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                      Statistical books




Eurostat regional yearbook 2009
Statistical books




Eurostat regional yearbook 2009
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Luxembourg: Office for Official Publications of the European Communities, 2009

ISBN 978-92-79-11696-4
ISSN 1830-9674
doi: 10.2785/17776
Cat. No: KS-HA-09-001-EN-C

Theme: General and regional statistics
Collection: Statistical books

© European Communities, 2009
© Copyright for the following photos: cover: © Annette Feldmann; the chapters Introduction,
Population, Household accounts, Information society, Education and tourism: © Phovoir.com;
the chapter European cities: © Teodóra Brandmüller; the chapters Labour market, Gross domestic
product, Structural business statistics and Science, technology and innovation: © the Digital Photo
Library of the Directorate-General for Regional Policy of the European Commission;
the chapter Agriculture: © Jean-Jacques Patricola.

For reproduction or use of these photos, permission must be sought directly from the copyright
holder.
Preface
Dear Readers,
Five years ago, 2004, was a momentous year, with 10 new
Member States joining the European Union on 1 May. This
Eurostat regional yearbook 2009 is eloquent testimony to the
economic and social progress made by these regions since
then and highlights those areas where redoubled efforts will
be needed to reach our goal of greater cohesion.
The 11 chapters of this yearbook investigate interesting as­
pects of regional differences and similarities in the 27 Mem­
ber States and in the candidate and EFTA countries. The
aim is to encourage readers to track down the regional data
available on the Eurostat website and make their own ana­
lyses of economic and social developments.
In addition to the fascinating standard chapters on regional
population developments, the regional labour market, re­
gional GDP, etc., this year’s edition features a new contri­
bution on the regional development of information society
data. As in recent years, the description of regional devel­
opments is rounded off by a contribution on the latest findings of the Urban Audit, a data collection
containing a multitude of statistical data on European towns and cities.
We are constantly updating the range of regional indicators available and hope to include them as
topics in future editions, provided the availability and quality of these data are sufficient.
I wish you an enjoyable reading experience!




                                                                                Walter Radermacher
                                                                          Director­General, Eurostat




              Eurostat regional yearbook 2009                                                           3
Acknowledgements
    The editors of the Eurostat regional yearbook 2009 would like to thank all those who were involved in
    its preparation. We are especially grateful to the following chapter authors at Eurostat for making the
    publication of this year’s edition possible.

    • Population: Veronica Corsini, Monica Marcu and Rosemarie Olsson (Unit F.1: Population)
    • European cities: Teodóra Brandmüller (Unit E.4: Regional statistics and geographical informa­
      tion)
    • Labour market: Pedro Ferreira (Unit E.4: Regional statistics and geographical information)
    • Gross domestic product: Andreas Krüger (Unit C.2: National accounts — production)
    • Household accounts: Andreas Krüger (Unit C.2: National accounts — production)
    • Structural business statistics: Aleksandra Stawińska (Unit G.2: Structural business statistics)
    • Information society: Albrecht Wirthmann (Unit F.6: Information society and tourism)
    • Science, technology and innovation: Bernard Félix, Tomas Meri, Reni Petkova and Håkan Wilén
      (Unit F.4: Education, science and culture)
    • Education: Sylvain Jouhette, Lene Mejer and Paolo Turchetti (Unit F.4: Education, science and
      culture)
    • Tourism: Ulrich Spörel (Unit F.6: Information society and tourism)
    •	 Agriculture: Céline Ollier (Unit E.2: Agriculture and fisheries)

    This publication was edited and coordinated by Åsa Önnerfors (Unit E.4: Regional statistics and geo­
    graphical information) with the help of Berthold Feldmann (Unit E.4: Regional statistics and geo­
    graphical information) and Pavel Bořkovec (Unit D.4: Dissemination). Baudouin Quennery (Unit E.4:
    Regional statistics and geographical information) produced all the statistical maps.

    We are also very grateful to:
    —    the Directorate-General for Translation of the European Commission, and in particular the
         German, English and French translation units;
    —    the Publications Office of the European Union, and in particular Bernard Jenkins in Unit B.1,
         Cross­media publishing, and the proofreaders in Unit B.2, Editorial services.




4                                                               Eurostat regional yearbook 2009
Contents

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Statistics on regions and cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
The NUTS classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
More regional information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1 POPULATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                   13
Unveiling the regional pattern of demography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                     14
Population density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .           14
Population change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .            14
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   23
Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .             23

2 EUROPEAN CITIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                      25
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     26
    Enhanced list of indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                  26
    Moving from five-year periodicity to annual data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                  26
    Extended geographical coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                             26
Discovering the spatial dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                        26
    Core cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    28
    Larger urban zones. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .              28
Geography matters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .            33

3 LABOUR MARKET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                        35
Regional working time patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                       36
Brief overview for 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .              36
Regional work patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .               39
Part-time jobs: lowering the average working time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                        41
Employees spend less time at work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                          44
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   45
Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .             46
    Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    46

4 GROSS DOMESTIC PRODUCT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                         49
What is regional gross domestic product?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                50
Regional GDP in 2006. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .              50
Average GDP over the three-year period 2004–06 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                         52
Major regional differences even within the countries themselves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                      52
Dynamic catch-up process in the new Member States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                              52
Different trends even within the countries themselves. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                           56
Convergence makes progress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                       56
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   57
Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .             59
    Purchasing power parities and international volume comparisons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                      59




                         Eurostat regional yearbook 2009                                                                                                                                                                    5
5 HOUSEHOLD ACCOUNTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                    61
    Introduction: measuring wealth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                       62
    Private household income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                   62
    Results for 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .       62
        Primary income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                 62
        Disposable income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                    64
    Dynamic development on the edges of the Union . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                            68
    Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   70
    Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .             71

    6 STRUCTURAL BUSINESS STATISTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                               73
    Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     74
    Regional specialisation and business concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                         74
    Specialisation in business services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                      80
    Employment growth in business services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                 84
    Characteristics of the top 30 most specialised regions in business services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                          84
    Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   87
    Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .             87

    7 INFORMATION SOCIETy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                89
    Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     90
    Access to information and communication technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                 90
    Use of the Internet and Internet activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                            93
    Non-users of the Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                96
    Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   97
    Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .             99

    8 SCIENCE, TECHNOLOGy AND INNOvATION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
    Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
    Research and development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
    Human resources in science and technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
    High-tech industries and knowledge-intensive services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
    Patents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
    Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
    Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

    9 EDUCATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                  113
    Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     114
    Students’ participation in education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                         114
    Participation of 4-year-olds in education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                            114
    Students in upper secondary education and post-secondary non-tertiary education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                                          116
    Students in tertiary education. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                    119
    Tertiary educational attainment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                      119
    Lifelong learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .        119
    Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   123
    Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .             123




6                                                                                                                                                  Eurostat regional yearbook 2009
10 TOURISM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .           125
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     126
Accommodation capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                   127
Overnight stays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .        127
Average length of stay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .             130
Tourism intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .        130
Tourism development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                133
Inbound tourism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .          135
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   135
Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .             137

11 AGRICULTURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                 139
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     140
Utilised agricultural area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .             140
    Proportion of area under cereals to the utilised agricultural area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                   140
    Proportion of permanent crops to the utilised agricultural area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                                    140
Agricultural production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .              143
    Wheat production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .               143
    Grain maize production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                   143
    Rapeseed production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                  146
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   146
Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .             148


ANNEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    149
European Union: NUTS 2 regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                         149
Candidate countries: statistical regions at level 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                                  152
EFTA countries: statistical regions at level 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                             153




                         Eurostat regional yearbook 2009                                                                                                                                                                   7
Introduction
Introduction


                                   Statistics on regions and cities                         throughout Europe and offers a couple of expla­
                                                                                            nations for why they vary so much from region
                                   Statistical information is essential for under­          to region. The three economic chapters on Gross
                                   standing our complex and rapidly changing                domestic product, Household accounts and
                                   world. Eurostat, the Statistical Office of the Euro­     Structural business statistics all give us detailed
                                   pean Communities, is responsible for collecting          insight into the general economic situation in re­
                                   and disseminating data at European level, not            gions, private households and different sectors of
                                   only from the 27 Member States of the Euro­              the business economy.
                                   pean Union, but also from the three candidate
                                   countries (Croatia, the former Yugoslav Repub­           We are particularly proud to present a new and
                                   lic of Macedonia and Turkey) and the four EFTA           very interesting chapter on the Information so-
                                   countries (Iceland, Liechtenstein, Norway and            ciety, which describes the use of information
                                   Switzerland).                                            and communication technologies (ICT) among
                                                                                            private persons and households in European
                                   The aim of this publication, the Eurostat regional       regions. This chapter tells us, for example, how
                                   yearbook 2009, is to give you a flavour of some of       many households use the Internet regularly and
                                   the statistics on regions and cities that we collect     how many have broadband access. The next two
                                   from these countries. Statistics on regions enable       chapters are on Science, technology and innova-
                                   us to identify more detailed statistical patterns        tion and Education, three areas of statistics that
                                   and trends than national data, but since we have         are often seen as key to monitoring achievement
                                   271 NUTS 2 regions in the EU­27, 30 statisti­            of the goals set in the Lisbon strategy to make
                                   cal regions on level 2 in the candidate countries        Europe the most competitive and dynamic
                                   and 16 statistical regions on level 2 in the EFTA        knowledge­based economy in the world.
                                   countries, the volume of data is so great that one
                                   clearly needs some sorting principles to make it         In the next chapter we learn more about regional
                                   understandable and meaningful.                           statistics on Tourism, and which tourist desti­
                                                                                            nations are the most popular. The last chapter
                                   Statistical maps are probably the easiest way for the    focuses on Agriculture, this time mainly crop
                                   human mind to sort and ‘absorb’ large amounts of         statistics, revealing which kind of crop is grown
                                   statistical data at one time. Hence this year’s Euro­    where in Europe.
                                   stat regional yearbook, as in previous editions,
                                   contains a lot of statistical maps where the data
                                   is sorted by different statistical classes represented   The NUTS classification
                                   by colour shades on the maps. Some chapters also
                                   make use of graphs and tables to present the statis­     The nomenclature of territorial units for statistics
                                   tical data, selected and sorted in some way (differ­     (NUTS) provides a single uniform breakdown of
                                   ent top lists, graphs with regional extreme values       territorial units for the production of regional sta­
                                   within the countries or only giving representative       tistics for the European Union. The NUTS classi­
                                   examples) to make it easier to understand.               fication has been used for regional statistics for
                                                                                            many decades, and has always formed the basis
                                   We are proud to present a great variety of subjects      for regional funding policy. It was only in 2003,
                                   tackled in the 11 chapters in this years’ edition        though, that NUTS acquired a legal basis, when
                                   of the Eurostat regional yearbook. The first chap­       the NUTS regulation was adopted by the Parlia­
     (1) More information on
                                   ter on Population gives us detailed knowledge of         ment and the Council (1).
         the NUTS classification   different demographic patterns, such as popula­
         can be found at http://
         ec.europa.eu/eurostat/    tion density, population change and fertility rates      Whenever new Member States join the EU, the
         ramon/nuts/splash_        in the countries examined. This chapter can be           NUTS regulation is amended to include the re­
         regions.html
                                   considered the key to all other chapters, since          gional classification in those countries. This was
                                   all other statistics depend on the composition of        the case in 2004, when the EU took in 10 new
                                   the population. The second chapter focuses on            Member States, and in 2007 when Bulgaria and
                                   European cities and explains in detail the defini­       Romania also joined the European Union.
                                   tions of the various spatial levels used in the Ur­      The NUTS regulation states that amendments of
                                   ban Audit data collection, with some interesting         the regional classification, to take account of new
                                   examples on how people travel to work in nine            administrative divisions or boundary changes in
                                   European capitals.
                                                                                            the Member States, may not be carried out more
                                   The chapter on the Labour market mainly de­              frequently than every three years. In 2006, this
                                   scribes the differences in weekly working hours          review took place for the first time, and the re­




