regions: statistical yearbook 2003

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Regions: Statistical

yearbook 2003

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Luxembourg: Office for Official Publications of the European Communities, 2003

ISBN 92-894-5366-4ISSN 1681-9306

© European Communities, 2003

PRELIMINARES EN 18-08-2003 09:19 Página 2

Foreword

This 2003 edition of the Eurostat regional yearbook is the last before the planned enlargement in

2004, the most extensive in the history of the European Union. With the arrival of a further 10

Member States, regional statistics at European level will undoubtedly alter in character, but, in fact,

many of the trends are already clearly identifiable, reflecting the increasingly complete integration

of the individual countries within the data-collection system of Eurostat.

Clearly, regional variations within these countries will continue to interest policy-makers but there

will be some shift in emphasis. The small size of no fewer than six of the accession countries

(Cyprus, Estonia, Latvia, Lithuania, Malta and Slovenia) means that, in most statistical fields,

Eurostat will be collecting data from these countries only at national level — whereas only two of

the current 15 Member States (Denmark and Luxembourg) are in this situation. This does not, of

course, mean that conditions are similar throughout each of these countries, or that no data are

available. With the exception of Cyprus, each of the above countries has agreed with Eurostat a

regional breakdown at a lower level, permitting the collection of regional accounts, unemployment

and certain other data.

It is understandable that attention should focus on the 2004 enlargement date but it is important

to recognise the great efforts made by Bulgaria and Romania to comply with European standards

in statistics and to supply data on an equal footing with the remaining 10 accession countries. The

regional yearbook’s maps and commentaries, of course, continue to feature these two countries.

Although it is uncertain how far negotiations (and statistical cooperation) will have advanced in

the meantime, it is quite possible that the 2004 edition may already feature data from Turkey. The

broader the European canvas becomes, the more important it is to have accurate and comparable

regional statistics to build up a clear overall picture of the social and economic realities of a

growing European Union.

Pedro Solbes MiraEuropean Commissioner for Economic

and Monetary Affairs, responsible for Eurostat


CONTENTS

■ INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2003 — a year of remarkable changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

New nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Enlargement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Content and structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Readers’ views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Specialist input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

NUTS 99 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

More regional information needed? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Regional interest group on the web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Closure date for the yearbook data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

■ POPULATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Population change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Components of population increase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Net migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

In- and out-migration flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

■ AGRICULTURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

The contribution of agriculture to GDP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Animal share of agricultural goods output . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Output/input ratios in agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Eurofarm data — women in agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Income diversification of the farming community . . . . . . . . . . . . . . . . . . . . . . . 32

The LUCAS survey — statisticians monitor territory . . . . . . . . . . . . . . . . . . . . . . 34

Objectives of the survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Two-stage area frame systematic sampling design . . . . . . . . . . . . . . . . . . . . . . . 35

A multi-purpose information system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Realisation of the survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Main results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Land use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Mixed land cover/land use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Landscape photos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

■ REGIONAL GROSS DOMESTIC PRODUCT . . . . . . . . . . . . . . . . . . . . . . . . . . 43

What is gross domestic product? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Purchasing power parities and international volume comparisons . . . . . . . . . . . . . . . 45

Regional GDP in euro for the year 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

R e g i o n s : S t a t i s t i c a l y e a r b o o k 2 0 0 3 5

■ REGIONAL UNEMPLOYMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

The regional dimension of overall unemployment . . . . . . . . . . . . . . . . . . . . . . . 53

Women and the labour markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Youth unemployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

The problem of long-term unemployment . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Revised methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

■ LABOUR FORCE SURVEY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Overall rate of employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Employment rate for women . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Average number of hours normally worked . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Lifelong training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Levels of education and employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

■ SCIENCE AND TECHNOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Human resources in science and technology . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Employment in high-tech and knowledge-intensive fields . . . . . . . . . . . . . . . . . . . . 80

Patent applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

■ STRUCTURAL BUSINESS STATISTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Services are the largest employer in the regions . . . . . . . . . . . . . . . . . . . . . . . . . 92

Employees better paid around the capital cities . . . . . . . . . . . . . . . . . . . . . . . . . 92

Employment in industry unevenly divided among the regions . . . . . . . . . . . . . . . . . 94

Capital-intensive industries in the regions . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

■ TRANSPORT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101


Transport infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Road network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

Railway network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Transport equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Sea transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Air transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

■ HEALTH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Comments on methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Mortality in the EU regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

Incidence of tuberculosis in the EU regions . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

Healthcare resources in the EU regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

R e g i o n s : S t a t i s t i c a l y e a r b o o k 2 0 0 36

■ TOURISM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133


Capacity (infrastructure) statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

Occupancy data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

■ URBAN STATISTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Tight schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Selection of cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Spatial units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

The variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Next steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

■ HOUSEHOLD ACCOUNTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Income of private households and gross domestic product . . . . . . . . . . . . . . . . . . . 151

How rich are Europe’s regions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

■ INTERACTIVE DATA PRESENTATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

Interactive visualisation of regional data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

How to use the applet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

■ EUROPEAN UNION: NUTS 2 REGIONS . . . . . . . . . . . . . . . . . . . . . . . . . . 167

■ REGIONS IN THE CANDIDATE COUNTRIES . . . . . . . . . . . . . . . . . . . . . . . . 169


I N T R O D U C T I O N

2003 — a year ofremarkable changesOver the past year, the future structure and natureof regional statistics at European level have beenshaped by the adoption of the nomenclature ofterritorial units for statistics (NUTS) regulationand by the steady march towards enlargement in2004.

New nomenclatureIn April 2003, after more than two years of prepa-rations — involving thorough consultations at na-tional level, wide-ranging discussions with datausers and two separate passages through the Eu-ropean Parliament — the NUTS regulation wasadopted by Parliament. The NUTS nomenclaturethus at last gains a legal base, along with a well-defined procedure for managing modifications tothe regional breakdowns in individual countries.

At the same moment that the regulation itself be-came part of European legislation, so too did itsannex, which sets out the nomenclature agreedbetween Eurostat and the member countries.Whereas (until the regulation was signed) region-al statistics in Europe had been collected in accor-dance with the 1999 version of the nomenclature(known as ‘NUTS 99’), the annex lays down anew nomenclature, ‘NUTS 2003’, which is nowthe only valid and acceptable regional breakdownfor supplying data to Eurostat.

The snag, of course, is that data supplied in themonths following the adoption of the regulationwere necessarily collected on the basis of theNUTS 99 breakdown. For this reason, Eurostathas adopted a target date of 21 November 2003for completing the reorganisation of its databasesthat contain regional data, in particular the RE-GIO database. The same reasoning applies all themore forcefully in the case of the yearbook, wherethe initial extractions of data for this year’s chap-ters actually occurred before the regulation be-came law. Accordingly, this 2003 yearbook, its listof regions and all maps, graphs and commentariesare based on NUTS 99. A summary of the classi-fication may be found at the end of thisintroduction.

In fact, NUTS 2003 strongly resembles its prede-cessor. Of the over 200 NUTS 2 regions featuring

in the NUTS 99 breakdown, only 10 have beenmodified and, in every, case the underlying NUTS3 structure remains unaffected, greatly helpingdata recalculation. In all, 10 Member States arecompletely unaffected by the changes at NUTS 2level. Changes at the NUTS 3 level are even lessextensive. Full details of the NUTS 2003 break-down may be found on Eurostat’s RAMONserver (1).

EnlargementWith regard to data for the candidate countries,there has been further improvement in coveragesince last year’s edition, though not as extensive asone would wish.

Although in almost all thematic fields the acces-sion countries have been integrated into the data-collection process, this has in a number of casesbeen too recent for the data to be available for thisyear’s edition. Accordingly, several chapters con-tain maps still limited to current EU MemberStates. By contrast, the 2004 edition should fea-ture complete integration of all accessioncountries.

While attention naturally has focused on the 2004enlargement date, it should be stressed that nodistinction is made in the yearbook between coun-tries scheduled to become Member States in 2004and those due to join around 2007: wherever dataare available for Bulgaria and Romania, these, ofcourse, also feature in the maps and commen-taries. In the case of Turkey, the situation is ratherdifferent. Although a regional breakdown hasbeen agreed between Turkey and Eurostat, littleregional data have as yet been collected and thecoverage is certainly too thin for inclusion in the2003 yearbook.

Content andstructureThe year 2003 sees the return of the tourism chap-ter, reflecting the biennial nature of the data-collection process in this field. By contrast, theavailability of new environmental data is not suf-


(1) From the Eurostat home page (www.europa.eu.int/comm/eurostat), just select your preferred language, click on‘Metadata’, then on ‘Classifications’ and finally on‘RAMON’.

ficient to justify retaining the chapter this yearand we look forward to restoring it in 2004. De-spite the absence of new results from the urbanaudit, the urban chapter is retained because it of-fers a forum to discuss the methodological andother challenges that have been encountered dur-ing the past year’s work on this project and to de-scribe the progress expected. Similarly, the agri-culture chapter has been expanded to include areview of the LUCAS project which, while notNUTS based, seeks to obtain highly detailed in-formation on the variation of land use and landcover across Europe. Finally, a new chapter onhousehold accounts has been included in the 2003edition of the yearbook, further expanding the re-gional economic information so valued by users.

In keeping with earlier years, regional distribu-tions are described in commentaries that exploitcarefully selected colour maps and graphs, high-lighting individual regions where appropriate. Adeliberate attempt has been made to choose vari-ables which are different from those examined in2002 and 2001, thus not only offering regularreaders new insights, but also giving a better im-pression of the still wider range of data availablein the REGIO database. Again in line with previ-ous editions, the enclosed CD-ROM containsdata for the latest available year in each REGIOdomain, the data series used to draw the mapsand the PDF versions of each of the three lan-guage editions of the yearbook. A special efforthas been made to ensure that the CD-ROM isboth interesting and more user-friendly. In recog-nition of the widespread use of the yearbook inreference and educational settings, the CD-ROMincludes this year for the first time an interactiveapplet that allows users to compare a whole seriesof variables for each of 27 NUTS 2 regions, thesebeing the regions containing the capital cities ofall Member States and accession countries.

Readers’ viewsIn January 2003, a questionnaire was distributedto some 300 regular individual and institutionalusers of the regional yearbook. No fewer than154 replied, providing feedback on the approachso far adopted. In general, the current format wassupported, as is shown by the following graphs.

Although the wide variety of users would presup-pose very different uses for the yearbook, itappears that the approach adopted does manageto meet most readers’ needs.

Again, it is inevitable that different users willvalue most those chapters more directly related totheir work, but overall it would appear that allchapters enjoy a measure of support and indeedmany specifically commented that they felt allchapters were useful. High individual rankingswere achieved by the regional GDP, regional un-employment and population chapters.

Most users regarded the commentaries as bothhelpful and offering the appropriate depth ofanalysis. As the following graphs indicate, themaps, too, were valued.

Do you find the choice of imformation displayedin the maps useful?

Yes

PartlyNot answered

62 %5 %

33 %

Does the product meet your needs?

Partly

NoNot answered

Yes

79 %

9 %

11 %1 %

Satisfaction with the content of the regional yearbook (1)

Very satisfied

Satisfied

Not answered

(1) Users were offered two other options, ‘Not really satisfied’ and ‘Not at allsatisfied’, but no one used either option.

11 %

49 %

40 %


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Some nine users voiced criticisms, two of them ad-vocating coverage at a different level than NUTS2. A further two regretted the absence of data ta-bles, such as were featured in the regional year-book until 1999. It would appear that this aspectis outweighed by the greater readability, especial-ly for non-specialist users. Indeed, it is doubtfulwhether even specialists are greatly inconve-nienced, since, for research purposes, the full ex-tent of the REGIO database is available throughthe Data Shops, with the further advantage of up-dates since the data closure of the yearbook.There was also nostalgia for the many linguisticversions of the yearbook when it was still only acompilation of tables (up to 1997). On grounds ofcost, let alone editorial complexity, however, theregional yearbook will continue to be ‘limited’ toEnglish, French and German versions.

Specialist inputOnce again, the commentaries within each of thethematic chapters reflect the specialist knowledgeof Eurostat’s thematic units (2). By exploiting theirexperience of data at the national level, the au-thors are in a position to place the regional varia-tion noted in an appropriate context.

The regional statistics team gratefully acknowl-edges the contribution made by the following au-thors, each of whom has had to find the necessarytime within an already overcrowded schedule.

Chapter Author(s)

1. Population Aarno LaihonenErik Beekink

2. Agriculture Pol MarquerUlrich EidmannMaxime KayadjanianManola Bettio

3. Regional gross domestic product Axel Behrens

4. Regional unemployment Axel Behrens

5. Labour force Ana Franco

6. Science and technology August GoetzfriedSimona Frank

7. Structural business statistics Paul FeuvrierFranca Faes-Cannito

8. Transport Josefine Oberhausen

9. Health Didier DupreToni Montserrat

10. Tourism Hans-Wilhelm Schmidt

11. Urban statistics Berthold FeldmannTorbiörn Carlquist

12. Household accounts Axel Behrens

Working interactively Joachim Mittagwith data Jana Cernovska

Ulrich Marty

NUTS 99The present version of NUTS (NUTS 99) subdi-vides the economic territory of the Member Statesof the European Union (EU) into 78 regions atNUTS 1 level, 211 regions at NUTS 2 level and 1093 regions at NUTS 3 level.

Because of their relatively small area or popula-tion, some countries do not have all three region-al levels. It should be noted that, in the case ofboth Denmark and Luxembourg, the entire coun-try is one NUTS 2 region.

In the maps in this yearbook, the statistics are pre-sented at NUTS level 2. A map giving the codenumbers of the regions may be found in the sleeveof this publication. At the end of the publication,there is a list of all the NUTS 2 regions in the Eu-ropean Union, together with a list of the level 2statistical regions in the candidate countries, asagreed between Eurostat and the individual statis-tical offices of the candidate countries. It must benoted that this classification does not precludeany decision on the NUTS that will be taken asand when individual countries join the EU.

Is the presentation of maps clear enough?

Yes

NoNot answered

67 %1 %

32 %


(2) In the case of the chapters on regional gross domestic prod-uct, regional unemployment, urban statistics and house-hold accounts, the authors are simultaneously members ofthe regional statistics team and the subject specialists with-in Eurostat.

Full details of these national regional break-downs, including lists of level 2 and level 3 re-gions and the appropriate maps, may be consult-ed on the RAMON server using the web siteaddress in footnote 1.

More regionalinformation needed?The REGIO database contains more extensivetime series (which may go back as far as 1970)and more detailed statistics than those given inthis yearbook (for example, population by singleyears of age — deaths by single years of age —births by age of the mother — detailed results ofthe Community labour force survey — economicaccounts aggregates for 17 branches — detailedbreakdown of agricultural production — data onthe structure of agricultural holdings, etc.). More-over, there is coverage in REGIO of a number ofindicators at NUTS level 3 (such as area, popula-tion, births and deaths, gross domestic product,unemployment rates). This is important becausetwo EU Member States (Denmark and Luxem-bourg) and four candidate countries (the threeBaltic States and Slovenia) do not have a level 2breakdown.

All REGIO data may be obtained by contactingyour nearest Data Shop at the address listed at theback of the publication.

For more detailed information on the content ofthe REGIO database, please consult the Eurostatpublication European regional statistics — Refer-ence guide 2003, a copy of which is available inPDF format on the accompanying CD-ROM.

Regional interestgroup on the webEurostat’s regional statistics team maintains apublicly accessible interest group on the web(‘CIRCA site’) with many useful links and docu-ments.

To access it, simply click on the URL:

http://forum.europa.eu.int/Public/irc/dsis/regstat/information.

Among other resources, you will find:

• a list of all regional coordination officers in theMember States and the candidate countries;

• the Regional Gazette published at intervals bythe regional team;

• the latest edition of the REGIO reference guide;

• Powerpoint presentations of Eurostat’s workconcerning regional statistics;

• the regional classification NUTS for the Mem-ber States and the regional classification for thecandidate countries;

• a link to a list of all Eurostat Data Shops.

Closure date for theyearbook dataThe cut-off date for this issue is 31 May 2003.


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IntroductionChanges in the size of a population are the resultof the number of births, the number of deaths andthe number of people who migrate.

The difference between the number of births andthe number of deaths we call natural growth.With regard to migration, we can distinguishbetween international migration, i.e. across a

national boundary, and internal migration, i.e. in-side a national territory.

Furthermore, a distinction can be made betweenimmigration and emigration (international migra-tion) and arrivals and departures due to internalmigration. Migration flows are often expressed innet migration rates.

In the following sections, a description is present-ed of regional dynamics at the NUTS 2 level


Total population change rateas a percentage

(1996–2000) — NUTS 2

> 3.01.5–3.00.0–1.5– 1.5–0.0≤ – 1.5Data not available

DED1, DED2, DED3, FR91, FR92, FR93, FR94,IE01, IE02: 1998–2000UKK1, UKK2, UKK3, UKK4, UKL1, UKL2: 1997–2000

Statistical data: Eurostat database REGIO© EuroGeographics, for the administrative boundariesCartography: Eurostat — GISCO, June 2003

Map 1.1

concerning migration flows in the second half ofthe 1990s in the Member States of the EuropeanUnion and the accession countries. The first sec-tion analyses the population increase during theperiod 1996–2000, this then being followed by asection presenting an overview of the componentsof the population increase in the year 1999. Thethird section focuses on the migration componentof the population increase, discussing net migra-tion rates in 1999. The chapter concludes withsome case studies on in- and out-migration flowsduring the years 1996–99.

Population changeAs stated in the introduction, population increaseor decrease is a result of natural growth and a pos-itive or negative migration balance.

Map 1.1 shows the relative population increase inper cent over the five-year period 1996–2000(population at 1 January 2000, minus populationat 1 January 1996, divided by the population at 1January 1996 and multiplied by 100).

In the period 1996–2000, the relative total popu-lation increase was negative in more than onequarter of the regions in the European Union (59out of 211) and nearly 70 % of the regions in theaccession countries (38 out of 55). The overallpopulation increase for the EU was 1 %; for theother 12 countries, there was an overall decreaseof 2.1 %.

The five regions with the strongest relative popu-lation increase during this period were: Flevoland(the Netherlands), Islas Balearas and Canarias(both in Spain), Luxembourg and Uusimaa(Finland).

The five regions with the fastest relative popula-tion decrease during this period were: Alentejo(Portugal), Halle, Dessau and Magdeburg (all inGermany) and Mellersta Norrland (Sweden).

By looking at the colour scheme, some remark-able patterns can be observed. Focusing on theblue-coloured (population decrease) and red-coloured regions (population increase), it can benoticed, for example, that, in the regions in thecentre and the north of Sweden (other than Stock-holm) and Finland, the population decreased,while in the southern part of both countries, thepopulation increased. In the United Kingdom, thecentral and the southern part of the countryrecorded a population increase, whereas the pop-

ulation decreased in the northern regions. A simi-lar picture is observed in Spain. All regions in theNetherlands experienced a growth in populationduring this period, but the opposite trend was ap-parent in Bulgaria and Hungary.

Components ofpopulation increaseWhat can be said about the balance between nat-ural increase and migration? In Map 1.2, an at-tempt is made to reproduce both components atthe NUTS 2 level in one map for the year 1999.The complexity of the map needs some further ex-planation. If natural increase is denoted by N andnet migration by M, there are six combinations ofthese components which determine the sign (+, –)of the total population growth. Positive growth(an increase in the total population) will resultfrom three possible combinations of these compo-nents. One of these, N+, M+ (both natural in-crease and net migration are positive), has beenfurther divided in the map into two subclassesshowing which of the components has a biggerrole in the positive total increase, N+ < M+ andN+ > M+. The other two combinations leading tooverall population growth are |N–| < |M+| (the ab-solute value of negative natural increase is small-er than the absolute value of positive net migra-tion) and, finally, |N+| > |M–| (the absolute valueof positive natural increase is greater than the ab-solute value of negative net migration).

Negative growth (a decrease in the total popula-tion) will result from combinations N–, M– (bothnatural increase and net migration are negative),|N–| > |M+| (the absolute value of negative natur-al increase is greater than the absolute value ofpositive net migration), and |N+| < |M–| (the ab-solute value of positive natural increase is small-er than the absolute value of negative net migra-tion).

Because of low fertility levels, migration has be-come the decisive factor for the still positive, butslow, population increase in the European Unionas a whole. It is important also at regional level.In 1999, there were 92 NUTS 2 regions (out ofthe 211) in the European Union with negativenatural population increase. Because of positivenet migration, the total increase was negative inonly half of those regions. This effect does notoccur in the accession countries: 41 regions (out


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of the 55) showed a negative natural growth,while 35 regions showed a negative populationgrowth.

EU regions with severe population decrease (withboth negative natural increase and negative netmigration and a total population decrease of 7.5


Map 1.2

Components ofpopulation change

1999 — NUTS 2

M = Net migrationN = Natural increase

E: provisional


Natural increase

Positive

N+ < M+

Negative

Negative

Positive

Population in

crease

Net

mig

ratio

nN

egat

ive

Pos

itive

N+ > M+

N+ > M–

N+ < M–

N– < M+

N– > M+

N– M–

per 1 000 or more) can mainly be found in Ger-many (Thüringen, Halle, Magdeburg, Chemnitz,Dresden, Berlin and Mecklenburg-Vorpommern).Among the accession countries, these regions areSeverozapaden and Severen tsentralen inBulgaria.

EU regions with a strong population increase (de-fined as having positive natural increase, positivenet migration and a total population increase of7.5 per 1 000 or more) are, besides Denmark, forexample, situated in the Netherlands (Gelderland,Flevoland, Noord- and Zuid-Holland, Noord-Brabant and Utrecht), in Sweden (Stockholm), inFinland (Uusimaa) and in Spain (Cataluña, An-dalucía, Canarias, Islas Baleares, Comunidad deMadrid and Región de Murcia). In the accessioncountries, there are only two regions (Nord-Est inRomania and Malopolskie in Poland) that complywith all the abovementioned conditions. Thereare, however, three further regions with both pos-itive natural increase and positive net migration(Malta and the regions of Pomorskie andWielkopolskie in Poland).

Net migrationFollowing from the general overview of the com-ponents that contribute to positive or negativepopulation increase in a given region, this sectionfocuses particularly on one of these components,i.e. migration. As mentioned before, despite lowfertility in the European Union as a whole, migra-tion has made it possible for the total populationto continue to rise, albeit slowly.

In Map 1.3, the difference between in- and out-migration per 1 000 inhabitants on the regionallevel in 1999 is presented, using the crude rate ofnet migration. This crude rate is based on the cal-culation of population increase minus the naturalgrowth (per 1 000 inhabitants).

Nearly one quarter of the EU regions showed anegative migration figure in 1999. For the regionsin the accession countries, this proportion wasmore than twice as high. Especially in Poland andRomania, the vast majority of regions had a neg-ative migration balance. As a result, the overallnet migration rate for the EU regions was 2.5 in1999, while for the accession countries an averagefigure of – 1.3 was recorded. However, the topfive regions losing population due to migrationare to be found in Italy (Calabria and Campania),Germany (Halle and Dessau) and Finland (Itä-

Suomi). Other EU regions with strong negativemigration figures are located in the southern partof Italy, the northern part of France, central andeastern Germany and central and northern Swe-den and Finland. The first region from an acces-sion country is in only 17th place: Severozapadenin Bulgaria, followed in 19th, 20th and 25thplaces, respectively, by Yugoiztochen in Bulgaria,Opolskie in Poland and Severoiztochen, onceagain in Bulgaria.

Regions that received relatively many migrantsare mainly located in the southern part of the UK,the southern part of France and the central andnorthern part of Italy. Apart from one region inthe Netherlands (Flevoland), the top five regionsfor incoming migration are situated in Spain (IslasBaleares and Canarias), Portugal (Algarve) andGreece (Ionia Nissia and Ipeiros). In the accessioncountries, there are two regions with a crude netmigration rate that equals or exceeds 5 per 1 000,namely Strední Cechy in the Czech Republic andSlovenia.

Summarising, it may be said that there are signif-icant net migration flows in England going fromnorth to south, in France, again from north tosouth, and in Italy from the southern to the cen-tral and northern parts of the country. Economicpush and pull factors, which often cause youngpeople to move to other regions, are the maincause of these shifts. As an example, in the nextsection an attempt is made to show in more detailthe migration flows in particular countries, basedon real migration figures.

In- and out-migration flowsIn the previous sections, the dynamics behindpopulation developments were shown. After ex-amining changes in population size at the region-al level during the period 1996–99, we focused onthe components which influence populationgrowth or decrease: natural increase and migra-tion. Next, we discussed one of the components,(crude) net migration. The crude figures present-ed are based on the figures for population increaseminus those for natural growth.

In this section, a short case study is made, basedon real migration figures for a few Member States(Sweden, Finland, the Netherlands and Spain) andtwo accession countries (the Czech Republic and


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Hungary). The countries were selected on the ba-sis of the availability and completeness of thedata.

Map 1.4 shows the countries mentioned above.Using Map 1.1 as background information, Map1.4 also features pie charts for every region. Thesecharts show the balance between in- and out-migration as a percentage of the average popula-

tion in that region, while the size of the pie chartindicates the relative size of the migration flows.

In-migration to a particular region is the sum ofinterregional immigration and international im-migration. Similarly, the region’s out-migration isthe total of interregional emigration and interna-tional emigration. In- and out-migration rates arecalculated as, respectively, the average annual in-and out-migration during the years 1996–99


Crude rate of net migrationper 1 000 inhabitants

1999 — NUTS 2

EU-15 = 2.53> 5.02.5–5.00.0–2.5– 2.5–0.0≤ – 2.5Data not available

E: provisional


Map 1.3


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Map 1.4

In- and out-migration ratesper 1 000 inhabitants

1996–99, NUTS 2, in six countries

Statistical data: Eurostat© EuroGeographics, for the administrative boundariesCartography: Eurostat — GISCO, Mai 2003

In-migration

Out-migration

3–991.5–30–1.5– 1.5–0< – 1.5

divided by the mean population during the period((1.1.1996 + 1.1.2000)/2) multiplied by 1 000.

In three of the eight regions in Sweden, in-migra-tion is higher than out-migration. The highest in-migration occurs in the region of Stockholm(30.08 per 1 000). Migration into the region, of-ten by young people, is common and is a conse-quence of the prevalence of job opportunities inthis region. Other regions with a higher in- thanout-migration rate are Sydsverige (with Malmö)and Västsverige (with the city of Gothenburg).The region with the highest out-migration duringthe second half of the 1990s was Mellersta Norr-land (27.67 per 1 000). The migration, mainly tothe southern regions, is partly a result of increas-ing industrial automation.

Also in Finland, only the southern regions, Uusi-maa (with Helsinki), Etelä-Suomi and Ahvenan-maa/Åland, show in-migration higher than out-migration. Especially the in-migration to Uusimaais high (25.11 per 1 000), caused by the job op-portunities in the Helsinki region. The regions inthe north show a considerable out-migration. Thehighest out-migration can be observed in Itä-Suomi with 21.99 per 1 000.

Earlier in the chapter, it was noted that all regionsin the Netherlands showed a positive populationincrease during the period observed. Again in allregions, the migration rate was positive. The re-gion with the highest in-migration is Flevoland(66.83 per 1 000), but the same region alsorecorded the highest out-migration at 41.46 per1 000.

