

SK CZ DK

IE

SI

BG LT RO LV

LU EE

Source: Eurostat

2007

2002

Most EU Member State governments finance themselves through the issue of securities other than shares, e.g. government 
bonds, treasury bills, etc, rather than through direct loans. In 2007, securities other than shares made up over 80% of EU 
and euro area government debt, whereas loans accounted for just above 15%. Additionally, governments tend to rely on 
long-term financing (maturity over one year) rather than on short-term financing, by a factor of around 10 to one.

Luxembourg is the only EU Member State that has not reported any debt securities at all in the structure of its government 
debt at end-2007. Other Member States that rely more on loans than debt securities are Estonia (76% of total government 
consolidated gross debt) and Romania (56%). In Bulgaria, Germany, Latvia and Cyprus, the share of loans in government 
debt is also relatively high (over 20%). 

The share of currency and deposits in government debt in the EU is just below 5% and in euro area it is even smaller (3.3%). 
However, for some EU Member States, the share of this item is above 10%: in the United Kingdom and Ireland it accounted 
for over 16%, in Portugal 13%, whereas in Italy and Romania it reached 9% of total government debt in 2007. 

In Norway, government debt was composed at end-2006 only of securities other than shares (24%, of which almost four 
fifths were long-term securities) and short-term loans (76%).

74

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Figure 2.3.27: Composition of government consolidated gross debt at end-2007

Statistical analysis

2

100%

80%

l

a
t
o
t

f

o
%

60%

40%

20%

0%

EU
27

EA
13

BE BG CZ DK DE

EE

IE GR ES

FR

IT CY LV

LT

LU HU MT NL AT

PL

PT RO SI

SK

FI

SE UK

Currency and deposits

Short-term securities other than shares*

Long-term securities other than shares*

Short-term loans

Long-term loans

Source: Eurostat. *Securities other than shares exclude financial derivatives.

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Statistical analysis

2.4

Inflation, interest rates and exchange rates

Introduction

The Harmonised Indices of Consumer Prices (HICPs) provide the best measure for international comparisons of con-
sumer  price  inflation  in  the  EU  and  the  euro  area,  and  for  assessing  price  convergence  and  stability  in  the  context 
of monetary policy analysis. Annual average inflation for the euro area in the period 2000–2007 was relatively stable 
around the level of 2.2% and stood at 2.1% for 2007. In the EU as a whole, annual average inflation in 2007 stood at 2.3%, 
its highest level since 2000. 

Long-term  interest  rates  are  a  convergence  criterion  for  European  monetary  union.  Following  the  market  turmoil  that 
began in summer 2007 and central banks’ interventions to safeguard liquidity, the Maastricht criterion interest rates in the 
euro area decreased from 4.60% in July 2007 to 4.06% in March 2008.

Money market rates, also known as inter-bank rates, are interest rates used by banks for operations among themselves. In 
general the rates decreased between 2000 and 2004. Later, this important benchmark for short-term interest rates increased 
continuously and in December 2007 reached 4.85%. 

The introduction of the euro eliminated exchange rates between an increasing number of EU Member States. In contrast 
to the moderate fluctuations between the majority of European currencies, the value of the euro increased against the cur-
rencies of important trading partners between 2002 and 2007: the Swiss franc (+12.0%), the Japanese yen (+36.6%) and the 
US dollar (+44.9%).

2.4.1  Trends in consumer price inflation 2000–2007

Consumer price indices have a variety of potential uses, for example in indexing social benefits or contracts and as inputs 
into various types of economic analysis. The Harmonised Indices of Consumer Prices (HICPs) have been set up to provide 
the best measure for international comparisons of consumer price inflation in the EU and the euro area and for assessing 
price convergence and stability in monetary policy analysis. Since 1999, when the euro area was created, the European Cen-
tral Bank’s main focus of interest has been assessing price stability in the euro area. Figure 2.4.1 shows that the Euro area 
annual average inflation varied around 2.2% during the period 2000-2007.

Figure 2.4.1: 

EU and euro area – HICP all-items, annual average inflation rates (in %)

2.4

2.3

2.2

2.1

2.0

1.9

1.8

2000

2001

2002

2003

2004

2005

2006

2007

European Union (EU)

Euro area (EA)

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Statistical analysis

2

Annual average inflation for the euro area in the period 2000–2007 was relatively stable at a level of around 2.2%. The high-
est value, 2.3%, was observed in 2001, compared with 2.1% in 2000. In 2003-2004 inflation fell to 2.1%, and then it rose to 
2.2% for two consecutive years. In 2007, annual average inflation declined again to 2.1%. 

In 2001, extraordinary inflation rates were recorded for food at 5.0%. This was significantly above the price increases meas-
ured for these products in 2000 or between 2002 and 2007. These price increases in 2001 might be explained by the out-
breaks of BSE and foot-and-mouth disease. Other main components with high annual average rates for the euro area in 
2001 were restaurants and hotels (3.4%), miscellaneous goods and services (3.1%), and education (3.0%).

In 2007, the three main headings with the highest weights in household final monetary consumption expenditure for the 
euro area showed annual average rates above the overall inflation rate of 2.1%. These headings are food, housing (with rates 
of 2.7% each) and transport (2.4%). Other components with upward impacts on inflation were education (7.8%), alcohol 
and tobacco (3.4%), restaurants and hotels (3.2%), and ‘miscellaneous’ (2.3%). Downward impacts on overall inflation in 
the euro area came from communications (-1.9%), recreation and culture (0.2%), clothing (1.0%), household equipment and 
health (1.7% each). 

Figure 2.4.2:  Euro area - HICP main headings, annual average inflation rates (in %)

Miscellaneous

Restaurants and hotels

Education

Recreation and culture

Communications

-4.1

-1.9

Transport

Health

Household equipment

Housing

Clothing

Alcohol and tobacco

Food

All-items

2.3

3.1

3.2

3.4

3.0

7.8

0.2

1.4

2.4

1.2

1.2

1.7

1.7
1.8

1.0

0.4

2.7

2.9

3.4

2.8

2.7

2.1

2.3

5.0

-6

-4

-2

0

2001

2

4

6

8

10

2007

In the EU as a whole, annual average inflation in 2007 was at its highest level since 2000. It had been below euro area infla-
tion until 2004. Then in 2005 and 2006 both country groups showed the same annual average inflation rates and in 2007 
EU inflation was higher than that in the euro area.

In  2000,  when  the  inflation  measured  in  the  European  Union  was  1.9%  and  the  EU  had  15  Member  States,  the  low-
est  annual  average  inflation  rates  were  recorded  for  the  United  Kingdom  (0.8%),  Sweden  (1.3%)  and  Germany 
(1.4%).  The  main  headings  with  low  rates  in  2000  in  the  European  Union  were  communications  (-6.8%),  clothing 

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Statistical analysis

(-0.5%) and recreation and culture (0.1%), and those with the highest rates were transport (4.8%), education (3.8%) and 
housing (3.6%).

Figure 2.4.3:  EU - HICP main headings, annual average inflation rates (in %)

Miscellaneous

Restaurants and hotels

Education

Recreation and culture

Communications

-6.8

Transport

Health

Household equipment

Housing

Clothing

Alcohol and tobacco

Food

HICP all-items 

2.3

2.1

3.4

2.9

3.8

8.6

-0.1

0.1

-2.0

2.5

2.2

1.9

4.8

1.6

0.6

0.1

-0.5

3.3

3.6

3.8

2.7

3.5

-8

-6

-4

-2

0

2000

1.0

2.3

1.9

2

2007

4

6

8

10

In 2007, the seven highest annual average inflation rates among the 27 EU Member States were those of countries that 
had joined the EU in 2004 or 2007. The highest rate was recorded by Latvia (10.1%), followed by Hungary (7.9%) and Bul-
garia (7.6%); the lowest rates were for Malta (0.7%), France, the Netherlands and Finland (1.6% each). The main headings 
with the highest annual average rates were education (8.6%), alcohol and tobacco (3.8%), food (3.5%), and restaurants 
and hotels (3.4%).

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Statistical analysis

2

10.1

7.9

7.6

6.7

5.8

4.9

3.8

Figure 2.4.4:  HICP all-items, annual average inflation rates (in %), 2007

12

10

8

6

4

2

0

1.6

1.6

1.6

1.7

1.7

1.8

0.7

1.9

2.0

2.1

2.2

2.2

2.3

2.3

2.3

2.4

2.6

2.7

2.8

2.9

3.0

3.0

Malta

Netherlands
France

Finland
Denmark

Sweden

Belgium

Slovakia

Italy
euro area

Cyprus

Germany
Portugal
Austria
United Kingdom EU 

Luxembourg
Poland
Spain

Czech Republic
Ireland
Greece

Slovenia

Romania

Lithuania

Estonia

Bulgaria

Hungary

Latvia

Looking at annual average inflation rates for all EU Member States during the period 2000–2007 and at the countries with 
the highest weights in the European Union (see information in Box 2.4.3), the highest inflation rates were those in Spain 
(3.6%) in 2002 and 2006 and the lowest in the United Kingdom (0.8%) in 2000.

Permanent versus transitory price changes

There are many prices that substantially affect the overall index and that may quickly reverse, so that such changes are 
transitory and volatile. Experts are always trying to construct inflation measures to be independent of these effects (short-
term changes in energy prices, fresh fruit and vegetables) but to reflect that part of inflation caused by monetary effects or 
permanent price changes.

Special aggregates enable detection of the factors responsible for certain behaviours of inflation rates. In order to facilitate 
medium-term decisions by the European Central Bank, Eurostat releases a series of special aggregates, including:

– HICP all items excluding energy;
– HICP all items excluding energy, food, alcohol and tobacco;
– HICP all items excluding energy and unprocessed food;
– HICP all items excluding energy and seasonal food;
– HICP all items excluding tobacco;
– Energy
– Food, alcohol and tobacco.

When price changes are measured excluding energy, inflation rates in the EU turn out to be more volatile. In fact, the im-
pact of changes in energy prices had a rather high upward impact on overall inflation in 2000, 2005 and 2006 ranging from 
0.6 to 0.9 percentage points. This was due to inflation rates for energy of between 8% and 12% in these three years. In 2003 
and 2004, inflation measured excluding energy was 1.8% and thus 0.2 percentage points below the headline HICP inflation 
rate. Only in 2002 did falling energy prices have a downward impact of 0.2 percentage points on overall inflation; in 2001 
the impact of energy prices on all-items inflation was neutral.

Apart from energy prices, food40 prices were also more volatile than overall inflation. Increases in food prices had a substan-
tial upward impact on headline inflation in the EU in 2001 and this phenomenon returned in 2006 with an upward impact 
of 0.3 percentage points in 2007.

40

including alcohol and tobacco

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Statistical analysis

Figure 2.4.5:  EU - HICP all items & special aggregates, annual average inflation rates (in %)

2.5

2.0

1.5

1.0

0.5

2000

2001

2002

2003

2004

2005

2006

2007

HICP all-items

HICP all items excluding energy

HICP all items excluding energy, food, alcohol and tobacco

BOX 2.4.1: CHANGING COMPOSITION OF COUNTRy AGGREGATES

The euro area HICP aggregate is compiled as a weighted average for the countries in the euro area. The country weights 
are derived from national accounts data for household final monetary consumption expenditure (HFMCE), naturally 
expressed in euro. The index is computed as an annual chain index allowing country weights to change each year and, 
consequently, new Member States to be added as they join the euro area.

For the EU and EEA HICP aggregates, the euro area is treated as a single entity to which data for the other countries 
is then added (the weights again use national accounts data, converted into purchasing power standards). Note that 
for the EU enlargement in May 2004 chain-linking was also added in May to maintain the correct country coverage for 
both the EU and EEA aggregates.

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2

BOX 2.4.2: HOUSEHOLD CONSUMPTION PATTERNS

The consumption patterns of households determine the relative importance (weight) of household monetary expendi-
ture that is attached to each of the categories of goods and services covered by the HICP. The impact on the all-items 
index of any price change is proportional to the size of the corresponding weight. There is no uniform basket applying 
to all Member States. The structure of the weights may vary considerably between the HICPs for individual Member 
States as well as between the HICP for an individual Member State and the average weighting structure according to 
the EU or the euro area. The HICP is computed as an annual chain-index, allowing weights to change each year. 

In 2007, the three main headings food, housing and transport, each accounting for around 15% of consumption ex-
penditure, were those with the largest weights in both country groups: the EU and the euro area. A weight of around 
one tenth is attached to recreation and culture, though it is a little more important for the whole EU than for the euro 
area. Only just below are the weights for restaurants and hotels, which again are slightly higher for the EU. 

Within the national HICPs the weight for food varies between 10–12% (the United Kingdom, Luxembourg, Germany 
and Austria) and 36% (Romania). The share for transport in HFMCE ranges from 8–12% (Romania, Lithuania and Latvia) 
to 19–22% (Slovenia, Portugal and Luxembourg). Consumption expenditure on recreation and culture ranges from 5% 
(Romania, Portugal and Greece) to 12–15% (Sweden and the United Kingdom). The weight for housing ranges from 
9% (Malta, Cyprus, Greece and Luxembourg) to 19–23% (Romania, Germany, Slovakia and Poland). In the housing cat-
egory, it should be noted that HICPs reflect only monetary expenditure; unlike national accounts or household budget 
surveys, they do not cover services provided by owner occupied dwellings. This means that countries in which a larger 
proportion of the population lives in rented dwellings tend to have a larger weight for housing than countries in which 
a larger proportion of households live in their own dwellings.

Share of HICP main headings in HFMCE, 2007
Figure 2.4.6:  Share of HICP main headings in HFMCE, 2007
Figure 2.4.6: 

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

EU

euro area

Belgium
Czech Republic
Bulgaria
Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Cyprus
Italy

Lithuania
Latvia
Hungary
Luxembourg

Malta
Netherlands
Austria

Poland

Portugal
Roumania

Slovenia

Slovakia

United Kingdom
Finland
Sweden

Food

All items, excluding Food and Transport

Transport

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Statistical analysis

BOX 2.4.3: IMPORTANCE OF MEMBER STATES’ CONSUMPTION EXPENDITURE

The  weight  of  a  Member  State  in  the  euro  area  or  in  the  EU  is  its  share  of  household  final  monetary  consumption 
expenditure in the totals. The country weights used in 2007 are based on national accounts data for 2005 updated to 
December 2006 prices. For the euro area, weights in national currencies are converted into euro using the irrevocably 
locked exchange rates. For the EU, weights in national currencies are converted into purchasing power standards. The 
weight of the euro area reflects its share in the EU total. 

EU and euro area - country weights, 2007
Figure 2.4.7:  EU and euro area - country weights, 2007
Figure 2.4.7: 

EU country weights
(share per thousand in EU) 

Euro area country weights
(share per thousand in euro area)

United Kingdom  172.35

Poland  41.52

Sweden  16.98

Czech Republic  13.62

Romania  12.75

Hungary  12.66

Denmark  10.51

Slovakia  6.1

Bulgaria  6.05

Lithuania  4.28

Malta  0.78

Estonia  1.53

France  207.44

Germany  281.62

Euro area   696.64

Italy  182.75

Spain  122.91

Luxembourg  2.41

Slovenia  3.35

Ireland  14.08

Finland  16.16

Portugal  20.79

Greece  30.6

Austria  31.2

Belgium  33.83

Netherlands  52.85

Latvia  2.28

Cyprus  1.96

Source: Eurostat

2.4.2  Trends in interest rates 2000–2007

Long-term interest rates: 10-year government bond yields (Maastricht criterion)

Long-term interest rates are one of the convergence criteria indicators for European monetary union (Article 121 of the 
Treaty establishing the European Community). Article 4 of the Protocol on the convergence criteria annexed to the Treaty 
states that a Member State has to have an average nominal long-term interest rate that does not exceed by more than two 
percentage points that of, at most, the three best performing Member States in terms of price stability. The interest rate levels 
are to be measured on the basis of long-term government bonds or comparable securities, taking into account differences in 
national definitions. This means in practice that, for each country, data have to be collected on long-term (close to 10-year 
maturity) central government bonds (or a basket of several of these bonds) which are liquid on the secondary market (the 
interest rates for Cyprus are based on primary market rates). For all countries except Luxembourg and Estonia, the same 
principles for the calculation of long-term interest rates have been used.

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2

Long-term interest rates in the EU still vary across countries

In 2007, the gap between EU Member States’ rates remained significant. The lowest rates were recorded for Sweden (4.17%), 
Germany (4.22%) and the Czech Republic (4.28%), while the highest rates were found in Romania (7.15%) and Hungary 
(6.74%). 

Figure 2.4.8:  Maastricht criterion rates, annual average for 2007

7.15

6.74

5.69

5.48

5.28

5.06

4.72

4.17 4.22 4.28 4.29 4.29 4.29 4.29

4.3

4.31 4.31 4.33

4.42 4.48

4.49 4.49

4.5

4.52 4.54 4.55 4.56 4.58

7.5

7.0

6.5

6.0

5.5

5.0

4.5

4.0

3.5

3.0

Sweden
Austria
Germany 
Czech Republic
Denmark

Netherlands
Finland

France

Ireland

Belgium
Spain

Portugal

Cyprus

Italy
Slovakia

Greece

Slovenia

Bulgaria

Luxembourg 
Lithuania

Latvia

Poland

Estonia

Hungary

Romania

EU

Malta

United Kingdom

Source: Eurostat, Economy and finance, Interest rates, Long term interest rates, Maastricht criterion interest rates (ECB). 

Annex Table 4.27 shows changes in long-term interest rates for EU Member States, EU aggregates, the euro area and for 
some OECD countries. In 2000 and 2001 long-term interest rates were higher than in subsequent years. The lowest rate in 
2001 was recorded for Germany (4.80%). EU and US 10-year government bond yields both stood at 5%. The highest value 
was recorded for Poland in 2001 (10.68%).Between 2000 and 2005, long-term interest rates decreased significantly in the 
euro area, by 202 basis points, to 3.42%. The lowest rate in 2005 was recorded for Ireland (3.33%), the highest in Hungary 
(6.60%).In 2006 and 2007 increasing long-term interest rates were reported by most of the Member States providing data, 
with the exception of Cyprus, Malta, the UK and Hungary. Following the market turmoil that began in summer 2007 and 
central banks’ interventions to safeguard liquidity, the Maastricht criterion interest rates in the euro area decreased from 
4.60% in July 2007 to 4.06% in March 2008.

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Statistical analysis

Figure 2.4.9:  Long term interest rates - euro area, US and Japan (annual average)

7

6

5

4

3

2

1

0

2000

2001

2002

2003

2004

2005

2006

2007

Euro area 

United States

Japan

In 2007 annual average long term government bond yields stood at 4.32% in the euro area, 4.63% in the US and 1.68% in 
Japan. 

Short-term rates: three-month money market rates (three-month EURIBOR)

Money market rates, also known as inter-bank rates, are interest rates used by banks for operations among themselves. In 
the money market, banks are able to trade their surpluses and deficits.

Annex Table 4.28 shows the change in three-month money market interest rates in the euro area (EURIBOR) and in other 
Member States that had not adopted the euro before 2007. For the period 2000 to 2007, to provide a global picture, data is 
given for the US and Japan. 

In 2000 the three-month money market interest rates in the euro area, the US and Japan were higher than in subsequent 
years. The lowest rates were recorded for Sweden (4.06%) and the euro area (4.39%), the highest for Romania (50.71%) and 
Poland (18.77%).

In general the rates decreased between 2000 and 2004. In the euro area the three-month EURIBOR fell by 228 basis points 
to 2.11% in 2004 and remained below 2.20% until September 2005. Later this important benchmark for short-term interest 
rates rose continuously and in December 2007 reached 4.85%. 

The lowest annual rates in 2007 were noted in the Czech Republic (3.10%) and in Sweden (3.89%), the highest in Latvia 
(8.68%) and in Hungary (7.86%). 

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Figure 2.4.10:  3-month money market rates in the euro area, the US and Japan

Statistical analysis

2

7

6

5

4

3

2

1

0

2000

2001

2002

2003

2004

2005

2006

2007

Euro area 

United States

Japan

In 2007, the three-month EURIBOR was 4.28%. The relevant short-term rate in the United States was 5.30% and in Japan 
only 0.79%.

The increase in three-month money market rates between 2005 and 2007 was a global phenomenon, not limited to the euro 
area (+209 basis points). It was felt in all the Member States outside the euro area with the exception of Cyprus, Poland and 
Romania.

However, in the United States three-month money market rates followed a slightly different pattern. Only the lowest rate 
was recorded for both in March 2004 (euro area 2.03%, US 1.11%). After that, US money market interest rates increased 
continuously — exceeding the euro area level in November 2004 — to 5.50% in July 2006. In that time the three-month 
EURIBOR rose only to 3.10%. However, the gap of 240 basis points closed in subsequent months. Since January 2008, US 
short-term interest rates have been lower than those of euro area.

On a global scale, Japanese interest rates were always the lowest. Japanese three-month interest rates remained below 0.1% 
until March 2006. Since then Japanese rates have increased significantly. However, even in December 2007 three-month 
interest rates were below 1%, which was still a moderate rate compared with European countries (lowest level in December 
2007 in the Czech Republic (4.05%) and Slovakia (4.31%). In the euro area the three-month EURIBOR was 4.85% and in the 
US three-month interest rates stood at 4.97%.

In December 2007 the highest three-month interest rates were observed in Latvia (10.78%), Romania (7.93%) and Hungary 
(7.63%).

2.4.3 Trends in euro exchange rate developments 2000–2007

Exchange rate developments were less relevant in the last decade. The introduction of the euro eliminated exchange rates 
between an increasing number of EU Member States. At the outset, in 1999, the euro area covered 11 Member States (BE, 
DE, IE, ES, FR, IT, LU, NL, AT, PT, FI). Later several other Member States joined: Greece (2000), Slovenia (2007) and Cyprus 
and Malta (2008). In addition, many other European currencies have remained stable against the euro in recent years.

Especially in relation to two of the currencies of Member States that have not joined the euro area since its creation, the Swedish 
krona and the Danish krone, the euro maintained a reasonably stable exchange rate. However, taking into account recent de-
velopments the euro appreciated significantly against the pound sterling, by 17.1% by the end of March 2008 (year-on-year).

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A small number of currencies appreciated against the euro during the period 2004–2007, measured by the annual average 
exchange rates in 2007 as against 2004 (see Annex Table 4.29). The most significant gains were for the Rumanian leu (17.7%), 
the Polish zloty (16.4%), the Slovak koruna (15.6%) and the Czech koruna (12.9%).

In  contrast  to  the  moderate  fluctuations  between  the  majority  of  European  currencies,  the  value  of  the  euro  increased 
against  the  following  currencies  of  important  trading  partners  between  2002  and  2007:  the  Swiss  franc  (+  12.0%),  the 
Japanese yen (+36.6%) and the US dollar (+ 44.9%). At the end of March 2008 the value of the euro stood at USD 1.5812, i.e. 
+18.7% year-on-year. 

Fluctuations upward and downward since 2000 against the euro were exceptional. They occurred for the Hungarian forint 
and the Icelandic koruna.

Figure 2.4.11:  Euro exchange rates vs. US-Dollar and yen

1.8

1.6

1.4

1.2

1.0

0.8

2000

2001

2002

2003

2004

2005

2006

2007

Yen (Japan)

United States Dollar

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2.5 

External dimension of the economy

2.5.1  

Introduction

The EU has a common trade policy (known as the Common Commercial Policy). In other words, wherever trade issues, 
including issues related to the World Trade Organisation (WTO), are concerned, the EU acts as a single entity. In these 
cases, the European Commission negotiates trade agreements and represents Europe’s interests on behalf of the Union’s 
27 Member States. The EC Treaty establishes the overall aims and objectives of EU trade policy: Article 2 sets the general 
aims, including promoting the development of economic activities, high employment and competitiveness, and environ-
mental protection. Articles 131 explains how the common commercial policy must operate in principle: “to contribute, in 
the common interest, to the harmonious development of world trade, the progressive abolition of restrictions on interna-
tional trade and the lowering of customs barriers”. Article 133 sets out the scope, instruments and decision-making pro-
cedures. Article 300 establishes the current inter-institutional procedure for the conclusion of international agreements, 
principally by the Council. 

The EU’s external trade policy contributes to Europe’s competitiveness in foreign markets. Being an open economy, the 
EU’s aim is to secure improved market access for its industries, services and investments, as well as to enforce the rules of 
free and fair trade. A coordinated foreign trade policy takes on even greater importance in an era of globalisation, when 
economies and borders are opening up, leading to an increase in trade and capital movements, and the spread of informa-
tion, knowledge and technology, and involving a process of deregulation. The economic impacts of globalisation on the 
EU are obviously felt through trade in goods and services, financial flows ranging from foreign direct investment to more 
short-term forms, such as portfolio investment, as well as the movement of persons linked to cross-border economic activ-
ity, ranging from workers’ remittances to the provision of services.

Globalisation becomes noticeable when it is measured by actual trade flows. According to World Development Indicators 
(published by the World Bank) trade grew, on average, almost twice as fast as GDP between 1990 and 2006. Global trade is 
expected to hit about US$ 16 trillion in 2007, equal to 31% of world GDP. At the same time, stocks of foreign direct invest-
ment grew almost five times as fast as world GDP. The domestic sales of foreign affiliates are larger than world exports and 
are critically reliant on trade in intermediate goods, further underscoring the importance of the integration of trade in 
modern economic activity. 

Within the EU, there are two main sources of statistics on international trade. One is external trade statistics (ETS), which 
provide information on trade in merchandise goods, collected on the basis of customs and VAT declarations. ETS pro-
vide highly detailed information on the value and volumes (quantity) of international trade in goods as regards the type 
of commodity. The second main source is balance-of-payments statistics (BoP), which register all the transactions of an 
economy with the rest of the world. The purpose of this chapter is to give an overview of the EU’s trade in merchandise 
goods (within the ETS framework), as well as its trade in services, current account, and foreign direct investments (within 
the BoP framework). 

2.5.2   Trade in goods

The European Union is one of the major players in international trade, accounting for about one fifth of world trade in 
goods; it is the world’s biggest exporter and its second biggest importer after the USA. The trade balance for EU27 has been 
persistently negative over recent years, with deficits rising from €45 bn in 2002 to €192 bn in 2006, and then falling back to 
€186 bn in 2007. However, the USA is the biggest net importer, with a trade deficit of about €700 bn in 2006, while the other 
main traders, China, Japan and Canada, recorded a surplus.

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Figure 2.5.1: Main world traders: exports, imports and trade balance, 2006 (EUR bn)

1500

1250

1000

750

500

250

0

-250

-500

-750

EU-27

USA

China (1)

Exports

Imports

Japan

Balance

Canada

Source: Comtrade, Eurostat (EU27) (1) Excluding Hong Kong

An analysis by product can be made at aggregated level by using the first digit (section) of the Standard International Trade 
Classification (SITC). The EU’s main exports in 2007 were in Machinery & vehicles (SITC 7), Other manufactured articles 
(SITC 6+8) and Chemicals (SITC 5), while the main imports were Machinery & vehicles, Other manufactured articles and 
Energy products (SITC 3). The EU had a €130 bn surplus in the trade of Machinery & vehicles - a sector which showed a 
clear comparative advantage in international trade. The EU surplus in the trade in Chemicals amounted to €77 bn. In con-
trast, the EU was a net importer of fossil fuels and it had a €270 bn deficit in the trade in Energy products in 2007, reflecting 
the EU’s comparative disadvantage in this sector. 

Figure 2.5.2:  EU27 imports, exports and balance, by SITC-1 product group, 2007 (EUR Bn)

Food products

Crude materials

Energy products

Chemical products

Other manufactured products

Machinery & vehicles

-300

-100

0

100

Exports

Imports

300

Balance

500

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In first position as the EU’s most-imported product, ‘Petroleum & petroleum products’ went from a 9% share of total im-
ports in 1999 to 18% in 2007. The other main imported goods are included in SITC section 7: ‘Telecommunications, audio, 
TV, video’, ‘Electrical machinery and equipment’ and ‘Office machinery & computers’ each had shares of around 5% of total 
EU imports in 2007.

While the USA remained the EU’s main trading partner in terms of total trade, China became the EU’s main provider of goods 
imports for the first time in 2006. During 1999 to 2007, buoyant growth contributed to a linear rise in EU exports to China. 
However, high levels of imports of (mainly) manufactured goods from China have resulted in a growing trade deficit. 

China has taken a leading role as a supplier of manufactured goods to the EU. Indeed, by 2007 China had confirmed its 
leading position as provider of ‘Articles of apparel and clothing accessories’ and had gained the major share as provider of a 
further three of the six main imported products: ‘Office machinery and computers’, ‘Electrical machinery and equipment’ 
and ‘Telecommunications, audio, TV, video’. For the same three categories, both the USA and Japan lost significant market 
shares in the EU import market.

Endowed with high-technology industries such as pharmaceuticals, the USA and Switzerland remained in first and second 
position as suppliers of the EU’s imports of ‘Chemical products’ in recent years, together accounting for 51% of EU imports 
in that category in 2007. Russia and Norway were clearly the main providers of energy products to the EU in 2007. Brazil 
became the largest single provider of ‘Food products’ (SITC 0+1) to the EU in 2001 and of ‘Crude materials’ (SITC 2+4) in 
2004, closely followed in the latter category by the USA.

Figure 2.5.3:  Extra-EU27 imports by SITC group, 2007 (Share by main partner, %)

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

21.8

21.7

21.4

21.2

Food products

Crude materials

Energy products

Chemical products

China

USA

Switzerland

Norway

Russia

Turkey

Other
manufactured
products

Machinery and
vehicles

Japan

Others

With 44% of EU exports in 2007, ‘Machinery & vehicles’ were the category of products that was most exported outside 
the EU. The US remained the EU’s main customer by far, with sales over €100 bn during the last seven years and ac-
counting for 20% of exports in 2007. While EU exports of Machinery & vehicles to Switzerland and Japan were constant, 
exports to Turkey and especially to Russia and China rose strongly between 2000 and 2007. Exports of Machinery & 
vehicles to the latter two partners displayed average annual growth rates of 26% and 14% respectively. Going into fur-

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ther detail, the most exported products in the SITC 7 section in 2007 were ‘Road vehicles’, ‘General-purpose industrial 
machinery’’ and Electrical machinery and equipment’ , while the most exported chemical products were the ‘Medicinal 
and pharmaceutical products’.

Making up 25% of EU exports in 2007, exports of ‘Other manufactured products’ to the EU’s top ten partners (mainly ‘Iron 
and steel’, ‘Professional, scientific and controlling instruments and apparatus’ and ‘Non-metallic mineral manufactures’) 
rose in all cases between 2000 and 2007, with the exception of the USA and Japan. The USA and Switzerland remained first 
and second respectively as the EU’s main export destinations in this product category, while sales to Russia and China grew 
at impressive annual rates of 19% and 18% respectively. Exports of Chemical products displayed robust growth throughout 
the same period, accounting for just under 16% of total EU exports in 2007, while around 50% of all Chemical product 
exports went to the USA, Switzerland, Russia and Turkey. While the USA developed its position as the leading destination 
for exports of Chemical products from the EU, with 28% of the total in 2007, exports to Russia and China grew strongly 
between 2000 and 2007, at annual rates of 21% and 17% respectively. In 2007, a large share of Energy exports went to the 
USA (29%), followed by Switzerland (8%), while China purchased the most of EU Crude materials exports (17%), followed 
by the USA (10%). Despite a period of stagnation in 2003 and 2004, exports of Food products grew over the entire period 
between 2000 and 2007. For this category the USA remained the EU27’s main export partner; over the same period, EU27 
exports to China rose by 15% and to Russia by 13%, making Russia the destination of 11% of EU27 Food exports in 2007 
and thus the EU27’s second-main partner for this product.

Figure 2.5.4:

Extra-EU27 exports by SITC group, 2007 (Share by main partner, %)

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

21.8

21.7

21.2

Food products

Crude materials

Energy products

Chemical products

China

USA

Switzerland

Norway

Russia

Turkey

Other
manufactured
products

Machinery and
vehicles

Japan

Others

Considering the extra-EU trade by individual Member State, Germany is the biggest trader in terms of both exports and 
imports, with a share of 27.5% and 18.8% respectively in 2007. The other biggest EU exporters are Italy, France and United 
Kingdom, with shares above 10% (but declining) over the entire period 2000-2007. In extra-EU imports, Germany is fol-
lowed by United Kingdom, the Netherlands, Italy and France, with shares in 2007 ranging from 15% to 10%. However, 
imports into the Netherlands, and therefore its trade deficit, are over-estimated because of the so-called ‘Rotterdam effect’: 
that is goods bound for other EU countries arrive in Dutch ports and, according to Community rules, are recorded as extra-
EU imports by the Netherlands (the country where goods are released for free circulation).

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Close to two thirds of the EU’s total external trade was carried out within the Union in 2007. The weight of intra-EU trade 
(dispatches plus arrivals) measured as a percentage of the individual Member States’ total trade ranged between 83% in the 
Czech Republic and 56% in the United Kingdom. The growth rates of imports and exports were slightly below the annual 
average growth rates displayed by intra-EU arrivals (5.8%) and dispatches (5.6%) over the period 2000-2007, reflecting the 
growing expansion of the EU internal market. Throughout, the share of intra-EU dispatches in total exports was higher 
than  that of intra-EU arrivals  in total  imports;  in  2007 these shares were  68.1% and 64.3% respectively. Dispatches are 
shown broken down by main exporting Member State, as well as by SITC-1 product group. With shares similar to those in 
total extra-EU exports, ‘Machinery & vehicles’ and ‘Other manufactured products’ together made up about two thirds of 
intra-EU dispatches in 2007.

Figure 2.5.5:

Intra-EU27 dispatches by main 
declaring Member State, 2007 (%)

Figure 2.5.6: 

Intra-EU27 dispatches by SITC-1 
product group, 2007 (%)

Other
18%

Germany 
24%

Products n.e.s.
1%

Crude materials
3%

Energy producrs
6%

New MS 12 *
12%

United Kingdom
7% 

Italy
8%

Machinery and
vehicles
39% 

Netherlands 
12%

France 
10%

Belgium
9%

Other 
manufactured
products 
28%

* ‘New MS 12’ = Member States that joined the EU in the 2004 and 2007 enlargements

Food products
8%

Chemical
products 
15% 

BOX 2.5.1: TRADE IN GOODS – INTRASTAT vS. EXTRASTAT

External trade statistics measure the value and quantity of goods traded between the Member States of the European 
Union (Intrastat) as well as between Member States and third countries (Extrastat). Community legislation ensures that 
the statistics provided to Eurostat are based on specific legal texts, and on definitions and procedures which, to a large 
extent, have been harmonised. 

Statistical information on extra-EU trade in goods is provided by declaring parties when completing the customs for-
malities, and is then collected by the Member States from the statistical copy of the customs declaration (Single Ad-
ministrative Document or ‘SAD’). 

The  Intrastat  system  came  into  operation  on  1  January  1993  when  the  Single  Market  was  set  up,  leading  to  the 
disappearance of customs formalities within the EU. This system provides for direct collection of information from 
companies and is closely linked to the vAT system. In order to reduce the burden on enterprises, the Intrastat system 
is designed in such a way that the workload for providers of statistical information varies according to the amount 
of trade in which they are engaged. To achieve this, each Member State applies a system of thresholds, expressed 
in annual values of intra-EU trade that either exempts enterprises from providing statistical information or limits 
the information collected. While Extra-EU trade statistics cover, in principle, all imports and exports, in the Intrastat 
system Member States may exclude small scale transactions (of a value below 1000 Euro or a net mass of less than 
1000  kg).  Once  the  annual  thresholds  have  been  set,  estimates  must  be  made  for  the  below-  threshold  trade  in 
order to have a complete coverage of trade. Similarly, trade data must be adjusted to compensate for any missing 
information. 

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Another important difference between Intrastat and Extrastat is the definition of the partner country in the case of 
imports. For both extra-EU and intra-EU exports, the trading partner is the country (or Member State) of final destina-
tion of the goods. For extra-EU imports, the trading partner is the country of origin of the goods. For intra-EU imports 
(arrivals), the trading partner is the Member State of consignment of the goods. 

The method of trade allocation to a partner country is one major reason for the differences between national and Com-
munity figures. The valuation of the reference period can also be influenced by the different systems of data collection. 
For Intrastat the reference period might be assigned by the Member States to a given month on the basis of the date 
on which vAT becomes chargeable on intra-Community acquisitions. For Extrastat, information is generally assigned 
to the month in which the customs authority accepts the declaration. 

Last but not least, the deadlines for the transmission of detailed monthly data to Eurostat differ: they are 42 days for 
Extrastat and 70 days for Intrastat. As a consequence, extra-EU detailed data are generally released within two months 
after the end of the reference period. For intra-EU trade, as well as for intra- and extra-Euro area trade, detailed data 
are released one month later.

2.5.3   Trade in Services

Whereas section 2.5.2 described trade in merchandise goods (within the External Trade Statistics framework) this section 
will concentrate on trade in services. Trade in services statistics are compiled within the balance of payments framework.

Services are increasingly important in modern economies. In 2007, their shares of EU27 gross value added and employment 
were 69.5% and 68.9% respectively. The strong and growing role of services is not, however, reflected in international trade. 
Due to the intangible nature of services, this sector is inherently subject to more constraints than trade in goods. Also, serv-
ices are often difficult to separate from goods with which they may be associated or bundled to varying degrees, and trade 
in  goods  may  indiscriminately  include  service  charges  such  as  insurance,  maintenance  contracts,  transport  charges,  or 
royalty/licence payments. However, with the increasing tradability of large parts of the service economy, trade in services is 
now growing at a similar rate to trade in goods, and thus is growing at a substantially higher rate than GDP. The expansion 
of trade in services has continued to outpace the growth in service sector output by a sizeable margin. 

Since the 1990s, growth in the export of goods and services in the EU has evolved in a broadly similar pattern, with both 
sectors growing by about 6% per year on average. Consequently, services maintained their roughly 22% share of overall 
international trade during this period. However, in 2007 – as figure 2.5.7 shows - after lagging behind for four consecutive 
years, services exports recorded a higher growth rate (6.8%) than that of goods (4.5%).

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Figure 2.5.7:  EU GDP and exports of goods and services, annual variation (%) (1)

Statistical analysis

2

e
g
n
a
h
c
e
g
a
t
n
e
c
r
e
p

l

a
u
n
n
A

14.0

12.0

10.0

8.0

6.0

4.0

2.0

0.0

2000

2001

2002

2003

2004

2005

2006

2007

(1) At 1995 prices and exchange rates

GDP

Goods

Services

In 2007, the European Union remained the world’s largest exporter and importer of services. The EU27 accounted for about 
one quarter of global exports and imports (intra-EU transactions are excluded from this calculation since the EU is treated 
as a single entity). EU27 trade in services was marked by an increase of 13.6% in exports (credits) and 10.7% in imports 
(debits) over 2006 in value terms. As a result the surplus continued to grow, reaching €88.4bn in 2007. 

Figure 2.5.8:  EU27 trade in services, credit, debit and net (EUR bn)

o
r
u
E
n
o

i
l
l
i

b
n

I

600

500

400

300

200

100

0

2004

2005

Credit

Debit

2006

Net

2007

Figure  2.5.9  shows  that  transportation  services,  travel  and  other  business  (which  covers  merchanting  and  other  trade-
related services, operational leasing services and miscellaneous business, professional and technical services) made up 68% 
of total EU exports and 74% of total EU imports. The increased surplus in 2007 was mainly due to an improved balance in 
transportation services, construction services, financial services and computer and information services, and other busi-
ness services. These numbers were partially offset by the growing deficit in travel. 

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Statistical analysis

Figure 2.5.9:  EU international trade in services with the rest of the World (€ bn), 2007

Transportation

Travel

Communications services

Construction services

Insurance services

Financial services

Computer and information
services

Royalties and license fees

Other business services

Personal, cultural and
recreational services

Government services, n.i.e.

-20.0

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

Credit

Debit

Net

The significant deficit in royalties and licence fees remained more or less stable, as did the surplus in insurance services and 
government services and the deficits in communications services and personal cultural and recreational services. 2007 saw 
the biggest annual increases in relative terms in construction services, financial services, and other business services, both 
for exports and imports. 

An analysis of the breakdown by partner, and of the underlying trend of EU transactions with the rest of the world (extra-
EU transactions), shows that the USA continued to be the EU’s biggest trading partner. In 2007, 27.7% of total exports from 
the EU27 went to the USA and 30.9% of total imports came from the USA. Exports to the USA increased at a slightly lower 
rate than imports from the USA, pushing down the trade surplus from €12.6bn in 2006 to €11.2bn in 2007. Exports to Swit-
zerland in recent years grew faster than imports from that country, resulting in a considerable increase in the trade surplus. 
Countries that increased their share in EU trade in services, although starting from a relatively low level, were China (3.5% 
and 3.2% of total EU exports and imports respectively in 2007, compared to 2.5% and 2.4% in 2004), and Russia (3.6% and 
2.8% of total EU exports and imports respectively in 2007, compared to 2.6% and 2.3% in 2004). 

The EU had considerable surpluses with most of its trading partners; however the largest deficits were recorded with Mo-
rocco, Croatia, Thailand, Egypt and Turkey, mainly due to deficits recorded under the ‘travel’ category.

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Figure 2.5.10:  Extra-EU trade in services, share by main partner (%)

Credits

Debits

i

s
e
c
v
r
e
s
n

i

e
d
a
r
t

U
E
n

i

e
r
a
h
S

40.0

35.0

30.0

25.0

20.0

15.0

10.0

5.0

0.0

i

s
e
c
v
r
e
s
n

i

e
d
a
r
t

U
E
n

i

e
r
a
h
S

40.0

35.0

30.0

25.0

20.0

15.0

10.0

5.0

0.0

US

CH

JP

RU

2004

CN

CA

HK

IN

US

CH

JP

2007

RU

2004

CN

CA

HK

IN

2007

The United Kingdom continued to be the largest exporter of services in 2007. Almost one quarter of all EU exports to the 
rest of the world came from the UK, followed by Germany and France. Germany was the biggest importer, accounting for 
more than 19% of total EU imports, followed by the UK and France. The United Kingdom also recorded the largest surplus 
in 2007 (€54.0bn), followed by Sweden (€9.2bn) and Greece (€ 8.2bn). The highest deficit in 2006 was recorded by Ireland 
(-€ 12.4bn), followed by Germany (-€ 3.8bn) and Italy (-€ 3.7 bn).

Figure 2.5.11:  Member States’ share in total extra-EU ITS transactions (%), net (EUR bn), 2006

%
n

i

e
r
a
h
s
S
M

25.0

20.0

15.0

10.0

5.0

0.0

60.0

50.0

40.0

30.0

20.0

10.0

0.0

-10.0

-20.0

o
r
u
E
n
o

i
l
l
i

b
n

i

t
e
N

UK DE

FR

IT NL

IE

ES DK BE

SE

EL

LU AT

FI

PL HU CZ PT RO CY

SI

BG LV

LT

SK

EE MT

MS share (%)

Net

Left-hand scale for MS share in percentage; Right-hand scale for balance in bn euro

It is noteworthy that about 57% of EU trade in services in 2007 was between EU Member States (intra-EU transactions). 
This share has remained more or less stable during the last decade. 

