ISSN 1830-9674



                      Statistical books




Eurostat regional yearbook 2009
Statistical books




Eurostat regional yearbook 2009
Europe Direct is a service to help you find answers
 to your questions about the European Union
 Freephone number (*):

 0...
Preface
Dear Readers,
Five years ago, 2004, was a momentous year, with 10 new
Member States joining the European Union on ...
Acknowledgements
    The editors of the Eurostat regional yearbook 2009 would like to thank all those who were involved in...
Contents

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....
5 HOUSEHOLD ACCOUNTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
10 TOURISM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....
Introduction
Introduction


                                   Statistics on regions and cities                         throughout Euro...
Introduction


sults of these changes to the NUTS classification      given on the three candidate countries (Croatia,
hav...
Population
1    Population


     Unveiling the regional pattern                        million (1960) to almost 500 million (497 mil...
Population   1
Map 1.1: Population density, by NUTS 2 regions, 2007
          Inhabitants per km2




           Eurostat ...
1                      Population


     Map 1.2: Total population change, by NUTS 2 regions, average 2003–07
            ...
Population                    1
In nearly all western and south­western regions of              2003–07. The resulting neg...
1                      Population


     Map 1.3: Natural population change (live births minus deaths), by NUTS 2 regions,...
Population                       1
Greece, Italy and Portugal in the south. The other                        Relatively hi...
1                    Population


     Map 1.4: Net migration, by NUTS 2 regions, average 2003–07
              Per 1 000 ...
Population       1
2.1 children per women is considered to be the          • regions in the north­east of France and the
r...
1                     Population


     Map 1.5: Percentage of population aged 65 years old and more, by NUTS 2 regions, 2...
Population       1
Conclusion                                            nomena have been identified, spreading across
   ...
European cities
2    European cities


     Introduction                                               Moving from five-year periodicity
 ...
European cities   2
Map 2.1: Boundaries of cities participating in the Urban Audit data collection




            Eurosta...
2                                      European cities


                                       and inter­urban relations ...
European cities   2
Map 2.2: Defining the boundaries of the core city — Hamburg (DE) and Lyon (FR)

          Hamburg (DE)...
2                     European cities


     Map 2.3: Defining the boundaries of the larger urban zone — Barcelona (ES) an...
European cities                         2
insufficient. It is commonly agreed that we have to              Map 2.3 display...
2    European cities


     This demarcation process was used in most par­           percentage suggests that the core cit...
European cities                                      2
So far we have seen that larger urban zones tend       Geography ma...
Labour market
3    Labour market


     Regional working time patterns                          10 percentage points below the overall e...
Labour market   3
Map 3.1: Employment rate for the 15–64 age group, by NUTS 2 regions, 2007
         Percentage




      ...
3                    Labour market


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



...
Eurostat Regional Yearbook
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Eurostat Regional Yearbook

  1. 1. ISSN 1830-9674 Statistical books Eurostat regional yearbook 2009
  2. 2. Statistical books Eurostat regional yearbook 2009
  3. 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. 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. 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. 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. 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. 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. 9. Introduction
  10. 10. 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
  11. 11. 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
  12. 12. Population
  13. 13. 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
  14. 14. Population 1 Map 1.1: Population density, by NUTS 2 regions, 2007 Inhabitants per km2 Eurostat regional yearbook 2009 15
  15. 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
  16. 16. 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
  17. 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
  18. 18. 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
  19. 19. 1 Population Map 1.4: Net migration, by NUTS 2 regions, average 2003–07 Per 1 000 inhabitants 20 Eurostat regional yearbook 2009
  20. 20. 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
  21. 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
  22. 22. 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
  23. 23. European cities
  24. 24. 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
  25. 25. European cities 2 Map 2.1: Boundaries of cities participating in the Urban Audit data collection Eurostat regional yearbook 2009 27
  26. 26. 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
  27. 27. 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
  28. 28. 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
  29. 29. 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
  30. 30. 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
  31. 31. 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
  32. 32. Labour market
  33. 33. 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
  34. 34. 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
  35. 35. 3 Labour market Map 3.2: Unemployment rate, by NUTS 2 regions, 2007 Percentage 38 Eurostat regional yearbook 2009

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