For Spain, only in-migration rates are available.The NUTS 2 regions with the highest in-migrationare Islas Baleares and Ceuta y Melilla. In the re-gion of Cataluña, the number of arrivals due to

internal migration is lower than international im-migration.

All regions in Hungary show a negative popula-tion increase. All regions also have a negative nat-ural growth. Two of the seven regions also have ahigher out- than in-migration: Észak-Mag-yarország and Észak-Alföld, both in the easternpart of the country. In the region of Dél-Dunántúlin the south, in- and out-migration are nearlyequal. The region with the highest in-migration isKözép-Magyarország (which contains Budapest).

In the Czech Republic, almost all the regions havea higher in- than out-migration. The regions ofPraha and Ostravsko are the only ones featuring ahigher out-migration but for quite different rea-sons. Although Praha is unusual among capital-city regions in recording out-migration, this is be-cause of residents moving out of the region to lessexpensive accommodation from which they thencommute to work in the city. By contrast, the out-migration from Ostravsko reflects the rise in un-employment following the decline of the heavy in-dustry that once underpinned the region’seconomy. The highest in-migration was recordedby Strední Cechy, which entirely surrounds Praha(21.55 per 1 000).

Earlier, it was stated that migration flows are usu-ally caused by economic push and pull factors:young people moving from a region with few jobopportunities to a region with more job opportu-nities. While this is indeed the explanation for themajority of the flows mentioned above, the casesof Praha and Flevoland (not to mention Ceuta yMelilla, where the military garrisons are a majorcomponent of the region’s population) demon-strate that other factors may also be involved.


2A G R I C U L T U R E

IntroductionThis edition of the regional yearbook interpretsagriculture in an unusually wide sense to includeland use and land cover. The result is a veryheterogeneous chapter comprising three quite sep-arate sections. Firstly, the very large amount of in-formation available from regional agricultural ac-counts is exploited to provide a new perspective

on the distribution of agricultural activities.Secondly, the reader is introduced to the potentialoffered by Eurofarm data, as well as the method-ological considerations involved. Finally, the opportunity is taken to introduce the innovativeLUCAS project. Although this project is not limitedto agriculture, it offers enormous potential interms of understanding Europe’s landscapes,whether or not these are agriculturally exploited.


Contribution of agriculture to GDP2000 — NUTS 2

> 63–61.5–3≤ 1.5Data not available

B, E, BG, CZ, HU, PL, SK: national levelSK: 1999NL: estimationsUK: provisional data


Map 2.1

The contribution ofagriculture to GDP

In national accounts terminology, gross domesticproduct (GDP) at market prices is the final resultof the production activity of the various branches(resident producer units) of an economy. It corre-sponds to the sum of gross value added (at marketprices) of the various branches, and the compari-son of the gross value added of a given branchwith the overall GDP therefore makes it possibleto establish a rough measure of the importance ofthat particular branch. It is only a rough measurebecause, given the close economic relationshipsbetween the individual branches, it would beslightly short-sighted to consider each of them inan isolated way. In the case of agriculture, thecontribution of which to GDP is generally quitelow, a more sophisticated analysis would there-fore need to also take into account agriculture’slinks with the various branches of industry, bothupstream and downstream, in particular with theagro-food industry.

Nevertheless, the indicator chosen for the firstmap in this section refers to agriculture only, moreprecisely to agriculture’s contribution to GDP.With regard to the Member States, the first find-ing of this analysis is that, in terms of the numberof regions, this contribution is equivalent to orhigher than 3 % in only about one of four regions.Most of the regions with a higher contributionfrom agriculture are located in Greece. In fact, ifone focuses on those regions where agriculturecontributes 6 % or more to GDP, 10 of the 12 EUregions in this situation can be found in Greece,the other two regions of this group being Açores(Portugal) and Champagne-Ardenne (France).

Amongst the candidate countries, for which nodata were available at the NUTS 2 level, Bulgariaand Romania are those countries where agricul-ture is most important, contributing more than10 % to the countries’ GDP. In the other coun-tries, agriculture’s share in overall GDP varies be-tween 1.5 % (Czech Republic) and 3.8 %(Cyprus, 1999 data). In Poland, the largest agri-cultural producer amongst the countries joiningthe European Union in 2004, agriculture con-tributes 2.7 % to GDP.

Animals’ share ofagricultural goodsoutputMap 2.2 gives information, at a rather high levelof aggregation, on the composition of agricultur-al production. The indicator chosen is the share ofanimal output in the overall output of agricultur-al goods; it is a measure of the relative importanceof animal (and indirectly crop) production in thevarious regions.

This analysis should be interpreted with cautionas the composition of agricultural output is sub-ject to a number of factors which can have a con-siderable impact when looked at on a year-to-yearbasis, but which may have little impact on themedium-term development of agriculturalproduction. This is particularly the case for cropproduction, where differences in the climatic con-ditions between years may lead to considerablechanges in the level of output. The use of three-year averages, for example, would give a more ac-curate picture of the structure of agricultural pro-duction but this would increase the time lag in theavailability of data by a further year.

Taking the EU-15 average, animal production isslightly less important than crop production. In2001, the reference year of the present analysis,the ratio was about 45:55 (animal versus cropoutput). One therefore expects a certain predom-inance of regions ‘specialised’ in crop output, i.e.where the share of crop output is more importantthan that of animal output. This expectation canbe confirmed by the findings of this analysis,though the number of regions where crop pro-duction is more important than animal produc-tion is higher than one would expect — outrank-ing animal-oriented regions by a ratio of 2:1.

As shown in Map 2.2, the regions with the high-est degree of specialisation on animal productionare located in the Netherlands (Overijssel andFriesland), Ireland and France (Bretagne and Lim-ousin). However, in the Portuguese islands of theAzores (Açores) and in the Italian region of Valled’Aosta, animal production accounts for aboutfour fifths of agricultural output. Most of the re-gions where crops represent the predominant typeof production (at least three quarters of total out-put) are located in Greece, France and Italy. InSpain, for which in this context no regional break-


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down is available, crop production is clearly pre-dominant (a ratio of 60:40).

Amongst the candidate countries, animal produc-tion is most important in Estonia (more than60 % of total output). At the other end of thescale is Romania, where crops account for almosttwo thirds of total output. In Poland, thecrops/animals ratio is 50:50.

Output/input ratiosin agricultureThe output/input ratios which are the basis ofMap 2.3 give information on the value of outputgenerated by one unit of input. They can be re-garded as a relatively simple type of productivityindicator. Their usefulness is limited, however, by


Animal output aspercentage of agricultural goods output

2001 — NUTS 2

> 7050–7030–50≤ 30Data not available

B, E, BG, CZ, HU, PL, SK: national levelNL, P: 2000NL: estimationsUK: provisional data


Map 2.2

the fact that output is seen as a function of inter-mediate consumption of goods and services only,while the other factors of production (labour, landand capital) are disregarded. The difference be-tween output and intermediate consumption is(gross) value added, and the output/input ratiosare therefore also referred to as ‘value added rates’.

The map shows that most of the EU regions withhigh output/input ratios are located in the south-ern part of Europe (Italy, Greece, southern France

and Spain). To a certain degree, this seems to belinked with the type of production that is pre-dominant in the respective regions. In most of theregions with an output/input ratio of 3.0 or high-er, crop products account for at least 70 %, andpermanent crops (fruit, olive oil, wine) are of par-ticular importance.

Those regions where the output/input ratio is rel-atively low (no more than 1.5) are mainly locatedin the north of Europe, namely in Finland and


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Output/input ratios in agriculture2001 — NUTS 2

> 3.02.5–3.02.0–2.51.5–2.0≤ 1.5Data not available

B, E, BG, CZ, HU, PL, RO, SK: national levelNL, P: 2000NL: estimationsUK: provisional data


Map 2.3

Sweden. This seems to be the consequence mainlyof difficult climatic conditions and of a relativelyshort vegetation period.

As regards the candidate countries, the differencesare less pronounced. The output/input ratios arehighest in Malta and Cyprus (2.4 and 2.3 respec-tively), and lowest in Slovakia and the Czech Re-public (1.4 and 1.5 respectively). In Poland, theoutput/input ratio is 1.7.

Eurofarm data —women in agricultureThe proportion of women who are farm man-agers (see Map 2.4) is in stark contrast to usualgender maps of the EU, which show a more bal-anced distribution of men and women in manage-ment and leadership positions in Europe’s north-ern regions. Among farm managers, however, the


Female farm managersas percentage of farm managers

(including group holdings)1999/2000 — NUTS 2

> 3020–3015–2010–15≤ 10Data not available


Map 2.4

proportion of women is higher in southern than innorthern Europe. A number of other considera-tions should nevertheless be taken into account.

• In all EU regions, more men than women man-age farms. The highest proportion of femalemanagers is 49 % (in Galicia in Spain), whilethe EU average is a modest 22 %.

• The decision-making powers of the managerare not the same as the economic power en-joyed by the farm holder. Some national trendsare very strong, reflecting underlying culturalvalues. In the United Kingdom and Austria, thefemale rate is higher for farm managers thanfor farm holders. The reverse phenomenon isrecorded in Spain.

• In every Member State, the farms managed bywomen are physically and economically small-er units on average than those managed bymen.

Incomediversification of thefarming communityThe level of other gainful activities (OGAs) re-ported by farm holders/managers (see Map 2.5)gives an indication of both the viability of farmsand the availability of alternative employment op-portunities in the local economy. Three types ofcases can be described.

• In rural areas (northern Europe, the extremesouth of Europe and mountain areas), the highproportion of OGAs reflects the insufficient in-come generated by the actual farm itself. Mostof these OGAs are nevertheless linked withagriculture (forestry, agricultural product pro-cessing, agri-tourism).

• In urban areas (south-eastern UK, central Ger-many, the Spanish Mediterranean coast), thefarms are relatively small and more intensive.An additional income can be found near to theholding, possibly in other branches of the econ-omy.

• Elsewhere, agriculture is more professional,generating incomes that make alternativesources of income less necessary.

Certain NUTS 2 regions illustrate each of thesetypical cases. For example, Sweden, Ireland, Scot-land and the Aegean Islands could be classified inthe first type. The urban Länder (such as Bremen,Hamburg or Berlin) and Saarland in Germanyrepresent the second, while France’s Bretagne andÎle-de-France regions and Italy’s Emilia-Romagnaand Lombardia could be regarded as examples ofthe third type.

It should be noted that these trends have beenmapped at the NUTS 2 level. In a number of cas-es, the NUTS 2 region is relatively large and ratescalculated across the whole of the region tend toblur some combinations of these typologies. Bycontrast, the smaller NUTS 2 regions show moreextreme cases and better illustrate these location-dependent phenomena.


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Other gainful activitiesof farm holders/managers

(including group holdings)1999/2000 — NUTS 2> 5040–5030–4020–30≤ 20Data not available


Map 2.5

The LUCAS survey —statisticians monitorterritoryEurostat, in close cooperation with the Direc-torate-General for Agriculture and with the tech-nical support of the Joint Research Centre, haslaunched the pilot project ‘Land use/cover areaframe statistical survey (LUCAS)’, thus imple-menting a decision in 2000 of the European Par-liament and the Council on the application of area

frame survey and remote-sensing techniques tothe agricultural statistics for 1999 to 2003.

In 2001, the first LUCAS pilot survey was carriedout in 13 of the 15 Member States of the Euro-pean Union. It had to be postponed to 2002 in Ire-land and the United Kingdom because of foot-and-mouth disease. The survey is organised intwo phases: a field survey in springtime (phase I)to collect data on land use/cover, as well as on theenvironment, and a farmer interview survey in au-tumn (phase II) to gather additional informationon yields and agricultural practices.


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Table 2.1 — Managing Eurofarm (FSS DB) exceptions to NUTS

NUTS 2 Eurofarm regions/districts

Eurofarm units defined BE1 + BE24 BE24_1

as aggregates of NUTS 2 DE3 + DE5 + DE6 DE3_90_5_6

ES61 + ES63 ES61

UKC1 + UKC2 UKC

UKD3 + UKD4 + UKD5 UKD3_4_5

UKE3 + UKE4 UKE3_4

UKG1 + UKG3 UKG1_3

UKI1 + UKI2 + UKJ1 UKI_J1

NUTS 2 obtained by FI13 FI131 + FI132 + FI133 + FI134

aggregating Eurofarm districts FI14 FI141 + FI142 + FI143 + FI144

FI15 FI151 + FI152

FI17 FI171 + FI172 + FI173 + FI174 + FI175 + FI176 + FI177

IT31 IT311 + IT312

Matching NUTS and FSS geographicalunits

The farm structure survey (FSS) uses its owngeographical units at three different levels: coun-try, region and district. Each level has been de-fined as a compromise, making it possible to aggregate data as precisely and reliably as possi-ble. The district is the level of aggregation of cen-sus data, every 10 years. The region permits asufficient quality level for intermediate surveys(sampling).

Accordingly, the FSS geographical levels do notmatch with any given NUTS level right acrossthe whole EU. Within each Member State, theFSS geographical level is more consistent withthe NUTS. The FSS regions are mainly eitherNUTS 1 or 2, whereas districts follow NUTS 2or 3.

There are exceptions to the above rules wherethe number of agricultural holdings is low. In

these cases, urban or natural regions are aggre-gated at a higher level.

Until now, the FSS geographical units could notbe modified between censuses, because they areused as a reference for sample extrapolation. Inthe run-up to the 1999/2000 census, Eurostatasked to Member States to provide a more pre-cise location of holdings (NUTS 4/5), so that itwould be possible to keep track of changes in thenomenclature. The aim was to ensure consisten-cy not with the NUTS classification, but ratherwith the changes in the Structural Funds ‘objec-tive zone’ definitions.

The above move does not solve either the relia-bility problem or the fact that the number offarms in urban or very sparsely populated areasdoes not match with any NUTS level throughoutthe EU. After all, the FSS statistics reflect realityrather than that reality is shaped by thestatistics.

Objectives of thesurveyThe LUCAS survey, still in a pilot phase, has twomain purposes:

1. Implementation of the surveys themselves in2001 and 2003.

2. Detection of changes in land use/cover. Besidesthe picture given in a specific year by LUCASestimates, one of the strengths of the project isthe opportunity it offers to monitor and quan-tify changes over time in land cover, land useand landscape structure.

The survey can already be seen as a dynamic andefficient approach capable of meeting the follow-ing objectives:

• to obtain harmonised information (lacking atEuropean level);

• to extend a purely land use/land cover informa-tion system so it becomes a multi-purpose andmulti-user system;

• to offer a common methodology and nomen-clature for data collection and the computationof estimates;

• to get early estimates of areas that refer to thecurrent year, and the possibility to quantify inreal-time the changes with respect to previoussituations;

• to provide the statistical information needed tomonitor the integration of environmental con-cerns into the common agricultural policy.

Two-stage area framesystematic samplingdesignThe sampling design adopted was systematic areaframe sampling, since LUCAS is designed to pro-vide multi-purpose information and thereforeneeds to cover not only the agricultural area, butall the territory of EU Member States.

Figure 2.1 — The LUCAS two-stage sampling

This sampling design enables the production ofarea estimates for land cover/land use categoriesat the European level. If countries so wish, theycan acquire results at national or regional level byincreasing the number of sampled points.

The LUCAS phase I survey adopts a two-stagesampling design: at the first level, primary sam-pling units (PSUs) are defined as cells of a regular18 x 18 km grid, while the secondary samplingunits (SSUs) are 10 points regularly distributed (ina 1 500 x 600 m rectangle (3)) around the centreof each PSU (see Figure 2.1), with reference num-bers 11, 12, 13, 14, 15, 21, 22, 23, 24 and 25.

The sampling results in approximately 10 000PSUs covering all EU territory. The number ofPSUs was chosen to optimise the cost structureand the precision at European level.

The observation unit of the LUCAS phase I surveyis the point, defined as a circle of 3 m diameter(see Figure 2.2). In each SSU, data on land coverand land use as well as on environmental featuresare collected in the field. Considering the hetero-


(3) With the exception of Spain and Italy, in which the LUCASsampling plan was slightly adapted to comply with alreadyestablished area frame systems.

geneity of land cover types, in some particularcases an enlargement of the observation windowup to a circle of 20 m radius is foreseen (4).

Data are also collected along the straight line thatconnects the observation points located in the firstrow (the transect) (Figure 2.2).

Figure 2.2 — The point and the transect

The phase II survey, carried out in autumn, dealswith the collection of environmental informationand farming practices. The sample for LUCASphase II is a subsample of about 5 750 SSUs (in-cluding a reserve of 15 %) classified as arable landduring phase I.

Observation units of the phase II survey are theagricultural holding itself and the plot in whichthe sampled SSU is located.

A multi-purposeinformation systemThe survey is intended to record not only agricul-tural aspects, but also a much wider range of pos-sible land cover types (i.e. built-up areas, forestsand wooded lands, bushes and grassland,wetland, water and bare soil areas) and land usecategories (residential, industrial, commercial,recreational, etc.). Some environmental informa-tion is also collected.

Land cover and land use information

‘Land cover’ is the observed physical cover of theearth’s surface, while ‘land use’ is the descriptionof the same areas in terms of their socioeconom-ic function. The LUCAS concept of ‘land’ is ex-tended to inland water areas (lakes, rivers,coastal areas), but it does not embrace uses belowthe earth’s surface (mine deposits, subways,

mushroom beds, underground levels of buildings,etc.).

The land cover classification is defined in three hi-erarchical levels of detail with 57 classes at thethird level. By contrast, the land use nomenclaturedistinguishes 14 classes at the third level. Thecomplete nomenclature scheme is available on theCIRCA site land use group (5).

Environmental information

Qualitative information, including the presence ofirrigation and drainage infrastructure, isolatedtrees, evidence of damage from natural hazardsand soil erosion/accumulation, is collected in thefield (LUCAS phase I) within an extended obser-vation window of a 20 m circle around the obser-vation point. Along the transect, the change ofland cover and the occurrence of linear features (6)are registered.

Photographs of the landscape are taken at SSU13;these pictures create a photo sample of Europeanlandscapes.

The farmer interviews (LUCAS phase II) provideinformation regarding agricultural techniques(e.g. crop rotation, sowing method, quantities offertilisers per type, treatment with weed killers,etc.), and data on areas and yield of crops.

Realisation of thesurvey Location of the points on the ground

Three different tools are used to locate the pointson the ground:

• aerial photographs;• topographic maps;• compasses and, in certain cases, global posi-

tioning system (GPS).

First, topographic maps (1:100 000 to 1:50 000scale) are used to define the best route to reachPSUs. At the level of SSUs, it is primarily the aeri-al photographs that are used for a precise loca-tion, with the additional support of GPS, com-passes, and of larger-scale topographic maps(1:25 000 to 1:5 000).

Transect(300 m)

Extended observationwindow (r = 2 m)

Point(r = 1.5 m)


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(5) http://forum.europa.eu.int/Public/irc/dsis/landstat/library.(6) Hedge and tree rows, stone walls and dykes, water chan-

nels, tracks and roads, railways, electricity lines.

(4) The extended window of observation is applied for wood-ed areas, shrubland and permanent grassland. The exis-tence of certain features (e.g. isolated trees, soil erosion) isalso observed in this extended observation window.

Period and observation time

On average, the survey began in May and lastedfor two to three months. In Ireland and the Unit-ed Kingdom, the 2001 data collection had to bedeferred to 2002 due to the foot-and-mouth epi-demic. For similar reasons, the survey in theNetherlands did not start until 18 June 2001.

An average of two hours was required to visiteach PSU (including walking 10 x 300 m betweeneach SSU). On steeply sloping or heavily over-grown ground, as much as 10 hours were needed.

Figure 2.3 — Distance of observation of the points

SSUs located at more than 2 000 m altitude (or insuch isolated locations as in the middle of theScandinavian forest or on islands not served by aregular transport service) were photo-interpretedrather than visited on the ground. Nevertheless,88 % of the SSUs were observed on the ground.

Main resultsLand cover

Table 2.2 — Land cover estimates

Land cover km2 % CV

Woodland 1 134 606 35.0 1.0

Cropland 837 536 25.8 1.3

Permanent grassland 509 573 15.7 1.4

Shrubland 268 693 8.3 2.9

Water and wetland 236 111 7.3 3.0

Artificial land 153 912 4.8 2.7

Bare land 99 729 3.1 5.3

Total 3 240 160 100.0

NB: CV = Coefficent of variation

At EU-15 level (see Table 2.2), the main type ofland cover consists of areas that are entirely or al-most in their natural state. By contrast, the artifi-cial areas represent less than 5 % of the surface ofthe EU.

The concentration of wooded areas and of inlandwater (lakes and wetlands) in the Scandinaviancountries is very high, especially when comparedwith the rest of EU territory (see Map 2.6).

The intensive agricultural zones are located inDenmark, the eastern part of England, the north-western half of France, the Po river plain andAdriatic coast in Italy, and the south of Portugaland Spain. Shrublands are mainly concentrated inMediterranean countries, but their presence is no-ticeable on the north-west border of Sweden andalso in the highlands of Scotland.

Photo-interp.12 %

>100 m11 %

At the point53 %

50–100 m7 %

3–50 m17 %


Land useAgriculture accounts for more than 41 % of theterritory, making it the leading type of land use inthe 15 countries investigated (see Table 2.3). Thiscategory includes land used directly for produc-tion as well as land used generally for farmingpurposes (buildings, farmyards, etc.). Apart from

the extreme situations of the Nordic countries andAustria, on average around half of the territory ormore is used for farming. Forestry comes second,with 30 %. Almost 19 % of the territory of the 15countries is classified as being without apparentuse. These three headings (agriculture, forestry,unused) account for 90 % of EU territory.


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(1) Warning: maps report the most represented class for each PSU.

Map 2.6 (1)

Table 2.3 — Land use estimates

Land use km2 % CV

Agriculture 1 343 180 41.5 0.9

Forestry 972 952 30.0 1.2

Unused 603 630 18.6 1.6

Recreation, leisure, sport 131 805 4.1 4.7

Residential 74 584 2.3 4.4

Transport, communication,storage, protective works 65 644 2.0 3.6

Community services 11 745 0.4 16.6

Land use km2 % CV

Fishing 9 743 0.3 17.5

Industry, manufacturing 6 861 0.2 16.1

Commerce, finance, business 6 458 0.2 16.3

Mining, quarrying 6 137 0.2 22.4

Construction 2 668 0.1 23.7

Water, waste treatment 2 566 0.1 21.4

Energy production 2 187 0.1 31.8

Total 3 240 160 100.0

NB: CV = Coefficient of variation.


Map 2.7

Mixed landcover/land useSome 82 % of areas under permanent grass areused for agricultural purposes, 9 % are unused,residential, leisure and recreation areas accountfor 8 % and the remaining 1 % for transport,storage,communication and protective works.The use of areas under permanent grass variesgreatly. In Germany, Greece, Spain and France,over 80 % of this type of land is used for agricul-ture. ‘Other uses’ increase from south to north,the trend being for permanent grassland to beused for dwellings (lawns) or recreational purpos-

es (sports grounds). The extreme is Finland,where just 4 % of the area under permanent grassis devoted to agriculture and almost 60 % todwellings (see Map 2.8).

Landscape photosLUCAS data on land cover/land use were supple-mented by photographing the landscape from asystematic observation point for each PSU (SSUNo 13). Surveyors took one photo in each of thefour directions North, East, South and West (seeImage 2.1); each photo is referenced to the num-ber of the PSU, to preserve its location in space.


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Map 2.8

Some 20 000 photos were taken in the 2001 datacollection. These photos constitute a uniquearchive of European landscapes, to be exploited

with the aim of opening new perspectives in theanalysis of European landscapes.


Image 2.1 — Example of four landscape photos taken in France

West North

East South

ConclusionThe experience acquired with the LUCAS pilotsurvey has made it possible to validate the areaframe methodology applied, and the survey hasproved its reliability in providing for the first timeharmonised and comparable data at EU level.

Due to its flexible design, LUCAS can be seen as aplatform for many kinds of applications, especial-ly those related to environmental assessments.

REGIONAL GROSS DOMESTIC PRODUCT 3

What is grossdomestic product?The economic development of a region is oftenexpressed in terms of its gross domestic product(GDP). It is also an indicator frequently used as abasis for comparisons between regions. But whatexactly does it mean? And how can comparabili-ty be established for regions of different size anddifferent currencies?

Regions of differing size achieve different GDPlevels. A comparison can be made by indicatingGDP per inhabitant of the region in question.Here, the distinction between place of residenceand place of employment is important, since ref-erence to GDP per inhabitant is only straightfor-ward if all employees engaged in generating thisvalue are also residents of the region in question.Gross domestic product measures the economicperformance achieved within national or regionalboundaries, regardless of whether this was attrib-utable to domestic or foreign economic entities. Inareas with a high proportion of commuters, re-gional GDP per inhabitant can be extremely high,particularly in such economic centres as Londonor Vienna, and relatively low in the surroundingregions, even if these are characterised by highhousehold purchasing power or disposable in-come. Regional GDP per inhabitant should not,therefore, be equated with regional disposableincome.

As has already been pointed out, regional GDPrepresents a ‘cash value’. International compara-bility can be easily achieved by converting nation-al currencies into euro or another currency, butexchange rates will not reflect all internationalprice-level differences. In order to correct this im-balance, GDP is converted into purchasing powerstandards (PPS). This allows a comparison basedon units of volume or goods units rather than val-ues. The section below explains in detail whatpurchasing power parities (PPPs) are.

Purchasing powerparities andinternational volumecomparisonsIntroduction

International differences in GDP values, even af-ter conversion via exchange rates to a commoncurrency, are not due simply to differing volumesof goods and services. The ‘level of prices’ com-ponent can sometimes assume sizeable propor-tions. Exchange rates reflect many factors relatedto demand and supply in the currency markets,such as international trade and interest rate dif-ferentials. In other words, exchange rates usuallyreflect other components as well as pricedifferences. Conversions via exchange rates aretherefore of only limited use for internationalcomparisons.

To obtain a pure comparison of volumes, it is es-sential to use special conversion rates (spatial de-flators) which remove the effect of price-level dif-ferences between countries. Purchasing powerparities are such currency conversion rates thatconvert economic indicators expressed in nation-al currencies to an artificial common currency,called purchasing power standards.

In other words, PPPs are used to convert nominalfinal expenditures on product groups, national ac-counts aggregates and GDP of different countriesto comparable pure expenditure volumes, ex-pressed in PPS units. With the introduction of theeuro, prices can be compared directly for the firsttime between countries in the euro zone. How-ever, the euro has different purchasing power in thedifferent countries of the euro zone, depending onthe national price level. PPPs must therefore con-tinue to be used to calculate pure volume aggre-gates in PPS. In other words, for countries not inthe euro zone, PPPs are currency conversion ratesand eliminate the effects of different price levels,while for the euro-zone countries they fulfil onlythe abovementioned price deflator function.

How are PPPs calculated and what arePPS?