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2.5.4  Current account

The current account measures a country’s economic position in the world, covering all transactions that occur between resi-
dent and non-resident entities. Besides trade in goods and services, discussed in the two previous sections, it also includes 
income and current transfers. Economies with a current account deficit are net debtors to the world economy (this deficit is 
financed by the various items of the financial and capital account), while economies with a surplus are creditors. 

The position of the EU economy can be weighed against that of other major world economies, comparing the current ac-
count balance measured as a share of GDP. As figure 2.5.12 shows, in the most recent years the EU recorded values close 
to zero, though on the deficit side, whereas the USA ran a deficit which has grown steadily from -3.8% in 2001 to -5.3% in 
2007. Japan, on the other hand, had a surplus, which rose from 2.1% in 2001 to 4.8% in 2007. However, the most significant 
change was recorded in China, where the current account surpluses soared from 1.3% in 2001 to 9.4% in 2006. India, on the 
other hand, has moved from a small surplus (in 2001 up to 2003) to a small deficit (since 2004). Russia, whose economy is 
dominated by raw materials and energy products exported to foreign markets, runs a significant current account surplus, 
which recorded its lowest value (6.1% of GDP) in 2007.

Figure 2.5.12:  Current account balance as share of GDP (%)

12.0%

10.0%

8.0%

6.0%

4.0%

2.0%

0.0%

-2.0%

-4.0%

-6.0%

-8.0%

2001

2002

2003

2004

2005

2006

2007

EU (*)

China

India

Japan

Russia

USA

(*) 2001-2003: EU25; 2004-2007: EU27

A sharper picture of the economic situation can be provided by the contribution of the various components to the current 
account balance: international trade in goods, trade in services, income and current transfer. As shown in figure 2.5.13, 
based on yearly data for 2007, the EU current account was fairly well balanced among the four components, with a small 
deficit that was mainly due to a deficit in trade in goods. The small value, in terms of GDP, of the net trade in goods means 
that exports almost matched imports. The slight imbalance in favour of imports had a determining effect on the current 
account deficit.

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Trade in goods was also the main driver of the current account balance for Russia, China and the USA. In the first two cases 
it generated a significant surplus, whereas for the USA it led to a deficit. The situation in Japan and India was quite different. 
In Japan, a large surplus on income and, to a lesser extent, in trade in goods triggered the current account balance. In India, 
on the other hand, the surpluses in trade in services and in current transfers all but offset the considerable deficit in trade 
in goods, thus leading to a slight deficit in the current account.

Figure 2.5.13: Current account by component as share of GDP, 2007 (%)

12.0%

10.0%

8.0%

6.0%

4.0%

2.0%

0.0%

-2.0%

-4.0%

-6.0%

-8.0%

(*) 2006 data

EU

USA

India (*)

Russia

Japan (*)

China (*)

Goods

Services

Income

Current transfer

Total current account

The contribution of the various components to the EU current account changed over time. As figure 2.5.14 shows, the cur-
rent account has moved sharply from a surplus of about 10 bn EUR to a deficit of 72 bn EUR – that is from +0.1% to -0.6% 
as a share of GDP. The main driving force was trade in goods, where the deficit continued to worsen from 2003 to 2006, and 
improved only marginally in 2007. On the other hand, current transfers’ deficit remained fairly stable in the last few years, 
at around 50 bn EUR. The trend defined by trade in goods was partially offset by income and by trade in services. Whereas 
the surplus in income played a comparatively more important role in 2004 and 2005, trade in services was the item which 
recorded the largest surplus in 2006 and 2007 (65 bn EUR and 88 bn EUR, respectively).

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Statistical analysis

Figure 2.5.14:  EU Current account by component (bn EUR)

100

75

50

25

0

-25

-50

-75

-100

-125

-150

-175

2004

2005

2006

2007

Current account

Goods

Services

Income

Current transfers

The analysis of the geographical breakdown of the EU current account, which can be found in figure 2.5.15 for 2007, shows 
that the net creditor position of the EU with the USA was more than counterbalanced by the debtor position with China. 
The deficit position with Russia and Japan further weakened the total current account deficit. 

Figure 2.5.15: EU current account balance with main partners, 2007 (EUR bn)

150

100

50

0

-50

-100

-150

-200

Russia

USA

China

India

Japan

Other
Countries

Total Extra
EU27

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BOX 2.5.2:  

DIFFERENCES BETWEEN ETS AND BOP IN DATA ON INTERNATIONAL TRADE IN  
GOODS 

Balance of Payments (BOP) and External Trade Statistics (ETS) both provide data for the external trade in goods of a 
given country. Although these statistics serve different purposes and address different users’ needs, users may never-
theless be puzzled to find that, for the same period, reporter and partner country, the two data sets generally do not 
show the same figures. Different methodologies are the main reason for the differences between the two data sets.

The main conceptual differences between BOP and ETS data on goods originate from the different recommendations 
on computing, as defined in the Balance of Payments Manual (BPM5) and International Merchandise Trade Statistics 
(IMTS) respectively.

Coverage and time of recording

ETS data cover goods which add to or subtract from the stock of material resources of a country by entering (imports) 
or leaving (exports) its economic territory. Consequently, trade statistics are recorded on the basis of customs docu-
ments reflecting physical movement of goods across the national or customs frontier of an economy, with the date of 
lodgement of the customs declaration as a suitable approximation. 

In contrast, goods in BOP statistics, in addition to general merchandise, also covers goods for processing, repairs on 
goods, goods procured in ports by carriers, and non-monetary gold. Transactions involving general merchandise are 
recorded at the time of the change of ownership of the goods, i.e. when the parties enter the goods in their books as a 
real asset and make a corresponding change to their financial assets and liabilities. 

Actual movement of the goods may not occur at the same time as the change of ownership of the goods. There are 
a number of special transactions where the movement of goods does not reflect a change of ownership, e.g. goods 
sent or received for processing, repair of goods, mobile equipments that change ownership outside the country of 
residence of the original owner, etc. As ETS are the most common source for the goods component of the BOP, for the 
purposes of balance of payments statistics source data coming from ETS should be adjusted by removing merchandise 
movements recorded during the period that did not involve a change of ownership, and by adding merchandise that 
changed ownership during the period but was recorded in the ETS in earlier or later periods.

Valuation

The principle of valuation of general merchandise in BOP is the market value of goods at the customs frontier of the 
economy from which the goods are first exported, i.e. free on board (FOB). In contrast, IMTS use FOB-type valuation 
as the statistical value of exports only. For imports, CIF-type valuations are used, including cost, insurance and freight 
(CIF) at the border of the importing country. For BOP purposes, in order to convert imports from CIF to FOB valuation, 
the value of freight and insurance premiums incurred from the frontier of the exporting country to the border of the 
importing country should be deducted. When the customs point of the exporting and importing territory are contigu-
ous, the CIF and FOB values should be the same.

2.5.5   Foreign direct investments

Foreign direct investment (FDI) is playing a growing role in economic globalisation. For the investing firm, it means access 
to new markets and marketing channels, possibly cheaper production facilities, access to new technology, products, skills 
and financing. For a host country or the firm which receives the investment, it can provide a source of new technologies, 
capital, products and management skills, which can lead to higher competition and give an impetus to economic devel-
opment. FDI complements and fuels the expansion of trade flows and is seen as an important cornerstone of economic 
globalisation. 

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Statistical analysis

Figure 2.5.16:  World FDI flows as a % of the world GDP, 1970-2006

4.0%

3.5%

3.0%

2.5%

2.0%

P
D
G
%

1.5%

1.0%

0.5%

0.0%

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

Source: UNCTAD World Investment Report 2007 and UN statistics division.

The world FDI flows have increased six-fold since 1970. As a percentage of world GDP, the FDI flows remained below 1 % 
from 1970 until 1989. From 1994 they grew steeply, with 2000 being the peak year (3.9 % of GDP). The following three years 
were characterised by severe slumps; global FDI flows fell by 61 % between 2000 and 2003, dropping to 1.5 % of world GDP. 
Since then, increasing world FDI flows were recorded until 2006.

Figure 2.5.17:  World FDI flows by origin, 2001-2006 (EUR bn)

EU27 for 2004-2006, EU-25 for 2001-2003

)

n
b
R
U
E
(

1000

750

500

250

0

-250

100

75

50

25

0

)

%

(
e
r
a
h
s
U
E

2001

2002

2003

2004

2005

2006

EU (to extra-EU)

Other developped countries

EU share (right-hand scale)

USA

Other countries

Source: Eurostat, UNCTAD.

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Figure 2.5.17 demonstrates the important role that the EU plays in world FDI flows. After the peak investment years at the 
beginning of this century when the EU had an almost 50 % share of world FDI outward flows, the EU continued to be the 
largest investor through the global decline in FDI flows and the following upturn; the exception was 2004, when the United 
States surpassed the EU in investments. In 2006, when world FDI flows grew by 85 %, the EU held the leading position 
amongst the principal investing countries, with a share of 34 % of the total world FDI flows.

Figure 2.5.18:  EU FDI flows and stocks 2001-2007 (EUR bn) 41

EU27 for 2004-2007, EU25 for 2001-2003

3500

3000

2500

s
k
c
o
t
s

2000

1500

1000

500

0

500

400

s
w
o
fl

300

200

100

0

2001

2002

2003

2004

2005

2006

2007*

Extra EU outward stocks

EU outflows to Extra EU

Extra EU inward stocks

EU inaflows from Extra EU

* Estimated stock data for 2007.

The trend in EU FDI flows in extra-EU countries has been upwards since 2002. Especially since 2004, constant growth has 
been the major characteristic of EU outward flows throughout, reaching EUR 420 bn in 2007 and representing 3.4 % of the 
EU GDP. 

EU FDI inflows followed a downward curve from 2001, reaching their lowest point in 2004 with EUR 58 bn; however, since 
then they have continued to expand, reaching the unprecedented amount of EUR 319 bn in 2007. The EU remained a net 
investor: in 2007; FDI outflows exceeded inflows by EUR 101 bn, which was a similar result to those of previous years. 

EU FDI stocks in extra-EU countries amounted to EUR 3 126 bn in 2007, following a 16 % increase compared to 2006 
(EUR 2 706 bn). These investments were highly diversified, but in terms of activities, services accounted for the major share. 
In 2007, EU FDI inward stocks (EUR 2 376 bn) increased by 83 % over 2001 (EUR 1 296 bn). The EU was a net investor, with 
EU FDI outward stocks exceeding EU FDI inward stocks by EUR 750 bn.

41 The FDI flow figures for 2007 are based on preliminary data from the Member States; FDI stock data for 2007 are estimated by Eurostat.

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Statistical analysis

Figure 2.5.19:  EU27 FDI outward stocks by main destination (end-2006)

not allocated 
1% 

Africa
5%

Europe
(non-EU)
23%

North American
countries
39%

Central and
South America 
17%

Oceania 
2% 

Asia
13%

North America continued to be by far the favourite destination of EU FDI, hosting EU FDI stocks of EUR 1 078 bn at the 
end of 2006. Its share of the total EU FDI outward stocks remained stable from 2004 to 2006, at around 40 %. The United 
States, with EUR 934 bn, attracted the lion’s share of the EU outward stocks targeting the Northern American countries, 
representing a share of 35 % of all EU FDI investments outside the EU. The United Kingdom was the biggest EU investor in 
the United States with FDI stocks of EUR 276 bn.

Non-EU Europe, with EUR 620 bn, was the second biggest extra-EU partner, as its stocks accounted for 23 % of EU outward 
stocks at end-2006. Switzerland, alone, attracted EUR 333 bn for the same period, accounting for 65 % of the total EU FDI 
stocks  targeting  the  non-EU  European  countries  and  12 %  of  all  extra-EU  investments. The  Netherlands  together  with 
France were the most significant EU investors in Switzerland with cumulative investments of EUR 77 bn at end-2006.

The combined share of South and Central America increased marginally from 16 % at end-2004 to 17 % at end-2006. 
However,  over  the  same  period,  EU  FDI  stocks  invested  in  South  America  increased  by  16 %  and  those  in  Central 
America by 36 %.

Asia, with EUR 368 bn at end-2006, saw its share of EU FDI outward stocks decrease slightly to 13 % (16 % at end-2004), 
although it remained the fourth biggest destination area. The main recipients of EU outward stocks in Asia were China, 
including Hong Kong, with a share of 32 % of the EU outward stocks targeting Asia (4 % of total EU FDI stocks held abroad). 
With EUR 76 bn, Japan was the second biggest destination of EU FDI outward stocks to Asia. France was the biggest EU 
investor with 35 % of the total EU FDI outward stocks targeting Japan.

From 2004 to 2006 the share of total extra-EU FDI stocks in Africa remained stable at 5 %, but investments grew by 14 % in 
absolute terms, from EU 110 bn at end-2004 to EUR 125 bn at end-2006. South Africa was the primary destination, receiv-
ing 35 % of the total EU outward FDI stocks directed at Africa.

Oceania kept its share of total extra-EU FDI stocks at fairly stable levels of around 2 % from 2004 to 2006. The value of 
the EU outward stocks invested in Oceania dropped from EUR 67 bn at end-2004 to EUR 56 bn at end-2006. This fall was 
mainly due to decreased investments in Australia.

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Figure 2.5.20:  EU27 FDI inward stocks by extra-EU main investor (end-2006)

not allocated 
2% 

Africa
1%

Europe
(non-EU)
22%

North America
50%

Central and
South America 
14%

Oceania 
1% 

Asia
10%

At end-2006 the EU FDI inward stocks from extra-EU partner countries stood at EUR 2 057 bn - a 19 % increase from 
end-2004 levels. It is worth noting that, at end-2006, half of the EU inward stocks originated in North America, which con-
tinued to be the biggest investor in the European Union throughout the period in question. The United States was the big-
gest investor in the EU, with EUR 954 bn, and accounted for 94 % of the total EU inward stocks from Northern American 
countries and 46 % of all extra-EU investments. The United Kingdom, with EUR 265 bn, hosted 28 % of the total EU FDI 
inward stocks from the United States at the end of 2006.

The share of Europe (non-EU) rose from 20 % at the end-2004 to 22 % at end-2006, totalling EUR 457 bn. Switzerland 
was the main investor country into the EU, accounting for 54 % of the total EU inward stocks from European (non-EU) 
countries, and 12 % of all inward stocks. France, with EUR 44 bn, was the biggest EU recipient of FDI stocks coming from 
Switzerland.

EU FDI inward stocks coming from South and Central America remained steady at around 14 % throughout 2004-2006. 
Asia’s share increased slightly to 10 % at end-2006 from 9 % at end-2004. The shares of EU FDI inward stocks of other inves-
tor zones remained unchanged at fairly low levels between 2004 and the end of 2006. 

2.5.6  Outward Foreign affiliates statistics 

The European Union is one of the world’s biggest investors, and foreign affiliates of European companies play a very impor-
tant role in the global economy. Therefore, Outward Foreign Affiliates Statistics (FATS), which can be defined as statistics 
describing the activity of foreign affiliates abroad controlled by the compiling economy, are increasingly relevant to the for-
mulation of the European Union economic policies, as they provide information on the role that European capital groups 
play in the world’s economy, especially in terms of sales and employment.

Reporting of outward FATS data in Europe still takes place on a voluntary basis only, and for most countries the variables 
covered are turnover and number of persons employed. For 2004, which is the most recent year for which figures are avail-
able, seven Member States provided data. 

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Statistical analysis

The scale of activities of EU-controlled foreign affiliates was bigger within the EU than outside it. For the reference year 
2004, the intra EU27 share of the number of persons employed was 57.7% (the figures ranged from 34.5% for Portugal to 
81.2% for Austria) and the share of turnover was 54.5% (ranging from 50.9% for Germany to 89.8% for Czech Republic). 
The only exception was in relation to the number of persons employed in Portuguese-controlled foreign affiliates, where the 
extra-EU share was 65.5%. 

It should be noted that the most substantial activity by foreign affiliates takes place in the neighbouring countries (France 
for Belgium, Slovakia for Czech Republic, Cyprus for Greece, Germany for Austria, Spain for Portugal or Sweden for Fin-
land). The United States was the principal destination in terms of both turnover and number of persons employed only for 
German foreign affiliates.

The activity of the European affiliates outside the EU was highest in North America, with shares of 38.6% for number of 
persons employed and 57% for turnover (USA counting for 91% of the total). 

Figure 2.5.21:  Number of persons employed and turnover in foreign affiliates located outside the EU27, 2004, 

shares by region (%) (1)

Number of persons employed

Turnover

Oceania
2%

Other
European countries 
15%

Africa 
6%

North
America
39% 

Oceania
3%

Other
European countries 
12%

Africa 
3%

North
America
57% 

Asia
23% 

South America
11%

Central
America
4%

Asia
17% 

South America
4%

Central America
4%

(1) Reporting countries: Austria, Belgium, Czech Republic, Finland, Germany, Greece, Portugal

In terms of turnover, the services sector was the main field of activity for EU affiliates in 2004 with 54.7% of total, followed 
by manufacturing with 40.9%. Only in Finland did manufacturing take a higher share of total turnover than services. How-
ever, the manufacturing sector accounted for 54.8% of the total number of persons employed in foreign affiliates, compared 
with 41% for services. 

Among the activity categories of the services sector, ‘Trade and Repairs’ had the biggest share in terms of both number of 
persons employed and turnover (47.6% and 67.2% respectively). However, this share differed from country to country, rang-
ing from 10.4% in Greece to 62.5% in Finland for number of persons employed and from 12.9% in Greece to 88.3% in Czech 
Republic for turnover. ‘Trade and Repairs’ was followed by ‘Transport and Communication’ in terms of employments and 
by Financial Intermediation in terms of turnover. 

The impact of foreign affiliates on the labour market differs significantly from country to country, being substantial in some 
countries and almost negligible in others. German affiliates were by far the biggest employer abroad, but compared against 
total employment within the compiling country Finland had the highest ratio with the total number of persons employed 
in foreign affiliates accounting for 13.9% of total employment in the country. It was followed by Austria and Germany, both 
with a ratio of about 11%. On the other hand, the ratio for Czech Republic is only 0.3% and for Portugal 0.6%. Taking only 
affiliates outside the EU into account, these figures ranged from 5% for Finland and Germany to 0.1% for Czech Republic 
and 0.4% for Portugal. 

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BOX 2.5.3:  

FDI vERSUS FATS

FDI and (outward) FATS are closely related statistical domains. Their subject of interest is the same – businesses inter-
nationalising by investing abroad in other business units, existing ones and/or newly founded ones. This similarity in 
substance is also expressed in compilation practice, as outward FDI stock and outward FATS data are often compiled 
with the help of the same survey. yet, despite all these similarities there are a number of important methodological 
differences between them. These differences limit the scope of comparability between the two datasets. The most 
important methodological differences are:

50% (FATS) rule versus 10% (FDI) rule

FATS comprises all affiliates that are foreign-controlled (more than 50% of voting rights) while FDI statistics include all 
foreign interests amounting to 10% or more of the voting power. Broadly speaking, it could be said that the outward 
FATS population is a sub-group of the population of foreign direct investments relevant for FDI statistics. 

The principle of the Ultimate Controlling unit (UCI) versus Immediate Counterparty Country

FATS, including outward FATS, are based on the concept of control when assigning statistical values to institutional 
units. According to Eurostat’s FATS recommendations manual, control is defined as “the ability to determine the gen-
eral policy of an enterprise by choosing appropriate directors, if necessary.” Typically, equity-ownership is taken as a 
proxy to determine control and, whilst cases of minority ownership are also acknowledged, the ownership of more 
than 50% of the voting power or of the shares (directly or indirectly) is generally taken as an indication of effective 
control over another institutional unit. As control and ownership chains often extend across a number of institutional 
units,  FATS  statistics  according  to  the  FATS  recommendations  manual  are  always  assigned  to  the  Ultimate  Control-
ling Institution (UCI), which is colloquially (although less accurately) referred to as the ‘parent company’. The core FDI 
statistics, on the other hand, are based on the immediate counterparty country principle. FDI flows and positions are 
attributed to the country of the immediate investor or recipient of the investment, even if the capital may be passing 
through to a third country. 

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Statistical analysis

2.6 

Labour market

The labour market provides a complementary insight into analyses of economic performance and growth. This chapter 
describes the current situation and the changing patterns in the European labour force market. It starts by presenting the 
evolution  of  employment  growth,  its  relationship  with  the  economic  cycle,  the  distribution  of  employment  by  industry 
and the pattern of actual hours worked. Other aspects relating to the composition of the labour force are also analysed, 
in particular the participation of women and other specific groups, such as immigrant workers. Job-related features of the 
European Union labour market, in particular whether people are working in full-time or part-time jobs and the relevance 
of fixed-term contracts, are also discussed. To complete the analysis of the active population, levels of unemployment and 
employment trends are reviewed.

2.6.1  Employment growth and employment rates

In 2007, employment42 grew by 1.6% in the EU27 and by 1.8% in the EA13. Employment growth in 2007 was equal to or 
stronger than that of any previous year since 2001, when the expansion during the period 1995-2000 came to end. The 
figures for 2007 underpin the recovery since 2003 after the slowdown in 2001-2002. This pattern of employment growth is 
broadly consistent with the evolution of real GDP in EU27. However, the employment cycle has a smaller amplitude than 
GDP (see also section in Chapter 2.1 on labour productivity). Employment growth - particularly since 2003 - was stronger 
in the EA13 countries than in the EU27, although only by a small margin. 

Figure 2.6.1:

Employment and GDP growth

4.0%

3.0%

2.0%

1.0%

0.0%

2000

2001

2002

2003

2004

2005

2006

2007

GDP EU27

employment EU27

employment EA13

Source: National Accounts

All Member States except one had positive employment growth in 2007. In general, small and medium-sized countries 
performed better. The Member States with the strongest growth were: Poland (+4.4%), Luxembourg (+4.1%), Latvia (+3.4%), 
Ireland (+3.4%), Cyprus (+3.2%) and Spain (+3.1%). 

42 Unless otherwise stated, employment in this chapter is measured as the number of persons employed or (the equivalent number of) job holders. Employment can be measured in 
jobs or in full-time equivalents. One job holder could work in two or more jobs. Full-time and part-time jobs can be transformed into full-time equivalents. Hence these units are 
different yardsticks to measure employment. 

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Figure 2.6.2:  Employment growth, 2007

Statistical analysis

2

5.0%

4.0%

3.0%

2.0%

1.0%

0.0%

-1.0%

PL LU LV IE CY ES BG SI MT SE FI SK NL LT AT DK EA
13

DE EU
27

CZ BE GR RO IT FR EE UK PT HU HR TR IS NO CH

Source: National Accounts

On average, 223.4 million men and women worked in the EU27 during the year 2007. This represented a net increase43 of 
14.0 million persons since 2000, or an overall 6.7% growth over the period 2000-2007. In the EA13 the increase was 10.6 
million persons, making a total of 143.8 million persons in 2007, i.e. an overall growth for 2000-2007 of 7.9%. The Member 
States with the highest net employment creation over the period 2000-2007 were Spain (+4.2 million persons), Italy (+2.2), 
United Kingdom (+1.7) and France (+1.2). The development in Spain is even more remarkable if expressed in terms of em-
ployment growth for the period 2000-2007. Spain increased employment by 25.6 percent, significantly ahead of other large 
Member States such as Italy (+9.7 %), United Kingdom (+6.3 %) and France (+4.9 %).

Figure 2.6.3:

Employment growth, total 2000-2007

30%

25%

20%

15%

10%

5%

0%

-5%

-10%

-15%

LU ES IE CY LV BG EE GR IT

FI

LT MT EA
13

SK EU
27

UK SI BE AT NL SE FR PL CZ DK PT DE HU RO * HR TR IS NO CH

Source: National Accounts  

* Note: Growth 2001-2007 for Romania

43

‘Net increase’ means number of persons that entered in employment minus persons that abandoned employment.

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Statistical analysis

This growth went in parallel with increased overall employment rates and, in particular, with a stronger participation by 
women in the labour market. The employment rate in the EU27 measured by the Labour Force Survey (LFS) grew to reach 
65.4% in 2007 (see definition in box 2.6.1). The positive result in 2007 follows numbers of 64.5% in 2006 and 63.5% in 2005. 
The increase by 0.9 percentage point in 2007 after 1.0 pp in 2006 maintains and reinforces the positive upward trend in the 
EU labour market. The EU27 employment rate was 62.2% in 2000. In the EA13 the employment rate reached 65.7% in 2007, 
slightly above the average for the EU27. This follows rates of 64.8% in 2006 and 61.5% in 2000.

Employment rates grew faster for women than men, although the levels for women still remain lower. The EU27 female 
employment rate in 2007 rose by 1.0 percentage point to 58.3%. The employment rate for men rose by 0.9 pp to reach 72.5%. 
The sustained stronger growth of the female employment rate in all recent years has narrowed the gender gap from 17.1pp 
in 2000 to 14.2pp in 2007. In the EA13, the gender gap stood at 15.4pp in 2007. The employment gender gap is smallest in 
Finland (3.6pp in 2007), Sweden (4.8pp) and Lithuania (5.7pp). On the other hand, few Member States present particularly 
high gender gap values: these include Malta (37.3pp in 2007), Greece (27.0pp) and Italy (24.1pp). Spain and Luxembourg 
are the two countries that show a significant reduction in the employment gender gap since 2000 - down 8.4pp and 8.0pp 
respectively - although they are still above the EU27 average in 2007 (21.5pp and 16.9pp respectively in 2007). 

Figure 2.6.4:  Employment rates by gender

75.0%

72.0%

69.0%

66.0%

63.0%

60.0%

l

s
e
a
M

2000

2001

2002

2003

2004

2005

2006

2007

Males

Females

65.0%

62.0%

59.0%

56.0%

53.0%

50.0%

l

s
e
a
m
e
F

Source: EU Labour Force Survey

Older workers also made a significant contribution to employment growth. The employment rate for older people (aged 
55-64) reached 44.7% in 2007 in the EU27. This was an increase of 1.2 percentage points over the previous year and an 
overall increase of 7.8pp since 2000. Correspondingly, the average value in the EA13 is 43.3% in 2007, 1.5pp higher than 
2006 and up 9.0pp since 2000. Among EU Member States, Sweden recorded the highest employment rate for older workers 
(70.0% in 2007), and the biggest improvement since 2000 was in Bulgaria (+21.8pp), now at 42.6%. 

The improved employment rate in 2007 represents the biggest step so far towards reaching the overall employment target 
set by the Lisbon European Council of 2000 which was to raise the overall employment rate to as close to 70% as possible by 
2010, and to raise the employment rate for women to more than 60% by the same year. The Stockholm European Council 
of 2001 set an additional target, namely to raise the average EU employment rate for older men and women (aged 55-64) to 
50% by 2010. The target of 70% by 2010 remains very ambitious; in order to reach it, significantly stronger growth would be 
needed in the next few years. At present, Denmark (77.1%), the Netherlands (76.0%), Sweden (74.2%), Austria (71.4%), Unit-
ed Kingdom (71.3%), Cyprus (71.0%) and Finland (70.3%) are the only countries to have reached the target, although many 
others are not far away. Compared to the Lisbon target, the record on female employment rate is better: 15 Member States 
have already reached the 60% target, namely: Denmark (73.2%), Sweden (71.8%), the Netherlands (69.6%), Finland (68.5%), 
Estonia  (65.9%),  United  Kingdom  (65.5%),  Austria  (64.4%),  Latvia  (64.4%),  Germany  (64.0%),  Slovenia  (62.6%),  Cyprus 
(62.4%), Lithuania (62.2%), Portugal (61.9%), Ireland (60.6%) and France (60.0%). In addition Bulgaria, Czech Republic, 

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Belgium and Luxembourg have rates above 55%. For the category of older workers, reaching the Stockholm 50% target for 
the EU remains a challenge, although it is worth pointing out that the countries of the EU15, for which the Stockholm target 
was originally set, should have a smaller gap to close, as the EU15 employment rate for older people in 2007 is 46.6%.

BOX 2.6.1:  

DEFINITIONS OF EMPLOyMENT AND UNEMPLOyMENT

For  the  sake  of  international  comparability,  labour  market  statistics  from  Eurostat  use  key  definitions  based  on  the 
International Labour Organisation guidelines44 (ILO guidelines). The EU labour force survey (EU LFS) uses those guide-
lines and further improves comparability within the EU with a more precise definition of unemployment (stipulated 
in Commission Regulation (EC) No 1897/2000), while remaining fully compatible with the ILO standards. Chapter 11 of 
the European System of Accounts 1995 (ESA95) includes definitions of employment and unemployment that are fully 
consistent with the ILO guidelines. 

The EU LFS uses the following concepts: 

–  Employed persons are persons aged 15 and over (Spain, United Kingdom: 16 and over; Denmark, Estonia, Hungary, 
Latvia, Sweden, Finland and Norway: 15-74; Iceland: 16-74) who during the reference week performed work, even 
for just one hour per week, for pay, profit or family gain or were not at work but had a job or business from which 
they were temporarily absent because of, e.g., illness, holidays, industrial dispute, education or training. 

–  Unemployed persons are persons aged 15-74 (in Spain, United Kingdom, Iceland: 16-74) who were without work 
during the reference week, were currently available for work and were either actively seeking work in the past four 
weeks or had already found a job to start within the next three months. 

–  The economically active population comprises employed and unemployed persons. 

Those three concepts (employed, unemployed and active) plus the population are combined to compile three ratios, 
as follows:

–  Employment rate is the percentage of employed persons over the population.

–  Unemployment rate is the percentage of unemployed persons over the active population.

–  Activity rate is the percentage of active persons over the population.

Those rates are regularly calculated by gender and by age groups.

44

In some countries, it is not only female workers and older workers that account for employment growth, but also the in-
creased contribution of foreign-born workers. Foreign-born workers are defined as persons who were born in a country 
other than the one in which they work, irrespective of their citizenship. According to the LFS, the number of foreign-born 
workers in Spain increased by +2.6 million during the period 2000-2007, while in the United Kingdom it rose by +1.2 mil-
lion. In Spain, about 74% of these workers were born outside the EU27, the remainder being born in another EU Member 
State. The United Kingdom’s share was 64%.

The  industries  showing  stronger  growth  in  the  EU27  during  2007  were  NACE  F  (construction),  recording  +3.8%,  and 
NACE J-K (financial intermediation; real estate, renting and business activities) at +3.5%. These were also the activities with 
stronger growth in the EA13 in 2007, both having growth rates of +3.8%. The employment trends by NACE in EU27 and 
EA13 are similar except in agriculture, where significant differences appear: the number of persons in agriculture, forestry 
and fishery fell by 0.1% in the EU27 and by 0.8% in the EA13. The difference was due to a growth rate of 0.4%in the non-
EA13 countries. A similar situation had already been observed in 2006.

Indeed, since 2000 there was a net movement of workers from agriculture and manufacturing activities towards construc-
tion and services. The EU27 saw a net decrease of 1.4 million persons working in agriculture and a reduction of 2.5 million 
persons in manufacturing (excluding construction), whereas there were net increases of 2.1 million persons in construction 
and 15.8 million in services.

44 More precisely those guidelines contained in the Recommendation of the 13th International Conference of Labour Statisticians, convened in 1982 by the ILO.

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Figure 2.6.5:

EU27, 2000-2007

l

s
r
e
d
o
h
b
o

j

n
o

i
l
l
i

M

70

60

50

40

30

20

10

0

Agriculture
and fishing

Industry
(excl construction)

Construction

Trade, hotels, restaurants,
transport and
communication

Financial intermediation,
real estate and business
activities

Public administration,
health, education and
other social services

year 2000

year 2007

Source: National Accounts 

Those developments led to the following distribution of employment by activity in 2007: in the EU27, 68.9% of persons 
worked in service activities (+2.9 percentage points since 2000), 17.5% in manufacturing other than construction (-2.3pp 
since 2000), 7.4% in construction (+0.5pp) and the remaining 6.2% in agriculture, forestry and fishery (-1.1pp). Correspond-
ingly, in the EA13, the share of services in 2007 was 71.1% (+2.8pp since 2000), 17.1% in manufacturing other than construc-
tion (-2.3pp since 2000), 7.7% in construction (+0.3pp) and 4.1% in agriculture (-0.8pp)

However, those averages conceal significant differences in the distribution of employment by activities among Member 
States, which result from structural differences. The following table shows the Member States reporting the highest and 
lowest share of employment in each main activity group, in 2007: 

Table 2.6.1: 

Employment distribution by NACE and Member State

Agriculture, hunting, forestry and fishing

Total industry (excluding construction)

Construction

Wholesale and retail trade, repair of motor vehicles, motorcycles and personal 
and household goods; hotels and restaurants; transport, storage and 
communication

Financial intermediation; real estate, renting and business activities

Public administration and defence, compulsory social security; education; 
health and social work; other community, social and personal service activities; 
private households with employed persons

Total  

Note: CZ, LU and PL data for 2006. RO data for 2005

Source: National Accounts

EU27 average

Lowest

Highest

Luxembourg 
(1.6%)

Cyprus 
(10.4%)

Poland
(4.6%)

Romania
(16.7%)

Romania
(4.5%)

Romania
(15.7%)

Romania
(33.3%)

Czech Rep 
(29.4%)

Ireland
(13.3%)

Cyprus
(35.6%)

Luxembourg 
(28.2%)

Sweden
(38.9%)

6.2%

17.5%

7.4%

25.2%

15.0%

28.8%

100%

110

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Statistical analysis

2

2.6.2  Professional status and main job features45

Most persons employed in Europe are employees rather than self-employed workers: at least 75% of non-agricultural job-
holders in all Member States in 2007 are employees46. The share of employees in the EU27 was 87.7% and in the EA13 it was 
86.9%. These shares are extremely stable over time, mostly because the number of employees dwarfs the number of self-
employed, which means that the rates of growth of total employment and of employees follow very similar patterns. 

Most employment consists of full-time jobs, even though the share of part-time jobs has shown a tendency to increase. In 
2000, 16.2% of workers in the EU27 classified their main job as part-time; in 2007 this share rose to 18.2%. This upward 
trend is stronger in the EA13, rising from 15.9% in 2000 to 19.7% in 2007. The EU LFS gathers information on part-time and 
full-time jobs based on a spontaneous self-classification by respondents; by way of exception, in Germany, Ireland and the 
Netherlands this is done in terms of the number of hours worked reported by individuals. 

While the growing trend in part-time employment is broadly similar for male and female workers, the proportion of part-
time employment differs significantly by gender, as part-time employment is much more common among women than 
men. In 2007, female workers classifying their main job as part-time accounted for 31.2% of total female workers in the 
EU27, whereas the corresponding share for males was only 7.7%. In EA13 the gender gap was even wider, the shares being 
respectively 35.2% and 7.5% in 2007.

Figure 2.6.6:

Part-time jobholders, as % of total jobholders

40%

35%

30%

25%

20%

15%

10%

5%

0%

Source: EU LFS

EU27, females

EU27, males

EA13, females

EA13, males

year 2000

year 2007

The countries having the highest percentage of female part-time workers in 2007 were the Netherlands (75.0%), followed 
some way behind by Germany (45.8%) and the United Kingdom (42.3%). 

45 All the information in this section refers only to main jobs, unless otherwise stated. This is because the LFS does not gather information on certain of the variables analysed here 

for secondary jobs. If secondary jobs are left out of consideration, the statistics for persons employed and jobs are the same. The wording of this section is focused on the jobholders 
(e.g. ‘the number of persons reporting their main job as full-time is XX’), but occasionally for the sake of simplicity and clarity it will refer to jobs (e.g. ‘the number of full-time jobs 
is XX’).

46 The shares in this paragraph exclude agriculture for the following reason: in a few EU Member States, a very significant percentage of self-employed persons work in agriculture – 
for example in Romania (90% of self-employed persons worked in agriculture, fisheries or forestry in 2005), Poland (70% in 2006), Bulgaria (65% in 2007), Lithuania (54%), Latvia 
(50%) and Slovenia (49%), whereas in all the other Member States this share is below 30%. Actually, in some countries these numbers reflect a large number of people spending a few 
hours raising agricultural products purely for own-consumption. Statistics record them as self-employed (or unpaid family workers) in agriculture, but this kind of labour clearly 
has a different economic significance from other self-employment in manufacturing and services.

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There is a consistent trend towards more part-time work in parallel with the increase in female participation in the labour 
force, which was noted above. During the period 2000-2007, the Member States showing the strongest increase in part-time 
female employment were Luxembourg (+13.5 percentage points increase to 38.6% in 2007), Italy (+10.4pp), Malta (+9.4pp), 
Austria (+9.0pp) and Germany (+7.9pp). The figure for the Netherlands – the leading country in female part-time work – 
was also up by +4.0pp from 71.0% in 2000. This rise was higher than in 18 Member States. In general, the countries with 
a stronger increase in part-time female employment during the period 2000-2007 are those which were already above the 
EU27 average in 2000 (see figure 2.6.7). This means that, according to this indicator, the gap between Member States has 
widened with the passage of time.

Figure 2.6.7:

Female part-time jobholders, as a % of all female jobholders, 2007

80%

70%

60%

50%

40%

30%

20%

10%

0%

-10%

NL DE UK AT BE

SE

LU DK EA13 IE EU27 FR

IT MT ES

FI

PT

PL

EE

SI CY RO LT GR CZ LV HU SK

TR NO

year 2000

Increase 2000-2007

Source: EU LFS

Some  of  the  countries  experiencing  the  strongest  growth  in  female  part-time  work  also  report  the  highest  increases  in 
male part-time employment. These are: Germany (a +4.4pp increase from 5% in 2000), the Netherlands (+4.2pp), Sweden 
(+3.6pp) and Denmark (+3.3pp). The increases in male part-time work are generally less pronounced than for women, al-
though there are exceptions, such as Denmark. All in all, part-time employment is becoming increasingly widespread, more 
so among women than among men, and is revealing a widening gender gap.

An important factor in part-time work is whether or not it is voluntary. Some 22.0% of EU2747 part-time workers in 2006 
aspired to a full-time job, with men (at 28.4%) accounting for a higher share than women (20.1%). An analysis by Member 
State presents a mixed picture. Since 2000, the share of involuntary part-time workers fell in nine Member States, led by 
Lithuania (down 14.6 percentage points), but rose in another 16 Member States, principally in Poland (+14.1pp). However, 
Member States are also showing signs that they are slowly converging towards similar percentages of involuntary part-time 
workers, as the coefficient of variation among them fell from 1.5pp in 2000 to 1.3pp in 2006.

Fixed-term employment has shown a sustained rise in recent years. The EU27 average reached 14.5% of employees in 2007, 
up from 14.4% in 2006 and 14.0% in 2005. The upwards movement has been very consistent since 2000, when the figure 
was  12.3%. Similar trends were  observed  for  the  EA13,  reporting  16.8% in 2007, the same as in 2006 and following on 
from 16.3% in 2005. The incidence of this phenomenon varies widely from country to country: in Spain one out of three 
employee jobs is fixed-term (31.7% in 2007, although down from 34.0% in 2006), with Poland coming second on 28.2% and 
Portugal third on 22.4%. Meanwhile, at the other end of the spectrum, in Romania and Estonia fewer than 3% of employees 
have fixed-term contracts. The evolution in recent years has been most noteworthy in Poland, which saw fixed-term work 
rise from 5.8% in 2000 to 28.2% in 2007 (i.e. by +22.4 percentage points). Other significant increases took place in Slovenia 

47 Data not available for 2007. The EU27 aggregate for 2006 was calculated using 2004 figures for Ireland.

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Statistical analysis

2

(+4.8pp), the Netherlands (+4.4pp) and Italy (+3.1pp), while the biggest decreases in fixed-term contracts were in Latvia 
(-9.7pp since 2002) and Greece (-2.6pp since 2000). Most other Member States reported fairly stable results. Norway and 
Iceland recorded sustained increases in fixed-term contracts (+6.9pp since 2000 and +4.4pp since 2003, respectively).

Fixed-term contracts are more common among women than among men, although the difference is far smaller than for 
part-time work: the gender gap in 2007 was 1.3pp in the EU27 and 1.8pp in the EA13. In 2007, only a few Member States had 
a higher proportion of fixed-term contracts among men, and these are mostly the new Member States: Latvia, Lithuania, 
Estonia, Hungary, Poland, Romania and Germany. 

Contract length and part-time/full-time job status are inter-related. Among the persons in the EU27 reporting their main 
job as part-time, 21.2% had fixed-term contracts in 2007 (the remaining 78.8% having permanent contracts), whereas for 
the full-time jobholders the share of fixed-term contracts is lower at 15.8%. Similarly, in the EA13, 21.4% of part-time job-
holders had fixed-term contracts, whereas the share among full-time jobholders is 15.6%. Beyond this general statement, 
the profiles of Member States show a good deal of variation. A first group of countries, which includes most of the new 
Member States plus the United Kingdom, have very few (i.e. under 10%) fixed-term contracts among full-time jobs, whereas 
10%-30% of part-time jobs are covered by fixed-term contracts. At the other extreme, a second group of countries, which 
includes Slovenia, Poland, Spain, Portugal, Greece and Czech Republic, report a large number of fixed-term contracts, more 
frequently among part-time jobs (45-65%) than among full-time jobs (some 10-30%). Finally, in the majority of Member 
States about 10-30% of part-time jobs and about 10-20% of full-time jobs are based on fixed-term contracts. This group also 
includes European aggregates. Austria and Germany are the only countries where fixed-term contracts are proportionally 
more frequent among full-time jobholders than among part-time jobholders. The figure below highlights the three groups 
that can be identified. This figure uses averaged data for the years 2004 to 2007 to enhance stability of the results. 

Figure 2.6.8:  Fixed-term contracts by full-time / part-time job status, average 2004-2007

40%

)
e
m

i
t
-
l
l

u

f

l

a
t
o
t

%

(

m
r
e
t
-
d
e
x
fi
e
m

i
t
-
l
l

u
F

30%

20%

10%

0%

0%

Source: EU LFS 

ES

PL

SI

PT

EL

CZ

DE

EA-27

NL

DK

BE

LT

IE

UK

EA-13

CY

FR

IT

HU
LV

MT

RO

SE

AT

LU

EE

FI

BG
SK

10%

20%

30%

40%

50%

60%

70%

Part-time fixed-term (% total part-time)

2.6.3  Working time

Analyses of employment in terms of number of workers or jobs are usually supplemented by the numbers of hours worked. 
This is because actual working time is generally considered to be the most appropriate measure of labour input for econom-
ic production. Working time arrangements are of interest in the social domain too. However, analysis of working time is 
not straightforward. Several concepts of working time co-exist (actual hours worked, usual hours of work, etc.) and several 
indicators are possible (total annual hours, average hours per person, weekly hours). This multitude of measures can create 
confusion. Another hurdle is that measuring actual hours worked is difficult and data availability is limited.

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Statistical analysis

The total number of annual hours actually worked in the EU2748 rose in 2006 by +1.4%, after rising +0.8% in 2005. The 
growth in the upward trend in terms of hours worked began in 2004.