10                                                                                                 Eurostat regional yearbook 2009
Introduction


sults of these changes to the NUTS classification      given on the three candidate countries (Croatia,
have been valid since 1 January 2008.                  the former Yugoslav Republic of Macedonia and
                                                       Turkey) and the four EFTA countries (Iceland,
Since these NUTS changes were introduced quite
                                                       Liechtenstein, Norway and Switzerland).
recently, the statistical data are still missing in
some cases or have been replaced with national         Regions in the candidate countries and the EFTA
values on some statistical maps, as indicated in       countries are called statistical regions and they
the footnotes to each map concerned. This ap­          follow the same rules as the NUTS regions in
plies in particular to Sweden, which introduced        the European Union, except that there is no legal
NUTS level 1 regions, to Denmark and Slovenia,         base. Data from the candidate and EFTA coun­
which introduced new NUTS level 2 regions,             tries are not yet available in the Eurostat database
and to the two northernmost Scottish regions,          for some of the policy areas, but the availability
North Eastern Scotland (UKM5) and Highlands            of data is constantly improving, and we hope to
and Islands (UKM6), where the border between           have even more complete coverage from these
the two regions has changed. The regional data         countries in the near future.
availability for these countries will hopefully
soon be improved.
                                                       More regional information
Please also note that some Member States have a
relatively small population and are therefore not      In the subject area ‘Regions and cities’ under the
divided into more than one NUTS 2 region. Thus,        heading ‘General and regional statistics’ on the
for these countries the NUTS 2 value is exactly        Eurostat website you will find tables with statis­
the same as the national value. Following the lat­     tics on both ‘Regions’ and the ‘Urban Audit’, with
est revision of the NUTS classification, this now      more detailed time series (some of them going
applies to six Member States (Estonia, Cyprus,         back as far as 1970) and with more detailed sta­
Latvia, Lithuania, Luxembourg and Malta), one          tistics than this yearbook contains. You will also
candidate country (the former Yugoslav Republic        find a number of indicators at NUTS level 3 (such
of Macedonia) and two EFTA countries (Iceland          as area, demography, gross domestic product and
and Liechtenstein). In all cases the whole country     labour market data). This is important since some
consists of one single NUTS 2 region.                  of the countries covered are not divided into
A folding map on the inside of the cover accom­        NUTS 2 regions, as mentioned above.
panies this publication and it shows all NUTS          For more detailed information on the content
level 2 regions in the 27 Member States of the         of the regional and urban databases, please con­
European Union (EU­27) and the correspond­             sult the Eurostat publication European regional
ing level 2 statistical regions in the candidate and   and urban statistics — Reference guide — 2009
EFTA countries. In the annex you will find the         edition, which you can download free of charge
full list of codes and names of these regions. This    from the Eurostat website. You can also down­
will help you locate a specific region on the map.     load Excel tables containing the specific data used
                                                       to produce the maps and other illustrations for
Coverage                                               each chapter in this publication on the Eurostat
                                                       website. We do hope you will find this publication
The Eurostat regional yearbook 2009 mainly con­        both interesting and useful and we welcome your
tains statistics on the 27 Member States of the        feedback at the following e­mail address: estat­
European Union but, when available, data is also       regio@ec.europa.eu




              Eurostat regional yearbook 2009                                                                 11
Population
1    Population


     Unveiling the regional pattern                        million (1960) to almost 500 million (497 million
                                                           on 1 January 2008). Including candidate coun­
     of demography                                         tries and EFTA countries, the total population
     Demographic trends have a strong impact on the        has grown over the same period from under 450
     societies of the European Union. Consistently low     million to 587 million.
     fertility levels, combined with extended longevity    The total population change has two compo­
     and the fact that the baby boomers are reaching       nents: the so­called ‘natural increase’, which is
     retirement age, result in demographic ageing of       defined as the difference between the numbers of
     the EU population. The share of the older gen­        live births and deaths, and net migration, which
     eration is increasing while the share of those of     ideally represents the difference between inward
     working age is decreasing.                            and outward migration flows (see ‘Methodologi­
     The social and economic changes associated with       cal notes’). Changes in the size of a population are
     population ageing are likely to have profound         the result of the number of births, the number of
     implications for the EU — and also to be visible      deaths and the number of people who migrate.
     at regional level, stretching across a wide range     Up to the end of the 1980s, natural increase
     of policy areas and impacting on the school­age       was by far the major component of population
     population, healthcare, labour force participa­       growth. However, there has been a sustained de­
     tion, social protection and social security issues    cline in the natural increase since the early 1960s.
     and government finances, etc.                         On the other hand, international migration has
     The demographic development is not the same           gained importance and became the major force
     in all regions of the EU. Some demographic phe­       of population growth from the beginning of the
     nomena might have a stronger impact in some           1990s onwards.
     regions than in others.                               The analysis on the following pages is mainly
     This chapter presents the regional pattern of de­     based on demographic trends observed over the
     mographic phenomena as it is today.                   period from 1 January 2003 to 1 January 2008. For
                                                           this purpose, five­year averages have been calcu­
                                                           lated of the total annual population change and its
     Population density                                    components. Given that demographic trends are
                                                           long­term developments, the five­year averages
     On 1 January 2007, 584 million people inhabited the   provide a stable and accurate picture. They help to
     European Union and candidate and EFTA coun­           identify regional clusters, which often stretch well
     tries. The population distribution is varied across   beyond national borders. For the sake of compara­
     the 317 NUTS 2 regions that make up this area.        bility, the population change and its components
     Map 1.1 shows the population density on 1 Janu­       are presented in relative terms, calculating the
     ary 2007. The population density of a region is the   so­called crude rates, i.e. they relate to the size of
     ratio of the population of a territory to its size.   the total population (see ‘Methodological notes’).
     Generally, capital city regions are among the most    Maps 1.2, 1.3 and 1.4 show these figures on total
     densely populated, as Map 1.1 shows. Inner Lon­       population change and its components.
     don was by far the most densely populated, but the    In most of the north­east, east and part of the
     Bruxelles­Capitale, Wien, Berlin, Praha, Istanbul,    south­east of the area made up by the European
     Bucureşti — Ilfov and Attiki (Greece) regions also    Union and the candidate and EFTA countries, the
     have densities above 1 000 inhabitants per km².       population is on the decrease. Map 1.2 is marked
     The least densely populated region was the region     by a clear divide between the regions there and in
     of Guyane (France), while the next least densely      the rest of the EU. Most affected by the decreasing
     populated regions, with fewer than 10 inhabitants     population trend are Germany (in particular the
     per km², were all in Sweden, Finland, Iceland and     former eastern Germany), Poland, Bulgaria, Slo­
     Norway. By comparison, the European Union has         vakia, Hungary and Romania, and to the north
     a population density of 114 inhabitants per km².      the three Baltic States and the northern parts
                                                           of Sweden and the Finnish region of Itä­Suomi.
     Population change                                     Decreasing population trends are also evident
                                                           in many regions of Greece. To the east, on the
     During the last four and a half decades, the pop­     other hand, the total population change is positive
     ulation of the 27 countries that make up today’s      in Cyprus and, to a lesser extent, in the former
     European Union has grown from around 400              Yugoslav Republic of Macedonia and Turkey.




14                                                                Eurostat regional yearbook 2009
Population   1
Map 1.1: Population density, by NUTS 2 regions, 2007
          Inhabitants per km2




           Eurostat regional yearbook 2009                          15
1                      Population


     Map 1.2: Total population change, by NUTS 2 regions, average 2003–07
               Per 1 000 inhabitants




16                                                                      Eurostat regional yearbook 2009
Population                    1
In nearly all western and south­western regions of              2003–07. The resulting negative ‘natural popu­
the EU the population increased over the period                 lation change’ is widespread and affects almost
2003–07. This is particularly evident in Ireland                50 % of the EU’s regions.
and in almost all regions of the United Kingdom,
                                                                A single extended cross­border region can be
Italy, Spain, France and Portugal, including the
                                                                identified showing a natural increase of popu­
French overseas departments and the Spanish and
                                                                lation, made up of Ireland, the central United
Portuguese islands in the Atlantic Ocean. There
                                                                Kingdom, most regions in France, Belgium, Lux­
has also been positive total population change in
                                                                embourg, the Netherlands, Switzerland, Iceland,
Austria, Switzerland, Belgium, Luxembourg and
                                                                Lichtenstein, Denmark and Norway: in these
the Netherlands.
                                                                regions, in the period 2003–07, live births were
The picture provided by Map 1.2 can be refined by               more numerous than deaths.
analysing the two components of total population
                                                                Deaths are more numerous than births in Ger­
change, namely natural change and migration.
                                                                many, the Czech Republic, Slovakia, Hungary,
Map 1.3 shows that in many regions of the EU                    Slovenia, Croatia, Romania and Bulgaria, and also
more people died than were born in the period                   in the Baltic States and Sweden in the north and



Figure 1.1: Total fertility rates by country, 1986 and 2006
            Children per woman
SK
 PL
 LT
  SI
RO
DE
CZ
HU
 LV
PT
  IT
BG
HR
 ES
GR
AT
MT
  LI
MK
CY
CH
 EE
LU
NL
BE
DK
UK
  FI
 SE
  IE
NO
FR
  IS
TR
   0.0                  0.5                 1.0                  1.5                  2.0                 2.5                     3.0   3.5
      1986                    2006

   Source: Eurostat Demographic Statistics
   Notes: 1986 data: EE, PL, MT: national estimates; LI: 1985 national estimate; HR: 1990; TR: 1990 national estimate; MK: 1994
           2006 data: IT, BE, TR: national estimates




                  Eurostat regional yearbook 2009                                                                                             17
1                      Population


     Map 1.3: Natural population change (live births minus deaths), by NUTS 2 regions, average 2003–07
               Per 1 000 inhabitants




18                                                                       Eurostat regional yearbook 2009
Population                       1
Greece, Italy and Portugal in the south. The other                        Relatively high fertility rates tend to be recorded in
countries have an overall more balanced situation.                        countries that have implemented a range of family­
                                                                          friendly policies, such as the introduction of acces­
A major reason for the slowdown of the natural
                                                                          sible and affordable childcare and/or more flexible
increase of the population is the fact that inhabit­
                                                                          working patterns; this is the case for France, the
ants of the EU have fewer children. At aggregat­
                                                                          Nordic countries and the Netherlands.
ed level, in the 27 countries that today form the
European Union, the total fertility rate has de­                          The (slight) increase in the total fertility rate that
clined from a level of around 2.5 in the early                            is observed in some countries between 1986 and
1960s to a level of about 1.5 in 1993, where it has                       2006 may be partly attributable to a catching­up
remained since (for the definition of the total fer­                      process following postponement of the decision
tility rate, see the ‘Methodological notes’).                             to have children. When women give birth later in
                                                                          life, the total fertility rate first indicates a decrease
At country level, in 2006, a total fertility rate of
                                                                          in fertility, followed later by a recovery.
less than 1.5 was observed in 17 of the 27 Member
States. To compare, Figure 1.1 also includes figures                      By comparison, in the more developed parts of
for 1986 and for the candidate and EFTA countries.                        the world today, a total fertility rate of around



Figure 1.2: Crude birth rates, by NUTS 2 regions, 2007
            Births per 1 000 inhabitants
BE                      Prov. West-Vlaanderen                          Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest
BG                      Severozapaden                 Yugoiztochen
CZ                                Střední Morava         Střední Čechy
DK                                      Sjælland             Hovedstaden
DE                       Saarland                Hamburg
 EE
  IE                                          Border, Midland and Western         Southern and Eastern
 EL                              Ipeiros                       Kriti
 ES          Principado de Asturias                                                                  Ciudad Autónoma de Ceuta
FR                                         Corse                                                                                          Guyane
 IT                           Liguria                        Provincia Autonoma Bolzano/Bozen
CY
LV
 LT
LU
HU                      Nyugat-Dunántúl                  Észak-Alföld
MT
NL                         Limburg (NL)                                  Flevoland
AT                   Burgenland (A)                      Vorarlberg
 PL                         Opolskie                           Pomorskie
PT                             Alentejo                         Região Autónoma dos Açores
RO                      Sud-Vest Oltenia                       Nord-Est
  SI                     Vzhodna Slovenija                 Zahodna Slovenija
SK                   Západné Slovensko                          Východné Slovensko
  FI                              Itä-Suomi                           Pohjois-Suomi
 SE                       Norra Mellansverige                             Stockholm
UK                  Cornwall and Isles of Scilly                                      Inner London
HR                        Središnja i Istočna           Sjeverozapadna Hrvatska
                       (Panonska) Hrvatska
MK
TR
  IS
  LI
NO                     Hedmark og Oppland                                 Oslo og Akershus
CH                               Ticino                    Région lémanique
       0                  5                        10                    15                  20                 25                   30            35
               National value
       Source: Eurostat Demographic Statistics.
       Notes: FR, UK: 2006
               TR: national level




                   Eurostat regional yearbook 2009                                                                                                      19
1                    Population


     Map 1.4: Net migration, by NUTS 2 regions, average 2003–07
              Per 1 000 inhabitants