In their simplest form, PPPs are a set of pricerelatives, which show the ratio of the prices innational currency of the same good or service in


different countries (e.g. a loaf of bread costs EUR1.87 in France, EUR 1.68 in Germany, GBP 0.95in the UK, etc.). For the price collections, a basketof comparable goods and services is used which isselected to represent the whole range of goods andservices, and to be representative of consumptionpatterns in the various countries. The simple priceratios at product level are aggregated to PPPs forproduct groups, then for overall consumptionand, finally, for GDP. In order to have a referencevalue for the calculation of the PPPs, a country isusually chosen and used as the reference country.For the European Union, the selection of a singlecountry (or currency) as a base seemed inappro-priate. Therefore, PPS is the artificial common ref-erence currency unit used in the European Unionto express the volume of economic aggregates forthe purpose of spatial comparisons in real terms.Economic volume aggregates in PPS are obtainedby dividing their original value in national cur-rency units by the respective PPPs.

The empirical evidence

Graph 3.1 shows the conversion rates in order ofsize used to convert the GDP values in ecu/eurointo the PPS described above. Values are shownfor both 1993 and 2003. The graph reads as fol-lows: if Denmark has a GDP of EUR 100 in 2003,this converts into approximately 79 PPS; if Esto-nia also has a GDP of EUR 100 in 2003, this con-verts into approximately 200 PPS.

It can be seen that between 1993 and 2003 the dif-ferences between the countries, in particular be-

tween the EU Member States and the candidatecountries, diminished considerably, and there is aclear convergence of price levels. However,differences do remain, particularly in relation tolittle-developed countries, such as Bulgaria orRomania.

It is also clear that strong economic developmentcan affect price levels. In percentage terms, Ire-land has recorded the greatest reduction in theecu/euro–PPS rate: Whereas in 1993 its GDP wasconverted at a rate of ECU 100 to 114 PPS, the2003 conversion rate is only EUR 100 to 86 PPS.

Unfortunately, no conversion rates at regional lev-el are available, so that, for example, French over-seas departments (departéments d’outre mer) suchas Réunion or Guadeloupe are converted usingthe rate for France. With regional conversion fac-tors, the GDP in PPS for these areas would cer-tainly be higher. Similarly, the same conversionfactor is used for northern and southern Italy orwestern and eastern Germany. A certain amountof distortion should therefore be expected. Theorder of magnitude of the PPPs is also significant.Assuming a statistical margin of error for bothGDP and PPPs, the effect of converting euro toPPS dominates the statistical inexactitude of re-gional GDP when the conversion factor from euroto PPS is very high.

The regions are ranked differently when calcula-tions are based on PPS rather than the euro: Slove-nia, for example, has a much higher per capitaGDP in euro (EUR 9 815) than the capital regionof Bratislava (Slovakia) (EUR 8 426), whereas in


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Graph 3.1 — Value of GDP in PPS, if GDP in euro = 100

NB: For Malta, no PPS figure is available for 2000.

PPS, Bratislava comes out higher than Sloveniawith 22 134 PPS per capita as opposed to 15 183PPS per capita.

In terms of distribution, the use of PPS rather thanthe euro has a levelling effect, reducing the rangeof NUTS 2 regions in Europe from more than60 000 to around 50 000, and the coefficient ofvariation from 54.3 % for GDP in euro to 41.0 %for GDP in PPS.

Regional GDP in eurofor the year 2000Map 3.1 shows the regional distribution of GDPfor the European Union and the candidate coun-tries. It ranges from EUR 1 251 per capita insouth-east Romania to EUR 62 788 per capita inthe UK Inner London region. Brussels and


GDP per inhabitant, in EUR2000 — NUTS 2

EU-15 = 22 603> 25 00020 000–25 00015 000–20 0007 500–15 000≤ 7 500Data not available


Map 3.1

Luxembourg follow in second and third places,with Stockholm in fifth place, although it is onlyninth for GDP in PPS. An even clearer example ofthe difference between the different calculationsof GDP is Prague, which is 29th for GDP in PPS,but 196th for GDP in euro.

There are also differences within the countries, asGraph 3.2 shows. The largest regional differencesare in the United Kingdom, where the Inner Lon-don region, in particular, stands out with its veryhigh GDP per capita. However, this disparity ismostly due to the borders of the regions and theresulting commuter effect. If London is removedfrom the equation, the range of regional GDP isfairly average.

When these values are converted into PPS, theGDP per capita for Europe’s regions is as follows.The range of values is lower, i.e. the difference be-tween the regions is larger in monetary units thanin volume comparisons. Map 3.2 again shows theprominent position of the capital regions. Lisbon,Madrid, Brussels, London, Berlin, Prague,Bratislava, Budapest, Sofia and Stockholm allstand out clearly.

For the sake of completeness, the regional dispar-ities in GDP in PPS of the individual countries arealso shown. Regional GDP per capita in PPS iscurrently the key variable for determining Euro-pean structural policy. Regions under a specificthreshold value are eligible for aid under


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Yuzhen tsentralen Yugozapaden

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PrahaStrední Morava

Közép-MagyarországÉszak-Alföld

MazowieckieLubelskie

20 000 30 000 40 000 60 000 70 00050 00010 0000

Graph 3.2 — Gross domestic product, NUTS level 2, 2000 (million EUR per capita)

Objective 1. This variable therefore generates agreat deal of interest. Interestingly, whilst a largeamount of regional aid is based on the concept of

PPS, Member States’ contributions are deter-mined on the basis of figures expressed in nation-al currency or euro.


GDP per inhabitant, in PPS2000 — NUTS 2

EU-15 = 22 603> 25 00020 000–25 00015 000–20 0007 500–15 000≤ 7 500Data not available


Map 3.2


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Extremadura


Calabria

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Inner LondonCornwall and Isles of Scilly

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Nord-Est Bucure‚sti

PrahaStrední Morava

Közép-MagyarországÉszak-Alföld

MazowieckieLubelskie

Graph 3.3 — Gross domestic product, NUTS level 2, 2000 (million PPS per capita)

REGIONAL UNEMPLOYMENT 4

IntroductionUnemployment is one of the key problems of Eu-rope. It is not merely a matter of idle capacity inlabour as a factor of production: unemploymentoften leads to social distortions. The human fac-tor plays a key part in this — unemployment is of-ten accompanied by social exclusion and cancause depression in those affected. It also presentsthe countries’ social systems with immense prob-lems. Health and pension insurance systems losecontributors and run into financial difficulties.For governments, the pressure for reform growsto an extent that would not have been possiblewith full employment.

Unemployment rates are defined as the propor-tion of unemployed persons in the overall labourforce of a region. The numerators and denomina-tors of these rates are calculated in accordancewith the recommendations of the 13th Interna-tional Conference of Labour Statisticians. Re-gional estimates are based on the results of theCommunity labour force survey (CLFS) at na-tional level, which are adapted to refer to April ofthe year in question.

To estimate regional unemployment rates, the nu-merator (labour force) and the denominator (un-employed) are first separated and calculated forfour sub-populations:

• men under 25 years of age and women under25 years of age;

• men over 25 years of age and women over 25years of age.

The number of unemployed people is brokendown over the regions either directly on the basisof the results of the second quarter of the labourforce survey or by using information on registeredunemployed persons. In both cases, the results atnational level are taken and the number of unem-ployed people is broken down over the various re-gions in proportion to the regional results of thelabour force survey or to the regional figures forregistered unemployed persons.

However, regionalisation of the labour forcedown to NUTS 2 level is based on the secondquarter of the labour force survey. Depending onthe data situation, any further breakdown isbased, again, on the results of the labour forcesurvey, on registers or on the latest available cen-sus results.

Figures for long-term unemployment are estimat-ed directly from the labour force survey, but areavailable only for NUTS 2 level and are not bro-ken down by gender or age.

The regionaldimension of overallunemploymentIn April 2001, the unemployment rate in theNUTS 2 regions of the European Union varied be-tween 1.2 and 33.3 %, and between 2.0 and32.8 % in the candidate countries. Breakdownsby gender and age highlight even greater regionaldifferences. The rate of unemployment amongwomen, for example, ranged from 1.1 to 36.4 %,while between 2.1 and 59.9 % of under-25s wereout of work. The breakdown by gender showsthat the female unemployment rate in the candi-date countries has a narrower range (between 2.0and 28.5 %) than that of the EU. However, thedifferences in the rates for under-25s are larger,ranging from 3.0 to 75.5 %.

The unemployment rate in the European Union,i.e. the ratio of unemployed persons to the totaleconomically active population, stood at 7.6 % inApril 2001. At national and, in particular, region-al level, there were marked deviations from thisaverage figure.

Taking only the NUTS 2 regions into considera-tion, the unemployment rate varied between1.2 % in the Dutch region of Utrecht and 33.3 %in the French region of Réunion. Related in eachcase to 100 members of the economically activepopulation, Réunion thus had around 27 timesmore jobless people than the region of Utrecht.

Of the 265 regions under consideration, as manyas 53 achieved an unemployment rate in April2001 of, at most, 3.8 % — lower than half the EUaverage of 7.6 %. These 53 NUTS 2 regions werespread over 11 Member States, with Greece,Spain and France being the only countries whereno NUTS 2 region had an unemployment rate of3.8 % or less. This was also the case for Denmark.The Netherlands and the United Kingdom arewell represented in this leading group, with morethan 10 regions each; the group, however, con-tains only three regions from the candidate coun-tries — two of them in Hungary and one in theCzech Republic. At the other extreme, 16 regions


(in Germany, Spain, France and Italy) had unem-ployment rates of over 15.2 %, at least double therate for the European Union as a whole.

Within the countries, there are also differences inthe unemployment rates. Graph 4.1 shows the re-gions with the lowest and the highest unemploy-ment rates in April 2001.

Clearly, the trends in the EU Member States andthe central and east European countries (CEECs)run in opposite directions. Whereas the unem-ployment rate in the EU countries fell from 9.2 %in 1999 to 8.3 % in 2000 and stood at 7.6 % in2001, it rose in the CEECs from 10.4 % in 1999to 12.5 % in 2000, before climbing to 13.1 % in2001.


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Statistical data: Eurostat database REGIO© EuroGeographics, for the administrative boundariesCartography: Eurostat — GISCO, March 2003

Map 4.1


0 5 10 15 20 25 30 35

Bucure‚sti Sud-Est

Yugozapaden Severozapaden

BratislavskyVychodné Slovensko

Malopolskie Dolnoslaskie

Közép-Magyarország Észak-Magyarország

Praha Moravskoslezko

Berkshire, Buckinghamshireand Oxfordshire

Merseyside

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Oberösterreich Wien

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Border, Midland and Western Southern and Eastern

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Oberbayern Halle

Vlaams Brabant Hainaut

Graph 4.1 — Unemployment rate, total percentage, NUTS level 2, 2001

Average

Min. % value

Max. % value

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Bulgaria

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United Kingdom

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Netherlands

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Women and thelabour marketsIn many European countries, the social situationof men and women varies. It is therefore useful toanalyse female unemployment.

Female unemployment rates in the regions of Eu-rope in April 2001 ranged from 1.1 % to 36.4 %.

The lowest rates were recorded by the regions ofUtrecht (the Netherlands) at 1.1 % and Ahvenan-maa/Åland (Finland) at 1.4 %. The highest figureswere in the Italian region of Calabria (36.4 %),and the Spanish regions of Ceuta y Melilla(34.3 %) and Extremadura (34.1 %). The break-down of unemployment by gender shows that thefemale unemployment rate in the candidate coun-tries is more or less as high as that for men, i.e. be-tween 2.0 % for the Hungarian capital region of


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Female unemployment rateTotal percentage2001 — NUTS 2



Map 4.2

Közép-Magyarország and 28.5 % for the Bulgar-ian region of Severozapaden. This may indicatethat the participation of men and women in thelabour markets in the accession countries is morebalanced than in the European Union.

Graph 4.2 shows the regions with the lowest andthe highest unemployment rates for women inApril 2001.

Youth unemploymentAnother dimension of unemployment relates tothe under-25s. Whilst unemployment at a moreadvanced age leads in many cases to depression orsocial withdrawal, youth unemployment can re-sult in strong social discontent or violence. If itcan be assumed that professional experiencemakes reinsertion into the labour market easier,people who have become unemployed at an earlystage in life will have even fewer opportunities.

In many of the regions under consideration, theyouth unemployment rate decreased betweenApril 2000 and April 2001. The biggest falls oc-curred in the Italian regions of Umbria with10.5 %, Liguria with 9.7 %, and Molise (9.5 %,a value shared also by Cantabria in Spain). Strik-ing in this context is the different development be-tween western and eastern Germany. While therates in all regions of the former West Germanyare decreasing, they are increasing strongly in theregions of the new Länder. Poland has alsorecorded a sharp rise in youth unemployment. In

Estonia, youth unemployment has increased, al-though the overall rate has fallen.

Regional differences in the youth unemploymentrate, i.e. the rate of unemployment among the ac-tive population under 25 years of age, are muchmore pronounced than in the overall unemploy-ment rate. In April 2001, youth unemploymentvaried between 2.1 % in the Dutch region ofUtrecht and 75.5 % in the Bulgarian region ofSeverozapaden. The figures for the Dutch regionof Zeeland are not suitable for publication due tothe small sample size, but experience shows that,in this region, youth unemployment is particular-ly low. Zeeland is therefore in all probability theregion with the lowest youth unemployment inEurope.

On the youth unemployment front, too, a wholeseries of regions posted rates differing markedlyfrom the EU average of 15.1 %. In April 2001, therate was below the EU average in as many as 52regions, while 48 regions recorded levels of overtwice the average.


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Graph 4.2 — Female unemployment rate, total percentage, NUTS level 2, 2001

The 53 regions with relatively low unemploymentamong young people were mainly in northern andcentral Europe: 17 in Germany, 8 in the Nether-lands, 8 in Austria and 7 in the United Kingdom.The rest were spread over Hungary, Ireland, Italy,Luxembourg, Portugal, Finland and Sweden.With regard to youth unemployment, it should be

pointed out that there are major differences in theeducation systems. In Germany, for example, therelatively low youth unemployment rate may bedue to the availability of school or non-schooltraining: young people attending who would oth-erwise be unemployed do not appear in unem-ployment statistics.


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4Youth unemployment rate

Total percentage2001 — NUTS 2


Statistical data: Eurostat d,atabase REGIO© EuroGeographics, for the administrative boundariesCartography: Eurostat — GISCO, March 2003

Map 4.3


0 10 20 30 40 50 60 8070

Nord-VestSud-Vest


BratislavskyVychodné Slovensko

Mazowieckie Swietokrzyskie



Berkshire, Buckinghamshireand Oxfordshire Merseyside

Stockholm Norra Mellansverige


Madeira Alentejo

Oberösterreich

UtrechtZeeland

Trentino-Alto AdigeCampania

Border, Midland and Western Southern and Eastern

Alsace Réunion

RiojaCeuta y Melilla

Ionia NissiaSterea Ellada

Oberbayern Halle

West-Vlaanderen Hainaut

Kärnten

Graph 4.3 — Youth unemployment rate, total percentage, NUTS level 2, 2001

Average

Min. % value

Max. % value

Romania

Bulgaria

Slovakia

Poland

Hungary

Czech Republic

United Kingdom

Sweden

Finland

Portugal

Austria

Netherlands

Italy

Ireland

France

Spain

Greece

Germany

Belgium

The 48 regions with particularly high rates, on theother hand, were nearly all in the Mediterraneanarea — mainly in Spain, Italy and Greece — andthe French overseas departments. Poland is repre-sented in all 16 Voivodships. There is also highyouth unemployment in Bulgaria, Lithuania andSlovakia.

Graph 4.3 shows the regions with the highest andlowest youth unemployment rates in April 2001.

The problem of long-term unemploymentThere is a considerable difference between em-ployees becoming unemployed in the short termdue to normal economic restructuring and long-term unemployment. ‘Long term’ is usuallydefined here as ‘longer than a year’. If a person


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Long-term unemploymentas percentage of unemployment

2001 — NUTS 2

EU-15 = Not Available> 5035–5020–35≤ 20Data not available


Map 4.4

cannot be reemployed within a year, the reason isstructural, and the problem lies either with theperson concerned or with structural deficits in thesectoral or regional sector.

In order to show the effect of long-term unem-ployment, separately and to ensure that the resultsof the regions are comparable, the proportion oflong-term unemployment is viewed below in rela-tion to the overall unemployment figure. It is, forexample, possible for the proportion of long-termunemployment to be low even where unemploy-ment figures are high. In this case, there will bemany instances of ‘job-ready’ unemployed people(for whom finding a suitable job is simply a mat-ter of time) and unemployment caused by theeconomy, but the structural problem is only slight.It is also possible for there to be a high percentageof long-term unemployment in a country whereunemployment is low. In these cases, there is veryprobably a structural problem.

Map 4.4 shows the proportion of long-term un-employment. From this map, unlike the mapsabove, which showed a more peripheral problem,it is clear that there is a major problem of long-term unemployment in eastern Europe, whichalso extends to Greece and southern Italy. A fur-ther feature of the map is a band with a high pro-portion of long-term unemployment passingthrough central Germany into Belgium. Interest-ingly, the often observed disparity between west-ern and eastern Germany does not appear to existhere, although, clearly, there are job-creation

measures in the new Länder which may be reduc-ing long-term unemployment figures.

Data on long-term unemployment are availablefor 253 regions, 23 of which have a proportion oflong-term unemployment in relation to overallunemployment of under 20 %. Of these, 10 are inthe United Kingdom and 4 are in Sweden; the restare spread over Belgium, Greece, Spain, Italy,Austria and Finland. Even in the below-30 %range, Cyprus is the only candidate country rep-resented. At the other end of the scale, 46 regionshave a share of between 50 and 60 %; 17 have aneven higher proportion. Of those 17, eight are inItaly and three are in Greece; the others are in Bel-gium, Germany, Poland, Portugal, Slovakia andSlovenia. However, this list may not be complete,as no data are available from the French overseasdepartments or Bulgaria.

Within the countries, the breakdown is illustratedin Graph 4.4.

Revised methodologyThe year 2003 will see a reform of regional un-employment rates, with the focus switching fromthe results of the second quarter to annualaverages.

Within the framework of a major quality projecton regional indicators, Eurostat created a task


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Limburg (B) Hainaut

Niederbayern Dessau

Notio Aigaio Ipeiros

Navarra Ceuta y Melilla

Alsace Languedoc-Roussillon

Valle d'AostaCampania

Salzburg Wien

Algarve Madeira

Pohjois-Suomi Väli-Suomi

Stockholm Mellersta Norrland

Hampshire and Isle of Wight Northern Ireland


Közép-Dunántúl Nyugat-Dunántúl

Lubelskie Podkarpackie

Bratislavsky ZápadnéSlovensko

Sud Nord-Vest

0 20 40 8060

Graph 4.4 — Long-term unemployment as percentage of unemployment, NUTS level 2, 2001

force to examine in detail and, if necessary, revisethe methodology used to estimate regionalunemployment rates. The task force presentedrecommendations for a revised system of calculat-ing regional unemployment rates, which werediscussed in the relevant Eurostat working partiesbetween Eurostat and the Member States. Theworking parties asked Eurostat to implement thenew rules as soon as the required data becameavailable.

Some background information is required to un-derstand the new method: the Community labourforce survey (CLFS) is one of the main compo-nents of the calculation. However, for many yearsdata from the CLFS were available only for thesecond quarter. The Member States have madegreat efforts to remedy this situation, and, at pre-sent, almost all countries are able to provide datafor all four quarters. Since regional information ispublished only once a year, it would be a shamenot to make use of this new information, particu-larly in view of the fact that annual averages in-crease statistical reliability at regional level. Thisnew methodology has already been used to a cer-tain extent for candidate countries.

The new methods are simpler and more transpar-ent than the old ones. Down to NUTS 2 level, onlyannual averages from the CLFS are used, both forunemployment and active population figures.

The next step is to determine the regional struc-tures below the NUTS 2 level. For some countries,CLFS results at NUTS 3 level are not reliableenough, whilst, for others, they are. Some coun-tries have new census results, whilst others havedecided not to conduct a census. The reliability ofthe countries’ registers also varies. The structure isdetermined by Eurostat and the national statisti-cal offices on the basis of the data situation.

Unemployment figures and economically activepopulation figures are broken down into regionseither directly on the basis of the reliable CLFS orusing the option of the annual average of a three-year period. Information on the registered unem-ployed can also be used. Census results can alsoserve as a source of information.

Regional unemployment rates based on these newmethods will be published for the first time inOctober 2003.


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L A B O U R F O R C E S U R V E Y 5

Overall rate ofemploymentThe European employment strategy (EES),launched at the European Summit on Employ-ment in Luxembourg in 1997, was devised againsta background of high unemployment. In 2000,the European Council in Lisbon updated EES pri-orities by adopting the aim of full employment,

setting interim objectives and incorporating thestrategy into a broader framework of policycoordination.

In spring 2000, the Lisbon Summit set the objec-tive of achieving an employment rate of around70 % by 2010 for all persons aged between 15and 64. In March 2001, the Stockholm EuropeanCouncil set an interim objective of 67 % for thepopulation as a whole by 2005.


Employment rates: men and womenaged between 15 and 64 years

Population employed as percentage of total population aged 15–64Spring 2001 — NUTS 2


DEB1, DEB2, DEB3: 1999

Statistical data: Eurostat database REGIO© EuroGeographics, for the administrative boundariesCartography: Eurostat — GISCO, May 2003

Map 5.1

Five years after the EES was launched, all the EUMember States generally show a clear rise in em-ployment rates, albeit with regional differences. Itis nevertheless difficult to determine to what ex-tent this general improvement in employmentrates over the last five years can be attributed tothe EES, and to what extent it is the result of bet-ter economic circumstances.

The employment rates for all those aged 15–64ranges between 40.7 % (Campania, Italy) and80.7 % (Ahvenanmaa/Åland, Finland). For 2001,Map 5.1 shows that the 2005 target of 67 % hadbeen exceeded in Denmark, most regions in Aus-tria, Portugal, Finland and the United Kingdomand every region in the Netherlands and Sweden,as well as in the south of Germany (Baden-Württemberg and Bayern).

The situation was less promising in the candidatecountries: only Cyprus, the Praha and Jihozápadregions in the Czech Republic, the Sud-Vest re-gion in Romania and Bratislavsky in Slovakia hadachieved a rate of more than 67 %.

In the last five years, the biggest improvements inthe employment rates were recorded in Spain (upby 6 percentage points for all regions, with an in-crease of nearly 11 points for the Comunidad deMadrid), Ireland’s two regions (6 and 9 points),France (5 points in Basse-Normandie and Lor-raine), Italy (more than 5 points in Piemonte, Lig-uria, Toscana and Umbria), almost every region inthe Netherlands and Portugal, the north of Fin-land, north and central Sweden and the regions ofSouth Yorkshire and East Anglia in the UnitedKingdom.

In some regions, the employment rates, while notfalling, are showing less spectacular progress.This applies to Denmark, the German regions ofBrandenburg, Mecklenburg-Vorpommern,Lüneburg, Sachsen-Anhalt, Schleswig-Holsteinand Thüringen, throughout Greece (apart fromAttiki) and Austria, and in most regions in theUnited Kingdom.

Employment rate forwomenThe Lisbon and Stockholm objectives for womenin employment are 57 % by 2005 and 60 % by2010.

Employment rates for women are rising fasterthan the overall rate. The trends for 1997–2001show that women contributed more to the overallgrowth in employment, although the main rea-sons for growth differed.

A comparison of Maps 5.1 and 5.2 shows that the2005 employment target for women had been at-tained in 2001 in more regions than the target forthe population as a whole. This was especially thecase in certain regions in Germany, France, Fin-land and the United Kingdom. Among the candi-date countries, Estonia, Latvia, Slovenia and partof Romania and the Czech Republic also had em-ployment rates for women of more than 57 %.

The high rate of participation by women is veryoften associated with high percentages of womenworking part-time in order to reconcile employ-ment and family commitments. This does not ap-ply to Portugal and Finland, where less thanone in every five women is in a part-time job. Part-time work is also not very common in the candi-date countries.

Most of the regions in Greece and Spain, as wellas in the south of Italy and France, have rates be-low 50 %.

Since 1997, in the European Union as a whole, thegap between the employment rates for men andwomen has narrowed by nearly 2 percentagepoints. However, there are still big differences atregional level, where the gap exceeds 25 percent-age points in almost every region in Greece, Spainand the south of Italy.

Differences below 10 percentage points occur pri-marily in the northern and eastern countries. Thisis true of Denmark, some regions in the UnitedKingdom (Northumberland, Tyne and Wear,Greater Manchester, South Yorkshire, Devon andEastern Scotland), almost every region in Finlandand Sweden (where occasionally the employmentrate for women is even higher than for men), theformer east German regions of Berlin, Mecklenburg-Vorpommern, Sachsen and Sachsen-Anhalt andmost regions in the candidate countries apartfrom Cyprus, the Czech Republic and Hungary.

The differences in the rates for men and womenare relatively low for those aged 15–24 in everycountry, but they rise quickly for those over 25. InBelgium, Cyprus, most German regions, Greece,Ireland, Italy, Luxembourg, the Netherlands, Por-tugal, Romania and Spain, the gap widens asworkers get older because women tend to leaveemployment because of family commitments.


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In France, Finland, Sweden, the German regionsof Bremen, Berlin, Sachsen, Sachsen-Anhalt andThüringen and in all the candidate countries notmentioned earlier, there is a return to employmentafter the age of 35. In the 55–64 age group, somecountries also show large differences in the em-ployment rates for men and women, because of alower retirement age for women.

Average number ofhours normallyworkedThe average number of hours worked shown inMap 5.3 corresponds to the number of hours


Employment rates: womenaged between 15 and 64 years

Population employed as percentage of total population aged 15–64Spring 2001 — NUTS 2


DEB1, DEB2, DEB3: 1999


Map 5.2

normally worked by the person. It covers allhours including overtime, whether paid or not,normally worked and excludes travel time be-tween the home and place of work and breaks fora main meal.

Every region in the Netherlands has an averagenumber of hours below 32 per week on accountof a large percentage of people working part-time.At the other extreme, in most of the regions in

Greece, where part-time work is virtually non-existent (only 4.1 % of those in employment), theaverage number of hours worked is in excess of42, especially in Notio Aigaio and AnatolikiMakedonia, Thraki (where the figure is 46 hoursper week). These are regions where the ‘hotels andrestaurants’ and ‘agriculture’ sectors, in the for-mer and latter respectively, employ a sizeable per-centage of the population.


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Average number of hours usually workedper week

Spring 2001 — NUTS 2

EU-15 = 37.7> 4340–4337–4035–37≤ 35Data not available

DEB: NUTS 1

Statistical data: Eurostat database REGIO© EuroGeographics, for the administrative boundariesCartography: Eurostat — GISCO, April 2003

Map 5.3

The sector of activity, occupational status (em-ployer, employee, self-employed) and the inci-dence of part-time work are factors which deter-mine the number of hours worked. Consequently,regions where there is a high percentage of peopleworking in the hotel trade or agriculture havelong working hours, as do regions where there isa noticeable percentage of employers or self-employed persons.

In every country and region, the average numberof hours worked by women is lower than the fig-ure for men. This is partly explained by the factthat more women work part-time, but also be-cause they tend to work in the service sectorwhere working hours are shorter than in the man-ufacturing and agriculture sectors. Women arealso more likely than men to be employees. Inmost of the candidate countries, the differencesbetween the hours normally worked by men andwomen are relatively narrow compared with thefigures for the European Union. The difference isless than 3 hours in Bulgaria, Estonia, Hungary,Latvia, Lithuania, Romania, Slovakia and Slove-nia, and less than 6 hours in every region in theCzech Republic and most regions in Poland, butmore than 10 hours in most regions in the Nether-lands and the United Kingdom.