However, most of that positive development is due to the increase in the number of workers. The average number of annual 
hours worked per person in 2006 in the EU27 was 1 693, which is very close to the worked hours in the two previous years 
(1 695 and 1 697 respectively), and down compared to earlier in the decade. 

Figure 2.6.9:  Total and average annual hours worked, EU2549

s
r
u
o
h
e
g
a
r
e
v
A

1.730

1.720

1.710

1.700

1.690

1.680

1.670

1.660

350

348

346

344

342

340

338

336

334

332

)
s
n
o

i
l
l
i

m

(

s
r
u
o
h

l

a
t
o
T

2000

2001

2002

2003

2004

2005

2006

Total hours

Average hours

Source: National Accounts

The pattern is therefore broadly consistent over time, showing a gradual downwards trend; this could be explained by a 
combination of several factors, but no single factor dominates. First, there is an increased incidence of part-time work, as 
we saw in the previous section. Each part-time job reduces the average number of hours worked. A second reason is that 
the number of hours actually worked in part-time jobs fell slightly, from 20.3 weekly hours in 2000 to 20.1 hours in 2006. 
Finally, the dynamics of second jobs indicate that, compared to previous years, fewer hours were actually worked in second 
jobs (on average 12.3 weekly in 2006 against an average of 13.1 weekly hours in 2001). 

Usual weekly hours of work statistics are used to measure working patterns. The usual hours of work in a job depend on 
several factors, first among them being whether the job is full-time or part-time. Other factors are the status of the jobholder 
(employee or self-employed) and the gender of the jobholder.

An analysis of the usual hours of work in full-time jobs in the EU27 for 2007 gives a mode value of 40 hours per week (i.e. the 
most frequent number of hours in full-time jobs). The distribution is very concentrated around the mode, meaning that a 
majority of full-time jobholders work around 40 hours (almost 60% of them usually work between 38 and 42 weekly hours). 
Secondly, the distribution is fairly symmetrical but slightly skewed towards values higher than 40 weekly hours, indicating 
that there are more full-time jobholders working over 42 weekly hours (24% of them) than working less than 38 hours (18% 
of them). In fact, this symmetry around 40 weekly hours masks gender-based differences, as more male full-time jobhold-
ers work more than 42 hours rather than less (29% vs. 14% of men full-time jobholders, respectively), whereas the opposite 
holds for female full-time jobholders (16% vs. 25% of female full-time jobholders).

48 Data not available for the year 2007.

49 Data not available for Romania before 2002.

114

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Figure 2.6.10:  Usual weekly hours EU27, 2007

Statistical analysis

2

70%

60%

50%

40%

30%

20%

10%

0%

<7

8-12

13-17

18-22

23-27

28-32

33-37

38-42

43-47

48-52

53-57

58-62

63-67

68-72

>73

Part-time jobs

Full-time jobs

Source: EU LFS

For part-time jobs, the most frequent number of usual weekly hours worked is 20 hours. Only 27% of part-time workers 
are in this mode group (i.e. those usually working 18-22 hours per week) whereas, in the case of full-time workers, 58% of 
workers are in the corresponding mode group of 38-42 hours per week. Linked to this, the distribution of hours worked 
in part-time jobs is in the form of a bell curve, with longer tails than the distribution of hours in full-time jobs, showing 
that proportionally more part-time jobholders work a few hours more or less than the mode value. This indicates a wide 
range of length of working time for part-time jobs compared to the mode for full-time jobs, where there is a much narrower 
spread. There are gender differences in the distribution of usual hours worked in part-time jobs too: more women part-time 
jobholders work more than 23 hours (39% of them) than less than 17 hours (33%). Note that the distribution above or below 
the mode is the opposite of that observed in full-time jobs. For men, the distribution of usual hours of work is fairly sym-
metrical: 39% of male part-time jobholders work less than 17 hours, whereas 36% work more than 23 hours. 

BOX 2.6.2: 

LABOUR FORCE SURvEy vS. NATIONAL ACCOUNTS

LFS and National Accounts are the two main sources of employment data. Although they use common definitions (see 
previous box), LFS and National Accounts have their own aims and measurement approaches, which may lead to dif-
ferent results.

The LFS is a sample survey of households. The survey focuses on employment and unemployment, but another 100 or 
so variables are also collected, including: gender, age, educational level attained, features of each job held, occupation, 
hours worked etc. The LFS is unique in providing social breakdowns and interrelations of employment/unemployment 
with other variables, mostly demographic and social.

National Accounts is a conceptual framework comprising definitions, classifications, variables and presentational ar-
rangements, compiled by comparing and combining all the relevant data sources available in the country. This is a key 
feature of National Accounts: it enables the best to be taken from each source, increasing coherence and obtaining a 
more comprehensive result. This integration is performed differently in each country. Whenever LFS is used in National 
Accounts, some alignments of scope are needed prior to any integration, the main ones being: 

– Different geographical coverage. Geographical coverage can either follow the domestic concept of employment 
(i.e.  employment  in  resident  production  units)  or  the  national  concept  (i.e.  resident  workers).  ESA95  uses  more

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Statistical analysis

frequently the domestic concept of employment as this allows putting it in relation to GDP. For its part, LFS gives 
information on the national concept. Adjustments for cross-border workers are needed in order to transform one 
concept into the other.

– Other coverage issues: LFS excludes persons below 16 years old from the definition of employment. For their part, 
National Accounts do not exclude individuals from employment on the basis of age. LFS leaves the following out 
of its scope: persons living in institutional households, staff of national embassies working abroad and crews of 
national fishing boats. 

Because of those scope alignments, plus the integration of LFS with other sources (in countries where this is done), 
National Accounts employment is different from LFS. The policies on revision of the two statistics may also highlight 
the differences. 

All  in  all,  National  Accounts  are  judged  more  suitable  to  measure  employment  levels,  employment  growth  and  in-
dustry breakdowns. LFS is more appropriate for measuring participation in the labour market (i.e. employment rates, 
activity rates, flows between employment and unemployment, etc.), demographic or social breakdowns (e.g. by age, 
gender or educational level) and it is more suitable for socio-demographic studies.

More  details  about  differences  between  National  Accounts  employment  and  LFS  can  be  found  in  the  Eurostat  LFS 
metadata pages, under section 3 here:

http://circa.europa.eu/irc/dsis/employment/info/data/eu_lfs/LFS_MAIN/LFS/LFS_COMPARABILITy.htm

2.6.4  Unemployment rates

Consistent with the positive outlook for employment, 2007 was a good year for unemployment. The average EU27 unem-
ployment rate dropped further to 7.1%, down from 8.1% in 2006 and 8.9% in 2005 (see definition of unemployment rate in 
box 2.6.1). Indeed, 2007 saw the strongest decrease in unemployment in Europe in recent years. Unemployment fell in every 
Member State, which is remarkable. The biggest shrinkage in unemployment was in Poland (falling by 4.2pp), Slovakia, Bul-
garia and Czech Republic. Slovakia and Poland are still suffering from the highest unemployment rates in the EU, in spite of 
the improvement in 2007 and in previous years. Since 2002, when unemployment peaked in Europe, the best progress has 
been seen in Bulgaria (down from 18.1% to 6.9%) and in Poland (down from 19.9% to 9.6%).

Figure 2.6.11:  Unemployment rates 2007

12.0%

10.0%

8.0%

6.0%

4.0%

2.0%

0.0%

NL DK CY LT AT

IE

EE LU SI UK CZ LV

IT

SE MT RO BG FI

EU
27

EA
13

HU BE PT GR ES FR DE PL SK HR TR * NO

Source: EU LFS

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2

In 2007 the unemployment rate for women also showed a positive movement in the EU27, down to 7.8% from 8.9% in 2006. 
Poland  reported  the  best  improvements  for  female  unemployment  (which  fell  by  4.6pp),  followed  some  way  behind  by 
Czech Republic, Bulgaria and Slovakia, all around 2pp. On average, the gender gap between unemployment of women and 
men decreased slightly to 1.2pp, from 1.3pp in 2006 (female unemployment was higher). It narrowed significantly in Lux-
embourg, falling by 1.0pp in 2007, and also in Estonia (down 0.9pp) and Spain (down 0.8pp), although it widened in other 
Member States, mostly in Germany (+0.6pp) and Portugal (+0.5pp). Since 2002, the largest decreases in gender disparities 
in the unemployment rate were reported in Spain (-3.1pp), Italy (-1.8pp) and Greece (-1.2pp):

Table 2.6.2: 

Unemployment rates, gender differences (pp)

2002

2007

Improvement

Men

Women

Difference

Men

Women

Difference

Men

Women

Difference

Spain

Italy

Greece

Source: EU LFS

8.1

15.7

7.6

6.7

11.5

4.8

6.8

15.6

8.8

6.4

10.9

4.5

4.9

7.9

3

5.2

12.8

7.6

3.1

1.8

1.2

In 2007, there were also improvements for young unemployed persons and the long-term unemployed. The EU27 average 
unemployment rate of young people (aged 15-24) fell sharply from 17.1% in 2006 to 15.4% in 2007. This is a remarkable im-
provement after a peak of 18.4% in 2004. Long-term unemployment is also down to 3.0% from 3.7% and has seen a sustained 
improvement since 2004. 

Figure 2.6.12:  Unemployment of specific groups, EU27

s
n
o
s
r
e
p
g
n
u
o
Y

19.0%

18.0%

17.0%

16.0%

15.0%

14.0%

13.0%

12.0%

Source: EU LFS

9.0%

8.0%

7.0%

6.0%

5.0%

m
r
e
t
-
g
n
o
L

4.0%

3.0%

2.0%

2000

2001

2002

2003

2004

2005

2006

2007

Young persons (aged 15-24)

Long-term (12-months or more)

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Methodological articles
Methodological articles

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Methodological articles

3

3.1   Updating statistical methodology

One of the key attributes of economic statistics is that they are comparable across countries, and that these countries adopt 
best practices in statistical compilation. In this sense, the work of statisticians in defining, promulgating and maintaining 
common methodological standards across countries is of vital importance.

The following two articles describe the ongoing work of updating statistical methodology in two central macroeconomic 
datasets — national accounts (the System of National Accounts and the European System of Accounts) and Balance of Pay-
ments and International Investment (BPM6). Both articles show the extensive investment and cross-country collaboration 
which is taking place to allow these reference texts to be renewed, so that they continue to be relevant and compilable in a 
changing world.

One might also draw attention to the interdependence between economic datasets. National accounts draw extensively on 
balance of payments as source data, as they draw on other economic databases (for example consumer prices). Datasets may 
be used together, as illustrated later in the article on European sector accounts for national accounts and balance of pay-
ments statistics. The work on individual economic datasets cannot, and does not, take place in isolation — hence the close 
overlap between the exercises described below.

Statisticians must nevertheless take account of the impact of methodological revisions on the statistics available to users, 
notably on the comparability of data over time, and the articles seek to describe the main areas where planned revisions 
would impact the statistics.

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3.1.1   Update of the SNA 1993 and revision of ESA 95

Christian Ravets
Eurostat, National accounts: methodology and analysis

The national accounts are a complete system of macroeconomic accounts and are a key input into economic analysis and 
policymaking.  International  comparability  is  extremely  important  for  these  statistics  and  therefore  the  development  of 
related methodological standards, and their implementation, must proceed in a coordinated fashion. This is particularly 
relevant in Europe, where national accounts figures are also used for administrative purposes. 

As one of the five international organisations in charge of the world-level System of National Accounts (SNA 1993), Eurostat 
is actively participating in its update process. 

At the same time, Eurostat has started the process of revising the European System of Accounts (ESA 95), which is the 
European version of the SNA 1993. 

Update of SNA 1993

Background and process of review

In 2003, the UN Statistical Commission called for an update of the SNA 1993 to bring the accounts into line with the new 
economic environment, advances in methodological research, and user needs. The Commission stipulated that the update 
not recommend fundamental or comprehensive changes to the SNA 1993, and identified consistency with related manuals 
such as the Balance of Payments Manual as an important consideration.

The Inter-secretariat Working Group on National Accounts (ISWGNA) — comprising Eurostat, the International Mon-
etary Fund, the Organisation for Economic Cooperation and Development, the United Nations, and the World Bank — was 
mandated to coordinate and manage the update project.

The Statistical Commission emphasised the need for transparency in the update project and the broadest possible involve-
ment of the global statistical community. In line with this emphasis, an Advisory Expert Group (AEG), including 20 coun-
try experts from all regions of the world, was established in 2003 to take a key role in the update. Proposals for change, for 
the most part, have come from standing and short-term expert groups. The single most important tool to promote transpar-
ency and wide involvement in the update is the project website at http://unstats.un.org/unsd/nationalaccount/snarev1.asp, 
maintained by the United Nations Statistics Division. 

Reaching agreement on recommendations

The ISWGNA, with the help of the AEG, identified a list of 44 substantive issues to be considered, endorsed by the Statisti-
cal Commission and posted on the project website. By its fourth meeting (January-February 2006), the AEG had agreed on 
recommendations on almost all points of the issues before it.

The ISWGNA then engaged in an in-depth review of country comments on these recommendations. This review provided input 
into the preparation of the full set of consolidated recommendations agreed by the UN Statistical Commission in early 2007.

Two volumes

The new SNA will be provided in two volumes. Volume 1, to be published in 2008, is the full set of chapters that represent 
the SNA framework in terms of accounting conventions, the accounts, and the integration of the accounts. Volume 2 will 
comprise mainly the interpretation of the accounts and various extensions such as satellite accounts, and will be published 
in March 2009. 

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Outcome of the UN Statistical Commission discussion on the SNA update (26-28 February 2008)

The Statistical Commission accepted the proposal in the ISWGNA report to the Commission that Volume 1 of the updated 
SNA be approved in principle, but with an extra two months being allowed for countries to comment on the revised draft 
chapters. As a result, the evaluation period for Volume 1 has been extended until 30 April. 

The UNSC mandated the ISWGNA to proceed with the finalisation and publication of volume 1, after advising the Bureau 
of the Commission, in pre-edit electronic and paper versions, taking into account the substantive observations made during 
the two months review period. 

The UNSC requested that the ISWGNA takes appropriate measures to ensure that volume 2 of the updated 1993 SNA is duly 
completed and presented for adoption to UNSC in 2009.

The UNSC also requested the ISWGNA to present a strategy for the implementation of the updated 1993 SNA, reflecting the 
need for regional and sub-regional coordination given the different levels of statistical development, and elaborate on the 
advocacy strategy to engage users, especially those engaged in policy formulation and analysis.

Preparation of Volume 2 of the updated 1993 SNA

Key points in the timetable for this year are:

Until July, drafts of the chapters of Volume 2 will progressively be posted for countries’ comments.

Mid-September will be the deadline for comments on the last draft chapter(s).

The revision of Volume 2 chapters will be finalised at the end of October.

In mid-December, the revised Volume 2 will be ready for submission to the UNSC meeting (24-27 February 2009).

Implementation of the SNA

From available information it is clear that most countries worldwide will implement the updated SNA 1993 between 2009 
and 2014. In Europe (as described below) the revised ESA, based on the updated SNA 1993, will be implemented in 2014. 
Major countries outside Europe will generally implement the updated SNA 1993 a little earlier than this; in 2009, the US, 
Canada and Australia will be the first countries to implement the updated system.

Main recommendations 

Changes in the structure and functioning of economies since the SNA 1993 was completed have led to the need to review 
certain important issues. It was also felt necessary to better harmonise the SNA 1993 with other macroeconomic manuals, 
and to deal with some questions left open when the SNA 1993 was finalised.

The update recommendations involved all parts of the SNA, in particular the treatment of non-financial assets, financial 
instruments, stocks and flows characteristics of economic globalisation, government and the public sector.

Major changes in the updated SNA 1993 are the following:

Pension schemes

Defined benefit schemes

Under the SNA 1993, the actual social contributions represent the amount actually paid into a pension fund. If the contri-
bution is pre-defined, but not the pension payment (defined contribution scheme), this treatment is correct.

But if the contribution is not pre-defined, and the pension payment is pre-defined by a formula (defined benefit scheme), the 
amounts set aside by the employer in the scheme may not match the liability to the employees.

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The revised SNA will take into account these particularities of defined benefit schemes, on the basis of actuarial calculations.

Unfunded government employer schemes

The case of unfunded government employer schemes was much debated, because countries have different institutional ar-
rangements.

In 2005, the Advisory Expert Group supported the recommendation of the IMF Task Force on pensions to include the 
liabilities of unfunded pension schemes in the core accounts of the revised SNA, while recording the liabilities of social 
security pension schemes in supplementary tables outside the core accounts. This treatment would, in particular, entail a 
sharp increase in many countries’ government liabilities.

There have been some divergences of opinion between countries worldwide on the recommendation to include liabilities of 
all unfunded employer schemes in the “core” national accounts. In particular, the position of European countries was that 
the diversity of administrative arrangements means that it is difficult and not particularly meaningful to separate unfunded 
government employers’ pension schemes from social security schemes.

The international statistical organisations have formulated a compromise which involves introducing some flexibility as to 
which kind of pension liabilities are recorded in the core national accounts, whilst requesting all countries to complete a 
new supplementary table with full details of all pension schemes.

The updated SNA 1993 will recommend filling in a standard supplementary table, which has been defined by a Eurostat/
ECB Task Force, which will show the pension entitlements of households for all pension schemes. Internationally agreed 
criteria will be developed to indicate whether the pension entitlements corresponding to unfunded government employer 
schemes have to be included or not in the core accounts.

Impacts on the accounts

The recording as liabilities of defined benefit pension schemes in the core accounts will generally lead to increased recorded 
assets of households and increased liabilities for the pension schemes (which are recorded as corporations, either separate 
financial corporation units or with the employer as non-financial corporations or non-profit institutions serving house-
holds), with employers’ imputed social contributions (as part of employee compensation) increasing. For government data, 
the impact will depend on which defined benefit pension schemes’ liabilities are recorded in the core national accounts, but 
if some schemes are so classified the liabilities of government would increase and, generally, government compensation of 
employees and (by convention) government output would increase.

The standard supplementary table, to be implemented by all countries, is expected to provide a useful source for economic 
analysis of pensions and will provide the basis for deriving comparable data across countries, irrespective of the application 
in different countries of the flexibility of recording government pension schemes.

Cost of capital services

Capital services for assets used in market production are implicitly included in the SNA 1993 but are not separately iden-
tified. Given the importance of identifying them for productivity measurement and other analysis, a new chapter will be 
added to the SNA 1993 Rev 1 explaining the role and appearance of capital services in the system and stressing the desir-
ability of calculating capital services, capital stock and consumption of fixed capital in an integrated and consistent manner. 
No changes will be made to standard entries in the accounts to show capital services but an explanation will be provided of 
how supplementary items or tables could be derived and presented.

The AEG had recommended also including in the value of government output a return to capital on non-financial assets 
used in non-market production. The non-market output being valued at the sum of costs, this would have increased GDP. 
However, this recommendation was not approved by many countries, particularly in Europe. Given the difference between 
non-market and market production, this recommendation will not be included in the updated SNA 1993.

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Impacts on the accounts

As it was considered inappropriate to price capital used by government in its production of goods and services by including 
the rate of return to capital in addition to depreciation, the core accounts will not be affected.

Research and development

The SNA 1993 does not recognise research and experimental development as capital formation, despite the fact that it is 
thought to make a major contribution to future economic growth. The result is that GDP is underestimated.

According to the AEG recommendation, the SNA 1993 should be changed to recognise the outputs of R&D as assets and 
the acquisition, disposal and depreciation of R&D fixed assets should be treated in the same way as other fixed assets. The 
recommendation, as reformulated by the ISGWNA, is the following: R&D should be treated as capital formation and its 
value should be determined in terms of the economic benefits it provides. In principle, R&D that does not provide an eco-
nomic benefit to its owner does not constitute a fixed asset and should be treated as intermediate consumption. Because it 
is difficult to quantify the benefits of R&D, by convention, it may be valued at the sum of costs.

The AEG agreed to use the definition of R&D provided by the Frascati Manual (OECD Manual on Proposed Standard 
Practice for Surveys on Research and Experimental Development), with the clarifying explanation that this does not imply 
that human capital is treated as an asset in the SNA.

Some research is still needed in this context. In practice, research and development expenditure should be recognised as 
part of capital formation. However, recognising the difficulties to be overcome before this objective can be reached, satellite 
accounts will provide a useful way of working towards solutions that give the appropriate level of confidence in the resulting 
measures, and their international comparability.

The SNA 1993 Rev.1 will describe the objective and recognise that for many countries implementation in the core accounts 
will take time.

Impacts on the accounts

The capitalisation of research and development in the core accounts will be a major change from the point of view of quan-
titative impact on GDP: as this expenditure will no longer be treated as intermediate consumption, it will no longer be 
deducted in the calculation of the value added of the corresponding sectors. The impact on GDP can be roughly estimated 
as between 1.5 and 2%.

Military expenditures

The present SNA distinguishes military acquisitions of offensive weapons and their means of delivery (treated as intermedi-
ate consumption regardless of their life length) from other military acquisitions (treated as capital formation). This treat-
ment does not correspond to the economic reality if weapons are used and even exported after several years.

Considering that destructive weapons have investment characteristics, the AEG recommendation is to treat as capital for-
mation all expenditure by the military which meets the definition of being used in production over a period in excess of one 
year, regardless of the nature of the expenditure or the purpose intended for it. All equipment will be treated as fixed capital 
formation except for consumables, which will be treated as inventories. Separate items will identify weapons systems within 
fixed capital formation and military inventories apart from other inventories.

The required figures for national accounts purposes would be aggregate and not at a level of detail that would create prob-
lems for military secrecy.

Impacts on the accounts

Since government output is measured, by convention, as the sum of costs (including consumption of fixed capital), govern-
ment value added will increase by an amount equal to this consumption of fixed capital. The impact of the capitalisation 

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of destructive military weapons on GDP (equal to the consumption of fixed capital50 of these weapons) will differ greatly 
among countries; an average increase of around 0.5% of GDP is a first rough estimate.

Goods for processing

The SNA 1993 and the fifth edition of the Balance of Payments Manual treat goods that are sent abroad for processing and 
then returned to the country from which they were dispatched as exports for their full value when they leave the first coun-
try and in imports when they return to it.

The fundamental question addressed by the review was whether recording of imports and exports should follow a change 
of ownership recording or that of physical movement. The recommendation is for the former; that is, for recording on a 
change-of-ownership basis. This is a change from the SNA 1993. This recommendation recognises that many goods move 
from one country to another without entailing a consequential payment from the recipient country to the sending country. 
In the revised SNA, net figures of processing services will be substituted to gross figures of export of goods for processing 
and subsequent re-import or vice-versa.

The consequences affect the recording of transactions both in the national economy and in international transactions. The 
decision to record on a pure change of ownership basis implies that no transactions will be recorded for intra-enterprise (in-
ter-establishment) deliveries when goods are passed from one establishment to another for processing and then returned. 
Therefore, input-output tables will reflect what each unit contributes to the production process rather than the physical 
technology, as was previously the case.

Also, the physical movements of goods, captured in merchandise trade statistics, have to be reconciled with the interna-
tional flows to be recorded in the balance of payments and the national accounts.

Impacts on the accounts

In the light of globalisation, goods for processing will no longer inflate both imports and exports figures, which is important 
for international trade analysis.

Some examples of other important recommendations

– Employee stock options: guidance is provided to permit further harmonisation with international business accounting 

standards;

– non-life insurance: the calculation of output is reconsidered to avoid its volatility; 

– contracts, leases and licences: the updated SNA sets out the principles of appropriate treatment corresponding to the 

different possible arrangements;

– borderline between withdrawal of owner’s equity and dividends; concept of “super dividends”;

– exceptional payments between government and public corporations and quasi-corporations: treatment has been redefined. 

Revision of ESA 95

Eurostat has started to revise the European System of Accounts (ESA 95), the European manual of national accounting 
based on the SNA 1993. The new ESA should continue to be the appropriate methodological reference for producing the 
high-quality national accounts data required to support economic policymaking and the implementation of major EU poli-
cies. Revision will also provide an opportunity to further improve the standards of ESA 95 and target them more better to 
the various uses in the EU. A more integrated system will be developed, to the largest possible extent. 

50 This is the national accounts term for a concept similar to depreciation in business accounting.

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Specificities of ESA 95 and needs for change

The purpose of ESA 95 as defined in Article 1 of Council Regulation (EC) No 2223/96 of 25 June 1996 on the European 
system of national and regional accounts in the Community is to provide:

– a) a methodology on common standards, definitions, classifications and accounting rules intended to be used for com-

piling accounts and tables on a comparable basis for the purposes of the Community;

– b) a programme for transmitting, for Community purposes and on precise dates, the accounts and tables according to 

ESA 95.

ESA 95 is an essential tool at European level, used for major administrative purposes (e.g. own resources, the excessive 
deficit procedure, structural funds) and for analysis of the coordination and convergence of Member States’ economic 
policies.

To achieve the objectives set by the Treaty on the European Union, in particular economic and monetary union, ESA pro-
vides EU institutions, governments and economic and social operators with a set of harmonised and reliable statistics on 
which to base their decisions.

ESA 95 is broadly consistent with the current international System of National Accounts (SNA 1993) as regards definitions, 
accounting rules and classifications. There are nevertheless some differences, particularly in its presentation, which is more 
in line with its use with the European Union. This specific use in fact requires greater accuracy in definitions, classifications 
and accounting rules.

Unlike the SNA, the ESA is based on a Regulation laying down binding rules to ensure comparability at EU level and a 
compulsory data transmission programme. Where the SNA is flexible and offers several options, ESA generally chooses one 
particular option to achieve more consistency at EU level.

ESA 95 constitutes a version of the SNA adapted to the structures of the Member States’ economies and must follow the 
layout of the SNA to the greatest possible extent so that the European Union’s data are comparable with those compiled by 
its main international partners.

Scope of revision

The revision of ESA 95 will decide the national accounts standards and the ways in which key economic aggregates are 
calculated in the EU for many years.

The starting point will be the consolidated text, i.e. the text of (EC) Council Regulation No 2223/96 of 25 June 1996 as 
amended by several regulations concerning:

– the allocation of FISIM

– the definition of general government expenditure and revenue

– taxes and social contributions unlikely to be collected

– the reclassification of settlements under swaps and forward rate arrangements

– the revised classification of expenditure according to purpose

– the use of ESA95 in the determination of Member States’ payments to the VAT based own resources

– transmission of data.

The revision will also cover all the recommendations and clarifications agreed at international level and included in the 
updated SNA 1993.

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The project will affect many statistical areas linked to national accounts, both at Eurostat and in each National Statistical 
Institute: research and development, environmental, agricultural and tourism accounts, population, labour and social pro-
tection statistics, balance of payments, etc.

Presentation of ESA 95

The current presentation and clarifications of ESA will be kept:

a)   In ESA, there are separate chapters on transactions in products, distributive transactions and financial transactions. By 

contrast, in the SNA these transactions are explained in several chapters arranged by account.

b)   ESA describes a concept by providing a definition and a listing of what is included and what is excluded. The SNA de-

scribes concepts in more general terms and endeavours also to explain the rationale behind the conventions.

c)   ESA adds specific clarifications (e.g. 50% rule for the market/non-market split; method of allocating FISIM; thresholds 

for the GFCF/intermediate consumption split, etc.).

Two important issues

Research and Development 

There is a consensus among EU countries that work should be continued by means of a Task Force and in close coopera-
tion with the OECD. The objective of introducing compulsory supplementary tables on R&D in the new ESA Regulation 
is strongly supported; this will make it possible to address the difficulties and to create the necessary basis to achieve the 
long-term objective of including research and development in the core national accounts.

Pension schemes

The recommendations of the ECB/Eurostat Task Force on pensions will be implemented in such a way that comparability 
of Member States’ national accounts is ensured, including guidelines for the distinction between unfunded government 
pension schemes that should be recorded in the core accounts and those that should not.

Changes in existing chapters and new chapters

For most existing chapters, the structure of ESA 95 will be kept (or only slightly amended), as it will continue to follow the 
classification of sectors (Chapter 2), flows (Chapters 3 to 6), assets (Chapter 7), and accounts (Chapter 8), and to present 
the different types of leases, licences, contracts, insurance and pensions (Chapters 15, 16 and 17) or population concepts 
(Chapter 11). Chapter 14 on FISIM will present the details of the method of allocation.

Three existing chapters will be more detailed in the new ESA. 

– The input/output framework (Chapter 9), whilst maintaining a structure similar to the current one, will be more de-
tailed and cover the supply table, the use table, valuation, imports, the supply and use tables as an integral part of na-
tional accounts, symmetric input/output tables and the price issue.

– The chapter on quarterly economic accounts (Chapter 12) will focus on the scope and coverage of quarterly economic 
accounts, compilation and sources, consistency with annual accounts (including revisions), volume measures in quar-
terly economic accounts, seasonal adjustment and calendar effect correction.

– The chapter on regional accounts (Chapter 13) will cover the scope and coverage of regional accounts and will explore 
specific regional issues like the treatment of FISIM, the methodology for the transition from GVA to GDP, the method-
ology for calculating real growth rates of regional GDP, clearer prioritisation of methods for regionalisation, and more 
details on specific industries and ancillary units.

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The provisional structure for the new chapters is as follows.

– The chapter on fundamental principles of national accounts will be an introduction to the system with a general objec-
tive of presenting the different basic concepts and structures of national accounts. This presentation will help users to 
understand how national accounts can give them a clear picture of what is happening in the economy.

– The chapter on links between business accounts and national accounts: the chapter will first present the main general 
principles of business accounts and of the transition between business accounts and national accounts, and then will 
explain how these principles can be applied in specific cases.

– The chapter on European accounts will introduce the objective and scope of European accounts and the particularities 
of their compilation including EU institutions, the treatment of the rest of the world, the aggregation and balancing is-
sue, and consistency with sources and with other European macroeconomic statistics.

– The chapter on the rest of the world accounts will present the concept of residence, the general accounting rules, the 
external accounts of goods and services and of primary incomes and current transfers, external accumulation accounts 
and the relationship with balance of payments accounts.

– The chapter on government accounts will present the basic principles concerning the delimitation of the general govern-
ment sector, the relations between the government and public corporations, accounting issues related to government 
and corporations, and government net lending/borrowing and its relationship with government debt.

– The chapter on satellite accounts will present a common framework for functionally oriented satellite accounts, with 
a particular focus on satellite accounts to be included in the core accounts in the medium or long term (research and 
development).  Satellite  accounts  for  which  a  fairly  complete,  agreed  and  operational  methodological  framework  has 
already been developed will also be presented briefly, with a reference to the specialist manuals: Economic Accounts of 
Agriculture (EAA), Economic Environmental Accounts (SEEA 2003), and Social Protection (ESSPROS).

Review process

To ensure the success of such a project, an evaluation report on the implementation of ESA 95 in the EU is needed. The 
evaluation report would identify weaknesses and the improvements required to implement ESA95. This is a way to analyse 
what has to be done to ensure optimal implementation.

An impact assessment of the introduction of the new ESA will be carried out with respect to both resources and the impact 
on main aggregates, notably GDP, GNI and other key indicators such as government deficit and debt. The results of the im-
pact assessment will help, at EU and at national level, to identify the human and financial resources needed to successfully 
complete the project.

National financial accountants from European countries (both from statistical institutes and central banks) will naturally 
be closely involved in the revision process. A publicly accessible website is being set up to provide a forum for comments 
from statisticians and also users of the accounts as the drafting process proceeds in 2008.

The current timetable for the revised ESA is that it will be completed and proposed (as a legal instrument) in 2009, with 
adoption of the final text expected in 2011. European countries are committed to implementing the revised ESA by 2014, 
when complete datasets including historical series will be available to users.

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3.1.2   The Balance of Payments and International Investment Manual (BPM6)

Robert Heath and Robert Dippelsman
Statistics Department, International Monetary Fund

Introduction

International standards for balance of payments statistics have been a priority for the Fund from its earliest days. The first 
edition of the Balance of Payments Manual was released by the Fund in January 1948. The latest is the fifth edition (BPM5), 
released in 1993.

Preparation of a sixth edition of the Balance of Payments and International Investment Position Manual (BPM6) is now well 
under way and is planned to be finalised by end-2008. Like its predecessors, it provides guidance for compilers on produc-
ing statistics to internationally agreed standards. As a result, basic variables for the external sector such as the current ac-
count balance and reserve assets can be reported by member countries in an internationally comparable way.

Background

The new manual is the culmination of several years work by the IMF’s Statistics Department in collaboration with compil-
ers and other interested parties. The project was initiated in the early part of this decade by the IMF Committee on Balance 
of Payments Statistics (Committee), which meets annually, has broad geographic national representation, and was created 
by the Fund to advise it on balance of payments statistics. The project involved extensive consultation with data users, in-
cluding those within the Fund. As well, national statistical compilers were involved with specialized expert groups51 and 
took advantage of opportunities to comment on disseminated papers and material on a dedicated internet site. An Anno-
tated Outline, released in 2004, proposed general directions and sought feedback on a range of questions. 

Guiding principles

There were three guiding principles in preparing BPM6. 

First, the basic framework for the balance of payments data in BPM5 is retained in BPM6, and so the changeover to BPM6 is 
likely to be less significant for countries than from BPM4 to BPM5. Nonetheless, the new manual brings increased elabora-
tion and some changes in concepts, presentation, and emphasis. The sixth edition will be about twice as long as BPM5, in 
response to requests for more conceptual explanation and more detail on specific cases.

Second, the revision was undertaken to coincide with the update of the System of National Accounts (SNA), thereby main-
taining and enhancing the harmonisation with national accounts statistics that was introduced in BPM5.52

The work programmes of the Committee and of the relevant groups revising the SNA were closely coordinated, not least in 
the production of issues papers. So, for instance, a decision to regard research and development (R&D) output such as pat-
ents and copyrights as produced assets means that sales of R&D are included in services in BPM6, rather than in the capital 
account as non-produced assets as in BPM5. 

There are also some changes to bring BPM6 closer to the SNA. These include renaming the “income” and “current transfers” 
categories “primary income” and “secondary income,” replacing the monetary authorities sector with the central bank sec-
tor, and introducing a new category of rent for use of natural resources. Similarly, there are to be some changes in the SNA to 
bring it into line with the BPM, such as taking closer account of reinvested earnings on direct investment. The relationship 
between the SNA and international accounts is set out in Chapter 2 of BPM6. 

51 There were four technical expert groups: Direct Investment (DITEG), Reserve Assets (RESTEG), Currency Unions (CUTEG), and other balance of payments issues (BOPTEG). As well 
as national compilers, representatives from international agencies also participated. The issues and outcome papers produced by these groups are posted on the IMF’s external website.

52 The harmonization of macroeconomic statistics is elaborated in The System of Macroeconomic Accounts Statistics: An Overview (Statistics Department, IMF Pamphlet Series No. 56).

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Third,  BPM6  adopts  advances  since  1993  in  methodology  that  were  included  in  other  guides  and  manuals,  such  as  the 
Monetary  and  Financial  Statistics  Manual  (2000),  the  Government  Finance  Statistics  Manual  (2001),  the  External  Debt 
Guide (2003), and the Guidelines for a Data Template (2001). In turn, these manuals and guides will be revised to maintain 
harmonisation among the major macroeconomic datasets.

Presentation of BPM6

BPM6 has 14 chapters and a series of appendices. It states general principles that are intended to be applicable in a wide 
range of circumstances. It also applies the principles to some specific topics that have been identified as needing additional 
guidance. 

After the introductory chapters, like BPM5, BPM6 starts with basic principles. Two chapters include a detailed discussion of 
accounting principles, economic territory, units, residence, and institutional sectors and sub-sectors—in particular, com-
pared with BPM5, there is more discussion of the financial sector. 

Thereafter the structure of BPM6 is significantly reorganized from BPM5 to take account of the increasing importance of 
financial flows and stocks in analyzing external stability, as highlighted in the 2007 Surveillance Decision adopted by the 
IMF Executive Board. There is greater prominence given to positions, as discussed ahead, and “other flows,” highlighting 
the relevance of analysing holding gains and losses. Chapters 5 to 9 and the title of the Manual reflect this emphasis. 

The current account is covered in Chapters 10 to 12, covering the same ground as Chapters 10 to 15 in BPM5. On services, 
there was close cooperation with the Interagency Task Force on Trade in Services in developing the methodology. The capi-
tal account is covered in Chapter 13 (Chapter 17 in BPM5), and Chapter 14 discusses selected issues in the analysis of the 
international accounts (Appendix V in BPM5).

The appendices bring more detail on exceptional financing and debt reorganization than in BPM5. Supplementary infor-
mation showing the benefits arising from concessional debt as one-off transfers at the point of loan origination is included, 
calculated as the difference between the nominal value of the debt and its present value using a relevant market discount 
rate. Further, although arrears are to be recorded as part of the value of the instrument and not as a separate transaction in 
the financial account, an exception is made in the analytical presentation53 given the relevance of arrears to the concept of 
exceptional financing.

The topical summary appendices bring together in one place issues relating to direct investment, insurance, and financial 
leases that are discussed across different chapters of BPM6. Appendices on “multinational enterprises” and “remittances” 
explain datasets that are related to those set out in BPM6. Remittances include the new concept of personal transfers that is 
replacing “workers’ remittances” in the balance of payments.54

An  appendix  on  currency  and  other  economic  unions  is  introduced.  A  currency  union  and  its  economic  territory  are 
defined, the currency union central bank is recognized as an institutional unit, and various aspects of recording that are 
special to currency and economic unions are described. 

Another appendix sets out the detailed changes between BPM6 and BPM5, while the standard components and additional 
position data tables are provided as the final appendix, not least so that it is easy to find. 

Major themes 

BPM6 reflects changes that have occurred in the global economy since 1993. This period has been characterized by a sig-
nificant  growth  in  cross-border  activity  and  policy  challenges  arising  from  increased  financial  flows.  So  consequently, 
globalization, financial innovation, and the balance sheet approach are major themes that underlay many of the detailed 
changes made in BPM6. 

53 The analytical presentation includes the concept of exceptional financing, with the relevant transactions reclassified from that shown in the standard components. 

54 Personal transfers comprise all transfers in cash or in kind made or received by resident households to or from other nonresident households.

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Methodological articles

The statistical implications of globalization  have been  a  major  focus  of BPM6,  as global  goods and financial  flows have 
grown substantially in recent years. 

After extensive discussion, there is a revised treatment of outsourced processing (so-called goods for processing), which 
will  better  reflect  the  actual  flows  and  identify  this  activity  separately.  In  BPM6,  the  fees  received  by  economies  for 
processing goods that have not changed ownership, and so are not owned by the entity undertaking the processing, are 
to be recorded as service earnings.55 For those economies that are very active in outsourced processing activity, there 
could be a significant change in their trade balances arising from the introduction of BPM6, and possibly the current 
account. This is because the inward and outward flow of processed goods recorded gross in the trade account in BPM5
are recorded at the value of the processing fee in services under BPM6.56 Also, repairs on goods are to be classified as a 
service not goods.

Using the same principles, merchanting activity, where a resident transacts in goods (buys then sells) with nonresidents 
outside of the domestic economy, is to be classified under goods transactions within exports; so imports of goods under 
merchanting are classified as negative exports. In BPM5 merchanting is recorded as a service transaction. 

In a world of increasingly mobile individuals there are several modifications included in BPM6. The residence of households 
is  to  be  based  on  “predominant  center  of  economic  interest,”  while  cross-border  movements  of  personal  effects  during 
migration are no longer to be recorded as transactions but as reclassifications, given that no transaction takes place. High 
value goods acquired by travelers, such as occurs in the activity known as “shuttle trade,” are to be recorded as merchandise 
goods rather than under travel services.

Within direct investment, elaborations are provided to identify direct investment relationships in complex multi-country 
company structures, which are becoming increasingly common. More generally, the 10 percent level for establishing a di-
rect investment relationship is maintained, but with a focus on voting power; direct investment positions and transactions 
are to be recorded on a gross basis as assets and liabilities. 

Identification of the residence and activities of special purpose entities (SPEs) and similar corporate structures with little 
or no physical presence are dealt with in detail. SPEs are to be classified as separate institutional units in the economy 
in which they are incorporated, a clarification of the residence principles in BPM5. Further, reflecting that governments 
may create special rules for certain entities or zones, so restricting the free flow of capital, goods and services within an 
economy, the definition of economic territory is modified to a territory under the effective economic control of a single 
government. 

Financial innovation

Many of the issues raised by compilers and users concerned developments on financial markets. There is additional discus-
sion of a range of topics identified as of increasing importance such as financial derivatives and employee stock options, 
which are now included together as separate category in the financial account. Also, the financial instrument classification 
is set out explicitly in Chapter 5, whereas in BPM5 the discussion was mixed in with the functional categories.

There is guidance on the treatment of short positions, when a trader sells a borrowed security, and on the related fees for 
borrowing the security—to be classified as interest regardless of whether the security is debt or equity. When securities are 
lent or provided under reverse transactions, ownership in the balance of payments is assumed not to have changed, as the 
risk and rewards of ownership remain with the original owner. For this and for other cases like goods under financial leases, 
the concept of “change of ownership” in BPM5 is modified to “change in economic ownership.”

55 The specific item in the services account is entitled “manufacturing services on physical inputs owned by others.”

56 The BPM5 treatment of goods for processing differed depending upon where the goods were subsequently sold. If the goods were sold to a resident of the economy undertaking the 
processing or to a resident of a third party economy, the processing economy recorded a service earnings, otherwise all goods for processing were recorded under goods account on 
a gross basis. 

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For index-linked instruments, a distinction is to be drawn depending on whether indexation has a holding gain motive 
(usually when based on a narrow index), or not (usually when more broadly based, such as with a consumer price index). For 
the former, the rate of interest accrual is to be fixed at issuance with any subsequent changes in value classified as holding 
gains and losses. Foreign currency indexed-linked debt with both principal and coupons indexed to a foreign currency is to 
be classified and treated as being denominated in that foreign currency.

Implicit financial services (FISIM) previously included in interest are to be recorded in services, as in SNA. The treatment 
in BPM5 of buy-sell margins on foreign exchange transactions as service earnings of financial intermediaries is extended 
to transactions in securities. Also, the measurement of insurance services is improved by reducing the impact of volatile 
insurance claims on their calculation. 

Loans continue to be valued at nominal value in the position data, but a memorandum item is included for the creditor to 
show the likely realizable value. So-called standardized guarantees are to be treated in the same way as insurance, and the 
treatment of “one-off” guarantees is clarified. 

Research into the gold market revealed that gold is commonly traded in accounts whereby the dealer has a claim for a 
certain quantity of gold to be delivered without having ownership over specific gold bullion. Such accounts, known as unal-
located gold accounts, are to be classified as a deposit (or monetary gold if held in reserves). The consequence of this change 
and of the SDR allocation decision noted below, is that only gold bullion held in reserves will be an financial asset in the 
system without a counterpart liability.

In BPM6, primary income of investment funds is to include reinvested earnings, like direct investment. 