20                                                                Eurostat regional yearbook 2009
Population       1
2.1 children per women is considered to be the          • regions in the north­east of France and the
replacement level, i.e. the level at which the popu­      French overseas departments;
lation would remain stable in the long run if there
                                                        • a few regions in the south of Italy, in the Neth­
were no inward or outward migration. At present
                                                          erlands and in the United Kingdom.
(2006 data), practically all of the EU and the can­
didate and EFTA countries, with the exception           Regions where the two components of population
of Turkey and Iceland, are still well below the re­     change do not compensate for, but rather add to,
placement level.                                        one another are often exposed to major develop­
                                                        ments, upwards or — in some regions — down­
The analysis of Map 1.3 can also be refined by iso­
                                                        wards. In Ireland, Luxembourg, Belgium, Malta,
lating the contribution of live births to the natural
                                                        Cyprus, Switzerland, Iceland, many regions
population change. Figure 1.2 shows the regional
                                                        in France and in Norway and some regions in
differences within each country of the so­called
                                                        Spain, the United Kingdom and the Netherlands,
crude birth rates (see the ‘Methodological notes’).
                                                        a natural increase has been accompanied by posi­
The largest regional differences in 2007 were
                                                        tive net migration. However, in eastern German
in France, where the highest crude birth rate is
                                                        regions, Lithuania and Latvia and some regions
more than three times the lowest, followed by
                                                        in Poland, Slovakia, Hungary, Bulgaria and Ro­
Spain, where the highest crude birth rate is also
                                                        mania, both components of population change
three times the lowest. For the other countries,
                                                        have moved in a negative direction, as can also be
regional differences in crude birth rates are less
                                                        seen from Map 1.2. In these regions this trend has
pronounced but still significant.
                                                        led to sustained population loss.
The third determinant of population change
                                                        In 2007, the average population in the EU­27 aged
(after fertility and mortality) is migration. As
                                                        65 and older was 17 %, which means an increase
many countries in the EU are currently at a point
                                                        of 2 percentage points in the last 10 years. This
in the demographic cycle where ‘natural popula­
                                                        ageing population, especially in rural areas,
tion change’ is close to being balanced or nega­
                                                        raises issues about infrastructure and the need
tive, the importance of immigration increases
                                                        for social services and healthcare.
when it comes to maintaining population size.
Moreover, migration also contributes indirect­          The highest percentage of population aged 65
ly to natural change, given that migrants have          and older can be found in Liguria (Italy), at 27 %.
children. Migrants are also usually younger and         Germany follows with up to 24 % in the region of
have not yet reached the age at which death is          Chemnitz and a further 14 regions above 20 %.
more frequent.                                          Some regions in Greece, Portugal, France and
                                                        Spain also show high figures, with up to 23 % of
In some regions of the European Union, negative
                                                        their population aged 65 years and older. These
‘natural change’ has been offset by positive net mi­
                                                        regions also show low and even negative natural
gration. This is at its most striking in Austria, the
                                                        population change, with more people dying than
United Kingdom, Spain, the northern and central
                                                        being born.
regions of Italy and some regions of western Ger­
many, Slovenia, southern Sweden, Portugal and           In Turkey the percentage of the population aged
Greece, as can be seen in Map 1.4. The opposite is      65 and older is as low as 3 % in the region of Van,
much rarer: in only a few regions (namely in the        and on average 8 % in the other regions. Although
northern regions of Poland and of Finland and           Turkey has negative net migration, the high fertil­
in Turkey) has positive ‘natural change’ been can­      ity results in a young population. Similarly, with
celled out by negative net migration.                   high fertility, coupled with high net migration,
                                                        only 11 % and 12 % of the population in the two
Four cross­border regions where more people
                                                        regions of Ireland are 65 and older.
have left than arrived (negative net migration)
can be identified on Map 1.4:                           According to projections, elderly people would
                                                        account for an increasing share of the population
• the northernmost regions of Norway and Fin­
                                                        and this is due to sustained reductions in mortal­
  land;
                                                        ity in past and future decades. The ageing process
• an eastern group, comprising most of the re­          can be typified as ageing from the top, as it large­
  gions of eastern Germany, Poland, Lithuania           ly results from projected increases in longevity,
  and Latvia and most parts of Slovakia, Hun­           moderated by the impact of positive net migra­
  gary, Romania, Bulgaria and Turkey;                   tion flows and some recovery in fertility.




               Eurostat regional yearbook 2009                                                                 21
1                     Population


     Map 1.5: Percentage of population aged 65 years old and more, by NUTS 2 regions, 2007




22                                                                     Eurostat regional yearbook 2009
Population       1
Conclusion                                            nomena have been identified, spreading across
                                                      national boundaries. While population decline is
This chapter highlights certain features of region­   evident in several regions, at aggregated level the
al population development in the area made up by      EU­27 population still increased in that period
the EU­27 Member States and the candidate and         by around 2 million people every year. The main
EFTA countries over the period from 1 January         driver of population growth in this area is migra­
2003 to 1 January 2008. As far as possible, typolo­   tion, which counterbalanced, as seen in the maps,
gies of regions in the different demographic phe­     the negative natural change in many regions.



Methodological notes
Sources: Eurostat — Demographic Statistics. For more information please consult the Eurostat
website at http://www.ec.europa.eu/eurostat.
Total fertility rate is defined as the average number of children that would be born to a
woman during her lifetime if she were to pass through her childbearing years conforming to the
age-specific fertility rates that have been measured in a given year.
Migration can be extremely difficult to measure. A variety of different data sources and definitions
are used in the Member States, meaning that direct comparisons between national statistics can
be difficult or misleading. The net migration figures here are not directly calculated from immigra-
tion and emigration flow figures. Since many countries either do not have accurate, reliable and
comparable figures on immigration and emigration flows or have no figures at all, net migration is
generally estimated on the basis of the difference between total population change and natural in-
crease between two dates (in the Eurostat database, it is then called net migration including cor-
rections). The statistics on net migration are therefore affected by all the statistical inaccuracies in
the two components of this equation, especially population change. In effect, net migration equals
all changes in total population that cannot be attributed to births and deaths.
Crude rate of total population change is the ratio of the total population change during the year
to the average population of the area in question in that year. The value is expressed per 1 000
inhabitants.
Crude rate of natural change is the ratio of natural population increase (live births minus deaths)
over a period to the average population of the area in question during that period. The value is
expressed per 1 000 inhabitants. It is also the difference of the crude birth rate minus the crude
death rate, which are, respectively, the ratio of live births during the year over the average popula-
tion and of deaths over the average population.
Crude rate of net migration is the ratio of net migration during the year to the average popula-
tion in that year. The value is expressed per 1 000 inhabitants. As stated above, the crude rate of
net migration is equal to the difference between the crude rate of total change and the crude
rate of natural change (i.e. net migration is considered as the part of population change not at-
tributable to births and deaths).
Population density is the ratio of the population of a territory to the total size of the territory (in-
cluding inland waters), as measured on 1 January.




              Eurostat regional yearbook 2009                                                               23
European cities
2    European cities


     Introduction                                               Moving from five-year periodicity
                                                                to annual data collection
     Data on European cities were collected in the Ur­
     ban Audit project. The project’s ultimate goal is to       Four reference years have been defined so far for
     help improve the quality of urban life: it supports        the Urban Audit: 1991, 1996, 2001 and 2004. For
     the exchange of experience among European cit­             the years 1991 and 1996, data were collected ret­
     ies; it helps to identify best practices; it facilitates   rospectively only for a reduced number of 80 var­
                                                                iables. Where data for these years were not avail­
     benchmarking at European level; and it provides
                                                                able, data from adjacent years were also accepted.
     information on the dynamics both within the cit­
                                                                In 2009 Eurostat launched an annual Urban Au­
     ies and with their surroundings.
                                                                dit, requesting data for a limited number of vari­
     The Urban Audit has become a core task of Euro­            ables. The annual data will help users to monitor
     stat. Even so, the project would not have been pos­        certain urban developments more closely.
     sible without sustained help and support from a
     wide range of colleagues. In particular, we would          Extended geographical coverage
     like to acknowledge the effort made by the cities
                                                                The pilot study in 1999 covered 58 cities from 15
     themselves, the national statistical institutes and
                                                                countries. Since then the number of participating
     the Directorate­General for Regional Policy of
                                                                countries has doubled and the number of cities
     the European Commission.
                                                                has grown sixfold. At present the Urban Audit
     The Urban Audit celebrates its 10th anniversary            covers 362 cities from 31 countries — including
     this year. The ‘Urban Audit pilot project’ was the         the EU­27, Croatia, Turkey, Norway and Swit­
     first attempt to collect comparable indicators on          zerland. The 321 Urban Audit cities in the EU­27
     European cities, and was first conducted by the            have more than 120 million inhabitants, covering
     Commission in June 1999. The past 10 years have            approximately 25 % of the total population. This
     brought many changes, and we have constantly               extended sample ensures that the results give a
     made efforts to improve the quality of the data            reliable portrait of urban Europe.
     — including coverage, comparability and rele­              The number of cities was limited and the ones
     vance. So, where we are now? The list of indica­           selected should reflect the geographical cross­
     tors has been enhanced to take account of new              section of each country. Consequently, in a few
     policy needs; the periodicity has been reduced to          countries some large cities (over 100 000 inhab­
     satisfy users; and geographical coverage has been          itants) were not included. To complement the
     extended following successive rounds of EU en­             Urban Audit data collection in this respect, the
     largement.                                                 Large City Audit was launched. The Large City Au­
                                                                dit includes all ‘non­Urban Audit cities’ with more
     Enhanced list of indicators                                than 100 000 inhabitants in the EU­27. For these
     There have been three major revisions of the list so       cities a reduced set of 50 variables is collected.
     far. Policy relevance, data availability and experi­       We invite all readers to explore the wealth of in­
     ence with previous collections have been reviewed          formation gathered in the past 10 years by brows­
     to produce the current list of more than 300 in­           ing the Urban Audit data on Eurostat’s website.
     dicators. These indicators cover several aspects
     of quality of life, such as demography, housing,
     health, crime, labour market, income disparity,
                                                                Discovering the spatial dimension
     local administration, educational qualifications,          Cities are usually displayed as distinct uncon­
     the environment, climate, travel patterns, the             nected dots on a map. This visualisation method
     information society and cultural infrastructure.           increases visibility but it misrepresents reality
     They are derived from the variables collected by           and distorts the understanding of linkages be­
     the European Statistical System. Data availability         tween a city and its hinterland and the under­
     differs from domain to domain: in the domain of            standing of linkages between cities. Cities can
     demography, for example, data are available for            no longer be treated as discrete unrelated enti­
     more than 90 % of the cities, whereas for the envi­        ties without a spatial dimension. The recent de­
     ronment data are available for less than half of the       velopments in transport, communication and
     cities. In 2009 we will introduce new indicators to        information technology infrastructure ease the
     symbolise the relationship between the city and            flow of people and resources from one area to
     its hinterland.                                            another considerably. Urban–rural connectivity




26                                                                     Eurostat regional yearbook 2009
European cities   2
Map 2.1: Boundaries of cities participating in the Urban Audit data collection




            Eurostat regional yearbook 2009                                                27
2                                      European cities