Lifelong trainingLifelong training is measured by means of a ques-tion in the labour force survey. It refers to instruc-tion or training received during the four weeksprior to the survey. In France, the reference periodis reduced to only one week, which makes com-parison with other countries difficult and whichexplains to some extent the fact that the resultscollected for this question are relatively sparse. Infact, in 2001, the question was not asked in theCzech Republic, Ireland, Latvia or Slovakia.

The indicator shown in Map 5.4 has been calcu-lated for the population aged 25–64 in order toexclude, as far as possible, those attending schoolor university.

The rate of involvement in education and trainingamong the adult population is still fairly low atabout 8.4 % for the EU (compared with 5.8 % in1997).

Northern Europe has rates of involvement in edu-cation and training which are above those of therest of the European Union. Most regions in the

United Kingdom, Mellersta Norrland in Swedenand Uusimaa in Finland, for example, record fig-ures above 20 %. At the other extreme, most re-gions in Bulgaria, Greece and Romania have ratesbelow 2 %.

The rates vary according to age and level of edu-cation. Those aged 25–34 are five times more like-ly to be involved in education and training thanthose aged 55–64. Those with poorer qualifica-tions are six times less likely to be involved in ed-ucation and training than those with higher qual-ifications. Women are more involved in lifelongtraining than men.

Levels of educationand employmentIn this chapter, the level of education refers to thehighest educational attainment at the time of leav-ing continuous education. The lower level of edu-cation refers to primary or lower secondary edu-cation. Medium-level education covers peoplewho have completed secondary or post-secondarynon-tertiary education. Higher education com-bines graduates with a first- or second-stage ter-tiary qualification.

In the European Union, 42.4 % of people aged be-tween 25 and 64 have completed medium-level,36.2 % lower-level and 21.5 % higher-level edu-cation. Graph 5.1 reveals marked disparities inthe distribution of the population by level of edu-cation from one country to another. People withhigher education qualifications represent morethan one quarter of the population aged between25 and 64 in Belgium, Cyprus, Estonia, Denmark,Finland, Lithuania, Sweden and the United King-dom, but less than 15 % in Austria, the Czech Re-public, Hungary, Italy, Poland, Portugal, Roma-nia, Slovakia and Slovenia. In Belgium, Greece,Spain, Ireland, Italy, Luxembourg and Portugal,on the other hand, more than 40 % of people havecompleted only primary or lower secondary edu-cation. The gaps revealed by these results are real,although they may also derive from the differentcountries incorrectly applying the internationalstandard classification of education (ISCED).

In the EU Member States and in the candidatecountries, generally, employment rates rise withdiploma levels. In the European Union, 85 % oftertiary education graduates are in employment,as against 73 % of people with medium-level


education and 50 % of those who completed lower-level education.

According to Map 5.5, in Portugal, several re-gions in the United Kingdom, Vorarlberg in Aus-tria, Detmold in Germany, Flevoland in theNetherlands, Norra Mellansverige in Sweden, insouthern Romania, Bratislavsky in Slovakia andPraha in the Czech Republic, more than 90 % ofhigher education graduates are in employment. At

the other extreme, in certain regions of Germany(including Berlin, Brandenburg, Sachsen, Sach-sen-Anhalt and Thüringen), France (Aquitaine,Midi-Pyrénées and Corse), Greece (north andwest), southern Italy, almost all of Spain (with theexception of Este, Nordeste and the Comunidadde Madrid) and all of Bulgaria, Estonia, Lithua-nia, Nord-Vest and Centru in Romania andSwietokrzyskie in Poland, the rate falls below


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(adult participation in education and training)Spring 2001 — NUTS 2

> 2010–205–10≤ 5Data lacking reliabilityData not available

Percentage of the population aged 25–64 participating ineducation and training over the four weeks prior to thesurvey


Map 5.4

80 %. Only two regions record employment ratesof less than 70 % among tertiary education grad-uates: Corse (France) and Severozapaden(Bulgaria).

While employment rates rise with education lev-els, the proportion of the population which hascompleted tertiary education is also unevenly dis-tributed from one country and from one region toanother. In Lithuania, where there are five timesas many graduates as in Portugal (45 % as against9.1 %), the graduate employment rate is 10 per-centage points lower than in Portugal.

The difference between the employment rates ofpeople with higher and lower-level education re-veals marked contrasts between regions (seeGraph 5.2). At one extreme, differences in excessof 50 percentage points are found in Slovakia

(except for Bratislavsky) and in Poland (Ku-jawsko-Pomorskie Lubuskie, Slaskie, Warminsko-Mazurskie, Zachodniopomorskie), whereas, atthe other extreme, the differences are less than20 % in Nord-Est, Sud-Vest and Nord-Vest in Ro-mania, in Portugal, in southern Sweden and inAnatoliki Makedonia, Thraki, Kriti and Pelopon-nissos in Greece.

The regions in a single country sometimes presentcontrasts in how employment rates vary with edu-cation levels. Thus, in Romania, Poland andSpain, the scale of the differences exceeds 30points.

In Bulgaria, Ireland, Italy, the Netherlands, Portu-gal and Finland, the regions are more uniform,with differences of less than 15 points.


B DK D EL E F IRL I L NL A P FIN S UK BG CY CZ EE HU LT LV PL RO SI SK0

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Graph 5.1 — Population aged 25–64 years old by level of education and by country(national level, EU and candidate countries)

Differences in employment rates between persons having attained tertiary education (ISCED 5 and 6) and personshaving attained at most a lower secondary education (ISCED 0–2)

At most, lower secondary level Upper secondary level Third level


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with high level of educationSpring 2001 — NUTS 2


DEB: NUTS 1


Map 5.5


20 30 40 60 705010

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Île-de-France


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Salzburg

Centro (P)

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Galicia


Sicilia

Nord - Pas-de-Calais

Steiermark

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Övre Norrland

Merseyside

Dél-Alföld

Malopolskie

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Småland med öarna

Berkshire, Bucksinghamshire and Oxfordshire

Észak-Alföld

Lubuskie

Severoiztochen Severen tsentralen

OstravskoStrední Cechy

Sud-Vest Bucure‚sti

BratislavskyVychodnéSlovensko

Graph 5.2 — Difference in the employment rate by educational attainment level: high and low(population aged 25–64) NUTS level 2, 2001

S C I E N C E A N D T E C H N O L O G Y 6

IntroductionOne of the strategies identified by the LisbonSummit in March 2000 in order to make theEuropean Union the most competitive economy,capable of a rapid response to the changing re-quirements of the global marketplace, was greateremphasis on Europe’s transition to a knowledge-based economy.

At the Barcelona Summit, the European Councilremarked that a significant boosting of the overallspending on research and development (R & D)and innovation in the EU would be necessary inorder to close the gap between the EU and its ma-jor competitors. The objective agreed by EU gov-ernments at Barcelona was to increase R & Dspending to 3 % of GDP by 2010, with two thirdsof this new investment to come from the privatesector.

It is evident that economic growth is increasinglyrelated to the capacity of an economy to changeand to innovate. Considerable effort should beput into creating an environment that encouragesresearch, thus facilitating the transition to aknowledge-based economy. Such a policy needsstatistical information on science and technology,a wide field that includes data on research,patents, high-tech manufacturing sectors, andhigh-tech and knowledge-intensive services.

The best-performing economic sectors from thepoint of view of research and innovation are thoseknown as high technology. Such sectors have beendefined for both manufacturing and services(more information on the definition may be foundin the methodological notes). For this reason, thisedition of the regional yearbook focuses on thesame sectors as the last edition.

Innovation is a process that requires investment,for example in key aspects such as education andR & D, which may be difficult to measure as aninput. On the other hand, we can measure thenumber of patents as an intermediate element ofthis process. Some other indicators can be used inorder to measure the performance of the innova-tion process, for example employment in high-and medium-high-tech manufacturing sectors andin high-tech and knowledge-intensive services.

This chapter examines the dynamism of regionsfrom the point of view of regional indicators suchas human resources in science and technology(S & T), employment in high- and medium-high-

tech manufacturing sectors and in high-tech andknowledge-intensive services.

The reference year is 2001 for data on employ-ment; for data on patents, 2001 provisional dataare used.

Human resources inscience andtechnologyIn recent years, there has been increasing recogni-tion of the importance of human capital as an en-gine of growth and it became very important toquantify these highly qualified staff in order to seeto what degree the countries, and, in particular,individual regions, have the capacity to turn thehuman potential into innovative practice.

Map 6.1 represents the percentage of the labourforce working in an S & T occupation, as a pro-fessional or as a technician according to the inter-national standard classification of occupations(ISCO) definition. Only a few countries show avery high percentage (over 35 %) of the labourforce working in an S & T occupation: six regionsin Germany (e.g. Berlin, Oberbayern and Karl-sruhe), two each in the Netherlands (e.g. Noord-Holland) and Sweden (e.g. Sydsverige) and one inFinland (Uusimaa). With the exception of threeGerman regions, all the remaining regions in thesefour countries also show high percentages (morethan 25 %).

The rest of the European regions record an aver-age percentage of between 15 and 25 %, withsome exceptions in Portugal (e.g. Norte and Cen-tro), Greece (e.g. Kriti and Peloponnissos) andSpain (Galicia), where the percentage in someregions is below 15 %.

Map 6.2 shows the percentage of the populationaged between 25 and 64 who have a third-leveleducation.

As in previous years, in 2001 some countriesagain show a concentration of highly educated in-habitants in the capital regions when compared tothe rest of the country. Examples include Berlin inGermany, Comunidad de Madrid in Spain, Lon-don in the United Kingdom, Uusimaa in Finlandand Wien in Austria.


High levels of populations who have a third-leveleducation are also noticeable in all Finnish re-gions and in the regions of the former EastGermany.

In Spain, the northern and eastern part of thecountry dominates, particularly País Vasco andComunidad Foral de Navarra.


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6Human resources in science and technologyby occupation as percentage of the labour force

2001 — NUTS 2


A: 1997; DEB: 1999; S, UK: 2000PT2, PT3, FI2: Eurostat estimate


Map 6.1


Human resources in science and technologyPercentage of population aged 25–64

with third-level education2001 — NUTS 2


A: 1997; DEB: 1999; S, UK: 2000IT2, PT15, PT2, FI2: Eurostat estimatePT3: confidential


Map 6.2

Employment in high-tech andknowledge-intensivefieldsThe importance of high- and medium-high-techmanufacturing sectors and high-tech andknowledge-intensive services has increased con-siderably in the last few years and has a significantimpact on the structure and organisation of em-ployment.

Map 6.3 classifies European regions with regardto the level of employment in high- and medium-high-tech manufacturing sectors, expressed as apercentage of total employment.

In 2001, at the EU level, 12 million people wereemployed in these manufacturing sectors, which is7.6 % of total employment.

There are noticeable disparities across EU regions:the ratio of employment in these sectors rangedfrom 0.7 in Extremadura (Spain) to 21 % inStuttgart (Germany). The region with the highestratio of people employed in high- and medium-high-tech manufacturing sectors was Stuttgart,which recorded 393 000 people in these sectors.

In all, 13 of the top 20 regions are located in Ger-many; there are two each in France, Italy and theUnited Kingdom and one in Spain (see Table 6.1).

In Italy, the highest rates are in the northern partof the country, with the exception of Basilicata inthe south, which has almost 11 %.

With the exception of Denmark, Greece, Irelandand Portugal, all countries have at least oneregion with more than 7.5 % of all employees em-ployed in high- and medium-high-tech manufac-turing sectors


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Table 6.1 — Top 20 regions for employment in high- and medium-high-tech manufacturing sectors (1)

Employment in high- and medium-high-tech manufacturing

Code NUTS 2 region 1 000 Percentage Percentage of employmentof total employment in manufacturing

1 DE11 Stuttgart 393 21.0 58.12 DE14 Tübingen 152 18.1 51.13 DE91 Braunschweig 123 17.8 61.74 DEB3 Rheinhessen-Pfalz 152 17.0 59.35 DE12 Karlsruhe 209 16.9 55.26 FR43 Franche-Comté 82 16.6 54.27 DE22 Niederbayern 92 16.2 49.88 DE26 Unterfranken 96 15.6 19.39 DE25 Mittelfranken 118 14.6 49.7

10 DE27 Schwaben 122 14.4 47.611 DE13 Freiburg 139 14.1 46.312 IT11 Piemonte 245 13.8 44.813 UKG1 Herefordshire, Worcestershire and Warwickshire 81 13.3 57.214 DE21 Oberbayern 270 13.0 57.415 DE71 Darmstadt 230 13.0 58.616 FR42 Alsace 99 129 48.617 UKG3 West Midlands 134 11.9 48.818 IT2 Lombardia 428 10.9 34.019 DEA2 Köln 197 10.7 48.920 ES51 Cataluña 263 10.6 37.7

(1) With at least 80 000 people working in high- and medium-high-tech manufacturing sectors.Exceptions to the reference year 2001 — Swedish regions: 2000; Koblenz, Trier, Rheinhessen-Pfalz: 1999.

Map 6.4 portrays the European regions in termsof the level of employment in high-tech services.

The proportion of employment in high-tech ser-vices varies across the EU regions, ranging from0.9 % in Extremadura (Spain) to 10.3 % in Berk-shire, Buckinghamshire and Oxfordshire (UK)

The higher-ranking regions are widespread allover Europe, but are particularly dominant innorthern Europe (e.g. Sweden, Finland, Denmark,the UK and Ireland) and in southern France. Thecapital regions again show a concentration of per-sons employed in high-tech services.


Employment in high- and medium-high-tech manufacturing sectors as

a percentage of total employment2001 — NUTS 2

> 117.5–115.5–7.5≤ 5.5Data not available

EU-15: 7.57 (Eurostat estimate)S: 2000


Map 6.3

Table 6.2 shows that employment in high-techservices is focused in the UK, which includes 8 ofthe leading 20 regions.

Knowledge-intensive services (KIS) are defined ac-cording to the proportion of employees with at

least third-level education. Map 6.5 shows theemployment in KIS across European regions.

Employment in KIS is becoming increasingly im-portant. At the EU level, in 2001, there were 53million people employed in these services, whichis 33 % of total employment.


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Map 6.4

Inner London (UK), with 61.1 % of all employ-ment in knowledge-intensive services, is the EUregion with the highest proportion of peopleworking in these sectors, followed by Stockholm(Sweden) and Outer London (UK).

Knowledge-intensive services dominate in north-ern Europe, especially in Sweden (e.g. Stockholmand Mellersta Norrland), Denmark, the UnitedKingdom (e.g. Inner London and Outer London),Belgium (e.g. Vlaams Brabant) and the Nether-lands (e.g. Utrecht). Southern parts of Francescore highly as well.

A number of other clusters can be identified, suchas in Spain (Comunidad de Madrid), in Germany(Berlin and Hamburg), in Italy (Lazio and Cal-abria), Austria (Wien) and Greece (Attiki). Again,regions containing the capital tend to have higherrates.

Patent applicationsMap 6.6 shows the dominant sector for eachregion according to the international patentclassification (IPC).

Each of the colours refers to specialisation in aparticular IPC sector. By contrast, the intensity ofthe colours on the map depends on the number ofpatent applications, a light colour meaning thatthe number of patent applications is 10 or less anda darker one implying that there are more than 10patents within the dominant IPC sector in theregion.

The most widespread IPC sector across Europeanregions is that of ‘performing operations andtransporting’, particularly in France, Germany,Austria and northern Italy.

Only one European region (Väli-Suomi in Fin-land) records a predominance of patent applica-tions in the textile and paper sector.

A high degree of specialisation in electricityoccurs in the northern regions of Finland andSweden. Several German regions also showspecialisation of patent applications in this sector,for example Oberbayern and Dresden.

There are some smaller clusters of patentapplications relating to chemistry and metallurgy,for example in Belgium, UK (Northern Ireland),Germany, Spain and France.


Table 6.2 — Top 20 regions for employment in high-tech services (1)

Employment in high-techservices

Code NUTS 2 region 1 000 Percentage of Percentage of employmenttotal employment in services

1 UKJ1 Berkshire, Buckinghamshire and Oxfordshire 120 10.3 13.62 SE01 Stockholm 79 8.4 9.93 UKH2 Bedfordshire and Hertfordshire 66 7.8 10.44 FR1 Île-de-France 383 7.5 9.25 BE24 Vlaams Brabant 32 7.2 8.96 ES3 Comunidad de Madrid 151 7.1 9.67 NL31 Utrecht 42 7.1 8.98 UKI1 Outer London 154 7.1 8.59 FI16 Uusimaa (Suuralue) 52 7.1 9.0

10 UKI1 Inner London 88 6.9 7.911 UKJ3 Hampshire and Isle of Wight 55 6.1 8.312 UKJ2 Surrey, East and West Sussex 77 6.0 7.613 UKH1 East Anglia 62 5.6 7.914 IT6 Lazio 109 5.6 7.215 DE71 Darmstadt 97 5.5 7.816 AT13 Wien 41 5.4 6.817 UKK1 Gloucestershire, Wiltshire and North Somerset 59 5.3 7.018 DE12 Karlsruhe 65 5.2 8.519 DE21 Oberbayern 108 5.2 7.920 FR62 Midi-Pyrénées 52 5.0 7.4

(1) With at least 30 000 people working in high-tech services.Exceptions to the reference year 2001 — Swedish regions: 2000; Koblenz, Trier, Rheinhessen-Pfalz: 1999.

The Iberian peninsula is dominated by lightcolours, meaning that there is a low degree ofspecialisation, with the exception of Andalucía(specialisation in human necessities), ComunidadValenciana (chemistry, metallurgy), Cataluña and

País Vasco (predominant in performing opera-tions and transporting).

Map 6.7 presents the patent applications in high-tech sectors in both absolute terms and permillion inhabitants.


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as percentage of total employment2001 — NUTS 2




Map 6.5

The low population in some regions, such asPohjois-Suomi in Finland, explains why a lowscore in terms of the total number of patentapplications may coincide with a high scoreexpressed as patent applications per millioninhabitants.

At the other extreme, there are some regions witha high population density such as Île-de-France(France) with 886 applications, which is, in rela-tive terms, only 81 patent applications per millioninhabitants.


Patents applications to the EPOby IPC section (total number)

2001 — NUTS 2Numberof patents: Patents predominantly in:1 10 1 076

Human necessitiesPerforming operations, transportingChemistry, metallurgyTextiles, paperFixed constructionMechanical engineering; lighting; heating; weapons; blastingPhysicsElectricity

0

Provisional values


Map 6.6


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Total and per million inhabitants2001 — NUTS 2

Per million inhabitants Total numberof applications

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Map 6.7

1 000400200

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Oberbayern in Germany and Noord-Brabant inthe Netherlands show a very large number ofpatents and also a high score expressed as patentapplications per million inhabitants, despite adense population within the region.

South European regions show rather low ratios ofpatent applications per million inhabitants as wellas in the total number of applications.

Methodological notesThe maps in this chapter are compiled from New-Cronos: Theme 9 — Science and technology/domains HRST, EHT and Patents.

Data on human resources in science and technol-ogy (HRST) are collected in line with the recom-mendations of the Manual of human resources de-voted to S & T (Canberra manual). HRST byoccupation (HRSTO) are classified according tothe international standard classification of occu-pations (ISCO) developed by the InternationalLabour Organisation (ILO).

Human resources in science and technology byoccupation include those people working in S & Toccupations, i.e. ISCO major group 2 (profession-als) and ISCO major group 3 (technicians andassociate professionals).

The population with third-level educationincludes those persons belonging to ISCED cate-gories 5A, 5B and 6.

HRST and data on employment in high-tech sec-tors are extracted from the Community labourforce survey (CLFS). The CLFS data are based ona sample of the population and, therefore, the re-sults are subject to the usual types of error associ-ated with sampling techniques, as well as to anumber of other non-sampling errors. All resultsconform to the Eurostat guidelines on sample sizeand therefore are not published if the degree ofsampling error is likely to be high. Due to lowsample sizes, data quality problems arise in cer-tain regions (see the information given in New-Cronos).

A patent is a public title of industrial propertyconferring on its owner the exclusive right to ex-ploit the invention for a limited area and time.Patents are the most widely used sources of datafor measuring innovative activity and technologi-

cal development, as well as for comparisons oftechnology growth. The patent data reported hereinclude the patent applications filed at the Euro-pean Patent Office (EPO) during the referenceyear, classified according to the inventor’s regionof residence and to the international patents clas-sification of applications.

High-tech patents are counted in accordance withthe trilateral statistical report definition, wherethe following technical fields are included:computer and automated business equipment,micro-organism and genetic engineering;aviation; communication technology; semicon-ductors; lasers.

The high-tech economic sectors are defined interms of the R & D intensity of the sector, follow-ing the definition applied by the Organisation forEconomic Cooperation and Development (OECD— 1997). The R & D intensity is calculated as theratio of the sector’s R & D expenditure to its val-ue added. To this is added the indirect R & D in-tensity, which expresses the R & D ratio of the in-put to the sector, relating both to intermediaryproducts and to capital investments. Applying thisapproach to the manufacturing sectors of the Eu-ropean economic activity classification NACERev. 1, there are 10 main high- and medium-high-tech sectors identified: aerospace; computers andoffice machinery; electronics/communications;pharmaceuticals; scientific instruments; motor ve-hicles; electrical machinery; chemicals; othertransport equipment; non-electrical machinery.

Three NACE service sectors have been identifiedas being ‘high-tech’, these being post and telecom-munications, computer and related activities andresearch and development.

As R & D intensity does not serve as a suitable in-dicator with regard to services, a broader defini-tion of knowledge-intensive services (KIS) hasbeen proposed, based on the concept of knowl-edge intensity, which includes the proportion ofemployees with at least third-level education.Knowledge-intensive services include: watertransport, air and space transport, post andtelecommunications; financial intermediation;computer and related activities; research and de-velopment; real estate; renting and business activ-ities; education; health and social work; recre-ational, cultural and sporting activities, radio andtelevision activities; libraries, archives, museums,etc.


STRUCTURAL BUSINESS STATISTICS 7

IntroductionRegional business statistics are a vital source ofinformation for anyone requiring details of eco-nomic activity in the European regions. How isemployment changing in the regions? What arewage rates and investment rates in any particularregion or sector? A detailed analysis of the struc-ture of the European economy by sector is possi-

ble only at regional level. In fact, it often happensthat a country’s flagship industry is concentratedin a few regions, and, conversely, within a verydynamic country, there may be regions where eco-nomic growth is lagging because there is a crisis incertain key sectors in those regions.

Maps 7.1, 7.2, 7.3 and 7.4 have been based onstructural business statistics (SBS) at regional levelwhich are available in NewCronos in the SBS


Share of trade and services in total employment

Percentage of employment in services as percentageof total employment (manufacturing and services)

2000 — NUTS 2> 6456–6449–5636–49≤ 36Data not available

Population covered = NACE Rev. 1, Sections C, D, E, F,G, H, I, KIRL, FIN: 1999DK, D, L, NL: 1999 + 2000IRL: NUTS 1


Map 7.1

domain, theme4/sbs/region, and in the REGIOdomain, theme1/regio/sbs-r. The maps presentedhere are a brief summary of the available regionalbusiness statistics. The full database has muchmore information.

Regional business statistics are presented not onlyfor the EU Member States, but also for accessioncountries, for which data availability is currentlyalmost as good as for Member States. In the caseof Bulgaria and Hungary, however, business datawere not yet available when this publication wasprepared.

Services are thelargest employer inthe regionsMap 7.1 shows the share of trade and services innon-financial market employment in the regionsof Europe.

‘Employment’ refers to persons in employment,i.e. those working in the unit concerned and thoseworking outside the unit while remaining part ofit and being paid by it. The map shows results forall the market sector, with a rough distinction be-tween industry and services. Industry is taken tobe Sections C, D, E and F and services Sections H,I and K of NACE Rev. 1. However, this analysismay be broken down for each sector at a detailedlevel of economic activity using the REGIO data-base in NewCronos.

Traditionally, the German economy, which fo-cuses on manufacturing, is contrasted with theUK economy, which is to a greater extent gearedto services. This national generalisation broadlyapplies at local level in these two countries aswell, but there are a few UK regions, most of themin the centre and west (such as Leicestershire, Rut-land and Northamptonshire, and West Wales andthe Valleys), which are almost as industrialised asGerman regions.

The pattern of employment in France is unlikethat in any other EU country. In terms of total em-ployment, jobs in services are particularly evidentaround the capital, whereas there are much fewerin the rest of the country. This high proportion ofservices in the Île-de-France region is due to themarked concentration of the population in thatregion, with the major industrial areas now being

some way from the capital. Map 7.2 shows thatthese jobs in services tend to be more skilled thanthose in other French regions.

The coastal regions of the Mediterranean have aparticularly high concentration of services, with aband stretching from Algarve in Portugal, via An-dalucía, Provence-Alpes-Côte d’Azur, Lazio andCampania, to Calabria in the south of Italy. Corse(Corsica) and Sardegna (Sardinia) also have ahigh density of jobs in services. How should thisgrouping of jobs be interpreted? On the one hand,these are very much tourist regions and, on theother hand, the influence of traditional, job-intensive sectors such as retail trade or sea trans-port is still felt. As Map 3.1 on per capita grossdomestic product in Chapter 3 shows, the southof Italy is still desperately trying to catch up withthe economy in the north of the country, wherethe high level of industrialisation is evidence of adynamic economy.

In Spain, France and Italy, there is a sharp con-trast between a very highly industrialised area inthe north of the country and another in the southwhich is more geared to trade and services. InFrance, the Languedoc-Roussillon and Provence-Alpes-Côte d’Azur regions are very serviceintensive.

Belgium, the Netherlands and the north of Swe-den also have very service-intensive regions. In theNetherlands, in particular, there is a great deal ofcommercial and transport activity around theports of Amsterdam and Rotterdam, i.e. in theNoord-Holland and Zuid-Holland regions.

The regions of the accession countries are gener-ally more heavily industrialised than the EU aver-age. Nevertheless, services predominate in Latviaand in the region of Mazowieckie in Poland.

Employees betterpaid around thecapital citiesMap 7.2 shows wages and salaries per capita forthe whole of the non-financial market sector.‘Wages and salaries’ means all sums in cash andbenefits in kind paid to persons who are countedas employees, including home workers, in returnfor their labour during the accounting year,whether they are paid by the hour, by output or at


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piece rates, and whether or not they are paidregularly.

Wages and salaries per capita are a good proxy forthe qualification of the labour force in industry inthe region in question, i.e. the average wage orsalary received by a person working in that sectorof activity. According to how they are assessed by

an observer, high average wages and salaries in aregion or a country may, as has been suggested,denote a qualified workforce, but they may alsomake a region less competitive.

There is, in general, a remarkable variety of wagesand salaries in Europe, and in the euro zone inparticular. In a unified monetary zone, differences


Wages and salaries per personemployed in market activities

Wages and salaries related to number ofpersons employed — 1 000 EUR/person

2000 — NUTS 2> 23.119.0–23.114.4–19.08.1–14.4≤ 8.1Data not available

Population covered = NACE Rev. 1, Sections C, D, E, F,G, H, I, KI: 1998; IRL: 1999EL: 1998 + 1999; DK, D, NL: 1999 + 2000EL: national level


Map 7.2

in wages and salaries or in productivity can nolonger be masked by exchange rate fluctuations.While it is true that wage and salary levels are byno means the only criterion of competitiveness,this noticeable difference between regions willhave economic consequences in the long term inan area where people and capital can circulatefreely.