In the area of reserves, the definition of liquidity — including the ability to sell the asset without unduly affecting the value 
— has been clarified and guidance is given on the treatment in reserves of assets held in pooled funds and in special purpose 
government funds, usually known as sovereign wealth funds. 

Balance sheet approach

In response to increasing interest in balance sheet analysis, as noted above there is more emphasis on the international in-
vestment position (IIP). Over the past decade, user interest in issues such as external debt and vulnerability has increased, 
and a growing number of countries are reporting IIP data to the Fund. As the graph shows, the number of reporters has 
tripled in the last ten years to well over one hundred. As a result of the shift in emphasis, the contents and title of the new 
manual include a reference to the IIP (although it is still abbreviated as BPM6). 

Compared with BPM5, more of a statistical explanation of balance sheet changes is given in BPM6, to show changes that 
arise outside of transactions—such as from exchange rate and other valuation changes, and write-offs. Furthermore, debt 
instruments are identified separately, and additional supplementary breakdowns of debt instruments by remaining matu-
rity and particularly currency emphasised. A memorandum item on reserves-related liabilities is included to help analysis 
of reserve assets. And, in this context, SDR allocations are to be recognised as a liability. The analytical chapter in BPM6
includes a discussion of the balance sheet approach. 

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Methodological articles

Figure 3.1.1:

International Investment Position Reporters

120

100

r
e
b
m
u
n

80

60

40

20

0

37

1998

113

2007

Future developments

A new draft of BPM6 was available early in 2008 at http://www.imf.org/external/pubs/ft/bop/2007/bopman6.htm57 and was 
promoted in regional outreach seminars during the first half of 2008. A final version would be prepared for Committee’s 
agreement in October 2008, and then be posted as the final version, subject to editing, by end-2008. 

Countries are expected to update their data collections, methodologies, and procedures over the next several years, with 
2012 or 2013 the most likely target dates for converting data for International Financial Statistics and the Balance of Pay-
ments  Statistics  Yearbook  to  the  new  manual.  The  Fund  will  support  countries  undertaking  conversion  in  its  training, 
documentation, and technical assistance programs. The Balance of Payments Compilation Guide and Textbook will also be 
updated. 

57 The website also has background materials, including the Annotated Outline and papers presented to various expert groups and the results of these discussions. 

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3.2   European Sector Accounts

Denis Leythienne and Béatrice Thiry58
Eurostat, National accounts: production

I.

Introduction

The sector accounts are the part of the national accounts that records the economic flows and stocks of the institutional 
agents classified in “sectors” according to their economic behaviour and function in the economy.

They are necessary to answer questions such as: are households spending or saving a bigger share of their income? Are 
corporations investing more or do they prefer to retain their earnings? Is the economy financing its investments through its 
total savings or is it borrowing from abroad?

Among sector accounts, one generally distinguishes between the non-financial accounts, the financial accounts and the 
balance sheets. Non-financial sector accounts record flows according to their economic nature, such as output, wages, taxes, 
subsidies etc. The financial accounts record flows by financial instrument (e.g. currency, loans, shares etc.). The balance 
sheets record the stocks of non-financial assets (e.g. building, machinery) and financial assets and liabilities (e.g. currency, 
loans, shares, etc.)  

For about ten years, the annual sector accounts of the Member States of the European Union have been compiled according 
to one common methodology described in the European System of Accounts 1995 (ESA 95)59.

With the start of the euro area, it was felt necessary to supplement national sector accounts as provided by the Member States 
with genuine European sector accounts that would reflect the economic situation of the euro area / European Union as a 
whole. In order to “consolidate” the accounts of the Member States across borders, Eurostat and the European Central Bank 
worked out a special methodology, in close cooperation with the national statistical institutes and national central banks. 

This article provides methodological information on the compilation of European non-financial sector accounts. Section 
II outlines the basic features of sector accounts. Section III describes the methods used for the compilation of European 
aggregates. 

II. 

Sector accounts

The European sector accounts aim to provide a comprehensive and comparable overview of the European economy as a 
whole. They record all “transactions” between economic agents grouped by “sector”. The system forms a sequence of inter-
linked accounts.

2.1 

Institutional sectors

The institutional sectors bring together economic agents with broadly similar behaviour: non-financial corporations, fi-
nancial corporations, general government and households. Transactions between domestic economic agents and economic 
players residing abroad are recorded in the “rest of the world” accounts.

The sector accounts thus show the interactions between the different sectors of a given economy, and between them and the 
rest of the world.

58 With the help of Peeter Leetmaa, Hervé Rénnié and Tatjana Smokova (European Commission / Eurostat).  

Béatrice Thiry is on temporary leave from the National Bank of Belgium and Tatjana Smokova is on secondment from Statistics Estonia.  

59 For more details, see http://forum.europa.eu.int/irc/dsis/nfaccount/info/data/esa95/en/titelen.htm.

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Methodological articles

2.2 

Transactions

Transactions are classified according to their economic nature (e.g. payment and receipt of wages or taxes, consumption, 
granting of a loan, etc.).

They belong to two main categories: transactions in goods and services and distributive transactions.

Transactions in goods and services

All flows related to the supply and use of goods and services during the period considered are recorded as transactions in 
goods and services.

The supply of goods and services provided by each producing sector of the economy is recorded as “output”. Goods and 
services bought abroad are recorded as “imports” in the Rest of the World accounts.

The different uses of goods and services in the domestic economy are: “intermediate consumption”, “final consumption” 
and “gross capital formation” depending on their role in the economic process. “Intermediate consumption” is the value 
of goods and services consumed as inputs by a production process; “final consumption” stands for the goods and services 
used for satisfaction of individual needs (by households) or of collective needs (by general government); “gross capital 
formation”  means  investment  in  fixed  assets  (e.g.  buildings,  machinery,  private  dwellings,  agricultural  livestock,  and 
intangible assets) and changes in inventories. Goods and services sold abroad are recorded as “exports” in the Rest of the 
World accounts.

For goods and services as a whole, total supply should equal total use, which leads to the following equation:

(1) Output + Imports = Intermediate Consumption + Final Consumption + Investment + Exports

Or:

GDP = Output — Intermediate Consumption = Final Consumption + Investment + Exports — Imports

Where GDP stands for Gross Domestic Product and measures the value of goods and services produced during the period 
considered.

Distributive transactions

Distributive transactions consist of economic flows that involve the (re-)distribution of income created by the production 
of goods and services.

For each distributive transaction, the amounts that are paid by a sector are recorded as “uses” whereas the amounts received 
are recorded as “resources”. For instance, the “uses” side of the transaction category “compensation of employees” records 
the amounts of wages and salaries and social contributions payable by all the country’s employers, namely non-financial 
corporations, financial corporations, government and households (as individual entrepreneurs), and employers residing 
abroad (rest of the world sector).

The resources side shows the compensation of employees receivable by the relevant sectors of the economy, namely:

– the household sector with respect to the households that reside in the country considered;

– the rest of the world sector for households that are paid in the country considered but reside abroad.

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3

Table 3.2.1:

Resources/uses balance for compensation of employees

(Billion euros, 2006 data for EU27)

USES

RESOURCES

s
n
o

i
t
a
r
o
p
r
o
c
l
a
i
c
n
a
n
fi
-
n
o
N

s
n
o

i
t
a
r
o
p
r
o
c
l
a
i
c
n
a
n
i
F

t
n
e
m
n
r
e
v
o
g

l
a
r
e
n
e
G

d
l
r
o
w
e
h
t

f
o
t
s
e
R

s
d

l

o
h
e
s
u
o
H

S
E
S
U

-
L
A
T
O
T

S
E
C
R
U
O
S
E
R
L
A
T
O
T

Compensation of 
employees

3583

287

1241

550

13

5674

5674

s
n
o

i
t
a
r
o
p
r
o
c
l
a
i
c
n
a
n
fi
-
n
o
N

0

n
o

i
t
a
r
o
p
r
o
c
l
a
i
c
n
a
n
i
F

0

t
n
e
m
n
r
e
v
o
g

l
a
r
e
n
e
G

0

d
l
r
o
w
e
h
t

f
o
t
s
e
R

6

s
d

l

o
h
e
s
u
o
H

5668

As shown in the numerical example above, for each type of distributive transaction, total resources of all sectors and the 
rest of the world accounts equal total use. On the use side, two thirds of the total amount of compensation of employees is 
paid by non-financial corporations. The other third is paid either by general government, households (as individual entre-
preneurs), financial corporations or non-residents (rest of the world sector). On the resources side, almost all compensation 
of employees is received by resident households.

Sequence of accounts

Transactions are grouped into a sequence of accounts covering a specific aspect of the economic process, ranging from 
production, generation and (re)distribution of income through consumption and investment to borrowing and lending. 
Each account is closed by a balancing item defined as total resources minus total use, and this is carried over to the next 
account.

Some balancing items are of great analytical interest, for example gross operating surplus, disposable income and saving. 
The final balancing item of the non-financial sector accounts is net lending/borrowing, which is equal to all flows received 
minus all flows paid by each institutional sector. It then reflects whether a given sector has increased/decreased its financial 
wealth through economic activities carried out during the period considered.

III. 

European sector accounts

The euro area/European Union accounts are based on, but are not the simple sum of, the national accounts of the Member 
States. First, the accounts of European institutions and bodies need to be added. Second, cross-border transactions between 
European countries have to be eliminated from the rest of the world accounts. Third, imbalances which are mainly caused 
by the removal of intra-flows, called “asymmetries”, are eliminated in order to re-balance the accounts.

In the diagram below, these transformations are presented using a simple example, assuming that Europe is made up of two 
countries, A and B. All economic flows are displayed with arrows.

In the first diagram, the European Union accounts are compiled as the mere sum of the national accounts of countries A 
and B. Both intra-flows between countries A and B and flows vis-à-vis third countries are taken into account. The EU insti-
tutions are not part of the European aggregate.

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Methodological articles

EU
institutions

Country A

Country B

Europe as sum of countries

In this second diagram, the European Union is considered as a single entity. Only flows vis-à-vis third countries are taken 
into account. The EU institutions are included in the European aggregate.

EU
institutions

Country A

Country B

Europe as a single entity

Since 2006, European sector accounts at current prices have been compiled according to the single-entity principle. The 
other European aggregates, in particular volume data, are still compiled using the first approach.

The main steps in the compilation of European sector accounts are detailed below.

3.1

European Union institutions and other European bodies

The European Union institutions (EUI) and other European bodies are not considered to be part of the domestic economy 
in the national accounts compiled by the Member States. Consequently, the sector accounts provided by the Member States 
do not record the activities of institutions and bodies set up by European treaties as resident entities. By contrast, the Euro-
pean institutions are part of the domestic sectors of the European Union economy (not the euro area). 

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With the exceptions of the European Central Bank and the European Investment Bank, which are both classified in the 
financial corporations sector, all European institutions are classified in the government sector.

For the general government sector, the EUI are the following:

– The Council
– The European Commission
– The European Parliament
– The European Court of Justice
– The European Court of Auditors
– The Social and Economic Committee
– The Committee of the Regions
– European agencies whose accounts are part of the general budget of the European Union

Significant transactions, especially transfers, take place between the above institutions and Member States. This is particu-
larly the case for the Commission, which administers European policies that involve financial transfers. 

European institutions are not included in the euro area accounts because their administrative competence goes beyond the 
boundaries of the monetary union. The ECB is the only institution included in the financial corporations sector in both the 
euro area and the European Union accounts. 

3.2  

The rest of the world in the European accounts

The rest of the world accounts, as compiled by the Member States, record transactions between the national economies and 
all non-resident units, including those in other European Member States. For instance, imports/exports recorded in Mem-
ber States’ national accounts include the goods and services bought from or sold abroad, whether from or to a resident of 
the euro area, of the European Union or of a third country. 

To  reflect  appropriately  the  transactions  between  European  areas  and  third  countries,  it  is  therefore  necessary  to  re-
move, from the sum of national rest of the world sectors, the economic flows within the area considered (“intra-European 
flows”).

There is insufficient data on the rest of the world sector breakdown into intra/extra euro area/European Union in the na-
tional accounts data sets. Consequently, the European accounts draw on both the national and the European balance of 
payments statistics. In other words, the “intra flows” are estimated using the geographical breakdown provided by balance 
of payments (BoP) data. 

Note that, because of different data revision policies and conceptual differences, it is not possible to ensure full consistency 
between the European rest of the world sector and BoP statistics at this stage. 

Moreover, for “intra flows”, total resources should theoretically equal total uses. For instance, within the euro area, total 
“intra”-imports should equal total “intra”-exports. However, this is not the case in practice. The comparison of total intra-
flows in resources and uses reveals imbalances called “asymmetries”. 

As a consequence, European accounts cannot be derived by simply removing the intra-flows of each transaction. The result-
ing discrepancies have to be allocated to the various resident sectors in order to re-balance the European accounts. Table 2 
illustrates the removal of the “intra” European flows using interest flows as an example.

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Methodological articles

Table 3.2.2:  

Removal of intra-European flows of interest

(Billion euros, 2006 data for EU27)

USES

H
S
I

P
N
d
n
a
s
d

l

o
h
e
s
u
o
H

I

s
w
o
fl
-
a
r
t
n
—
d
l
r
o
w
e
h
t

f
o
t
s
e
R

s
w
o
fl
-
a
r
t
x
E

—
d
l
r
o
w
e
h
t

f
o
t
s
e
R

s
n
o

i
t
a
r
o
p
r
o
c
l
a
i
c
n
a
n
fi
-
n
o
N

s
n
o

i
t
a
r
o
p
r
o
c
l
a
i
c
n
a
n
i
F

t
n
e
m
n
r
e
v
o
g

l
a
r
e
n
e
G

RESOURCES

s
w
o
fl
-
a
r
t
x
E

—
d
l
r
o
w
e
h
t

f
o
t
s
e
R

I

s
w
o
fl
-
a
r
t
n
—
d
l
r
o
w
e
h
t

f
o
t
s
e
R

s
n
o

i
t
a
r
o
p
r
o
c
l
a
i
c
n
a
n
fi
-
n
o
N

s
n
o

i
t
a
r
o
p
r
o
c
l
a
i
c
n
a
n
i
F

t
n
e
m
n
r
e
v
o
g

l
a
r
e
n
e
G

H
S
I

P
N
d
n
a
s
d

l

o
h
e
s
u
o
H

S
E
S
U
—

S
E
C
R
U
O
S
E
R

s
e
c
r
u
o
s
e
R

l

a
t
o
T

s
e
s
U

l

a
t
o
T

383

1440

311

255

475

275

3139

0

3139

334

502

211

1709

44

339

383

1440

311

255

275

2664

-27

2637

334

211

1709

44

339

With  
intra-flows

“Intra-
flows”
removed

In the case of the European Union, about two thirds of the cross-border flows of interest are paid to or received from other 
countries of the European Union. For example, on the use side of the Rest of the World accounts, EUR 475 (out of 750) bil-
lion has been removed as “intra-European” flows of interest.

3.3  

Balancing the European accounts

As illustrated in the example above, removing the intra-flows from the European Rest of the World accounts creates imbal-
ances between total resources and total uses when conceptually they should be equal.

In the case of transactions in goods and services, these imbalances are created by the difference between intra-imports and 
intra-exports called “asymmetries”.

Goods and Services

Asymmetries in goods and services are, in absolute terms, the most important discrepancies in the European sector ac-
counts. In 2006, they reached EUR 46 billion for the euro area and EUR 124 billion for the European Union. These amounts 
represent respectively 0.3% and 0.5% of the total supply of goods and services for the areas concerned. These asymmetries 
have to be allocated to the different transactions in goods and services so that total supply and total uses remain balanced 
in the European accounts [see equation 1) in §2.2 above]. 

To  allocate  asymmetries  in  goods,  intra-community  trade  statistics  collected  under  the  Intrastat  regulation60  are  used. 
These  data  provide  a  breakdown  of  intra-flows  of  goods  by  final  use  categories.  These  proportions  are  used  to  allocate 
asymmetries in goods to the intermediate consumption and gross fixed capital formation (“investment”) of households and 
non-financial corporations.

60 Statistics relating to the trading of goods by the Community and its Member States with non-member countries are based on Council Regulation No 1172/95 and Commission 

Regulation (EC) No 1917/2000.

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Asymmetries in services are allocated to the intermediate consumption of non-financial corporations and to the final con-
sumption of households on a pro rata basis. This balancing process has an impact on goods and services transactions, and 
therefore on the level of the Gross Domestic Product of the areas concerned. As illustrated in the figure below, the GDP of the 
European Union is about 0.2% lower after balancing. In the euro area case, balancing caused a decrease of about 0.3% in the 
beginning of the period (1999 and 2000) whereas the impact decreased significantly in the recent years (-0.04% in 2006).

Figure 3.2.1:

Adjustment of GDP due to the balancing process

(in percentage points)

0.00%

-0.05%

-0.10%

-0.15%

-0.20%

-0.25%

-0.30%

-0.35%

1999

2000

2001

2002

Euro area 13

2003

EU27

2004

2005

2006

Distributive transactions

Total resources must also equal total uses for each distributive transaction, when summed over all institutional sectors and 
the rest of the world. For example, total interest revenue of all sectors and the rest of the world combined must be equal to 
total interest expenditure.

For distributive transactions as well, the removal of “intra” European flows creates imbalances as illustrated in Table 2 in 
the case of interest.

In order to balance distributive transactions, the method consists of identifying the items to which the discrepancies should 
be allocated.

In practice, the following rules are applied:

– For each transaction, discrepancies are allocated to the sectors whose data are deemed less reliable according to experts’ 

judgment;

– government data, which are considered more reliable, are not affected by the balancing procedure;

– rest of the world data are not affected by the balancing procedure either except where they conflict with government data 

(e.g. in the case of taxes and subsidies on products).

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For compensation of employees, the discrepancy is totally allocated to the amounts received by the households sector. In-
deed, in most Member States, this item is indirectly estimated as the sum of the compensation of employees paid by all other 
sectors. It is also the most important item for this transaction so that the impact of balancing is minimised. 

The same kind of reasoning is applied to other transactions which are more or less directly related to salaries, i.e. social 
contributions, social benefits and the adjustment for the change in net equity of households in pension funds reserves.

In the case of interest, imbalances (total resources minus total use) are allocated on a pro rata basis as illustrated in Table 3 
(follow-up of table 2).

Table 3.2.3:

Balancing intra-European flows of interest

(Billion euros, 2006 data for EU27) 

USES

H
S
I

P
N
d
n
a
s
d

l

o
h
e
s
u
o
H

I

s
w
o
fl
-
a
r
t
n
—
d
l
r
o
w
e
h
t

f
o
t
s
e
R

s
w
o
fl
-
a
r
t
x
E

—
d
l
r
o
w
e
h
t

f
o
t
s
e
R

s
n
o

i
t
a
r
o
p
r
o
c
l
a
i
c
n
a
n
fi
-
n
o
N

s
n
o

i
t
a
r
o
p
r
o
c
l
a
i
c
n
a
n
i
F

t
n
e
m
n
r
e
v
o
g

l
a
r
e
n
e
G

RESOURCES

s
w
o
fl
-
a
r
t
x
E

—
d
l
r
o
w
e
h
t

f
o
t
s
e
R

I

s
w
o
fl
-
a
r
t
n
—
d
l
r
o
w
e
h
t

f
o
t
s
e
R

s
n
o

i
t
a
r
o
p
r
o
c
l
a
i
c
n
a
n
fi
-
n
o
N

s
n
o

i
t
a
r
o
p
r
o
c
l
a
i
c
n
a
n
i
F

t
n
e
m
n
r
e
v
o
g

l
a
r
e
n
e
G

H
S
I

P
N
d
n
a
s
d

l

o
h
e
s
u
o
H

S
E
S
U
—

S
E
C
R
U
O
S
E
R

s
e
c
r
u
o
s
e
R

l

a
t
o
T

s
e
s
U

l

a
t
o
T

383

1440

311

255

275

2664

-27

2637

334

211

1709

44

339

-2

-9

0

-2

0

-13

14

0

1

11

0

2

381

1431

311

253

275

2651

0

2651

334

212

1720

44

341

“intra-flows” 
removed

Allocation of 
imbalances

Balanced
accounts

As was shown in Table 2, removing intra-European flows creates an imbalance of EUR -27 billion between total interest 
received and total interest paid. This imbalance between resources and use of interest has been distributed among the resi-
dent sectors except general government as shown in Table 3. For example, the amounts recorded for the interest received by 
financial corporations were EUR 1709 billion before adjustment and EUR 1720 billion afterwards.

The same method is used for the other transactions related to property income and other current transfers, as there is no 
obvious single absorbing item. Consequently, imbalances are allocated to the domestic sectors (other than government) on 
a pro-rata basis.

For  the  transactions  “net  non-life  insurance  premiums”  and  “current  taxes  on  income  and  wealth”,  the  discrepancy  is 
proportionally allocated among the domestic sectors (other than government) on the use side. For the transaction “non-
life insurance claims”, it is the opposite: the discrepancy is allocated among the non-government domestic sectors on the 
resources side.

For taxes and subsidies on products, as government data are considered more reliable and should not be affected by the 
balancing method, the adjustment falls on the rest of the world accounts. As regards other taxes and subsidies on produc-
tion, the discrepancy is totally allocated to the non-financial corporations that represent by far the biggest amount for taxes 
paid and subsidies received.

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For capital taxes, the most important taxpayer, namely the households sector, was chosen to absorb the discrepancy.

As regards the other capital transfers, the imbalance was allocated between the amount received by non-financial corpora-
tions and households.

IV.  

Conclusion

With the start of the euro area and the successive enlargements of the European Union, new user needs have emerged for 
genuine European accounts.

In order to compile such accounts, it was necessary to include European institutions and consolidate cross-border flows 
within the areas considered. The removal of intra-flows has led to discrepancies between resources and uses that have been 
resolved by balancing the accounts, thus impacting on aggregates like the European GDP at current prices. 

The result of the whole compilation process, from the individual country data to the final balancing procedure, gives a bal-
anced and consistent set of sector accounts for the euro area and the European Union.

The European sector accounts are no longer the sum of national accounts transmitted by the Member States. They are also 
different from other euro area/European Union national accounts publications, in which cross-border flows within the area 
concerned have not yet been removed.

The methodology broadly described above has been used to compile the European sector accounts that were published for 
the first time in 2006 (annual accounts) and in 2007 (quarterly accounts). These data are available, together with methodo-
logical information in English, French and German, on the following website: http://ec.europa.eu/eurostat/sectoraccounts.

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Methodological articles

3.3   Harmonised indices of consumer prices61

Christine Wirtz
Eurostat, Price statistics

Introduction

The Harmonised Indices of Consumer Prices (HICPs) are a set of European Union consumer price indices (CPIs) calculated 
according to a harmonised approach and a single set of definitions. This article outlines the aims and methodology of the 
HICPs, summarises the main harmonised standards and notes some key items on the agenda for further harmonisation. 
The HICP development project is ongoing. This article describes the state of development of the HICPs at the end of 2007. 
The HICPs have a legal basis in that their production, and many elements of the specific methodology to be used, is laid 
down in a series of legally binding European Union Regulations.

The main HICPs

The HICPs on which most attention is focused are:

– the  Monetary  Union  Index  of  Consumer  Prices  (MUICP)  —  an  aggregate  index  covering  the  countries  in  the  euro 

area;

– the European Index of Consumer Prices (EICP) — for the euro area plus the other European Union countries;

– the national HICPs — for each of the Member States of the European Union (EU).

Beyond these there are also the European Economic Area Index of Consumer Prices (EEAICP) and HICPs for the individu-
al countries in the European Economic Area (EEA) and Switzerland. There are also interim HICPs for candidate countries. 
It is expected that once those countries accede to the EU their HICPs will be fully comparable with those of the existing 
Member States. The national HICPs are produced by the national statistical institutes, while the country-group aggregates 
are produced by Eurostat.

The  official  country-group  aggregates  reflect  the  evolution  of  European  monetary  union  (EMU),  the  EU  and  the  EEA. 
New Member States are chained into the index at the time of accession. In addition to these official aggregates, Eurostat 
computes also country aggregates with stable composition over time. For example, the aggregate EU27 shows price indices 
covering all current 27 Member States since 1997.

Uses of the HICPs

Consumer price indices have a variety of potential uses, for example for indexing social benefits or contracts, or as inputs 
into various types of economic analyses. Following the Maastricht Treaty, the main thrust of the harmonisation project has 
been the use of the HICPs as convergence criteria and the main measure for monitoring price stability in the euro area. The 
HICPs have been set up to provide the best measure for international comparisons of consumer price inflation in the EU 
and the euro area, and for assessing price convergence and stability in the context of monetary policy analysis.

In the early stages of the project until 1998, the main use of the HICPs was in assessing the price stability and price con-
vergence required for entry into European monetary union. Since then, convergence in terms of price stability for new 
potential Members of the EMU has been assessed regularly by means of the HICP. 

From 1999 onwards, the focus of interest shifted towards country-group aggregates — and in particular the euro area. This 
change of emphasis reflects the European Central Bank’s objective of price stability and the view that the HICPs are the 

61 Comments on this paper by K. Hayes and C. Stewart (Eurostat) are gratefully acknowledged.

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most appropriate price measure for assessing price stability. The focus of the HICPs on measuring price stability and con-
vergence, and on international comparisons, does not mean that a wider range of users should not or cannot use HICPs for 
other purposes. Depending on the precise purpose the user has in mind, the HICPs may be the best available price statistics. 
All users of the HICPs should note, however, that the HICPs are revisable; the indices may change after the initial results 
are published.

A harmonised methodology for the HICP and minimum standards

On 23 October 1995, the European Union’s Council of Ministers adopted a Regulation providing the legal basis for the 
establishment of a harmonised methodology for compiling consumer price indices in the Member States and European 
Economic  Area  Countries.  This  Regulation62  (the  HICP  Framework  Regulation)  required  that  HICPs  be  produced  and 
published, use a common reference base, provide common coverage of consumer goods and services, and share a common 
classification. A series of specific measures has been adopted to implement the HICP Framework Regulation.

The approach taken to harmonisation has been to build, as far as possible, on the EU Member States’ existing data sources 
and methodologies for their national CPIs. The legal standards typically take the form of minimum standards, whereby 
more than one solution to a harmonisation issue may usually be allowed so long as comparability is not jeopardised. Within 
this framework, by end-2007, a series of 17 legally binding standards and some additional guidelines had been drawn up 
and implemented.

Compliance monitoring

Given the importance accorded to the accuracy, reliability and comparability of the HICPs in the EU, Eurostat operates a 
system of compliance monitoring to ensure that the legal framework is adhered to. This includes compliance assessments 
on the basis of questionnaires and visits by Eurostat officials to the EU national statistical institutes to study their work on 
their HICPs in more detail.

Compliance monitoring is crucial in promoting confidence in HICP data and Eurostat needs to be assured that Member 
States are complying with the regulations in order to support the obvious need for high quality HICP Statistics. Recom-
mendations are published and followed up by Eurostat. The follow-up process ensures that the recommendations are taken 
up. If required, further follow-up visits by Eurostat can be made. 

Basic concepts and definitions

Aim and scope of the HICPs

The aim of the HICPs was stated to be to measure inflation on a comparable basis, taking into account differences in na-
tional definitions. This, however, requires an operational definition of the term “inflation”.

Given the opinion and the needs of the HICPs’ main users, it was decided to compute the HICPs as Laspeyres-type price 
indices, based on the prices of goods and services available for purchase in the economic territory of each EU Member State 
for the purpose of directly satisfying consumer needs.

Based on this concept and by reference to national accounts, specifically the European System of Accounts (ESA 95), the 
coverage in practice of the HICPs was taken to be household final monetary consumption expenditure (HFMCE). This 
definition effectively prescribes the goods and services, the population and the geographic territory to be covered, as well as 
the prices and the weights to be used.

The HICP may thus be described as a Laspeyres-type “consumer inflation” or “pure price” index, which measures average 
price changes on the basis of changed expenditure on maintaining the consumption pattern of households and the compo-
sition of the consumer population in the base or reference period.

62 Council Regulation (EC) No 2494/95.

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The term “pure price index” indicates that it is only changes in prices that should be reflected in the HICP measure between 
the current and the base or reference period. The HICP is therefore not a cost of living index. That is, it is not intended to 
be a measure of the change in the minimum cost for achieving the same standard of living (i.e. constant utility) from two 
different consumption patterns realised in the two periods compared, and where factors other than pure price changes may 
enter the index.

Household final monetary consumption expenditure (HFMCE)

The coverage of the HICPs is delimited by HFMCE, and so concerns that part of final consumption expenditure which is:

– by households, irrespective of their nationality or residence status;
– in monetary transactions;
– on the economic territory of the EU Member State;
– on goods and services that are used for the direct satisfaction of individual needs or wants;
– in one or both of the time periods being compared.

The prices used in the HICP should be the prices paid by households to purchase individual goods and services in monetary 
transactions. The purchaser’s price is the price for the products that the purchaser actually pays at the time of purchase.

The weights of the HICP are the aggregate expenditures by households on any set of goods and services covered by the 
HICP, expressed as a proportion of the total expenditure on all goods and services within the coverage of the HICP.

The HICPs are classified according to the four-digit categories and sub-categories of the COICOP/HICP (Classification of 
Individual Consumption according to Purpose, adapted to the needs of HICPs).

The concept of HFMCE not only specifies the coverage, the prices and the weights for the HICP, but also establishes a link 
between HICPs and ESA 95 that has proved useful to analysts and policy-makers. HICP definitions follow ESA 95 wherever 
possible and when to do so is consistent with the aims and uses of the HICP. That said, there are some differences between 
the coverage of the HICPs and that of household final consumption expenditure (HFCE) as defined by national accounts, 
in particular the treatment of owner-occupied housing.

Some basic requirements for HICPs

The relative distribution of consumers’ expenditure on individual products varies from country to country. Hence, there 
is no uniform basket applying to all EU Member States. The weights used in the compilation of HICPs may relate to a 
reference period up to seven years prior to the current year. In practice, this results in a complete weight and sample 
revision of national HICPs in at least five-yearly intervals, taking into account that a period of about two years may be 
needed to incorporate the results of a full consumer expenditure survey. Adjustments must nevertheless be made each 
year for any especially large changes in expenditure patterns, to minimise any disparities that could arise from different 
update frequencies.

To keep the HICPs broadly in step with each other and up to date, new products must be included when they achieve a 
significant relative importance. HICPs must also be shown to be based on appropriate sampling procedures, taking into 
account national diversity in products and prices.

The samples must be kept up to date, in particular by banning the practice whereby missing prices are simply assumed to be 
equal to the last observed prices. In order to measure pure price changes, the prices included in HICPs need to be adjusted 
for changes in the quality of goods and services. Certain inappropriate quality adjustment practices, such as so-called au-
tomatic linking, may not be used.

HICP aggregates for country groups are calculated as the weighted average of the national HICPs, using the weights of the 
countries and sub-indices concerned. The weight of a country is its share of HFMCE in the total.

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Coverage

Goods and services

The  coverage  of  goods  and  services  in  the  HICPs  has  been  expanded  over  time.  The  HICPs  now  cover  virtually  all  of 
HFMCE. The main difference from the ESA 95 concept of HFCE is the exclusion of owner-occupied housing.

The initial coverage of goods and services in the HICPs, although fairly comprehensive, reflected for the most part what 
was common to the national consumer price indices. Since then, with considerable effort and cooperation by EU Member 
States, coverage has been extended to virtually all consumer expenditure, in the sense of HFMCE. In particular, the dif-
ficult areas of health, education and social protection services are now covered, as are insurance and financial services. 
These are included in the HICPs according to agreed definitions, thus ensuring comparability despite major institutional 
differences.

Geographic and population coverage

The HICP Framework Regulation required the HICPs to be based on the prices of goods and services available for purchase 
on the economic territory of the EU Member State for the purposes of directly satisfying consumer needs. As regards the 
economic territory and the consumers concerned, a harmonised definition of the geographic and population coverage of the 
HICP was necessary both to achieve comparability and to avoid gaps or double counting when aggregating national HICPs.

HICP coverage includes expenditure by foreign visitors and expenditure by individuals living in institutions, but excludes 
expenditure by residents whilst in a foreign country (this is the so-called domestic concept). All private households are to 
be included, irrespective of the area in which they live or their position in income distribution. Expenditure incurred for 
business purposes should be excluded.

The choice of the domestic concept reflected the role of the MUICP in measuring price stability in the euro area. Price 
changes in the euro area are measured by aggregating price changes taking place within the individual EU Member States. 
Expenditure and price changes to be measured within the economic territory should include those affecting foreign visitors 
and exclude those affecting residents whilst in a foreign country.

It is an HICP requirement that HICPs should be compiled using weights which reflect the HFMCE of all households. HICPs 
which cover only a subset of households should nevertheless be regarded as comparable if this difference in practice ac-
counts for less than one part per thousand of the total expenditure to be covered by the HICP.

Computation issues and price sampling

Weights for the HICP sub-indices

The weights for the HICP sub-indices are the aggregate expenditure by households on any set of goods and services covered 
by the HICP, expressed as a proportion of the total expenditure on all goods and services within the coverage of the HICP.

The quality requirements for HICP weights call for a minimum action of review and adjustment to ensure sufficient quality. 
A comparability threshold determines when weights must be reviewed and updated. Imposing the cost of high precision for 
all weights or frequent updating of weights was not considered justified at the outset of the project.

The review requirement involves checking each year those weights which are judged to be most critical for reliability and 
relevance and, hence, for the comparability of the overall HICP. These are primarily the weights for index components where 
significant market changes have accompanied atypical price movements. Where a weight is identified as deficient, EU Mem-
ber States should make an improved estimate and introduce an appropriate adjustment, from the following January index, 
where this would exceed the threshold effect of 0.1 percentage points (on average for one year compared with the previous 
year). The aim is to ensure that the adjusted weights are the best estimates that can be made on the information available.

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Index formulae

Methodological articles

The choice of the index formula to be used for the HICP is made at two levels:

– the level of the macro-formula; that is, the choice between a chained index with annual links and a fixed base index with 

links up to five years;

– the level of the micro-formula; within each level there is the issue of reference period, both for prices and for weights.

Macro index formula

The HICP is required to be a Laspeyres-type index. Although the HICPs produced by the EU Member States differ in detail, 
they can all be broadly described as Laspeyres-type indices. They are all price indices in which the month-to-month move-
ments in prices are measured as an average of price indices using expenditure weights which are an appropriate reflection 
of the consumption pattern of the consumer population in the weight reference period.

In practice, there are three types of common reference or base periods used in the construction of HICPs:

– the “weighting reference period” is defined as the 12-month period to which the volumes of the current expenditure 

weights refer;

– the “price reference period” is the period from which the current price change is measured and from which prices 
are used as denominators in the index calculations; it refers to the prices used for the volume valuation in the current 
weights;

– the “index reference period” is the period for which the index is set to 100 index points.

The HICP is, depending on the macro-formula applied in practice for its computation, potentially a chained index. It should 
be stressed that this is the equivalent chain form of the fixed base index which simply allows chained and fixed indices to 
be expressed by a common formula. The chaining becomes effective if and only if there are changes to the weights currently 
used, for instance on the grounds of the HICP review requirements for weights.

In practice, some EU Member States compile fixed base HICPs while others compute chain HICPs with annual weight 
updating. In order to obtain a set of HICPs with sub-indices allowing consistent aggregations, it is necessary to present 
the HICPs as if they were all computed with the same formula. Hence, it was necessary to apply a common price reference 
period and a common index reference period.

Since 2006, the index reference period has been set to 2005 = 100. In order to obtain a common price reference period, too, 
the weights for each year are “price updated” to December of the previous year.

Elementary aggregate indices

HICP methodology defines an elementary aggregate by reference to the expenditure or consumption covered by the most 
detailed level of stratification of the HICP. In practice, reliable expenditure information is normally not available for weight-
ing purposes within an elementary aggregate. As a consequence, an elementary aggregate index is a price index for an el-
ementary aggregate comprising only price data.

For the HICPs, elementary aggregate indices are computed as the ratio of geometric mean prices or the ratio of arithmetic 
mean prices. The arithmetic mean of price relatives may only be applied in exceptional cases and where it can be shown 
that it is comparable.

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The level at which macro-aggregation changes into elementary aggregation

The level of elementary aggregation interacts with other design features such as sampling procedures and the availability of 
weighting information. Depending on the sources of the weights used, elementary aggregation may start at different levels 
in different countries in the product, geographic and outlet hierarchies.

Differences in national practices can affect the resulting HICPs but this issue was, in the first instance, not considered to 
be a priority for harmonisation and no action has been undertaken up until now. This issue is likely to be taken up again as 
the harmonisation process develops.

Computation of country aggregates

The HICP country group aggregates for the euro area, the EU and the EEA are calculated by Eurostat using the HICPs 
provided by the Member States.

The computation consists of three main steps. For all countries, price changes since December of the previous year are 
derived from the HICPs. Then the weighted average of these national price changes is computed, using the weights of the 
countries and sub-indices concerned. The weight of a country is its share of HFMCE in the total of the country group. The 
annual price change of the country group is then chain-linked to December of the previous year in order to provide a series 
with a common reference period.

The euro area aggregate, the MUICP, is compiled as a weighted average of the countries comprising the euro area. The coun-
try weights are derived from national accounts data for HFMCE, naturally expressed in euro. The index is computed as an 
annual chain index allowing for country weights to change each year and, consequently, for adding new Member States as 
they join the euro area.

For the EU and EEA HICP aggregates, the euro area is treated as a single entity to which data for the other countries is 
then added (the weights again use national accounts data, converted into purchasing power standards). Note that for EU 
enlargement in May 2004, chain-linking was also added in May to maintain the correct country coverage for both the EU 
and EEA aggregates.

Sampling of prices

To achieve a reliable and comparable representation of the HICP target universe, each country should select a target sample, 
which is a set of products. Prices should then be observed for the selected products over time. Where products cease to be 
available on the market they should be replaced by comparable items from the same consumption segment.

There are three important sampling dimensions to take into account:

– the elementary product groups, defined as a set of products that are sampled in order to represent one or more consump-

tion segments in the HICP;

– the outlet dimension;

– the regional dimension.

An elementary product group can be stratified, for instance by regions, cities or outlet types. The entity at the lowest level 
of stratification is referred to as an “elementary aggregate”.

Random sampling is not easily achieved when it comes to collecting prices for a CPI and in practice most EU Member 
States follow purposive sampling procedures for their HICPs. Sufficiently large sample sizes are necessary to ensure that 
comparability is achieved. HICPs which have enough elementary aggregates to represent the target universe and enough 
prices within each elementary aggregate to take account of the variation of price movements in the population are regarded 
as reliable and comparable.

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Methodological articles

Where no price observation is possible because the product offer is not available, estimated prices can be used for a maxi-
mum of two consecutive months. From the third month onwards, the product offer needs to be replaced in the sample. 
Appropriate quality adjustment needs to be applied in this case.

In 2007, more than 2.7 million prices were collected for the HICP every month in the 27 EU Member States. Approximately 
370 000 outlets are surveyed in more than 2 300 cities or municipalities.

Price collection and treatment of price observations

Timing of price collections and of entering prices into the HICP

Price collections for goods must take place across at least one working week period at, or near, the middle of the calendar 
month to which the index pertains. For products that are known to typically show sharp irregular price changes within the 
same month, prices are collected over a period of more than one working week. This holds in particular for energy products 
and for fresh food, such as fruit and vegetables.

While prices for goods are entered into the HICP for the month in which they are observed, prices for services are entered 
into the HICP for the month in which the consumption of the service at the observed prices can commence.

The treatment of price reductions

HICP methodology requires that price reductions must be: (i) attributable to the purchase of an individual good or service; 
(ii) available to all potential consumers with no special conditions attached; (iii) known to the buyer at the time when he 
or she entered into the agreement to buy the product concerned; and (iv) claimable at the time of purchase or within such 
a time period from the actual purchase that they might be expected to have a significant influence on the quantities buyers 
are willing to buy.

Specific guidelines advise on how various price reduction schemes should be treated, such as sales prices, credit and pay-
ment arrangements, inducements, discounts, rebates or refunds.

Missing or rejected price observations

In order to ban practices which can lead to serious bias, EU Member States are asked to maintain and provide a statement of their 
target sample from month to month. Where prices are not observed, they must be estimated by an appropriate procedure.

In general, the prices reported by the price collectors should be accepted. Rejection or adjustment of reported prices, for 
example the correction of an unusually high or low price change, should not be carried out by automatic procedures, but 
only by reference to specific information on the individual price observation, such as a repeat observation. If, following a 
validation procedure, the reported price must nevertheless be rejected, the rejected price should be treated according to the 
rules for missing observations.

The guidelines leave it to EU Member States to apply methods other than those specified. Where an EU Member State does 
not use the methods specified, Eurostat may request it to show that the resulting HICP does not differ systematically from 
an HICP constructed in line with the specified methods by more than 0.1 percentage points on average, taking one year 
against the previous year.

Prices for new products

The HICP Framework Regulation requires HICPs to be kept relevant, meaning broadly in step with each other and up to 
date in terms of market developments. Neither the formulae used to calculate the index nor the frequency with which the 
basket of goods and services is renewed can fully address the basic problem: the risk of bias if the introduction of new prod-
ucts is used as an opportunity to increase or decrease prices.

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What is meant by the term “new product” is not always precise. In particular, there is no sharp dividing-line between new 
models and varieties of previously existing products and genuinely new innovative products which fulfil needs that could 
not be fulfilled before. 

In the HICP, new varieties are mostly introduced as a replacement and the prices are then subject to quality adjustment, 
while new innovative products are introduced by addition.

Quality adjustment

For the HICPs, quality change is said to occur whenever the change in specification has resulted in a significant difference in 
utility (or functionality) to the consumer between a new variety or model of a good or service and the good or service previ-
ously selected for pricing. A quality change does not arise when there is a comprehensive revision of the HICP sample.

Quality adjustment is defined as the procedure of making an allowance for a quality change by increasing or reducing the ob-
served current or reference prices by a factor or an amount equivalent to the value of that quality change. Quality adjustments 
should be based on explicit estimates of the value of the quality change. Where no estimates are available, price changes 
should be estimated as the whole difference between the price of the substitute and that of the item it has replaced. 

EU Member States are required to examine their quality adjustment procedures and to avoid the so-called automatic link-
ing method, which is equivalent to the assumption that the difference in price between two successive models is wholly 
attributable to a difference in quality. They should monitor the incidence of quality changes and the adjustments made in 
order to demonstrate their compliance with HICP standards.

Despite the existing legal standards, differences between HICPs may arise because the same changes in the physical char-
acteristics of an item are still perceived and treated in different ways in different countries. This is not to say that the same 
quality characteristic must be valued to the same extent in different EU Member States, only that the principles and pro-
cedures for valuation should be harmonised. In practice, differences in quality adjustment procedures between countries 
may not average out across the goods and services covered by the indices. On the contrary, they are likely to cumulate to 
differences well in excess of 0.1 percentage points.

Quality adjustment is one of the most, if not the most, intractable harmonisation issues for the HICP. Eurostat and the EU 
Member States are currently involved in developing and rating quality adjustment methods. So far, standards have been 
agreed for clothing, footwear, books, recorded media, computer games, and for cars and other vehicles.