                                       and inter­urban relations have become critical           the cites. Different land covers were grouped into
     (2) A detailed description        for balanced regional development.                       44 classes in the CLC2000 (2). Each colour on
         of the CLC2000 project
                                                                                                the map represents a different land cover class.
         and the UMZ creation is       To facilitate the analysis of the interaction be­
         available on the website of                                                            Some of these classes are particularly important
         the European Environment      tween the city and its surroundings for each
         Agency (http://www.eea.                                                                for our analysis of cities. Red areas, for instance,
                                       participating city, different spatial levels were de­
         europa.eu).
                                                                                                are territories covered with urban fabric: roads,
                                       fined. Most of the data are collected at core city
                                                                                                residential buildings, buildings belonging to the
                                       level, i.e. the city as defined by its administrative/
                                                                                                local administration or to public services, etc.
                                       political boundaries. In addition, a level called
                                                                                                Purple areas are used for commercial or industri­
                                       the larger urban zone was described. The larger
                                                                                                al purposes. Light purple represents green urban
                                       urban zone is an approximation of the functional
                                                                                                areas like parks, botanical gardens, etc. The areas
                                       urban area extending beyond the core city.               of these three land cover classes lying less than
                                       Map 2.1 illustrates the cities participating in          200 m apart were merged together to define
                                       the Urban Audit data collection, showing the             ‘built­up’ area. Port areas, airports and sport fa­
                                       boundaries of core cities and larger urban zones.        cilities were included if they were neighbours of
                                       Not surprisingly, the largest cities in Europe in        the previously defined ‘built­up’ area.
                                       terms of population — London, Paris, Berlin and          As a next step, road and rail networks and water
                                       Madrid — tend to have the greatest larger urban          courses were added if they were within 300 m of
                                       zones in terms of area, and are readily identifiable     the area defined beforehand. The area identified by
                                       on the map. In most cases the larger urban zone          this procedure is called the ‘urban morphological
                                       includes only one core city. However, there are          zone’ (UMZ). The urban morphological zones of
                                       exceptions, such as the German Ruhr area, which          Hamburg and Lyon are shown in the middle row
                                       includes several core cities (see inset in Map 2.1).     of Map 2.2. These maps also make it possible to
                                       The demarcation of core cities is illustrated in de­     compare the UMZ and core city in terms of area.
                                       tail in Map 2.2 while the larger urban zones are         In Hamburg 82 %, and in Lyon 73 %, of the area
                                       shown in Map 2.3. The spatial data used to pro­          of the UMZ lies within the boundaries of the core
                                       duce most of the maps presented in this chapter          city. In terms of population the intersections are
                                       are available from the Geographic Information            even greater: 90 % of the population of the core
                                       System of the European Commission (GISCO) —              city of Hamburg lives in the UMZ, and in Lyon
                                       a permanent service of Eurostat (for more infor­         the respective figure is 98 %. As we expected, the
                                       mation, visit Eurostat’s website).                       two areas are not identical but they overlap each
                                                                                                other to a large extent, thus ensuring that the data
                                       Core cities                                              collected at core city level are relevant and mean­
                                       Throughout Europe’s history — in ancient Greece,         ingful for the morphological city as well.
                                       in ancient Rome and in the Middle Ages — a city          To measure spatial inequalities within the city,
                                       was as much a political entity as a collection of        the area of the core city was divided into sub­city
                                       buildings. This collection of buildings was usu­         districts. Sub­city districts were defined in such
                                       ally surrounded by fortified walls. As the city          a way as to keep to the population thresholds
                                       grew the walls were expanded. In the modern              set — minimum 5 000 and maximum 40 000 in­
                                       era the significance of the city walls as part of the    habitants — as far as possible. The bottom row of
                                       defence system declined and most of them were            Map 2.2 illustrates the sub­city districts of Ham­
                                       demolished. The boundary of the city as a politi­        burg and Lyon. Key demographic and social indi­
                                       cal entity and the boundary of the built­up area         cators are available in the Urban Audit database
                                       were no longer linked and the location of these          for the more than 6 000 sub­city districts.
                                       boundaries is no longer evident. Nowadays, a city
                                       could be designated as an urban settlement or as a       Larger urban zones
                                       legal, administrative entity. The Urban Audit uses
                                                                                                City walls, even if they are preserved, no longer
                                       this later concept and demarcates the core city by
                                                                                                function as barriers between the people living in­
                                       political boundaries. This ensures that data are
                                                                                                side and outside of the city. Students, workers and
                                       directly relevant to policymakers.
                                                                                                persons looking for healthcare or for cultural fa­
                                       Map 2.2 illustrates the difference between the           cilities regularly commute between the city and
                                       two concepts using the examples of Hamburg               the surrounding area. Economic activity, transport
                                       (Germany) and Lyon (France). Maps in the top             flows and air pollution clearly cross the adminis­
                                       row show the land cover based on Corine land             trative boundaries of a city as well. Consequently,
                                       cover 2000 (CLC2000) in the area surrounding             collecting data exclusively at core city level is



28                                                                                                     Eurostat regional yearbook 2009
European cities   2
Map 2.2: Defining the boundaries of the core city — Hamburg (DE) and Lyon (FR)

          Hamburg (DE)                        Lyon (FR)




           Eurostat regional yearbook 2009                                             29
2                     European cities


     Map 2.3: Defining the boundaries of the larger urban zone — Barcelona (ES) and Zagreb (HR)

               Barcelona (ES)                     Zagreb (HR)




30                                                                      Eurostat regional yearbook 2009
European cities                         2
insufficient. It is commonly agreed that we have to              Map 2.3 displays the different commuting rates.
widen our territorial perspective. However, the way              A commuting rate of 10 % means that one in 10
to measure how far the functional influences of a                residents living in the municipality commutes to
city go beyond its immediate boundaries varies.                  work to the core city. As we can see on the map,
Map 2.3 uses the examples of Barcelona (Spain)                   large cities like Barcelona and Zagreb attract
and Zagreb (Croatia) to illustrate how the func­                 people living up to 100 kilometres away to work in
tional urban area was demarcated in the Urban                    the city. As a second step, a threshold was set for
Audit. Maps in the top row are similar to the top                looking at the commuting pattern. Municipali­
row of Map 2.2 portraying the land cover of the                  ties above this threshold were to be included but
selected area. The larger urban zone around the                  ones below not. Given the different national and
core city tends to be more ‘green’, both on the                  regional characteristics, different thresholds were
map and also in real terms. Areas covered with                   used within the range of 10–20 %. Finally, the
forests and shrubs are coloured green on the map.                list of municipalities to be included in the larger
Yellow and orange indicate areas in agricultural                 urban zone was revised to ensure spatial contiguity
use, such as arable land and fruit trees. As a first             and data availability. By definition the larger
step to demarcate the larger urban zones, we                     urban zone always includes the entire core city.
looked at the number of people commuting from                    The boundaries of the larger urban zone of Barce­
municipalities to the core city. The middle row of               lona and Zagreb are displayed in the bottom row.


Figures 2.1 and 2.2: Comparison of core city, kernel and larger urban zone in terms
                     of population and area in European capitals, 2004
                           Share of population living in core cities and              Share of area of core cities and kernels
                           kernels (larger urban zone = 100 %)                        (larger urban zone = 100 %)
           Ankara (TR)
        Bucureşti (RO)
             Sofia (BG)
           Helsinki (FI)
            Vilnius (LT)
            Tallinn (EE)
       Stockholm (SE)
           Zagreb (HR)
            Lisboa (PT)
              Roma (IT)
         Lefkosia (CY)
               Riga (LV)
           Athina (GR)
              Wien (AT)
        Budapest (HU)
        Bratislava (SK)
            Berlin (DE)
     København (DK)
        Warszawa (PL)
          London (UK)
             Praha (CZ)
          Valletta (MT)
              Paris (FR)
 Bruxelles/Brussel (BE)
         Ljubljana (SI)
           Madrid (ES)
     Amsterdam (NL)
              Oslo (NO)
              Bern (CH)
             Dublin (IE)
    Luxembourg (LU)
                           0%        20 %      40 %       60 %       80 %     100 %   0%       20 %     40 %      60 %   80 %    100 %
                              core city        kernel         larger urban zone
                           Notes: HU 2005; FI 2003; HR 2001




                 Eurostat regional yearbook 2009                                                                                         31
2    European cities


     This demarcation process was used in most par­           percentage suggests that the core city of Luxem­
     ticipating countries, but there were also excep­         bourg is slightly under­bounded — meaning that
     tions and departures from this which limit the           a considerable share of the urban population lives
     overall comparability of the larger urban zones          outside the administrative city limits. For very
     to some extent. That said, demarcating a perfect         under­bounded capitals — like Paris (France) or
     functional urban area — based on a perfectly har­        Lisboa (Portugal) — an additional spatial level,
     monised methodology across Europe for which              the ‘kernel’, was introduced. The kernel is an ap­
     no statistical information is available — would          proximation of the built­up area around the core
     be completely in vain. Figures 2.1 and 2.2 com­          city. The only exception is London (United King­
     pare the different spatial levels used for European      dom), where the kernel was defined to match the
     capitals in terms of population and area. In Bu­         core city of Paris in terms of population to make
     curesti (Romania) more than 80 % of the larger           for easier comparison between the two largest cit­
     urban zone population lives within the core city.        ies in Europe. In terms of area, the picture is more
     At the other extreme, in Luxembourg (Luxem­              uniform, as for the majority of capitals the core
     bourg) less than 20 % of the larger urban zone           city makes up less than 20 % of the area of the
     population lives within the core city. This low          larger urban zone.



     Figure 2.3: Proportion of journeys to work in European capitals, 2004
             København                                  Tallinn                                      Dublin




               Madrid                               Amsterdam                                     Bratislava




               Helsinki                              Stockholm                                        Bern




                                        by car         by bicycle          on foot            by public transport

                                     Notes: SE 2005; DK, NL 2003; CH 2000.
                                     For DK, FI and SE the kernel level was used instead of the larger urban zone




32                                                                    Eurostat regional yearbook 2009
European cities                                      2
So far we have seen that larger urban zones tend       Geography matters
to have a lower population density and a higher
percentage of green areas than core cities. Using      The book entitled The Spatial Economy (3), co­         (3) Masahisa Fujita, Paul R.
the indicators calculated in the Urban Audit we        authored by Paul Krugman, winner of the 2008               Krugman and Anthony
                                                                                                                  Venables, The spatial
can analyse the demographic, economic, envir­          Nobel Memorial Prize in Economic Sciences,                 economy: Cities, regions
                                                                                                                  and international trade.
onmental, social and cultural characteristics          states: ‘Agglomeration […] occurs at many lev­             MIT Press, 2001.
(similarities and differences) of the two spatial      els, from the local shopping districts that serve
levels. To illustrate this, Figure 2.3 compares the    residential areas within cities to specialised eco­
travel to work patterns in selected capitals at dif­   nomic regions like Silicon Valley or the City of
ferent levels. The inner circle of the pie charts      London that serve the world market as a whole.
shows the modal split in the core city. In the core    […] Yet although agglomeration is a clearly pow­
city of København (Denmark), for example, the          erful force, it is not all­powerful: London is big,
majority of people ride their bikes to work, 30 %      but most Britons live elsewhere, in a system of cit­
of them use public transport and 25 % travel by        ies with widely varying sizes and roles. It should
car. The outer circle shows the share of transport     not, in other words, be hard to convince econo­
modes in the larger urban zone. As expected, the       mists that economic geography […] is both an in­
proportion of journeys to work by car is consist­      teresting and important subject.’ In this chapter
ently higher in the larger urban zone than in the      we have focused on the various spatial levels used
core city, with the exception of Bratislava.           in the Urban Audit. These provide a platform
Where do families settle? Where do companies           for analysing the dramatically uneven distribu­
locate? Where do tourists stay? In the core city or    tion of population across the landscape and the
in the area of the larger urban zone outside of the    agglomeration at district, at city and at regional
core city? We encourage readers to probe deeper        level. Our intention was to convince readers that
into the Urban Audit database and to explore the       ‘statistical geography’ is both an interesting and
indicators depicting the spatial dimension.            an important subject.