In Spain, Italy and Portugal, in particular, averagewages and salaries are under EUR 17 000 percapita and no region stands out by virtue of reallyhigh pay.

Nevertheless, average wages and salaries per capi-ta may show marked imbalances between regionswithin a single country. In Île-de-France, pay ismuch higher than in other regions of the country.In fact, there are an enormous number of highlyskilled jobs in this region, particularly in the headoffices of the country’s major businesses. Similar-ly, in Finland, wages are higher in the Uusimaa re-gion than in the rest of the country. In general,wages and salaries are highest in the regions clos-est to the capital cities, with the striking exceptionof Portugal and the Lisboa e Vale do Tejo region,which includes Lisbon.

The proximity of northern Europe seems to behaving an effect in Spain and Italy, where wagesand salaries are highest in the north of the coun-try. In Italy, the higher level in the north and inLombardia is to some extent a sectoral effect: inthe north, industry is more productive andemployees are better paid than in the traditionalregions of the south.

Pay levels are lower in the former East Germanythan in the rest of the country. In fact, the old Län-der contrast with both the eastern areas of thecountry and the rest of Europe in that wages andsalaries are fairly high in all regions, in particulararound Stuttgart and Darmstadt (includingFrankfurt as a financial centre). This high level islargely due to the method of wage negotiation inGermany, where trade unions play an importantpart. This is typical of ‘Rhineland capitalism’,where pay is negotiated through collective agree-ments rather than at branch or business level. Inthis respect, it differs from Anglo-Saxoncapitalism.

Users who are interested in further details couldusefully break down this study on wages andsalaries in the regions at sectoral level using theREGIO database. It is at this sectoral level, in par-ticular, that regional competitiveness can be

assessed. For example, users will be able to findrelative pay levels in the automobile industry inPiemonte in Italy and in Niedersachsen inGermany.

Throughout the regions of the accession coun-tries, average salaries are well below the EU aver-age. The disparity is emphasised by the fact that,in this study, salaries are calculated in euro usinga nominal exchange rate, i.e. not taking intoaccount purchasing power parities. Were theseparities to be taken into account, there would def-initely be a smaller gap in salaries between Mem-ber States and accession countries. Nevertheless,Chapter 3 demonstrates that even once the pur-chasing power parities have been taken into ac-count, the accession country regions remain over-all poorer than those in Member States.

Employment inindustry unevenlydivided among theregionsMap 7.3 shows the density of employment in in-dustry in Europe, i.e. the number of industrialjobs per km2. ‘Industry’ is used here in the broadsense, covering Sections C, D, E and F of NACERev. 1, i.e. mining and quarrying, manufacturingand construction. Many of the regions with highjob density also have a high population density,but we have already seen that some regions maybe poor in terms of industrial jobs, but rich interms of services jobs.

The north of Italy, the west of Germany, Belgiumand the Netherlands are highly industrialised re-gions with job density in most cases higher than20 industrial jobs per km2. Similarly, the easterncoast of Spain, the Madrid region and País Vascoare more highly industrialised than the rest of thecountry.

The regions around capital cities generally haveboth high job density in industry and the higherwages and salaries which go with skilled jobs.This is the case in Paris, in particular, with Île-de-France, Madrid with Comunidad de Madrid andHelsinki with Uusimaa. Head offices and seniormanagement tend to be located in capital cities.


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Employment density and high salaries do not nec-essarily go hand in hand, however. Salaries arefairly low in certain regions of central England,even though these have high industrial employ-ment density. In East Midlands (United Kingdom)or Lisboa e Vale do Tejo in Portugal, the predom-inant industries are labour intensive and, as a

result, average wages and salaries are fairly lowdespite high industrial job density.

Southern Poland, particularly the regions ofSlaskie and Malopolskie that surround the city ofKrakow, has an especially high level of employ-ment in industry. This is also the case for Bucurestiin Romania, the region of Západné Slovensko


Density of employment in manufacturing and constructionNumber of persons employed per km 2

2000 — NUTS 2> 36.016.6–36.09.0–16.64.3–9.0≤ 4.3Data not available

Population covered = NACE Rev. 1, Sections C, D, E, FDK, IRL, FIN: 1999EL: 1998 + 1999; D, L, NL: 1999 + 2000IRL: NUTS 1


Map 7.3

around the Slovak capital of Bratislava and forthe region of Yugozapaden in the south-west ofBulgaria.

Capital-intensiveindustries in theregionsMap 7.4 shows the rate of investment in manu-facturing industry, i.e. physical investment in rela-tion to employment in industry. It illustrates theincrease in capital associated with each person inemployment in industry in the regions. The in-vestments in question are those made during thereference period in all kinds of tangible goods, i.e.all those purchased from third persons or pro-duced for own account (capitalised production oftangible goods) which have a useful life of morethan one year.

Since this investment rate is likely to fluctuatemarkedly from one year to the next, the capital in-tensity of a given region cannot necessarily be in-ferred from the fact that investment may havebeen high in 2000. Investment flows would haveto be looked at over several years to enable capi-tal stock figures to be calculated.

The data shown here are business statistics, whichare not, as has already been suggested, the same asnational accounts. But investment is still one ofthe major components of gross domestic product,along with household consumption and the tradebalance. Thus, those regions which invest themost are often the wealthiest, as can be seen fromthe similarity with the map showing per capitaGDP.

A few results stand out, however. The former EastGermany invests more than the former West Ger-many, which is more highly geared to light indus-

try. Investment is particularly high in the Halleand Dresden regions. It is also fairly high in rela-tion to the European average in the north of Italyand in all the Austrian regions — Kärnten in par-ticular — with the exception of the area aroundthe capital, Vienna. Finally, in contrast to theircounterparts in Ireland, the industries in the southand the centre of the United Kingdom investedsurprisingly little in 2000.

The relatively low per capita investment in the ac-cession countries is further emphasised by the factthat the exchange rates used do not take purchas-ing power parities into account.

ConclusionDomains SBS: theme4/sbs/region and REGIO:theme1/regio/sbs-r offer users who are interestedin regional sectoral data a detailed, harmonisedoverview of economic activity by sector in the re-gions. Those who wish to know more can use thefull database, of which the four maps presentedhere give only a brief view. In particular, they cancompare per capita wage costs from one region ofEurope to another or observe the regions’ relativespecialisation in different sectors of the economy.

To take one example: Which are the main Euro-pean regions specialising in the chemical industry?Users can establish how employment in this fieldis distributed within the different regions of Eu-rope. They can also compare the relative share ofchemical industry jobs in total industrial employ-ment within the different regions. They can lookat investment in the regions in a given year and atinvestment in the past, because it does have a sub-stantial cyclical component. Finally, they can cor-relate employment in the regions with the numberof local units, and this provides a good proxyvalue for the concentration of the sector with theaverage size of the local units in the sector in theregion.


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Investment rate in manufacturingInvestment per person employed – 1 000 EUR/person

2000 — NUTS 2


Population covered = Manufacturing, NACE Rev. 1, Section DDK, D, IRL: 1999


Map 7.4

Methodology of regional businessstatistics

The regional data collected under the SBSregulation are the number of local units,employment, wages and salaries, and materialinvestment.

The statistics are in the main available from ref-erence year 1995. However, 1995–98 was atransition period in implementation of the regu-lation, during which the national statistical in-stitutes adapted to a system complying withCouncil Regulation (EC) No 58/97.

Availability is better from 1999, the first refer-ence year after the transition period. The quali-ty is also better. For example, for the first time,the Belgian data for 1999 cover all enterprises’local units. In previous years, the populationcovered by Belgian regional statistics was limit-ed to local units of enterprises with more than20 employees. Similarly, for the first time, theGerman data cover all local units as from refer-ence year 2000, whereas German regional statis-tics in previous years covered only local units inenterprises with over 20 persons in employment.

Regional statistics also comprise the third of thefour sections of the SBS collection. The first twoare the national and size-class series (the resultsof small and medium-sized enterprises in partic-ular) and the last consists of the other structuralseries (such as statistics on environmental pro-tection expenditure).

Regional business statistics are broken down byregion (NUTS 2 level) and activity (NACE Rev.1, two- or three-digit level, depending on the sec-tor). The population covered is market employ-ment in the non-financial sectors, correspondingto NACE Rev. 1 Sections C to K excluding J,which covers the financial sectors.

The collection unit is the local unit. In mostcases, its principal activity is calculated at locallevel, but in some countries the principal activi-ty which counts is that of the enterprise of whichthe local unit forms a part, given that an indus-trial enterprise may consist of several local units.As the statistical unit is not the same in the twocollections, the results broken down by size class(available in NewCronos in the domain sizclass:theme4/sbs/sizclass) and by region may divergeto some extent, even if the scale is the same. Thisdivergence is no reflection on the quality ofeither collection.

Value added, on the other hand, is not recordedat local level under the SBS regulation, but is cal-culated at enterprise rather than local unit level.Business statistics differ from national accounts(which calculate a regional gross domestic prod-uct) in that they are drawn directly from the dataobserved and have not undergone any economicintegration.


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T R A N S P O R T 8

IntroductionLike EU regional policy, EU transport policy de-pends on having reliable, up-to-date transport sta-tistics available in the European Union. Growth inthe transport sector is closely linked to growth inthe economy as a whole.

Since the 1970s, there has been a steady increasein both passenger and goods transport. An effi-cient infrastructure is needed to cope with the in-crease in mobility and the rise in passenger andgoods flows. It is not merely the liberalisation ofthe single market which has led to an increase inthe volume of traffic: changes in the structure andlocation of manufacturing industry, changes inproduction processes leading to a need for just-in-time deliveries, increased leisure time and higherdisposable incomes also play a part in thesedevelopments.

Although relatively dense in the EU as a whole,the transport network has not been developed inall regions to the same extent: infrastructure ca-pacity reflects differences in supply and demand,as well as in population density and the degree ofurbanisation and industrialisation.

Eurostat’s regional transport statistics hope tomake a contribution in this field by presentingquantitative information on a range of infrastruc-ture aspects along with specific flows of goodsand passengers.

Methodological notesRegional transport statistics and the metadatawhich go with them can be found on two sites inthe NewCronos reference database. Theme 7(Transport) contains NUTS 2 indicators in the‘Tranlink’ domain showing the infrastructure ofroad, rail and inland waterway networks, vehiclenumbers, journeys by lorry, road safety and thetransport of passengers and goods by sea and byair. The same data are available under Theme 1(General statistics) in the REGIO database.

The NewCronos database has a total of 19 tableson regional transport statistics.

In seven tables, the same variables are at presentdivided into Member States and accession coun-tries. With EU enlargement next year, the tablesfor the accession countries will be merged withthose of the Member States.

Journeys by lorry at present cover only the regionsof the current Member States.

There are four tables each in NewCronos forfreight transported by sea and by air and for pas-sengers, but the methodologies are different. Sincereference year 1999, these data have been ob-tained from surveys of ports and airports carriedout by Eurostat under existing legislation.

All tables contain annual data and, apart from thetables on regional sea and air transport (with newmethodology) and the table on safety, they startwith reference year 1978. National traffic flowsfrom one region of a country to another are nolonger included in REGIO but can be found insimplified form in Theme 7 (Transport) in the do-mains ‘Road’, ‘Rail’ and ‘Inlandww’. Here, the‘Aviation’ and ‘Maritime’ domains also offer fur-ther data on traffic flows between airports.

The maps and graphs below give an overview ofregional transport statistics, which can then becompared with other regional data in New-Cronos, so that readers can investigate the inter-actions which may help explain the differencesnoted between the regions.

TransportinfrastructureOverall, the EU has a dense transport network,which is being expanded as a result of increasingdemand for both passenger and goods transportservices.

Information on road, rail and inland waterwaynetworks can be found in the NewCronos data-base at NUTS 2 level. In all tables, the unit is kilo-metres of route length.

Roads are divided into motorways and otherroads. Railway links are classified according totwo criteria: two or more tracks, and whether ornot they are electrified. Data on inland waterways(navigable canals and navigable rivers and lakes)are patchy because many Member States have nosignificant network. The data sent by the MemberStates make no distinction, either, between high-capacity broad canals and lower-capacity narrowones.

The next section gives an overview of the Euro-pean road network, looking at motorway densityin particular.


Road networkAn extensive network of major roads and motor-ways generally gives regions a competitive anddevelopmental advantage.

Map 8.1 shows the density of the motorway net-work in the NUTS 2 regions in 2001, expressed askilometres of motorway per 100 km2.

There are definitional reasons why certain areasin the north of the United Kingdom are white onthe map: the dual-carriageway roads there do notqualify as motorways. A further point to note isthat area-based indicators in small regions, in par-ticular, may lead to relatively high values.


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Density of motorwaysin kilometres of motorway per 100 km 2

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Map 8.1

• Very high motorway density in many cases in-dicates a high level of urbanisation, as can beseen in the central regions of the Netherlands.

• Regions which include major conurbationsmay well have high motorway densities, too.These are frequently regions with a great dealof commuter activity, such as the Vlaams Bra-bant region around Brussels.

• Some regions which include important indus-trial areas also have a very dense network ofmotorways. Examples would be Greater Man-chester (including Manchester), Merseyside (in-cluding Liverpool) and West Midlands in theUnited Kingdom.

• Conurbations just as often have high motorwaydensities. There are numerous examples of this:Vienna in Austria, Hamburg, Bremen and Düs-seldorf in Germany, Bratislava in Slovakia, theSpanish region Comunidad de Madrid, Lisboae Vale do Tejo (including Lisbon) in Portugal,and Île-de-France in France.

• Similarly, in some countries, regions which in-clude important ports have extensive motor-way networks for onward transport of goodsunloaded. Examples would be Nord-Pas-de-Calais in France, some regions in VlaamsGewest in Belgium and Liguria in Italy.

• Motorway densities are also noticeably high inthe German Saarland region and in the Spanishregion País Vasco.

• Sweeping around the Mediterranean coastfrom Comunidad Valenciana in Spain throughthe highly developed region of Cataluña andProvence-Alpes-Côte d’Azur to Sicilia, an arc ofregions with relatively high motorway densitiesreflects the importance of a modern transportinfrastructure in tourist areas.

• Peripheral regions in Greece, Sweden, Poland,Romania and the United Kingdom, have lowmotorway densities, as do island regions suchas Corse (France), Sardegna (Italy) and Kriti(Greece).

• With the exception of Estonia and the Slova-kian region of Bratislavsky, many regions in theaccession countries have a less dense motorwaynetwork, and are comparable with those re-gions in the Member States which have lowerlevels of urbanisation (most regions in Spain,France, Ireland and Portugal).

Railway networkThe indicator showing the number of inhabitantsper kilometre of railway is a measure of the ac-cessibility of the railway network. It takes accountof population density and is thus more informa-tive than the length of the network per unit ofarea.

Graph 8.1 shows this indicator for the NUTS 2 re-gions. For each Member State (i.e. all those whichcan be broken down into NUTS 2 regions), the re-gions with the highest and lowest values havebeen graphed, along with the national average(the broken orange vertical line). This indicator isextremely stable over time since — in contrast toroad building — it is comparatively rare for sec-tions of railway to be closed or new sectionsopened.

• It is striking that the range of the indicator forthe regions varies enormously from one coun-try to another. Whereas in Greece the numberof inhabitants per kilometre of railway networkand thus accessibility is on a scale from 1 485to 28 709, the difference is smallest in Bulgaria,ranging from 1 473 (Severen tsentralen) to 2 357 (Yugozapaden). The relatively thinlypopulated areas in the north of Greece, Anato-liki Makedonia, Thraki, contrast with thedensely populated region of Attiki which in-cludes Athens.

• Owing to their relatively high population den-sities, capital cities tend to have a very high den-sity value. Interestingly enough, in a few casesthe regions with the lowest national values arethose immediately surrounding these conurba-tions (Berlin and Brandenburg, Vienna andNiederösterreich, Comunidad de Madrid andCastilla-La Mancha).

• Since there are no NUTS 2 regions forDenmark or Luxembourg, only the nationalaverages have been given.

Transport equipmentThis may be defined as all vehicles carrying goodsand/or passengers, and hence covers buses, lor-ries, trains, inland waterway vessels, aircraft,semi-trailers, railway wagons, bicycles and two-wheeled motor vehicles, as well as passenger cars.


NewCronos has regional data on vehicles atNUTS 2 level divided by category of vehicle: pas-senger cars, buses, goods vehicles, road tractors,special-purpose road vehicles, trailers, semi-trailers and motorcycles.

This section, however, covers only one of theseindicators, namely passenger cars.

Map 8.2 shows the passenger car fleet measuredin terms of the number of private cars per 10 in-habitants. This mobility indicator, which alsocontinues to show a rising tendency, ties in close-ly in many cases — but not in all, as the mapshows — with the level of economic developmentin a region (measured by GDP per capita — seealso Chapter 3). There are many German regionswhich could be quoted as examples, with highvalues for both GDP and numbers of cars, where-as most Greek regions have low values for bothindicators.

However, Map 8.2 shows that there are also a fewregions which do not follow this trend.

• In general, larger city core regions have an ex-tensive local public transport network, and thenumber of cars in these regions may thus be onthe low side. Insufficient parking places or veryhigh parking costs in city centres also lead to adrop in the number of cars per head of the citypopulation. The age and social structure of theurban population may also have an effect. Atthe same time, car density is in many casesrelatively high in regions around large cities, re-flecting the amount of commuter traffic. Exam-ples would be Berlin with the surrounding re-gion of Brandenburg and Bremen with itssurrounding regions in Germany, London withthe South-East and Eastern regions and Viennawith Niederösterreich in Austria.


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80 10 000 15 000 20 000 25 000 30 0005 000

B

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BG

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LV

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SK

NL

L

A

S

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CZ

EE

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PL

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P

WienNiederösterreich

Rég. Bruxelles-Cap./Brussels Hfdst. Gew.Luxembourg (B)

Comunidad de MadridCastilla-La Mancha

UusimaaItä-Suomi

Île-de-FranceLimousin

Attiki

Anatoliki Makedonia,Thraki

CampaniaAbruzzo

NorteAlentejo

StockholmÖvre Norrland

PrahaJihozápad

Közép-MagyarországDél-Dunántúl

MazowieckieWarminsko-Mazurskie

BratislavskyStredné Slovensko

YugozapadenSeveren tsentralen

Bucure‚stiVest

NB: F: 2000; A: 1997; population: 2000.

Graph 8.1 — Regional variation in per capita access to railways, NUTS level 2, 2001(inhabitants per km of railway)

• In the larger NUTS 2 regions which have a corecity and an extensive hinterland, car densitytends to be distributed more or less evenly. Thisis the case in Comunidad de Madrid and Île-de-France, where these factors more or lessbalance each other out.

• High car density may also indicate relativeprosperity, particularly in regions with compar-

atively high average incomes such as the GrandDuchy of Luxembourg, numerous German re-gions which include major financial centres inthe Länder of Bayern, Baden-Württemberg,Rheinland-Pfalz, and Niedersachsen and Prahain the Czech Republic.

• The recent expansion of the automobile indus-try may in some European regions also be a


Number of private carsper 10 inhabitants2001 — NUTS 2

> 5.24.5–5.23.2–4.5≤ 3.2Data not available

P, UKI, UKL, UKM: NUTS 1B, P: 1999; EL: 1998Population: 2000 (B, EL: Eurostat estimates)


Map 8.2

motor for higher car density. This is the case inthe eastern German regions, in Chemnitz inparticular.

• Regions whose economies are very much de-pendent on tourism also seem to have high cardensities. The Spanish, French and ItalianMediterranean regions in particular (includingthe island regions), in some of which there arelarge numbers of residents who are retired for-eign nationals, have a relatively large car fleet.

• In a few thinly populated regions, a car may beessential if the population is to be at all mobile.Such areas include the central Spanish regionsaround the Comunidad de Madrid, the Frenchregions of Champagne-Ardenne, Auvergne,Limousin and Midi-Pyrénées, and thoseFinnish and Swedish regions which are outsidethe capital cities.

• Car density in the central European regionsfrom Estonia to Greece have comparable val-ues, between 3.2 and 4.5 passenger cars per 10inhabitants.

Sea transportSea transport statistics by region exist in New-Cronos at the NUTS 2 level for both passengersand freight. They provide information on goodsand passengers transported in the various regions.In NewCronos, two time series go with these in-dicators. One goes back to 1978 and ends withreference year 1998. Since 1999, a new method-ology has been used in the Member States to ob-tain these regional statistics, which are also shownin separate tables in the database. The two timeseries are no longer directly comparable owing tothe differences in methodology. In the case of theaccession countries, these data are still compiledon the basis of questionnaires.

The present regional data on passengers andfreight transported come directly from surveys ofsea ports conducted under current legislation(Council Directive 95/64/EC). The methodologyfor intraregional traffic is thus the same at bothnational and NUTS 2 levels (eliminating thedouble counting which had existed hitherto).

The data collected under the above Council direc-tive are obtained only for ports handling passen-gers or freight in excess of a certain threshold.Traffic at small ports is not taken into account,and so Eurostat’s aggregate regional values are

not necessarily identical to national totals. How-ever, the regional distribution of the volume oftraffic can be represented fairly accurately.

Finally, in the data now collected on regional pas-senger and freight volumes transported by sea,ports are allocated to the NUTS regions on the ba-sis of geography alone, and not with reference toeconomic interrelationships (as was the case in thepast).

The passenger data are divided into passengersboarding and passengers disembarking andfreight data in Map 8.3 are divided into tonnes offreight loaded and unloaded. The information inthe map refers only to coastal regions with freightports.

• One striking feature is the marked concentra-tion of substantial volumes of goods unloadedin the region of Zuid-Holland, which includesthe port of Rotterdam. This region has overthree times the volume of the next highest EUregion. The Antwerpen region, with the port ofAntwerp, is a further example of this regionalcentralisation. The effect of these enormousvolumes of freight and the need for onwardtransport has a noticeable impact on goodstraffic through much of the European Union.

• In most European coastal regions, many moregoods are unloaded than are loaded, a clearindication of the EU economy’s importdependency.

• There are some regions, however, where moregoods are loaded than unloaded, including thenorthern regions of Highlands and Islands,Eastern Scotland, Tees Valley and Durham(United Kingdom) and Estonia, Latvia andLithuania and the Polish coastal regions. Thefigures in this case show a close correlationwith the highly developed industry in those re-gions and the presence of natural resources,which, in many cases, are transported by sea sothat the products can be marketed. A similarargument applies to the Baltic regions and tothe region of Zuid-Holland, namely that theseare important for onward transport (with verygood connections to other modes of transport).

• There are only a few regions where the volumesof freight loaded and unloaded are roughlyequal, notable examples being Denmark andthe Swedish regions of Sydsverige andVästsverige.


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Maritime cargo loadedand unloaded

2001 — NUTS 2

Goods Freight (1 000 t)

LoadedUnloadedData not availableTotal traffic < 1 000 000 t

EL: 2000


Map 8.3

230 000

100 00050 000

10 000

• Some ports are known for environmentally sus-tainable ‘short sea shipping’ (including betweenrivers and seas). Considerable volumes aretransported in this way, influencing the spatialdistribution pattern illustrated.

• The ratio of goods unloaded to goods loaded isparticularly unbalanced in island regions, i.e.more goods are unloaded than loaded (e.g. inSardegna in Italy, Islas Baleares in Spain andKriti in Greece). This could well tie in with themain branch of the economy in these regions,namely tourism, and indicate the possiblestocking of supplies and equipment. In con-trast, these regions seem to produce no freightfor shipping.

• Övre Norrland in northern Sweden has similar-ly high figures for the loading of freight, eventhough it is very thinly populated. This is prob-ably due to the production of large volumes ofraw materials.

Air transportNewCronos contains regional statistics at NUTS2 level on the transport by air of passengers andfreight. Two series are also available here, basedon different methodologies. The series going backto 1978 ended with reference year 1998 and wasreplaced by a new time series with differentdefinitions as from 1999.

In EU Member States, the present methodologyobtains regional statistics on passengers andfreight directly from data collections relating toairports for which there is a legal basis (Regula-tion (EC) No 437/2003 of the European Parlia-ment and of the Council). This type of data col-lection ensures that the national and regional datapublished by Eurostat tally, which was not alwaysthe case hitherto. Traffic within a single NUTS re-gion, i.e. between airports belonging to the sameNUTS region, is ascertained correctly with thecurrent methodology, which was not the case inthe time series from 1978 to 1998, owing todouble counting, which frequently overestimatedvalues.

As with transport by sea, data on air transport arecollected only for airports which exceed specificthreshold values for passengers and freight. Thismay, of course, lead to differences if nationaltransport figures are aggregated from regionaldata and compared with total traffic at national

level. Traffic at small airports is not taken into ac-count. However, the regional distribution of thevolume of traffic can be represented fairlyaccurately

The data on airports also are now allocated toNUTS regions on the basis of geography, ignoringthe economic interrelationships which were takeninto account in the past. In the case of the acces-sion countries, these data are still compiled on thebasis of questionnaires.

The data on passengers are divided into passen-gers boarding and passengers disembarking.

The data included hitherto in NewCronos ontransit passengers cannot be estimated at presentowing to a lack of available data using the newmethodology.

Map 8.4 illustrates and analyses air passengertransport.

Although data on regional air transport are pre-pared at NUTS 2 level, the catchment area for amajor airport (i.e. the area from which it draws itscustomers) will in most cases be much larger thanthis regional level. For Map 8.4, therefore, NUTS1 regions have been chosen. The area of the circlerepresents the total number of passengers usingthe airports in the NUTS 1 region concerned.

London’s five international airports are dividedup over three NUTS 1 regions (Eastern, Londonand the South-East).

For Denmark, Ireland, Luxembourg, and Sweden,the NUTS 1 level is the national level.

There is only one value available covering thewhole of the French overseas departments (dé-partements d’outre mer), and the total passengerfigures are thus not shown here by region.

• Bassin parisien is a good example of how air-ports can attract customers. Although thiscatchment area is much larger than the Île-de-France region, which it entirely surrounds, itsown air transport needs are almost entirely metby Paris airports within Île-de-France.

• The region which includes the capital city can-not always be assumed to be a country’s busiestair transport region. In Spain, the tourist regionof Este has higher absolute figures than Comu-nidad de Madrid. The picture is similar in Ger-many, where Hessen, whose economy is highlydeveloped and which includes the financial cen-tre of Frankfurt, which is responsible for exten-sive business traffic, has an enormous volume


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Air passenger trafficTotal and related to population

2001 — NUTS 1

Passengers per inhabitant Passengers (1 000)

> 6.5

3.1–6.5

1.5–3.1

≤ 1.5

Data not available

Population: 2000EL, NL1: 2000


Map 8.4

60 00030 00010 000

2 500

of traffic (both in terms of absolute numbersand per capita), as well as acting as a hub forlong-distance flights. Business traffic is withoutdoubt the reason for the high volumes of trafficin the Italian region of Lombardia, in whichMilan is situated, and in the Netherlands re-gions of Utrecht, Noord-Holland and Zuid-Holland around Amsterdam.

• Increased passenger traffic in island regionscorrelates closely with the most importantbranch of the economy in these regions, name-ly tourism. Examples are Nisia Aigaiou, Kriti inGreece and Canarias in Spain, where the figuresfor air passengers per inhabitant are particu-larly high.