Newly significant goods and services

Newly significant products should be incorporated in the HICP as soon as they achieve a sales volume of one part per thou-
sand of total consumer expenditure in the EU Member State.

Additions are brought into the index for two main reasons:

– a new product (e.g. MP3 player) was not represented in the index, and would not normally be considered as a replace-

ment because it was radically different from the existing products;

– a product was previously available, but was not explicitly represented in the index because consumption of it was too low.

In the case of new products added, the price of the new product is collected in addition to the products already observed; 
weights for the relevant category of COICOP/HICP are adjusted.

Tariff prices

Many tariff prices faced by consumers relate to products which are or have been regulated by government, or are or have 
been provided in a monopoly or a monopoly-like situation. Changes are, however, taking place in many EU countries in 

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3

Methodological articles

such markets, as the markets are opened up, and it is important that the impact on consumer inflation is appropriately 
captured in the HICPs since such products account for a large proportion of total expenditure.

HICP sub-indices involving tariff prices are, in practice, often obtained centrally or directly from suppliers such as major 
retail chains, or computed by the Member States based on data on tariff prices and their underlying consumption patterns 
provided by suppliers.

The requirement for the HICP is not only to ensure that EU Member States measure the same price change in a comparable 
and reliable way, but also to give such legal powers as are necessary to ensure that Member States are in position to have 
access to the data they need.

Insurance

Since January 2000, the HICPs have covered all insurance connected with a dwelling which is typically paid by the tenant, 
not only contents insurance, and private health, civil liability and travel insurance. Life insurance is excluded from the cov-
erage of the HICP, as it is considered a household saving.

Weights and prices for insurance should be measured net of claims. However, a price index for gross premiums may be used 
as a proxy or estimate for changes in the “prices” of net premiums.

Health, education and social protection services

The purchaser prices of goods and services in the health, education and social protection sectors to be used in the HICP 
should, in accordance with ESA 95, be the amounts to be paid by consumers net of reimbursements.

Reimbursements are defined as payments to households by government units, social security administrations or non-profit 
institutions serving households, which are made as direct consequences of purchases of individually specified goods and 
services, initially paid for by households. Payments of claims to households by insurance companies do not constitute re-
imbursements.

A specific legal standard clarifies the treatment of health care reforms in the HICP. In fact, changes in consumer prices 
should not be measured simply as a result of changes in the eligibility and access rules for social health insurance. Rather 
changes of the prices within one and the same scheme should be accounted for by means of adjusting the weights and chain-
ing the price indices.

Financial services

EU Member States traditionally followed different practices in measuring prices of financial services in their national CPIs 
and applied different methods of defining the weights. There was scope for non-comparability in the exclusion of service 
charges expressed as a proportion of transaction values. A harmonised methodology for the treatment of such charges was 
thus considered necessary.

The  harmonised  methodology  says  that  where  service  charges  are  defined  as  a  proportion  of  the  transaction  value,  the 
purchaser prices should be defined as the proportion itself, multiplied by the value of a representative unit transaction in 
the base or reference period. The HICP should include charges expressed as a flat fee or flat rate but exclude interest pay-
ments and interest-like charges. Changes in purchaser prices which reflect changes in the rules determining them, as well 
as changes in the purchaser prices resulting from changes in the values of the representative unit transactions, should be 
shown as price changes in the HICP. The change in the values of the representative unit transactions may be estimated using 
the change in a price index which represents appropriately the unit transactions concerned.

152

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Methodological articles

3

Release and timeliness of the HICPs

Full HICPs

The full set of HICPs is published each month according to a pre-announced schedule — in general between 14 and 16 days 
after the end of the month in question. This schedule has advanced significantly since the HICP was first published, as a 
result of a series of improvements to timeliness made in both the EU Member States and at Eurostat.

Flash estimate of the MUICP

Eurostat also publishes a monthly flash estimate for the MUICP — the HICP for the euro area as a whole. This flash esti-
mate is based on the results from the first countries to publish their national estimates and on energy price data. It gives an 
early indication of what the MUICP is likely to show when the full data set is available. The estimation procedure combines 
historical information with partial information on price developments in the most recent months to give a total index for 
the euro area. No detailed breakdown is available.

Over the two years up to December 2007, the flash estimate exactly anticipated the inflation rate 17 times, and seven times 
differed by 0.1. The MUICP flash estimate is generally released on the last working day of the month in question.

Data

The HICP data which are released each month cover the price indices themselves, annual average price indices and rates of 
change, monthly and annual rates of change, and 12 month moving rates of change. None of these are seasonally adjusted.

As well as the all-items HICPs, the full range of around 100 COICOP/HICP indices for different goods and services are 
made available. The main headings are as follows:

– food;
– alcohol and tobacco;
– clothing;
– housing;
– household equipment;
– health;
– transport;
– communications;
– recreation and culture;
– education;
– hotels and restaurants;
– miscellaneous.

In addition, a series of special aggregates is released, for example:

– the HICP all items, excluding energy;
– the HICP all items, excluding energy, food, alcohol and tobacco;
– the HICP all items, excluding unprocessed food;
– the HICP all items, excluding energy and seasonal goods;
– the HICP all items, excluding tobacco.

The weights for the full range of indices including the special aggregates are made available for the individual countries 
and for all country groups. All of the HICPs, including the complete list of component indices and special aggregates, are 
accessible via the Eurostat website63.

63 http://ec.europa.eu/eurostat

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3

Metadata 

Methodological articles

The HICP section64 on the Eurostat website provides access to a full range of HICP information. It gathers together in one 
place the explanations, reference documents and data already available on Eurostat’s website and will be progressively ex-
tended to give further information on key projects, methods and data. 

Revisions

Since the main purpose of the HICP is to inform ECB monetary policy for the euro area, and the HICP is a revisable index, 
clear and transparent policy on revisions is of paramount importance. Also, for the HICP harmonisation process, a deci-
sion was needed on how to implement improvements whilst at the same time minimising the difficulties caused to users by 
introducing discontinuities into the published HICP series.

The published HICP series may be revised for mistakes, new or improved information, and changes in the system of har-
monised rules. In particular: 

– Mistakes should be corrected and any revisions that may result from such corrections should be implemented without 

unnecessary delay.

– New or improved information, for example a more up-to-date weighting structure, may result in revision, provided that 

Eurostat does not oppose the timing of the revisions to be made.

– Changes in the system of harmonised rules should not require revision of published HICPs unless otherwise stated in 
the particular implementing measure. The impact of such changes should be assessed. Only if the impact is likely to be 
significant should it then be estimated for each of the 12 following months, starting with the index for the January in 
which the change takes place.

Agenda for further harmonisation

The progress made on harmonising consumer price indices does not mean that development is at an end. There are several 
major issues where further harmonisation will still be necessary. Currently, work is in progress on:

– Quality adjustment: A HICP standard addressing this issue was adopted in 2007 and laid down the legal basis for im-
plementing concrete methods. Eurostat and the EU Member States are following up an action plan to implement previ-
ously agreed best practices for a range of specific goods and services, in particular for cars, consumer durables, books, 
recordable media, clothing, computers and telecommunications services. Further specific standards will be developed 
in close cooperation between Eurostat and the EU Member States.

– Sampling and weightings: Eurostat and the EU Member States plan to develop additional requirements for the HICP.

– Owner-occupied housing: services provided by owner-occupied housing are currently excluded from the HICPs. Pi-
lot calculations are being carried out using an approach based on the acquisition prices of housing that is new to the 
household sector — mainly newly constructed dwellings. Indices will be compiled separately from the HICPs on an 
experimental basis before any decision is made to incorporate them in the HICPs.

– Assessment  and  follow  up  of  Member  States’  compliance  with  the  legal  framework  and  documentation  of  Member 
States’ methods: The implementation of the new strategy on HICP compliance monitoring started in the second half 
of 2006 and will be further developed in the coming years. This strategy has involved the introduction of a “Country 
Desk” approach which facilitates the development of country-specific expertise. This means that each country has a 
nominated Eurostat officer who is responsible for monitoring developments in each country and overseeing all aspects 
of compliance monitoring. This requires identification of issues, analysis of data, and follow-up and implementation of 
the recommendations emanating from the compliance monitoring exercises.  

64 http://epp.eurostat.ec.europa.eu/pls/portal/url/page/PGP_DS_HICP.

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Methodological articles

3

– Impact of indirect taxes and administered prices on the HICP: Pilot work is ongoing for the definition and computation 

of experimental indices in order to describe the impact of taxes and administered prices on the HICPs.

Other issues currently on the agenda include:

– the need for more comprehensive quality assurance of the HICP compilation process in the widest sense;

– support for those countries seeking to join the EU to ensure that their HICPs are fully comparable;

– consolidation of the legal framework for HICPs, and the production in due course of a methodological manual to assist 

both compilers and users;

– treatment of seasonal items: Eurostat and the EU Member States plan to develop additional requirements.

  European Economic Statistics

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Statistical annex
Statistical annex

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Statistical annex

4

Introduction

This statistical annex contains tables of the key data presented in the data analysis section of this publication. The tables 
are presented for the most recent time periods available, broken down by country and including European aggregates as 
appropriate. It is important to note that the data presented are those available at end-April 2008, and therefore readers 
are encouraged to visit the Eurostat public database (accessible through the Eurostat website www.ec.europa.eu\eurostat) 
for more recent data. 

  European Economic Statistics

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4

Statistical annex

Table 4.1: 

GDP at current prices, millions of euros

European Union

9,175,444

9,552,881

9,912,884

10,079,553

10,580,833

11,024,384

11,621,711 12,276,233

2000

2001

2002

2003

2004

2005

2006

2007

Euro area 

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

6,745,504

7,039,536

7,285,212

7,500,988

7,805,362

8,080,455

8,459,246 8,866,577

251,741

258,883

267,652

274,726

289,690

301,966

316,622

330,800

13,704

61,495

15,250

69,045

16,623

80,004

17,767

80,924

19,875

88,262

21,882

25,238

28,899

100,320

114,021

128,130 e 

173,598

179,226

184,744

188,500

197,070

207,756

220,069

227,665

2,062,500

2,113,160

2,143,180

2,163,800

2,211,200

2,244,600

2,322,200 2,423,800

6,103

104,620

137,929

6,916

116,939

146,261

7,757

130,215

157,586

630,263

680,678

729,206

8,693

139,414

171,258

782,929

9,582

11,210

13,234

15,547

148,502

185,225

841,042

161,498

198,609

908,450

174,705

185,632 e 

213,985

228,949

980,954 1,049,848

1,441,373

1,497,187

1,548,559

1,594,814

1,660,189

1,717,921

1,791,953 1,867,345 e 

1,191,057

1,248,648

1,295,226

1,335,354

1,391,530

1,428,375

1,479,981 1,535,540

10,079

8,496

12,360

22,001

52,025

4,221

417,960

210,392

185,714

122,270

40,346

21,125

22,017

132,272

266,422

10,801

9,320

13,562

22,572

59,512

4,301

447,731

215,878

212,294

129,308

44,904

22,423

23,520

139,868

251,340

11,170

9,911

15,023

23,992

70,714

4,489

465,214

220,841

209,617

135,434

48,442

24,134

25,955

143,974

264,244

11,785

9,978

16,452

25,726

74,682

4,421

476,945

226,175

191,644

138,582

52,613

25,328

29,465

145,938

275,657

12,728

11,176

18,126

27,439

82,322

4,488

491,184

236,149

204,237

144,128

60,842

26,739

34,023

152,345

287,689

13,659

13,012

20,673

30,032

88,914

4,756

14,631

16,047

23,721

33,852

15,561

19,936

28,018

35,982 e 

89,901

100,951

5,067

5,377

508,964

534,324

559,537

245,330

244,420

149,123

79,587

28,252

38,480

157,335

294,674

257,897

272,766

272,131

307,346

155,278

162,919

97,718

121,431

30,454

44,571

33,542

54,827

167,041

178,759

313,327

332,303

United Kingdom

1,573,359

1,613,355

1,678,980

1,615,984

1,745,051

1,804,586

1,912,656 2,023,589

Iceland

Liechtenstein

Norway

Switzerland

e  
:  

Estimate
Estimated value

9,421

2,693

182,579

270,918

8,830

2,784

190,956

284,886

9,475

2,857

204,074

296,018

9,711

2,718

199,146

287,754

10,657

2,782

208,256

292,382

13,118

2,942

242,935

299,473

13,305

14,600

:

:

267,892

284,712 e 

309,096

309,415

160

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Table 4.2:

GDP per capita in Purchasing Power Standards, EU27=100

2000

2001

2002

2003

European Union

Euro area 

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Iceland

Norway

Switzerland

b 
e 

Break in series
Estimated value

100

114

126

28

69

132

119

45

131

84

98

116

117

89

37

39

244

56

84

135

134

48

78

26

79

50

118

127

117

132

165

145

100

114

124

29

70

128

117

46

133

87

98

116

118

91

39

42

235

59

78

134

127

48

78

28

79

52

116

122

118

133

162

140

100

113

125

31

71

129

115

50

138

91

101

116

112

89

41

44

241

62

80

134

128

48

77

29

81

54

116

121

119

130

155

140

100

112

123

33

74

124

117

55

141

92

101

112

111

89

43

49

247

63

79

130

129

49

77

31

82

56

113

123

120

126

157

136

Statistical annex

4

2004

100

111

121 b

34

75

126

117

57

142

94

101

110 b

107

91

46

50

253

63

77

129

129

51

75

34

85

57

117

125

122

131

165

135

2005

2006

100

111

121

35

77

127

115

63

144

96

103

112

105

93

50

53

264

64

77

131

129

51

75

35

87

61

115

124

119

135

180

134

100

110

120

37

79

126

114

68

145

97

105

111

103

92

54

56

279

65

77

131

128

52

75

39 f

88

64

117

125

118

131

186

135

  European Economic Statistics

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4

Statistical annex

Table 4.3: 

Gross value Added by industry, % total Gross value Added, 2006

Agriculture, 
hunting, 
forestry and 
fishing

Total industry 
(excluding 
construction)

Construction

Trade, 
transport and 
communication 
services

Financial 
services and 
business 
activities

Other services

European Union

Euro area 

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Iceland

Norway

Switzerland

1.8

1.8

0.9

8.5

2.6

1.6

0.9

3.1

1.7

3.7

2.9

2

2.1

2.6

3.5

5.2

0.4

4.2

2.8

2.2

1.7

4.3

2.9

8.8

2.3

3.9

2.5

1.3

0.9

6.1

1.5

1.2

20.2

20.3

19.2

24.1

31.7

20.5

25.4

21

25

15.7

18.2

14.4

20.7

10.8

14.5

24.9

9.3

25.4

17.7

18.6

23

24.7

17.8

27.5

27.4

28.6

26.4

23.7

17.5

15.5

40.5

21.9

6.2

6.4

5

6.8

6.4

5.5

4

7.4

9.9

8.6

12.2

6.3

6.1

8.3

7.4

8.8

5.3

4.8

3.9

5.5

7.7

6.5

6.5

8.4

6.2

6.9

6

4.8

5.4

10.5

4.6

5.6

21.2

20.8

23.1

23.8

25.5

21.5

17.9

29.6

16.8

30.1

24.6

18.6

22.8

27.5

34.2

31.1

21

20.6

27.6

21.9

23.6

27.5

24.4

25.4

22

26.6

22.1

19.5

21.3

18.4

16

22.1

28

27.9

28.4

21.5

16.8

24

29.5

23.2

26.4

18.2

21.3

32.8

27.2

26.4

21.7

14.3

48.5

22.3

21.3

27.7

23.4

18.2

22

17.6

21.7

18.7

21

24.2

32.7

27.6

17.5

23

22.6

22.8

23.4

15.3

17

27

22.3

15.8

20.2

23.7

20.9

25.8

21.1

24.4

18.7

15.7

15.6

22.7

26.7

24.1

20.7

18.8

26.3

12.2

20.3

15.2

22

26.5

22.3

21.9

19.9

26.1

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Statistical annex

4

Table 4.4: 

Expenditure Components, % of GDP, 2006

Private final 
consumption

Government final 
consumption

Gross capital 
formation

External balance of 
goods and services

European Union

Euro area 

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Iceland

Norway

Switzerland

57.8

57

52.5

70.4

48.8

49.1

58.5

54.1

45.5

71

57.4

56.7

59.1

64.5

65.2

65.3

36

53.6

63.3

47.4

55.4

62

65

68.9

53.4

56.8

51.4

47.4

63.5

58.7

40.8

59.2

20.8

20.3

22.4

16.6

21.2

25.7

18.3

16.4

16

15.8

18.1

23.6

20.2

18.6

16.6

18

15.3

22.9

19.9

25.4

18

18.3

20.7

16.6

19.2

19

21.8

26.3

22

24.4

19.3

11.1

21.1

21.6

22

31.7

26.9

22.5

17.8

38.2

27.2

25.7

30.6

21.1

21.5

20.5

39.7

27

19

22.9

20.7

19.7

20.9

21.1

22.2

26.5

28.4

28

20.7

18.1

18

34.8

21.7

22.1

0.2

1.1

3.2

-18.8

3.1

2.7

5.4

-11.3

10.7

-12.6

-6.1

-1.4

-0.8

-3.7

-21.5

-10.4

29.7

0.6

-4

7.4

5.7

-1.4

-8

-12

-1

-3.8

5

8.2

-3.6

-18

18.2

7.5

  European Economic Statistics

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4

Statistical annex

Table 4.5: 

Income components, % of GDP, 2006

Compensation of 
employees

Wages and 
salaries

Employer’s social 
contributions

Gross operating 
surplus and 
mixed income

Taxes on 
production and 
imports less 
subsidies

48.7

47.7

50.0

32.3

42.8

52.9

49.5

44.5

41.8

35.9

46.5

51.9

41.1

44.8

43.9

42.7

45.2

47.8

43.9

49.2

48.5

35.6

50.2

40.7

51.4

36.8

48.3

53.7

55.5

57.5

40.6

62.1

38.4

36.8

37.0

26.2

32.3

48.1

39.9

33.8

38.8

28.1

36.3

38.1

30.1

39.3

37.0

33.7

39.3

:

39.5

38.5

39.1

30.7

:

:

44.1

28.5

38.7

39.9

46.6

:

33.2

:

10.3

10.8

12.9

6.2

10.5

4.8

9.6

10.7

3.0

7.8

10.2

13.8

11.1

5.5

7.0

9.0

5.9

:

4.4

10.8

9.4

4.8

:

:

7.3

8.3

9.6

13.8

8.9

:

7.4

:

38.9

40.1

38.2

49.1

48.2

31.9

39.6

43.4

46.0

53.8

42.4

34.4

44.9

38.8

44.4

47.4

43.9

41.6 e

42.6

38.9

40.9

51.0

: e

: e

35.2

53.5

39.7

31.3

32.4

24.8

49.1

34.7

12.4

12.2

11.8

18.6

9.0

15.2

10.9

12.1

12.8

10.4

11.1

13.6

13.9

16.4

11.7

9.9

10.9

13.1

13.5

11.8

10.6

13.4

:

:

13.4

9.7

12.0

15.0

12.2

17.7

10.4

3.1

European Union

Euro area 

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Iceland

Norway

Switzerland

e  
:  

Estimated value
not available

164

European Economic Statistics 

2008_1393_anto.indd   164

7-07-2008   10:20:29

Statistical annex

4

Table 4.6: 

volume GDP growth rate, percentage change on previous year

2000

2001

2002

2003

2004

2005

2006

2007

European Union

Euro area 

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Iceland

Norway

Switzerland

e  

Estimated value

3.9

3.8

3.7

5.4

3.6

3.5

3.2

9.6

9.4

4.5

5

3.9

3.7

5

6.9

4.1

8.4

5.2

:

3.9

3.4

4.3

3.9

2.1

4.1

1.4

5

4.4

3.8

3.3

3.6

2

1.9

0.8

4.1

2.5

0.7

1.2

7.7

6.1

4.5

3.6

1.9

1.8

4

8

6.6

2.5

4.1

-1.6

1.9

0.8

1.2

2

5.7

3.1

3.4

2.6

1.1

2.4

2

1.2

1.2

0.9

1.5

4.5

1.9

0.5

0

8

6.6

3.9

2.7

1

0.5

2.1

6.5

6.9

4.1

4.4

2.6

0.1

0.9

1.4

0.8

5.1

3.7

4.8

1.6

2.4

2.1

1.5

0.4

1.3

0.8

1

5

3.6

0.4

-0.2

7.2

4.5

5

3.1

1.1

0

1.9

7.2

10.3

2.1

4.2

-0.3

0.3

1.2

3.9

-0.8

5.2

2.8

4.8

1.8

1.9

2.8

1

-0.2

2.5

2.1

3

6.6

4.5

2.3

1.1

8.3

4.4

4.6

3.3

2.5

1.5

4.2

8.7

7.3

4.9

4.8

0.2

2.2

2.3

5.3

1.5

8.5

4.4

5.2

3.7

4.1

3.3

3.9

2.5

1.9

1.6

1.7

6.2

6.4

2.5

0.8

3.1

2.8

2.8

6.3

6.4

3.9

2.9

10.2

11.2

6

3.8

3.6

1.7

0.6

3.9

5.7

4.2

3.9

2

1.8

4

10.6

12.2

7.9

5

4.1

3.4

1.5

2

3.6

0.9

4.2

4.1

6.6

2.8

3.3

1.8

2.7

2.4

7.7

6.1

3.9

3.4

3

3.3

6.2

1.3

7.9

5.7

8.5

4.9

4.1

2.9

2.2

3.2

2.9

2.6

2.8

6.2

6.5 e 

1.8

2.5

7.1

5.3 e 

4

3.8

1.9 e 

1.5

4.4

10.3

8.8

5.1 e 

1.3

3.8

3.5

3.4

6.5

1.9

6 e 

6.1

10.4

4.4

2.6

3

3.5 e 

3.1

  European Economic Statistics

165

2008_1393_anto.indd   165

7-07-2008   10:20:30

4

Statistical annex

Table 4.7: 

Labour productivitity per person employed, % change

European Union

Euro area 

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Iceland

Norway

Switzerland

e  

Estimated value

2002

2003

2004

2005

2006

2007

2000

2.17

1.38

1.74

0.47

3.83

3

1.31

11.21

4.51

3.98

-0.04

1.2

1.72

3.31

10.14

8.39

2.73

3.87

:

1.65

2.25

2001

0.99

0.37

-0.64

4.86

1.99

-0.2

0.8

6.77

2.97

4.24

0.47

0.08

-0.2

1.79

5.74

10.86

-2.86

3.81

-3.31

-0.13

0.25

0.86

0.19

1.66

4.26

1.33

0.43

0.56

6.63

4.71

1.89

0.29

0.4

-1.23

0.02

4.79

3.18

0.84

4.33

2.04

-0.42

0.85

0.93

0.34

0.94

1.99

5.02

1.48

0.74

5.7

2.43

3.11

-0.02

0.96

-1.49

-1.8

5.41

7.91

0.28

2.84

-1.32

0.83

0.97

1.85

1.34

2.28

3.95

4.13

2.93

0.66

8.29

1.23

3.69

-0.26

2.36

1.09

0.4

7.48

7.35

2.6

5.52

0.82

3.13

1.67

0.92

0.67

0.45

3.45

5.35

1.61

0.87

8.02

1.26

2.3

-0.44

1.26

-0.02

0.34

8.72

5.32

2.09

4.14

2.01

1.27

0.68

5.92 e

3.48 e

4.57 e

5.13 e

3.99 e

1.26 e

1.8

:

2.8

3.42

2.74

1.9

2.6

2.25

2.67

2.51

0.2

:

2.63

2.8

1.15

-1.02

1.53

2.22

1.63

-0.46

0.17

:

2.09

4.67

0.68

2.36

1.27

1.62

1.12

0

-0.24

5.27

3.22

3.64

1.67

2.51

1.8

2.27

2.07

-0.03

1.62

10.29

4.12

5.45

3.3

4.86

2.19

8.14

3.38

2.25

1.24

5.79

3.97

5.12

1.43

3.03

0.82

4.07

1.5

1.99

1.44

1.16

1.57

2.88

4.4

2.24

2.23

5.51

1.37

1.7

0.11

1.23

-0.15

2.26

7.2

5.85

2.33

3.19

2.21

1.13

2.28

2.77 e

0.62 e

4.93 e

4.48

6.11

3.04

2.34

2.04

-0.68 e

-1.15

0.95

1.24

0.82

1.07

3.25

4.61 e

-0.02

0.74

6.31

1.61 e

2.72

0.66

0.64 e

0.32

1.08

6.61

6.7

0.59 e

1.47

1.07

1.07 e

1.41

2 e

1.69 e

4.73 e

3.3

8.07

2.15

0.34

2.32

-0.67 e

-0.29 e

1.44 e

166

European Economic Statistics 

2008_1393_anto.indd   166

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Statistical annex

4

Table 4.8: 

Household saving rate

Calculated in % as: gross saving / gross disposable income 
(adjusted for changes in the net equity of households in pension funds reserves)

EU 27

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

2000

11.7%

13.5%

15.4%

:

8.5%

4.9%

15.1%

4.1%

:

10.5%

11.1%

14.9%

14.2%

:

2.9%

4.1%

:

:

:

12.1%

12.8%

10.7%

10.2%

:

13.9%

11.1%

7.4%

8.2%

5.1%

2001

12.5%

14.2%

16.4%

:

7.4%

8.8%

15.2%

3.1%

:

9.7%

11.1%

15.6%

16.0%

:

-0.4%

3.7%

:

:

:

14.7%

12.0%

12.1%

10.9%

:

15.4%

9.1%

7.6%

12.7%

6.4%

2002

12.4%

14.8%

15.8%

:

8.1%

8.8%

15.7%

0.5%

11.3%

8.3%

11.4%

16.7%

16.8%

:

1.2%

1.8%

:

:

:

13.9%

12.1%

8.4%

10.6%

:

16.2%

8.9%

7.7%

13.4%

5.0%

2003

12.3%

14.6%

14.7%

:

7.4%

9.4%

16.0%

-1.1%

12.2%

8.3%

11.9%

15.6%

16.0%

:

2.4%

0.9%

:

:

:

13.1%

13.2%

7.8%

10.5%

:

13.5%

7.1%

8.3%

13.2%

4.9%

2004

11.9%

14.5%

13.3%

:

4.9%

5.8%

16.1%

-1.0%

12.5%

8.9%

11.4%

15.6%

16.1%

:

2.5%

0.4%

:

11.3%

:

13.0%

13.3%

7.2%

9.7%

:

14.4%

6.2%

9.2%

12.4%

3.7%

2005

11.8%

14.0%

12.2%

:

5.8%

2.5%

16.3%

-0.8%

13.0%

:

10.6%

15.0%

15.9%

:

1.1%

1.5%

:

11.0%

:

12.1%

13.7%

7.7%

:

:

14.2%

7.2%

7.7%

11.8%

5.6%

2006

11.4%

13.8%

12.5%

:

4.9%

:

16.2%

-0.7%

11.0%

:

10.1%

15.3%

14.9%

:

:

:

:

:

:

12.5%

14.1%

:

:

:

:

6.5%

5.5%

:

5.0%

  European Economic Statistics

167

2008_1393_anto.indd   167

7-07-2008   10:20:31

4

Statistical annex

Table 4.9: 

Investment rate of non-financial 
corporations

Table 4.10: 

Profit share of non-financial 
corporations

Calculated in % as: gross fixed capital formation / gross
value added of non-financial corporations

Calculated in % as: gross 
operating surplus / gross value 
added of non-financial corporations

2000

2001

2002

2003

2004

2005

2006

2000

2001

2002

2003

2004

2005

2006

EU 27

22.7% 22.2% 21.4% 21.1% 21.1% 21.9% 22.2%

36.7% 36.7% 36.6% 36.9% 37.6% 37.6% 38.0%

Euro area 13

22.7% 22.1% 21.2% 20.9% 21.1% 21.6% 22.1%

37.6% 38.2% 38.1% 38.2% 38.6% 38.7% 39.0%

Belgium

Bulgaria

23.4% 22.5% 20.8% 20.8% 20.9% 21.4% 21.3%

35.2% 33.9% 34.1% 35.2% 37.1% 38.8% 39.4%

:

:

:

:

:

:

:

:

:

:

:

:

:

:

Czech Republic

33.2% 33.6% 31.9% 30.6% 28.7% 26.8% 25.8%

47.6% 48.1% 47.2% 45.7% 46.7% 47.6% 48.1%

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

25.3% 24.7% 26.0% 25.7% 25.2% 26.2%

:

40.7% 38.1% 37.4% 37.3% 37.9% 39.3%

:

21.1% 19.8% 17.9% 17.7% 17.5% 17.7% 18.1%

36.3% 37.2% 37.5% 37.7% 38.7% 40.0% 41.1%

31.4% 30.4% 33.7% 36.1% 34.5% 31.5% 33.3%

44.7% 46.1% 46.8% 48.0% 48.6% 49.5% 48.2%

:

:

16.1% 15.6% 15.6% 17.3% 16.0%

:

: 58.9% 57.3% 55.4% 54.4% 52.0%

45.0% 49.5% 50.9% 54.5% 52.5%

:

:

53.1% 54.3% 54.9% 56.3% 56.1%

:

:

30.5% 29.9% 29.8% 30.4% 31.5% 33.3% 34.3%

35.8% 35.8% 36.0% 36.0% 36.6% 36.6% 36.9%

19.7% 19.8% 18.7% 18.2% 18.6% 18.9% 19.5%

31.2% 31.3% 30.7% 31.0% 30.8% 30.3% 30.7%

23.8% 23.8% 24.8% 24.1% 24.0% 24.2% 24.9%

46.9% 47.0% 46.3% 45.6% 45.7% 44.3% 43.2%

:

:

:

:

:

:

35.9% 36.0% 34.8% 34.8% 37.6% 37.1%

Lithuania

24.2% 25.5% 23.8% 24.0% 24.0% 23.1%

Luxembourg

Hungary

Malta

:

:

:

:

:

:

:

:

:

:

:

:

:

:

25.1% 17.6%

:

:

:

:

:

:

:

:

:

:

:

:

:

:

49.8% 52.7% 55.7% 54.1% 52.8% 49.6%

51.7% 56.8% 56.7% 56.6% 55.9% 56.3%

:

:

:

:

:

:

:

:

:

:

:

:

:

:

41.4% 43.8%

:

:

:

:

:

:

:

:

Netherlands

18.2% 17.4% 16.8% 15.2% 15.1% 15.0% 15.4%

39.2% 38.8% 38.9% 38.1% 38.5% 39.9% 40.5%

30.2% 29.1% 26.5% 28.2% 27.5% 27.0% 27.2%

40.3% 39.6% 39.7% 40.2% 41.8% 42.2% 43.0%

38.2% 29.4% 25.2% 23.8% 22.4% 22.3%

33.0% 31.8% 29.7% 28.4% 27.0%

:

:

:

:

:

:

:

30.8% 27.9% 26.3% 27.5% 28.1% 28.0%

:

:

:

:

36.7% 33.8% 38.0% 42.1% 47.4% 46.6%

36.7% 37.2% 37.0% 35.5% 36.9%

:

:

:

:

:

:

:

28.4% 29.1% 29.8% 31.8% 31.6% 31.4%

:

:

:

:

32.3% 40.0% 38.0% 36.6% 35.4% 40.9% 38.7%

48.5% 49.9% 47.3% 49.6% 54.4% 51.5% 53.2%

19.8% 20.4% 17.6% 17.1% 16.8% 17.6% 17.9%

45.7% 45.8% 45.5% 45.0% 45.2% 43.8% 45.2%

23.6% 22.8% 21.0% 20.0% 20.1% 21.6%

United Kingdom 18.3% 18.0% 17.7% 16.7% 16.2%

:

:

:

29.3% 25.8% 26.3% 26.8% 28.2% 29.4%

34.6% 33.6% 33.4% 34.1% 34.8%

:

:

:

168

European Economic Statistics 

2008_1393_anto.indd   168

7-07-2008   10:20:31

Statistical annex

4

Table 4.11: 

Total general government expenditure

euro per inhabitant

% of GDP

2000

2001

2002

2003

2004

2005

2006

2007

2000 2001 2002 2003 2004 2005 2006 2007

1,421

5,261

EU27

EA13

Belgium

Bulgaria

:

:

9,562

9,797

10,136 10,544

10,951

11,392

10,167

10,796

11,188

11,562

11,820

12,148

12,497

12,889

12,059

12,368

12,909

13,543

13,676

14,931

14,567

15,286

:

:

854

918

1,018

1,111

1,193

Czech Republic

2,503

3,004

3,632

3,754

3,903

4,404

4,845

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

17,423

18,123

18,757

19,257

19,902

20,203

20,732

21,104

11,320

12,206

12,498

12,715

12,626

12,764

12,803

12,932

1,622

1,775

2,031

2,219

2,422

2,789

3,252

3,905

8,682

10,100

11,139

11,676

12,386

13,170 14,046

15,557

5,899

6,057

6,427

6,987

7,598

7,715

8,111

8,884

6,123

6,458

6,864

7,157

7,660

8,054

8,594

9,067

12,251

12,619

13,230

13,694

14,142

14,669

15,073

15,589

9,660

10,523

10,738

11,201

11,419

11,744

12,262

12,556

5,371

1,338

1,381

5,875

1,368

1,433

6,330

1,511

7,339

1,493

1,507

1,584

7,361

1,729

1,760

7,854

8,251

2,013

2,660

2,037

2,370

8,671

3,329

2,952

Luxembourg 

18,860

19,494

22,330 23,834 25,506

26,996

27,678

28,788

Hungary

Malta

2,370

4,437

2,763

4,710

3,572

4,893

3,618

5,301

3,981

4,401

4,634

5,032

5,120

5,308

5,465

5,588

Netherlands

11,595

12,657

13,313 13,846

13,910

14,090

15,079

15,694

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

13,507

13,636

13,799

14,177

15,213

14,809

15,326

15,803

1,994

2,429

2,425

2,240

2,280

5,158

5,580

5,782

6,039

6,384

731

5,035

2,067

777

5,422

1,940

880

813

944

5,696

5,980

6,223

2,167

2,201

2,390

2,776

6,731

1,235

6,491

2,719

3,126

3,434

6,798

7,019

1,598

2,083

6,870

3,072

7,193

3,747

12,349

12,872

13,502

14,010

14,632

15,126

15,499 16,048

16,697

15,682

16,798

17,526

17,773

18,012

18,746

19,095

United Kingdom 10,623

11,148

11,824

11,628

12,645

13,441

14,115

14,829

Iceland

Norway

14,028

13,195

14,580

15,311

16,050

18,723

17,707

:

17,197 18,664

21,156

21,016 20,609

22,135

23,347

24,643

:  

missing value

:

46.3

49.1

:

41.8

53.6

45.1

36.5

31.5

46.7

39.1

51.6

46.2

37.0

37.3

39.1

37.6

46.5

41.0

44.2

51.4

41.1

43.1

40.6

47.4

50.7

48.3

55.6

39.8

41.9

42.3

:

46.8

47.4

46.8

46.9

47.6

49.8

40.3

46.3

54.6

48.1

35.6

33.6

44.8

38.9

52.6

48.1

51.1

40.3

47.3

55.1

48.5

34.6

33.4

45.0

38.4

53.3

47.5

49.2

39.7

45.1

54.6

47.1

34.1

33.9

45.4

38.9

53.2

47.4

51.8

39.2

44.9

52.7

46.9

33.5

33.8

43.1

38.5

53.4

46.3

46.8

48.5

36.4

43.6

51.2

45.4

33.0

34.2

42.3

38.6

52.7

47.4

48.3

47.7

48.2

48.8

40.2

35.6

34.8

41.5

51.3

43.2

46.2

50.5

44.2

44.3

39.6

47.1

44.9

48.8

56.7

41.8

44.2

45.0

34.8

33.2

41.8

49.1

47.8

47.1

50.9

44.6

45.5

33.6

47.1

40.2

50.0

57.0

42.9

45.6

47.1

48.2

42.8

35.8

33.4

42.6

48.9

45.8

46.1

52.7

42.6

46.5

33.6

46.5

37.8

50.2

55.6

42.6

44.1

45.4

43.6

35.6

33.6

41.8

49.9

45.0

45.2

49.7

43.3

43.6

37.9

33.9

38.6

51.9

43.9

46.1

49.2

43.8

47.6

46.3

33.5

46.0

38.1

50.4

55.2

44.0

42.2

42.1

35.3

45.3

37.2

48.9

54.3

43.9

40.5

40.6

47.3

49.1

:

44.5

54.2

47.6

35.1

33.3

45.3

38.6

51.6

48.0

38.2

34.6

36.8

38.1

47.3

43.1

45.4

50.8

43.8

44.4

38.8

48.2

44.4

47.7

55.5

40.8

42.6

44.1

45.8

46.3

48.9

37.8

42.4

50.6

43.9

33.7

36.4

43.3

38.8

52.6

48.5

43.9

38.0

35.6

37.5

50.1

42.5

45.9

48.2

42.4

45.8

36.9

43.3

36.9

47.5

52.6

43.7

:

40.9

  European Economic Statistics

169

2008_1393_anto.indd   169

7-07-2008   10:20:32

4

Statistical annex

Table 4.12: 

Total general government expenditure; millions of euro

2000

2001

2002

2003

2004

2005

2006

2007

:

:

4,642,714

4,777,071

4,964,412

5,186,503

5,407,046

5,645,777

3,119,942

3,328,487

3,469,021

3,607,282

3,711,263

3,836,235

3,966,013

4,110,916

123,554

127,150

133,354

140,477

142,458

156,385

153,582

161,486

:

25,715

93,002

:

30,717

97,084

6,697

37,047

100,839

7,160

38,292

103,793

7,898

39,838

107,531

8,576

45,070

109,477

9,186

49,737

112,718

10,937

54,282

115,229

930,400

1,005,060

1,030,840

1,049,210

1,041,690

1,052,590

1,054,490

1,063,770

2,225

32,990

64,406

246,542

744,253

550,032

3,728

3,172

4,834

8,270

24,203

1,730

184,612

108,210

76,293

52,740

16,390

10,016

11,165

63,917

148,132

625,566

3,944

77,232

2,426

38,975

66,318

262,982

772,060

599,587

4,121

3,222

4,987

8,607

28,144

1,851

203,063

109,676

92,913

57,432

17,417

10,800

10,436

66,778

139,508

658,985

3,761

84,230

2,764

43,735

70,614

283,597

815,144

613,734

4,496

3,533

5,226

9,964

36,284

1,937

214,960

111,544

92,703

59,945

19,171

11,365

11,657

70,226

149,923

701,417

4,193

96,029

3,009

46,598

77,016

300,643

849,587

645,251

5,305

3,471

5,470

10,766

36,650

2,112

224,621

115,085

85,561

63,057

17,659

11,938

11,838

73,035

156,998

692,471

4,429

95,939

3,272

50,276

84,044

327,015

883,073

664,303

5,445

3,998

6,048

11,684

40,234

2,054

226,403

124,365

87,053

67,040

20,460

12,428

12,864

76,484

159,849

756,606

4,696

94,616

3,758

54,639

85,662

349,524

921,454

688,254

5,952

4,631

6,955

12,559

44,393

2,141

229,905

121,929

105,937

71,009

26,698

12,987

14,649

79,339

162,646

809,386

5,539

4,374

59,743

90,424

378,725

952,516

722,751

6,375

6,086

8,043

13,083

46,668

2,223

246,410

126,926

119,186

71,947

34,489

13,793

16,559

81,623

170,235

855,157

5,389

5,242

67,558

99,249

406,883

991,049

744,817

6,829

7,575

9,965

13,743

50,601

2,287

257,026

131,411

130,871

74,538

44,819

14,520

20,222

84,865

174,683

901,320

:

102,310

108,822

115,779

EU27

EA13

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Iceland

Norway

:  

missing value

170

European Economic Statistics 

2008_1393_anto.indd   170

7-07-2008   10:20:32

Table 4.13: 

Main components of total general government expenditure; millions of euro; 2007

Statistical annex

4

Social  
transfers

Compen-
sation of 
employees

Intermedi-
ate con-
sumption

Property 
income, incl. 
interest

Public in-
vestments

EU27

EA13

Belgium

Bulgaria

Czech Republic

Denmark

Germany

2,359,050

1,282,168

1,877,041

74,594

3,512

23,040

37,411

893,715

39,063

2,597

9,662

38,074

597,940

168,400

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

1,662

21,510

39,666

148,398

436,097

307,006

1,903

1,609

2,972

6,488

18,364

704

114,631

62,609

50,988

31,308

11,645

5,595

8,695

31,199

60,602

1,428

18,438

25,300

107,361

243,236

164,645

2,266

2,295

2,828

2,658

11,543

703

52,180

24,644

29,482

21,006

11,739

3,640

3,754

23,145

50,055

United Kingdom

258,905

Norway

40,417

222,027

34,725

784,456

441,327

12,049

2,311

7,783

19,966

101,490

1,012

10,121

11,624

54,176

95,786

79,738

792

1,377

1,517

1,093

6,502

293

38,657

11,761

18,192

6,670

7,362

2,056

2,488

16,106

31,086

242,448

17,439

336,589

264,976

12,803

297

1,454

3,413

67,140

23

1,766

9,940

16,664

51,789

76,180

505

107

200

79

4,135

182

13,014

7,713

7,945

4,676

903

441

760

2,770

6,187

45,502

3,551

Other 
current 
transfers

261,437

169,715

7,004

367

1,637

5,811

Others

Total ex-
penditure

307,572

237,505

10,395

460

4,540

6,372

5,645,777

4,110,916

161,486

10,937

54,282

115,229

314,505

226,637

5,579

1,393

6,166

4,181

36,210

36,270

56,320

1,063,770

685

7,768

6,876

39,481

61,764

36,134

479

1,134

1,469

1,388

3,632

218

19,122

2,768

12,699

3,920

6,731

1,238

1,029

4,389

10,377

37,674

8,527

264

4,163

3,769

15,696

52,875

23,810

701

801

464

980

3,007

88

10,034

6,618

7,180

3,350

3,501

656

1,704

4,491

7,220

58,977

5,748

168

3,793

2,074

25,107

49,502

57,304

183

253

515

1,057

3,418

99

9,388

15,299

4,386

3,608

2,938

893

1,793

2,765

9,157

35,786

5,372

5,242

67,558

99,249

406,883

991,049

744,817

6,829

7,575

9,965

13,743

50,601

2,287

257,026

131,411

130,871

74,538

44,819

14,520

20,222

84,865

174,683

901,320

115,779

  European Economic Statistics

171

2008_1393_anto.indd   171

7-07-2008   10:20:33

4

Statistical annex

Table 4.14: 