              Eurostat regional yearbook 2009                                                                                                33
Labour market
3    Labour market


     Regional working time patterns                          10 percentage points below the overall employ­
                                                             ment target set for 2010.
     Flexible working hours are one of the most valu­
                                                             A cluster of regions right in the centre of Europe,
     able ways for individuals to reconcile work with
                                                             comprising regions in southern Germany and in
     other aspects of life, particularly family duties.
                                                             Austria, recorded relatively high employment.
     Working part time can be a positive thing, as
                                                             The northern EU regions, comprising regions in
     long as the decision is voluntary and not due to
                                                             the Netherlands, the United Kingdom, Denmark,
     underemployment. The different legal systems
                                                             Sweden and Finland, also recorded relatively high
     and the different collective agreements across EU
                                                             employment. Low regional employment rates
     countries governing working hours provide some
                                                             were mainly found in the southern regions of
     flexibility, providing scope, to a greater or lesser
                                                             Spain and Italy and in east European countries.
     extent, for more free time.
                                                             The range between the lowest and the highest re­
     And how about the situation at regional level? Are
                                                             gional employment rate in 2007 was still signifi­
     there significant differences among regions of the
                                                             cant, with the highest employment rate almost
     same country in how much time people spend at
                                                             twice as high as the lowest. The figures ranged
     work? It is clear that the national legal system has
                                                             from 43.5 % in Campania (Italy) to 79.5 % in
     a big influence in all regions of a country. But on
                                                             Åland (Finland).
     top of this, do any regional factors influence the
     differences in weekly hours spent at work?              Employment throughout the EFTA regions was
     In this chapter we will look at how much time           above 70 %. In the candidate countries, employ­
     people spend at work in European regions and we         ment rates ranged from 25.7 % in Mardin (Turkey)
     will offer some possible explanations for the dif­      to 62.4 % in Sjeverozapadna Hrvatska (Croatia).
     ferent time patterns. First we will give you a snap­    The other two Lisbon targets set for employment —
     shot of the regional labour market in 2007.             for the female employment rate to exceed 60 % and
                                                             for the older­worker employment rate to exceed
                                                             50 % — are closer to being fulfilled, but still appear
     Brief overview for 2007                                 increasingly unlikely to be achieved by 2010.
     The EU­27 employment rate rose from an average          The female employment rate in the EU­27 in­
     of 64.4 % in 2006 to 65.3 % in 2007. It is still 4.6    creased in 2007 by 1 percentage point to 58.3 %.
     percentage points short of achieving the Lisbon         Out of the three targets, this seems the most
     employment target. Looking back to employ­              promising, but the negative impacts on the la­
     ment figures for 2000, when the targets were set,       bour market that are likely to be felt in the com­
     it is clear that the rise in employment fell short      ing years should not be overlooked. Regional
     of ambitions. It now seems increasingly unlikely        female employment rates varied widely in 2007,
     that the Lisbon targets for employment will be          from a minimum of 27.9 % in Campania (Italy) to
     achieved by 2010, since there are only three years      a maximum of 76.4 % in Åland (Finland).
     left, and especially given the recession and eco­
     nomic difficulties we are currently facing, which       The employment rate of older workers, i.e. em­
     are highly likely to have a negative impact on em­      ployed persons aged 55–64 years, was 44.7 % in
     ployment in the coming years.                           2007, which is 1.2 percentage points higher than
                                                             in 2006. At regional level, older­worker employ­
     The latest quarterly data available at national level   ment rates ranged from a low of 21.8 % in Śląskie
     confirm this. The employment rate for the EU­27         (Poland) to a high of 72.8 % in Småland med
     in the last quarter of 2008 was 65.8 % and 64.6 %       öarna (Sweden). The EU­27 unemployment rate
     in the first quarter of 2009.                           fell significantly in 2007 by 1 percentage point to
     Social and territorial cohesion is one of the EU’s      7.2 %, the steepest fall since 2000.
     goals, so it is important to look at regional labour
                                                             Unemployment is distributed quite evenly
     markets and how they change over time. Map 3.1
                                                             throughout the EU. Map 3.2 shows that, in spite of
     shows the regional employment rate for the 15–64
                                                             the good performance in 2007, some regions still
     age group, by NUTS 2 regions, in 2007.
                                                             record a double­digit unemployment rate. These
     In 2007, only 81 of the 264 NUTS 2 regions in the       are mainly located in the south of Spain, the south
     EU­27 for which data was available had already          of Italy and the eastern regions of Germany. Some
     achieved the Lisbon target (shaded with the dark­       regions in Slovakia, Poland and Hungary also re­
     est colour in Map 3.1), while 59 regions were still     corded unemployment rates above 10 % in 2007.




36                                                                  Eurostat regional yearbook 2009
Labour market   3
Map 3.1: Employment rate for the 15–64 age group, by NUTS 2 regions, 2007
         Percentage




           Eurostat regional yearbook 2009                                           37
3                    Labour market


     Map 3.2: Unemployment rate, by NUTS 2 regions, 2007
              Percentage




38                                                         Eurostat regional yearbook 2009
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Eurostat Regional Yearbook