SafetyIn the NewCronos database, safety is covered atregional level in terms of numbers of personskilled and injured in road accidents. The data arecollected at NUTS 2 level, with series going backto 1988.

Map 8.5 classifies the regions according to the ra-tio of persons killed and injured in road accidentsto the total population in the region concerned.Relating the figures to the population smoothsout the differences arising from the fact that someareas have a larger population than others.

The deeper the colours on the map, the more per-sons killed in accidents. At the same time, thenumber of persons injured in accidents increasesas the colours of the regions change from green(via blue) to red. The dark red regions are thusthose where accidents have the most serious con-sequences, as regards both deaths and injuries.

The standard definition of a road accident deathcovers all deaths within 30 days of the accident.However, this definition is not applied in allMember States, and so some countries which ap-ply a shorter time span have comparatively lowindicators. Corrective coefficients for use in thesecases are available in the REGIO reference guide,but the data used here have not been adjustedwith these coefficients.

• The number of persons killed and injured in ac-cidents varies considerably from one region ofEurope to another, from 21 deaths in accidentsand 3 007 persons injured per million inhabi-tants in Ceuta y Melilla in Spain to 369 deaths

and 7 253 persons injured per 1 million inhab-itants in Alentejo in Portugal.

• The ratio of persons killed to persons injured intraffic accidents also varies considerably. Roadaccidents have the most serious consequencesin the Portuguese regions of Centro, Alentejoand Algarve and in the Belgian region of Lux-embourg and the French region of Corse. Bothnumbers killed and numbers injured are highestin these regions.

• There are very high figures for traffic deaths(over 220) but comparatively few persons in-jured (under 2 400) in the Greek regions ofAnatoliki Makedonia, Thraki and Sterea Ella-da, indicating that the chances of survival aftera road accident are comparatively low.

• The central European regions from Lithuaniato the Greek region of Kriti have a similar pat-tern to the above in that road traffic accidentsare generally fatal and comparatively few peo-ple survive their injuries, although the numberof persons killed is generally on the low side(90–220 per million inhabitants). Along thisaxis, it is only in Latvia and the Greek region ofPeloponnissos that fatalities and injuries areboth high.

• There are over 220 deaths and between 2 400and 5 400 persons injured in the Spanish re-gions of Castilla y León, Castilla-la Manchaand La Rioja.

• There are noticeably few people killed and in-jured in accidents in many of the regions of theNetherlands, in the Swedish regions of Stock-holm, Norra Mellansverige and Övre Nor-rland, in the three northern Finnish regions andUusimaa, in the Italian region of Campania andin the French region of Nord-Pas-de-Calais, aswell as in the Romanian region of Bucuresti.

• In contrast, many more persons survive theirinjuries in many west German regions fromMünster to Freiburg and in central regions inthe United Kingdom from Lancashire to Surreyand East and West Sussex, as well as in the Ital-ian region of Liguria.

• Regions surrounding major conurbations suchas Comunidad de Madrid in Spain, Berlin inGermany, Brussels in Belgium, Wien in Austria,Praha in the Czech Republic and Bucuresti inRomania, generally have fewer deaths and in-juries than areas around them, possibly becausepeople living in the cities make greater use of


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public transport, average speeds are lower andthere are more motorways.

• In island regions where tourism is important,such as those in France and Greece, the impactof the seasonal influx of tourists on the highvalues for these indicators should not be under-estimated, but owing to the reference to thepopulation figures this effect has not beentaken into account here.

It would not be logical to reduce the causes ofdeaths and injuries in road traffic accidents toonly a few factors, since the reasons are generallyto be found in a number of different factors. Thefollowing could play a part (but the list is not ex-haustive): driver training, observance of speedlimits and restrictions on drivers’ alcohol con-sumption, increasing car ownership, vehicle qual-ity, distance travelled, antiquated road networks,


Deaths and persons injuredin road traffic accidents

by million inhabitants2001 — NUTS 2

220

Persons 150

killed 90

0 2 400 5 400 Data not Persons injured available

D, I, P, UK: 2000B: 1999EL: ProvisionalNL: 1998 (injured)Population: 2000 (B, EL: Eurostat estimates)


Map 8.5

national safety standards, the presence of well-equipped and well-trained rescue services, etc.

ConclusionIn many respects, regional transport statisticsshow trends which could also be ascertained fromeconomic indicators, and this illustrates the closelink between these two fields, a link which is par-ticularly clear if regional infrastructure, car own-ership and numbers of passengers and goodstransported are correlated with regional gross do-mestic product (GDP). In many cases, this com-parison shows an increase in traffic coupled withgrowth in the economy. It is also clear that thistrend does not arise from individual traffic flowsbut from networks which are linked to one an-other, in which integration is essential.

The patterns of distribution shown here alsoindicate that there are direct links between thetransport variables covered, and that economicdevelopment in the regions is essentially under-pinned by an adequate transport infrastructure.Conversely, a lack of appropriate infrastructuremay be a limiting factor in regional development.It has also become clear that, owing to their dis-proportionately high volumes of traffic, certainregions are much more seriously affected by envi-ronmental problems than fringe regions whichhave less traffic.

For the accession countries, the pattern of distrib-ution is similar to that of the Member States, ex-cept that the volume of traffic is not concentratedto the same extent on regions with highly devel-oped economies.


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H E A L T H 9


IntroductionThe regional health indicators for the EuropeanUnion, developed by Eurostat to help set objec-tives in the field of health, comply with standard-ised definitions and methods which aim to makecomparisons possible. If they are to yield high-quality, comparable information on the generalhealth of the population, the data will have to becomparable from one region to another and re-flect changes over time. The main non-medicalfactors governing the health of the population atregional level will also have to be taken intoaccount.

Regional-level health statistics cover two separateaspects. On the one hand, there are data onmortality and morbidity, where the illnesses ordiseases in question are defined according to aninternational classification and comparable meth-ods of diagnosis. The first two sections of thischapter deal with these statistics. Eurostat alsocollects health sector data on infrastructure, in thebroad meaning of the term, and on staffing in thehealth sector. The third section of this chapteranalyses these figures.

Comments onmethodologyThe provision of medical and hospital services atnational and regional levels is closely linked to to-tal expenditure on healthcare. Between 1980 and2000, the share of GDP spent on healthcareincreased in most Member States, but sincehealthcare is organised and defined differently atnational or regional level, it is difficult to interpretcomparisons between countries, whether they re-late to figures on given dates or to tendencies (forexample, where should the dividing line be drawnbetween health services and social services?). TheEU’s healthcare systems depend more and moreon gate-keeping and referral systems to ensurethat they function properly and that there is con-tinuity of care. Structures for public health differmarkedly across countries and public health ac-tivities as a whole are highly fragmented, withvarious authorities involved. Most secondary careis provided by general hospitals. Daycare hospi-tals and day surgery are gradually emerging as al-ternatives to inpatient care in countries such asBelgium, Denmark, France, Ireland, Italy, the

Netherlands and the United Kingdom. Daysurgery is growing in importance in Germany,Luxembourg and Portugal, but remains uncom-mon in Greece and Spain. There is also an in-creasing tendency to locate specialised mentalhealthcare within general hospitals, to coordinateprovision with community care and to close downlarge psychiatric institutions. With regional gov-ernments becoming more important, the regionsare also increasingly important for the politicaland administrative management of health issues.

(a) Socio-health regions

Socio-health regions are defined in very differentways from one regional, provincial or local gov-ernment to another or from one Member State toanother. With regional governments becomingmore important, the regions are also increasinglyimportant in Europe as units for the political andadministrative management of health issues. InSpain, for example, regional governments haveacquired a great deal of autonomy, one practicaleffect of which is that they manage the whole ofthe health budget. The situation is very similar inBelgium. Since 1996, France’s healthcare reform,introduced to put healthcare planning on a re-gional footing, has allowed hospitals to be re-sponsible for allocating the budget. Healthcaremanagement is also being drastically reorganisedin the United Kingdom, with NHS trusts havingvarying levels of responsibility. In other MemberStates, such as the Netherlands and Sweden, themunicipalities are responsible for healthcare.

Hence, the difficulty with statistics on health andon medical/health/hospital services at regionallevel stems from the fact that regional, provincialor local government statistics or the regionalbreakdown which is of interest to health authori-ties in the Member States do not coincide with theNUTS and problems may arise with cross-referencing to compare regional statistics.

(b) Mortality indicators

Eurostat collects data on the absolute number ofdeaths (at national level and at NUTS 1 andNUTS 2 regional levels). Coding is based on theprimary cause of death (Section B) on the deathcertificate. The causes of death are defined on thebasis of the World Health Organisation’s (WHO)international classification of diseases (ICD), withall the Member States using the 9th or 10th revi-sion. The standardised death rate (SDR) is aweighted mean of age-specific death rates. The


HE

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TH

9weighting factor is the age distribution of the pop-ulation whose mortality is being observed. Com-paring the SDRs of two or more populations (atthe NUTS 2 level in the present publication)means comparing a combination of different age-specific death rates and different populationstructures which do not reflect the ‘real’ mortalitydifferences, but also include the effect of the pop-ulation structure on the total number of deathsand on the crude death rates. ‘Premature’ mortal-ity (before the age of 65) is in many cases linkedto a cause of death whose frequency could be re-duced by a change in behaviour (alcoholism,smoking, violent deaths) and that behaviour is inturn linked to social, economic and cultural riskfactors. Typologies by cause of death and by agedefine ‘mortality profiles’ showing excess mortal-ity or lower death rates than expected. These havebeen drawn up using ascending hierarchical clas-sification methods, a ‘tree’ for which is shown oneach map.

(c) Tuberculosis indicators

At national level, it is the WHO programme ofcollaborating centres for the surveillance of tuber-culosis in Europe (EuroTB) which aims to moni-tor tuberculosis and standardise methods of sur-veillance. It is largely financed by the EuropeanUnion (Directorate-General for Health and Con-sumer Protection) and is managed jointly by theInstitut de veille sanitaire (InVS) in France and theRoyal Netherlands Tuberculosis Association(KNCV) in the Netherlands. The 51 countries inthe WHO Europe region are taking part in theproject. At regional level, it is Eurostat which col-lects surveillance data based on the EuroTB pro-tocols. The criterion for notifying a case of the dis-ease is the presence in a culture of tuberculousbacilli (Mycobacterium tuberculosis bovis orafricanum) . Under Commission Decision2002/253/EC of 19 March 2002 laying down casedefinitions for reporting communicable diseasesto the Community network, the Member Stateshave to send in information on the epidemiologi-cal development and emergence of public healththreats due to communicable diseases. Tubercu-losis is one of the diseases for which notificationis mandatory in the EU.

(d) Resource indicators

For the indicators of available health resourcesused in this publication, Eurostat collects region-al-level statistics on healthcare workers (numbers

of doctors and of other professions) and numbersof hospital beds.

At national level, it collects data on numbers ofdoctors divided according to existing definitions(doctors qualified to practise, who may be work-ing, retired, unemployed or abroad, practisingdoctors, who are those consulted by patients in ahospital, in the doctor’s surgery or elsewhere, oractive doctors, i.e. those employed in the healthsector. At regional level, information is not alwaysavailable in terms of these three concepts, and, inthis case, the Member States establish the numberof doctors in each region on the basis of differentconcepts and registers. In most Member Statesand candidate countries, the number of doctorsrefers to the number of practising doctors. In Bel-gium, Italy, the Netherlands and Finland, it refersto doctors qualified to practise and in Spain toactive doctors. Ireland and the United Kingdominclude the public sector only.

The data on numbers of beds reported to Eurostatare normally presented in the form of annual av-erage numbers of beds used during the referenceyear, or according to recording concepts or bud-getary or planned approval. Not all the figures arereadily comparable and they should be inter-preted with care, since the definitions of ‘hospital’and ‘hospital bed’ vary from one Member State toanother. In general, however, differences in num-bers of beds are affected by accounting practices(annual average, years ending 31 March or 31 De-cember, ‘official’, ‘budgetary’ or ‘planned’ beds).Only beds used for full inpatient accommodationare counted. The ‘total inpatient care beds’ coversall beds in general hospitals (with the exception ofcots for infants in good health) and in specialisedhospitals, psychiatric hospitals and other estab-lishments treating those with mental disorders,nursing homes, etc. Hospital beds available fornursing care during the day, in medical centres forchildren, in crèches under medical supervisionand in establishments for those with sensory defi-ciencies are not necessarily included.

Mortality in the EUregionsBy adjusting for the effects of population struc-ture, standardised rates can be used to highlightgeographical inequalities in the risk of death.After standardisation by age, the rates vary in a


ratio of 1:2. The geography which emerges isvery different from that highlighted by cruderates. There is generally excess male mortality,with rates up to twice as high as those forwomen. Despite these differences, most of theworst-affected regions have high rates for bothmen and women.

Disparities in mortality linked to anaccumulation of factors

Many of the regions most affected are thosewhose economies are lagging. In Germany,France and the United Kingdom, the regionswhich used to have heavy industry and that arenow changing to other types of employment,such as Saarland, Nord-Pas-de-Calais, Lan-cashire and Yorkshire, have high mortality ratesfor both men and women. The same applies toeastern Germany, Andalucía (Spain) and Campa-nia (Italy), which, within the countries con-cerned, are relatively poor regions badly hit byunemployment at present. However, this socio-economic factor is not sufficient in itself to explainmortality levels. The correlation between socio-economic level and level of mortality is notalways a close one. Denmark, one of the richestMember States, ranks on a par with Portugal andIreland where mortality is concerned and, con-versely, Greece has below-average mortalityoverall, at the same level as Sweden. The moun-tainous European regions (Alps, Pyrenees andPeloponnese) are favourably situated, unlike theformer industrialised regions. As well as socio-economic and environmental factors, which fre-quently interact, one key feature determining dif-ferences in mortality is health practices.Differences in mortality figures from one regionto another may also reveal inequalities in effi-ciency or access to healthcare in the EuropeanUnion.

Widespread excess male mortality, butvarying from one Member State to another

Even after adjusting for age structures, differencesin mortality between men and women are on theincrease. The high proportion of women in theolder age groups in the European population ex-plains the similarity between male and femalecrude rates. But for any given age, the risk ofdeath is much higher for men. Although there isgenerally excess male mortality in the EU as awhole, the gap is larger in some Member Statesthan others, being widest in France, Finland andSpain, while Sweden, the United Kingdom, Den-

mark and Greece have relatively low excess malemortality ratios.

Split geography for ischaemic heart disease

The contrast between the north and the south ofthe European Union, in particular for mortalitylinked to diseases of the circulatory system viewedas a whole, is, in fact, largely determined by thegeography of ischaemic heart disease, which issimilar for both sexes (see Maps 9.1 and 9.2) andis highly specific. There are two opposing groupsof countries, one which has clear excess mortality,made up of the British Isles, the Scandinaviancountries, the Netherlands and the Germaniccountries, and the other with lower-than-expectedmortality, comprising Luxembourg, Belgium andthe Mediterranean countries including France.There are noticeable contrasts between these twogroups, with rates up to five times higher for menand seven times higher for women in one groupthan in the other. In the south, France, northernSpain and Portugal have the most favourable Eu-ropean rates. In the north, the northernmost re-gions along with the eastern Länder and Saarlandin Germany, and Wien are particularly badlyaffected.

Before these disparities can be interpreted in thelight of risk factors or characteristics of healthcaresystems, the comparability of certification prac-tices has to be examined. For example, some sud-den deaths for which heart disease is responsiblemay be recorded — depending on certificationpractice — as ill-defined causes of death or in-farction. A recent study comparing France and theUnited Kingdom showed, however, that, evenwhen data were adjusted according to firm hy-potheses, death rates were still much lower inFrance. Apart from these potential methodologi-cal biases, the differences between Member Statesin deaths from ischaemic heart disease may be ex-plained by eating habits, such as a rich or unbal-anced diet with too much fat in the northernMember States. Finally, with ischaemic disease,and infarction in particular, death is sudden, inmany cases occurring before the patient reacheshospital. The question of the density of healthcareservices, their quality and how quickly patients re-ceive care, both at the time of the attack (emer-gency services) and upstream (hospital cardiologydepartments) should also be taken into account asexplanatory factors, but specific studies areneeded.


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A marked correlation for men betweenindustrial and urban areas and the level ofmortality from respiratory tract cancers

Mortality rates in the male population vary in aratio of 1:4 according to region. There are notice-able contrasts within national borders (see Map9.3). Deaths from cancers of the respiratory tractare frequent before the age of 65 (survival timesfor this type of cancer are short). A map showing

the breakdown of premature mortality rateswould be very different from the map of mortali-ty at all ages. The northernmost Member Stateswith their relatively low figures contrast with therest of the European Union. Despite the markeddisparities within most Member States, there arenational tendencies. In France, for example, therates are high in all regions. Geographical over-laps across national borders should also be

Standardised death rateof ischaemic heart diseases

Males, all ages1997–99 — NUTS 2

Three-year average/per 100 000 inhabitants> 250200–250150–200100–150≤ 100Data not available

B, DK, EL: average 1994–96D, UKM: NUTS 1


Map 9.1


considered. The majority of coastal regions andMediterranean islands have excess mortality,from Andalucía to Campania, as do the Atlanticregions from Galicia to Bretagne. With the excep-tion of Austria and Portugal, all countries in thesouth have high rates, whereas in the northern EUcountries the rates are lower than might be ex-pected. Sweden and Finland appear to be veryuniform, whereas within the British Isles there are

some notable disparities although the mortalitylevel is on the whole low.

The link between smoking and death from respi-ratory cancer is now well established. Regionswith excess mortality are those where tobaccoconsumption is or has been higher than elsewhere.However, we do not have sufficiently reliable dataon the history of tobacco consumption in Euro-pean regions to allow an accurate measure of this

Standardised death rateof ischaemic heart diseases

Females, all ages1997–99 — NUTS 2




Map 9.2


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correlation. In industrial areas, the high deathrates from respiratory cancer probably indicatemortality in a male population with both high to-bacco consumption and more frequent exposureto a polluting environment at work.

Breast cancers: sharp geographicaldistinction

Breast cancers are the commonest of cancers af-fecting women, responsible for more than 4 % ofdeaths in the female population of Europe. Theyfrequently affect young women: over half ofdeaths occur before the age of 65. This pathologyis the main cause of female mortality between the

Standardised death rateof malignant neoplasm of larynx

and trachea/bronchus/lungMales, age = less than 65

1997–99 — NUTS 2

Three-year average/per 100 000 inhabitants> 4030–4020–30≤ 20Data not available



Map 9.3


ages of 45 and 64 (over 12 % of deaths). Al-though the geography of female mortality frombreast cancer shows clear gradations, differencesin mortality within Europe are noticeably lessmarked than with other cancers, in particularthose of the respiratory tract or upper aerodiges-tive tract. Death rates are low, in a ratio of 1:2.6,compared with those for these other cancers.

The regional map of breast cancer (see Map 9.4),which is similar for all ages taken together and forpersons below the age of 65, shows that the geo-graphical breakdown is not random and thatthere is a certain amount of continuity. One vastarea where there is excess mortality is made up ofDenmark, which has the highest rates in Europe,Belgium, the west of Germany, the north of

Standardised death rateof malignant neoplasm of breast





Map 9.4


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9France, the north of Italy, Luxembourg, theNetherlands, Austria and the British Isles. In therest of the EU, rates are much lower, especially inGreece, Spain, Finland and Sweden. In Portugal,there is a marked contrast between the north andthe south, with the northern regions having lowerrates. The Mediterranean islands — Corse,Sardegna, Sicilia and Islas Baleares but excludingthe Greek islands — show similar and relatively

high rates, higher than in the countries to whichthey belong. With the exception of Germany,France, Italy and Portugal, where there are no-ticeable regional contrasts, the breakdown of thelevel of mortality from breast cancer is generallyin line with national trends. There are severalrecognised risk factors for breast cancer and thegeographical breakdown reflects the unevenspread of these factors. Hormonal factors and

Standardised death rateof suicide and intentional self-harm

Males, all ages1997–99 — NUTS 2




Map 9.5


excessive consumption of fats are frequently men-tioned as being likely to increase the risk of breastcancer, whereas the consumption of fresh foodand green vegetables is thought likely to lower therisk. Genetic factors are also quoted as a riskfactor, but more rarely.

Maps showing suicides reveal nationaltendencies

Suicide has a major impact on premature mor-tality. It is the second most important cause ofdeath among young people aged 15–24, afterroad accidents. Three quarters of suicides are inthe population aged under 65. Excess male mor-tality is very high, with an average European rate

Standardised death rateof suicide and intentional self-harm





Map 9.6


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9which is 3.3 times higher than that for women.Male and female rates vary in a ratio of 1:20 and1:50 respectively, depending on the region. Al-though male rates are much higher, in most Euro-pean regions there is a correlation between maleand female rates (see Maps 9.5 and 9.6). Finland,which has more suicides than any other country inEurope, has high rates in all regions. It is the onlyEuropean country where suicide is the primecause of death among young people aged 15–24,ahead of road accidents. The French and Austrianregions as a whole also have high rates, but theyare lower in Alsace, Île-de-France and Midi-Pyrénées. The rates are close to the European av-erage in Belgium, Denmark, Germany, Ireland,Luxembourg and Sweden. In Germany, the high-est rates are in the Länder in the east for men andin urban Länder for women. In the Netherlands,the rates are higher for women than for men.Male rates in the Netherlands are close to those inthe United Kingdom. These two countries, in thenorth of the EU, are the exception in having sui-cide rates which are low in general. The main con-trast in the EU is between the southern MemberStates — Greece, Spain, Italy, and Portugal — andthe rest of Europe. In the southern countries, inGreece in particular among the female popula-tion, suicide has very little impact on mortality.There are a few nuances, however. The rate ishigher in the north of Italy than in the south and,conversely, in Portugal the rates are higher in thesouth than in the north for men. In Galicia andAsturias, the rates are higher than in other Span-ish regions.

It is very difficult to interpret these marked dis-parities. Among causes of death overall, suicidehas led to more discussion than any other as re-gards the validity of the data both within MemberStates and in terms of international comparabili-ty. The problems raised have to do with the lackof specific criteria for reporting cases of suicideand the lack of autopsies which would enablecauses of death to be checked more efficiently, es-pecially in cases where intention is not clear. Thepropensity to report a death as suicide may alsodepend on the type of doctor in charge of certifi-cation or the socio-demographic characteristics ofthe deceased. Finally, this propensity may vary inline with cultural or religious criteria. The verylow rates recorded in southern countries may thusbe due in part to under-reporting. Most studiesconclude that deaths by suicide have been under-estimated in official statistics, but differences inmortality levels are such that the differences

observed cannot be explained altogether by biases in reporting.

Incidence oftuberculosis in EUregionsThe return of tuberculosis

In the industrialised countries, the steady declinein the incidence of tuberculosis came to an end atthe start of the 1990s, reflecting problems in or-ganising the anti-tuberculosis campaign and exac-erbated by the epidemic of HIV infection. As theInVS in France indicated, 20 years ago it was ‘po-litically correct’ in Europe to consider that tuber-culosis was well on the way to being eradicated(and the figures really did show this), that it was aThird World infectious disease. Many of the struc-tures for combating tuberculosis had been dis-mantled, and few structures outside hospitalswere able to look after patients suffering from it.In addition to these basic factors, there are otherswhich helped this disease to make such a strongcomeback: human migration, much of it betweencountries with a high prevalence of tuberculosisand the EU countries which have a low preva-lence; the fact that the population is not ade-quately covered by health services, especially inisolated rural areas and on the outskirts of majorconurbations; the HIV pandemic; the decline inresources allocated to public healthcare pro-grammes to control tuberculosis. The emergenceof HIV infection means that seropositive patientsfrequently develop tuberculosis. The economiccrisis of the 1980s in the industrialised countriesled to an increase in poverty which, in turn, led tothe return of tuberculosis, a disease linked topoverty. Success in controlling tuberculosis de-pends very much on improvements in socio-economic conditions.

Cases of tuberculosis reported in 2000

In 2000, 46 846 cases of tuberculosis werereported in the EU, i.e. 12.4 cases per 100 000 in-habitants. Between 1995 and 2000, the rates no-tified fell by 3 % per annum overall in the WHOwestern Europe region but increased in Denmark,Luxembourg, Norway and the United Kingdomowing to a rise in the number of cases among peo-ple born in other countries. According to EuroTB,


the annual fall in the number of cases is higher innationals (– 7 %) than in people from other coun-tries (– 1.5 %) (data from 10 countries). In centralEurope, annual rates fell by 3 to 6 % in nine coun-tries and increased by 2 to 4 % in Bulgaria andRomania. In the east (former USSR countries), therates in 2000 were 57 % higher than in 1995,with annual average increases ranging from 5 to12 % in most countries. In the EU, the incidence

is currently falling, after stabilising and then risingin many countries at the end of the 1980s and thestart of the 1990s. In many EU countries, how-ever, there is a worsening tendency owing to theincreasing numbers of cases reported in patientsof foreign origin. The rates were lower than 20per 100 000 inhabitants in all countries exceptSpain (21.3) and, the highest of all, Portugal(44.1). Between 1995 and 2000, the incidence fell

TuberculosisIncidence rates per 100 000 inhabitants

2000 — NUTS 2


D, FR9, IRL, UK: NUTS 1


Map 9.7


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9by 15.9 %. Exceptions were Denmark, Greeceand Spain.

At regional level (see Map 9.7), there are high lev-els of incidence in regions where there are majorcities (Île-de-France, Greater London, Lisboa eVale do Tejo, Région de Bruxelles-Capitale/Brus-sels Hfdst. Gew., etc.) contrasting markedly withother regions in the same country (especially inFrance and the United Kingdom). Elsewhere, theBaltic regions (Estonia, Latvia and Lithuania) andmany of the Polish regions (in particular Lódzkie,Swietokrzyskie and Mazowieckie) and Hungarianregions (in particular Közép-Magyarország andÉszak-Alföld) also have a very high incidence. Fi-nally, almost all the Portuguese regions (especial-ly Algarve, Lisboa e Vale do Tejo, Norte and Cen-tro) and some Spanish regions (Ceuta y Melilla,Galicia and Principado de Asturias) are clearlyabove the European average. The distribution ofcases is very uniform in remaining Member Statesother than Germany and Finland, where there is anoticeable contrast between regions in the northand the south.

Healthcare resourcesin the EU regionsChanges in the number of doctors

There has been a steady increase in the number ofpractising doctors/physicians in most MemberStates over the past 20 years. The number of doc-tors qualified to practise is higher than the num-ber actually practising in all countries eventhough the ratio reported in 2000 varies from onecountry to another. In Luxembourg, there arecomparatively few differences, whilst in Spainthey are substantial. Density rates for practisingdoctors (doctors per 100 000 inhabitants) haveincreased in all Member States and all the candi-date countries over the past 20 years. In 1999,Greece reported rates above 400. In five MemberStates (Belgium, Germany, Austria, Luxembourgand France), there were over 300 practising doc-tors per 100 000 inhabitants. In two MemberStates (the Netherlands and the United Kingdom),the rates were below 200 but the figures for Ire-land and the United Kingdom refer only to doc-tors working in the National Health Service andare therefore not strictly comparable. However,the wide range of densities for doctors may alsobe explained by differences in healthcare systems.