Total government expenditure by COFOG functions; % of GDP; 2006

General 
public 
services

Defence

Public 
order and 
safety

Econom-
ic affairs

Environ-
ment pro-
tection

Housing 
and com-
munity 
amenities

Health

Recreation, 
culture and 
religion

Educa-
tion

Social 
protec-
tion

6.5

7.0

9.0

5.1

4.9

6.0

6.0

2.6

3.6

8.1

4.6

7.3

8.7

9.9

6.1

4.2

4.0

9.6

6.7

7.3

7.0

5.9

6.9

3.4

6.2

1.6

1.4

1.1

1.9

1.2

1.6

1.1

1.4

0.5

2.3

1.1

1.9

1.4

2.3

1.4

1.6

0.2

1.4

0.8

1.5

0.9

1.2

1.3

1.7

1.4

1.8

1.6

1.6

2.9

2.1

1.0

1.6

2.1

1.4

1.1

1.8

1.4

1.9

2.2

2.2

1.8

0.9

2.2

1.5

1.8

1.4

1.8

1.9

2.4

1.7

3.9

3.9

4.8

5.1

6.9

3.5

3.3

4.2

4.5

4.5

5.0

3.0

5.9

4.4

6.0

4.0

4.5

6.3

5.7

4.7

4.8

4.4

3.8

6.7

4.1

0.7

0.7

0.6

0.8

1.1

0.5

0.5

0.7

0.6

0.6

0.9

0.9

0.8

0.3

1.0

0.8

1.0

0.7

1.7

0.8

0.4

0.6

0.6

0.2

0.4

1.0

1.0

0.4

0.6

1.2

0.5

0.9

0.0

1.3

0.4

0.9

1.8

0.7

2.5

0.8

0.4

0.6

1.1

0.8

1.0

0.6

1.2

0.6

2.1

0.6

6.4

6.5

7.0

4.2

7.2

7.0

6.2

4.0

7.8

4.7

5.6

7.4

7.0

3.1

2.9

4.7

4.6

5.5

6.4

5.9

6.9

4.7

7.2

5.8

6.2

1.0

1.0

1.3

0.8

1.3

1.6

0.6

2.4

0.6

0.3

1.5

1.5

0.8

1.2

1.3

1.0

1.7

1.7

0.6

1.4

1.0

1.1

1.0

0.9

1.2

5.2

5.0

6.0

4.2

4.9

7.7

4.0

6.0

4.2

2.3

4.3

6.1

4.5

7.2

6.3

5.5

4.5

5.8

5.5

5.1

5.9

6.0

7.1

4.1

6.4

18.7

19.3

17.7

13.0

12.7

21.8

21.2

9.5

9.7

17.9

12.8

22.3

18.2

10.4

9.6

10.0

16.4

17.7

14.0

16.5

20.4

16.9

16.0

10.8

17.1

6.4 p

1.6 p

2.0 p

3.8 p

0.7 p

0.8 p

5.0 p

0.9 p

4.0 p

11.6 p

6.5

7.7

4.8

4.3

1.5

1.7

2.5

1.6

1.5

1.3

2.5

0.9

4.5

4.8

2.7

3.6

0.3

0.4

1.0

0.6

0.3

0.7

0.9

0.6

6.8

6.8

6.9

6.9

1.1

1.1

0.9

1.0

5.8

7.1

5.7

5.5

20.4

22.7

15.6

15.6

EU27**

EA13*

Belgium*

Bulgaria**

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France*

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania*

Slovenia

Slovakia*

Finland

Sweden

United Kingdom*

Norway

*  

**  

2005 data

2004 data

172

European Economic Statistics 

2008_1393_anto.indd   172

7-07-2008   10:20:34

Statistical annex

4

Table 4.15: 

Total general government revenue

euro per inhabitant

% of GDP

EU27

EA13

Belgium

Bulgaria

2000 2001 2002 2003 2004 2005 2006 2007

2000 2001 2002 2003 2004 2005 2006 2007

:

:

9,047

9,154

9,516

9,985 10,607 11,171

:

: 44.3 44.3

43.9 44.4 44.9 44.9

10,166 10,375 10,583 10,822 11,093 11,494 12,137 12,711

46.2

45.4

45.0

45.0 44.6 44.9

45.5

45.6

12,072 12,496 12,900 13,534 13,655 14,236 14,652 15,220

49.1

49.6

49.8

51.1

49.1

49.4 48.8 48.7

:

:

833

907

1,054

1,162

1,291

1,548

:

:

39.3

39.8

41.2

41.0

39.4

41.2

Czech Republic

2,281

2,617

3,101

3,230

3,649

4,054

4,551

5,068

38.1

38.7

39.5 40.7

42.2

41.4

41.0 40.8

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

18,161 18,518 18,838 19,221 20,582 22,118 22,703 22,962

55.8

55.3 54.8

55.0

56.4

57.7

56.1

55.1

11,650 11,482 11,548 11,657 11,614 11,848 12,350 12,934

46.4 44.7 44.4 44.5

43.3

43.5

43.8

43.9

1,611

1,772

2,052

2,337

2,543

2,941

3,602

4,277

36.2

35.0 36.0

36.4

35.9

35.4 36.6

36.9

9,987 10,398 11,014 11,827 12,899 13,809 15,276 15,684

36.3 34.3

33.2

33.9

35.3

35.5

37.2

36.7

5,428

5,463

5,738

6,104

6,380

6,767

7,554

8,243

43.0

40.9 40.0

39.3

38.1

37.8

39.4 40.2

5,967

6,348

6,780

7,115

7,590

8,256

8,992

9,584

38.1 38.0

38.4 38.2 38.5

39.4 40.4

41.0

11,901 12,239 12,434 12,635 13,177 13,855 14,386 14,792

50.2

50.0

49.5

49.2

49.6

50.4

50.3

49.9

9,479

9,843 10,055 10,380 10,568 10,672 11,411 12,074

45.3 44.9 44.4 44.8 44.2

43.8

45.4 46.6

5,035

5,531

5,637

6,281

6,660

7,418

8,024

9,315

34.7

35.9

35.8 38.5

38.7

41.2

42.4

47.2

1,239

1,286

1,414

1,423

1,679

1,993

2,645

3,326

34.6

32.5

33.4

33.2

34.7

35.2

37.7 38.0

Lithuania

1,268

1,294

1,425

1,523

1,679

2,006

2,338

2,850

35.9

33.2

32.9

32.0

31.8

33.1

33.4 34.3

Luxembourg 

21,855 22,616 23,459 24,129 24,769 26,924 28,607 31,034

43.6 44.2

43.6

42.4

41.4

41.7

39.9 40.5

Hungary

Malta

2,220

2,525

2,949

3,091

3,457

3,714

3,806

4,480

43.6

43.2

42.4

41.9

42.4

42.1

42.6 44.6

3,769

4,007

4,274

4,208

4,600

4,959

5,148

5,354

34.8 36.6

37.7

37.9

41.1

42.1

41.3 40.7

Netherlands

12,113 12,586 12,706 12,919 13,377 13,999 15,239 15,823

46.1

45.1

44.1

43.9 44.3 44.9 46.6 46.3

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

13,026 13,595 13,574 13,757 14,100 14,339 14,820 15,585

49.6

50.7

49.7

49.4 48.8

48.1

47.6

47.5

1,847

2,145

2,150

1,926

1,975

2,499

2,855

3,270

38.1 38.6

39.2

38.4

36.9

39.0 40.0 40.4

4,803

5,037

5,404

5,647

5,919

5,876

6,227

6,612

40.2

40.1

41.4

42.5

43.1

41.6

42.4

43.1

788

736

835

776

910

1,189

1,498

1,944

43.8

36.7

37.6

32.1

32.4

32.3

33.1

34.4

4,634

4,969

5,395

5,635

5,921

6,285

6,682

7,181

43.6

44.1 44.6 44.4 44.2 44.5

44.1

43.2

1,569

1,656

1,772

2,051

2,241

2,519

2,773

3,528

38.5

37.9

36.7

37.4

35.4

35.3

33.5

34.7

14,121 14,225 14,650 14,684 15,266 15,927 16,770 17,821

55.3

52.8

52.9

52.5

52.4

53.1

52.9

52.7

17,822 16,157 16,378 17,170 17,955 18,662 19,501 20,339

59.3

57.2

55.3

55.8

56.1

57.2 56.5 56.0

United Kingdom 10,995 11,331 11,283 10,730 11,657 12,422 13,308 13,875

41.2

41.5

39.9

39.5

39.2 40.7

41.4

40.9

Iceland

Norway

:  

missing value

14,596 12,982 13,740 14,373 16,113 21,039 19,981

:

43.6

41.9

41.7

42.8 44.2

47.4

45.7

:

23,447 24,299 25,297 24,198 25,661 30,065 33,983 35,158

57.7

57.4 56.3

55.5 56.6

57.2

59.1 58.3

  European Economic Statistics

173

2008_1393_anto.indd   173

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4

Statistical annex

Table 4.16: 

Total general government revenue; millions of euro

2000

2001

2002

2003

2004

2005

2006

2007

:

:

4,392,958

4,463,560

4,660,683

4,911,693

5,236,911

5,536,165

3,119,617

3,198,773

3,281,359

3,376,411

3,483,125

3,629,702

3,851,830

4,054,168

123,686

128,469

133,254

140,390

142,244

149,103

154,480

160,791

:

23,426

96,941

:

26,753

99,199

957,490

945,450

2,211

37,949

59,255

240,259

723,013

539,744

3,495

2,937

4,436

9,583

22,670

1,470

192,856

104,361

70,669

49,114

17,674

9,218

8,472

73,088

158,113

647,450

4,104

2,422

40,125

59,816

258,490

748,775

560,854

3,880

3,027

4,504

9,985

25,727

1,575

201,922

109,348

82,034

51,844

16,490

9,898

8,910

73,797

143,728

669,802

3,701

6,533

31,633

101,273

952,500

2,793

43,245

63,041

280,121

766,134

574,725

4,004

3,308

4,945

10,467

29,954

1,692

205,155

109,729

82,207

56,032

18,197

10,763

9,533

76,193

146,176

669,351

3,951

7,074

32,954

103,602

961,930

3,168

47,200

67,290

298,850

783,903

597,932

4,540

3,309

5,259

10,899

31,313

1,677

209,580

111,680

73,552

58,964

16,872

11,249

11,033

76,545

153,812

638,983

4,158

105,300

109,660

114,824

110,463

8,183

37,243

111,204

958,130

3,436

52,359

70,578

324,030

822,858

614,802

4,927

3,883

5,769

11,347

34,939

1,845

217,724

115,262

75,413

62,164

19,716

11,826

12,061

79,802

161,486

697,454

4,714

117,810

8,971

41,491

119,859

977,020

3,963

57,289

75,140

358,276

870,325

625,439

5,622

4,585

6,850

12,525

37,467

2,000

228,426

118,057

95,370

61,986

25,719

12,575

13,570

83,541

168,514

748,010

6,225

9,942

46,722

11,918

52,288

123,435

125,370

1,017,230

1,064,000

4,844

64,975

84,214

396,280

909,129

672,606

6,199

6,052

7,935

13,523

38,329

2,094

249,014

122,737

108,879

65,912

32,320

13,416

14,949

88,315

177,084

806,300

6,081

5,741

68,108

92,085

430,052

940,406

716,234

7,337

7,567

9,619

14,815

45,048

2,191

259,128

129,600

124,624

70,213

41,820

14,496

19,037

94,241

186,057

843,379

:

138,961

158,393

165,183

EU27

EA13

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Iceland

Norway

:  

missing value

174

European Economic Statistics 

2008_1393_anto.indd   174

7-07-2008   10:20:35

Statistical annex

4

Table 4.17: 

Main components of general government revenue; millions of euro; 2007

EU27

EA13

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Norway

Taxes

3,350,454

2,337,705

99,343

7,113

25,536

108,298

579,060

3,295

49,544

46,193

263,906

510,513

459,888

5,317

4,435

5,942

9,554

26,152

1,526

141,116

74,771

70,576

40,443

24,139

8,045

9,545

55,329

118,640

602,236

97,189

Social  
contributions

Government 
sales

Property  
income

1,673,219

1,354,506

52,551

2,500

20,775

4,250

400,760

1,709

12,018

31,571

136,198

340,954

204,772

1,254

1,893

2,542

3,999

13,726

401

81,923

42,702

37,167

20,702

12,927

4,800

6,505

21,556

42,388

170,678

25,701

275,010

188,111

5,658

992

3,466

5,997

46,210

310

2,545

3,380

12,904

60,303

18,604

475

686

322

644

2,700

109

18,587

4,980

7,734

4,097

2,554

915

585

9,285

13,882

47,085

7,180

122,003

85,889

1,996

414

1,053

3,690

17,560

231

2,039

2,180

10,379

14,768

9,321

113

147

147

550

916

78

14,513

3,803

4,869

1,183

1,168

235

831

7,363

8,190

14,266

33,909

Others

115,481

87,958

1,244

899

1,458

3,137

20,410

195

1,963

8,761

6,665

13,868

23,649

177

406

666

69

1,555

77

2,989

3,344

4,278

3,788

1,033

501

1,571

708

2,957

9,114

1,203

Total   
revenue

5,536,165

4,054,168

160,791

11,918

52,288

125,370

1,064,000

5,741

68,108

92,085

430,052

940,406

716,234

7,337

7,567

9,619

14,815

45,048

2,191

259,128

129,600

124,624

70,213

41,820

14,496

19,037

94,241

186,057

843,379

165,183

  European Economic Statistics

175

2008_1393_anto.indd   175

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4

Statistical annex

Table 4.18: 

Main types of tax revenues of general government and EU institutions; % of GDP; 2006

EU27

EA13

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Norway

VAT

7.0

6.8

7.1

12.4

6.6

10.3

6.3

9.2

7.9

7.1

6.4

7.2

6.3

10.4

8.6

7.7

5.5

7.6

8.1

7.5

7.7

8.1

8.9

7.9

8.7

7.4

8.7

9.1

6.6

8.0

Other taxes on 
products and 
production

Taxes on  
income

Social  
contributions

Others

Total tax  
revenues

6.9

7.1

6.7

6.9

4.6

7.7

6.1

4.5

6.4

5.1

6.3

8.3

8.7

7.5

4.5

3.9

7.0

7.7

7.3

5.8

6.6

6.3

6.8

4.8

6.8

4.2

5.2

7.9

6.3

4.3

12.3

11.6

16.0

6.3

8.7

28.8

10.4

7.1

12.9

7.5

11.3

10.8

14.0

10.1

8.3

9.7

12.5

9.1

11.3

10.7

12.4

7.0

8.5

5.8

9.0

5.6

16.6

19.3

14.5

22.1

13.7

15.4

15.7

8.7

16.2

1.9

17.2

10.3

6.3

13.2

12.9

18.1

12.9

7.8

9.1

8.8

10.8

12.6

7.7

15.1

16.0

12.2

12.5

10.4

14.5

11.9

12.2

12.8

8.2

8.7

1.0

0.7

1.2

-0.2

0.1

1.3

0.6

0.0

0.5

0.5

0.4

1.2

0.4

0.7

0.2

-0.1

0.6

0.3

0.8

1.3

0.7

0.1

0.3

0.3

0.3

0.4

0.9

0.5

2.6

0.9

40.9

41.6

46.8

34.2

36.3

50.0

40.6

31.1

34.0

33.5

37.3

45.7

42.4

36.6

30.6

30.0

36.4

37.3

35.2

40.4

43.4

33.8

37.0

29.2

39.3

29.5

43.6

49.7

38.2

44.0

176

European Economic Statistics 

2008_1393_anto.indd   176

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Statistical annex

4

Table 4.19: 

Taxes on consumption

% of GDP

implicit tax rate (%)

% of 
total 
taxation

2000 2001 2002 2003 2004 2005 2006

2000 2001 2002 2003 2004 2005 2006

2006

EU27

EA15

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

11.4

11.1

11.4

14.4

10.6

15.7

10.5

11.8

12.2

12.4

9.9

11.6

10.9

10.6

11.3

11.8

10.8

15.3

12.1

11.7

12.1

11.3

12.4

:

14.1

12.0

13.6

12.4

United Kingdom 12.0

Norway

12.7

:  

missing value

11.2

10.8

11.0

14.0

10.2

15.7

10.5

11.8

10.9

12.6

9.5

11.3

10.4

11.8

10.6

11.5

10.6

14.5

12.7

11.9

12.3

11.1

12.4

:

13.6

10.9

13.1

12.5

11.8

12.6

11.1

10.8

11.0

13.7

10.1

15.8

10.4

11.9

11.0

12.3

9.4

11.3

10.2

12.4

10.6

11.7

10.8

14.1

13.4

11.6

12.5

11.8

12.7

11.3

14.0

11.1

13.4

12.7

11.7

12.8

11.1

10.7

11.0

15.1

10.4

15.6

10.5

11.6

10.9

11.5

9.6

11.1

9.9

14.7

11.4

11.1

10.6

14.6

12.4

11.8

12.3

11.9

12.7

11.5

14.0

11.8

13.9

12.7

11.8

12.4

11.1

10.7

11.2

16.8

11.2

15.8

10.2

11.8

11.2

11.3

9.7

11.2

10.0

15.2

11.3

10.6

11.2

15.0

13.2

11.9

12.3

11.8

12.7

11.3

13.8

12.1

13.6

12.6

11.6

12.2

11.1

10.7

11.2

18.1

11.3

16.1

10.1

12.8

11.5

10.9

9.8

11.3

10.0

15.2

12.2

10.9

11.0

14.5

14.1

12.0

12.1

12.2

13.5

12.4

13.7

12.5

13.7

12.7

11.3

11.7

11.1

10.8

11.3

18.9

10.7

16.2

10.1

13.1

11.6

11.3

9.8

11.2

10.3

15.4

12.6

11.0

9.8

13.9

14.1

12.3

11.7

12.5

13.8

12.0

13.4

11.3

13.5

12.5

11.1

11.8

20.1

19.7

21.8

19.7

19.4

33.4

18.9

19.8

26.1

19.0

15.7

20.9

17.9

12.7

18.7

17.8

23.1

27.5

15.9

23.7

21.3

17.8

19.2

:

24.0

21.3

28.6

26.5

19.4

31.1

19.7

19.2

21.0

18.9

18.9

33.5

18.5

19.9

24.1

19.5

15.2

20.3

17.3

14.3

17.5

17.4

22.7

25.6

16.5

24.4

21.4

17.2

19.3

:

23.5

18.5

27.6

26.9

19.0

30.6

19.7

19.3

21.4

18.7

19.3

33.7

18.5

20.0

25.0

18.8

15.4

20.3

17.1

15.4

17.4

17.8

22.8

25.4

18.1

23.8

21.9

17.9

19.9

16.6

24.6

19.1

27.7

27.4

18.9

29.7

19.7

19.1

21.3

20.7

19.6

33.3

18.6

19.9

24.9

17.9

15.8

20.0

16.6

18.9

18.6

17.0

23.6

26.0

16.5

24.2

21.5

18.3

19.8

17.6

24.7

21.0

28.1

27.5

19.2

19.7

19.2

22.0

23.2

21.8

33.3

18.1

20.3

25.9

17.6

16.0

20.1

16.8

20.0

18.5

16.0

25.1

27.6

17.3

24.9

21.5

18.5

19.7

16.5

24.5

21.5

27.7

27.6

19.1

28.5

28.9

19.7

19.2

22.2

24.4

22.2

33.6

18.0

22.8

26.5

17.0

16.3

20.1

16.8

20.0

20.2

16.5

25.5

26.4

19.1

25.3

21.2

19.6

20.6

18.0

24.2

22.2

27.6

28.1

18.7

29.7

19.9

19.4

22.4

25.9

21.2

34.0

18.2

23.6

26.9

17.6

16.4

20.0

17.2

20.4

20.0

16.7

25.1

25.8

19.8

26.9

20.9

20.2

21.1

17.7

24.2

20.2

27.3

28.1

18.5

31.1

27.8

26.7

25.3

54.8

29.4

32.9

25.8

42.3

35.7

36.0

26.7

25.3

24.5

42.2

41.8

36.9

27.6

37.4

41.7

31.2

28.1

36.8

38.4

41.9

34.4

38.6

31.0

25.6

29.7

26.7

  European Economic Statistics

177

2008_1393_anto.indd   177

7-07-2008   10:20:36

4

Statistical annex

Table 4.20: 

Taxes on labour

% of GDP

implicit tax rate (%)

% of 
total 
taxation

2000 2001 2002 2003 2004 2005 2006

2000 2001 2002 2003 2004 2005 2006

2006

EU27

EA15

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

20.4

21.5

24.3

13.5

17.1

26.6

24.3

17.4

11.5

12.4

15.9

23.2

19.7

9.8

15.3

16.3

15.3

19.1

9.7

20.3

23.7

14.2

14.1

:

21.0

15.1

23.7

31.0

United Kingdom 14.2

Norway

17.5

:  

missing value

20.2

21.3

24.9

12.1

17.0

26.9

24.0

16.9

11.0

12.1

16.2

23.1

20.0

10.3

14.6

15.4

15.9

19.3

10.7

17.8

24.0

14.4

14.3

:

21.3

15.1

23.6

31.2

14.2

18.0

19.9

21.2

25.0

11.3

17.8

26.1

23.9

16.9

10.0

13.0

16.3

22.9

20.0

10.3

14.6

14.9

15.3

19.4

10.2

18.1

24.0

13.4

14.5

12.3

21.1

15.1

23.6

30.0

13.6

19.0

20.0

21.2

24.7

12.4

18.1

26.0

23.8

16.6

9.8

13.2

16.2

23.1

20.1

11.0

14.6

14.6

15.2

18.7

10.3

18.4

24.1

13.2

14.7

11.2

21.1

14.5

23.3

30.3

13.6

18.8

19.5

20.6

24.2

12.2

17.8

25.2

22.8

16.4

10.5

12.7

16.0

22.7

19.9

10.5

14.6

14.7

15.4

18.2

10.7

18.0

19.5

20.5

23.9

11.6

17.9

25.1

22.2

15.2

10.4

12.8

16.2

23.0

20.3

11.3

14.0

14.6

15.5

18.5

10.6

17.6

19.6

20.7

23.1

10.0

17.6

24.7

22.3

15.4

10.5

12.9

16.6

23.2

20.6

11.1

14.5

14.8

14.7

18.6

10.3

19.1

23.6

23.3

23.3

12.5

14.7

10.8

21.1

13.0

22.7

30.0

13.8

18.2

12.6

15.0

10.9

21.0

12.6

23.2

29.7

14.2

17.0

13.0

15.1

:

20.7

11.5

22.8

29.3

14.3

16.4

37.2

39.6

43.9

38.8

40.7

41.0

40.7

37.8

28.5

38.2

28.7

42.1

43.4

22.3

36.7

41.2

29.9

41.8

20.6

34.3

40.2

33.6

27.0

:

37.7

36.3

44.1

47.2

25.3

38.3

36.9

39.2

43.6

34.4

40.3

40.8

40.5

37.3

27.4

37.7

29.5

41.7

43.5

23.6

36.5

40.3

29.5

41.0

21.3

30.3

40.7

33.2

27.4

:

37.5

37.1

44.1

46.3

25.0

38.4

36.5

36.6

39.0

43.7

33.0

41.2

38.8

40.4

37.8

25.9

40.0

29.8

41.2

43.3

23.0

37.8

38.1

28.3

40.6

20.8

30.4

40.8

32.4

27.6

31.2

37.7

37.0

43.8

44.8

24.1

38.7

39.0

43.5

35.5

41.4

38.1

40.3

36.9

24.7

41.2

30.0

41.5

43.3

23.4

36.6

36.9

28.8

38.8

20.3

30.8

40.9

32.7

27.8

30.1

37.8

36.3

42.5

44.7

24.3

39.0

36.1

38.4

44.1

36.3

41.8

37.4

39.1

36.1

25.7

38.0

29.9

41.1

42.8

22.8

36.7

36.0

29.2

37.7

21.3

30.4

41.1

32.7

27.9

29.2

37.5

34.3

41.6

44.7

24.8

39.2

36.2

38.4

43.9

34.7

41.7

37.5

38.6

34.1

25.1

37.8

30.6

41.7

42.8

24.5

33.2

34.9

30.0

37.8

21.9

30.5

41.0

33.1

28.4

29.1

37.5

32.9

41.5

44.7

25.3

38.5

36.7

39.0

42.8

30.9

41.0

37.0

39.6

33.9

25.1

38.1

31.6

42.1

43.0

24.2

33.5

34.1

29.6

39.0

21.5

33.5

41.2

34.4

28.5

:

37.6

30.3

41.5

44.5

25.5

38.0

49.1

51.2

51.8

29.1

48.4

50.3

56.8

49.6

32.4

41.1

45.4

52.5

48.6

30.4

48.2

49.8

41.3

50.0

30.6

48.3

55.8

38.5

42.1

:

53.0

39.4

52.5

59.8

38.3

37.2

178

European Economic Statistics 

2008_1393_anto.indd   178

7-07-2008   10:20:37

Statistical annex

4

Table 4.21: 

Taxes on capital

% of GDP

implicit tax rate (%)

% of 
total 
taxation

2000 2001 2002 2003 2004 2005 2006

2000 2001 2002 2003 2004 2005 2006

2006

EU27

EA15

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

9.0

8.8

9.5

5.3

6.2

7.2

7.0

2.1

8.0

9.8

8.7

9.6

8.6

8.3

9.3

6.0

6.7

6.0

5.5

1.7

7.8

8.3

8.3

9.8

8.2

8.0

9.3

5.8

6.9

6.1

5.2

2.2

7.4

8.2

8.8

9.1

8.1

8.0

9.2

5.8

7.2

6.6

5.4

2.6

8.4

7.6

8.7

8.8

8.4

8.2

9.7

5.6

8.4

8.1

5.8

2.7

8.7

7.4

9.3

9.4

8.8

8.5

9.9

5.6

7.9

9.7

6.4

2.5

8.9

7.6

10.1

9.6

11.1

11.1

10.7

11.3

10.7

10.3

9.6

2.9

2.3

8.9

3.3

2.0

8.5

3.0

2.0

7.3

2.5

2.5

7.7

2.6

3.1

9.0

2.8

3.3

9.4

9.0

32.468 30.694 29.521 28.949 29.808 31.391 33.329

30.577 28.534 28.113 28.262 28.923 30.264 31.897

10.2

29.333 29.357 30.688 31.745 32.697 32.146 32.326

6.3

8.0

8.4

6.8

2.5

11.401 13.184

:

:

:

:

:

20.906 22.310 23.765 24.849 28.072 25.501 24.922

35.987 30.828 30.676 36.730 45.519 47.728 40.911

29.157 22.722 21.174 21.436 21.599 22.887 23.406

6.579 5.377 6.871

8.211 8.589 7.940 8.399

10.4

32.199 33.213 32.339 35.968 38.301 37.532 42.508

7.2

20.338 17.365 17.283 15.864 15.499

-

-

10.7

10.0

11.4

10.0

3.0

4.0

29.759 28.338 29.966 30.299 32.695 36.012 38.691

37.469 37.865 36.591 35.644 38.457 40.028 41.452

29.939 29.372 29.510 31.901 30.311 30.391 34.378

26.200 24.335 25.195 24.352 26.248 31.013 36.590

11.209 11.542 9.276 8.006

7.799 9.620

:

10.612 8.069

7.527 9.227 10.780 11.549 14.137

Luxembourg 

13.1

13.2

13.2

12.4

10.7

11.3

10.9

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

4.1

6.3

8.0

6.9

7.2

7.8

:

3.0

6.9

9.9

8.4

4.4

6.9

8.6

8.4

7.0

7.2

:

3.3

7.1

7.8

6.2

4.4

7.9

8.0

7.2

7.8

7.4

4.5

3.5

7.0

7.6

5.2

4.4

8.7

7.3

6.8

7.4

7.4

5.0

3.6

6.8

6.8

5.3

4.4

9.3

7.6

7.0

7.5

6.7

5.3

4.0

6.5

7.1

6.1

4.4

9.0

8.3

6.7

8.4

6.6

4.7

4.8

6.3

7.1

7.1

4.7

9.4

8.1

6.8

8.7

7.0

:

5.0

6.4

7.2

7.1

United Kingdom 11.1

Norway

12.2

11.1

12.1

10.3

11.3

10.0

11.0

10.3

12.9

11.0

14.8

11.9

15.9

:  

missing value

:

:

:

:

:

:

:

:

:

:

:

:

:

:

:

:

:

:

:

:

:

20.549 22.950 25.244 22.342 22.201 20.672 19.977

26.018 33.001 27.934 25.636 25.303 23.187 23.364

20.618 20.810 22.787 20.961 18.494 22.188

32.665 30.635 32.190 31.613 27.940 28.057

:

:

:

:

:

:

:

:

:

:

:

:

:

:

:

:

23.018 21.821 22.297 22.245 21.478 19.097 18.094

36.375 25.658 27.433 25.777 26.306 27.548 24.587

42.790 33.688 29.529

:

:

:

:

40.952 41.957 37.118 32.927 33.999 36.769 39.741

:

:

:

:

:

:

:

23.4

22.3

22.8

18.2

22.2

17.1

17.3

8.2

32.0

22.9

29.2

22.6

26.9

27.4

10.0

13.5

30.6

12.6

27.7

20.5

16.2

25.8

19.5

:

12.7

21.9

16.5

14.6

32.0

36.1

  European Economic Statistics

179

2008_1393_anto.indd   179

7-07-2008   10:20:38

4

Statistical annex

government surplus (+)/ government deficit (-)

% of GDP

primary 
balance

interest 
paid

2000

2001

2002

2003

2004

2005

2006

2007

2007

EU27

EA13

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Norway

:  

missing value

0.6

0.0

0.1

:

-3.7

2.2

1.3

-0.2

4.7

:

-1.0

-1.5

-0.8

-2.3

-2.8

-3.2

6.0

-2.9

-6.2

2.0

-1.7

-3.0

-2.9

-4.4

-3.8

-12.2

6.9

3.8

3.6

:

-1.4

-1.8

0.6

0.4

-5.7

1.3

-2.8

-0.1

0.9

:

-0.6

-1.5

-3.1

-2.2

-2.1

-3.6

6.1

-4.0

-6.4

-0.2

0.0

-5.1

-4.3

-3.5

-4.0

-6.5

5.0

1.6

0.5

13.5

-2.5

-2.5

0.0

-1.0

-6.8

0.2

-3.7

0.4

-0.4

-4.7

-0.5

-3.1

-2.9

-4.4

-2.3

-1.9

2.1

-8.9

-5.5

-2.1

-0.6

-5.0

-2.9

-2.0

-2.5

-8.2

4.1

-1.2

-2.0

9.3

-3.1

-3.0

0.0

-0.5

-6.6

-0.1

-4.0

1.8

0.4

-5.6

-0.2

-4.1

-3.5

-6.5

-1.6

-1.3

0.5

-7.2

-9.9

-3.1

-1.4

-6.3

-2.9

-1.5

-2.7

-2.7

2.6

-0.9

-3.3

7.3

-2.8

-2.9

0.0

1.4

-3.0

1.9

-3.8

1.6

1.4

-7.4

-0.3

-3.6

-3.5

-4.1

-1.0

-1.5

-1.2

-6.5

-4.6

-1.7

-3.7

-5.7

-3.4

-1.2

-2.3

-2.4

2.4

0.8

-3.4

11.1

-2.5

-2.5

-2.3

1.8

-3.6

5.0

-3.4

1.8

1.6

-5.1

1.0

-2.9

-4.2

-2.4

-0.4

-0.5

-0.1

-7.8

-3.0

-0.3

-1.5

-4.3

-6.1

-1.2

-1.5

-2.8

2.9

2.2

-3.4

15.2

-1.4

-1.3

0.3

3.0

-2.7

4.8

-1.6

3.4

3.0

-2.6

1.8

-2.4

-3.4

-1.2

-0.2

-0.5

1.3

-9.2

-2.6

0.5

-1.5

-3.8

-3.9

-2.2

-1.2

-3.6

4.1

2.3

-2.6

19.3

-0.9

-0.6

-0.2

3.4

-1.6

4.4

0.0

2.8

0.3

-2.8

2.2

-2.7

-1.9

3.3

0.0

-1.2

2.9

-5.5

-1.8

0.4

-0.5

-2.0

-2.6

-2.5

-0.1

-2.2

5.3

3.5

-2.9

:

1.8

2.4

3.6

4.4

-0.4

5.9

2.8

2.9

1.2

1.3

3.8

0.0

3.1

6.5

0.5

-0.5

3.1

-1.4

1.6

2.7

2.2

0.6

0.2

-1.8

1.2

-0.8

6.8

5.3

-0.7

:

2.7

3.0

3.8

1.0

1.2

1.5

2.8

0.1

0.9

4.1

1.6

2.7

5.0

3.2

0.5

0.7

0.2

4.1

3.4

2.3

2.7

2.6

2.8

0.7

1.3

1.4

1.5

1.8

2.2

:

180

European Economic Statistics 

2008_1393_anto.indd   180

7-07-2008   10:20:38

Statistical annex

4

Table 4.22: 

Government consolidated gross debt; % of GDP

2000

:

:

107.8

74.3

18.5

51.5

59.7

5.2

37.9

103.2

59.3

57.3

109.1

58.8

12.3

23.7

6.2

54.3

55.9

53.8

65.6

36.8

50.5

24.7

:

50.4

43.8

54.4

41.0

:

EU27

EA13

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Norway

:  

missing value

2001

61.0

68.2

106.5

67.3

25.1

48.7

58.8

4.8

35.6

103.6

55.5

56.9

108.7

60.7

14.0

23.1

6.3

52.1

62.1

50.7

66.1

37.6

52.9

26.0

27.2

49.0

42.3

55.3

37.7

29.2

2002

60.3

68.0

103.4

53.6

28.5

48.3

60.3

5.6

32.2

100.6

52.5

58.8

105.6

64.7

13.5

22.4

6.3

55.7

60.1

50.5

65.9

42.2

55.6

25.0

28.4

43.4

41.3

53.7

37.5

36.1

2003

2004

2005

2006

2007

61.8

69.1

98.6

45.9

30.1

45.8

63.8

5.5

31.1

97.9

48.7

62.9

62.1

69.6

94.2

37.9

30.4

43.8

65.6

5.1

29.5

98.6

46.2

64.9

62.6

70.2

92.1

29.2

29.7

36.4

67.8

4.5

27.4

98.0

43.0

66.4

61.3

68.5

88.2

22.7

29.4

30.4

67.6

4.2

25.1

95.3

39.7

63.6

58.7

66.4

84.9

18.2

28.7

26.0

65.0

3.4

25.4

94.5

36.2

64.2

104.3

103.8

105.8

106.5

104.0

68.9

14.6

21.2

6.1

58.0

69.3

52.0

64.7

47.1

56.9

21.5

27.9

42.4

44.3

53.5

38.7

44.3

70.2

14.9

19.4

6.3

59.4

72.6

52.4

63.8

45.7

58.3

18.8

27.6

41.4

44.1

51.2

40.4

45.6

69.1

12.4

18.6

6.1

61.6

70.4

52.3

63.5

47.1

63.6

15.8

27.5

34.2

41.3

50.9

42.1

43.8

64.8

10.7

18.2

6.6

65.6

64.2

47.9

61.8

47.6

64.7

12.4

27.2

30.4

39.2

45.9

43.1

48.9

59.8

9.7

17.3

6.8

66.0

62.6

45.4

59.1

45.2

63.6

13.0

24.1

29.4

35.4

40.6

43.8

:

  European Economic Statistics

181

2008_1393_anto.indd   181

7-07-2008   10:20:39

4

Statistical annex

Table 4.23: 

Structure of government consolidated gross debt; millions of euro; 2007

Total

7,240,784

5,898,832

280,507

5,257

38,284

59,115

1,576,305

531

47,199

216,362

379,742

1,209,497

1,596,762

9,262

1,948

4,836

2,493

65,958

3,368

253,818

161,328

146,798

103,552

14,528

8,071

16,194

63,196

132,042

843,832

EU27

EA13

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

*  
:  

excluding derivatives
missing value

Currency and 
deposits

Short term 
securities other 
than shares*

Long-term  
securities other 
than shares*

Short-term 
loans

Long-term loans

341,641

196,318

1,116

:

0

1,783

6,934

0

7,727

674

3,307

18,661

143,029

0

35

0

161

32

:

573

:

0

13,679

1,303

40

30

417

3,709

138,432

450,315

382,840

27,782

0

2,955

2,615

40,273

0

6,121

57

33,366

112,240

127,869

202

65

109

0

8,489

355

16,749

2,185

6,522

9,287

470

134

0

6,778

21,238

24,455

5,355,760

4,364,584

220,571

3,278

29,539

43,093

159,802

103,113

1,957

1

276

148

1,102,936

54,477

126

31,775

196,226

279,780

891,497

1,189,972

6,363

1,091

4,368

0

50,777

2,753

191,644

135,290

124,044

72,535

4,458

6,967

14,476

45,390

85,755

621,055

5

222

43

4,419

21,789

9,182

0

51

10

336

304

25

6,054

1,109

154

3,051

0

138

3

337

8,380

47,334

933,268

851,979

29,081

1,978

5,513

11,477

371,685

400

1,354

19,362

58,871

165,310

126,711

2,697

706

349

1,996

6,356

235

38,798

22,744

16,079

5,000

8,297

793

1,686

10,274

12,960

12,556

182

European Economic Statistics 

2008_1393_anto.indd   182

7-07-2008   10:20:39

Statistical annex

4

Table 4.24: 

Annual average inflation rates (in %) 2000 – 2007 by product group, for EU and euro area

Euro area

HICP all-items 

COICOP – main headings

Food

Alcohol and tobacco

Clothing

Housing

Household equipment

Health

Transport

Communications

Recreation and culture

Education

Restaurants and hotels

Miscellaneous

Selected special aggregates

All-items excluding energy

All-items excl. energy, food, alcohol & tobacco

Energy

Food, alcohol & tobacco

EU

HICP all-items 

COICOP – main headings

Food

Alcohol and tobacco

Clothing

Housing

Household equipment

Health

Transport

Communications

Recreation and culture

Education

Restaurants and hotels

Miscellaneous

Selected special aggregates

All-items excluding energy

All-items excl. energy, food, alcohol & tobacco

Energy

Food, alcohol & tobacco

Source: Eurostat

2000

2001

2002

2003

2004

2005

2006

2007

2.1

2.3

2.2

2.1

2.1

2.2

2.2

2.1

1.2

2.2

0.7

3.9

1

1.7

5.2

-7.1

-0.2

2.8

2.8

2.3

1.1

1

13

1.4

5

2.8

0.4

2.9

1.8

1.2

1.2

-4.1

1.4

3

3.4

3.1

2.4

1.8

2.2

4.5

2.8

4.1

2.1

1.3

1.7

2.5

1.7

2.1

5.9

0.9

2.5

1.3

2.2

2.3

-0.3

-0.6

1.4

4

4.6

2.9

2.5

2.4

-0.6

3.1

0.2

3.5

3.2

2.6

2

1.8

3

2.8

1

7.5

0.7

2.5

0.9

7.9

3.1

-2

-0.1

3.3

2.8

2.2

1.9

1.8

4.5

2.3

0.7

4.9

0.1

4.7

0.8

2.1

4.4

-2.3

-0.1

3.1

2.5

1.8

1.4

1.4

10.1

1.6

2.3

2.7

0.4

4.7

1

1.4

3.1

-3.2

0.1

2.9

2.6

2.1

1.6

1.4

7.7

2.4

2.7

3.4

1

2.7

1.7

1.7

2.4

-1.9

0.2

7.8

3.2

2.3

2.1

1.9

2.6

2.8

2000

2001

2002

2003

2004

2005

2006

2007

1.9

2.2

2.1

1

2.7

-0.5

3.6

0.6

1.9

4.8

4.7

3

-0.7

2.9

1.5

1.6

1

-6.8

-4.4

0.1

3.8

2.9

2.1

1

0.9

12.1

1.4

1.4

3.7

3.6

3.1

2.2

1.7

1.7

4.3

2.5

3.5

0.8

1.5

1.5

2.7

1.6

-0.1

1.4

4.2

4.3

2.8

2.3

2.2

-0.5

2.7

2

1.9

4.9

0.2

2.6

1

2.3

2.5

-0.5

0

4.7

3.2

2.5

1.8

1.7

3.1

2.6

2

2.2

2.2

2.3

1.1

6.1

-0.2

2.7

0.7

7.1

3.2

-1.8

-0.4

3.8

2.9

2.5

1.8

1.7

4.5

2.2

0.8

4

-0.8

4.9

0.6

2.4

4.3

-2.1

-0.3

3.7

2.8

2.3

1.4

1.4

9.6

1.6

2.3

2.6

-0.6

5.4

0.6

1.7

3

-2.5

-0.3

4

2.8

2.6

1.6

1.4

8.4

2.4

3.5

3.8

0.1

3.3

1.6

2.2

2.5

-2

-0.1

8.6

3.4

2.3

2.2

1.9

3.1

3.5

  European Economic Statistics

183

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4

Statistical annex

Table 4.25: 

Annual average inflation rates 2000-2007 - by MS

2000

2001

2002

2003

2004

2005

2006

2007

1.9

2.1

2.7

10.3

3.9

2.7

1.4

3.9

5.3

2.9

3.5

1.8

2.6

4.9

2.6

1.1

3.8

10.0

3.0

2.3

2.0

10.1

2.8

45.7

8.9

12.2

2.9

1.3

0.8

4.4

3.0

1.9

EU 

Euro area

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Roumania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Iceland

Norway

EEA

Switzerland

Source: Eurostat

2.2

2.3

2.4

7.4

4.5

2.3

1.9

5.6

4.0

3.7

2.8

1.8

2.3

2.0

2.5

1.6

2.4

9.1

2.5

5.1

2.3

5.3

4.4

2.1

2.2

1.6

5.8

1.4

2.4

1.4

3.6

4.7

3.9

3.6

1.9

2.6

2.8

2.0

0.3

2.1

5.2

2.6

3.9

1.7

1.9

3.7

2.0

2.1

1.5

2.3

-0.1

2.0

1.0

1.4

4.0

3.4

3.1

2.2

2.8

4.0

2.9

-1.1

2.5

4.7

1.9

2.2

1.3

0.7

3.3

2.0

2.1

1.9

6.1

2.6

0.9

1.8

3.0

2.3

3.0

3.1

2.3

2.3

1.9

6.2

1.2

3.2

6.8

2.7

1.4

2.0

3.6

2.5

34.5

22.5

15.3

11.9

8.6

7.2

2.7

2.7

1.2

6.6

2.7

2.2

7.5

3.5

2.0

1.9

1.3

5.3

0.8

2.1

5.7

8.4

1.3

2.3

1.4

1.4

2.0

2.0

3.7

7.5

0.1

1.0

1.3

2.3

0.6

2.0

2.2

2.2

2.5

6.0

1.6

1.7

1.9

4.1

2.2

3.5

3.4

1.9

2.2

2.0

6.9

2.7

3.8

3.5

2.5

1.5

2.1

2.2

2.1

9.1

2.5

2.8

0.8

0.8

2.1

1.4

1.5

2.2

2.2

2.2

2.3

7.4

2.1

1.9

1.8

4.4

2.7

3.3

3.6

1.9

2.2

2.2

6.6

3.8

3.0

4.0

2.6

1.7

1.7

1.3

3.0

6.6

2.5

4.3

1.3

1.5

2.3

4.6

2.5

2.2

1.0

2.3

2.1

1.8

7.6

3.0

1.7

2.3

6.7

2.9

3.0

2.8

1.6

2.0

2.2

10.1

5.8

2.7

7.9

0.7

1.6

2.2

2.6

2.4

4.9

3.8

1.9

1.6

1.7

2.3

3.6

0.7

2.3

0.8

184

European Economic Statistics 

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Statistical annex

4

Table 4.26: 