  • 1. ISSN 1830-9674 Statistical books Eurostat regional yearbook 2009
  • 3. Europe Direct is a service to help you find answers to your questions about the European Union Freephone number (*): 00 800 6 7 8 9 10 11 (*) Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed. More information on the European Union is available on the Internet (http://europa.eu). Luxembourg: Office for Official Publications of the European Communities, 2009 ISBN 978-92-79-11696-4 ISSN 1830-9674 doi: 10.2785/17776 Cat. No: KS-HA-09-001-EN-C Theme: General and regional statistics Collection: Statistical books © European Communities, 2009 © Copyright for the following photos: cover: © Annette Feldmann; the chapters Introduction, Population, Household accounts, Information society, Education and tourism: © Phovoir.com; the chapter European cities: © Teodóra Brandmüller; the chapters Labour market, Gross domestic product, Structural business statistics and Science, technology and innovation: © the Digital Photo Library of the Directorate-General for Regional Policy of the European Commission; the chapter Agriculture: © Jean-Jacques Patricola. For reproduction or use of these photos, permission must be sought directly from the copyright holder.
  • 4. Preface Dear Readers, Five years ago, 2004, was a momentous year, with 10 new Member States joining the European Union on 1 May. This Eurostat regional yearbook 2009 is eloquent testimony to the economic and social progress made by these regions since then and highlights those areas where redoubled efforts will be needed to reach our goal of greater cohesion. The 11 chapters of this yearbook investigate interesting as­ pects of regional differences and similarities in the 27 Mem­ ber States and in the candidate and EFTA countries. The aim is to encourage readers to track down the regional data available on the Eurostat website and make their own ana­ lyses of economic and social developments. In addition to the fascinating standard chapters on regional population developments, the regional labour market, re­ gional GDP, etc., this year’s edition features a new contri­ bution on the regional development of information society data. As in recent years, the description of regional devel­ opments is rounded off by a contribution on the latest findings of the Urban Audit, a data collection containing a multitude of statistical data on European towns and cities. We are constantly updating the range of regional indicators available and hope to include them as topics in future editions, provided the availability and quality of these data are sufficient. I wish you an enjoyable reading experience! Walter Radermacher Director­General, Eurostat Eurostat regional yearbook 2009 3
  • 5. Acknowledgements The editors of the Eurostat regional yearbook 2009 would like to thank all those who were involved in its preparation. We are especially grateful to the following chapter authors at Eurostat for making the publication of this year’s edition possible. • Population: Veronica Corsini, Monica Marcu and Rosemarie Olsson (Unit F.1: Population) • European cities: Teodóra Brandmüller (Unit E.4: Regional statistics and geographical informa­ tion) • Labour market: Pedro Ferreira (Unit E.4: Regional statistics and geographical information) • Gross domestic product: Andreas Krüger (Unit C.2: National accounts — production) • Household accounts: Andreas Krüger (Unit C.2: National accounts — production) • Structural business statistics: Aleksandra Stawińska (Unit G.2: Structural business statistics) • Information society: Albrecht Wirthmann (Unit F.6: Information society and tourism) • Science, technology and innovation: Bernard Félix, Tomas Meri, Reni Petkova and Håkan Wilén (Unit F.4: Education, science and culture) • Education: Sylvain Jouhette, Lene Mejer and Paolo Turchetti (Unit F.4: Education, science and culture) • Tourism: Ulrich Spörel (Unit F.6: Information society and tourism) • Agriculture: Céline Ollier (Unit E.2: Agriculture and fisheries) This publication was edited and coordinated by Åsa Önnerfors (Unit E.4: Regional statistics and geo­ graphical information) with the help of Berthold Feldmann (Unit E.4: Regional statistics and geo­ graphical information) and Pavel Bořkovec (Unit D.4: Dissemination). Baudouin Quennery (Unit E.4: Regional statistics and geographical information) produced all the statistical maps. We are also very grateful to: — the Directorate-General for Translation of the European Commission, and in particular the German, English and French translation units; — the Publications Office of the European Union, and in particular Bernard Jenkins in Unit B.1, Cross­media publishing, and the proofreaders in Unit B.2, Editorial services. 4 Eurostat regional yearbook 2009
  • 6. Contents INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Statistics on regions and cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 The NUTS classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 More regional information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1 POPULATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Unveiling the regional pattern of demography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Population density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Population change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2 EUROPEAN CITIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Enhanced list of indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Moving from five-year periodicity to annual data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Extended geographical coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Discovering the spatial dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Core cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Larger urban zones. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Geography matters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3 LABOUR MARKET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Regional working time patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Brief overview for 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Regional work patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Part-time jobs: lowering the average working time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Employees spend less time at work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4 GROSS DOMESTIC PRODUCT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 What is regional gross domestic product?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Regional GDP in 2006. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Average GDP over the three-year period 2004–06 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Major regional differences even within the countries themselves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Dynamic catch-up process in the new Member States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Different trends even within the countries themselves. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Convergence makes progress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Purchasing power parities and international volume comparisons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Eurostat regional yearbook 2009 5
  • 7. 5 HOUSEHOLD ACCOUNTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Introduction: measuring wealth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Private household income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Results for 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Primary income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Disposable income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Dynamic development on the edges of the Union . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6 STRUCTURAL BUSINESS STATISTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Regional specialisation and business concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Specialisation in business services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Employment growth in business services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Characteristics of the top 30 most specialised regions in business services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 7 INFORMATION SOCIETy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Access to information and communication technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Use of the Internet and Internet activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Non-users of the Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 8 SCIENCE, TECHNOLOGy AND INNOvATION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Research and development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Human resources in science and technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 High-tech industries and knowledge-intensive services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Patents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 9 EDUCATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Students’ participation in education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Participation of 4-year-olds in education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Students in upper secondary education and post-secondary non-tertiary education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Students in tertiary education. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Tertiary educational attainment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Lifelong learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6 Eurostat regional yearbook 2009
  • 8. 10 TOURISM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Accommodation capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Overnight stays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Average length of stay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 Tourism intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 Tourism development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Inbound tourism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 11 AGRICULTURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 Utilised agricultural area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 Proportion of area under cereals to the utilised agricultural area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 Proportion of permanent crops to the utilised agricultural area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 Agricultural production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Wheat production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Grain maize production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Rapeseed production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 Methodological notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 ANNEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 European Union: NUTS 2 regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Candidate countries: statistical regions at level 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 EFTA countries: statistical regions at level 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Eurostat regional yearbook 2009 7
  • 9.
  • 11. Introduction Statistics on regions and cities throughout Europe and offers a couple of expla­ nations for why they vary so much from region Statistical information is essential for under­ to region. The three economic chapters on Gross standing our complex and rapidly changing domestic product, Household accounts and world. Eurostat, the Statistical Office of the Euro­ Structural business statistics all give us detailed pean Communities, is responsible for collecting insight into the general economic situation in re­ and disseminating data at European level, not gions, private households and different sectors of only from the 27 Member States of the Euro­ the business economy. pean Union, but also from the three candidate countries (Croatia, the former Yugoslav Repub­ We are particularly proud to present a new and lic of Macedonia and Turkey) and the four EFTA very interesting chapter on the Information so- countries (Iceland, Liechtenstein, Norway and ciety, which describes the use of information Switzerland). and communication technologies (ICT) among private persons and households in European The aim of this publication, the Eurostat regional regions. This chapter tells us, for example, how yearbook 2009, is to give you a flavour of some of many households use the Internet regularly and the statistics on regions and cities that we collect how many have broadband access. The next two from these countries. Statistics on regions enable chapters are on Science, technology and innova- us to identify more detailed statistical patterns tion and Education, three areas of statistics that and trends than national data, but since we have are often seen as key to monitoring achievement 271 NUTS 2 regions in the EU­27, 30 statisti­ of the goals set in the Lisbon strategy to make cal regions on level 2 in the candidate countries Europe the most competitive and dynamic and 16 statistical regions on level 2 in the EFTA knowledge­based economy in the world. countries, the volume of data is so great that one clearly needs some sorting principles to make it In the next chapter we learn more about regional understandable and meaningful. statistics on Tourism, and which tourist desti­ nations are the most popular. The last chapter Statistical maps are probably the easiest way for the focuses on Agriculture, this time mainly crop human mind to sort and ‘absorb’ large amounts of statistics, revealing which kind of crop is grown statistical data at one time. Hence this year’s Euro­ where in Europe. stat regional yearbook, as in previous editions, contains a lot of statistical maps where the data is sorted by different statistical classes represented The NUTS classification by colour shades on the maps. Some chapters also make use of graphs and tables to present the statis­ The nomenclature of territorial units for statistics tical data, selected and sorted in some way (differ­ (NUTS) provides a single uniform breakdown of ent top lists, graphs with regional extreme values territorial units for the production of regional sta­ within the countries or only giving representative tistics for the European Union. The NUTS classi­ examples) to make it easier to understand. fication has been used for regional statistics for many decades, and has always formed the basis We are proud to present a great variety of subjects for regional funding policy. It was only in 2003, tackled in the 11 chapters in this years’ edition though, that NUTS acquired a legal basis, when of the Eurostat regional yearbook. The first chap­ the NUTS regulation was adopted by the Parlia­ (1) More information on ter on Population gives us detailed knowledge of ment and the Council (1). the NUTS classification different demographic patterns, such as popula­ can be found at http:// ec.europa.eu/eurostat/ tion density, population change and fertility rates Whenever new Member States join the EU, the ramon/nuts/splash_ in the countries examined. This chapter can be NUTS regulation is amended to include the re­ regions.html considered the key to all other chapters, since gional classification in those countries. This was all other statistics depend on the composition of the case in 2004, when the EU took in 10 new the population. The second chapter focuses on Member States, and in 2007 when Bulgaria and European cities and explains in detail the defini­ Romania also joined the European Union. tions of the various spatial levels used in the Ur­ The NUTS regulation states that amendments of ban Audit data collection, with some interesting the regional classification, to take account of new examples on how people travel to work in nine administrative divisions or boundary changes in European capitals. the Member States, may not be carried out more The chapter on the Labour market mainly de­ frequently than every three years. In 2006, this scribes the differences in weekly working hours review took place for the first time, and the re­ 10 Eurostat regional yearbook 2009
  • 12. Introduction sults of these changes to the NUTS classification given on the three candidate countries (Croatia, have been valid since 1 January 2008. the former Yugoslav Republic of Macedonia and Turkey) and the four EFTA countries (Iceland, Since these NUTS changes were introduced quite Liechtenstein, Norway and Switzerland). recently, the statistical data are still missing in some cases or have been replaced with national Regions in the candidate countries and the EFTA values on some statistical maps, as indicated in countries are called statistical regions and they the footnotes to each map concerned. This ap­ follow the same rules as the NUTS regions in plies in particular to Sweden, which introduced the European Union, except that there is no legal NUTS level 1 regions, to Denmark and Slovenia, base. Data from the candidate and EFTA coun­ which introduced new NUTS level 2 regions, tries are not yet available in the Eurostat database and to the two northernmost Scottish regions, for some of the policy areas, but the availability North Eastern Scotland (UKM5) and Highlands of data is constantly improving, and we hope to and Islands (UKM6), where the border between have even more complete coverage from these the two regions has changed. The regional data countries in the near future. availability for these countries will hopefully soon be improved. More regional information Please also note that some Member States have a relatively small population and are therefore not In the subject area ‘Regions and cities’ under the divided into more than one NUTS 2 region. Thus, heading ‘General and regional statistics’ on the for these countries the NUTS 2 value is exactly Eurostat website you will find tables with statis­ the same as the national value. Following the lat­ tics on both ‘Regions’ and the ‘Urban Audit’, with est revision of the NUTS classification, this now more detailed time series (some of them going applies to six Member States (Estonia, Cyprus, back as far as 1970) and with more detailed sta­ Latvia, Lithuania, Luxembourg and Malta), one tistics than this yearbook contains. You will also candidate country (the former Yugoslav Republic find a number of indicators at NUTS level 3 (such of Macedonia) and two EFTA countries (Iceland as area, demography, gross domestic product and and Liechtenstein). In all cases the whole country labour market data). This is important since some consists of one single NUTS 2 region. of the countries covered are not divided into A folding map on the inside of the cover accom­ NUTS 2 regions, as mentioned above. panies this publication and it shows all NUTS For more detailed information on the content level 2 regions in the 27 Member States of the of the regional and urban databases, please con­ European Union (EU­27) and the correspond­ sult the Eurostat publication European regional ing level 2 statistical regions in the candidate and and urban statistics — Reference guide — 2009 EFTA countries. In the annex you will find the edition, which you can download free of charge full list of codes and names of these regions. This from the Eurostat website. You can also down­ will help you locate a specific region on the map. load Excel tables containing the specific data used to produce the maps and other illustrations for Coverage each chapter in this publication on the Eurostat website. We do hope you will find this publication The Eurostat regional yearbook 2009 mainly con­ both interesting and useful and we welcome your tains statistics on the 27 Member States of the feedback at the following e­mail address: estat­ European Union but, when available, data is also regio@ec.europa.eu Eurostat regional yearbook 2009 11
  • 13.