In some Member States, studies suggest that thenumber of doctors might increase (need for cer-tain specialists, increased need in the long-termcare sector, for example) and in others (the Unit-ed Kingdom, for example) discussions are underway on the need for more general practitionersand specialists due to the lack of housemen in hos-pitals. The density rates for doctors qualified topractise vary from 250 per 100 000 inhabitants inIreland to 599 per 100 000 in Italy, the range be-ing much higher than for practising doctors.

The relevant map (Map 9.8) shows the average re-gional density of doctors per 1 000 inhabitantsusing data at the NUTS 2 level for 2000. In someMember States, the rate is fairly uniform from oneregion to another, whilst in other countries itvaries. It is in metropolitan areas such as Île-de-France (France), Lazio (Italy), Région de Brux-elles-Capitale/Brussels Hfdst. Gew. (Belgium), At-tiki (Greece), Wien (Austria), Comunidad deMadrid (Spain), Praha (Czech Republic),Bratislavsky (Slovakia), Berlin and Hamburg(Germany) that the density rates are highest.Compared with 1986, the figures have risen in al-most all Member States’ regions. The lowest fig-ures are in areas with low population density. Inmost Italian regions and in northern Spanish re-gions, there is a high density of medical staff andthese regions are net ‘exporters’ of doctors to oth-er regions, in particular to the United Kingdom.This phenomenon is even more noticeable as re-gards nursing staff. The high density of doctors inthe Greek regions of Attiki and Kentriki Makedo-nia (which include the cities of Athens and Thes-saloniki respectively) may be explained by theexistence of less strict legislation on recognition ofmedical qualifications obtained in the candidatecountries. Nevertheless, there are no noticeabledifferences between EU regions and the regions ofcountries which have applied to join the EU. Allregions seem to have a sufficiently high density ofdoctors with the exception of a few in Greece,Portugal, Romania and the United Kingdom.

Changes in the number of hospital beds

The number of hospital beds per capita shows aquite different trend. Over the period 1980–2000,the number of beds declined sharply in mostMember States. For the EU as a whole, there wasa 30 % drop, probably due largely to the fact thatstays in hospital were cut from 17.4 days in 1980to under 11 days in 1997. In many countries, thelength of time patients spend in hospitals has de-clined substantially over the past 30 years. At the


same time, there is now less difference from onecountry to another. In 1980, the highest value(23.2 days) was recorded in Luxembourg andSweden, and was over 2.4 times higher than thelowest value (9.8 days) recorded in Ireland. In1996, the highest value was 15.3 days (Luxem-bourg) and the lowest 7.2 days (Denmark).

A further reason for this tendency lies in the grow-ing financial constraints of the 1990s, which

everywhere led to a rationalisation of healthcareservices. The increasing demand for healthcare forelderly people, often suffering from chronic dis-ability or illness, was in most cases met by a trans-fer of beds for acute or psychiatric care to beds forlong-term care, with a steady fall in total num-bers. Available resources expressed as the numberof hospital beds per capita vary noticeably fromone Member State to another. Nevertheless, the

Health personnelDoctors per 1 000 inhabitants

2000 — NUTS 2


DK, EE, SI: 1999D, IRL, UK: NUTS 1


Map 9.8

supply of hospital services at national and region-al levels correlates closely with total expenditureon healthcare.

In Sweden, the United Kingdom and Spain, thenumber of beds per 100 000 inhabitants is lowerthan in any of the other Member States (359, 408and 409 respectively in 1999/2000), whilst it ishighest in France (820). These figures includeboth public and private hospitals, but differ as re-

gards the inclusion of clinical beds and daycarebeds. The difference in bed density is still sub-stantial, even if differences in definition are nottaken into account.

The share of gross domestic product (GDP) whichthe Member States spent on healthcare in1998/2000 ranged from 6 to 10.4 %. There is acertain north–south (plus Ireland) divide, but thedifference is not great. Healthcare expenditure


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9Hospital beds

Rate per 1 000 inhabitants2000 — NUTS 2


DK, EL, IRL, L: 1999D, IRL, UK: NUTS 1


Map 9.9


accounted for a higher share of GDP in Germany(10.3 %), France (9.5 %) and Denmark (8.3 %)than in Slovakia (5.9 %) or Poland (6.2 %). Be-tween 1980 and 2000, the healthcare share ofGDP rose in most Member States. The level of ex-penditure depends partly on the prices of goodsand services and partly on quantities supplied. Inthis sector, the problem generally arises becausethe output of ‘health’ cannot be measured direct-ly. Whereas figures for goods and prices are read-ily available in most sectors of the economy, it isimpossible to record items such as outpatient orhospital services directly. However, it should bestressed yet again that differences in the way inwhich healthcare is organised and delimited at na-tional or regional level (e.g. where should the di-viding line be drawn between health services andsocial services?) make it difficult to interpret com-parisons between countries, whether these are offigures on given dates or of trends.

The north–south divide applies to hospital beds(see Map 9.9), but with certain provisos. TheGerman, French, Austrian and Finnish regions(headed by Mecklenburg-Vorpommern, Wien,Itä-Suomi, Saarland and Limousin) have a highdensity of beds, in marked contrast to the Span-

ish, Portuguese and Greek regions (Algarve andSicilia, in particular), the United Kingdom andIreland. Certain border regions such as Yugoiz-tochen (Bulgaria) or Itä-Suomi (Finland), whichborder on Turkey and Russia, respectively, alsohave a density higher than other regions owing,possibly, to inflows of patients from these neigh-bouring countries.

It is in Spain that the density of beds per 1 000 in-habitants is most uniform (between 3 and 5)whilst it varies most in Austria (between 6 and12). The number of beds per 1 000 inhabitants ishighest in relation to the EU average in Austria,France and Germany and lowest in Spain, Portu-gal and some regions of Greece. The tendency isvery different as regards the number of hospitalbeds per inhabitant. Between 1986 and 2000, thisfigure fell noticeably throughout the EU (from 8.3beds to 6.3). Here, again, there are no noticeabledifferences between the EU regions and the re-gions in the candidate countries. All the regions inthe latter have a bed density which, in many cas-es, is higher than in the EU regions. Examples areSeverozápad and Strední Morava (Czech Repub-lic), Bratislavsky (Slovakia), Bucuresti (Romania)and Zachodniopomorskie (Poland).

T O U R I S M10


IntroductionAt the time of the foundation of the EuropeanCommunity, tourism was limited in volume by fi-nancial constraints and geographically by trans-port limitations, frontier formalities and linguisticbarriers. In the European Union of 2003, the pic-ture is very different. Package holidays provide af-fordable access to geographically remote parts ofthe Union, while widespread car ownership and agood network of motorways has made frequentshorter holidays in nearby regions possible. Withthe accession of the Nordic countries to theSchengen Agreement, border formalities are fewerthan ever or non-existent and language skills areincreasingly valued in the tourist trade. Thesetrends have been accompanied in parallel by theemergence of many European regions with a pro-nounced orientation towards tourism, in terms ofboth the infrastructure provided for visitors andthe importance of the tourist industry for theregion’s economy.

Eurostat has collected statistics on tourism at re-gional level since 1994. The coverage is twofold:capacity and occupancy. Capacity refers to the ac-commodation infrastructure that is available tothe tourist in the region concerned. Occupancyprovides statistics on the number of nights spentin hired accommodation in a particular region.

Since the enlargement process is ongoing, Euro-stat has recently started to collect data from thefuture member countries. For the first time, thesedata are included in the respective maps.

Methodological notesAlthough throughout this chapter, for reasonspredominantly of cartographic clarity, the region-al level adopted for the analyses is that of NUTS2, Eurostat’s REGIO database, in fact, containsextensive data at NUTS 3 level.

Capacity(infrastructure)statisticsMap 10.1 clearly illustrates the total number ofbed places per region. It becomes evident that

most of the accommodation establishments are tobe found in tourist regions, above all in northernand central Italy, southern France and north-eastSpain as well as in Vorarlberg and Tirol in Austriaand Bayern in Germany. Regions with a lowerdensity of hotels are to be found in Portugal andin central France, as well as in parts of easternGermany.

Amongst the accession countries, those with thelargest tourism capacity are the Czech Republic(Severovychod), Poland (Zachodniopomorskie)and Hungary (Nyugat-Dunántúl). To give an ideaof their capacity, the Czech Republic and Polandcan be compared to Ireland or the Netherlands,while Hungary’s tourist capacity is comparable toBelgium’s.

Turning specifically to campsites, Map 10.2 ex-amines the availability of this kind of accommo-dation, but in a form which takes account of theregion’s permanent population. Unsurprisingly,urban areas, especially regions around capitalslike London, Berlin or Vienna, have few campsiteplaces per head of population. Darker shadedareas of the map indicate regions with a muchgreater per capita prevalence of campsites.

• Although all of France has, in general, an ex-cellent supply of sites, they are concentratedparticularly on the Atlantic seaboard, from Bre-tagne to Aquitaine, and in Languedoc-Roussillon on the Mediterranean.

• In Belgium, there are especially two distincthigh-density camping zones. West-Vlaanderenon the North Sea coast is similar to neighbour-ing Zeeland in the Netherlands, while the highnumber of campsites in the province of Luxem-bourg, in the Ardennes, is a pattern that con-tinues into the Grand Duchy of Luxembourg,and, to some extent, into the region of Trier inGermany.

• Mountainous terrain can also be popular withcampers, as is evident from Kärnten in Austriaand Valle d’Aosta in Italy.

• Although France’s Corsica (Corse) has a rela-tively good supply of campsites, this is not trueof a number of other island holiday destina-tions in the Mediterranean, such as Crete (Kri-ti) in Greece, the Balearic Islands (Islas Balear-es) in Spain or Sicily (Sicilia) in Italy. It isprobable that package holidays combiningflights with hotel accommodation explain thepattern.


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• In the candidate countries, the highest numberof bed places in campsites per capita can befound in Hungary (Közép-Dunántúl and Dél-Dunántúl) and in Slovakia (Vychodné Sloven-sko). These regions are comparable as regardsthe capacity of campsites to regions such asAntwerpen (Belgium), Bourgogne (France) orLisboa (Portugal).

In a similar way to Map 10.2, the number of ho-tel beds in a particular region is shown in Map10.3 as a proportion of the region’s population.

Some classic destinations for package holidayflights, such as the Balearic Islands in Spain andAlgarve in Portugal do indeed have a very highsupply of hotel accommodation per head of

Number of establishments(total number of establishments)

2001 — NUTS 2

> 3 0001 000–3 000400–1 000≤ 400Data not available

DEB1, DEB2, DEB3, EL, UKL2: 1999UK: 2000


Map 10.1


population. To these traditional destinations inthe European Union, one can add the island ofCyprus which has a hotel capacity similar toAlgarve.

That tourism can be a year-round phenomenon isshown in a typical way by the two parts of theTirol region in Austria.

Many holidaymakers do not, of course, fly to

their destination, especially on shorter breaks,

which are becoming more and more popular. A

number of regions with an extensive hotel infra-

structure lie within comfortable driving range of

major concentrations of urban population. Exam-

ples include West Wales and the Valleys, and

Capacity of campsitesNumber of places per 1 000 inhabitants

2001 — NUTS 2


DEB: NUTS 1Population: 2000EL: number of bed places in campsites: 1999UK: number of bed places in campsites: 2000


Map 10.2


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Dorset and Somerset in the United Kingdom andthe Black Forest region in Germany. However,central Sweden is also quite attractive for shortholiday breaks.

While urban centres generally rank low in hotelbeds per head of population, in Europe there are

a number of cities which are of such extreme im-portance in world as well as European tourismthat they defy this trend. London and GreaterParis are the most striking examples.

Capacity of hotels(total number of beds)

2001 — NUTS 2

> 150 00050 000–150 00025 000–50 000≤ 25 000Data not available

DEB1, DEB2, DEB3: 1999UK: 2000EE: incl. ‘other collective accommodation establishments’


Map 10.3


Occupancy dataWhile tourist infrastructure figures, such as thoseexamined in Maps 10.1 to 10.5, yield an indica-tion of the accommodation capacity available in aspecific region, it is important to know the extentto which this capacity is actually used. Some mea-sure of occupancy is therefore required. At NUTS2 level and for the years 1994–2001, the REGIOdatabase holds data on arrivals and nights spent.These figures are further broken down into resi-dents and non-residents. Non-residents are de-fined as persons of a nationality other than that ofthe country in which the region is located.

Given that this indicator is measured here on a percapita basis, regions of high population density,such as those that include Madrid and the Ruhrregion in Germany, do not rank high in terms oftotal nights spent.

The most striking feature of Map 10.4 is analmost continuous belt of higher-than-average oc-cupancy, probably reflecting summer family holi-days, that runs from France’s Mediterraneancoasts to Marche in Italy and Comunidad Valen-ciana in Spain.

Within easy travelling distance of the heavilypopulated regions of Germany and the Benelux

countries, Mecklenburg-Vorpommern, south-eastBayern and the Trier region, the Grand Duchy ofLuxembourg and the Luxembourg province ofBelgium may owe their higher ranking to the ac-cessibility of these regions for short breaks andalso longer holidays.

Winter rather than summer holidays are probablythe key factor in explaining the zone of high oc-cupancy in Austria’s four westernmost regionsand the mountainous Italian regions of Valled’Aosta and Trentino-Alto Adige.

A very different picture emerges if the domestictourist traffic is excluded. Certain regions of highpopulation density such as the Paris region, Vien-na in Austria and Inner London are clearly keydestinations for foreign visitors, as is the Brusselsregion, due to the fact that business tourists cometo the ‘capital city of Europe’, and the region ofnorth-east Spain.

As regards the situation in the accession countries,it is clear that the largest proportion of nightsspent by foreign visitors is in hotels and camp-sites. Holiday dwellings play a very limited role,which is in contrast to most tourist regions in theEuropean Union.

0 % 20 % 40 % 60 % 80 % 100 %

I — Veneto (652 721)

F — Provence-Alpes-Côte d'Azur (431 767)

I — Toscana (405 259)

E — Canarias (377 635)

I — Trentino-Alto Adige (367 003)

F — Bretagne (319 794)

D — Oberbayern (232 491)

UK — Dorset and Somerset (173 580)

EL — Notio Aigaio (152 914)

P — Algarve (130 693)

E — Cataluña (648 382)

E — Islas Baleares (414 396)

F — Languedoc-Roussillon (392 513)

F — Rhône-Alpes (370 199)

E — Andalucía (334 409)

A — Tirol (264 746)

UK — London (188 139)

NL — Gelderland (166 514)

I — Sicilia (133 564)

P — Lisboa e Vale do Tejo (127 115)

Graph 10.1 — Top 20 tourist regions; bed places by accommodation type, 2001NUTS level 2

Hotels Campsites Dwellings Other


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ConclusionThe above examples are intended merely to high-light a few of the many possible ways of analysingtourism effects in the regions of the EU and theaccession countries. They show clearly that theeffect of tourism in the European regions is be-

coming increasingly evident, and that regionswhich are not dominated by tourism today aretrying to attract more and more tourists by meansof package tours, special events, etc. Especiallythe tendency of more and shorter trips encouragesregions to promote their attractiveness. The aboveexamples are no substitute for detailed analysis.

Nights spent in hotels and campsitesas a proportion of the population of the region

2001 — NUTS 2> 2010–205–102.5–5≤ 2.5Data not available

Population: 2000IRL: NUTS 1DEB1, DEB2, DEB3, GR43, UKK4: 1999EL, IRL, NL11, NL13, NL21, NL23, AT: 2000UKD2, UKE3: 1997BG01, BG04, BG05, EE, RO04, RO08, SK01, SK02:hotels only


Map 10.4


We hope, however, that they will encourage read-ers to probe deeper into the regional data and tomake many further interesting discoveries.

Nights spent in hotels and campsitesby non-residents

as a proportion of total nights2001 — NUTS 2


DE5, EL, NL, AT, UK: 2000


Map 10.5


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0

50 000

100 000

150 000

250 000

350 000

B DK D EL E F IRL I L NL A P FIN S UK CZ HU LT LV PL RO SI SKBG

300 000

200 000

NB: EL, IRL, A, UK: 2000.

Graph 10.2 — Inbound and domestic tourism in 2001: nights spent in hotels and campsite sby residents and non-residents

1 000 nights

Non-residentsResidents

U R B A N S T A T I S T I C S11


BackgroundThe prime objective of European regional policyis to improve social and economic conditionswithin the European Union whilst reducing thedisparities between regions. Since much of the EUis urbanised, cities and towns play an importantrole in the search for a better social and econom-ic balance in the Union. Whereas cities were large-ly ignored when regional policies were devised inearlier years, major changes are currently takingplace in this field.

The results of the pilot phase of the ‘Urban Audit’(see regional yearbook 2002, Chapter 11) showedclearly that serious economic and social inequali-ties exist at city as well as at regional level, insome cases even more noticeably. Political actionis thus justified. There are obvious inequalitiesfrom one city to another in the EU, as well aswithin one and the same city.

The 1998/99 pilot phase of the Urban Auditshowed that it was possible to collect and presentdata for a wide range of indicators on a consistentpan-European basis. After completion of the auditin the spring of 2000, the European Commissiontherefore decided to continue the project, to cre-ate a sound quantitative basis for future regionalpolicy.

The analysis which followed, assessing the pilotphase results in detail, led to a series of conclu-sions regarding the list of variables collected, thelist of cities taking part and the spatial dimensionfor the next phase, Urban Audit II.

The work on the Urban Audit II project isdescribed below.

Tight scheduleSince the results of Urban Audit II are to form thebasis for future European regional policy, it wasimportant for the Commission to include the firstresults of the survey in the next cohesion report,which is due to come out in November 2003.Hence, the first results had to be available by July2003 to enable the report to be drafted.

Since the actual work of collecting the necessarydata and figures could not begin until the autumnof 2002, there was a good deal of pressure on allthose taking part to complete on time what was,and still is, groundbreaking work from the statis-tical point of view. However, there is good reasonto hope that the first comparable results of thesecond Urban Audit will be available in thesummer of 2003.

These first data could not be reproduced in thisyearbook, since they were not available before itwent to print, but they may be requested from Eu-rostat. However, the 2004 yearbook will certain-ly include interesting examples of Urban Audit IIresults.

Selection of citiesFor the Urban Audit pilot phase, it was decided toinclude the largest conurbations in the EuropeanUnion, but to exclude London and Paris, since itwas considered too difficult to cover these twocities in a one-year pilot project. They will, ofcourse, be included in Urban Audit II, and manyother major European cities will be added.

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Graph 11.1 — Urban audit II population coverage

Medium-sized citiesLarge cities


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11Cities in the Urban Audit

Urban Audit I

Urban Audit II

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© EuroGeographics, for the administrative boundariesCartography: Eurostat — GISCO, June 2003

Map 11.1


One specific focal point, however, will bemedium-sized cities (50 000 to 250 000 inhabi-tants), which were not well covered in the pilotphase even though a substantial proportion of theEU population lives in such cities. Detailed infor-mation on the various aspects of the quality of lifein these cities is of enormous value for urban pol-icy support schemes at European level.

A total of 189 cities in the European Union aretaking part in the Urban Audit II project, covering21 % of the population.

At the same time, a separate Phare programmehas begun in order to add fresh information onurban topics by supplying new statistical data onsome 60 cities in the accession countries. Urbanstatistics for these countries were not available bythe summer of 2003, but it is hoped that all thehard work of colleagues there will result in com-parable data by the end the year. It will be ex-tremely interesting to compare the results of theaccession countries with those of the MemberStates.

With comprehensive figures for considerablymore than 200 European cities, a solid database isbeing created which will doubtless shed light onthe specific requirements of future regional policyaffecting cities.

The map shows all the cities in Urban Audit II,noting those which took part in the pilot phase. Itshows that the selection achieved a good geo-graphical distribution.

It is important to be clear at this point that theresults of Urban Audit II are not specifically in-tended to pick out certain cities or parts of citiesfor future support programmes. In some circum-stances, such an objective would distort the re-sults. Rather, Urban Audit II aims to provide asound basis for the quantitative figures which willbe the cornerstone of future regional policy decisions.

Spatial unitsAs in the first phase, there are three levels of spa-tial unit at which the relevant data are collected.

The first is the ‘central city’ or ‘core city’, i.e. theadministrative unit for which extensive data aregenerally available. In countries where that con-cept does not exist as such (Portugal and the Unit-ed Kingdom), a few adaptations were made.

Next, the ‘larger urban zone’ (LUZ) is investigat-ed, i.e. data are compiled which include the ‘hin-terland’. The urban zone is defined with referenceto the functional urban region, taking particularaccount of commuter flows.

Finally, as already mentioned, inner-city socialand economic discrepancies are to be measured,with data collected on individual parts of cities,which should have between 5 000 and 40 000 in-habitants in all the cities investigated to ensurethat the results are comparable.

In a few cases, it was extremely difficult and ex-pensive to define urban zones and, even more, in-dividual areas or parts of cities. It became clearthat particular geographical and administrativecircumstances which had developed in the Mem-ber States over many centuries often require spe-cial solutions. Urban zones and parts of citieswere delimited primarily as a way of finding spa-tial units for which the results were sufficientlycomparable from one country to another.

Comparability of results is without doubt themain quality requirement of the Urban Audit re-sults. It is extremely important for data use, but,at the same time, the most difficult requirement tofulfil.

The variablesIn the Urban Audit pilot phase, around 480 vari-ables were collected. The degree of response fromthe cities ranged from nil to complete coverage,but was frequently on the low side. On the basisof a detailed analysis, Eurostat decided to dropsome 300 of the variables for Urban Audit II, butto add 150 new ones which had not been avail-able to measure important phenomena in the pilotphase. Thus for Urban Audit II ‘only’ 333 vari-ables are being collected, but Eurostat hopes thatthe degree of coverage will be extremely high.


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111. DEMOGRAPHY

1.1. Population1.2. Nationality1.3. Household structures

2. SOCIAL ASPECTS2.1. Housing2.2. Health2.3. Crime

3. ECONOMIC ASPECTS3.1. Labour market3.2. The business world3.3. Income and poverty

4. CIVIC INVOLVEMENT4.1. Elections4.2. Local government

5. TRAINING AND EDUCATION5.1. Educational opportunities5.2. Level of education achieved

6. ENVIRONMENT6.1. Climate6.2. Air quality and noise6.3. Water6.4. Waste disposal6.5. Construction6.6. Consumption of energy

7. TRAFFIC AND TRANSPORT8. IT INFRASTRUCTURE 9. CULTURE AND RECREATION

9.1. Cultural facilities available9.2. Tourism

The variables selected cover a wide range of socialand economic conditions in the cities and shouldprovide a solid basis for measuring the quality oflife in Europe’s cities in quantity terms.

Below is an overview of the content of the UrbanAudit II project.

A detailed check on the 333 variables showed thatsome of the data are already available in the vari-ous countries somewhere in existing databanks(type A). Other variables can be estimated, sincesimilar data are available; advanced estimationprocedures could be used here (type B). For athird group of variables, fresh data have to be col-lected in a new survey (type C), since the data arenot available and it is not possible to obtain suffi-ciently good estimates by any other means. Theclassification of variables into three groups dif-fers, of course, from one country to another (seeGraph 11.2).

The graph shows that, in the Scandinavian coun-tries, France and the United Kingdom, many vari-ables are already available, whereas in Greece,Ireland and Italy a large percentage have to be es-timated or collected from scratch the next year.

It quickly became clear that it was impossiblewithin the narrow time frame (starting in October2002 with the data required in June 2003) to car-ry out new statistical surveys. For the summer of2003, no type C variable data were therefore to beexpected. These statistics were to be collectedafresh as from the end of 2003, with the first fig-ures likely to be ready in mid-2004 at the earliest.

A complete estimate of all type B variables wasnot possible within a few months, either. Thus, 75‘key’ variables were identified, to be considered asparticularly important. Priority was to be given toestimating these by the summer of 2003 whereverpossible.

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Graph 11.2 — Classification of urban audit variables

Needs a fresh surveyCan be estimatedAvailable


Graph 11.3 shows the number of variables (keyand standard) per topic.

OrganisationIt is not possible to put together all these variablesfrom 189 cities unless all those involved work to-gether in partnership. In particular, there must beclose cooperation between the national statisticaloffices and the cities.

To this end, national Urban Audit coordinators(NUACs) have been appointed for each country,to act as links between the cities, the national sta-tistical offices (or the bodies responsible for datacollection at national level) and Eurostat. TheCommission has given the national statistical of-fices financial support to enable them to compileat least all key variables by June 2003 at the latest.

Regular meetings of all those involved ensure thatinformation is channelled between the Commis-sion (REGIO and Eurostat), the national offices,the cities and specialists in methodology under

contract to assist with estimates of the type Bvariables.

Next stepsThe Urban Audit II project has not yet been com-pleted. The first data quality checks have beencarried out, but further careful checks are neededon consistency and other quality characteristics.

In addition, data from the 1999/2000 pilot phasehave to be checked and improved where neces-sary, so that they can be analysed over time in thefuture.

Further reliable indicators have to be calculatedfrom the 333 variables to be published on the In-ternet. Finally, the Urban Audit II results have tobe analysed carefully so that sound conclusionscan be drawn for future regional policy.

Also, type C variables will need to be collectedvery soon.

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Graph 11.3 — Number of variables by topic

Demography

Social aspects

Economic aspects

Civic involvement

Training and education

Environment

Travel and transport

Information society

Culture and recreation

Key variablesStandard variables

H O U S E H O L D A C C O U N T S12


Income of privatehouseholds and grossdomestic productIntroduction

One of the aims of studying regional statistics isundoubtedly to try and provide some informationon the wealth of regions. Adam Smith entitled hismajor work in the field of economics The wealthof nations, and it is but a small step from the na-tion to the region, especially as the nation Statebecomes less and less important with Europeanintegration and the wealth of the regions movescentre stage.

The key, and by far the most frequently used,indicator to measure the wealth of regions is re-gional gross domestic product (GDP). GDP is of-ten expressed in purchasing power standards(PPS) and per capita to make the figures compa-rable between regions. Regional GDP is coveredin Chapter 3 of this publication.

GDP at regional level is calculated using the out-put approach. It is the total value of the goods andservices produced in a region by persons em-ployed in the region. When considering to whatextent GDP contributes to the wealth of the re-gions, this depends on the generation of incomefor private households, even if the multitude of in-terregional links and measures taken by the Statedo now mean that there is absolutely no guaran-tee that this income actually reaches the inhabi-tants of a region.

Regional per capita GDP has some undesirablefeatures, one of which is that a ‘place-of-work’figure is divided by a ‘place-of-residence’ figure.This inconsistency is of relevance wherever thereare commuter flows — i.e. people who work inone region but live in another. The most obviousexample is the UK Inner London region, whichhas by far the highest regional per capita GDP.This GDP is not, however, directly translated intoincome for the inhabitants of Inner London, asthousands of commuters journey to work intoLondon every day, but live in neighbouring re-gions. Hamburg and Vienna offer other examplesof this.

Given this and other conceptual weaknesses in-volved with GDP, it therefore seems worthwhileto take a closer look at household incomedistribution.

Household income distribution

In market economies, which also have State redis-tribution mechanisms, a distinction is madebetween two types of income distribution.

The primary distribution of income indicates theincome of private households generated frommarket transactions, i.e. trade in the factors ofproduction and goods. The ‘resources’ side in-cludes the compensation of employees, i.e. incomefrom the sale of labour as a factor of production.Private households can also receive property in-come, and there is also, of course, income in theform of an operating surplus or self-employmentincome. Any interest payable is recorded as a neg-ative item. The balance of these transactions istermed the primary income of private households.