Household consumption pattern used for the HICP 2007 - by main heading, by MS

All-items 

Food

Alcohol and tobacco

Clothing

Housing

Household equipment

Health

Transport

EU

Euro area

BE

BG

CZ

DK

DE

EE

IE

EL

ES

1000

1000

1000

1000

1000

1000

1000

1000

1000

1000

1000

152.97

155.72

172.47

232.54

177.67

148.63

114.71

198.32

131.29

171.88

219.45

43.79

66.18

39.86

69.34

29.51

59.17

55.43

41.80

89.91

52.67

51.40

58.30

51.97

55.79

85.80

80.42

67.63

60.55

45.21

94.76

28.32

90.60

151.74

154.67

162.39

103.20

157.67

189.76

226.58

128.00

101.32

88.65

105.18

69.76

37.77

72.74

40.59

67.32

40.79

46.91

51.11

61.39

24.44

69.90

31.80

72.04

45.97

48.93

33.67

49.44

35.26

70.47

57.06

60.74

28.38

152.24

157.95

145.90

175.28

127.35

138.69

155.31

137.39

139.26

130.91

141.92

Communications

31.10

Recreation and culture

102.97

32.20

125.54

5.60

73.81

53.48

11.05

41.13

24.48

23.51

107.17

116.37

111.42

7.18

9.65

30.83

94.37

9.55

91.77

82.63

IT

11.39

95.51

84.60

FR

1000

90.02

119.47

83.93

54.20

69.10

35.93

69.49

106.82

CY

LV

LT

LU

1000

1000

1000

1000

1000

43.83

76.45

16.54

38.22

112.97

22.84

38.15

52.89

22.69

35.01

72.35

16.00

89.65

172.46

163.62

143.77

60.99

68.77

63.72

58.28

MT

1000

NL

AT

PL

1000

1000

1000

8.00

54.53

80.17

HU

1000

163.16

172.90

181.87

250.04

255.23

109.70

195.82

171.26

131.29

124.74

198.99

36.29

56.36

32.02

94.34

30.92

88.31

67.94

71.27

79.37

143.40

83.44

44.60

71.45

51.16

50.18

59.61

37.16

66.11

30.04

55.81

71.98

42.26

148.30

103.29

85.90

128.37

116.18

92.70

142.66

85.09

174.27

142.30

227.26

65.52

42.45

91.52

35.99

64.64

48.84

54.35

41.87

64.10

48.21

94.60

16.10

68.81

40.93

92.41

31.43

76.32

25.95

79.85

52.75

49.38

53.14

177.22

159.81

153.80

123.49

109.36

218.70

146.46

146.33

139.20

148.27

104.08

Communications

Recreation and culture

Education

32.12

98.30

5.57

29.72

72.05

9.85

36.99

67.83

28.02

Restaurants and hotels

71.34

114.27

125.09

50.93

71.68

13.49

78.91

47.64

SK

43.28

61.48

13.61

79.31

46.44

FI

16.00

84.70

4.00

85.30

90.20

SE

52.57

89.68

12.26

81.12

47.08

UK

26.11

97.02

10.12

54.45

24.35

114.73

117.92

6.75

10.34

174.43

63.44

146.50

56.00

110.33

IS

NO

1000

67.14

EEA

1000

38.34

66.34

17.48

33.39

97.38

CH

1000

1000

1000

1000

1000

1000

1000

103.36

PT

1000

84.24

RO

1000

87.79

SI

187.78

364.46

169.18

168.28

153.98

146.03

103.00

155.60

133.57

152.77

108.13

31.47

63.74

97.41

73.57

52.47

201.27

28.54

47.30

16.65

63.57

72.18

51.51

67.82

55.69

42.04

58.95

56.19

43.85

68.94

43.00

62.00

36.18

59.87

34.40

68.11

43.69

66.20

16.91

44.94

194.58

106.44

227.07

148.60

181.33

115.00

115.61

170.16

151.90

194.23

41.21

30.06

75.54

49.88

45.48

9.54

23.05

30.46

69.57

35.12

58.23

36.73

63.30

54.42

61.24

33.81

68.00

24.00

64.72

42.63

75.93

33.94

69.82

45.65

37.73

163.49

185.94

102.73

159.61

153.85

152.00

198.26

202.49

152.79

110.48

36.50

94.36

11.85

83.75

87.97

43.97

85.14

17.47

87.50

75.15

38.66

35.01

24.00

32.65

31.75

31.10

113.64

124.19

153.00

132.18

142.66

103.40

5.85

80.41

66.39

4.70

18.00

64.09

138.00

82.95

100.00

9.30

82.73

70.26

3.68

43.26

60.04

11.31

94.96

84.33

27.54

88.79

7.32

96.29

96.23

Education

Restaurants and hotels

Miscellaneous

All-items 

Food

Alcohol and tobacco

Clothing

Housing

Household equipment

Health

Transport

Miscellaneous

All-items 

Food

Alcohol and tobacco

Clothing

Housing

Household equipment

Health

Transport

Communications

Recreation and culture

Education

Restaurants and hotels

137.79

Miscellaneous

62.01

Source: Eurostat

  European Economic Statistics

185

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4

Statistical annex

Table 4.27: 

Long term interest rates for EU Member States, US, and JP, 10 y government bond yields -  
EMU convergence criterion

Annual averages from 2000 to 2007

time

2000

2001

2002

2003

2004

2005

2006

2007

European Union 

Euro area 

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia (1)

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg (2)

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

United States (3)

Japan (3)

5.44

5.44

5.59

:

:

5.64

5.26

10.48

5.51

6.10

5.53

5.39

5.58

:

:

:

5.52

:

:

5.40

5.56

:

5.59

:

:

:

5.48

5.37

5.33

6.33

1.76

5.01

5.00

5.13

:

6.31

5.08

4.80

10.15

5.01

5.30

5.12

4.94

5.19

7.63

7.57

8.15

4.86

7.95

6.19

4.96

5.07

10.68

5.16

:

:

8.04

5.04

5.11

5.01

5.01

1.34

4.93

4.91

4.99

:

4.88

5.06

4.78

8.42

5.01

5.12

4.96

4.86

5.03

5.70

5.41

6.06

4.70

7.09

5.82

4.89

4.97

7.36

5.01

:

8.71

6.94

4.98

5.30

4.91

4.60

1.27

4.24

4.14

4.18

6.45

4.12

4.31

4.07

5.25

4.13

4.27

4.12

4.13

4.25

4.74

4.90

5.32

4.03

6.82

5.04

4.12

4.15

5.78

4.18

:

6.40

4.99

4.13

4.64

4.58

4.00

0.99

4.39

4.12

4.15

5.36

4.75

4.30

4.04

4.39

4.08

4.26

4.10

4.10

4.26

5.80

4.86

4.50

4.18

8.19

4.69

4.10

4.15

6.90

4.14

:

4.68

5.03

4.11

4.43

4.93

4.26

1.50

3.70

3.42

3.43

3.87

3.51

3.40

3.35

3.98

3.33

3.59

3.39

3.41

3.56

5.16

3.88

3.70

3.37

6.60

4.56

3.37

3.39

5.22

3.44

:

3.81

3.52

3.35

3.38

4.46

4.28

1.39

4.03

3.84

3.82

4.18

3.78

3.81

3.76

4.30

3.77

4.07

3.78

3.80

4.05

4.13

4.13

4.08

3.92

7.12

4.32

3.78

3.80

5.23

3.91

7.23

3.85

4.41

3.78

3.71

4.37

4.79

1.74

4.58

4.32

4.33

4.54

4.28

4.29

4.22

5.69

4.31

4.50

4.31

4.30

4.49

4.48

5.28

4.55

4.56

6.74

4.72

4.29

4.29

5.48

4.42

7.15

4.52

4.49

4.29

4.17

5.06

4.63

1.68

186

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Statistical annex

4

Table 4.28: 

3-month money market interest rates - Annual averages

time

2000

2001

2002

2003

2004

2005

2006

2007

European Union 

Euro area 

Bulgaria

Czech Republic

Denmark

Estonia

Cyprus

Latvia

Lithuania

Hungary

Malta

Poland

Romania

Slovenia (1)

Slovakia

Sweden

United Kingdom

United States

Japan

Source: ECB

4,78

4,39

4,63

5,37

5,00

5,68

6,44

5,40

8,64

11,39

4,89

18,77

50,71

10,94

8,57

4,06

6,19

6,53

0,28

4,40

4,26

5,06

5,17

4,70

5,31

5,93

6,86

5,93

10,87

4,93

16,07

41,28

10,87

7,77

4,12

5,04

3,77

0,15

3,48

3,32

4,91

3,54

3,54

3,88

4,40

4,35

3,74

9,21

4,01

8,98

2,60

2,33

3,68

2,27

2,42

2,92

3,90

3,84

2,84

8,51

3,29

5,68

27,31

17,73

8,03

7,77

4,27

4,06

1,79

0,08

6,78

6,18

3,24

3,73

1,22

0,06

2,64

2,11

3,74

2,36

2,20

2,50

4,74

4,23

2,68

11,53

2,94

6,20

19,14

4,66

4,68

2,31

4,64

1,62

0,05

2,72

2,19

3,62

2,01

2,22

2,38

4,25

3,07

2,43

6,70

3,18

5,28

8,35

4,03

2,93

1,89

4,76

3,56

0,06

3,50

3,08

3,69

2,30

3,18

3,16

3,37

4,38

3,11

7,23

3,49

4,21

8,09

3,58

4,33

2,57

4,85

5,20

0,30

4,62

4,28

4,90

3,10

4,44

4,66

4,15

8,68

5,11

7,86

4,26

4,74

7,20

:

4,34

3,89

6,00

5,30

0,79

  European Economic Statistics

187

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4

Statistical annex

Table 4.29: 

Euro exchange rates - Annual averages 2000 - 2007

unit

National currency 

time

2000

2001

2002

2003

2004

2005

2006

2007

currency

CZK

DKK

EEK

CyP

LvL

LTL

HUF

MTL

PLN

SKK

SEK

GBP

ISK

NOK

CHF

BGN

RON

JPy

USD

Source: ECB

Czech Koruna

Danish Krone

Estonian Kroon

Cyprus Pound

Latvian Lats

Lithuanian Litas

Hungarian forint

Malta lira

New Polish Zloty

Slovak Koruna

Swedish Krona

Pound Sterling

Iceland Krona

Norwegian Krone

Swiss Franc

New Bulgarian Lev

New Romanian leu

yen (Japan)

United States Dollar

35,599

7,4538

15,647

0,5739

0,5592

3,6952

260,04

0,4041

4,0082

42,602

8,4452

0,6095

72,58

8,1129

1,5579

1,9522

1,9922

99,47

0,9236

34,068

7,4521

15,647

0,5759

0,5601

3,5823

256,59

0,4030

3,6721

43,300

9,2551

0,6219

87,42

8,0484

1,5105

1,9482

2,6004

108,68

0,8956

30,804

7,4305

15,647

0,5753

0,5810

3,4594

242,96

0,4089

3,8574

42,694

9,1611

0,6288

86,18

7,5086

1,4670

1,9492

3,1270

118,06

0,9456

31,846

7,4307

15,647

0,5841

0,6407

3,4527

253,62

0,4261

4,3996

41,489

9,1242

0,6920

86,65

8,0033

1,5212

1,9490

3,7551

130,97

1,1312

31,891

7,4399

15,647

0,5819

0,6652

3,4529

251,66

0,4280

4,5268

40,022

9,1243

0,6787

87,14

8,3697

1,5438

1,9533

4,0510

134,44

1,2439

29,782

7,4518

15,647

0,5768

0,6962

3,4528

248,05

0,4299

4,0230

38,599

9,2822

0,6838

78,23

8,0092

1,5483

1,9558

3,6209

136,85

1,2441

28,342

7,4591

15,647

0,5758

0,6962

3,4528

264,26

0,4293

3,8959

37,234

9,2544

0,6817

87,76

8,0472

1,5729

1,9558

3,5258

146,02

1,2556

27,766

7,4506

15,647

0,5826

0,7001

3,4528

251,35

0,4293

3,7837

33,775

9,2501

0,6843

87,63

8,0165

1,6427

1,9558

3,3328

161,25

1,3705

188

European Economic Statistics 

2008_1393_anto.indd   188

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Statistical annex

4

Table 4.30: 

Main world traders: exports, imports and trade balance, 2000-2007 (EUR Bn)

2000

2001

2002

2003

2004

Exports

EU27

United States

China *

Japan

Canada

EU27

Imports

850

845

270

519

300

993

United States

1362

China *

Japan

Canada

Trade balance

EU27

United States

China *

Japan

Canada

excluding Kong Kong

*  
Source:  Comtrade, Eurostat (EU27)

244

411

260

-143

-517

26

108

40

885

816

297

450

291

979

1318

272

390

247

-94

-501

25

60

44

892

733

344

441

267

937

1271

312

357

235

-45

-538

32

84

32

869

640

387

417

241

935

1154

365

339

213

-66

-514

23

78

28

953

658

477

455

255

1028

1226

451

366

220

-75

-569

26

89

35

2005

1053

2006

1159

2007

1239

727

612

478

290

1180

1392

530

415

253

-127

-666

82

64

37

826

772

515

309

1351

1528

630

461

279

-192

-702

141

54

30

:

:

:

:

1424

:

:

:

:

-186

:

:

:

:

  European Economic Statistics

189

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4

Statistical annex

Table 4.31: 

Extra-EU-27 imports, exports and balance, by SITC-1 product group, 2000-2007 (EUR Bn)

sitc

Food products

Crude materials

Energy products

Chemical products

flow

Exports

Imports

Trade balance

Exports

Imports

Trade balance

Exports

Imports

Trade balance

Exports

Imports

Trade balance

Other manufactured products Exports

Machinery and vehicles

Total - All products

Imports

Trade balance

Exports

Imports

Trade balance

Exports

Imports

2000

2001

2002

2003

2004

2005

2006

2007

47.7

54.8

-7.1

17.8

49.2

-31.4

29.1

161.1

-132.0

118.9

70.5

48.4

224.1

250.5

-26.4

393.5

371.5

21.9

849.7

992.7

49.3

58.1

-8.8

17.0

48.0

-31.1

24.9

157.8

-132.8

130.2

76.9

53.3

232.7

253.5

-20.8

412.0

352.0

59.9

884.7

979.1

-94.4

50.1

58.1

-8.0

18.5

44.5

-26.1

26.2

149.1

-122.9

141.1

80.8

60.4

234.7

244.3

-9.6

401.5

329.1

72.4

891.9

937.0

-45.1

48.5

57.3

-8.8

18.3

43.1

-24.8

27.4

157.9

-130.4

141.1

80.5

60.6

223.9

238.5

-14.7

391.6

326.7

64.9

869.2

935.3

-66.0

48.5

58.9

-10.3

21.0

48.5

-27.5

32.9

183.6

-150.6

152.6

88.6

64.0

246.2

262.5

-16.3

430.1

354.6

75.5

52.0

63.0

-11.0

23.8

52.7

-28.9

45.9

272.5

57.9

67.9

-10.0

28.6

63.2

-34.7

58.7

61.8

75.2

-13.4

30.4

70.2

-39.8

61.8

339.5

331.5

-226.7

-280.8

-269.8

164.8

96.4

68.4

265.8

290.2

-24.4

470.9

379.1

91.8

184.6

109.0

75.6

293.6

340.8

-47.2

504.0

402.5

101.5

197.5

120.3

77.2

309.9

381.6

-71.7

543.5

413.7

129.8

952.9

1053.2

1159.2

1238.7

1027.5

-74.6

1179.9

-126.7

1351.4

1424.2

-192.2

-185.5

Trade balance

-143.0

190

European Economic Statistics 

2008_1393_anto.indd   190

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Table 4.32: 

Extra-EU-27 imports, exports and balance, by main partners, 2000-2007 (EUR Bn)

Statistical annex

4

partner

flow

United States

China *

Exports

Imports

Trade balance

Exports

Imports

Trade balance

Russian Federation

Exports

Imports

Trade balance

Exports

Imports

Trade balance

Exports

Imports

Trade balance

Exports

Imports

Trade balance

Exports

Imports

Trade balance

Exports

Imports

Trade balance

Exports

Imports

Trade balance

Exports

Imports

Trade balance

Switzerland

Japan

Norway

Turkey

South Korea 

India

Brazil

*  

excluding Kong Kong

2000

238.2

206.3

31.9

25.9

74.6

-48.8

22.7

63.8

-41.0

72.5

62.6

10.0

45.5

92.1

-46.6

26.4

47.2

-20.8

31.9

18.7

13.2

16.7

27.0

-10.2

13.7

12.9

0.8

16.9

18.7

-1.8

2001

245.6

203.3

2002

247.9

182.6

42.3

30.7

82.0

-51.3

31.6

65.9

-34.3

76.5

63.6

12.9

45.5

81.1

-35.6

27.2

46.4

-19.2

21.9

22.1

-0.2

15.8

23.3

-7.4

13.0

13.5

-0.5

18.6

19.6

-1.0

65.3

35.1

90.2

-55.1

34.4

64.5

-30.1

72.8

61.7

11.1

43.5

73.7

-30.2

28.2

48.0

-19.9

26.6

24.6

2.0

17.7

24.6

-6.9

14.3

13.7

0.7

15.7

18.4

-2.6

2003

227.3

158.1

69.2

41.5

106.2

-64.8

37.2

70.7

-33.5

71.4

59.1

12.3

41.0

72.4

-31.4

27.7

51.0

-23.4

30.9

27.3

3.6

16.5

26.0

-9.6

14.6

14.1

0.5

12.4

19.1

-6.7

2004

235.5

159.4

76.1

48.4

128.7

-80.3

46.0

84.0

-37.9

75.2

62.0

13.2

43.4

74.7

-31.3

30.8

55.3

-24.5

40.1

32.7

7.4

17.9

30.7

2005

252.9

163.8

89.1

51.9

160.4

-108.5

56.9

112.6

-55.7

82.6

66.6

16.0

43.7

74.1

-30.4

33.9

67.2

-33.3

44.6

36.1

8.6

20.2

34.4

-12.7

-14.2

17.2

16.4

0.8

14.2

21.7

-7.6

21.3

19.1

2.2

16.1

24.1

-8.0

2006

269.0

175.2

93.8

63.8

194.8

-131.0

72.3

140.8

-68.6

87.7

71.6

16.1

44.7

77.3

-32.5

38.5

79.2

-40.7

50.0

41.7

8.3

22.9

40.8

-17.9

24.4

22.6

1.8

17.7

27.2

-9.4

2007

261.3

180.9

80.4

71.7

231.3

-159.5

89.1

143.5

-54.5

92.7

76.8

15.9

43.7

77.9

-34.2

43.4

76.6

-33.3

52.6

46.9

5.7

24.8

39.4

-14.6

29.4

26.2

3.2

21.3

32.6

-11.3

  European Economic Statistics

191

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4

Statistical annex

Table 4.33: 

Member States’ contribution to the extra-EU27 trade

Share in the EU imports (%)

EU (27 countries)

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

2000

2001

2002

2003

2004

2005

2006

2007

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

5.6

0.3

0.9

1.4

5.7

0.4

1.1

1.4

6.1

0.4

1.3

1.4

5.9

0.4

1.4

1.4

6.1

0.5

1.1

1.6

6.1

0.4

1.0

1.5

5.8

0.4

1.1

1.4

6.1

0.6

1.2

1.4

20.0

19.6

19.0

19.4

19.2

18.8

19.4

18.8

0.1

2.0

1.3

5.5

12.1

10.2

0.1

0.1

0.3

0.2

1.2

0.1

0.2

1.9

1.3

5.5

12.2

10.3

0.2

0.1

0.3

0.3

1.3

0.1

0.2

2.0

1.6

5.7

11.7

10.4

0.2

0.1

0.4

0.2

1.5

0.1

0.2

1.9

1.8

6.0

11.2

10.3

0.2

0.1

0.4

0.3

1.6

0.1

0.2

1.7

1.6

6.5

11.2

10.5

0.1

0.1

0.4

0.4

1.5

0.1

0.2

1.6

1.5

7.0

11.1

10.6

0.1

0.1

0.4

0.4

1.4

0.1

0.2

1.4

1.6

7.4

9.8

11.1

0.1

0.2

0.4

0.5

1.4

0.1

0.2

1.3

1.6

7.4

9.7

11.1

0.1

0.2

0.4

0.4

1.5

0.1

11.1

11.0

11.1

11.3

11.7

12.5

12.3

12.5

1.6

1.7

1.0

0.5

0.3

0.4

1.2

2.5

1.7

1.7

1.0

0.6

0.3

0.5

1.1

2.2

1.7

1.9

0.9

0.6

0.3

0.5

1.2

2.2

1.7

2.0

0.9

0.7

0.3

0.5

1.3

2.2

1.6

1.7

1.0

0.9

0.2

0.5

1.3

2.2

1.7

1.7

1.0

1.0

0.3

0.5

1.4

2.2

1.6

2.0

1.0

1.1

0.3

0.7

1.5

2.3

1.7

2.3

1.0

1.0

0.4

0.8

1.5

2.3

United Kingdom

18.3

18.2

17.6

16.3

16.2

15.3

15.0

14.5

192

European Economic Statistics 

2008_1393_anto.indd   192

7-07-2008   10:20:44

Statistical annex

4

Share in the EU exports (%)

EU (27 countries)

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

2000

2001

2002

2003

2004

2005

2006

2007

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

5.6

0.3

0.5

1.9

5.3

0.3

0.6

2.0

6.3

0.3

0.7

2.1

5.9

0.3

0.6

2.0

5.9

0.3

0.7

1.9

5.9

0.3

0.9

1.9

5.9

0.4

0.9

1.8

5.9

0.4

1.1

1.8

24.8

26.3

26.7

26.8

27.2

26.5

27.7

27.5

0.0

3.5

0.6

4.0

14.7

11.8

0.0

0.0

0.1

0.1

0.6

0.2

5.6

2.2

0.8

0.6

0.4

0.3

0.2

2.2

4.4

0.1

3.7

0.5

3.8

14.6

12.0

0.0

0.1

0.1

0.1

0.6

0.1

5.4

2.2

0.9

0.6

0.4

0.3

0.1

2.2

3.9

0.1

3.6

0.5

3.8

13.7

11.8

0.0

0.1

0.2

0.1

0.6

0.1

5.7

2.3

0.9

0.6

0.4

0.4

0.2

2.1

4.0

0.1

3.5

0.5

3.9

13.3

11.5

0.0

0.1

0.3

0.1

0.7

0.1

5.9

2.4

1.0

0.6

0.4

0.4

0.3

2.2

4.3

0.1

3.3

0.5

4.0

13.0

11.4

0.0

0.1

0.3

0.1

0.8

0.1

6.1

2.6

1.2

0.6

0.5

0.4

0.3

2.2

4.3

0.1

3.0

0.5

4.1

12.9

11.0

0.0

0.1

0.3

0.1

0.9

0.1

6.3

2.7

1.5

0.6

0.6

0.5

0.3

2.2

4.1

0.2

2.7

0.5

4.2

11.8

11.1

0.0

0.1

0.4

0.2

1.1

0.1

6.6

2.6

1.6

0.7

0.7

0.5

0.4

2.3

4.0

0.2

2.6

0.5

4.3

11.4

11.6

0.0

0.1

0.4

0.2

1.2

0.1

7.1

2.6

1.7

0.7

0.7

0.5

0.5

2.3

3.9

United Kingdom

14.8

13.8

12.8

12.7

12.1

12.5

11.4

10.8

  European Economic Statistics

193

2008_1393_anto.indd   193

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4

Statistical annex

Table 4.34: 

Intra-EU-27 dispatches by SITC-1 product group, 2000-2007 (EUR Bn)

sitc

Food products

Crude materials

Energy products

Chemical products

Other manufactured products

Machinery and vehicles

Total - All products

2000

148.5

54.4

75.2

223.2

505.6

763.3

2001

157.6

52.7

75.3

239.5

520.6

787.6

2002

162.6

54.5

75.5

261.0

526.8

782.8

2003

167.9

55.9

80.1

268.0

530.5

771.9

2004

175.8

63.6

89.4

294.7

576.9

830.1

2005

187.9

67.3

129.3

325.7

611.4

858.8

2006

201.3

80.5

155.7

358.9

693.4

972.6

2007

220.6

89.1

151.2

396.3

750.7

1000.5

1805.8

1872.6

1897.4

1914.5

2071.8

2214.3

2497.1

2642.5

194

European Economic Statistics 

2008_1393_anto.indd   194

7-07-2008   10:20:45

Table 4.35: 

Member States’ contribution to the Intra-EU27 trade

Statistical annex

4

Intra-EU dispatches (EUR Bn)

EU (27 countries)

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

2000

2001

2002

2003

2004

2005

2006

2007

1805.8

1872.6

1897.4

1914.5

2071.8

2214.3

2497.1

2642.5

156.6

165.6

172.4

174.4

190.1

206.2

224.1

240.9

3.0

27.1

39.2

3.5

32.2

40.1

3.8

34.9

42.4

4.2

37.6

41.3

5.0

48.3

43.8

5.5

53.7

48.4

7.1

64.8

52.4

8.2

76.1

52.9

386.6

406.0

412.7

431.1

472.3

501.6

561.4

627.5

3.0

54.3

7.9

91.1

230.0

160.2

0.3

1.6

2.9

7.9

25.5

0.9

3.0

59.4

8.1

96.9

231.9

166.6

0.3

1.8

3.5

9.6

28.5

1.1

3.0

61.6

6.7

99.4

228.4

163.9

0.3

1.9

3.8

9.5

30.8

1.0

3.3

51.2

7.7

103.9

231.1

165.0

0.3

2.0

3.9

10.5

32.1

1.0

3.8

53.0

7.9

109.2

239.8

176.0

0.5

2.5

5.0

11.8

37.1

1.0

4.8

56.2

8.5

112.1

236.5

183.7

0.9

3.2

6.2

13.6

40.9

1.0

5.1

54.8

10.6

121.1

258.7

203.1

0.8

3.6

7.2

16.3

47.5

1.1

5.6

56.3

11.0

123.0

262.7

215.4

0.7

4.4

8.1

14.5

54.3

1.1

205.3

210.0

207.5

210.4

229.5

260.7

292.3

313.7

54.8

27.9

21.5

8.1

6.9

11.5

31.5

56.9

59.2

32.6

21.9

9.6

7.3

12.7

29.2

49.8

62.3

35.3

22.3

10.8

7.5

13.6

29.2

50.5

64.7

38.9

22.8

11.8

7.7

16.6

28.3

53.0

70.0

48.5

23.0

14.1

8.9

19.3

28.7

58.5

72.3

56.5

24.5

15.6

10.5

22.5

29.9

61.5

78.4

69.7

26.7

18.2

12.7

29.0

35.2

70.8

86.2

79.7

28.7

21.1

15.2

36.8

37.2

75.7

United Kingdom

183.5

182.4

181.9

160.0

164.2

177.4

224.9

185.5

  European Economic Statistics

195

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4

Statistical annex

Share in the intra-EU dispatches (%)

EU (27 countries)

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

2000

2001

2002

2003

2004

2005

2006

2007

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

8.7

0.2

1.5

2.2

8.8

0.2

1.7

2.1

9.1

0.2

1.8

2.2

9.1

0.2

2.0

2.2

9.2

0.2

2.3

2.1

9.3

0.3

2.4

2.2

9.0

0.3

2.6

2.1

9.1

0.3

2.9

2.0

21.4

21.7

21.8

22.5

22.8

22.7

22.5

23.7

0.2

3.0

0.4

5.0

0.2

3.2

0.4

5.2

0.2

3.2

0.4

5.2

0.2

2.7

0.4

5.4

0.2

2.6

0.4

5.3

0.2

2.5

0.4

5.1

0.2

2.2

0.4

4.9

12.7

12.4

12.0

12.1

11.6

10.7

10.4

8.9

0.0

0.1

0.2

0.4

1.4

0.0

8.9

0.0

0.1

0.2

0.5

1.5

0.1

8.6

0.0

0.1

0.2

0.5

1.6

0.1

8.6

0.0

0.1

0.2

0.5

1.7

0.1

8.5

0.0

0.1

0.2

0.6

1.8

0.0

8.3

0.0

0.1

0.3

0.6

1.8

0.0

8.1

0.0

0.1

0.3

0.7

1.9

0.0

0.2

2.1

0.4

4.7

9.9

8.2

0.0

0.2

0.3

0.5

2.1

0.0

11.4

11.2

10.9

11.0

11.1

11.8

11.7

11.9

3.0

1.5

1.2

0.5

0.4

0.6

1.7

3.2

10.2

3.2

1.7

1.2

0.5

0.4

0.7

1.6

2.7

9.7

3.3

1.9

1.2

0.6

0.4

0.7

1.5

2.7

9.6

3.4

2.0

1.2

0.6

0.4

0.9

1.5

2.8

8.4

3.4

2.3

1.1

0.7

0.4

0.9

1.4

2.8

7.9

3.3

2.6

1.1

0.7

0.5

1.0

1.3

2.8

8.0

3.1

2.8

1.1

0.7

0.5

1.2

1.4

2.8

9.0

3.3

3.0

1.1

0.8

0.6

1.4

1.4

2.9

7.0

196

European Economic Statistics 

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Statistical annex

4

Table 4.36: 

Trade in services with rest of the world, in € billion

2004

2005

2006

2007

Credit Debit

Net

Credit Debit

Net

Credit Debit

Net

Credit Debit

Net

42.4

39.5

2.9

0.7

0.5

2.7

45.2

3.6

9.5

35.4

41.2

2.7

8.2

30.3

4.0

0.8

1.2

5.1

47.4

4.1

10.6

41.8

-39.8

126.9

166.9

-40.0

139.1

5.1

1.0

1.2

5.2

56.5

4.6

12.3

45.0

51.6

3.5

10.3

39.5

4.9

1.1

2.0

5.4

-35.9

158.2

188.6

-30.4

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovak Republic

Finland

Sweden

3.3

7.7

29.4

117.4

2.3

42.4

26.7

69.4

90.9

68.2

5.0

1.4

2.0

27.1

8.8

1.4

68.3

39.5

10.8

11.9

2.9

2.8

3.0

12.2

31.3

2.6

7.2

26.8

157.2

1.4

52.6

11.3

47.6

79.2

67.0

2.1

1.0

1.3

0.9

-10.2

15.5

21.8

11.7

1.2

2.9

0.5

0.7

16.7

10.4

8.5

0.8

64.1

37.6

10.0

7.8

3.1

2.1

2.8

9.9

26.6

0.2

0.5

4.2

1.9

0.8

4.0

-0.2

0.7

0.2

2.3

4.7

2.6

48.2

27.6

76.2

95.6

71.9

5.2

1.8

2.5

32.9

10.3

1.6

74.0

40.6

13.1

12.3

4.1

3.1

3.5

13.7

34.5

1.7

57.5

11.9

54.0

85.0

72.4

2.2

1.3

1.7

19.8

9.2

1.0

67.9

36.4

11.5

8.4

4.4

2.3

3.3

12.2

26.2

0.8

-9.3

15.7

22.2

10.6

-0.5

3.1

0.5

0.8

13.1

1.1

0.6

6.1

4.1

1.6

3.8

-0.3

0.8

0.3

1.4

8.4

2.8

55.1

28.4

84.5

94.2

78.4

5.8

2.1

2.9

40.6

10.6

2.1

75.0

37.0

16.3

14.1

5.5

3.5

4.3

12.8

39.4

42.2

3.2

9.4

36.6

175.0

2.0

62.5

13.0

62.3

86.0

79.9

2.4

1.6

2.0

9.3

1.3

72.4

25.9

14.6

9.2

5.5

2.6

3.8

12.4

29.0

24.0

16.6

0.8

-7.4

15.3

22.1

8.3

-1.5

3.4

0.5

0.9

1.3

0.8

2.6

11.1

1.7

4.9

0.0

0.9

0.5

0.4

10.4

42.8

3.2

64.8

0.0

94.1

100.3

83.3

6.4

2.7

3.0

45.7

12.2

2.3

65.4

40.7

20.9

16.3

7.6

4.1

5.2

15.1

45.4

2.2

68.7

0.0

72.0

91.4

89.5

2.7

2.0

2.4

26.3

11.1

1.5

63.8

28.4

18.0

10.1

7.4

3.1

4.7

15.2

35.0

202.3

146.2

1.0

-4.0

0.0

22.1

8.9

-6.2

3.7

0.7

0.6

19.4

1.1

0.8

1.7

12.3

2.9

6.2

0.2

1.0

0.5

-0.1

10.4

56.1

United Kingdom

158.9

120.7

38.2

168.4

132.5

36.0

182.8

139.9

  European Economic Statistics

197

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4

Statistical annex

Table 4.37: 

EU trade in services, in € billion

Item Breakdown

Total Services

Transportation

Travel

Communications services

Construction services

Insurance services

Financial services

Computer and information 
services

Royalties and license fees

Other business services

Personal, cultural and 
recreational services

Government services, n.i.e.

Services not allocated

Partner breakdown

Switzerland

Russia

Canada

2004

2005

2006

2007

Credit Debit

Net

Credit Debit

Net

Credit Debit

Net

Credit Debit

Net

368.1

321.7

93.5

62.1

6.4

9.6

10.7

29.6

16.3

20.4

102.4

5.1

9.1

2.9

79.5

79.5

7.1

5.8

8.4

11.9

8.1

29.4

77.5

6.3

5.5

2.9

402.9

350.0

46.4

14.0

-17.4

-0.7

3.8

2.3

17.7

104.4

65.4

7.4

11.2

6.1

35.2

52.9

16.8

-18.4

-0.9

5.1

-2.2

87.7

83.8

8.3

6.1

8.4

441.6

109.7

71.1

8.7

12.1

14.2

41.9

373.1

96.2

85.1

9.8

6.6

7.2

68.5

13.6

-14.0

-1.2

5.5

7.0

17.4

24.5

501.4

413.0

118.5

102.0

94.1

10.7

7.9

7.7

76.7

9.8

15.9

14.3

54.3

88.4

16.5

-17.3

-0.9

8.0

6.5

14.4

20.8

21.3

33.0

8.2

17.3

8.7

8.6

21.0

9.7

11.3

23.6

10.3

13.2

-9.0

24.9

-1.2

3.7

0.1

23.7

117.1

4.9

7.9

2.3

32.1

85.8

6.3

6.1

2.4

-8.4

31.3

-1.4

1.8

-0.1

23.4

126.9

4.6

7.9

0.0

32.6

95.7

5.8

6.9

0.0

-9.2

31.2

-1.2

1.1

0.0

25.5

36.1

147.8

107.6

5.0

7.7

2.5

5.9

7.2

2.2

-10.6

40.2

-0.9

0.7

0.3

2004

2005

2006

2007

Credit Debit

Net

Credit Debit

Net

Credit Debit

Net

Credit Debit

Net

46.9

34.2

12.7

38.1

11.3

9.4

8.3

7.4

7.1

United States of America

117.9

109.3

Brazil

China

Hong Kong

India

Japan

3.7

9.3

7.1

3.9

3.5

7.7

5.2

3.9

18.4

11.1

49.4

12.3

9.0

9.1

7.6

123.2

118.2

4.6

12.3

8.3

5.4

4.0

9.6

5.6

4.8

19.6

12.3

2.0

1.2

8.6

0.3

1.7

1.9

-0.1

7.3

52.7

14.2

10.2

37.8

10.8

8.2

15.0

3.4

2.0

60.6

18.1

11.3

41.8

11.5

9.5

18.9

6.7

1.8

134.7

122.1

12.6

139.0

127.8

11.2

5.2

12.8

6.9

7.0

4.6

11.3

6.7

5.5

18.9

12.9

0.5

1.4

0.2

1.4

6.0

6.6

17.7

8.3

9.0

19.3

4.8

13.1

7.8

6.6

13.3

1.8

4.6

0.7

2.4

6.0

3.2

1.3

4.9

0.6

2.7

2.6

0.6

7.3

198

European Economic Statistics 

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Table 4.38: 

Current account of EU Member States, in € billion

Statistical annex

4

1000.2

897.3

102.9

1099.6

116.6

1268.1

1126.6

141.5

1395.7

1211.4

184.2

2004

Credit Debit

285.6

275.4

13.4

66.3

105.6

14.7

71.0

99.6

Net

10.2

-1.3

-4.7

5.9

7.8

163.2

48.2

261.9

546.3

411.8

7.4

6.3

10.2

9.0

164.1

-1.2

-0.9

58.7

-10.5

306.0

-44.2

537.8

424.9

8.0

7.8

11.6

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

103.4

100.2

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovak Republic

Finland

Sweden

56.4

4.5

384.2

149.9

86.5

54.0

25.5

16.8

26.8

73.5

159.2

United Kingdom 668.6

63.3

4.8

347.3

148.8

94.7

64.9

30.6

17.5

28.0

63.5

140.0

697.3

8.5

-13.0

-0.6

-1.4

-1.4

3.3

-6.9

-0.3

36.9

1.1

-8.2

-10.9

-5.1

-0.7

-1.2

10.0

19.2

-28.7

2005

2006

Credit Debit

Net

Credit Debit

311.4

303.5

7.9

-2.7

-1.8

9.0

334.4

326.0

18.3

93.4

22.8

97.2

138.5

132.6

2007

Credit Debit

363.6

20.5

109.5

147.0

357.2

26.8

113.0

144.5

Net

8.4

-4.5

-3.8

5.9

Net

6.4

-6.2

-3.5

2.5

18.0

80.2

115.4

983.0

10.9

185.7

65.5

349.4

601.5

463.3

9.1

9.5

14.6

117.6

70.1

5.4

392.6

162.0

107.2

70.7

38.6

19.7

35.0

73.8

15.2

78.4

124.4

9.7

180.0

51.5

282.5

585.8

439.9

8.3

7.9

13.1

120.9

64.1

5.0

429.2

164.8

104.2

56.5

31.7

19.1

31.7

79.5

175.8

777.3

-1.1

-5.7

-14.0

-66.9

-15.7

-23.4

-0.8

-1.6

-1.5

3.3

-6.0

-0.4

36.6

2.8

-3.0

-14.1

-6.9

-0.6

-3.2

5.7

11.8

13.8

203.5

210.8

-2.1

-7.3

12.8

15.5

229.5

238.8

-2.7

-9.3

322.1

648.8

485.5

9.3

9.4

15.7

151.6

77.0

6.3

487.7

174.0

126.4

66.1

38.8

22.2

40.5

91.8

202.4

915.8

409.8

671.3

523.3

10.2

13.0

18.3

148.1

82.5

6.7

-87.7

-22.5

-37.9

-0.9

-3.6

-2.6

3.5

-5.4

-0.4

443.2

44.5

167.7

133.7

81.7

49.0

23.0

44.2

84.2

175.9

990.2

6.3

-7.3

-15.7

-10.2

-0.9

-3.6

7.6

26.5

-74.5

458.7

-105.8

352.8

701.5

532.9

10.5

11.3

17.5

723.4

570.8

12.1

15.9

21.4

173.9

170.3

89.2

7.2

520.2

194.7

144.9

72.9

46.6

25.8

48.9

98.5

94.2

7.5

483.4

186.0

156.4

88.9

63.7

27.5

51.8

90.3

219.5

191.7

-21.9

-37.9

-1.5

-4.6

-3.8

3.6

-5.0

-0.3

36.8

8.8

-11.5

-15.9

-17.1

-1.6

-2.9

8.2

27.9

960.8

1045.9

-85.1

155.2

822.6

20.5

-45.3

  European Economic Statistics

199

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4

Statistical annex

Table 4.39: 

EU outward and inward FDI stocks by economic activity at end-2005 in € million

EU FDI stocks 
held outside 
the EU

% share

Investments 
from abroad 
held in the EU

% share

Net assets 
abroad

2 435 244

100%

1 823 203

100%

- Petroleum, chemical, rubber, plastic products

 196 541

Total

Agriculture, hunting and fishing

Mining and quarrying

Manufacturing

- Food products

- Textiles and wood activities

- Metal and mechanical products

- Machinery, computers, RTv, communication

- vehicles and other transport equipment

- Other manufacturing

Electricity, gas and water

Construction

Services

- Trade and repairs

- Hotels & restaurants

- Transport and communication

- Financial intermediation

- Business services

- Other services

Other sectors

 1 229

 84 020

 508 190

 57 936

 41 826

 99 201

 17 649

 53 196

 41 841

 47 463

 25 906

0%

3%

21%

2%

2%

8%

4%

1%

2%

2%

2%

1%

 1 103

 42 137

 317 396

 45 329

 44 221

 105 250

 47 090

 20 989

 21 586

 32 931

 13 901

 6 053

1 662 892

68%

1 359 059

 102 691

 15 929

 126 792

1 049 069

 284 900

 83 506

 105 544

4%

1%

5%

43%

12%

3%

4%

 86 245

 14 189

 46 449

 766 030

 410 661

 35 472

 83 554

0%

2%

17%

2%

2%

6%

3%

1%

1%

2%

1%

0%

75%

5%

1%

3%

42%

23%

2%

5%

 612 041

  126

 41 883

 190 794

 12 607

- 2 395

 91 291

 52 111

- 3 340

 31 610

 8 910

 33 562

 19 853

 303 833

 16 446

 1 740

 80 343

 283 039

- 125 761

 48 034

 21 990

200

European Economic Statistics 

2008_1393_anto.indd   200

7-07-2008   10:20:48

Table 4.40: 

Geographical distribution of EU FDI stocks 2004-2006*

Statistical annex

4

Outward stocks at end (EUR bn)

2004

2005

2006

Share (%) 
in 2006

Inward stocks at end (EUR bn)

2004

2005

2006

Share (%) 
in 2006

 2 199.9

 2 435.2

 2 706.2

100%

 1 732.5

 1 823.2

 2 057.3

100%

Extra EU27

Europe (non-EU)

   EFTA

      Switzerland

      Norway

   Russia

Africa

   North African countries

   Other African countries

      Republic of South Africa

  406.5

  297.7

  250.4

  45.0

  21.6

  110.2

  23.0

  87.2

  43.5

  509.5

  352.3

  310.8

  39.4

  33.3

  119.2

  26.1

  93.1

  47.1

  619.8

  376.8

  333.2

  41.5

  52.2

  125.1

  32.9

  92.1

  44.0

America

 1 252.5

 1 370.4

 1 533.6

   North American countries

  895.8

  947.2

 1 078.5

      Canada

      United States

   Central American countries

      Mexico

   South American countries

      Brazil

Asia

   Near and Middle East countries

   Other Asian countries

      China

      Hong Kong

      India

      Indonesia

      Japan

     South Korea

      Singapore

      Taiwan

Oceania and Polar regions

      Australia

      New Zealand

  80.5

  815.8

  211.6

  40.3

  145.1

  72.8

  345.5

  22.1

  323.4

  22.5

  97.7

  9.1

  7.1

  79.5

  20.6

  45.8

  7.1

  67.2

  60.3

  6.5

  97.0

  850.4

  255.4

  44.8

  167.8

  84.4

  368.4

  26.0

  342.4

  28.2

  85.4

  11.1

  10.9

  90.2

  28.9

  48.8

  10.4

  58.7

  52.8

  5.8

  119.6

  934.3

  287.1

  43.2

  168.0

  88.0

  367.7

  27.3

  340.5

  32.7

  83.4

  13.4

  9.5

  75.5

  29.2

  54.3

  9.3

  55.7

  50.6

  5.2

23%

14%

12%

2%

2%

5%

1%

3%

2%

57%

40%

4%

35%

11%

2%

6%

3%

14%

1%

13%

1%

3%

0%

0%

3%

1%

2%

0%

2%

2%

0%

339.4

269.8

233.5

28.6

5.8

14.7

3.1

11.6

4.5

402.4

298.8

240.1

45.9

12.3

19.5

3.6

15.9

4.2

457.4

324.7

247.8

63.0

12.7

23.5

4.4

19.2

4.0

 1 143.9

 1 180.6

 1 306.2

907.7

70.9

842.2

224.2

8.1

11.9

3.3

160.9

18.6

142.3

1.8

16.9

0.7

0.4

89.2

5.5

17.7

0.8

37.9

37.1

0.7

937.3

76.8

874.5

229.2

8.8

14.1

6.2

162.6

21.5

141.1

1.1

16.8

2.5

-2.6

82.7

6.1

28.2

0.6

23.5

22.8

1.0

1019.9

81.0

953.7

261.3

8.4

25.0

10.5

208.7

35.2

173.5

3.6

16.4

3.2

-3.3

99.3

7.6

40.0

0.8

17.8

17.3

1.8

22%

16%

12%

3%

1%

1%

0%

1%

0%

63%

50%

4%

46%

13%

0%

1%

1%

10%

2%

8%

0%

1%

-0%

0%

5%

0%

2%

0%

1%

1%

0%

*  

The sum of continents does not always equal total extra-EU because of not allocated flows.
Parts may be higher than totals because of disinvestment.