  • 15. 1 Population Unveiling the regional pattern million (1960) to almost 500 million (497 million on 1 January 2008). Including candidate coun­ of demography tries and EFTA countries, the total population Demographic trends have a strong impact on the has grown over the same period from under 450 societies of the European Union. Consistently low million to 587 million. fertility levels, combined with extended longevity The total population change has two compo­ and the fact that the baby boomers are reaching nents: the so­called ‘natural increase’, which is retirement age, result in demographic ageing of defined as the difference between the numbers of the EU population. The share of the older gen­ live births and deaths, and net migration, which eration is increasing while the share of those of ideally represents the difference between inward working age is decreasing. and outward migration flows (see ‘Methodologi­ The social and economic changes associated with cal notes’). Changes in the size of a population are population ageing are likely to have profound the result of the number of births, the number of implications for the EU — and also to be visible deaths and the number of people who migrate. at regional level, stretching across a wide range Up to the end of the 1980s, natural increase of policy areas and impacting on the school­age was by far the major component of population population, healthcare, labour force participa­ growth. However, there has been a sustained de­ tion, social protection and social security issues cline in the natural increase since the early 1960s. and government finances, etc. On the other hand, international migration has The demographic development is not the same gained importance and became the major force in all regions of the EU. Some demographic phe­ of population growth from the beginning of the nomena might have a stronger impact in some 1990s onwards. regions than in others. The analysis on the following pages is mainly This chapter presents the regional pattern of de­ based on demographic trends observed over the mographic phenomena as it is today. period from 1 January 2003 to 1 January 2008. For this purpose, five­year averages have been calcu­ lated of the total annual population change and its Population density components. Given that demographic trends are long­term developments, the five­year averages On 1 January 2007, 584 million people inhabited the provide a stable and accurate picture. They help to European Union and candidate and EFTA coun­ identify regional clusters, which often stretch well tries. The population distribution is varied across beyond national borders. For the sake of compara­ the 317 NUTS 2 regions that make up this area. bility, the population change and its components Map 1.1 shows the population density on 1 Janu­ are presented in relative terms, calculating the ary 2007. The population density of a region is the so­called crude rates, i.e. they relate to the size of ratio of the population of a territory to its size. the total population (see ‘Methodological notes’). Generally, capital city regions are among the most Maps 1.2, 1.3 and 1.4 show these figures on total densely populated, as Map 1.1 shows. Inner Lon­ population change and its components. don was by far the most densely populated, but the In most of the north­east, east and part of the Bruxelles­Capitale, Wien, Berlin, Praha, Istanbul, south­east of the area made up by the European Bucureşti — Ilfov and Attiki (Greece) regions also Union and the candidate and EFTA countries, the have densities above 1 000 inhabitants per km². population is on the decrease. Map 1.2 is marked The least densely populated region was the region by a clear divide between the regions there and in of Guyane (France), while the next least densely the rest of the EU. Most affected by the decreasing populated regions, with fewer than 10 inhabitants population trend are Germany (in particular the per km², were all in Sweden, Finland, Iceland and former eastern Germany), Poland, Bulgaria, Slo­ Norway. By comparison, the European Union has vakia, Hungary and Romania, and to the north a population density of 114 inhabitants per km². the three Baltic States and the northern parts of Sweden and the Finnish region of Itä­Suomi. Population change Decreasing population trends are also evident in many regions of Greece. To the east, on the During the last four and a half decades, the pop­ other hand, the total population change is positive ulation of the 27 countries that make up today’s in Cyprus and, to a lesser extent, in the former European Union has grown from around 400 Yugoslav Republic of Macedonia and Turkey. 14 Eurostat regional yearbook 2009
  • 16. Population 1 Map 1.1: Population density, by NUTS 2 regions, 2007 Inhabitants per km2 Eurostat regional yearbook 2009 15
  • 17. 1 Population Map 1.2: Total population change, by NUTS 2 regions, average 2003–07 Per 1 000 inhabitants 16 Eurostat regional yearbook 2009
  • 18. Population 1 In nearly all western and south­western regions of 2003–07. The resulting negative ‘natural popu­ the EU the population increased over the period lation change’ is widespread and affects almost 2003–07. This is particularly evident in Ireland 50 % of the EU’s regions. and in almost all regions of the United Kingdom, A single extended cross­border region can be Italy, Spain, France and Portugal, including the identified showing a natural increase of popu­ French overseas departments and the Spanish and lation, made up of Ireland, the central United Portuguese islands in the Atlantic Ocean. There Kingdom, most regions in France, Belgium, Lux­ has also been positive total population change in embourg, the Netherlands, Switzerland, Iceland, Austria, Switzerland, Belgium, Luxembourg and Lichtenstein, Denmark and Norway: in these the Netherlands. regions, in the period 2003–07, live births were The picture provided by Map 1.2 can be refined by more numerous than deaths. analysing the two components of total population Deaths are more numerous than births in Ger­ change, namely natural change and migration. many, the Czech Republic, Slovakia, Hungary, Map 1.3 shows that in many regions of the EU Slovenia, Croatia, Romania and Bulgaria, and also more people died than were born in the period in the Baltic States and Sweden in the north and Figure 1.1: Total fertility rates by country, 1986 and 2006 Children per woman SK PL LT SI RO DE CZ HU LV PT IT BG HR ES GR AT MT LI MK CY CH EE LU NL BE DK UK FI SE IE NO FR IS TR 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 1986 2006 Source: Eurostat Demographic Statistics Notes: 1986 data: EE, PL, MT: national estimates; LI: 1985 national estimate; HR: 1990; TR: 1990 national estimate; MK: 1994 2006 data: IT, BE, TR: national estimates Eurostat regional yearbook 2009 17
  • 19. 1 Population Map 1.3: Natural population change (live births minus deaths), by NUTS 2 regions, average 2003–07 Per 1 000 inhabitants 18 Eurostat regional yearbook 2009
  • 20. Population 1 Greece, Italy and Portugal in the south. The other Relatively high fertility rates tend to be recorded in countries have an overall more balanced situation. countries that have implemented a range of family­ friendly policies, such as the introduction of acces­ A major reason for the slowdown of the natural sible and affordable childcare and/or more flexible increase of the population is the fact that inhabit­ working patterns; this is the case for France, the ants of the EU have fewer children. At aggregat­ Nordic countries and the Netherlands. ed level, in the 27 countries that today form the European Union, the total fertility rate has de­ The (slight) increase in the total fertility rate that clined from a level of around 2.5 in the early is observed in some countries between 1986 and 1960s to a level of about 1.5 in 1993, where it has 2006 may be partly attributable to a catching­up remained since (for the definition of the total fer­ process following postponement of the decision tility rate, see the ‘Methodological notes’). to have children. When women give birth later in life, the total fertility rate first indicates a decrease At country level, in 2006, a total fertility rate of in fertility, followed later by a recovery. less than 1.5 was observed in 17 of the 27 Member States. To compare, Figure 1.1 also includes figures By comparison, in the more developed parts of for 1986 and for the candidate and EFTA countries. the world today, a total fertility rate of around Figure 1.2: Crude birth rates, by NUTS 2 regions, 2007 Births per 1 000 inhabitants BE Prov. West-Vlaanderen Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest BG Severozapaden Yugoiztochen CZ Střední Morava Střední Čechy DK Sjælland Hovedstaden DE Saarland Hamburg EE IE Border, Midland and Western Southern and Eastern EL Ipeiros Kriti ES Principado de Asturias Ciudad Autónoma de Ceuta FR Corse Guyane IT Liguria Provincia Autonoma Bolzano/Bozen CY LV LT LU HU Nyugat-Dunántúl Észak-Alföld MT NL Limburg (NL) Flevoland AT Burgenland (A) Vorarlberg PL Opolskie Pomorskie PT Alentejo Região Autónoma dos Açores RO Sud-Vest Oltenia Nord-Est SI Vzhodna Slovenija Zahodna Slovenija SK Západné Slovensko Východné Slovensko FI Itä-Suomi Pohjois-Suomi SE Norra Mellansverige Stockholm UK Cornwall and Isles of Scilly Inner London HR Središnja i Istočna Sjeverozapadna Hrvatska (Panonska) Hrvatska MK TR IS LI NO Hedmark og Oppland Oslo og Akershus CH Ticino Région lémanique 0 5 10 15 20 25 30 35 National value Source: Eurostat Demographic Statistics. Notes: FR, UK: 2006 TR: national level Eurostat regional yearbook 2009 19
  • 21. 1 Population Map 1.4: Net migration, by NUTS 2 regions, average 2003–07 Per 1 000 inhabitants 20 Eurostat regional yearbook 2009
  • 22. Population 1 2.1 children per women is considered to be the • regions in the north­east of France and the replacement level, i.e. the level at which the popu­ French overseas departments; lation would remain stable in the long run if there • a few regions in the south of Italy, in the Neth­ were no inward or outward migration. At present erlands and in the United Kingdom. (2006 data), practically all of the EU and the can­ didate and EFTA countries, with the exception Regions where the two components of population of Turkey and Iceland, are still well below the re­ change do not compensate for, but rather add to, placement level. one another are often exposed to major develop­ ments, upwards or — in some regions — down­ The analysis of Map 1.3 can also be refined by iso­ wards. In Ireland, Luxembourg, Belgium, Malta, lating the contribution of live births to the natural Cyprus, Switzerland, Iceland, many regions population change. Figure 1.2 shows the regional in France and in Norway and some regions in differences within each country of the so­called Spain, the United Kingdom and the Netherlands, crude birth rates (see the ‘Methodological notes’). a natural increase has been accompanied by posi­ The largest regional differences in 2007 were tive net migration. However, in eastern German in France, where the highest crude birth rate is regions, Lithuania and Latvia and some regions more than three times the lowest, followed by in Poland, Slovakia, Hungary, Bulgaria and Ro­ Spain, where the highest crude birth rate is also mania, both components of population change three times the lowest. For the other countries, have moved in a negative direction, as can also be regional differences in crude birth rates are less seen from Map 1.2. In these regions this trend has pronounced but still significant. led to sustained population loss. The third determinant of population change In 2007, the average population in the EU­27 aged (after fertility and mortality) is migration. As 65 and older was 17 %, which means an increase many countries in the EU are currently at a point of 2 percentage points in the last 10 years. This in the demographic cycle where ‘natural popula­ ageing population, especially in rural areas, tion change’ is close to being balanced or nega­ raises issues about infrastructure and the need tive, the importance of immigration increases for social services and healthcare. when it comes to maintaining population size. Moreover, migration also contributes indirect­ The highest percentage of population aged 65 ly to natural change, given that migrants have and older can be found in Liguria (Italy), at 27 %. children. Migrants are also usually younger and Germany follows with up to 24 % in the region of have not yet reached the age at which death is Chemnitz and a further 14 regions above 20 %. more frequent. Some regions in Greece, Portugal, France and Spain also show high figures, with up to 23 % of In some regions of the European Union, negative their population aged 65 years and older. These ‘natural change’ has been offset by positive net mi­ regions also show low and even negative natural gration. This is at its most striking in Austria, the population change, with more people dying than United Kingdom, Spain, the northern and central being born. regions of Italy and some regions of western Ger­ many, Slovenia, southern Sweden, Portugal and In Turkey the percentage of the population aged Greece, as can be seen in Map 1.4. The opposite is 65 and older is as low as 3 % in the region of Van, much rarer: in only a few regions (namely in the and on average 8 % in the other regions. Although northern regions of Poland and of Finland and Turkey has negative net migration, the high fertil­ in Turkey) has positive ‘natural change’ been can­ ity results in a young population. Similarly, with celled out by negative net migration. high fertility, coupled with high net migration, only 11 % and 12 % of the population in the two Four cross­border regions where more people regions of Ireland are 65 and older. have left than arrived (negative net migration) can be identified on Map 1.4: According to projections, elderly people would account for an increasing share of the population • the northernmost regions of Norway and Fin­ and this is due to sustained reductions in mortal­ land; ity in past and future decades. The ageing process • an eastern group, comprising most of the re­ can be typified as ageing from the top, as it large­ gions of eastern Germany, Poland, Lithuania ly results from projected increases in longevity, and Latvia and most parts of Slovakia, Hun­ moderated by the impact of positive net migra­ gary, Romania, Bulgaria and Turkey; tion flows and some recovery in fertility. Eurostat regional yearbook 2009 21
  • 23. 1 Population Map 1.5: Percentage of population aged 65 years old and more, by NUTS 2 regions, 2007 22 Eurostat regional yearbook 2009
  • 24. Population 1 Conclusion nomena have been identified, spreading across national boundaries. While population decline is This chapter highlights certain features of region­ evident in several regions, at aggregated level the al population development in the area made up by EU­27 population still increased in that period the EU­27 Member States and the candidate and by around 2 million people every year. The main EFTA countries over the period from 1 January driver of population growth in this area is migra­ 2003 to 1 January 2008. As far as possible, typolo­ tion, which counterbalanced, as seen in the maps, gies of regions in the different demographic phe­ the negative natural change in many regions. Methodological notes Sources: Eurostat — Demographic Statistics. For more information please consult the Eurostat website at http://www.ec.europa.eu/eurostat. Total fertility rate is defined as the average number of children that would be born to a woman during her lifetime if she were to pass through her childbearing years conforming to the age-specific fertility rates that have been measured in a given year. Migration can be extremely difficult to measure. A variety of different data sources and definitions are used in the Member States, meaning that direct comparisons between national statistics can be difficult or misleading. The net migration figures here are not directly calculated from immigra- tion and emigration flow figures. Since many countries either do not have accurate, reliable and comparable figures on immigration and emigration flows or have no figures at all, net migration is generally estimated on the basis of the difference between total population change and natural in- crease between two dates (in the Eurostat database, it is then called net migration including cor- rections). The statistics on net migration are therefore affected by all the statistical inaccuracies in the two components of this equation, especially population change. In effect, net migration equals all changes in total population that cannot be attributed to births and deaths. Crude rate of total population change is the ratio of the total population change during the year to the average population of the area in question in that year. The value is expressed per 1 000 inhabitants. Crude rate of natural change is the ratio of natural population increase (live births minus deaths) over a period to the average population of the area in question during that period. The value is expressed per 1 000 inhabitants. It is also the difference of the crude birth rate minus the crude death rate, which are, respectively, the ratio of live births during the year over the average popula- tion and of deaths over the average population. Crude rate of net migration is the ratio of net migration during the year to the average popula- tion in that year. The value is expressed per 1 000 inhabitants. As stated above, the crude rate of net migration is equal to the difference between the crude rate of total change and the crude rate of natural change (i.e. net migration is considered as the part of population change not at- tributable to births and deaths). Population density is the ratio of the population of a territory to the total size of the territory (in- cluding inland waters), as measured on 1 January. Eurostat regional yearbook 2009 23
  • 25.
  • 27. 2 European cities Introduction Moving from five-year periodicity to annual data collection Data on European cities were collected in the Ur­ ban Audit project. The project’s ultimate goal is to Four reference years have been defined so far for help improve the quality of urban life: it supports the Urban Audit: 1991, 1996, 2001 and 2004. For the exchange of experience among European cit­ the years 1991 and 1996, data were collected ret­ ies; it helps to identify best practices; it facilitates rospectively only for a reduced number of 80 var­ iables. Where data for these years were not avail­ benchmarking at European level; and it provides able, data from adjacent years were also accepted. information on the dynamics both within the cit­ In 2009 Eurostat launched an annual Urban Au­ ies and with their surroundings. dit, requesting data for a limited number of vari­ The Urban Audit has become a core task of Euro­ ables. The annual data will help users to monitor stat. Even so, the project would not have been pos­ certain urban developments more closely. sible without sustained help and support from a wide range of colleagues. In particular, we would Extended geographical coverage like to acknowledge the effort made by the cities The pilot study in 1999 covered 58 cities from 15 themselves, the national statistical institutes and countries. Since then the number of participating the Directorate­General for Regional Policy of countries has doubled and the number of cities the European Commission. has grown sixfold. At present the Urban Audit The Urban Audit celebrates its 10th anniversary covers 362 cities from 31 countries — including this year. The ‘Urban Audit pilot project’ was the the EU­27, Croatia, Turkey, Norway and Swit­ first attempt to collect comparable indicators on zerland. The 321 Urban Audit cities in the EU­27 European cities, and was first conducted by the have more than 120 million inhabitants, covering Commission in June 1999. The past 10 years have approximately 25 % of the total population. This brought many changes, and we have constantly extended sample ensures that the results give a made efforts to improve the quality of the data reliable portrait of urban Europe. — including coverage, comparability and rele­ The number of cities was limited and the ones vance. So, where we are now? The list of indica­ selected should reflect the geographical cross­ tors has been enhanced to take account of new section of each country. Consequently, in a few policy needs; the periodicity has been reduced to countries some large cities (over 100 000 inhab­ satisfy users; and geographical coverage has been itants) were not included. To complement the extended following successive rounds of EU en­ Urban Audit data collection in this respect, the largement. Large City Audit was launched. The Large City Au­ dit includes all ‘non­Urban Audit cities’ with more Enhanced list of indicators than 100 000 inhabitants in the EU­27. For these There have been three major revisions of the list so cities a reduced set of 50 variables is collected. far. Policy relevance, data availability and experi­ We invite all readers to explore the wealth of in­ ence with previous collections have been reviewed formation gathered in the past 10 years by brows­ to produce the current list of more than 300 in­ ing the Urban Audit data on Eurostat’s website. dicators. These indicators cover several aspects of quality of life, such as demography, housing, health, crime, labour market, income disparity, Discovering the spatial dimension local administration, educational qualifications, Cities are usually displayed as distinct uncon­ the environment, climate, travel patterns, the nected dots on a map. This visualisation method information society and cultural infrastructure. increases visibility but it misrepresents reality They are derived from the variables collected by and distorts the understanding of linkages be­ the European Statistical System. Data availability tween a city and its hinterland and the under­ differs from domain to domain: in the domain of standing of linkages between cities. Cities can demography, for example, data are available for no longer be treated as discrete unrelated enti­ more than 90 % of the cities, whereas for the envi­ ties without a spatial dimension. The recent de­ ronment data are available for less than half of the velopments in transport, communication and cities. In 2009 we will introduce new indicators to information technology infrastructure ease the symbolise the relationship between the city and flow of people and resources from one area to its hinterland. another considerably. Urban–rural connectivity 26 Eurostat regional yearbook 2009