Table 12.1 — Primary distribution of household income in accounts format

Uses Resources

D.4. Property income B.2/B.3. Operating surplus/self-employment income

B.5. Primary income (balance) D.1. Compensation of employees

D.4. Property income

The primary income is the point of departure forthe secondary distribution of income, which de-notes the State redistribution mechanism. All so-cial benefits and transfers other than in kind arenow added to primary income, and it is from thistotal that households have to pay taxes on income

and wealth, pay their social contributions and ef-fect transfers. The sum remaining after thesetransactions have been carried out, i.e. the bal-ance, is called the disposable income of privatehouseholds.


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Primary income and the disposable incomeof private households as incomedistribution balances

Eurostat uses NUTS level 2 data for the above-mentioned variables. These data are, of course, ofinterest in themselves, but primary income anddisposable income are the main pillars of theseaccounts.

Before these parameters can be compared, a quickdigression is required to look at the unit used toexpress this income in a meaningful manner sothat the corresponding comparisons make sense.

For the purposes of making comparisons betweenregions, regional GDP is generally expressed percapita and in purchasing power standards (PPS)so that volume comparisons can be made. Thesame process should thus be applied to the privatehousehold income parameters so that these canthen be compared with regional GDP and withone another.

There is a problem with this. PPS are designed toapply to GDP as a whole. The calculations use theexpenditure approach and PPS are also only sub-divided on the expenditure side. In the regionalaccounts, on the other hand, the expenditure ap-proach is not used — at least not for the EU re-gions — as this would require data on import andexport flows at regional level. These data are notavailable, so regional accounts are only calculatedfrom the output side. This means, however, thatthere is no exact correspondence between the in-come parameters and the PPS. PPS only exist forprivate consumption.

It can, however, be assumed that these conceptualdifferences are of little importance and the incomeparameters of private households should be con-verted with the consumer components of PPS andcalled purchasing power consumption standards(PPCS).

Results for 2000

The two maps on the following pages show the re-gional distribution of primary income (Map 12.1)and the regional distribution of disposable income(Map 12.2) at European level. Eurostat does notyet currently have complete NUTS level 2 data atits disposal. It hopes to have data for Austria bysummer 2003. Only NUTS level 1 data are avail-able for Germany. The data for France, Ireland,the Netherlands, Portugal and the United King-dom were estimated by extrapolation for 2000.There are no data for Bulgaria, Cyprus, Malta,Slovenia and Turkey.

Analysis of the regional distribution of primaryhousehold income indicates that there are ‘islandsof prosperity’ such as the London, Paris and Brus-sels regions, northern Italy and south Germany, aswell as Utrecht and Nordrhein-Westfalen and thecity states of Bremen and Hamburg. However,when we move from primary income to dispos-able household income, we find much greater uni-formity. Here, there are no discernible patterns orstructures, and the redistributing influence of theState is clear to see.

A reference framework is always required to in-terpret results. We use two types of reference here.First of all, the relationship between primary in-come and disposable household income is dis-cussed and then the ratio of income to regionalGDP is examined.

There are large differences in the ratio of dispos-able income to primary income. These are shownin Map 12.3. The greatest difference is in theStockholm and Helsinki capital regions, wherehouseholds are left with the lowest levels of pri-mary income. This clearly demonstrates thestrong influence of the State in the Nordiccountries.

There are also, however, some regions in whichthe disposable income of households is higherthan their primary income on account of socialbenefits other than social transfers in kind and

Table 12.2 — Secondary distribution of household income in accounts format

Uses Resources

D.5. Current taxes on income, wealth, etc. B.5. Primary income

D.61. Social contributions D.62. Social benefits other than social transfers in kind

D.7. Other current transfers D.7. Other current transfers

B.6. Disposable income (balance)


other transfers. It is they then who profit fromState redistribution policy.

Primary income exceeds disposable income forhouseholds in five Greek regions, six Polish re-gions and six UK regions. Romania has two suchregions and France, Hungary and Italy one each.In Germany, there are three Länder where thisratio is over 100 %.

The ratio of disposable income to regional grossdomestic product is also of particular interest.This is significant because regional GDP per capi-ta is an important indicator in determining Euro-pean structural policy. Regions receive (amongstother things) regional aid when their regionalGDP is below 75 % of the EU average. Capital re-gions often have a high regional GDP. It can be

Primary income of householdsper capita, in PPCS2000 — NUTS 2


D: NUTS 1F, IRL, NL, UK: Eurostat estimate


Map 12.1


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seen from Map 12.4 that this high GDP is gener-ally not reflected in disposable household income.This applies both for the EU regions and for thetwo highlighted candidate country regions ofPraha and Bratislavsky, which have a very highper capita GDP.

At the other end of the scale, there are regionswhere the ratio for the region is much morefavourable. Two Greek regions head this list, butthis situation can also be seen in the UnitedKingdom and the east of Germany. It is clear thattaking this indicator as the basis also changes therelative position of a region within Europe.

Disposable income of householdsper capita, in PPCS2000 — NUTS 2




Map 12.2


In conclusion, it can be seen that there are signifi-cant differences within Europe in the proportionof disposable household income which remainsfollowing the State redistribution of householdprimary income. Per capita disposable householdincome is also very different from regional GDP.The clear conclusion from these observations is

that, whilst comparing regions within the onecountry provides meaningful information, com-parisons between regions in different countriesare very problematic.

It is noticeable that disposable household incomeis particularly low in countries where State activi-ty is very high. This would seem to suggest that in

Disposable income of householdsas percentage of primary income

2000 — NUTS 2




Map 12.3


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these instances the State claims a very large pro-portion of income. On the other hand, this doesnot mean that these regions are particularly pooras they may perhaps benefit considerably fromthis State activity in the form of non-monetary

services, such as roads and kindergartens. Thenext section attempts to analyse this aspect inmore detail, presenting experimental calculationswhich seek to counterbalance these distortions.

Disposable income of householdsas percentage of GDP

2000 — NUTS 2




Map 12.4


How rich are Europe’sregions? Experimental calculations

Introduction

How rich are Europe’s regions? To answer thisquestion, we need first of all to explain what ismeant by the terms ‘Europe’, ‘region’ and ‘rich’.Europe is defined here as the European Unionplus the candidate countries. This is not a formaldefinition, but is based on the availability of comparable data. The NUTS classification(nomenclature of territorial units for statistics) isused to define the regions, with the analysis fo-cusing only on NUTS 2 regions. The term ‘rich’,however, is not so easy to define. In this case, itshould not be used in terms of wealth, but interms of income. Wealth only plays a role in so faras it can generate income, for example interest in-come. We would, however, like to go one step fur-ther and regard income, in economic theoryterms, as something which is important in that itallows people to consume and provides utility. Using this definition, public goods, for example,which are provided free of charge also provideutility and in this sense should also be regarded asa special type of income.

This work is based on currently available data. Itis a pragmatic analysis which does not seek todescribe the theoretically best indicator, but to de-velop an indicator which uses the available infor-mation in as meaningful a manner as possible.

Disposable household income

The arguments presented above with regard toregional GDP are well enough known and havealready been discussed at length. This is why re-gional accounts were included in the ESA 95 de-livery programme, which is compulsory for Mem-ber States. The information to be providedincludes disposable household income. The factthat this figure is residence based means that it caneasily be divided by the number of people in aregion.

As shown in the first section of this analysis, theproportion of disposable income in GDP variesenormously from country to country. In Swedenand Finland, it is around 45 %, in France, Spainand the United Kingdom it is about 60 %, fol-

lowed by Germany and Italy at approximately65 %, and in Greece it is over 70 %.

These huge differences make it difficult to com-pare (or rank) regional disposable household in-come. Differences between countries relating tofixed capital consumption and primary incomebalances or the balance of transfers to/fromabroad are not taken into consideration, and thewhole issue of differences in government activity,in particular, is completely neglected.

If such a comparison is nonetheless drawn, as inthe first section, the regions of Sweden and Fin-land end up in the bottom third of the table, as theState accounts for a large slice of economic per-formance in these countries, thus leaving house-holds with less income at their disposal.

It should, however, be borne in mind that the slicetaken initially by the State is then given back inone form or another. State activity is generally forthe benefit of citizens with the result, for example,that less of their disposable income has to bespent. One example should make this clear: if theState uses its income to finance good and cheapchildcare facilities, then private households do notneed to purchase this service at a high cost on theprivate market. Equally, a good public transportsystem reduces private expenditure on cars. Manyother examples could also be given. To sum up,however, it can be established that comparingregional disposable income does not reflect the ac-tual prosperity of a region, which should be ex-pressed in the consumption of private and publicgoods and services.

The two-stage approach

In the national accounts approach, there are clear-ly defined systems of equations. Taking regionalgross domestic product as the point of departure,the balance of income from abroad is certainlyrelevant for households as it expands their con-sumption capacity. Fixed capital consumption, onthe other hand, is regarded as a social cost whichreduces the household’s consumption capacitywhen its capital stock remains constant. The bal-ance of these transactions gives the net nationalincome at market prices. The net national incomeat market prices now has to be corrected for trans-fers to or from abroad. This then gives the dis-posable income of all sectors of the economy.

Following the national accounts approach, thepath from GDP to disposable household income isthus as follows:


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Gross domestic product at market prices+Balance of primary income from rest of the world–Fixed capital consumption

= Net national income at market prices–Balance of current transfers to/from rest of the world

= Disposable income of all sectors (100 %)–Disposable income of financial/non-financial corporationsand private non-profit organisations (average 4 %)–Disposable income of the State (average 25 %)

= Disposable income of private households (average 71 %)

the largest component of total disposable income,and makes up an average of 71 % of the total,within a range of 56 to 78 %. General govern-ment disposable income accounts for 25 % (rang-ing from 19 to 36 %). The rest makes up therather modest average total of 4 %, the range hererunning from 2 % in France to 14 % in theNetherlands.

Moving on to the regional distribution of thesecomponents, figures are available for the regionaldistribution and level of disposable household in-come, but no data are currently available for theregional distribution of disposable income forother sectors — a heading which includes theoperating surplus and property income of corpo-rations, and State activity. The latter includes re-distributive activities, infrastructure, defence ex-penditure, etc. All these transactions benefitprivate individuals in one form or another. Thisalso applies to the operating surplus and propertyincome of corporations, as these do ultimatelyalso belong to private individuals. Information isnot, however, available on the regional distribu-tion and relative magnitude of these two compo-nents. These components do, nonetheless,contribute substantially to wealth in the regions,and ignoring them makes it considerably harderto compare two regions from different countries.

Taking the arguments presented above into ac-count, the following procedure is now proposed:

1. The disposable income of households is divid-ed amongst the regions in accordance with theregional structure, which is already known.

2. The difference between the ‘disposable incomeof all sectors’ and the ‘disposable income ofhouseholds’ is then broken down per capitaacross the population of the individual regionsin each country.

This is the easiest and most transparent method ofdividing up the remaining balance. Information isnot available on the regional breakdown of theseparameters, so it is assumed that, on average,State activity benefits each citizen of a regionequally. This involves the assumption that each ofthe regions within the country has the same agestructure — a bold, but not totally unrealistic as-sumption. The problems are somewhat greaterwhen it comes to the operating surplus and prop-erty income, but, given the total lack of other datahere upon which to compile a regional structure,the per capita approach is again adopted.

This approach seems to be easier to justify for theState sector than for private organisations. How-ever, given how low a percentage of the total fig-ure is involved (4 % on average), this has only amarginal influence on the results. Experimentswith other distribution keys, such as value addedor persons in employment, resulted in a virtuallyidentical regional structure. The per capitaapproach was therefore chosen for transparency

The difference between the disposable income ofall sectors and the disposable income of privatehouseholds corresponds to the disposable income

of other sectors. It is therefore useful first of all toget an idea of the figures involved. Disposablehousehold income in the European Union is by far


reasons. The sum of activities 1 and 2 is dividedby the number of persons living in a region. Thefigure used here is the average annual population,which is also the denominator for GDP per capita.

Results

Caution should still be exercised in interpretingthe initial results for this new indicator. Austria

was not able to submit data, a gap which is due tobe filled by summer 2003. Germany was grantedan exemption allowing it to submit only NUTSlevel 1 data — i.e. for the Länder. There are no re-gional data available yet for disposable householdincome in Bulgaria and Turkey.

There are unfortunately no national accountsdata available for Cyprus, Hungary, Malta,

Regional disposable incomeper capita, all sectors

2000 — NUTS 2

> 22 50020 000–22 50017 500–20 00015 000–17 500≤ 15 000Data not available

D: NUTS 1; A: national levelEurostat estimate


Map 12.5


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12Poland and Slovakia, although regional data onhouseholds do exist. These gaps are also due to befilled shortly. The data from this first cycle arealso still subject to revision.

Map 12.5 shows the regional distribution of thisnew indicator. Subject to the abovementionedreservations, it is possible to state the following.

The Grand Duchy of Luxembourg is clearly therichest region in Europe. This finding comes as nosurprise and has been confirmed by other studies.

What is, however, astonishing is the performanceof northern Italian regions, with 5 of the 11 top-ranking regions being in Italy. One reason for thiscould be the fact that the State redistribution pol-

Rank changewhen regions are ranked according to regional

disposable income and not to GDP2000 — NUTS 2

> 3015–300–14– 15– – 1≤ – 16Data not available

D: NUTS 1; A: national levelEurostat estimate


Map 12.6


icy in Italy has a lower regional impact than inother countries, although it should be pointed outagain here that the State’s share of disposable in-come was divided up per capita. It may well bethat regional redistribution is carried out in someother way.

It is also clear that there are significantimprovements in the relative positions of regionsbordering on central London, and the fact that theOuter London region alone is up 87 places isclearly the result of commuter flows. This climbup the rankings is shown in Map 12.6.

Commuters also undoubtedly play a role inFlevoland and Namur, which each rise about 60places in the rankings.

There is a significant level of regional redistribu-tion in Germany. The five new Länder improve byan average of around 15 places, whilst Hesse andBerlin lose out badly, as do some of the larger-by-area Länder, such as Bayern (down 20) andBaden-Württemberg (down 6). In terms of rela-tive position, the largest falls are recorded by thecapital regions of Praha (Czech Republic) andComunidad de Madrid (Spain).

It is possible that this approach still needs to be re-fined. Nor are all the data complete. The initial re-sults of these calculations do, nonetheless, appearplausible and help to provide a more objectivecomparison of Europe’s regions.

INTERACTIVE DATA PRESENTATION



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© EuroGeographics, for the administrative boundariesCartography: Eurostat — GISCO, June 2003

Interactivevisualisation ofregional dataFor the first time, the CD-ROM enclosed with theregional yearbook contains not only static PDFfiles but also an applet which gives access to an in-teractive and user-friendly graphical presentationof regional differences. The visualisation is carriedout by means of bar charts complemented by tab-ular numerical information. The regions selectedfor inclusion are the NUTS 2 regions that containthe capital cities of the 15 Member States and 11of the 12 accession countries. It is important tonote that these regions are not the same as thecapital cities themselves. In certain cases (such asInner London), the city and its suburbs extend farbeyond the NUTS 2 region containing the heart ofthe city. By contrast, some other NUTS 2 regionsin the selection may be vastly bigger than the cap-ital city. In the case of two current Member States(Denmark and Luxembourg) and no fewer thanfive of the accession countries shown (Cyprus, Es-tonia, Latvia, Lithuania, Malta and Slovenia), thewhole country is a NUTS 2 region.

The applet allows you to explore the data by‘playing’ with them (without having to do anyprogramming) and to focus on the differences andsimilarities between these regions, always allow-ing for the abovementioned disparity in the natureof certain regions. In many cases, the maps andcommentaries contained in the rest of the year-book will allow the information for the ‘capital-city’ region to be placed in its national context.

How to use theappletThe applet is stored on the CD-ROM as a folderof files. When you open the folder, click on ‘Cap-italCityEU.htm’ in order to get the applet started.

The application offers two different views of thedata. Via the ‘View’ button, you may switch fromone perspective to the other.

The first ‘View’ option (‘comparison of regions’ isthe default option) enables you to compare all EUMember States or, alternatively, all accession

countries considered with respect to one of eightsocial and economic variables.

• The applet opens as a bar graph displaying thevariable ‘population’ in each of the capital-cityregions in the EU. Via the pull down menu‘Variable’, you may switch to a graphical com-parison of the same EU regions with respect toseven other variables — offering you a total ofeight different bar charts.

• Alternatively, rather than choosing EU coun-tries, you can select (via the pull down menu‘country set’) the accession countries and com-pare the capital-city regions of these countrieswith respect to one of the eight variables. Thisgenerates eight further graphs to study.

The second ‘View’ option (comparison of vari-ables) enables you to compare any of the 27 re-gions with any other of the 27 with respect topairs of two variables:

• The default for this view of the data is a com-parison of the Austrian and Belgian capital-cityregions with respect to the variables pair ‘pop-ulation’ and ‘population density’. You maynow choose via the pull down menus ‘Region 1’and ‘Region 2’ any other region combinationand visualise the differences with respect tothese two variables. This yields 27 x 26 = 702visualisations, each containing two bar charts.

• You may now also compare all combinations ofcapital-city regions with respect to three otherpairs of variables, namely the pairs ‘total area’and ‘agricultural area’, ‘unemployment rate’and ‘natural population increase rate’ or ‘GDP’and ‘number of cars per 10 inhabitants’, re-spectively. In total, 3 x 702 = 2 106 furthergraphs can be selected, each again consisting oftwo bar diagrams.

The explanations above are also accessible in amore condensed form by activating the ‘Help’function of the applet. Finally, there are explana-tory texts and methodological comments whichare accessible via the button ‘Notes’.



EUROPEAN UNION: NUTS 2 regionsBE Belgique-BelgiëBE1 Région de Bruxelles-

Capitale/BrusselsHfdst. Gew.

BE2 Vlaams GewestBE21 AntwerpenBE22 Limburg (B)BE23 Oost-VlaanderenBE24 Vlaams BrabantBE25 West-VlaanderenBE3 Région wallonneBE31 Brabant wallonBE32 HainautBE33 LiègeBE34 Luxembourg (B)BE35 NamurDK DanmarkDE DeutschlandDE1 Baden-WürttembergDE11 StuttgartDE12 KarlsruheDE13 FreiburgDE14 TübingenDE2 BayernDE21 OberbayernDE22 NiederbayernDE23 OberpfalzDE24 OberfrankenDE25 MittelfrankenDE26 UnterfrankenDE27 SchwabenDE3 BerlinDE4 BrandenburgDE5 BremenDE6 HamburgDE7 HessenDE71 DarmstadtDE72 GießenDE73 KasselDE8 Mecklenburg-

VorpommernDE9 NiedersachsenDE91 BraunschweigDE92 HannoverDE93 LüneburgDE94 Weser-EmsDEA Nordrhein-WestfalenDEA1 DüsseldorfDEA2 KölnDEA3 MünsterDEA4 DetmoldDEA5 ArnsbergDEB Rheinland-PfalzDEB1 KoblenzDEB2 TrierDEB3 Rheinhessen-Pfalz

DEC SaarlandDED SachsenDED1 ChemnitzDED2 DresdenDED3 LeipzigDEE Sachsen-AnhaltDEE1 DessauDEE2 HalleDEE3 MagdeburgDEF Schleswig-HolsteinDEG ThüringenGR ElladaGR1 Voreia ElladaGR11 Anatoliki Makedonia,

ThrakiGR12 Kentriki MakedoniaGR13 Dytiki MakedoniaGR14 ThessaliaGR2 Kentriki ElladaGR21 IpeirosGR22 Ionia NissiaGR23 Dytiki ElladaGR24 Sterea ElladaGR25 PeloponnissosGR3 AttikiGR4 Nissia Aigaiou, KritiGR41 Voreio AigaioGR42 Notio AigaioGR43 KritiES EspañaES1 NordesteES11 GaliciaES12 Principado de AsturiasES13 CantabriaES2 NoresteES21 País VascoES22 Comunidad Foral

de NavarraES23 La RiojaES24 AragónES3 Comunidad de

MadridES4 Centro (E)ES41 Castilla y LeónES42 Castilla-La ManchaES43 ExtremaduraES5 EsteES51 CataluñaES52 Comunidad

ValencianaES53 Islas BalearesES6 SurES61 AndalucíaES62 Región de MurciaES63 Ceuta y MelillaES7 Canarias

FR FranceFR1 Île-de-FranceFR2 Bassin parisienFR21 Champagne-ArdenneFR22 PicardieFR23 Haute-NormandieFR24 CentreFR25 Basse-NormandieFR26 BourgogneFR3 Nord-Pas-de-CalaisFR4 EstFR41 LorraineFR42 AlsaceFR43 Franche-ComtéFR5 OuestFR51 Pays de la LoireFR52 BretagneFR53 Poitou-CharentesFR6 Sud-OuestFR61 AquitaineFR62 Midi-PyrénéesFR63 LimousinFR7 Centre-EstFR71 Rhône-AlpesFR72 AuvergneFR8 MéditerranéeFR81 Languedoc-RoussillonFR82 Provence-Alpes-Côte

d’AzurFR83 CorseFR9 Départements

d’outre-merFR91 GuadeloupeFR92 MartiniqueFR93 GuyaneFR94 RéunionIE IrelandIE01 Border, Midland and

WesternIE02 Southern and EasternIT ItaliaIT1 Nord-OvestIT11 PiemonteIT12 Valle d’AostaIT13 LiguriaIT2 LombardiaIT3 Nord-EstIT31 Trentino-Alto AdigeIT32 VenetoIT33 Friuli-Venezia GiuliaIT4 Emilia-RomagnaIT5 Centro (I)IT51 ToscanaIT52 UmbriaIT53 MarcheIT6 Lazio


A

N

N

E

X


IT7 Abruzzo-MoliseIT71 AbruzzoIT72 MoliseIT8 CampaniaIT9 SudIT91 PugliaIT92 BasilicataIT93 CalabriaITA SiciliaITB SardegnaLU Luxembourg (Grand-

Duché)NL NederlandNL1 Noord-NederlandNL11 GroningenNL12 FrieslandNL13 DrentheNL2 Oost-NederlandNL21 OverijsselNL22 GelderlandNL23 FlevolandNL3 West-NederlandNL31 UtrechtNL32 Noord-HollandNL33 Zuid-HollandNL34 ZeelandNL4 Zuid-NederlandNL41 Noord-BrabantNL42 Limburg (NL)AT ÖsterreichAT1 OstösterreichAT11 BurgenlandAT12 NiederösterreichAT13 WienAT2 SüdösterreichAT21 KärntenAT22 SteiermarkAT3 WestösterreichAT31 OberösterreichAT32 SalzburgAT33 TirolAT34 VorarlbergPT PortugalPT1 ContinentePT11 NortePT12 Centro (P)PT13 Lisboa e Vale do Tejo

PT14 AlentejoPT15 AlgarvePT2 AçoresPT3 MadeiraFI Suomi/FinlandFI1 Manner-SuomiFI13 Itä-SuomiFI14 Väli-SuomiFI15 Pohjois-SuomiFI16 UusimaaFI17 Etelä-SuomiFI2 Ahvenanmaa/ÅlandSE SverigeSE01 StockholmSE02 Östra MellansverigeSE04 SydsverigeSE06 Norra MellansverigeSE07 Mellersta NorrlandSE08 Övre NorrlandSE09 Småland med öarnaSE0A VästsverigeUK United KingdomUKC North-EastUKC1 Tees Valley and

DurhamUKC2 Northumberland and

Tyne and WearUKD North-WestUKD1 CumbriaUKD2 CheshireUKD3 Greater ManchesterUKD4 LancashireUKD5 MerseysideUKE Yorkshire and the

HumberUKE1 East Riding and

North LincolnshireUKE2 North YorkshireUKE3 South YorkshireUKE4 West YorkshireUKF East MidlandsUKF1 Derbyshire and

NottinghamshireUKF2 Leicestershire,

Rutland andNorthamptonshire

UKF3 LincolnshireUKG West MidlandsUKG1 Herefordshire,

Worcestershire andWarwickshire

UKG2 Shropshire andStaffordshire

UKG3 West MidlandsUKH EasternUKH1 East AngliaUKH2 Bedfordshire and

HertfordshireUKH3 EssexUKI LondonUKI1 Inner LondonUKI2 Outer LondonUKJ South-EastUKJ1 Berkshire,

Buckinghamshire andOxfordshire

UKJ2 Surrey, East and WestSussex

UKJ3 Hampshire and Isle ofWight

UKJ4 KentUKK South-WestUKK1 Gloucestershire,

Wiltshire and NorthSomerset

UKK2 Dorset and SomersetUKK3 Cornwall and Isles of

ScillyUKK4 DevonUKL WalesUKL1 West Wales and the

ValleysUKL2 East WalesUKM ScotlandUKM1 North-Eastern

ScotlandUKM2 Eastern ScotlandUKM3 South-Western

ScotlandUKM4 Highlands and IslandsUKN Northern Ireland



Regions in the candidate countriesNOTE: The following list of regions in the candidate countries is intended to assist the reader to locateon the maps regions that are mentioned in the text. It is not an official list.

The current state of the nomenclature of statistical regions in the candidate countries may be consultedon the Eurostat site at: http://europa.eu.int/comm/eurostat/ramon/nuts/splash_regions.html

Code Country Level 2 regions Code Country Level 2 regionsBulgaria Malta

BG BALGARIJA MT MALTABG01 Severozapaden (North-West)BG02 Severen tsentralen (North Central) PolandBG03 Severoiztochen (North-East) PL POLSKABG04 Yugozapaden (South-West) PL01 DolnoslaskieBG05 Yuzhen tsentralen (South Central) PL02 Kujawsko-PomorskieBG06 Yugoiztochen (South-East) PL03 Lubelskie

PL04 LubuskieCyprus PL05 Lódzkie

CY ÊÕPÏÓ/CYPRUS/KIBRIS PL06 MalopolskiePL07 Mazowieckie

Czech Republic PL08 OpolskieCZ CESKÁ REPUBLIKA PL09 PodkarpackieCZ01 Praha PL0A PodlaskieCZ02 Strední Cechy PL0B PomorskieCZ03 Jihozápad PL0C SlaskieCZ04 Severozápad PL0D SwietokrzyskieCZ05 Severovychod PL0E Warminsko-MazurskieCZ06 Jihovychod PL0F WielkopolskieCZ07 Strední Morava PL0G ZachodniopomorskieCZ08 Moravskoslezko

RomaniaEstonia RO ROMÂNIA

EE EESTI RO01 Nord-EstRO02 Sud-Est

Hungary RO03 SudHU MAGYARORSZÁG RO04 Sud-VestHU01 Közép-Magyarország RO05 VestHU02 Közép-Dunántúl RO06 Nord-VestHU03 Nyugat-Dunántúl RO07 CentruHU04 Dél-Dunántúl RO08 BucurestiHU05 Észak-MagyarországHU06 Észak-Alföld SloveniaHU07 Dél-Alföld SI SLOVENIJA

Lithuania SlovakiaLT LIETUVA SK SLOVENSKÁ REPUBLIKA

SK01 Bratislavsky krajLatvia SK02 Západné Slovensko

LV LATVIJA SK03 Stredné SlovenskoSK04 Vychodné Slovensko


regions: statistical yearbook 2003

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