  European Economic Statistics

201

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4

Statistical annex

Table 4.41: 

Foreign Direct investment flows with rest of the world, in € billion

2004

2005

2006

2007

Outward Inward Net Outward Inward Net Outward Inward Net Outward Inward Net

84.0

234.5

127.0

107.5

168.9

106.1

319.2

100.8

EU27(1)

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary (2)

Malta

Netherlands (3)

Austria (3)

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

142.3

27.4

-0.2

0.8

:

11.9

0.2

14.6

:

48.8

45.7

15.5

0.6

0.1

0.2

67.7

0.9

0.0

23.5

6.7

0.6

6.3

:

0.4

-0.0

-0.9

16.7

73.3

58.3

35.1

2.7

4.0

:

-7.4

0.8

-8.5

:

19.9

26.2

13.6

0.9

0.5

0.6

63.7

3.6

0.3

3.7

3.1

10.1

1.9

:

0.6

2.4

2.3

9.4

-7.7

-2.9

-3.2

:

19.3

-0.6

23.1

:

28.8

19.5

2.0

-0.3

-0.4

-0.4

4.0

-2.7

-0.3

19.8

3.6

-9.4

4.4

:

-0.2

-2.5

-3.1

7.3

275.0

44.8

0.1

1.2

6.8

75.5

0.9

11.7

:

79.9

91.7

33.5

0.7

0.1

0.2

88.2

15.0

0.0

37.5

7.9

7.1

5.6

0.3

0.7

0.3

2.5

26.3

0.2

-0.0

13.0

44.6

0.5

11.5

:

33.6

97.3

33.6

0.4

0.1

0.3

99.7

1.8

-0.0

109.2

9.3

2.7

:

0.0

0.5

0.1

3.4

21.4

65.0

27.6

3.1

9.4

10.4

28.8

2.3

-25.5

:

20.1

65.2

16.0

1.0

0.6

0.8

93.6

6.2

0.5

38.4

9.0

8.3

:

5.2

0.7

2.0

3.8

8.2

-1.4

-2.9

-9.4

2.7

15.8

-1.7

37.0

:

13.5

32.1

17.6

-0.5

-0.5

-0.5

6.2

-4.4

-0.6

70.9

0.4

-5.6

:

:

-0.1

-1.8

-0.4

13.2

141.6

-76.6

419.9

37.9

0.2

1.0

11.9

122.3

1.1

12.1

:

87.4

159.3

64.2

0.8

0.2

0.4

25.8

0.0

22.8

23.2

2.4

4.5

-0.0

1.2

0.2

6.3

50.9

6.0

4.8

2.9

44.0

1.3

-0.7

:

21.4

64.6

31.2

1.2

1.3

1.4

99.6

15.8

1.5

6.4

4.5

15.2

9.0

9.1

0.5

3.3

4.4

-6.2

-5.8

-3.6

3.9

31.5

-0.5

12.5

:

58.5

27.1

2.3

-0.5

-1.2

-1.2

-0.7

-1.5

31.2

3.4

-8.1

-3.5

:

0.2

-3.0

-1.8

-0.8

-45.3

-11.4

132.9

28.5

6.1

6.7

8.3

37.2

1.8

18.9

:

39.0

109.5

22.7

1.5

1.6

1.4

86.8

26.8

0.7

72.7

22.6

12.8

4.1

7.3

1.1

2.2

6.2

9.3

-5.9

-5.7

3.6

85.1

-0.7

-6.8

:

48.4

49.8

41.5

-0.7

-1.4

-1.0

46.1

-1.0

-0.7

-49.9

0.5

-10.4

0.4

:

0.1

-2.0

0.1

13.1

29.7

45.0

28.2

17.5

72.4

18.3

117.7

26.8

165.4

13.7

135.7

missing or confidential data

Net =   Outward minus inward investment flows
Negative values denote disinvestment
:  
(1) EU27 investments with extra EU27
(2) Special purpose entities as included from 2006 onwards
(3) Special purpose entities are not included

202

European Economic Statistics 

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Table 4.42: 

Employment levels (thousand persons)

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

Forecast
Estimated value

f    
e   
Source:  National accounts

Statistical annex

4

2000

209,426

133,686

133,237

4,091

3,239

4,940

2,760

39,144

572

1,696

4,255

16,412

24,332

22,930

315

944

1,399

264

3,844

146

8,115

3,766

2005

216,464

139,664

139,146

4,225

3,495

4,988

2,762

2006

219,913

141,870

141,343

4,278

3,612

5,082

2,807

38,846

39,088

604

1,958

4,536

19,264

25,089

24,396

366

1,026

1,461

308

3,879

152

8,231

3,873

637

2,042

4,647

19,985

25,278

24,882

373

1,074

1,486

319

3,905

154

8,383

3,912

14,526 e

14,116 e

14,577 e

5,030

:

905

2,025

2,297

4,301

27,477

1,549

21,970 f

156

2,320

4,089

5,100

9,267

924

2,084

2,398

4,349

28,769

1,573 f

22,103 f

161

2,352

4,196

5,137 f

9,526 f

935

2,132

2,440

4,423

29,018

1,586 f

22,378 f

170 f

2,430

4,291

2007

223,448

144,370

143,827

4,337 f

3,714

5,161 f

2,858

39,737

642

2,111 f

4,705

20,614

25,530 f

25,165

385

1,111

1,515

332 f

3,899

158

8,548 f

3,988

15,218 f

5,150 f

9,643 f

960

2,177

2,494

4,522

29,219

1,627 f

22,718 f

171 f

2,488 f

4,323 f

  European Economic Statistics

203

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4

Statistical annex

Table 4.43: 

Employment growth (% over previous year)

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

Forecast
Estimated value

f    
e   
Source:  National accounts

2000

2005

2006

2007

1.7

2.4

2.4

2.0

4.9

-0.2

0.5

1.9

-1.5

4.6

0.5

5.1

2.7

1.9

1.6

-3.0

-4.0

5.6

1.3

:

2.2

1.1

0.9

0.9

0.9

1.2

2.7

1.0

0.8

-0.1

1.9

4.7

1.5

4.1

0.4

0.6

3.4

1.8

2.5

3.0

0.0

1.3

0.2

1.4

-1.6 e

2.3 e

2.1

:

1.3

-1.9

2.2

2.5

1.2

4.0

-0.4 f

2.0

0.6

1.1

-0.3

-1.5

0.2

1.4

1.4

0.3

1.0

0.8 f

1.4 f

3.2

1.2

0.4

1.6

1.6

1.6

1.3

3.3

1.9

1.6

0.6

5.5

4.3

2.4

3.7

0.8

2.0

1.9

4.7

1.7

3.6

0.7

1.3

1.8

1.0

3.3 e

0.7 f

2.8 f

1.2

2.3

1.8

1.7

0.9

0.8 f

1.2 f

5.6 f

3.3

2.3

1.6

1.8

1.8

1.4 f

2.8

1.6 f

1.8

1.7

0.8

3.4 f

1.2

3.1

1.0 f

1.1

3.2

3.4

2.0

4.1 f

-0.2

2.6

2.0 f

1.9

4.4 f

0.3 f

1.2 f

2.7

2.1

2.2

2.2

0.7

2.6 f

1.5 f

0.6 f

2.4 f

0.7 f

204

European Economic Statistics 

2008_1393_anto.indd   204

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Statistical annex

4

Table 4.44: 

Employment rates (15-64 years old), males plus females

2000

2005

2006

2007

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

62.2

61.5

61.5

60.5

50.4

65.0

76.3

65.6

60.4

65.2

56.5

56.3

62.1

53.7

65.7

57.5

59.1

62.7

56.3

54.2

72.9

68.5

55.0

68.4

63.0

62.8

56.8

67.2

73.0

71.2 b

:

63.5

63.8

63.8

61.1

55.8

64.8

75.9

66.0 b

64.4

67.6

60.1

63.3 b

63.9

57.6

68.5

63.3

62.6

63.6

56.9

53.9

73.2

68.6

52.8

67.5

57.6

66.0

57.7

68.4

72.5 b

71.7

55.0

48.8 i

46.0 i

:

77.5

83.8

74.8

64.5

64.8

64.8

61.0

58.6

65.3

77.4

67.5

68.1

68.6

61.0

64.8

63.8

58.4

69.6

66.3

63.6

63.6

57.3

54.8

74.3

70.2

54.5

67.9

58.8

66.6

59.4

69.3

73.1

71.5

55.6

45.9

84.6

75.4

78.3 i

77.2 i

77.9 i

65.4

65.7

65.7

62.0

61.7

66.1

77.1

69.4

69.4

69.1

61.4

65.6

64.6

58.7

71.0

68.3

64.9

63.6

57.3

55.7

76.0

71.4

57.0

67.8

58.8

67.8

60.7

70.3

74.2

71.3

:

:

85.1 p

76.8

78.6 i

Break in series
Provisional value
Explanatory text:
data source: national Labour Force Survey

b   
p   
i    
Turkey –  
Switzerland –  data refers to quarter 2 
Source:  

EU LFS

  European Economic Statistics

205

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4

Statistical annex

Table 4.45: 

Employment rates (15 to 64 years old), females

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

Break in series
Provisional value
Explanatory text:
data source: national Labour Force Survey

b   
p   
i    
Turkey –  
Switzerland –  data refers to quarter 2 
Source:  

EU LFS

2000

2005

2006

2007

53.7

51.4

51.4

51.5

46.3

56.9

71.6

58.1

56.9

53.9

41.7

41.3

55.2

39.6

53.5

53.8

57.7

50.1

49.7

33.1

63.5

59.6

48.9

60.5

57.5

58.4

51.5

64.2

70.9

64.7 b

:

56.3

55.7

55.7

53.8

51.7

56.3

71.9

60.6 b

62.1

58.3

46.1

51.2 b

58.5

45.3

58.4

59.3

59.4

53.7

51.0

33.7

66.4

62.0

46.8

61.7

51.5

61.3

50.9

66.5

70.4 b

65.9

48.6

25.8 i

23.8 i

:

73.6

69.3 i

80.5

71.7

57.3

56.8

56.9

54.0

54.6

56.8

73.4

62.2

65.3

59.3

47.4

53.2

58.8

46.3

60.3

62.4

61.0

54.6

51.1

34.9

67.7

63.5

48.2

62.0

53.0

61.8

51.9

67.3

70.7

65.8

49.4

23.9

80.8

72.2

70.4 i

71.1 i

58.3

58.0

58.0

55.3

57.6

57.3

73.2

64.0

65.9

60.6

47.9

54.7

60.0

46.6

62.4

64.4

62.2

55.0

50.9

36.9

69.6

64.4

50.6

61.9

52.8

62.6

53.0

68.5

71.8

65.5

:

:

80.8 p

74.0

71.6 i

206

European Economic Statistics 

2008_1393_anto.indd   206

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Table 4.46: 

Employment rates (15 to 64 years old), males

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

Statistical annex

4

2000

2005

2006

2007

70.8

71.6

71.6

69.5

54.7

73.2

80.8

72.9

64.3

76.3

71.5

71.2

69.2

68.0

78.7

61.5

60.5

75.0

63.1

75.0

82.1

77.3

61.2

76.5

68.6

67.2

62.2

70.1

75.1

77.8 b

:

70.8

71.9

71.9

68.3

60.0

73.3

79.8

71.3 b

67.0

76.9

74.2

75.2 b

69.3

69.9

79.2

67.6

66.1

73.3

63.1

73.8

79.9

75.4

58.9

73.4

63.7

70.4

64.6

70.3

74.4 b

77.6

61.7

71.8 i

68.2 i

:

81.3

86.9

77.8

71.6

72.7

72.7

67.9

62.8

73.7

81.2

72.8

71.0

77.7

74.6

76.1

69.0

70.5

79.4

70.4

66.3

72.6

63.8

74.5

80.9

76.9

60.9

73.9

64.6

71.1

67.0

71.4

75.5

77.3

62.0

68.1

88.1

78.4

87.3 i

83.9 i

84.7 i

72.5

73.4

73.4

68.7

66.0

74.8

81.0

74.7

73.2

77.4

74.9

76.2

69.3

70.7

80.0

72.5

67.9

71.9

64.0

74.2

82.2

78.4

63.6

73.8

64.8

72.7

68.4

72.1

76.5

77.3

:

:

89.1 p

79.5

85.6 i

Break in series
Provisional value
Explanatory text:
data source: national Labour Force Survey

b   
p   
i    
Turkey –  
Switzerland –  data refers to quarter 2 
Source:  

EU LFS

  European Economic Statistics

207

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4

Statistical annex

Table 4.47: 

Employment rates, older workers (aged 55-64), males plus females

2000

2005

2006

2007

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

Break in series
Provisional value
Explanatory text:
data source: national Labour Force Survey

b   
p   
i    
Turkey –  
Switzerland –  data refers to quarter 2 
Source:  

EU LFS

36.9

34.4

34.3

26.3

20.8

36.3

55.7

37.6

46.3

45.3

39.0

37.0

29.9

27.7

49.4

36.0

40.4

26.7

22.2

28.5

38.2

28.8

28.4

50.7

49.5

22.7

21.3

41.6

64.9

50.7 b

:

42.4

40.5

40.5

31.8

34.7

44.5

59.5

45.4 b

56.1

51.6

41.6

43.1 b

38.7

31.4

50.6

49.5

49.2

31.7

33.0

30.8

46.1

31.8

27.2

50.5

39.4

30.7

30.3

52.7

69.4 b

56.9

32.6

36.3 i

31.0 i

:

65.2

63.3 i

84.3

65.5

43.5

41.8

41.8

32.0

39.6

45.2

60.7

48.4

58.5

53.1

42.3

44.1

38.1

32.5

53.6

53.3

49.6

33.2

33.6

30.0

47.7

35.5

28.1

50.1

41.7

32.6

33.1

54.5

69.6

57.4

34.3

30.1

84.3

67.4

65.1 i

65.7 i

44.7

43.3

43.3

34.4

42.6

46.0

58.6

51.5

60.0

53.8

42.4

44.6

38.3

33.8

55.9

57.7

53.4

32.9

33.1

28.3

50.9

38.6

29.7

50.9

41.4

33.5

35.6

55.0

70.0

57.4

:

:

84.7 p

69.0

67.2 i

208

European Economic Statistics 

2008_1393_anto.indd   208

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Statistical annex

4

Table 4.48: 

Employment by NACE (thousands persons)

NACE A-B NACE C-E

NACE F

NACE G-I

NACE J-K

NACE L-P

Total

2007

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

13,902

5,852

5,832

:

732

:

84

850

30

:

534

923

:

39,093

24,720

24,638

:

821

:

407

7,905

148

:

534

3,226

:

1,016

5,245

18

110

164

:

180

:

:

:

:

:

:

87

79

120

99

:

:

:

:

:

:

40

186

293

:

945

:

:

:

:

:

:

253

577

460

755

:

:

:

:

:

:

16,433

11,180

11,130

:

231

:

187

2,199

74

:

395

2,695

:

1,930

38

128

165

:

325

:

:

:

:

:

:

80

169

185

271

:

:

:

:

:

:

56,290

36,659

36,477

33,436

22,677

22,622

:

910

:

745

9,909

166

:

1,511

5,761

:

6,122

137

321

396

:

:

252

:

434

6,876

58

:

413

2,390

:

3,719

43

99

97

:

64,293

43,281

43,128

:

768

:

1,001

11,998

165

:

1,318

5,619

223,448

144,370

143,827

4,349 f

3,714

5,172 f

2,858

39,737

642

2,116 f

4,705

20,614

:

25,581 f

7,134

25,165

110

268

400

:

385

1,111

1,515

333 f

3,899

158

8,583 f

3,988

15,240 f

5,147 f

9,645 f

960

2,177

2,494

4,522

29,219

1,618 f

22,651 f

177 f

2,523 f

4,361 f

1,045

369

1,035

:

:

:

:

:

:

209

625

574

975

:

:

:

:

:

:

:

:

:

:

:

:

132

219

328

663

:

:

:

:

:

:

:

:

:

:

:

:

199

507

826

1,759

:

:

:

:

:

:

  European Economic Statistics

209

2008_1393_anto.indd   209

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4

Statistical annex

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

Forecast
Estimated value

f    
e   
Source:  National accounts

NACE A-B NACE C-E

NACE F

NACE G-I

NACE J-K

NACE L-P

Total

2000

15,283

6,536

6,514

41,552

25,926

25,851

95

789

238

95

936

41

131

722

1,037

961

1,103

19

136

261

4

245

3

280

:

687

761

1,503

468

8,534

151

317

540

3,088

3,866

5,190

39

189

290

35

1,035

36

1,082

:

14,322

9,949

9,913

240

132

431

167

2,769

39

171

300

1,821

1,464

1,554

26

56

83

26

267

10

493

:

52,118

33,645

33,493

1,001

699

1,254

702

9,824

155

455

1,360

4,466

5,560

5,632

114

246

317

71

1,003

39

2,153

:

27,822

18,788

18,745

739

154

513

358

58,328

38,841

38,721

1,329

704

1,002

970

209,426

133,686

133,237

4,091

3,239

4,940

2,760

5,802

11,279

39,144

47

213

309

1,640

4,189

2,950

33

70

58

71

289

10

1,658

:

139

409

1,023

4,359

8,291

6,502

84

249

390

58

1,005

36

2,450

:

572

1,696

4,255

16,412

24,332

22,930

315

944

1,399

264

3,844

146

8,115

3,766

2,794 e

3,231 e

671 e

3,245 e

1,337 e

3,248 e

14,526 e

634

:

107

116

137

127

:

225

:

:

96

185

1,075

:

274

573

483

827

:

348

:

:

347

754

581

:

67

135

154

219

:

100

:

:

140

296

1,223

:

194

493

532

928

:

407

:

:

611

1,151

363

:

88

175

251

569

:

105

:

:

278

631

1,153

5,030

:

176

533

739

1,631

:

364

:

:

848

1,071

:

905

2,025

2,297

4,301

27,477

1,549

21,970 f

156

2,320

4,089

210

European Economic Statistics 

2008_1393_anto.indd   210

7-07-2008   10:20:53

Statistical annex

4

Table 4.49: 

Part-time jobholders, males plus females (% total jobholders)

2000

2005

2006

2007

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

16.2

15.8

15.9

18.9

:

5.3

21.3

19.4

8.1

16.4

4.5

7.9

16.7

8.4

8.4

11.3

10.2

10.4

3.5

6.8

41.5

16.3

10.5

10.9

16.5

6.5

2.1

12.3

19.5

25.2 b

:

9.2 i

:

25.8

30.5 i

17.8

18.9

18.9

22.0

2.1

4.9

22.1

24.0 b

7.8

:

5.0

12.4 b

17.1

12.8

8.9

8.3

7.1

17.4

4.1

9.6

46.1

21.1

10.8

11.2

10.2

9.0

2.5

13.7

24.7 b

25.4

10.1

5.9 i

22.2

28.2

18.1

19.5

19.5

22.2

2.0

5.0

23.6

25.8

7.8

:

5.7

12.0

17.2

13.3

7.7

6.5

9.9

17.1

4.0

10.1

46.2

21.8

9.8

11.3

9.7

9.2

2.8

14.0

25.1

25.5

9.4

7.9

17.1

28.7

33.1 i

33.3 i

18.2

19.6

19.7

22.1

1.7

5.0

24.1

26.0

8.2

:

5.6

11.8

17.2

13.6

7.3

6.4

8.6

18.0

4.1

11.1

46.8

22.6

9.2

12.1

9.7

9.3

2.6

14.1

25.0

25.5

:

:

16.7 p

28.2

33.5 i

Break in series
Provisional value
Explanatory text:
data source: national Labour Force Survey

b   
p   
i    
Turkey –  
Switzerland –  data refers to quarter 2 
Source:  

EU LFS

  European Economic Statistics

211

2008_1393_anto.indd   211

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4

Statistical annex

Table 4.50: 

Part-time jobholders, females (% total jobholders)

2000

2005

2006

2007

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

28.9

30.3

30.4

37.4

:

9.3

34.1

37.9

10.9

30.3

7.8

16.8

30.8

16.5

13.9

12.8

11.1

25.1

5.2

15.5

71.0

32.2

13.4

16.4

18.6

7.8

3.1

17.0

32.3

44.3 b

:

19.6 i

:

43.0

55.6 i

30.9

34.4

34.4

40.5

2.5

8.6

33.0

43.5 b

10.6

:

9.3

24.2 b

30.2

25.6

14.0

10.4

9.1

38.2

5.8

21.1

75.1

39.3

14.3

16.2

10.5

11.1

4.1

18.6

39.6 b

42.7

13.4

13.5 i

37.5

44.2

31.2

35.0

35.1

41.1

2.5

8.7

35.4

45.6

11.3

:

10.2

23.2

30.2

26.5

12.1

8.3

12.0

36.2

5.6

21.8

74.7

40.2

13.0

15.8

9.8

11.6

4.7

19.2

40.2

42.6

11.7

17.8

30.1

45.2

58.8 i

58.4 i

31.2

35.1

35.2

40.6

2.1

8.5

36.2

45.8

12.1

:

10.1

22.8

30.2

26.9

10.9

8.0

10.2

38.6

5.8

24.9

75.0

41.2

12.5

16.9

10.4

11.3

4.5

19.3

40.0

42.3

:

:

29.6 p

44.1

59.0 i

Break in series
Provisional value
Explanatory text:
data source: national Labour Force Survey

b   
p   
i    
Turkey –  
Switzerland –  data refers to quarter 2 
Source:  

EU LFS

212

European Economic Statistics 

2008_1393_anto.indd   212

7-07-2008   10:20:54

Statistical annex

4

2000

2005

2006

2007

Table 4.51: 

Part-time jobholders, males (% total jobholders)

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

Break in series
Provisional value
Explanatory text:
data source: national Labour Force Survey

b   
p   
i    
Turkey –  
Switzerland –  data refers to quarter 2 
Source:  

EU LFS

6.5

5.4

5.4

5.5

:

2.2

10.2

5.0

5.3

6.9

2.6

2.8

5.3

3.7

4.5

9.7

9.2

1.7

2.0

3.0

19.3

4.1

8.2

6.4

14.6

5.3

1.1

8.0

8.2

8.9 b

:

5.5 i

:

10.6

7.4

7.0

7.0

7.6

1.7

2.1

12.7

7.8 b

4.9

:

2.3

4.5 b

5.8

4.6

5.0

6.3

5.1

2.5

2.7

4.5

22.6

6.1

8.0

7.0

10.0

7.2

1.3

9.2

11.5 b

10.4

7.3

3.3 i

8.7

13.8

7.7

7.4

7.4

7.4

1.5

2.2

7.7

7.5

7.5

7.5

1.3

2.3

13.3

13.5

9.3

4.3

:

2.9

4.3

5.8

4.7

4.3

4.7

7.9

2.6

2.6

4.8

9.4

4.3

:

2.7

4.1

5.7

5.0

4.4

4.9

7.0

2.6

2.8

4.4

23.0

23.6

6.5

7.1

7.4

9.5

7.2

1.3

9.3

11.8

10.6

7.5

4.4

7.0

13.9

7.2

6.6

8.0

9.2

7.7

1.1

9.3

11.8

10.9

:

:

6.8 p

13.9

12.4 i

10.8 i

11.8 i

12.6 i

  European Economic Statistics

213

2008_1393_anto.indd   213

7-07-2008   10:20:55

4

Statistical annex

Table 4.52: 

Fixed-term contracts, males plus females (% total employees)

2000

2005

2006

2007

European Union (27 countries)

Euro area 15 

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

12.3

15.2

15.2

9.1

:

8.1

9.7

12.7

3.0

5.9

13.5

32.2

15.2

10.1

10.7

6.7

4.4

5.3

7.1

4.1

13.7

8.0

5.8

19.9

2.8

13.7

4.8

16.3

15.8

6.9 b

:

:

:

2.6

11.5 i

14.0

16.3

16.3

8.9

6.4

8.6

9.8

14.1 b

2.7

3.7

11.8

33.3 b

14.1

12.3

14.0

8.4

5.5

5.3

7.0

4.5

15.5

9.1

25.7

19.5

2.4

17.4

5.0

16.5

16.0 b

5.7

12.4

:

6.9

9.5

14.4

16.7

16.8

8.7

6.2

8.7

8.9

14.5

2.7

3.4

10.7

34.0

14.1

13.1

13.1

7.1

4.5

6.1

6.7

3.8

16.6

9.0

27.3

20.6

1.8

17.3

5.1

16.4

17.3

5.8

12.9

13.3

11.5

10.1

12.8 i

13.5 i

14.5

16.7

16.8

8.6

5.2

8.6

8.7

14.6

2.1

7.3

10.9

31.7

14.4

13.2

13.2

4.2

3.5

6.8

7.3

5.2

18.1

8.9

28.2

22.4

1.6

18.5

5.1

15.9

17.5

5.8

:

:

12.3 p

9.6

12.9 i

Break in series
Provisional value
Explanatory text:
data source: national Labour Force Survey

b   
p   
i    
Turkey –  
Switzerland –  data refers to quarter 2 
Source:  

EU LFS

214

European Economic Statistics 

2008_1393_anto.indd   214

7-07-2008   10:20:55

Statistical annex

4

Table 4.53: 

Fixed-term contracts, females (% total employees)

2000

2005

2006

2007

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

13.0

16.4

16.4

12.3

:

9.4

11.1

13.1

1.7

7.2

16.1

34.2

16.4

12.2

14.3

4.6

3.1

6.6

6.5

5.6

16.8

8.8

4.9

21.9

2.8

14.8

4.5

19.8

17.8

7.9 b

:

:

:

3.3

12.8 i

14.5

17.2

17.2

11.4

6.2

9.8

11.3

13.8 b

2.0

4.2

14.3

35.7 b

15.0

14.7

19.5

6.2

3.6

5.8

6.4

6.1

16.9

8.8

24.7

20.4

1.9

19.3

4.9

20.0

17.7 b

6.2

12.3

:

7.8

11.6

15.0

17.7

17.7

10.9

6.1

10.1

10.0

14.1

2.2

3.9

13.0

36.7

14.8

15.8

19.0

5.4

2.7

6.6

6.0

6.0

18.0

8.9

26.0

21.7

1.6

19.3

5.2

20.0

19.1

6.4

12.6

13.1

12.7

12.6

13.0 i

13.9 i

15.2

17.7

17.7

10.8

5.5

10.2

10.0

14.5

1.6

8.6

13.1

33.1

15.4

15.9

19.2

2.9

2.3

7.9

6.8

8.0

19.7

9.0

27.9

23.0

1.5

20.8

5.3

19.4

19.9

6.4

:

:

13.6 p

11.7

13.1 i

Break in series
Provisional value
Explanatory text:
data source: national Labour Force Survey

b   
p   
i    
Turkey –  
Switzerland –  data refers to quarter 2 
Source:  

EU LFS

  European Economic Statistics

215

2008_1393_anto.indd   215

7-07-2008   10:20:56

4

Statistical annex

Table 4.54: 

Fixed-term contracts, male (% total employees)

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

2000

2005

2006

2007

11.7

14.2

14.3

6.7

:

7.1

8.5

12.5

4.4

4.9

11.8

30.9

14.2

8.7

7.6

8.8

5.9

4.6

7.7

3.4

11.2

7.4

6.5

18.3

2.8

12.7

5.1

12.9

13.8

6.1 b

:

:

:

2.1

10.5 i

13.6

15.6

15.6

6.8

6.7

7.6

8.5

14.4 b

3.4

3.1

10.1

31.7 b

13.3

10.5

9.0

10.7

7.6

4.9

7.6

3.7

14.3

9.3

26.5

18.7

2.8

15.7

5.1

12.9

14.2 b

5.2

12.4

:

6.0

7.5

13.9

15.9

16.0

6.9

6.3

7.5

8.0

14.7

3.3

2.9

9.1

32.0

13.4

11.2

7.9

8.8

6.4

5.7

7.4

2.7

15.4

9.1

28.5

19.5

2.0

15.5

5.0

12.6

15.4

5.1

13.1

13.3

10.4

7.8

12.6 i

13.1 i

13.9

15.9

15.9

6.8

5.0

7.3

7.6

14.7

2.7

6.0

9.3

30.6

13.3

11.2

7.6

5.5

4.9

5.9

7.7

3.7

16.6

8.8

28.4

21.8

1.7

16.5

4.9

12.4

15.0

5.2

:

:

11.0 p

7.6

12.7 i

Break in series

b   
p   
i    
Turkey –  
Switzerland –  data refers to quarter 2 
Source:  EU LFS

Provisional value
Explanatory text:
data source: national Labour Force Survey

216

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Statistical annex

4

Table 4.55: 

Fixed-term contracts by full-time / part-time job status, averages 2004-2007

Part-time jobholders

Full-time jobholders

fixed-term permanent Total fixed-term permanent Total

European Union (27 countries)

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

Switzerland

19.9

20.8

14.7

31.7

44.5

13.3

12.4

7.7

16.2

50.7

53.0

22.8

20.9

21.2

21.7

13.9

6.9

21.8

20.8

21.9

6.7

55.8

48.2

24.1

64.2

31.2

30.4

26.2

11.0

45.1

80.1

79.2

85.3

68.3

55.5

86.7

87.6

92.3

83.8

49.3

47.0

77.2

79.1

78.8

78.3

86.1

93.1

78.2

79.2

78.1

93.3

44.2

51.8

75.9

35.8

68.8

69.6

73.8

89.0

54.9

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

12.8

15.3

6.8

5.8

7.0

8.0

14.4

2.2

3.3

9.5

30.2

12.0

11.4

12.9

6.3

4.4

5.5

6.4

2.4

11.2

9.8

23.8

19.0

2.0

14.5

4.4

14.3

10.8

3.9

12.2

87.2

84.7

93.2

94.2

93.0

92.0

85.6

97.8

96.7

90.5

69.8

88.0

88.6

87.1

93.7

95.6

94.5

93.6

97.6

88.8

90.2

76.2

81.0

98.0

85.5

95.6

85.7

89.2

96.1

87.8

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

55.3 i

12.6

16.9

8.5 i

44.7 i

100 i

12.1 i

87.9 i

100 i

87.4

83.1

100

100

7.8

7.0

92.2

93.0

100

100

91.5 i

100 i

14.9 i

85.1 i

100 i

Explanatory text:
data source: national Labour Force Survey

i    
Turkey –  
Switzerland –  data refers to quarter 2 
Source:  

EU LFS

  European Economic Statistics

217

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4

Statistical annex

Table 4.56: 

Actual hours worked, annual

EU27

2000

2001

2002

2003

2004

2005

2006

EU25

2000

2001

2002

2003

2004

2005

2006

Total hours worked

Average hours/ person

Levels

growth

Levels

growth

:

:

361,429,184

360,894,173

363,843,836

366,932,297

372,223,170

:

:

:

-0.1

0.8

0.8

1.4

:

:

1,703

1,694

1,697

1,695

1,693

:

:

:

-0.5

0.2

-0.1

-0.1

Total hours worked

Average hours/ person

Levels

growth

Levels

growth

338,547,068

339,808,210

338,232,470

337,872,396

340,810,388

343,963,573

349,053,676

:

0.4

-0.5

-0.1

0.9

0.9

1.5

1,723

1,711

1,696

1,688

1,690

1,689

1,686

:

-0.7

-0.9

-0.5

0.1

-0.1

-0.1

Source: National accounts

218

European Economic Statistics 

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Statistical annex

4

Table 4.57: 

Usual weekly hours worked, 2007 (% total distribution)

EU27

<7 hours

8-12 hours

13-17 hours

18-22 hours

23-27 hours

28-32 hours

33-37 hours

38-42 hours

43-47 hours

48-52 hours

53-57 hours

58-62 hours

63-67 hours

68-72 hours

>73 hours

Total

Source: EU LFS

Full-time jobholders

Part-time jobholders

males

females

total

males

females

total

0.0

0.0

0.0

0.4

0.3

1.1

12.0

57.6

7.3

11.2

2.3

4.6

0.6

1.6

1.0

100

0.0

0.0

0.1

1.3

1.3

3.5

18.2

60.0

4.9

6.4

1.1

1.8

0.3

0.7

0.5

100

0.0

0.0

0.1

0.7

0.6

2.0

14.3

58.5

6.4

9.4

1.8

3.6

0.5

1.2

0.8

100

11.8

15.6

11.7

24.6

12.6

19.3

3.2

0.9

0.1

0.1

0.0

0.0

0.0

0.0

0.0

100

8.0

13.6

11.7

27.4

15.6

20.2

2.7

0.6

0.0

0.1

0.0

0.0

0.0

0.0

0.0

100

8.9

14.0

11.7

26.8

14.9

20.0

2.8

0.7

0.1

0.1

0.0

0.0

0.0

0.0

0.0

100

  European Economic Statistics

219

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4

Statistical annex

Table 4.58: 

Unemployment rates, males plus females

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Norway

Break in series

b   
Source:  EU LFS

2000

2005

2006

2007

8.7

8.3

8.3

6.9

16.4

8.7

4.3

7.5

12.8

4.2

11.2

11.1

9.0

10.1

4.9

13.7

16.4

2.3

6.4

6.7

2.8

3.6

16.1

3.9

7.2

6.7

18.8

9.8

5.6

5.4

:

5.2

3.4

8.9

8.8

8.9

8.4

10.1

7.9

4.8

10.7

7.9

4.3

9.8

9.2

9.2

7.7

5.2

8.9

8.3

4.5

7.2

7.3

4.7

5.2

17.7

7.6

7.2

6.5

16.3

8.4

7.4 b

4.8

12.6

8.8

4.6

8.1

8.2

8.2

8.2

9.0

7.1

3.9

9.8

5.9

4.4

8.9

8.5

9.2

6.8

4.6

6.8

5.6

4.7

7.5

7.3

3.9

4.7

13.8

7.7

7.3

6.0

13.4

7.7

7.0

5.4

11.1

8.4

3.5

7.1

7.4

7.4

7.5

6.9

5.3

3.7

8.4

4.7

4.5

8.3

8.3

8.3

6.1

3.9

6.0

4.3

4.7

7.4

6.4

3.2

4.4

9.6

8.0

6.4

4.8

11.1

6.9

6.1

5.3

9.1

:

2.6

220

European Economic Statistics 

2008_1393_anto.indd   220

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Table 4.59: 

Unemployment rates, females

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Norway

Break in series

b   
Source: EU LFS

Statistical annex

4

2000

2005

2006

2007

9.8

10.0

10.0

8.5

16.2

10.3

4.8

7.5

11.8

4.2

17.1

16.0

10.8

13.6

7.2

12.9

14.1

3.1

5.6

7.4

3.6

4.3

18.1

4.9

6.4

7.0

18.6

10.6

5.3

4.8

:

5.1

3.2

9.6

9.9

9.9

9.5

9.8

9.8

5.3

10.1

7.1

4.0

15.3

12.2

10.2

10.1

6.5

8.7

8.3

5.8

7.4

9.0

5.1

5.5

19.1

8.7

6.4

7.0

17.2

8.6

7.4 b

4.3

13.8

8.5

4.4

8.9

9.3

9.3

9.3

9.3

8.8

4.5

9.4

5.6

4.1

13.6

11.6

10.1

8.8

5.4

6.2

5.4

6.2

7.8

8.9

4.4

5.2

14.9

9.0

6.1

7.2

14.7

8.1

7.2

4.9

12.7

8.4

3.4

7.8

8.4

8.4

8.4

7.3

6.7

4.1

8.3

3.9

4.1

12.8

10.9

8.9

7.9

4.6

5.6

4.3

5.7

7.7

7.6

3.6

5.0

10.3

9.6

5.4

5.8

12.7

7.2

6.4

4.9

10.5

:

2.5

  European Economic Statistics

221

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4

Statistical annex

Table 4.60:

Unemployment rates, males

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Norway

Break in series

b   
Source:  EU LFS

2000

2005

2006

2007

7.8

6.9

6.9

5.6

16.7

7.3

3.9

7.5

13.8

4.3

7.4

7.9

7.5

7.8

3.2

14.4

18.6

1.8

7.0

6.4

2.2

3.1

14.4

3.1

7.8

6.5

18.9

9.1

5.9

5.9

:

5.3

3.6

8.3

8.0

8.0

7.6

10.3

6.5

4.4

11.2

8.8

4.6

6.1

7.0

8.4

6.2

4.3

9.1

8.2

3.5

7.0

6.5

4.4

4.9

16.6

6.7

7.8

6.1

15.5

8.2

7.5

5.2

11.6

8.9

4.8

b

7.6

7.4

7.4

7.4

8.6

5.8

3.3

10.2

6.2

4.6

5.6

6.3

8.4

5.4

4.0

7.4

5.8

3.5

7.2

6.5

3.5

4.4

13.0

6.5

8.2

4.9

12.3

7.4

6.9

5.7

9.8

8.4

3.6

6.6

6.6

6.6

6.7

6.5

4.2

3.4

8.5

5.4

4.7

5.2

6.4

7.8

4.9

3.4

6.4

4.3

4.0

7.1

5.8

2.8

3.9

9.0

6.6

7.2

4.0

9.9

6.5

5.8

5.6

7.9

:

2.6

222

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Statistical annex

4

Table 4.61: 

Unemployment rates, young persons (aged 15-24), males plus females

2000

2005

2006

2007

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany (including ex-GDR from 1991)

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg (Grand-Duché)

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Norway

Break in series

b   
Source:  EU LFS

17.4

15.8

15.8

16.7

33.7

17.8

6.2

7.5

23.9

6.8

29.1

24.3

19.6

27.0

10.1

21.4

30.6

7.1

12.4

13.7

5.7

5.3

35.1

8.6

20.0

16.3

36.9

21.4

10.5

12.2

:

10.5

9.9

18.3

17.2

17.2

21.5

22.3

19.2

8.6

13.9

15.9

8.6

26.0

19.7

21.0

24.0

13.0

13.6

15.7

13.7

19.4

16.4

8.2

10.3

36.9

16.1

20.2

15.9

30.1

20.1

21.7 b

12.8

32.3

16.8

11.6

17.1

16.1

16.1

20.5

19.5

17.5

7.7

12.5

12.0

8.6

25.2

17.9

22.1

21.6

10.5

12.2

9.8

16.2

19.1

16.3

6.6

9.1

29.8

16.3

21.4

13.9

26.6

18.7

21.5

14.0

28.9

16.0

8.8

15.4

14.8

14.8

18.8

15.1

10.7

8.2

11.2

10.0

8.7

22.9

18.2

19.4

20.3

9.8

10.7

8.2

17.5

18.0

13.1

5.9

8.6

21.7

16.6

20.1

10.1

20.3

16.5

19.1

14.3

22.9

:

7.3

  European Economic Statistics

223

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4

Statistical annex

Table 4.62: 

Long-term unemployment rates, males plus females (% active population)

2000

2005

2006

2007

European Union (27 countries)

Euro area 15

Euro area 13

Belgium

Bulgaria

Czech Republic

Denmark

Germany 

Estonia

Ireland

Greece

Spain

France

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

United Kingdom

Croatia

Turkey

Iceland

Norway

4.0

3.9

4.0

3.7

9.4

4.2

0.9

3.8

5.9

1.6

6.1

4.6

3.5

6.3

1.2

7.9

8.0

0.6

3.1

4.4

0.8

1.0

7.4

1.7

3.7

4.1

10.3

2.8

1.4

1.4 b

:

1.1 i

:

0.3

4.1

3.9

3.9

4.4

6.0

4.2

1.1

5.7 b

4.2

1.5

5.1

2.2 b

3.8

3.9

1.2

4.1

4.3

1.2

3.2

3.4

1.9

1.3

10.2

3.7

4.0

3.1

11.7

2.2

1.4 b

1.0

7.4

3.5 i

0.3

0.9

3.7

3.7

3.7

4.2

5.0

3.9

0.8

5.5

2.8

1.4

4.8

1.8

3.9

3.4

0.9

2.5

2.5

1.4

3.4

2.9

1.7

1.3

7.8

3.8

4.2

2.9

10.2

1.9

1.1

1.2

6.7

2.5

0.2

0.8

3.0

3.2

3.2

3.8

4.0

2.8

0.6

4.7

2.3

1.4

4.1

1.7

3.3

2.9

0.7

1.6

1.4

1.3

3.4

2.6

1.3

1.2

4.9

3.8

3.2

2.2

8.3

1.6

0.8

1.3

:

:

0.2 p

0.5

Break in series
Provisional value
Explanatory text:
data source: national Labour Force Survey

b   
p   
i    
Turkey –  
Switzerland –  data refers to quarter 2 
Source:  

EU LFS

224

European Economic Statistics 

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2008_1393_anto.indd   226

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2008_1393_anto.indd   226

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European Commission

European Economic Statistics

Luxembourg: Office for Official Publications of the European Communities

2008 — 224 pp. — 21 x 29.7 cm

ISBN 978-92-79-08886-5

Price (excluding VAT) in Luxembourg: EUR 20

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2008_1393_anto.indd   226

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