  • 28. European cities 2 Map 2.1: Boundaries of cities participating in the Urban Audit data collection Eurostat regional yearbook 2009 27
  • 29. 2 European cities and inter­urban relations have become critical the cites. Different land covers were grouped into (2) A detailed description for balanced regional development. 44 classes in the CLC2000 (2). Each colour on of the CLC2000 project the map represents a different land cover class. and the UMZ creation is To facilitate the analysis of the interaction be­ available on the website of Some of these classes are particularly important the European Environment tween the city and its surroundings for each Agency (http://www.eea. for our analysis of cities. Red areas, for instance, participating city, different spatial levels were de­ europa.eu). are territories covered with urban fabric: roads, fined. Most of the data are collected at core city residential buildings, buildings belonging to the level, i.e. the city as defined by its administrative/ local administration or to public services, etc. political boundaries. In addition, a level called Purple areas are used for commercial or industri­ the larger urban zone was described. The larger al purposes. Light purple represents green urban urban zone is an approximation of the functional areas like parks, botanical gardens, etc. The areas urban area extending beyond the core city. of these three land cover classes lying less than Map 2.1 illustrates the cities participating in 200 m apart were merged together to define the Urban Audit data collection, showing the ‘built­up’ area. Port areas, airports and sport fa­ boundaries of core cities and larger urban zones. cilities were included if they were neighbours of Not surprisingly, the largest cities in Europe in the previously defined ‘built­up’ area. terms of population — London, Paris, Berlin and As a next step, road and rail networks and water Madrid — tend to have the greatest larger urban courses were added if they were within 300 m of zones in terms of area, and are readily identifiable the area defined beforehand. The area identified by on the map. In most cases the larger urban zone this procedure is called the ‘urban morphological includes only one core city. However, there are zone’ (UMZ). The urban morphological zones of exceptions, such as the German Ruhr area, which Hamburg and Lyon are shown in the middle row includes several core cities (see inset in Map 2.1). of Map 2.2. These maps also make it possible to The demarcation of core cities is illustrated in de­ compare the UMZ and core city in terms of area. tail in Map 2.2 while the larger urban zones are In Hamburg 82 %, and in Lyon 73 %, of the area shown in Map 2.3. The spatial data used to pro­ of the UMZ lies within the boundaries of the core duce most of the maps presented in this chapter city. In terms of population the intersections are are available from the Geographic Information even greater: 90 % of the population of the core System of the European Commission (GISCO) — city of Hamburg lives in the UMZ, and in Lyon a permanent service of Eurostat (for more infor­ the respective figure is 98 %. As we expected, the mation, visit Eurostat’s website). two areas are not identical but they overlap each other to a large extent, thus ensuring that the data Core cities collected at core city level are relevant and mean­ Throughout Europe’s history — in ancient Greece, ingful for the morphological city as well. in ancient Rome and in the Middle Ages — a city To measure spatial inequalities within the city, was as much a political entity as a collection of the area of the core city was divided into sub­city buildings. This collection of buildings was usu­ districts. Sub­city districts were defined in such ally surrounded by fortified walls. As the city a way as to keep to the population thresholds grew the walls were expanded. In the modern set — minimum 5 000 and maximum 40 000 in­ era the significance of the city walls as part of the habitants — as far as possible. The bottom row of defence system declined and most of them were Map 2.2 illustrates the sub­city districts of Ham­ demolished. The boundary of the city as a politi­ burg and Lyon. Key demographic and social indi­ cal entity and the boundary of the built­up area cators are available in the Urban Audit database were no longer linked and the location of these for the more than 6 000 sub­city districts. boundaries is no longer evident. Nowadays, a city could be designated as an urban settlement or as a Larger urban zones legal, administrative entity. The Urban Audit uses City walls, even if they are preserved, no longer this later concept and demarcates the core city by function as barriers between the people living in­ political boundaries. This ensures that data are side and outside of the city. Students, workers and directly relevant to policymakers. persons looking for healthcare or for cultural fa­ Map 2.2 illustrates the difference between the cilities regularly commute between the city and two concepts using the examples of Hamburg the surrounding area. Economic activity, transport (Germany) and Lyon (France). Maps in the top flows and air pollution clearly cross the adminis­ row show the land cover based on Corine land trative boundaries of a city as well. Consequently, cover 2000 (CLC2000) in the area surrounding collecting data exclusively at core city level is 28 Eurostat regional yearbook 2009
  • 30. European cities 2 Map 2.2: Defining the boundaries of the core city — Hamburg (DE) and Lyon (FR) Hamburg (DE) Lyon (FR) Eurostat regional yearbook 2009 29
  • 31. 2 European cities Map 2.3: Defining the boundaries of the larger urban zone — Barcelona (ES) and Zagreb (HR) Barcelona (ES) Zagreb (HR) 30 Eurostat regional yearbook 2009
  • 32. European cities 2 insufficient. It is commonly agreed that we have to Map 2.3 displays the different commuting rates. widen our territorial perspective. However, the way A commuting rate of 10 % means that one in 10 to measure how far the functional influences of a residents living in the municipality commutes to city go beyond its immediate boundaries varies. work to the core city. As we can see on the map, Map 2.3 uses the examples of Barcelona (Spain) large cities like Barcelona and Zagreb attract and Zagreb (Croatia) to illustrate how the func­ people living up to 100 kilometres away to work in tional urban area was demarcated in the Urban the city. As a second step, a threshold was set for Audit. Maps in the top row are similar to the top looking at the commuting pattern. Municipali­ row of Map 2.2 portraying the land cover of the ties above this threshold were to be included but selected area. The larger urban zone around the ones below not. Given the different national and core city tends to be more ‘green’, both on the regional characteristics, different thresholds were map and also in real terms. Areas covered with used within the range of 10–20 %. Finally, the forests and shrubs are coloured green on the map. list of municipalities to be included in the larger Yellow and orange indicate areas in agricultural urban zone was revised to ensure spatial contiguity use, such as arable land and fruit trees. As a first and data availability. By definition the larger step to demarcate the larger urban zones, we urban zone always includes the entire core city. looked at the number of people commuting from The boundaries of the larger urban zone of Barce­ municipalities to the core city. The middle row of lona and Zagreb are displayed in the bottom row. Figures 2.1 and 2.2: Comparison of core city, kernel and larger urban zone in terms of population and area in European capitals, 2004 Share of population living in core cities and Share of area of core cities and kernels kernels (larger urban zone = 100 %) (larger urban zone = 100 %) Ankara (TR) Bucureşti (RO) Sofia (BG) Helsinki (FI) Vilnius (LT) Tallinn (EE) Stockholm (SE) Zagreb (HR) Lisboa (PT) Roma (IT) Lefkosia (CY) Riga (LV) Athina (GR) Wien (AT) Budapest (HU) Bratislava (SK) Berlin (DE) København (DK) Warszawa (PL) London (UK) Praha (CZ) Valletta (MT) Paris (FR) Bruxelles/Brussel (BE) Ljubljana (SI) Madrid (ES) Amsterdam (NL) Oslo (NO) Bern (CH) Dublin (IE) Luxembourg (LU) 0% 20 % 40 % 60 % 80 % 100 % 0% 20 % 40 % 60 % 80 % 100 % core city kernel larger urban zone Notes: HU 2005; FI 2003; HR 2001 Eurostat regional yearbook 2009 31
  • 33. 2 European cities This demarcation process was used in most par­ percentage suggests that the core city of Luxem­ ticipating countries, but there were also excep­ bourg is slightly under­bounded — meaning that tions and departures from this which limit the a considerable share of the urban population lives overall comparability of the larger urban zones outside the administrative city limits. For very to some extent. That said, demarcating a perfect under­bounded capitals — like Paris (France) or functional urban area — based on a perfectly har­ Lisboa (Portugal) — an additional spatial level, monised methodology across Europe for which the ‘kernel’, was introduced. The kernel is an ap­ no statistical information is available — would proximation of the built­up area around the core be completely in vain. Figures 2.1 and 2.2 com­ city. The only exception is London (United King­ pare the different spatial levels used for European dom), where the kernel was defined to match the capitals in terms of population and area. In Bu­ core city of Paris in terms of population to make curesti (Romania) more than 80 % of the larger for easier comparison between the two largest cit­ urban zone population lives within the core city. ies in Europe. In terms of area, the picture is more At the other extreme, in Luxembourg (Luxem­ uniform, as for the majority of capitals the core bourg) less than 20 % of the larger urban zone city makes up less than 20 % of the area of the population lives within the core city. This low larger urban zone. Figure 2.3: Proportion of journeys to work in European capitals, 2004 København Tallinn Dublin Madrid Amsterdam Bratislava Helsinki Stockholm Bern by car by bicycle on foot by public transport Notes: SE 2005; DK, NL 2003; CH 2000. For DK, FI and SE the kernel level was used instead of the larger urban zone 32 Eurostat regional yearbook 2009
  • 34. European cities 2 So far we have seen that larger urban zones tend Geography matters to have a lower population density and a higher percentage of green areas than core cities. Using The book entitled The Spatial Economy (3), co­ (3) Masahisa Fujita, Paul R. the indicators calculated in the Urban Audit we authored by Paul Krugman, winner of the 2008 Krugman and Anthony Venables, The spatial can analyse the demographic, economic, envir­ Nobel Memorial Prize in Economic Sciences, economy: Cities, regions and international trade. onmental, social and cultural characteristics states: ‘Agglomeration […] occurs at many lev­ MIT Press, 2001. (similarities and differences) of the two spatial els, from the local shopping districts that serve levels. To illustrate this, Figure 2.3 compares the residential areas within cities to specialised eco­ travel to work patterns in selected capitals at dif­ nomic regions like Silicon Valley or the City of ferent levels. The inner circle of the pie charts London that serve the world market as a whole. shows the modal split in the core city. In the core […] Yet although agglomeration is a clearly pow­ city of København (Denmark), for example, the erful force, it is not all­powerful: London is big, majority of people ride their bikes to work, 30 % but most Britons live elsewhere, in a system of cit­ of them use public transport and 25 % travel by ies with widely varying sizes and roles. It should car. The outer circle shows the share of transport not, in other words, be hard to convince econo­ modes in the larger urban zone. As expected, the mists that economic geography […] is both an in­ proportion of journeys to work by car is consist­ teresting and important subject.’ In this chapter ently higher in the larger urban zone than in the we have focused on the various spatial levels used core city, with the exception of Bratislava. in the Urban Audit. These provide a platform Where do families settle? Where do companies for analysing the dramatically uneven distribu­ locate? Where do tourists stay? In the core city or tion of population across the landscape and the in the area of the larger urban zone outside of the agglomeration at district, at city and at regional core city? We encourage readers to probe deeper level. Our intention was to convince readers that into the Urban Audit database and to explore the ‘statistical geography’ is both an interesting and indicators depicting the spatial dimension. an important subject. Eurostat regional yearbook 2009 33
  • 35.
  • 37. 3 Labour market Regional working time patterns 10 percentage points below the overall employ­ ment target set for 2010. Flexible working hours are one of the most valu­ A cluster of regions right in the centre of Europe, able ways for individuals to reconcile work with comprising regions in southern Germany and in other aspects of life, particularly family duties. Austria, recorded relatively high employment. Working part time can be a positive thing, as The northern EU regions, comprising regions in long as the decision is voluntary and not due to the Netherlands, the United Kingdom, Denmark, underemployment. The different legal systems Sweden and Finland, also recorded relatively high and the different collective agreements across EU employment. Low regional employment rates countries governing working hours provide some were mainly found in the southern regions of flexibility, providing scope, to a greater or lesser Spain and Italy and in east European countries. extent, for more free time. The range between the lowest and the highest re­ And how about the situation at regional level? Are gional employment rate in 2007 was still signifi­ there significant differences among regions of the cant, with the highest employment rate almost same country in how much time people spend at twice as high as the lowest. The figures ranged work? It is clear that the national legal system has from 43.5 % in Campania (Italy) to 79.5 % in a big influence in all regions of a country. But on Åland (Finland). top of this, do any regional factors influence the differences in weekly hours spent at work? Employment throughout the EFTA regions was In this chapter we will look at how much time above 70 %. In the candidate countries, employ­ people spend at work in European regions and we ment rates ranged from 25.7 % in Mardin (Turkey) will offer some possible explanations for the dif­ to 62.4 % in Sjeverozapadna Hrvatska (Croatia). ferent time patterns. First we will give you a snap­ The other two Lisbon targets set for employment — shot of the regional labour market in 2007. for the female employment rate to exceed 60 % and for the older­worker employment rate to exceed 50 % — are closer to being fulfilled, but still appear Brief overview for 2007 increasingly unlikely to be achieved by 2010. The EU­27 employment rate rose from an average The female employment rate in the EU­27 in­ of 64.4 % in 2006 to 65.3 % in 2007. It is still 4.6 creased in 2007 by 1 percentage point to 58.3 %. percentage points short of achieving the Lisbon Out of the three targets, this seems the most employment target. Looking back to employ­ promising, but the negative impacts on the la­ ment figures for 2000, when the targets were set, bour market that are likely to be felt in the com­ it is clear that the rise in employment fell short ing years should not be overlooked. Regional of ambitions. It now seems increasingly unlikely female employment rates varied widely in 2007, that the Lisbon targets for employment will be from a minimum of 27.9 % in Campania (Italy) to achieved by 2010, since there are only three years a maximum of 76.4 % in Åland (Finland). left, and especially given the recession and eco­ nomic difficulties we are currently facing, which The employment rate of older workers, i.e. em­ are highly likely to have a negative impact on em­ ployed persons aged 55–64 years, was 44.7 % in ployment in the coming years. 2007, which is 1.2 percentage points higher than in 2006. At regional level, older­worker employ­ The latest quarterly data available at national level ment rates ranged from a low of 21.8 % in Śląskie confirm this. The employment rate for the EU­27 (Poland) to a high of 72.8 % in Småland med in the last quarter of 2008 was 65.8 % and 64.6 % öarna (Sweden). The EU­27 unemployment rate in the first quarter of 2009. fell significantly in 2007 by 1 percentage point to Social and territorial cohesion is one of the EU’s 7.2 %, the steepest fall since 2000. goals, so it is important to look at regional labour Unemployment is distributed quite evenly markets and how they change over time. Map 3.1 throughout the EU. Map 3.2 shows that, in spite of shows the regional employment rate for the 15–64 the good performance in 2007, some regions still age group, by NUTS 2 regions, in 2007. record a double­digit unemployment rate. These In 2007, only 81 of the 264 NUTS 2 regions in the are mainly located in the south of Spain, the south EU­27 for which data was available had already of Italy and the eastern regions of Germany. Some achieved the Lisbon target (shaded with the dark­ regions in Slovakia, Poland and Hungary also re­ est colour in Map 3.1), while 59 regions were still corded unemployment rates above 10 % in 2007. 36 Eurostat regional yearbook 2009
  • 38. Labour market 3 Map 3.1: Employment rate for the 15–64 age group, by NUTS 2 regions, 2007 Percentage Eurostat regional yearbook 2009 37
  • 39. 3 Labour market Map 3.2: Unemployment rate, by NUTS 2 regions, 2007 Percentage 38 Eurostat regional yearbook 2009